diff --git a/.github/workflows/dojo-e2e.yml b/.github/workflows/dojo-e2e.yml index 7f4dca09a4..8145b9c785 100644 --- a/.github/workflows/dojo-e2e.yml +++ b/.github/workflows/dojo-e2e.yml @@ -174,6 +174,10 @@ jobs: test_path: tests/agUiDotnetTests services: ["dojo", "ag-ui-dotnet"] wait_on: http://localhost:9999,tcp:localhost:8023 + - suite: openai-agents-python + test_path: tests/openaiAgentsPythonTests + services: ["dojo", "openai-agents-python"] + wait_on: http://localhost:9999,tcp:localhost:8024 # - suite: vercel-ai-sdk # test_path: tests/vercelAISdkTests # services: ["dojo"] diff --git a/.github/workflows/unit-python-sdk.yml b/.github/workflows/unit-python-sdk.yml index de5cd44725..9aaa9531bc 100644 --- a/.github/workflows/unit-python-sdk.yml +++ b/.github/workflows/unit-python-sdk.yml @@ -10,6 +10,7 @@ on: - "integrations/adk-middleware/python/**" - "integrations/aws-strands/python/**" - "integrations/langroid/python/**" + - "integrations/openai-agents/python/**" - "integrations/crew-ai/python/**" - ".github/workflows/unit-python-sdk.yml" pull_request: @@ -21,6 +22,7 @@ on: - "integrations/adk-middleware/python/**" - "integrations/aws-strands/python/**" - "integrations/langroid/python/**" + - "integrations/openai-agents/python/**" - "integrations/crew-ai/python/**" - ".github/workflows/unit-python-sdk.yml" @@ -336,6 +338,7 @@ jobs: working-directory: integrations/langroid/python run: uv run python -m unittest discover tests -v + openai-agents-python: crewai-python: runs-on: ubuntu-latest @@ -366,6 +369,16 @@ jobs: id: cached-uv-dependencies uses: actions/cache@55cc8345863c7cc4c66a329aec7e433d2d1c52a9 # v6.1.0 with: + path: integrations/openai-agents/python/.venv + key: ${{ steps.fork-check.outputs.prefix }}venv-${{ runner.os }}-openai-agents-python-${{ hashFiles('integrations/openai-agents/python/uv.lock') }} + + - name: Install dependencies + working-directory: integrations/openai-agents/python + run: uv sync + + - name: Run tests + working-directory: integrations/openai-agents/python + run: uv run pytest path: integrations/crew-ai/python/.venv key: ${{ steps.fork-check.outputs.prefix }}venv-${{ runner.os }}-crewai-${{ hashFiles('integrations/crew-ai/python/uv.lock') }} diff --git a/.gitignore b/.gitignore index dbb12bb6cc..252ee07879 100644 --- a/.gitignore +++ b/.gitignore @@ -52,3 +52,6 @@ node_modules # actionlint linter binary (dev tool, do not commit) /actionlint + +# Dev +.playwright-mcp/ diff --git a/README.md b/README.md index 8db4b044d5..1f2ab59769 100644 --- a/README.md +++ b/README.md @@ -116,7 +116,7 @@ AG-UI was born from CopilotKit's initial **partnership** with LangGraph and Crew | ---------- | ------- | ---------------- | | [Claude Agent SDK](https://github.com/ag-ui-protocol/ag-ui/tree/main/integrations/claude-agent-sdk) | โœ… Supported | ๐ŸŽฎ [Demos](https://dojo.ag-ui.com/claude-agent-sdk-python/feature/shared_state) | | [Langroid](https://github.com/ag-ui-protocol/ag-ui/tree/main/integrations/langroid) | โœ… Supported | ๐ŸŽฎ [Demos](https://dojo.ag-ui.com/langroid/feature/shared_state) | -| [OpenAI Agent SDK](https://openai.github.io/openai-agents-python/) | ๐Ÿ› ๏ธ In Progress | โ€“ | +| [OpenAI Agents SDK](https://openai.github.io/openai-agents-python/) | ๐Ÿ› ๏ธ In Progress | โ€“ | | [Cloudflare Agents](https://developers.cloudflare.com/agents/) | ๐Ÿ› ๏ธ In Progress | โ€“ | diff --git a/apps/dojo/e2e/aimock-setup.ts b/apps/dojo/e2e/aimock-setup.ts index 16cd32ffaf..e899bcb5d8 100644 --- a/apps/dojo/e2e/aimock-setup.ts +++ b/apps/dojo/e2e/aimock-setup.ts @@ -2,6 +2,7 @@ import { LLMock, type ChatMessage } from "@copilotkit/aimock"; import * as path from "node:path"; import { registerA2UIRecoveryFixtures } from "./a2ui-recovery-fixtures"; import { registerA2UIADKFixtures } from "./a2ui-adk-fixtures"; +import { registerOpenAIAgentsFixtures } from "./openai-agents-fixtures"; // Configurable so parallel worktrees / runs don't collide on one aimock port. const MOCK_PORT = Number(process.env.AIMOCK_PORT) || 5555; @@ -31,6 +32,7 @@ export async function setupLLMock(): Promise { // OSS-162 A2UI recovery showcase fixtures (predicate fixtures, must precede // the generic loadFixtureFile below). registerA2UIRecoveryFixtures(mockServer); + registerOpenAIAgentsFixtures(mockServer); // Extract text from message content โ€” handles both string and array-of-parts // (Strands SDK sends content as [{type: "text", text: "..."}]) @@ -1339,6 +1341,12 @@ export async function setupLLMock(): Promise { ); if (hasCrewExitTool && textOf(last.content) === "Crew exited") return false; + const hasOpenAIAgentsSupervisorTools = req.tools?.some((t) => + ["research_topic", "write_prose", "critique_draft"].includes( + t.function.name, + ), + ); + if (hasOpenAIAgentsSupervisorTools) return false; return true; }, }, diff --git a/apps/dojo/e2e/openai-agents-fixtures.ts b/apps/dojo/e2e/openai-agents-fixtures.ts new file mode 100644 index 0000000000..84337792ea --- /dev/null +++ b/apps/dojo/e2e/openai-agents-fixtures.ts @@ -0,0 +1,192 @@ +import { LLMock, type ChatMessage } from "@copilotkit/aimock"; + +function textOf(content: ChatMessage["content"] | undefined): string { + if (typeof content === "string") return content; + if (!Array.isArray(content)) return ""; + return content + .filter((part) => part.type === "text" && typeof part.text === "string") + .map((part) => part.text) + .join(""); +} + +export function registerOpenAIAgentsFixtures(mockServer: LLMock): void { + const hasTool = ( + req: { tools?: { function: { name: string } }[] }, + name: string, + ) => req.tools?.some((tool) => tool.function.name === name) ?? false; + const messagesText = (messages: ChatMessage[]) => + messages.map((message) => textOf(message.content)).join("\n"); + const systemText = (messages: ChatMessage[]) => + messages + .filter((message) => message.role === "system") + .map((message) => textOf(message.content)) + .join("\n"); + + mockServer.addFixture({ + match: { + predicate: (req) => + hasTool(req, "read_ag_ui_openai_agents_docs") && + messagesText(req.messages).includes( + "test the OpenAI Agents integration", + ), + }, + response: { + toolCalls: [ + { + name: "read_ag_ui_openai_agents_docs", + arguments: JSON.stringify({ heading: "Testing" }), + }, + ], + }, + }); + + mockServer.addFixture({ + match: { + predicate: (req) => + hasTool(req, "issue_refund") && + messagesText(req.messages).includes("ORD-1001"), + }, + response: { + toolCalls: [ + { + name: "issue_refund", + arguments: JSON.stringify({ order_id: "ORD-1001" }), + }, + ], + }, + }); + + const hasSupervisorTools = (req: { + tools?: { function: { name: string } }[]; + }) => + ["research_topic", "write_prose", "critique_draft"].every((name) => + hasTool(req, name), + ); + + mockServer.addFixture({ + match: { + predicate: (req) => + systemText(req.messages).includes("You research a topic") && + !hasSupervisorTools(req), + }, + response: { + content: + "AI research facts: symbolic AI began in the 1950s; machine learning later became dominant.", + }, + }); + + mockServer.addFixture({ + match: { + predicate: (req) => + systemText(req.messages).includes("You turn bullet-point facts") && + !hasSupervisorTools(req), + }, + response: { + content: + "AI evolved from symbolic systems into modern machine learning and generative models.", + }, + }); + + mockServer.addFixture({ + match: { + predicate: (req) => + systemText(req.messages).includes("You review a draft") && + !hasSupervisorTools(req), + }, + response: { + content: + "1. Improve the opening. 2. Connect the historical eras more clearly.", + }, + }); + + mockServer.addFixture({ + match: { + predicate: (req) => { + if (!hasSupervisorTools(req)) return false; + const text = messagesText(req.messages); + return !text.includes("AI research facts"); + }, + }, + response: { + toolCalls: [ + { + name: "research_topic", + arguments: JSON.stringify({ input: "history of AI" }), + }, + ], + }, + }); + + mockServer.addFixture({ + match: { + predicate: (req) => { + if (!hasSupervisorTools(req)) return false; + const text = messagesText(req.messages); + return ( + text.includes("AI research facts") && + !text.includes("AI evolved from symbolic systems") + ); + }, + }, + response: { + toolCalls: [ + { + name: "write_prose", + arguments: JSON.stringify({ input: "AI research facts" }), + }, + ], + }, + }); + + mockServer.addFixture({ + match: { + predicate: (req) => { + if (!hasSupervisorTools(req)) return false; + const text = messagesText(req.messages); + return ( + text.includes("AI evolved from symbolic systems") && + !text.includes("Improve the opening") + ); + }, + }, + response: { + toolCalls: [ + { + name: "critique_draft", + arguments: JSON.stringify({ + input: + "AI evolved from symbolic systems into modern machine learning and generative models.", + }), + }, + ], + }, + }); + + mockServer.addFixture({ + match: { + predicate: (req) => + hasSupervisorTools(req) && + messagesText(req.messages).includes("Improve the opening"), + }, + response: { + content: + "Artificial intelligence grew from early symbolic programs into machine learning and today's generative systems.", + }, + }); + + mockServer.addFixture({ + match: { + predicate: (req) => + systemText(req.messages).includes("Always reply in Arabic"), + }, + response: { content: "ู…ุฑุญุจู‹ุง! ุฃู†ุง ุฃุฌูŠุจ ุจุงู„ู„ุบุฉ ุงู„ุนุฑุจูŠุฉ." }, + }); + + mockServer.addFixture({ + match: { + predicate: (req) => + systemText(req.messages).includes("Always reply in German"), + }, + response: { content: "Hallo! Ich antworte auf Deutsch." }, + }); +} diff --git a/apps/dojo/e2e/tests/openaiAgentsPythonTests/agenticChatPage.spec.ts b/apps/dojo/e2e/tests/openaiAgentsPythonTests/agenticChatPage.spec.ts new file mode 100644 index 0000000000..0f7b0eb6d4 --- /dev/null +++ b/apps/dojo/e2e/tests/openaiAgentsPythonTests/agenticChatPage.spec.ts @@ -0,0 +1,45 @@ +import { test } from "../../test-isolation-helper"; +import { AgenticChatPage } from "../../featurePages/AgenticChatPage"; + +test("[OpenAI Agents Python] Agentic Chat sends and receives a greeting message", async ({ + page, +}) => { + await page.goto("/openai-agents-python/feature/agentic_chat"); + const chat = new AgenticChatPage(page); + await chat.openChat(); + await chat.sendMessage("Hi"); + await chat.assertUserMessageVisible("Hi"); + await chat.assertAgentReplyVisible(/Hello|Hi|hey/i); +}); + +test("[OpenAI Agents Python] Agentic Chat retains memory of previous questions", async ({ + page, +}) => { + test.slow(); + await page.goto("/openai-agents-python/feature/agentic_chat"); + const chat = new AgenticChatPage(page); + await chat.openChat(); + await chat.sendMessage("Hi, my name is Alex"); + await chat.assertUserMessageVisible("Hi, my name is Alex"); + await chat.assertAgentReplyVisible(/Hello|Hi|Alex/i); + await chat.sendMessage("What is my name?"); + await chat.assertUserMessageVisible("What is my name?"); + await chat.assertAgentReplyVisible(/Alex/i); +}); + +test("[OpenAI Agents Python] Agentic Chat retains user messages during a conversation", async ({ + page, +}) => { + test.slow(); + await page.goto("/openai-agents-python/feature/agentic_chat"); + const chat = new AgenticChatPage(page); + await chat.openChat(); + await chat.sendMessage("Hey there"); + await chat.assertAgentReplyVisible(/Hello|Hi/i); + await chat.sendMessage("My favorite fruit is Mango"); + await chat.assertAgentReplyVisible(/Mango/i); + await chat.sendMessage("and I love listening to Kaavish"); + await chat.assertAgentReplyVisible(/Kaavish/i); + await chat.sendMessage("Can you remind me what my favorite fruit is?"); + await chat.assertAgentReplyVisible(/Mango/i); +}); diff --git a/apps/dojo/e2e/tests/openaiAgentsPythonTests/backendToolRenderingPage.spec.ts b/apps/dojo/e2e/tests/openaiAgentsPythonTests/backendToolRenderingPage.spec.ts new file mode 100644 index 0000000000..33f46d0b2c --- /dev/null +++ b/apps/dojo/e2e/tests/openaiAgentsPythonTests/backendToolRenderingPage.spec.ts @@ -0,0 +1,12 @@ +import { expect, test } from "../../test-isolation-helper"; + +test("[OpenAI Agents Python] Backend Tool Rendering displays a weather card", async ({ + page, +}) => { + await page.goto("/openai-agents-python/feature/backend_tool_rendering"); + + await page.getByRole("button", { name: "Weather in San Francisco" }).click(); + + await expect(page.getByTestId("weather-card").last()).toBeVisible(); + await expect(page.getByText("Humidity").last()).toBeVisible(); +}); diff --git a/apps/dojo/e2e/tests/openaiAgentsPythonTests/customLifecycleEventsPage.spec.ts b/apps/dojo/e2e/tests/openaiAgentsPythonTests/customLifecycleEventsPage.spec.ts new file mode 100644 index 0000000000..77b319e344 --- /dev/null +++ b/apps/dojo/e2e/tests/openaiAgentsPythonTests/customLifecycleEventsPage.spec.ts @@ -0,0 +1,18 @@ +import { expect, test } from "../../test-isolation-helper"; +import { sendAndAwaitResponse } from "../../utils/copilot-actions"; + +test("[OpenAI Agents Python] Custom Lifecycle Events keeps usage with each reply", async ({ + page, +}) => { + await page.goto("/openai-agents-python/feature/custom_lifecycle_events"); + + await sendAndAwaitResponse(page, "Hi"); + await sendAndAwaitResponse(page, "Tell me one interesting fact about AI."); + + const usageLabels = page.getByLabel("Run usage"); + await expect(usageLabels).toHaveCount(2); + for (const usage of await usageLabels.all()) { + await expect(usage).toContainText(/\d+ input tokens/); + await expect(usage).toContainText(/\d+ output tokens/); + } +}); diff --git a/apps/dojo/e2e/tests/openaiAgentsPythonTests/docsCopilotPage.spec.ts b/apps/dojo/e2e/tests/openaiAgentsPythonTests/docsCopilotPage.spec.ts new file mode 100644 index 0000000000..26437a48de --- /dev/null +++ b/apps/dojo/e2e/tests/openaiAgentsPythonTests/docsCopilotPage.spec.ts @@ -0,0 +1,16 @@ +import { expect, test } from "../../test-isolation-helper"; +import { sendAndAwaitResponse } from "../../utils/copilot-actions"; + +test("[OpenAI Agents Python] Docs Copilot reads the integration documentation", async ({ + page, +}) => { + await page.goto("/openai-agents-python/feature/ag_ui_docs_copilot"); + + await sendAndAwaitResponse( + page, + "How do I test the OpenAI Agents integration?", + ); + + await expect(page.getByText("AG-UI OpenAI Agents docs").last()).toBeVisible(); + await expect(page.getByText('Read "Testing"').last()).toBeVisible(); +}); diff --git a/apps/dojo/e2e/tests/openaiAgentsPythonTests/dynamicSystemPromptPage.spec.ts b/apps/dojo/e2e/tests/openaiAgentsPythonTests/dynamicSystemPromptPage.spec.ts new file mode 100644 index 0000000000..3335d9f52e --- /dev/null +++ b/apps/dojo/e2e/tests/openaiAgentsPythonTests/dynamicSystemPromptPage.spec.ts @@ -0,0 +1,23 @@ +import { expect, test } from "../../test-isolation-helper"; +import { sendAndAwaitResponse } from "../../utils/copilot-actions"; + +test("[OpenAI Agents Python] Dynamic System Prompt changes the reply language", async ({ + page, +}) => { + test.slow(); + await page.goto("/openai-agents-python/feature/dynamic_system_prompt"); + + await page.getByRole("button", { name: /Arabic/ }).click(); + await sendAndAwaitResponse(page, "Which language are you using?"); + const latestReply = () => + page + .locator('[data-testid="copilot-assistant-message"]') + .last() + .locator("p") + .last(); + await expect(latestReply()).toContainText(/[\u0600-\u06ff]/); + + await page.getByRole("button", { name: /German/ }).click(); + await sendAndAwaitResponse(page, "Which language are you using now?"); + await expect(latestReply()).toContainText(/deutsch/i); +}); diff --git a/apps/dojo/e2e/tests/openaiAgentsPythonTests/humanInTheLoopApprovalPage.spec.ts b/apps/dojo/e2e/tests/openaiAgentsPythonTests/humanInTheLoopApprovalPage.spec.ts new file mode 100644 index 0000000000..49872b3137 --- /dev/null +++ b/apps/dojo/e2e/tests/openaiAgentsPythonTests/humanInTheLoopApprovalPage.spec.ts @@ -0,0 +1,22 @@ +import { expect, test } from "../../test-isolation-helper"; +import { + awaitLLMResponseDone, + sendChatMessage, +} from "../../utils/copilot-actions"; + +test("[OpenAI Agents Python] Approval resumes a paused refund tool", async ({ + page, +}) => { + test.slow(); + await page.goto("/openai-agents-python/feature/human_in_the_loop_approval"); + + await sendChatMessage(page, "Please refund ORD-1001."); + await awaitLLMResponseDone(page); + const card = page.getByTestId("approval-card"); + await expect(card).toBeVisible(); + await expect(card).toContainText("ORD-1001"); + + await page.getByTestId("approve-button").click(); + await awaitLLMResponseDone(page); + await expect(card).toBeHidden(); +}); diff --git a/apps/dojo/e2e/tests/openaiAgentsPythonTests/humanInTheLoopPage.spec.ts b/apps/dojo/e2e/tests/openaiAgentsPythonTests/humanInTheLoopPage.spec.ts new file mode 100644 index 0000000000..2bf6cc9674 --- /dev/null +++ b/apps/dojo/e2e/tests/openaiAgentsPythonTests/humanInTheLoopPage.spec.ts @@ -0,0 +1,20 @@ +import { expect, test } from "../../test-isolation-helper"; +import { HumanInTheLoopPage } from "../../featurePages/HumanInTheLoopPage"; + +test("[OpenAI Agents Python] Human in the Loop renders and submits selected steps", async ({ + page, +}) => { + test.slow(); + await page.goto("/openai-agents-python/feature/human_in_the_loop"); + + const humanInLoop = new HumanInTheLoopPage(page); + await humanInLoop.openChat(); + await humanInLoop.sendMessage( + "Give me a plan to make brownies, there should be only one step with eggs and one step with oven", + ); + + await expect(humanInLoop.plan).toBeVisible(); + await humanInLoop.uncheckItem("eggs"); + expect(await humanInLoop.isStepItemUnchecked("eggs")).toBe(true); + await humanInLoop.performSteps(); +}); diff --git a/apps/dojo/e2e/tests/openaiAgentsPythonTests/subagentsPage.spec.ts b/apps/dojo/e2e/tests/openaiAgentsPythonTests/subagentsPage.spec.ts new file mode 100644 index 0000000000..41f31984e1 --- /dev/null +++ b/apps/dojo/e2e/tests/openaiAgentsPythonTests/subagentsPage.spec.ts @@ -0,0 +1,26 @@ +import { expect, test } from "../../test-isolation-helper"; +import { sendAndAwaitResponse } from "../../utils/copilot-actions"; + +test("[OpenAI Agents Python] Subagents records all specialist delegations", async ({ + page, +}) => { + test.slow(); + await page.goto("/openai-agents-python/feature/subagents"); + + await sendAndAwaitResponse( + page, + "Write a short article about the history of AI", + ); + + // Scope to the delegation-log entries container (the div immediately after + // the "Delegation log" heading), NOT the whole sidebar โ€” the sidebar also + // statically renders the "Supervisor's team" chips labelled Researcher/Writer/ + // Critic, which would make these assertions pass even if no delegation ran. + const log = page + .getByText("Delegation log") + .locator("xpath=following-sibling::div[1]"); + await expect(log.getByText("Researcher").last()).toBeVisible(); + await expect(log.getByText("Writer").last()).toBeVisible(); + await expect(log.getByText("Critic").last()).toBeVisible(); + await expect(log.getByText("complete")).toHaveCount(3); +}); diff --git a/apps/dojo/e2e/tests/openaiAgentsPythonTests/toolBasedGenUIPage.spec.ts b/apps/dojo/e2e/tests/openaiAgentsPythonTests/toolBasedGenUIPage.spec.ts new file mode 100644 index 0000000000..f5f047fe66 --- /dev/null +++ b/apps/dojo/e2e/tests/openaiAgentsPythonTests/toolBasedGenUIPage.spec.ts @@ -0,0 +1,14 @@ +import { expect, test } from "../../test-isolation-helper"; +import { ToolBaseGenUIPage } from "../../featurePages/ToolBaseGenUIPage"; + +test("[OpenAI Agents Python] Tool Based Generative UI renders a haiku", async ({ + page, +}) => { + await page.goto("/openai-agents-python/feature/tool_based_generative_ui"); + + const haiku = new ToolBaseGenUIPage(page); + await expect(haiku.haikuAgentIntro).toBeVisible(); + await haiku.generateHaiku('Generate Haiku for "I will always win"'); + await haiku.checkGeneratedHaiku(); + await haiku.checkHaikuDisplay(page); +}); diff --git a/apps/dojo/scripts/generate-content-json.ts b/apps/dojo/scripts/generate-content-json.ts index 31798cc3ef..e0aa0055d8 100644 --- a/apps/dojo/scripts/generate-content-json.ts +++ b/apps/dojo/scripts/generate-content-json.ts @@ -91,7 +91,10 @@ async function getFeatureFrontendFiles(featureId: string) { const retrievedFiles = []; for (const fileName of featureFiles) { - retrievedFiles.push(await getFile(featurePath, fileName)); + const filePath = path.join(featurePath, fileName); + if (fs.existsSync(filePath)) { + retrievedFiles.push(await getFile(featurePath, fileName)); + } } return retrievedFiles; @@ -545,6 +548,21 @@ const agentFilesMapper: Record< {}, ); }, + "openai-agents-python": (agentKeys: string[]) => { + return agentKeys.reduce( + (acc, agentId) => ({ + ...acc, + [agentId]: [ + path.join( + __dirname, + integrationsFolderPath, + `/openai-agents/python/examples/agents_examples/${agentId}.py`, + ), + ], + }), + {}, + ); + }, // watsonx uses a single TS agent for all features โ€” no per-feature server files watsonx: () => ({ agentic_chat: [ diff --git a/apps/dojo/scripts/prep-dojo-everything.js b/apps/dojo/scripts/prep-dojo-everything.js index 458895aef9..18fa863e5a 100755 --- a/apps/dojo/scripts/prep-dojo-everything.js +++ b/apps/dojo/scripts/prep-dojo-everything.js @@ -134,6 +134,11 @@ const ALL_TARGETS = { name: "Dojo (dev)", cwd: gitRoot, }, + "openai-agents-python": { + command: "uv sync", + name: "OpenAI Agents SDK (Python)", + cwd: path.join(integrationsRoot, "openai-agents/python/examples"), + }, "claude-agent-sdk-python": { command: "uv sync", name: "Claude Agent SDK (Python)", diff --git a/apps/dojo/scripts/run-dojo-everything.js b/apps/dojo/scripts/run-dojo-everything.js index be3fbedde1..6efd8e6cc5 100755 --- a/apps/dojo/scripts/run-dojo-everything.js +++ b/apps/dojo/scripts/run-dojo-everything.js @@ -219,6 +219,14 @@ const ALL_SERVICES = { env: { PORT: 8014 }, }, ], + "openai-agents-python": [ + { + command: "uv run dev", + name: "OpenAI Agents SDK (Python)", + cwd: path.join(integrationsRoot, "openai-agents/python/examples"), + env: { PORT: 8024 }, + }, + ], "claude-agent-sdk-python": [ { command: "uv run dev", @@ -314,6 +322,7 @@ const ALL_SERVICES = { CLAUDE_AGENT_SDK_PYTHON_URL: "http://localhost:8019", CLAUDE_AGENT_SDK_TYPESCRIPT_URL: "http://localhost:8020", LANGROID_URL: "http://localhost:8021", + OPENAI_AGENTS_PYTHON_URL: "http://localhost:8024", NEXT_PUBLIC_CUSTOM_DOMAIN_TITLE: "cpkdojo.local___CopilotKit Feature Viewer", }, @@ -350,6 +359,7 @@ const ALL_SERVICES = { CLAUDE_AGENT_SDK_PYTHON_URL: "http://localhost:8019", CLAUDE_AGENT_SDK_TYPESCRIPT_URL: "http://localhost:8020", LANGROID_URL: "http://localhost:8021", + OPENAI_AGENTS_PYTHON_URL: "http://localhost:8024", NEXT_PUBLIC_CUSTOM_DOMAIN_TITLE: "cpkdojo.local___CopilotKit Feature Viewer", }, @@ -397,7 +407,7 @@ async function main() { OPENAI_BASE_URL: process.env.OPENAI_BASE_URL || "http://localhost:5555/v1", OPENAI_API_BASE: process.env.OPENAI_API_BASE || "http://localhost:5555/v1", OPENAI_API_KEY: process.env.OPENAI_API_KEY || "sk-mock", - OPENAI_CHAT_MODEL_ID: process.env.OPENAI_CHAT_MODEL_ID || "gpt-4o", + OPENAI_CHAT_MODEL_ID: process.env.OPENAI_CHAT_MODEL_ID || "gpt-5.5", }; // LLMock: inject GOOGLE_GEMINI_BASE_URL so ADK middleware agents (which keep diff --git a/apps/dojo/src/agents.ts b/apps/dojo/src/agents.ts index 0008ab56e1..489b52cf24 100644 --- a/apps/dojo/src/agents.ts +++ b/apps/dojo/src/agents.ts @@ -219,7 +219,7 @@ export const agentsIntegrations = { // Disabled until we can support Vercel AI SDK v5 // "vercel-ai-sdk": async () => ({ - // agentic_chat: new VercelAISDKAgent({ model: openai("gpt-4o") }), + // agentic_chat: new VercelAISDKAgent({ model: openai("gpt-5.5") }), // }), langgraph: async () => ({ @@ -339,7 +339,7 @@ export const agentsIntegrations = { // const agent = new LangChainAgent({ // chainFn: async ({ messages, tools, threadId }) => { // const { ChatOpenAI } = await import("@langchain/openai"); - // const chatOpenAI = new ChatOpenAI({ model: "gpt-4o" }); + // const chatOpenAI = new ChatOpenAI({ model: "gpt-5.5" }); // const model = chatOpenAI.bindTools(tools, { // strict: true, // }); @@ -623,6 +623,23 @@ export const agentsIntegrations = { tool_based_generative_ui: "tool_based_generative_ui", }), + "openai-agents-python": async () => + mapAgents( + (path) => + new HttpAgent({ url: `${envVars.openaiAgentsPythonUrl}/${path}/` }), + { + ag_ui_docs_copilot: "ag_ui_docs_copilot", + agentic_chat: "agentic_chat", + backend_tool_rendering: "backend_tool_rendering", + human_in_the_loop: "human_in_the_loop", + human_in_the_loop_approval: "human_in_the_loop_approval", + tool_based_generative_ui: "tool_based_generative_ui", + subagents: "subagents", + custom_lifecycle_events: "custom_lifecycle_events", + dynamic_system_prompt: "dynamic_system_prompt", + }, + ), + "claude-agent-sdk-python": async () => mapAgents( (path) => diff --git a/apps/dojo/src/app/[integrationId]/feature/(v2)/ag_ui_docs_copilot/README.mdx b/apps/dojo/src/app/[integrationId]/feature/(v2)/ag_ui_docs_copilot/README.mdx new file mode 100644 index 0000000000..ccac1e6327 --- /dev/null +++ b/apps/dojo/src/app/[integrationId]/feature/(v2)/ag_ui_docs_copilot/README.mdx @@ -0,0 +1,31 @@ +# ๐Ÿ“š AG-UI Docs Copilot + +## What This Demo Shows + +A developer assistant that answers questions using the documentation packaged +with `ag-ui-protocol` and `ag-ui-openai-agents`. + +The agent receives the available README headings in its instructions. When a +question needs documentation, it calls one of two backend tools to load only +the relevant section from the installed Python package metadata. The tool call +and its progress are rendered in the chat. + +No internet search or external retrieval service is used. + +## How to Interact + +- Ask how `AGUITranslator.to_openai()` and `to_agui()` connect an application + to AG-UI. +- Ask for a minimal FastAPI streaming endpoint. +- Ask how messages, tools, state, lifecycle events, or errors are translated. + +## Technical Details + +- `read_ag_ui_protocol_docs` reads the AG-UI Protocol Python README. +- `read_ag_ui_openai_agents_docs` reads the OpenAI Agents integration README. +- Each tool receives a heading and returns one matching Markdown section, + keeping the full documents out of the model prompt. +- The backend uses `AGUITranslator` directly around + `Runner.run_streamed()`, then encodes the resulting AG-UI events as SSE. +- For a larger documentation collection, replace the heading lookup with an + application-appropriate retrieval system. diff --git a/apps/dojo/src/app/[integrationId]/feature/(v2)/ag_ui_docs_copilot/page.tsx b/apps/dojo/src/app/[integrationId]/feature/(v2)/ag_ui_docs_copilot/page.tsx new file mode 100644 index 0000000000..397f949f44 --- /dev/null +++ b/apps/dojo/src/app/[integrationId]/feature/(v2)/ag_ui_docs_copilot/page.tsx @@ -0,0 +1,149 @@ +"use client"; + +import React from "react"; +import { CopilotKit } from "@copilotkit/react-core"; +import { + CopilotChat, + useConfigureSuggestions, + useRenderTool, +} from "@copilotkit/react-core/v2"; +import "@copilotkit/react-core/v2/styles.css"; +import { z } from "zod"; + +interface AGUIDocsCopilotProps { + params: Promise<{ integrationId: string }>; +} + +const AGUIDocsCopilot: React.FC = ({ params }) => { + const { integrationId } = React.use(params); + + return ( + + + + ); +}; + +const DocsChat = () => { + useRenderTool({ + name: "read_ag_ui_openai_agents_docs", + agentId: "ag_ui_docs_copilot", + parameters: z.object({ heading: z.string().optional() }), + render: ({ status, args, result }: any) => ( + + ), + }); + useRenderTool({ + name: "read_ag_ui_protocol_docs", + agentId: "ag_ui_docs_copilot", + parameters: z.object({ heading: z.string().optional() }), + render: ({ status, args, result }: any) => ( + + ), + }); + + useConfigureSuggestions({ + suggestions: [ + { + title: "Stream an existing agent", + message: + "Show me how to transfer an existing OpenAI Agents SDK streaming run to AG-UI. Give the smallest FastAPI endpoint using AGUITranslator.to_openai(), Runner.run_streamed(), AGUITranslator.to_agui(), EventEncoder, and StreamingResponse. Explain only the data flow at each boundary.", + }, + { + title: "Map SDK events to AG-UI", + message: + "Give me one concise table that maps OpenAI Agents SDK streaming events and items to AG-UI events. Include run lifecycle, text messages, tool calls and results, reasoning, state snapshots, messages snapshots, and errors. Include only mappings supported by this integration.", + }, + { + title: "Choose an integration layer", + message: + "Compare the three integration APIs: AGUITranslator, OpenAIAgentsAgent, and add_openai_agents_fastapi_endpoint. Give one concise table with what each does, when to choose it, and how much control it keeps over the SDK agent and server.", + }, + ], + available: "always", + consumerAgentId: "ag_ui_docs_copilot", + }); + + return ( +
+
+ +
+
+ ); +}; + +function DocsLookupProgress({ + source, + status, + heading, + result, +}: { + source: "AG-UI OpenAI Agents" | "AG-UI Protocol"; + status: "inProgress" | "executing" | "complete"; + heading?: string; + result?: string; +}) { + const complete = status === "complete"; + const missed = complete && !!result && result.startsWith("No section matches"); + + return ( +
+ + {missed ? "!" : complete ? "โœ“" : "๐Ÿ“–"} + +
+
+ + {source} docs + + {!complete && ( + + โ€ข + + โ€ข + + + โ€ข + + + )} +
+
+ {heading + ? `${complete ? "Read" : "Reading"} "${heading}"` + : complete + ? "Section read" + : "Opening the guide"} +
+
+
+ ); +} + +export default AGUIDocsCopilot; diff --git a/apps/dojo/src/app/[integrationId]/feature/(v2)/custom_lifecycle_events/README.mdx b/apps/dojo/src/app/[integrationId]/feature/(v2)/custom_lifecycle_events/README.mdx new file mode 100644 index 0000000000..ac7db1ef3d --- /dev/null +++ b/apps/dojo/src/app/[integrationId]/feature/(v2)/custom_lifecycle_events/README.mdx @@ -0,0 +1,30 @@ +# ๐Ÿช Custom Lifecycle Events + +## What This Demo Shows + +A custom AG-UI event can carry application-specific information alongside the +standard run events. This example emits a `run_usage` event containing the +actual input and output token counts, then displays those counts beneath the +completed assistant message. + +The usage is emitted at the end of the run because final token totals are only +available after the OpenAI Agents SDK stream has completed. + +## How to Interact + +- Ask: "Say hi in one sentence." +- Ask: "Tell me one interesting fact about AI." +- After the response completes, check the input and output token counts shown + directly below it. + +## Technical Details + +- The backend uses `AGUITranslator` directly so it can access the + `RunResultStreaming` object and its final usage totals. +- It passes a `CustomEvent` named `run_usage` through the translator's + `end_custom_event` option. The event is emitted immediately before + `RUN_FINISHED`. +- The event value is resolved after the stream finishes, when + `context_wrapper.usage` contains the actual totals. +- The frontend subscribes to `run_usage`, associates it with the completed + assistant message, and renders a compact usage label. diff --git a/apps/dojo/src/app/[integrationId]/feature/(v2)/custom_lifecycle_events/page.tsx b/apps/dojo/src/app/[integrationId]/feature/(v2)/custom_lifecycle_events/page.tsx new file mode 100644 index 0000000000..dda49c6a22 --- /dev/null +++ b/apps/dojo/src/app/[integrationId]/feature/(v2)/custom_lifecycle_events/page.tsx @@ -0,0 +1,134 @@ +"use client"; + +import React, { useEffect, useMemo, useRef, useSyncExternalStore } from "react"; +import "@copilotkit/react-core/v2/styles.css"; +import { + CopilotChat, + CopilotKitProvider, + useAgent, + useConfigureSuggestions, +} from "@copilotkit/react-core/v2"; + +interface CustomLifecycleEventsProps { + params: Promise<{ integrationId: string }>; +} + +interface RunUsage { + inputTokens: number; + outputTokens: number; +} + +const usageByMessageId = new Map(); +const usageListeners = new Set<() => void>(); + +function setMessageUsage(messageId: string, usage: RunUsage) { + usageByMessageId.set(messageId, usage); + usageListeners.forEach((listener) => listener()); +} + +function subscribeUsage(listener: () => void) { + usageListeners.add(listener); + return () => usageListeners.delete(listener); +} + +function UsageLabel({ + message, + position, +}: { + message: { id: string; role: string }; + position: "before" | "after"; +}) { + const usage = useSyncExternalStore( + subscribeUsage, + () => usageByMessageId.get(message.id), + () => undefined, + ); + if (message.role !== "assistant" || position !== "after" || !usage) { + return null; + } + + return ( +
+ โฌ‡๏ธ {usage.inputTokens} input tokens ยท โฌ†๏ธ {usage.outputTokens} output + tokens +
+ ); +} + +const CustomLifecycleEvents: React.FC = ({ + params, +}) => { + const { integrationId } = React.use(params); + const renderCustomMessages = useMemo(() => [{ render: UsageLabel }], []); + + return ( + + + + ); +}; + +const Chat = () => { + const { agent } = useAgent({ agentId: "custom_lifecycle_events" }); + const completedMessageId = useRef(null); + + useConfigureSuggestions({ + suggestions: [ + { title: "Say hi", message: "Say hi in one sentence." }, + { + title: "Tell me a fact about AI", + message: "Tell me one interesting fact about AI.", + }, + ], + available: "before-first-message", + consumerAgentId: "custom_lifecycle_events", + }); + + useEffect(() => { + const subscription = agent.subscribe({ + onRunStartedEvent: () => { + completedMessageId.current = null; + }, + onMessagesSnapshotEvent: ({ event }) => { + const assistant = [...event.messages] + .reverse() + .find((message) => message.role === "assistant"); + completedMessageId.current = assistant?.id ?? null; + }, + onCustomEvent: ({ event }) => { + if (event.name !== "run_usage") return; + if (!completedMessageId.current) return; + + const value = event.value as { + input_tokens: number; + output_tokens: number; + }; + setMessageUsage(completedMessageId.current, { + inputTokens: value.input_tokens, + outputTokens: value.output_tokens, + }); + }, + }); + return () => subscription.unsubscribe(); + }, [agent]); + + return ( +
+
+ +
+
+ ); +}; + +export default CustomLifecycleEvents; diff --git a/apps/dojo/src/app/[integrationId]/feature/(v2)/dynamic_system_prompt/README.mdx b/apps/dojo/src/app/[integrationId]/feature/(v2)/dynamic_system_prompt/README.mdx new file mode 100644 index 0000000000..04ba4ca74f --- /dev/null +++ b/apps/dojo/src/app/[integrationId]/feature/(v2)/dynamic_system_prompt/README.mdx @@ -0,0 +1,27 @@ +# ๐ŸŒ Dynamic System Prompt + +## What This Demo Shows + +The frontend sends the selected reply language through the AG-UI context +channel. The backend reads that context on every run and uses the OpenAI Agents +SDK dynamic-instructions API to rebuild the agent's system prompt. + +Changing the language affects the next response without restarting the server +or storing the selection in agent state. + +## How to Interact + +- Select English, Arabic, or German in the sidebar, then ask a question. +- Change the language and send another message. The next response follows the + new selection immediately. + +## Technical Details + +- `useAgentContext` adds `{ description: "Reply language", value: language }` + to `RunAgentInput.context` on every request. +- `OpenAIAgentsAgent` forwards the translated context to + `Runner.run_streamed(..., context=...)`. +- The agent's `instructions` callable reads `RunContextWrapper.context` and + returns a new system prompt for that run. +- Context is read-only request information in this example; no shared-state + bridge is required. diff --git a/apps/dojo/src/app/[integrationId]/feature/(v2)/dynamic_system_prompt/page.tsx b/apps/dojo/src/app/[integrationId]/feature/(v2)/dynamic_system_prompt/page.tsx new file mode 100644 index 0000000000..f8673be2c3 --- /dev/null +++ b/apps/dojo/src/app/[integrationId]/feature/(v2)/dynamic_system_prompt/page.tsx @@ -0,0 +1,85 @@ +"use client"; +import React, { useState } from "react"; +import "@copilotkit/react-core/v2/styles.css"; +import { CopilotChat, useAgentContext } from "@copilotkit/react-core/v2"; +import { CopilotKit } from "@copilotkit/react-core"; + +interface DynamicSystemPromptProps { + params: Promise<{ + integrationId: string; + }>; +} + +type Language = "English" | "Arabic" | "German"; + +const LANGUAGES: { value: Language; label: string; flag: string }[] = [ + { value: "English", label: "English", flag: "๐Ÿ‡ฌ๐Ÿ‡ง" }, + { value: "Arabic", label: "Arabic", flag: "๐Ÿ‡ธ๐Ÿ‡ฆ" }, + { value: "German", label: "German", flag: "๐Ÿ‡ฉ๐Ÿ‡ช" }, +]; + +const DynamicSystemPrompt: React.FC = ({ params }) => { + const { integrationId } = React.use(params); + + return ( + + + + ); +}; + +const DynamicSystemPromptView = () => { + const [language, setLanguage] = useState("English"); + + // The only thing this demo sends: one context item the agent's + // instructions callable reads on every turn to pick the reply language. + // No tool, no state โ€” just the AG-UI context channel. + useAgentContext({ + description: "Reply language", + value: language, + }); + + return ( +
+
+

Reply language

+
+ {LANGUAGES.map(({ value, label, flag }) => ( + + ))} +
+

+ Switching language updates the AG-UI context sent with the next + message โ€” the agent's system prompt is rebuilt every turn to + match. +

+
+ +
+
+ +
+
+
+ ); +}; + +export default DynamicSystemPrompt; diff --git a/apps/dojo/src/app/[integrationId]/feature/(v2)/human_in_the_loop_approval/README.mdx b/apps/dojo/src/app/[integrationId]/feature/(v2)/human_in_the_loop_approval/README.mdx new file mode 100644 index 0000000000..ee9c472376 --- /dev/null +++ b/apps/dojo/src/app/[integrationId]/feature/(v2)/human_in_the_loop_approval/README.mdx @@ -0,0 +1,31 @@ +--- +title: Human in the Loop Approval +description: Pause a backend tool call until a person approves or rejects it. +--- + +# ๐Ÿค Human in the Loop Approval + +## What This Demo Shows + +A backend-owned refund tool pauses before execution and asks the user for an +explicit decision. Approving runs the tool; rejecting resumes the agent +without issuing the refund. + +## How to Interact + +- Ask: "Refund order ORD-1001." +- Select **Approve** to execute the refund, or **Reject** to continue without + executing it. +- Ask about the order afterward to see how the agent handled the decision. + +## Technical Details + +- `issue_refund` uses the OpenAI Agents SDK's `needs_approval=True` option. +- After the SDK reports an interruption, the backend saves `result.to_state()` + under the AG-UI `thread_id` and emits an `approval_request` custom event. +- The frontend sends the selected decision and matching tool `call_id` in the + next request's `forwarded_props`. +- The backend applies `state.approve()` or `state.reject()` and resumes the + saved OpenAI Agents SDK run. +- This demo keeps paused runs in process memory. Production applications need + durable, concurrency-safe storage shared by all server instances. diff --git a/apps/dojo/src/app/[integrationId]/feature/(v2)/human_in_the_loop_approval/page.tsx b/apps/dojo/src/app/[integrationId]/feature/(v2)/human_in_the_loop_approval/page.tsx new file mode 100644 index 0000000000..3f677f7625 --- /dev/null +++ b/apps/dojo/src/app/[integrationId]/feature/(v2)/human_in_the_loop_approval/page.tsx @@ -0,0 +1,166 @@ +"use client"; +import React, { useState } from "react"; +import "@copilotkit/react-core/v2/styles.css"; +import { + CopilotChat, + useAgent, + useCopilotKit, + useConfigureSuggestions, +} from "@copilotkit/react-core/v2"; +import { CopilotKit } from "@copilotkit/react-core"; + +interface ApprovalProps { + params: Promise<{ integrationId: string }>; +} + +interface PendingApproval { + callId: string; + toolName: string; + arguments: string; +} + +const Approval: React.FC = ({ params }) => { + const { integrationId } = React.use(params); + + return ( + + + + ); +}; + +const Chat = () => { + const { agent } = useAgent({ agentId: "human_in_the_loop_approval" }); + const { copilotkit } = useCopilotKit(); + const [pending, setPending] = useState(null); + const [resolvedCallId, setResolvedCallId] = useState(null); + + useConfigureSuggestions({ + suggestions: [ + { title: "Refund an order", message: "I'd like a refund for ORD-1001." }, + { title: "Refund another order", message: "Please refund ORD-1002." }, + { title: "Unknown order", message: "Can you refund ORD-9999?" }, + ], + available: "always", + consumerAgentId: "human_in_the_loop_approval", + }); + + React.useEffect(() => { + const subscription = agent.subscribe({ + onCustomEvent: ({ event }) => { + if (event.name !== "approval_request") return; + // One CustomEvent can carry several pending calls if the model made + // more than one needs_approval call in a turn; this demo only ever + // triggers one, so showing the first is enough here. + const pendingList = event.value as Array<{ + call_id: string; + tool_name: string; + arguments: string; + }>; + const first = pendingList[0]; + if (!first) return; + setResolvedCallId(null); + setPending({ + callId: first.call_id, + toolName: first.tool_name, + arguments: first.arguments, + }); + }, + onRunFinishedEvent: () => setResolvedCallId(null), + onRunErrorEvent: () => setResolvedCallId(null), + }); + return () => subscription.unsubscribe(); + }, [agent]); + + const respond = (approve: boolean) => { + if (!pending) return; + setResolvedCallId(pending.callId); + copilotkit.runAgent({ + agent, + forwardedProps: { + approval: { call_id: pending.callId, approve }, + }, + }); + setPending(null); + }; + + return ( +
+ {pending && ( + respond(true)} + onReject={() => respond(false)} + /> + )} + {!pending && resolvedCallId && ( +
+ Decision sent โ€” waiting for the agent... +
+ )} +
+ +
+
+ ); +}; + +function ApprovalCard({ + pending, + onApprove, + onReject, +}: { + pending: PendingApproval; + onApprove: () => void; + onReject: () => void; +}) { + let args: Record = {}; + try { + args = JSON.parse(pending.arguments); + } catch { + // leave args empty โ€” raw string shown below is enough context either way + } + + return ( +
+

Approval needed

+

+ The agent wants to call{" "} + {pending.toolName} with: +

+
+        {JSON.stringify(args, null, 2)}
+      
+
+ + +
+
+ ); +} + +export default Approval; diff --git a/apps/dojo/src/app/[integrationId]/feature/(v2)/subagents/README.mdx b/apps/dojo/src/app/[integrationId]/feature/(v2)/subagents/README.mdx new file mode 100644 index 0000000000..997e70953e --- /dev/null +++ b/apps/dojo/src/app/[integrationId]/feature/(v2)/subagents/README.mdx @@ -0,0 +1,27 @@ +# ๐Ÿงญ Subagents + +## What This Demo Shows + +A supervisor agent delegates a writing task to three specialist agents: +researcher, writer, and critic. The supervisor remains responsible for the +conversation, calls the specialists as tools, and produces the final response. + +The sidebar shows which specialist is working and records every delegation in +order. Simple questions can be answered directly without calling the team. + +## How to Interact + +- Ask: "Write a short piece about the history of coffee." The supervisor + calls the researcher, writer, and critic before returning the final draft. +- Ask: "What is 2 + 2?" The supervisor answers directly without delegating. + +## Technical Details + +- The OpenAI Agents SDK exposes each specialist through `Agent.as_tool()`. + This is call-and-return delegation; it is not a handoff of the conversation. +- Each delegation becomes an AG-UI tool-call sequence followed by a + `TOOL_CALL_RESULT` event. +- A `useRenderTool` renderer for each specialist updates the role indicators + and delegation log. +- The specialists' internal model turns remain inside the OpenAI Agents SDK; + the client receives one supervisor-owned conversation. diff --git a/apps/dojo/src/app/[integrationId]/feature/(v2)/subagents/page.tsx b/apps/dojo/src/app/[integrationId]/feature/(v2)/subagents/page.tsx new file mode 100644 index 0000000000..6d35af32da --- /dev/null +++ b/apps/dojo/src/app/[integrationId]/feature/(v2)/subagents/page.tsx @@ -0,0 +1,245 @@ +"use client"; +import React, { useCallback, useEffect, useState } from "react"; +import "@copilotkit/react-core/v2/styles.css"; +import { CopilotChat, useConfigureSuggestions, useRenderTool } from "@copilotkit/react-core/v2"; +import { CopilotKit } from "@copilotkit/react-core"; +import { Badge } from "@/components/ui/badge"; +import { z } from "zod"; + +interface SubagentsProps { + params: Promise<{ + integrationId: string; + }>; +} + +type Role = "research" | "writer" | "critic"; +type Status = "inProgress" | "executing" | "complete"; + +const ROLES: { role: Role; label: string; emoji: string }[] = [ + { role: "research", label: "Researcher", emoji: "๐Ÿ”Ž" }, + { role: "writer", label: "Writer", emoji: "โœ๏ธ" }, + { role: "critic", label: "Critic", emoji: "๐Ÿง" }, +]; + +interface DelegationEntry { + id: string; + role: Role; + status: Status; + text: string; +} + +const Subagents: React.FC = ({ params }) => { + const { integrationId } = React.use(params); + + return ( + + + + ); +}; + +const SubagentsView = () => { + const [active, setActive] = useState>({ + research: null, + writer: null, + critic: null, + }); + const [log, setLog] = useState([]); + + // Called by each DelegationTracker instance as its tool call's status + // changes. Same shared state feeds both the role chips (who's working + // right now) and the log (what happened, in order). + const track = useCallback((entry: DelegationEntry) => { + setActive((prev) => ({ + ...prev, + [entry.role]: entry.status === "complete" ? null : entry.status, + })); + setLog((prev) => { + const idx = prev.findIndex((e) => e.id === entry.id); + if (idx === -1) return [...prev, entry]; + const next = [...prev]; + next[idx] = entry; + return next; + }); + }, []); + + useConfigureSuggestions({ + suggestions: [ + { + title: "History of AI", + message: "Write a short article about the history of AI", + }, + { + title: "History of programming languages", + message: "Write a short piece about the history of programming languages", + }, + ], + available: "always", + consumerAgentId: "subagents", + }); + + useRenderTool({ + name: "research_topic", + agentId: "subagents", + parameters: z.object({ input: z.string().optional() }), + render: ({ toolCallId, status, args, result }: any) => ( + + ), + }); + + useRenderTool({ + name: "write_prose", + agentId: "subagents", + parameters: z.object({ input: z.string().optional() }), + render: ({ toolCallId, status, args, result }: any) => ( + + ), + }); + + useRenderTool({ + name: "critique_draft", + agentId: "subagents", + parameters: z.object({ input: z.string().optional() }), + render: ({ toolCallId, status, args, result }: any) => ( + + ), + }); + + return ( +
+
+

Supervisor's team

+
+ {ROLES.map(({ role, label, emoji }) => { + const status = active[role]; + return ( +
+ {emoji} + {label} + {status && ( + + {status === "executing" ? "working" : "starting"} + + )} +
+ ); + })} +
+ +

+ Delegation log +

+
+ {log.length === 0 && ( +
No delegations yet.
+ )} + {log.map((entry) => { + const roleInfo = ROLES.find((r) => r.role === entry.role); + return ( +
+
+ + {roleInfo?.emoji} {roleInfo?.label} + + + {entry.status} + +
+ {entry.text && ( +
+ {entry.text} +
+ )} +
+ ); + })} +
+
+ +
+
+ +
+
+
+ ); +}; + +// Each tool call renders its own instance of this. `render` is invoked as a +// real component (not a plain function), so effects are legal here โ€” this +// is what reports status upstream into the shared chip/log state on every +// inProgress โ†’ executing โ†’ complete transition, in addition to rendering +// its own inline card in the chat transcript. +function DelegationTracker({ + role, + toolCallId, + status, + preview, + result, + onUpdate, +}: { + role: Role; + toolCallId: string; + status: Status; + preview?: string; + result?: string; + onUpdate: (entry: DelegationEntry) => void; +}) { + const text = status === "complete" ? (result ?? "") : (preview ?? ""); + + useEffect(() => { + onUpdate({ id: toolCallId, role, status, text }); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [toolCallId, role, status, text]); + + const roleInfo = ROLES.find((r) => r.role === role)!; + const label = + status === "complete" + ? `${roleInfo.label} finished` + : status === "executing" + ? `${roleInfo.label} workingโ€ฆ` + : `Calling ${roleInfo.label.toLowerCase()}โ€ฆ`; + + return ( +
+ {roleInfo.emoji} {label} +
+ ); +} + +export default Subagents; diff --git a/apps/dojo/src/app/globals.css b/apps/dojo/src/app/globals.css index 28208d8591..490a90592f 100644 --- a/apps/dojo/src/app/globals.css +++ b/apps/dojo/src/app/globals.css @@ -309,7 +309,7 @@ } @utility mix-from-* { - --tw-mix-from-color: --value(--color-*, color, [color]); + --tw-mix-from-color: --value(--color-*, [color]); --tw-mix-from-opacity: --modifier(--opacity-*, [percentage]); --tw-mix-from-opacity: calc(--modifier(number) * 1%); --tw-mix-from-opacity: calc(--modifier([number]) * 100%); @@ -326,7 +326,7 @@ } @utility mix-to-* { - --tw-mix-to-color: --value(--color-*, color, [color]); + --tw-mix-to-color: --value(--color-*, [color]); --tw-mix-to-opacity: --modifier(--opacity-*, [percentage]); --tw-mix-to-opacity: calc(--modifier(number) * 1%); --tw-mix-to-opacity: calc(--modifier([number]) * 100%); diff --git a/apps/dojo/src/config.ts b/apps/dojo/src/config.ts index fdb6f1d230..98e215c91d 100644 --- a/apps/dojo/src/config.ts +++ b/apps/dojo/src/config.ts @@ -17,6 +17,12 @@ function createFeatureConfig({ } export const featureConfig: FeatureConfig[] = [ + createFeatureConfig({ + id: "ag_ui_docs_copilot", + name: "AG-UI Docs Copilot", + description: "Ask questions about the OpenAI Agents integration and delegate code snippets to a specialist agent", + tags: ["AG-UI", "OpenAI Agents", "Documentation", "Copilot"], + }), createFeatureConfig({ id: "agentic_chat", name: "Agentic Chat", @@ -36,6 +42,13 @@ export const featureConfig: FeatureConfig[] = [ "Plan a task together and direct the Copilot to take the right steps", tags: ["HITL", "Interactivity"], }), + createFeatureConfig({ + id: "human_in_the_loop_approval", + name: "Human in the Loop (Backend Approval)", + description: + "A real backend tool (issue a refund) pauses for human approval before it runs, then resumes", + tags: ["HITL", "Interactivity", "Approval"], + }), createFeatureConfig({ id: "interrupt", name: "Interrupt (Suspend/Resume)", @@ -88,6 +101,27 @@ export const featureConfig: FeatureConfig[] = [ "Have your tasks performed by multiple agents, working together", tags: ["Chat", "Multi-agent architecture", "Streaming", "Subgraphs"], }), + createFeatureConfig({ + id: "subagents", + name: "Sub-agents", + description: + "Supervisor agent calls specialist agents as tools and synthesizes the result", + tags: ["Multi-agent architecture", "Sub-agents", "Orchestrator", "Agent as tool"], + }), + createFeatureConfig({ + id: "custom_lifecycle_events", + name: "Custom Lifecycle Events", + description: + "Manual CUSTOM events bracket the run โ€” a session marker right after start, a usage summary right before finish", + tags: ["Custom Events", "Lifecycle", "Translator"], + }), + createFeatureConfig({ + id: "dynamic_system_prompt", + name: "Dynamic System Prompt", + description: + "Reply language picked from the AG-UI context channel, baked fresh into the system prompt every turn", + tags: ["Context", "Dynamic System Prompt"], + }), createFeatureConfig({ id: "a2a_chat", name: "A2A Chat", diff --git a/apps/dojo/src/env.ts b/apps/dojo/src/env.ts index 5edd877153..8e481f1f38 100644 --- a/apps/dojo/src/env.ts +++ b/apps/dojo/src/env.ts @@ -25,6 +25,7 @@ type envVars = { awsStrandsTypescriptUrl: string; claudeAgentSdkPythonUrl: string; claudeAgentSdkTypescriptUrl: string; + openaiAgentsPythonUrl: string; langroidUrl: string; watsonxRegion: string; watsonxInstanceId: string; @@ -84,6 +85,8 @@ export default function getEnvVars(): envVars { process.env.CLAUDE_AGENT_SDK_PYTHON_URL || "http://localhost:8019", claudeAgentSdkTypescriptUrl: process.env.CLAUDE_AGENT_SDK_TYPESCRIPT_URL || "http://localhost:8020", + openaiAgentsPythonUrl: + process.env.OPENAI_AGENTS_PYTHON_URL || "http://localhost:8024", langroidUrl: process.env.LANGROID_URL || "http://localhost:8021", watsonxRegion: process.env.WATSONX_REGION || "", watsonxInstanceId: process.env.WATSONX_INSTANCE_ID || "", diff --git a/apps/dojo/src/files.json b/apps/dojo/src/files.json index 4bf043fcea..e4b90dd8b6 100644 --- a/apps/dojo/src/files.json +++ b/apps/dojo/src/files.json @@ -4944,5 +4944,197 @@ "language": "markdown", "type": "file" } + ], + "openai-agents-python::ag_ui_docs_copilot": [ + { + "name": "page.tsx", + "content": "\"use client\";\n\nimport React from \"react\";\nimport { CopilotKit } from \"@copilotkit/react-core\";\nimport {\n CopilotChat,\n useConfigureSuggestions,\n useRenderTool,\n} from \"@copilotkit/react-core/v2\";\nimport \"@copilotkit/react-core/v2/styles.css\";\nimport { z } from \"zod\";\n\ninterface AGUIDocsCopilotProps {\n params: Promise<{ integrationId: string }>;\n}\n\nconst AGUIDocsCopilot: React.FC = ({ params }) => {\n const { integrationId } = React.use(params);\n\n return (\n \n \n \n );\n};\n\nconst DocsChat = () => {\n useRenderTool({\n name: \"read_ag_ui_openai_agents_docs\",\n agentId: \"ag_ui_docs_copilot\",\n parameters: z.object({ heading: z.string().optional() }),\n render: ({ status, args, result }: any) => (\n \n ),\n });\n useRenderTool({\n name: \"read_ag_ui_protocol_docs\",\n agentId: \"ag_ui_docs_copilot\",\n parameters: z.object({ heading: z.string().optional() }),\n render: ({ status, args, result }: any) => (\n \n ),\n });\n\n useConfigureSuggestions({\n suggestions: [\n {\n title: \"Stream an existing agent\",\n message:\n \"Show me how to transfer an existing OpenAI Agents SDK streaming run to AG-UI. Give the smallest FastAPI endpoint using AGUITranslator.to_openai(), Runner.run_streamed(), AGUITranslator.to_agui(), EventEncoder, and StreamingResponse. Explain only the data flow at each boundary.\",\n },\n {\n title: \"Map SDK events to AG-UI\",\n message:\n \"Give me one concise table that maps OpenAI Agents SDK streaming events and items to AG-UI events. Include run lifecycle, text messages, tool calls and results, reasoning, state snapshots, messages snapshots, and errors. Include only mappings supported by this integration.\",\n },\n {\n title: \"Choose an integration layer\",\n message:\n \"Compare the three integration APIs: AGUITranslator, OpenAIAgentsAgent, and add_openai_agents_fastapi_endpoint. Give one concise table with what each does, when to choose it, and how much control it keeps over the SDK agent and server.\",\n },\n ],\n available: \"always\",\n });\n\n return (\n
\n
\n \n
\n
\n );\n};\n\nfunction DocsLookupProgress({\n source,\n status,\n heading,\n result,\n}: {\n source: \"AG-UI OpenAI Agents\" | \"AG-UI Protocol\";\n status: \"inProgress\" | \"executing\" | \"complete\";\n heading?: string;\n result?: string;\n}) {\n const complete = status === \"complete\";\n const missed = complete && !!result && result.startsWith(\"No section matches\");\n\n return (\n
\n \n {missed ? \"!\" : complete ? \"โœ“\" : \"๐Ÿ“–\"}\n \n
\n
\n \n {source} docs\n \n {!complete && (\n \n โ€ข\n \n โ€ข\n \n \n โ€ข\n \n \n )}\n
\n
\n {heading\n ? `${complete ? \"Read\" : \"Reading\"} \"${heading}\"`\n : complete\n ? \"Section read\"\n : \"Opening the guide\"}\n
\n
\n
\n );\n}\n\nexport default AGUIDocsCopilot;\n", + "language": "typescript", + "type": "file" + }, + { + "name": "README.mdx", + "content": "# ๐Ÿ“š AG-UI Docs Copilot\n\n## What This Demo Shows\n\nA developer assistant that answers questions using the documentation packaged\nwith `ag-ui-protocol` and `ag-ui-openai-agents`.\n\nThe agent receives the available README headings in its instructions. When a\nquestion needs documentation, it calls one of two backend tools to load only\nthe relevant section from the installed Python package metadata. The tool call\nand its progress are rendered in the chat.\n\nNo internet search or external retrieval service is used.\n\n## How to Interact\n\n- Ask how `AGUITranslator.to_openai()` and `to_agui()` connect an application\n to AG-UI.\n- Ask for a minimal FastAPI streaming endpoint.\n- Ask how messages, tools, state, lifecycle events, or errors are translated.\n\n## Technical Details\n\n- `read_ag_ui_protocol_docs` reads the AG-UI Protocol Python README.\n- `read_ag_ui_openai_agents_docs` reads the OpenAI Agents integration README.\n- Each tool receives a heading and returns one matching Markdown section,\n keeping the full documents out of the model prompt.\n- The backend uses `AGUITranslator` directly around\n `Runner.run_streamed()`, then encodes the resulting AG-UI events as SSE.\n- For a larger documentation collection, replace the heading lookup with an\n application-appropriate retrieval system.\n", + "language": "markdown", + "type": "file" + }, + { + "name": "ag_ui_docs_copilot.py", + "content": "\"\"\"AG-UI documentation assistant that reads local README sections on demand.\"\"\"\n\nfrom __future__ import annotations\n\nimport re\nfrom importlib.metadata import metadata\n\nfrom agents import Agent, Runner, function_tool\nfrom fastapi import FastAPI, Request\nfrom fastapi.responses import StreamingResponse\n\nfrom ag_ui.core import RunAgentInput\nfrom ag_ui.encoder import EventEncoder\nfrom ag_ui_openai_agents import AGUITranslator\nfrom .constants import DEFAULT_MODEL\n\n_HEADING_RE = re.compile(r\"^#{1,6}\\s+(.+)$\")\n\n\ndef _load_distribution_readme(distribution: str) -> str:\n \"\"\"Load the installed package README from its distribution metadata.\"\"\"\n document = metadata(distribution).get_payload()\n if not isinstance(document, str) or not document.strip():\n raise RuntimeError(f\"{distribution} has no README in its package metadata\")\n return document\n\n\nclass MarkdownSections:\n \"\"\"A Markdown document split by heading, read one section at a time.\n\n The Copilot sees only the heading list in its instructions and fetches\n the sections it needs on demand, so the full document never sits in a\n prompt.\n \"\"\"\n\n def __init__(self, document: str) -> None:\n sections: dict[str, list[str]] = {}\n heading = \"Overview\"\n for line in document.splitlines():\n match = _HEADING_RE.match(line)\n if match:\n heading = match.group(1).strip()\n sections.setdefault(heading, []).append(line)\n self._sections = {\n name: content\n for name, lines in sections.items()\n if (content := \"\\n\".join(lines).strip())\n }\n\n @property\n def headings(self) -> str:\n \"\"\"The table of contents: one heading per line.\"\"\"\n return \"\\n\".join(f\"- {name}\" for name in self._sections)\n\n def read(self, heading: str) -> str:\n \"\"\"Return one section by heading (case-insensitive, substring ok).\"\"\"\n wanted = heading.strip().lstrip(\"#\").strip().lower()\n for name, content in self._sections.items():\n if name.lower() == wanted:\n return content\n for name, content in self._sections.items():\n if wanted in name.lower():\n return content\n return f\"No section matches {heading!r}. Available headings:\\n{self.headings}\"\n\n\n# ag-ui-protocol docs: the core Python SDK README.\nAG_UI_PROTOCOL_DOCS = _load_distribution_readme(\"ag-ui-protocol\")\n_AG_UI_PROTOCOL_SECTIONS = MarkdownSections(AG_UI_PROTOCOL_DOCS)\n\n\n@function_tool\ndef read_ag_ui_protocol_docs(heading: str) -> str:\n \"\"\"Read one AG-UI Protocol Python documentation section by its heading.\n\n Args:\n heading: A heading from the \"AG-UI Protocol docs\" table of contents\n in your instructions.\n \"\"\"\n return _AG_UI_PROTOCOL_SECTIONS.read(heading)\n\n\n# ag-ui-openai-agents docs: this integration's README.\nAG_UI_OPENAI_AGENTS_DOCS = _load_distribution_readme(\"ag-ui-openai-agents\")\n_AG_UI_OPENAI_AGENTS_SECTIONS = MarkdownSections(AG_UI_OPENAI_AGENTS_DOCS)\n\n\n@function_tool\ndef read_ag_ui_openai_agents_docs(heading: str) -> str:\n \"\"\"Read one AG-UI OpenAI Agents integration documentation section.\n\n Args:\n heading: A heading from the \"AG-UI OpenAI Agents docs\" table of\n contents in your instructions.\n \"\"\"\n return _AG_UI_OPENAI_AGENTS_SECTIONS.read(heading)\n\n\ncopilot_instructions = f\"\"\"You are the developer-facing Copilot for an AG-UI\napplication. Answer only what the user asks. Be clear, practical, and concise;\ndo not add a tutorial, unrelated details, alternatives, or follow-up work\nunless requested. Handle ordinary conversation directly.\n\nFor any question about AG-UI, the OpenAI Agents SDK, translator APIs, FastAPI\nendpoints, streaming, tools, client tools, state, lifecycle events, errors,\ntests, or Python implementation, pick the most relevant heading from the\nmatching table of contents below and call the matching tool before answering.\nUse read_ag_ui_openai_agents_docs for OpenAI Agents SDK integration questions\nand read_ag_ui_protocol_docs for ag_ui.core protocol types and EventEncoder\nquestions. Treat the returned section as your only source of truth. Read\nanother section if the first one is not enough.\n\nUse only the part of the section that answers the user's question. Do not\ninvent behavior or claim details the documentation did not provide. For code\nrequests, provide only the smallest complete, production-readable Python\nsnippet needed, followed by a short explanation. Use documented APIs only.\n\nAG-UI Protocol docs table of contents:\n{_AG_UI_PROTOCOL_SECTIONS.headings}\n\nAG-UI OpenAI Agents docs table of contents:\n{_AG_UI_OPENAI_AGENTS_SECTIONS.headings}\n\"\"\"\n\ncopilot_agent = Agent(\n name=\"AG-UI Docs Copilot\",\n model=DEFAULT_MODEL,\n instructions=copilot_instructions,\n tools=[read_ag_ui_protocol_docs, read_ag_ui_openai_agents_docs],\n)\n\n# AGUI Translator Integration\napp = FastAPI(title=\"AG-UI Docs Copilot\")\ntranslator = AGUITranslator()\n\n\n@app.post(\"/\")\nasync def run_ag_ui_docs_copilot(\n body: RunAgentInput, request: Request\n) -> StreamingResponse:\n \"\"\"Translate one AG-UI request into an SDK run and stream it back.\"\"\"\n encoder = EventEncoder(accept=request.headers.get(\"accept\"))\n\n async def stream():\n # AGUI input -> OpenAI SDK\n translated_input = translator.to_openai(body)\n\n # normal OpenAI SDK streaming run\n result = Runner.run_streamed(\n copilot_agent,\n input=translated_input.messages,\n context=translated_input.context,\n )\n\n # OpenAI SDK -> AGUI events\n async for event in translator.to_agui(result, body):\n yield encoder.encode(event)\n\n return StreamingResponse(stream(), media_type=encoder.get_content_type())\n", + "language": "python", + "type": "file" + } + ], + "openai-agents-python::agentic_chat": [ + { + "name": "page.tsx", + "content": "\"use client\";\nimport React, { useState } from \"react\";\nimport \"@copilotkit/react-core/v2/styles.css\";\nimport { \n useFrontendTool,\n useRenderTool,\n useAgentContext,\n useConfigureSuggestions,\n CopilotChat,\n} from \"@copilotkit/react-core/v2\";\nimport { z } from \"zod\";\nimport { CopilotKit } from \"@copilotkit/react-core\";\n\ninterface AgenticChatProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\nconst AgenticChat: React.FC = ({ params }) => {\n const { integrationId } = React.use(params);\n\n return (\n \n \n \n );\n};\n\nconst Chat = () => {\n const [background, setBackground] = useState(\"--copilot-kit-background-color\");\n\n useAgentContext({\n description: 'Name of the user',\n value: 'Bob'\n });\n\n useFrontendTool({\n name: \"change_background\",\n description:\n \"Change the background color of the chat. Can be anything that the CSS background attribute accepts. Regular colors, linear of radial gradients etc.\",\n parameters: z.object({\n background: z.string().describe(\"The background. Prefer gradients. Only use when asked.\"),\n }) ,\n handler: async ({ background }: { background: string }) => {\n setBackground(background);\n return {\n status: \"success\",\n message: `Background changed to ${background}`,\n };\n },\n });\n\n useRenderTool({\n name: \"get_weather\",\n parameters: z.object({\n location: z.string(),\n }) ,\n render: ({ args, result, status }: any) => {\n if (status !== \"complete\") {\n return
Loading weather...
;\n }\n\n // Some integrations (e.g. LangGraph) deliver tool results as a JSON-encoded\n // string in the ToolMessage content rather than a parsed object. Normalize\n // so property access works in either case; otherwise every field reads as\n // undefined and the card renders empty values.\n let parsed: any = result;\n if (typeof parsed === \"string\") {\n try {\n parsed = JSON.parse(parsed);\n } catch {\n parsed = {};\n }\n }\n parsed = parsed ?? {};\n\n return (\n
\n Weather in {parsed.city ?? args.location}\n
Temperature: {parsed.temperature}ยฐC
\n
Humidity: {parsed.humidity}%
\n
Wind Speed: {parsed.windSpeed ?? parsed.wind_speed} mph
\n
Conditions: {parsed.conditions}
\n
\n );\n },\n });\n\n useConfigureSuggestions({\n suggestions: [\n {\n title: \"Change background\",\n message: \"Change the background to something new.\",\n },\n {\n title: \"Generate sonnet\",\n message: \"Write a short sonnet about AI.\",\n },\n ],\n available: \"always\",\n });\n\n return (\n \n
\n \n
\n \n );\n};\n\nexport default AgenticChat;\n", + "language": "typescript", + "type": "file" + }, + { + "name": "README.mdx", + "content": "# ๐Ÿค– Agentic Chat with Frontend Tools\n\n## What This Demo Shows\n\nThis demo showcases CopilotKit's **agentic chat** capabilities with **frontend\ntool integration**:\n\n1. **Natural Conversation**: Chat with your Copilot in a familiar chat interface\n2. **Frontend Tool Execution**: The Copilot can directly interacts with your UI\n by calling frontend functions\n3. **Seamless Integration**: Tools defined in the frontend and automatically\n discovered and made available to the agent\n\n## How to Interact\n\nTry asking your Copilot to:\n\n- \"Can you change the background color to something more vibrant?\"\n- \"Make the background a blue to purple gradient\"\n- \"Set the background to a sunset-themed gradient\"\n- \"Change it back to a simple light color\"\n\nYou can also chat about other topics - the agent will respond conversationally\nwhile having the ability to use your UI tools when appropriate.\n\n## โœจ Frontend Tool Integration in Action\n\n**What's happening technically:**\n\n- The React component defines a frontend function using `useCopilotAction`\n- CopilotKit automatically exposes this function to the agent\n- When you make a request, the agent determines whether to use the tool\n- The agent calls the function with the appropriate parameters\n- The UI immediately updates in response\n\n**What you'll see in this demo:**\n\n- The Copilot understands requests to change the background\n- It generates CSS values for colors and gradients\n- When it calls the tool, the background changes instantly\n- The agent provides a conversational response about the changes it made\n\nThis technique of exposing frontend functions to your Copilot can be extended to\nany UI manipulation you want to enable, from theme changes to data filtering,\nnavigation, or complex UI state management!\n", + "language": "markdown", + "type": "file" + }, + { + "name": "agentic_chat.py", + "content": "\"\"\"Agentic chat โ€” plain conversation, no tools.\"\"\"\n\nfrom __future__ import annotations\n\nfrom agents import Agent\nfrom fastapi import FastAPI\n\nfrom ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint\nfrom .constants import DEFAULT_MODEL\n\n\ndef create_agentic_chat_agent() -> Agent:\n return Agent(\n name=\"assistant\",\n model=DEFAULT_MODEL,\n instructions=\"You are a helpful assistant. Be concise.\",\n )\n\n\nagent = OpenAIAgentsAgent(create_agentic_chat_agent(), name=\"agentic_chat\")\napp = FastAPI(title=\"Agentic chat AG-UI demo\")\nadd_openai_agents_fastapi_endpoint(app, agent, \"/\")\n", + "language": "python", + "type": "file" + } + ], + "openai-agents-python::backend_tool_rendering": [ + { + "name": "page.tsx", + "content": "\"use client\";\nimport React from \"react\";\nimport \"@copilotkit/react-core/v2/styles.css\";\nimport \"./style.css\";\nimport { \n useRenderTool,\n useConfigureSuggestions,\n CopilotChat,\n} from \"@copilotkit/react-core/v2\";\nimport { z } from \"zod\";\nimport { CopilotKit } from \"@copilotkit/react-core\";\n\ninterface AgenticChatProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\nconst AgenticChat: React.FC = ({ params }) => {\n const { integrationId } = React.use(params);\n\n return (\n \n \n \n );\n};\n\nconst Chat = () => {\n useRenderTool({\n \n name: \"get_weather\",\n parameters: z.object({\n location: z.string(),\n }) ,\n render: ({ args, result, status }: any) => {\n if (status !== \"complete\") {\n return (\n
\n โš™๏ธ Retrieving weather...\n
\n );\n }\n\n // Some integrations (e.g. LangGraph) deliver tool results as a JSON-encoded\n // string in the ToolMessage content rather than a parsed object. Normalize\n // so property access works in either case; otherwise every field falls\n // through to its `|| 0` default and the card shows 0ยฐ C.\n let parsed: any = result;\n if (typeof parsed === \"string\") {\n try {\n parsed = JSON.parse(parsed);\n } catch {\n parsed = {};\n }\n }\n parsed = parsed ?? {};\n\n const weatherResult: WeatherToolResult = {\n temperature: parsed.temperature ?? 0,\n conditions: parsed.conditions ?? \"clear\",\n humidity: parsed.humidity ?? 0,\n windSpeed: parsed.wind_speed ?? parsed.windSpeed ?? 0,\n feelsLike:\n parsed.feels_like ?? parsed.feelsLike ?? parsed.temperature ?? 0,\n };\n\n const themeColor = getThemeColor(weatherResult.conditions);\n\n return (\n \n );\n },\n });\n\n useConfigureSuggestions({\n suggestions: [\n {\n title: \"Weather in San Francisco\",\n message: \"What's the weather like in San Francisco?\",\n },\n {\n title: \"Weather in New York\",\n message: \"Tell me about the weather in New York.\",\n },\n {\n title: \"Weather in Tokyo\",\n message: \"How's the weather in Tokyo today?\",\n },\n ],\n available: \"always\",\n });\n\n return (\n
\n
\n \n
\n
\n );\n};\n\ninterface WeatherToolResult {\n temperature: number;\n conditions: string;\n humidity: number;\n windSpeed: number;\n feelsLike: number;\n}\n\nfunction getThemeColor(conditions: string): string {\n const conditionLower = conditions.toLowerCase();\n if (conditionLower.includes(\"clear\") || conditionLower.includes(\"sunny\")) {\n return \"#667eea\";\n }\n if (conditionLower.includes(\"rain\") || conditionLower.includes(\"storm\")) {\n return \"#4A5568\";\n }\n if (conditionLower.includes(\"cloud\")) {\n return \"#718096\";\n }\n if (conditionLower.includes(\"snow\")) {\n return \"#63B3ED\";\n }\n return \"#764ba2\";\n}\n\nfunction WeatherCard({\n location,\n themeColor,\n result,\n status,\n}: {\n location?: string;\n themeColor: string;\n result: WeatherToolResult;\n status: \"inProgress\" | \"executing\" | \"complete\";\n}) {\n return (\n \n
\n
\n
\n

\n {location}\n

\n

Current Weather

\n
\n \n
\n\n
\n
\n {result.temperature}ยฐ C\n \n {\" / \"}\n {((result.temperature * 9) / 5 + 32).toFixed(1)}ยฐ F\n \n
\n
{result.conditions}
\n
\n\n
\n
\n
\n

Humidity

\n

{result.humidity}%

\n
\n
\n

Wind

\n

{result.windSpeed} mph

\n
\n
\n

Feels Like

\n

{result.feelsLike}ยฐ

\n
\n
\n
\n
\n \n );\n}\n\nfunction WeatherIcon({ conditions }: { conditions: string }) {\n if (!conditions) return null;\n\n if (conditions.toLowerCase().includes(\"clear\") || conditions.toLowerCase().includes(\"sunny\")) {\n return ;\n }\n\n if (\n conditions.toLowerCase().includes(\"rain\") ||\n conditions.toLowerCase().includes(\"drizzle\") ||\n conditions.toLowerCase().includes(\"snow\") ||\n conditions.toLowerCase().includes(\"thunderstorm\")\n ) {\n return ;\n }\n\n if (\n conditions.toLowerCase().includes(\"fog\") ||\n conditions.toLowerCase().includes(\"cloud\") ||\n conditions.toLowerCase().includes(\"overcast\")\n ) {\n return ;\n }\n\n return ;\n}\n\n// Simple sun icon for the weather card\nfunction SunIcon() {\n return (\n \n \n \n \n );\n}\n\nfunction RainIcon() {\n return (\n \n {/* Cloud */}\n \n {/* Rain drops */}\n \n \n );\n}\n\nfunction CloudIcon() {\n return (\n \n \n \n );\n}\n\nexport default AgenticChat;\n", + "language": "typescript", + "type": "file" + }, + { + "name": "style.css", + "content": ".copilotKitInput {\n border-bottom-left-radius: 0.75rem;\n border-bottom-right-radius: 0.75rem;\n border-top-left-radius: 0.75rem;\n border-top-right-radius: 0.75rem;\n border: 1px solid var(--copilot-kit-separator-color) !important;\n}\n\n.copilotKitChat {\n background-color: #fff !important;\n}\n", + "language": "css", + "type": "file" + }, + { + "name": "README.mdx", + "content": "# ๐Ÿค– Agentic Chat with Frontend Tools\n\n## What This Demo Shows\n\nThis demo showcases CopilotKit's **agentic chat** capabilities with **frontend\ntool integration**:\n\n1. **Natural Conversation**: Chat with your Copilot in a familiar chat interface\n2. **Frontend Tool Execution**: The Copilot can directly interacts with your UI\n by calling frontend functions\n3. **Seamless Integration**: Tools defined in the frontend and automatically\n discovered and made available to the agent\n\n## How to Interact\n\nTry asking your Copilot to:\n\n- \"Can you change the background color to something more vibrant?\"\n- \"Make the background a blue to purple gradient\"\n- \"Set the background to a sunset-themed gradient\"\n- \"Change it back to a simple light color\"\n\nYou can also chat about other topics - the agent will respond conversationally\nwhile having the ability to use your UI tools when appropriate.\n\n## โœจ Frontend Tool Integration in Action\n\n**What's happening technically:**\n\n- The React component defines a frontend function using `useCopilotAction`\n- CopilotKit automatically exposes this function to the agent\n- When you make a request, the agent determines whether to use the tool\n- The agent calls the function with the appropriate parameters\n- The UI immediately updates in response\n\n**What you'll see in this demo:**\n\n- The Copilot understands requests to change the background\n- It generates CSS values for colors and gradients\n- When it calls the tool, the background changes instantly\n- The agent provides a conversational response about the changes it made\n\nThis technique of exposing frontend functions to your Copilot can be extended to\nany UI manipulation you want to enable, from theme changes to data filtering,\nnavigation, or complex UI state management!\n", + "language": "markdown", + "type": "file" + }, + { + "name": "backend_tool_rendering.py", + "content": "\"\"\"Backend tool rendering โ€” a server-side ``@function_tool``.\n\nExercises ``TOOL_CALL_START/ARGS/END`` + ``TOOL_CALL_RESULT`` for a tool the\n*backend* owns and executes (as opposed to :mod:`human_in_the_loop`, where the\nfrontend owns execution). The SDK runs the tool body itself; the translator\njust reports the call and its result as they stream past.\n\nThe dojo's weather card (apps/dojo .../backend_tool_rendering/page.tsx) reads\nthe tool result as a JSON object โ€” temperature/conditions/humidity/\nwind_speed/feels_like โ€” and the argument as `location`, matching every other\nintegration's version of this demo. A plain sentence string or a `city`\nparam renders as a blank/all-zero card, since the frontend has nothing to\nparse. The fixed return value (same for every location) matches how most\nother integrations' versions of this demo work too โ€” the point of the demo\nis the tool-call plumbing, not real weather data.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom agents import Agent, function_tool\nfrom fastapi import FastAPI\n\nfrom ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint\nfrom .constants import DEFAULT_MODEL\n\n_WEATHER = {\"temperature\": 20, \"conditions\": \"sunny\", \"humidity\": 50, \"wind_speed\": 10, \"feels_like\": 20}\n\n\n@function_tool\ndef get_weather(location: str) -> dict:\n \"\"\"Get the current weather for a location.\"\"\"\n return _WEATHER\n\n\ndef create_backend_tool_agent() -> Agent:\n return Agent(\n name=\"weather_assistant\",\n model=DEFAULT_MODEL,\n instructions=(\n \"You are a helpful weather assistant. Use the get_weather tool \"\n \"whenever the user asks about weather in a specific location.\"\n ),\n tools=[get_weather],\n )\n\n\nagent = OpenAIAgentsAgent(create_backend_tool_agent(), name=\"backend_tool_rendering\")\napp = FastAPI(title=\"Backend tool rendering AG-UI demo\")\nadd_openai_agents_fastapi_endpoint(app, agent, \"/\")\n", + "language": "python", + "type": "file" + } + ], + "openai-agents-python::human_in_the_loop": [ + { + "name": "page.tsx", + "content": "\"use client\";\nimport React, { useState, useEffect } from \"react\";\nimport \"@copilotkit/react-core/v2/styles.css\";\nimport { \n useHumanInTheLoop,\n useConfigureSuggestions,\n CopilotChat,\n CopilotChatConfigurationProvider,\n} from \"@copilotkit/react-core/v2\";\nimport { CopilotKit,\nuseLangGraphInterrupt } from \"@copilotkit/react-core\";\nimport { z } from \"zod\";\nimport { useTheme } from \"next-themes\";\n\ninterface HumanInTheLoopProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\nconst HumanInTheLoop: React.FC = ({ params }) => {\n const { integrationId } = React.use(params);\n\n return (\n \n \n \n );\n};\n\ninterface Step {\n description: string;\n status: \"disabled\" | \"enabled\" | \"executing\";\n}\n\n// Shared UI Components\nconst StepContainer = ({ theme, children }: { theme?: string; children: React.ReactNode }) => (\n
\n \n {children}\n
\n \n);\n\nconst StepHeader = ({\n theme,\n enabledCount,\n totalCount,\n status,\n showStatus = false,\n}: {\n theme?: string;\n enabledCount: number;\n totalCount: number;\n status?: string;\n showStatus?: boolean;\n}) => (\n
\n
\n

\n Select Steps\n

\n
\n
\n {enabledCount}/{totalCount} Selected\n
\n {showStatus && (\n \n {status === \"executing\" ? \"Ready\" : \"Waiting\"}\n
\n )}\n
\n
\n\n \n 0 ? (enabledCount / totalCount) * 100 : 0}%` }}\n />\n \n \n);\n\nconst StepItem = ({\n step,\n theme,\n status,\n onToggle,\n disabled = false,\n}: {\n step: { description: string; status: string };\n theme?: string;\n status?: string;\n onToggle: () => void;\n disabled?: boolean;\n}) => (\n \n \n \n);\n\nconst ActionButton = ({\n variant,\n theme,\n disabled,\n onClick,\n children,\n}: {\n variant: \"primary\" | \"secondary\" | \"success\" | \"danger\";\n theme?: string;\n disabled?: boolean;\n onClick: () => void;\n children: React.ReactNode;\n}) => {\n const baseClasses = \"px-6 py-3 rounded-lg font-semibold transition-all duration-200\";\n const enabledClasses = \"hover:scale-105 shadow-md hover:shadow-lg\";\n const disabledClasses = \"opacity-50 cursor-not-allowed\";\n\n const variantClasses = {\n primary:\n \"bg-gradient-to-r from-purple-500 to-purple-700 hover:from-purple-600 hover:to-purple-800 text-white shadow-lg hover:shadow-xl\",\n secondary:\n theme === \"dark\"\n ? \"bg-slate-700 hover:bg-slate-600 text-white border border-slate-600 hover:border-slate-500\"\n : \"bg-gray-100 hover:bg-gray-200 text-gray-800 border border-gray-300 hover:border-gray-400\",\n success:\n \"bg-gradient-to-r from-green-500 to-emerald-600 hover:from-green-600 hover:to-emerald-700 text-white shadow-lg hover:shadow-xl\",\n danger:\n \"bg-gradient-to-r from-red-500 to-red-600 hover:from-red-600 hover:to-red-700 text-white shadow-lg hover:shadow-xl\",\n };\n\n return (\n \n {children}\n \n );\n};\n\nconst DecorativeElements = ({\n theme,\n variant = \"default\",\n}: {\n theme?: string;\n variant?: \"default\" | \"success\" | \"danger\";\n}) => (\n <>\n \n \n \n);\nconst InterruptHumanInTheLoop: React.FC<{\n event: { value: { steps: Step[] } };\n resolve: (value: string) => void;\n}> = ({ event, resolve }) => {\n const { theme } = useTheme();\n\n // Parse and initialize steps data\n let initialSteps: Step[] = [];\n if (event.value && event.value.steps && Array.isArray(event.value.steps)) {\n initialSteps = event.value.steps.map((step: any) => ({\n description: typeof step === \"string\" ? step : step.description || \"\",\n status: typeof step === \"object\" && step.status ? step.status : \"enabled\",\n }));\n }\n\n const [localSteps, setLocalSteps] = useState(initialSteps);\n const enabledCount = localSteps.filter((step) => step.status === \"enabled\").length;\n\n const handleStepToggle = (index: number) => {\n setLocalSteps((prevSteps) =>\n prevSteps.map((step, i) =>\n i === index\n ? { ...step, status: step.status === \"enabled\" ? \"disabled\" : \"enabled\" }\n : step,\n ),\n );\n };\n\n const handlePerformSteps = () => {\n const selectedSteps = localSteps\n .filter((step) => step.status === \"enabled\")\n .map((step) => step.description);\n resolve(\"The user selected the following steps: \" + selectedSteps.join(\", \"));\n };\n\n return (\n \n \n\n
\n {localSteps.map((step, index) => (\n handleStepToggle(index)}\n />\n ))}\n
\n\n
\n \n โœจ\n Perform Steps\n \n {enabledCount}\n \n \n
\n\n \n
\n );\n};\n\nconst Chat = ({ integrationId }: { integrationId: string }) => {\n return (\n \n \n \n );\n};\n\nconst ChatContent = () => {\n useConfigureSuggestions({\n suggestions: [\n { title: \"Simple plan\", message: \"Please plan a trip to mars in 5 steps.\" },\n { title: \"Complex plan\", message: \"Please plan a pasta dish in 10 steps.\" },\n ],\n available: \"always\",\n });\n\n // Langgraph uses it's own hook to handle human-in-the-loop interactions via langgraph interrupts,\n // This hook won't do anything for other integrations.\n useLangGraphInterrupt({\n \n render: ({ event, resolve }) => ,\n });\n useHumanInTheLoop({\n agentId: \"human_in_the_loop\",\n name: \"generate_task_steps\",\n description: \"Generates a list of steps for the user to perform\",\n parameters: z.object({\n steps: z.array(\n z.object({\n description: z.string(),\n status: z.enum([\"enabled\", \"disabled\", \"executing\"]),\n }),\n ),\n }) ,\n // Note: In v1, `available` was used to disable this for langgraph integrations.\n // In v2, availability is handled at the agent/backend level.\n render: ({ args, respond, status }: any) => {\n return ;\n },\n });\n\n return (\n
\n
\n \n
\n
\n );\n};\n\nconst StepsFeedback = ({ args, respond, status }: { args: any; respond: any; status: any }) => {\n const { theme } = useTheme();\n const [localSteps, setLocalSteps] = useState([]);\n const [accepted, setAccepted] = useState(null);\n\n useEffect(() => {\n if (status === \"executing\" && localSteps.length === 0 && Array.isArray(args?.steps) && args.steps.length > 0) {\n setLocalSteps(args.steps);\n }\n }, [status, args?.steps, localSteps]);\n\n if (!Array.isArray(args?.steps) || args.steps.length === 0) {\n return <>;\n }\n\n const steps = Array.isArray(localSteps) && localSteps.length > 0 ? localSteps : args.steps;\n const enabledCount = steps.filter((step: any) => step.status === \"enabled\").length;\n\n const handleStepToggle = (index: number) => {\n setLocalSteps((prevSteps) =>\n prevSteps.map((step, i) =>\n i === index\n ? { ...step, status: step.status === \"enabled\" ? \"disabled\" : \"enabled\" }\n : step,\n ),\n );\n };\n\n const handleReject = () => {\n if (respond) {\n setAccepted(false);\n respond({ accepted: false });\n }\n };\n\n const handleConfirm = () => {\n if (respond) {\n const confirmedSteps = localSteps.filter((step) => step.status === \"enabled\");\n setAccepted(true);\n respond({ accepted: true, steps: confirmedSteps });\n }\n };\n\n return (\n \n \n\n
\n {steps.map((step: any, index: any) => (\n handleStepToggle(index)}\n disabled={status !== \"executing\"}\n />\n ))}\n
\n\n {/* Action Buttons - Different logic from InterruptHumanInTheLoop */}\n {accepted === null && (\n
\n \n โœ—\n Reject\n \n \n โœ“\n Confirm\n \n {enabledCount}\n \n \n
\n )}\n\n {/* Result State - Unique to StepsFeedback */}\n {accepted !== null && (\n
\n \n {accepted ? \"โœ“\" : \"โœ—\"}\n {accepted ? \"Accepted\" : \"Rejected\"}\n
\n \n )}\n\n \n
\n );\n};\n\nexport default HumanInTheLoop;\n", + "language": "typescript", + "type": "file" + }, + { + "name": "README.mdx", + "content": "# ๐Ÿค Human-in-the-Loop Task Planner\n\n## What This Demo Shows\n\nThis demo showcases CopilotKit's **human-in-the-loop** capabilities:\n\n1. **Collaborative Planning**: The Copilot generates task steps and lets you\n decide which ones to perform\n2. **Interactive Decision Making**: Select or deselect steps to customize the\n execution plan\n3. **Adaptive Responses**: The Copilot adapts its execution based on your\n choices, even handling missing steps\n\n## How to Interact\n\nTry these steps to experience the demo:\n\n1. Ask your Copilot to help with a task, such as:\n\n - \"Make me a sandwich\"\n - \"Plan a weekend trip\"\n - \"Organize a birthday party\"\n - \"Start a garden\"\n\n2. Review the suggested steps provided by your Copilot\n\n3. Select or deselect steps using the checkboxes to customize the plan\n\n - Try removing essential steps to see how the Copilot adapts!\n\n4. Click \"Execute Plan\" to see the outcome based on your selections\n\n## โœจ Human-in-the-Loop Magic in Action\n\n**What's happening technically:**\n\n- The agent analyzes your request and breaks it down into logical steps\n- These steps are presented to you through a dynamic UI component\n- Your selections are captured as user input\n- The agent considers your choices when executing the plan\n- The agent adapts to missing steps with creative problem-solving\n\n**What you'll see in this demo:**\n\n- The Copilot provides a detailed, step-by-step plan for your task\n- You have complete control over which steps to include\n- If you remove essential steps, the Copilot provides entertaining and creative\n workarounds\n- The final execution reflects your choices, showing how human input shapes the\n outcome\n- Each response is tailored to your specific selections\n\nThis human-in-the-loop pattern creates a powerful collaborative experience where\nboth human judgment and AI capabilities work together to achieve better results\nthan either could alone!\n", + "language": "markdown", + "type": "file" + }, + { + "name": "human_in_the_loop.py", + "content": "\"\"\"Human-in-the-loop โ€” a *frontend*-owned tool, approved before execution.\n\nUnlike :mod:`backend_tool_rendering`, ``generate_task_steps`` has no server\nimplementation โ€” it arrives per-request as an AG-UI client tool\n(``RunAgentInput.tools``), gets wrapped into an SDK ``FunctionTool`` proxy by\n``AGUIToOpenAITranslator.translate_tools()``, and is merged onto this agent by\nthe run loop (see ``server.py``).\n\n``tool_use_behavior=StopAtTools(...)`` is the SDK's built-in for \"the model\nmay only *call* this tool, never execute it\": the run ends the moment the\nmodel emits the call, before the (dead-code) proxy body would ever run. The\nfrontend renders the steps for user approval, then sends the result back as\nan AG-UI ``ToolMessage`` in the *next* request โ€” ordinary multi-turn history,\nno custom pause/resume machinery needed.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom agents import Agent, StopAtTools\nfrom fastapi import FastAPI\n\nfrom ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint\nfrom .constants import DEFAULT_MODEL\n\n# Must match the AG-UI client tool name the frontend declares in\n# RunAgentInput.tools for this demo.\nFRONTEND_TOOL_NAME = \"generate_task_steps\"\n\nINSTRUCTIONS = \"\"\"You are a task planning assistant that breaks work into clear, actionable steps.\n\nWhen the user asks for help with a task:\n1. Immediately call the `generate_task_steps` tool with an array of steps.\n Each step is an object: {\"description\": \"...\", \"status\": \"enabled\"}.\n2. Do not restate the steps as plain text โ€” the frontend renders them.\n3. After the call, wait for the user to approve/select steps; do not call\n the tool again until they respond.\n\"\"\"\n\n\ndef create_human_in_the_loop_agent() -> Agent:\n return Agent(\n name=\"task_planner\",\n model=DEFAULT_MODEL,\n instructions=INSTRUCTIONS,\n tool_use_behavior=StopAtTools(stop_at_tool_names=[FRONTEND_TOOL_NAME]),\n )\n\n\nagent = OpenAIAgentsAgent(create_human_in_the_loop_agent(), name=\"human_in_the_loop\")\napp = FastAPI(title=\"Human in the loop AG-UI demo\")\nadd_openai_agents_fastapi_endpoint(app, agent, \"/\")\n", + "language": "python", + "type": "file" + } + ], + "openai-agents-python::human_in_the_loop_approval": [ + { + "name": "page.tsx", + "content": "\"use client\";\nimport React, { useState } from \"react\";\nimport \"@copilotkit/react-core/v2/styles.css\";\nimport {\n CopilotChat,\n useAgent,\n useCopilotKit,\n useConfigureSuggestions,\n} from \"@copilotkit/react-core/v2\";\nimport { CopilotKit } from \"@copilotkit/react-core\";\n\ninterface ApprovalProps {\n params: Promise<{ integrationId: string }>;\n}\n\ninterface PendingApproval {\n callId: string;\n toolName: string;\n arguments: string;\n}\n\nconst Approval: React.FC = ({ params }) => {\n const { integrationId } = React.use(params);\n\n return (\n \n \n \n );\n};\n\nconst Chat = () => {\n const { agent } = useAgent({ agentId: \"human_in_the_loop_approval\" });\n const { copilotkit } = useCopilotKit();\n const [pending, setPending] = useState(null);\n const [resolvedCallId, setResolvedCallId] = useState(null);\n\n useConfigureSuggestions({\n suggestions: [\n { title: \"Refund an order\", message: \"I'd like a refund for ORD-1001.\" },\n { title: \"Refund another order\", message: \"Please refund ORD-1002.\" },\n { title: \"Unknown order\", message: \"Can you refund ORD-9999?\" },\n ],\n available: \"always\",\n consumerAgentId: \"human_in_the_loop_approval\",\n });\n\n React.useEffect(() => {\n const subscription = agent.subscribe({\n onCustomEvent: ({ event }) => {\n if (event.name !== \"approval_request\") return;\n // One CustomEvent can carry several pending calls if the model made\n // more than one needs_approval call in a turn; this demo only ever\n // triggers one, so showing the first is enough here.\n const pendingList = event.value as Array<{\n call_id: string;\n tool_name: string;\n arguments: string;\n }>;\n const first = pendingList[0];\n if (!first) return;\n setResolvedCallId(null);\n setPending({\n callId: first.call_id,\n toolName: first.tool_name,\n arguments: first.arguments,\n });\n },\n onRunFinishedEvent: () => setResolvedCallId(null),\n onRunErrorEvent: () => setResolvedCallId(null),\n });\n return () => subscription.unsubscribe();\n }, [agent]);\n\n const respond = (approve: boolean) => {\n if (!pending) return;\n setResolvedCallId(pending.callId);\n copilotkit.runAgent({\n agent,\n forwardedProps: {\n approval: { call_id: pending.callId, approve },\n },\n });\n setPending(null);\n };\n\n return (\n
\n {pending && (\n respond(true)}\n onReject={() => respond(false)}\n />\n )}\n {!pending && resolvedCallId && (\n
\n Decision sent โ€” waiting for the agent...\n
\n )}\n
\n \n
\n
\n );\n};\n\nfunction ApprovalCard({\n pending,\n onApprove,\n onReject,\n}: {\n pending: PendingApproval;\n onApprove: () => void;\n onReject: () => void;\n}) {\n let args: Record = {};\n try {\n args = JSON.parse(pending.arguments);\n } catch {\n // leave args empty โ€” raw string shown below is enough context either way\n }\n\n return (\n \n

Approval needed

\n

\n The agent wants to call{\" \"}\n {pending.toolName} with:\n

\n
\n        {JSON.stringify(args, null, 2)}\n      
\n
\n \n Reject\n \n \n Approve\n \n
\n \n );\n}\n\nexport default Approval;\n", + "language": "typescript", + "type": "file" + }, + { + "name": "README.mdx", + "content": "---\ntitle: Human in the Loop Approval\ndescription: Pause a backend tool call until a person approves or rejects it.\n---\n\n# ๐Ÿค Human in the Loop Approval\n\n## What This Demo Shows\n\nA backend-owned refund tool pauses before execution and asks the user for an\nexplicit decision. Approving runs the tool; rejecting resumes the agent\nwithout issuing the refund.\n\n## How to Interact\n\n- Ask: \"Refund order ORD-1001.\"\n- Select **Approve** to execute the refund, or **Reject** to continue without\n executing it.\n- Ask about the order afterward to see how the agent handled the decision.\n\n## Technical Details\n\n- `issue_refund` uses the OpenAI Agents SDK's `needs_approval=True` option.\n- After the SDK reports an interruption, the backend saves `result.to_state()`\n under the AG-UI `thread_id` and emits an `approval_request` custom event.\n- The frontend sends the selected decision and matching tool `call_id` in the\n next request's `forwarded_props`.\n- The backend applies `state.approve()` or `state.reject()` and resumes the\n saved OpenAI Agents SDK run.\n- This demo keeps paused runs in process memory. Production applications need\n durable, concurrency-safe storage shared by all server instances.\n", + "language": "markdown", + "type": "file" + }, + { + "name": "human_in_the_loop_approval.py", + "content": "\"\"\"Tool approval โ€” a *backend*-owned tool gated by the SDK's own approval API.\n\nUnlike :mod:`human_in_the_loop` (a frontend-only tool with no server\nimplementation) and :mod:`backend_tool_rendering` (a server tool that always\nruns), ``issue_refund`` here is real server-side logic that only runs after a\nhuman approves it โ€” the SDK's ``needs_approval=True`` mechanism\n(``agents.tool.function_tool``), not an AG-UI concept.\n\nMechanically: when the model calls ``issue_refund``, the SDK stops the run\n*before* the tool body executes and surfaces a ``ToolApprovalItem`` on\n``result.interruptions``. That only becomes known once the stream is fully\ndrained โ€” there is no mid-stream event for it โ€” so it can't go through the\nnormal per-item translator dispatch the way ``MCPApprovalRequestItem`` does.\nThe example's run loop checks\n``result.interruptions`` right after ``to_agui()`` finishes, and if any are\npending:\n\n1. Serializes the paused run via ``result.to_state()`` and keeps it\n server-side, keyed by ``thread_id`` (an in-memory dict here โ€” a real app\n would use a session store; this survives one process, not a restart).\n2. Emits one ``CustomEvent(name=\"approval_request\")`` carrying every\n interruption, as ``to_agui()``'s ``end_custom_event`` โ€” right before\n ``RUN_FINISHED``, not after it. The client drops anything that arrives\n once a run is marked finished, so this has to land before that event,\n which means draining the raw SDK stream by hand first (interruptions\n aren't known until it's fully drained) instead of handing ``result``\n straight to ``to_agui()``.\n\nThe frontend renders Approve/Reject; either choice comes back as the next\n``RunAgentInput.forwarded_props[\"approval\"]`` (``{\"call_id\", \"approve\"}``).\nThe aggregate server looks up the stored state, calls ``state.approve()`` /\n``state.reject()``, and resumes with ``Runner.run_streamed(agent, state)``\ninstead of starting fresh from ``translated.messages``.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nfrom typing import Any\n\nfrom agents import Agent, Runner, function_tool\nfrom fastapi import FastAPI\nfrom fastapi.responses import StreamingResponse\n\nfrom ag_ui.core import CustomEvent, EventType, RunAgentInput\nfrom ag_ui.encoder import EventEncoder\nfrom ag_ui_openai_agents import AGUITranslator\nfrom ag_ui_openai_agents.engine import ClientToolPending\nfrom .constants import DEFAULT_MODEL\n\nlogger = logging.getLogger(__name__)\n\n# Fake order book โ€” good enough to make \"approved\" visibly do something.\n_ORDERS: dict[str, dict] = {\n \"ORD-1001\": {\"amount\": 49.99, \"status\": \"paid\"},\n \"ORD-1002\": {\"amount\": 129.50, \"status\": \"paid\"},\n}\n\n\n@function_tool(needs_approval=True)\ndef issue_refund(order_id: str) -> str:\n \"\"\"Issue a full refund for an order. Requires human approval before running.\"\"\"\n order = _ORDERS.get(order_id)\n if order is None:\n return f\"No such order: {order_id}\"\n order[\"status\"] = \"refunded\"\n return f\"Refunded ${order['amount']:.2f} for {order_id}.\"\n\n\ndef create_human_in_the_loop_approval_agent() -> Agent:\n return Agent(\n name=\"refund_assistant\",\n model=DEFAULT_MODEL,\n instructions=(\n \"You are a customer support assistant. When the user asks for a \"\n \"refund on an order, call issue_refund with that order id. \"\n \"Known orders: ORD-1001 ($49.99), ORD-1002 ($129.50). Don't ask \"\n \"for confirmation yourself โ€” the approval happens outside the \"\n \"conversation before the tool runs.\"\n ),\n tools=[issue_refund],\n )\n\n\nagent = create_human_in_the_loop_approval_agent()\napp = FastAPI(title=\"Human in the loop approval AG-UI demo\")\n_translator = AGUITranslator()\n_encoder = EventEncoder()\n_pending_approvals: dict[str, object] = {}\n\n\ndef resolve_approval(\n store: dict[str, Any],\n thread_id: str,\n forwarded_props: Any,\n) -> tuple[Any, Any, bool]:\n \"\"\"Claim the paused run for this request when a matching decision arrived.\n\n Pops first: whoever gets here wins, so a double-clicked Approve can't\n resume the same run twice. Anything other than a decision that matches a\n pending interruption abandons the paused run and starts a fresh turn โ€”\n the user moved on, and a thread should never be stuck waiting forever.\n\n ``approve`` has to be a real bool. Truthiness would read the string\n \"false\" as approval and run a refund the user declined, so a malformed\n decision is treated as no decision at all.\n\n Args:\n store: thread_id -> paused RunState.\n thread_id: The thread this request belongs to.\n forwarded_props: The request's forwarded_props.\n\n Returns:\n (pending_state, item, approve) to resume, or (None, None, False) to\n run the request fresh.\n \"\"\"\n pending_state = store.pop(thread_id, None)\n if pending_state is None:\n return None, None, False\n\n decision = None\n if isinstance(forwarded_props, dict):\n decision = forwarded_props.get(\"approval\")\n if not isinstance(decision, dict):\n return None, None, False\n\n approve = decision.get(\"approve\")\n if not isinstance(approve, bool):\n return None, None, False\n\n item = next(\n (\n item\n for item in pending_state.get_interruptions()\n if getattr(item.raw_item, \"call_id\", None) == decision.get(\"call_id\")\n ),\n None,\n )\n if item is None:\n return None, None, False\n return pending_state, item, approve\n\n\n@app.post(\"/\")\nasync def run(body: RunAgentInput) -> StreamingResponse:\n \"\"\"Run or resume the approval-gated agent.\"\"\"\n\n pending_state, item, approve = resolve_approval(\n _pending_approvals, body.thread_id, body.forwarded_props\n )\n\n async def stream():\n if item is not None:\n if approve:\n pending_state.approve(item)\n else:\n pending_state.reject(item)\n result = Runner.run_streamed(agent, pending_state)\n else:\n translated = _translator.to_openai(body)\n run_agent = agent\n if translated.tools:\n run_agent = run_agent.clone(tools=[*agent.tools, *translated.tools])\n result = Runner.run_streamed(\n run_agent, input=translated.messages, context=translated.context\n )\n\n # Collect as we go rather than in one comprehension: whatever streamed\n # before a mid-drain stop still has to reach the client. A client-owned\n # tool ends the run cleanly here; a real failure is kept and re-raised\n # from replay() below, so to_agui sees it and handles it the same way\n # it would on any other route.\n raw_events = []\n stream_error = None\n try:\n async for event in result.stream_events():\n raw_events.append(event)\n except ClientToolPending:\n pass\n except Exception as exc:\n stream_error = exc\n\n end_custom_event = None\n if stream_error is None and result.interruptions:\n _pending_approvals[body.thread_id] = result.to_state()\n end_custom_event = CustomEvent(\n type=EventType.CUSTOM,\n name=\"approval_request\",\n value=[\n {\n \"call_id\": getattr(item.raw_item, \"call_id\", None),\n \"tool_name\": item.tool_name,\n \"arguments\": getattr(item.raw_item, \"arguments\", None),\n }\n for item in result.interruptions\n ],\n )\n\n async def replay():\n for event in raw_events:\n yield event\n if stream_error is not None:\n raise stream_error\n\n try:\n async for event in _translator.to_agui(\n replay(), body, end_custom_event=end_custom_event\n ):\n yield _encoder.encode(event)\n except Exception:\n # to_agui already sent RUN_ERROR before re-raising; log the real\n # traceback here rather than let it escape the response.\n logger.exception(\"Agent run failed\")\n\n return StreamingResponse(stream(), media_type=_encoder.get_content_type())\n", + "language": "python", + "type": "file" + } + ], + "openai-agents-python::tool_based_generative_ui": [ + { + "name": "page.tsx", + "content": "\"use client\";\nimport React, { useState } from \"react\";\nimport \"@copilotkit/react-core/v2/styles.css\";\nimport { \n useFrontendTool,\n useConfigureSuggestions,\n CopilotSidebar,\n} from \"@copilotkit/react-core/v2\";\nimport { z } from \"zod\";\nimport {\n Carousel,\n CarouselContent,\n CarouselItem,\n CarouselNext,\n CarouselPrevious,\n} from \"@/components/ui/carousel\";\nimport { useURLParams } from \"@/contexts/url-params-context\";\nimport { CopilotKit } from \"@copilotkit/react-core\";\n\ninterface ToolBasedGenerativeUIProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ninterface Haiku {\n japanese: string[];\n english: string[];\n image_name: string | null;\n gradient: string;\n}\n\nexport default function ToolBasedGenerativeUI({ params }: ToolBasedGenerativeUIProps) {\n const { integrationId } = React.use(params);\n const { chatDefaultOpen } = useURLParams();\n\n return (\n \n \n \n \n );\n}\n\nfunction SidebarWithSuggestions({ defaultOpen }: { defaultOpen: boolean }) {\n useConfigureSuggestions({\n suggestions: [\n { title: \"Nature Haiku\", message: \"Write me a haiku about nature.\" },\n { title: \"Ocean Haiku\", message: \"Create a haiku about the ocean.\" },\n { title: \"Spring Haiku\", message: \"Generate a haiku about spring.\" },\n ],\n available: \"always\",\n });\n\n return (\n \n );\n}\n\nconst VALID_IMAGE_NAMES = [\n \"Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg\",\n \"Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg\",\n \"Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg\",\n \"Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg\",\n \"Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg\",\n \"Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg\",\n \"Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg\",\n \"Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg\",\n \"Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg\",\n \"Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg\",\n];\n\nfunction HaikuDisplay() {\n const [activeIndex, setActiveIndex] = useState(0);\n const [haikus, setHaikus] = useState([\n {\n japanese: [\"ไปฎใฎๅฅใ‚ˆ\", \"ใพใฃใ•ใ‚‰ใชใŒใ‚‰\", \"่Šฑใ‚’ๅ‘ผใถ\"],\n english: [\"A placeholder verseโ€”\", \"even in a blank canvas,\", \"it beckons flowers.\"],\n image_name: null,\n gradient: \"\",\n },\n ]);\n\n useFrontendTool(\n {\n agentId: \"tool_based_generative_ui\",\n name: \"generate_haiku\",\n parameters: z.object({\n japanese: z.array(z.string()).describe(\"3 lines of haiku in Japanese\"),\n english: z.array(z.string()).describe(\"3 lines of haiku translated to English\"),\n image_name: z.string().describe(`One relevant image name from: ${VALID_IMAGE_NAMES.join(\", \")}`),\n gradient: z.string().describe(\"CSS Gradient color for the background\"),\n }) ,\n followUp: false,\n handler: async ({ japanese, english, image_name, gradient }: { japanese: string[]; english: string[]; image_name: string; gradient: string }) => {\n const newHaiku: Haiku = {\n japanese: japanese || [],\n english: english || [],\n image_name: image_name || null,\n gradient: gradient || \"\",\n };\n setHaikus((prev) => [\n newHaiku,\n ...prev.filter((h) => h.english[0] !== \"A placeholder verseโ€”\"),\n ]);\n setActiveIndex(0);\n return \"Haiku generated!\";\n },\n render: ({ args }: { args: Partial }) => {\n if (!args.japanese) return <>;\n return ;\n },\n },\n [haikus],\n );\n\n const currentHaiku = haikus[activeIndex];\n\n return (\n
\n
\n \n \n {haikus.map((haiku, index) => (\n \n \n \n ))}\n \n {haikus.length > 1 && (\n <>\n \n \n \n )}\n \n
\n
\n );\n}\n\nfunction HaikuCard({ haiku }: { haiku: Partial }) {\n return (\n \n {/* Decorative background elements */}\n
\n
\n\n {/* Haiku Text */}\n
\n {haiku.japanese?.map((line, index) => (\n \n \n {line}\n

\n \n {haiku.english?.[index]}\n

\n
\n ))}\n
\n\n {/* Image */}\n {haiku.image_name && (\n
\n
\n \n
\n
\n
\n )}\n
\n );\n}\n", + "language": "typescript", + "type": "file" + }, + { + "name": "style.css", + "content": ".page-background {\n /* Darker gradient background */\n background: linear-gradient(170deg, #e9ecef 0%, #ced4da 100%);\n}\n\n@keyframes fade-scale-in {\n from {\n opacity: 0;\n transform: translateY(10px) scale(0.98);\n }\n to {\n opacity: 1;\n transform: translateY(0) scale(1);\n }\n}\n\n/* Updated card entry animation */\n@keyframes pop-in {\n 0% {\n opacity: 0;\n transform: translateY(15px) scale(0.95);\n }\n 70% {\n opacity: 1;\n transform: translateY(-2px) scale(1.02);\n }\n 100% {\n opacity: 1;\n transform: translateY(0) scale(1);\n }\n}\n\n/* Animation for subtle background gradient movement */\n@keyframes animated-gradient {\n 0% {\n background-position: 0% 50%;\n }\n 50% {\n background-position: 100% 50%;\n }\n 100% {\n background-position: 0% 50%;\n }\n}\n\n/* Animation for flash effect on apply */\n@keyframes flash-border-glow {\n 0% {\n /* Start slightly intensified */\n border-top-color: #ff5b4a !important;\n box-shadow: 0 10px 30px rgba(0, 0, 0, 0.07),\n inset 0 1px 2px rgba(0, 0, 0, 0.01),\n 0 0 25px rgba(255, 91, 74, 0.5);\n }\n 50% {\n /* Peak intensity */\n border-top-color: #ff4733 !important;\n box-shadow: 0 10px 30px rgba(0, 0, 0, 0.08),\n inset 0 1px 2px rgba(0, 0, 0, 0.01),\n 0 0 35px rgba(255, 71, 51, 0.7);\n }\n 100% {\n /* Return to default state appearance */\n border-top-color: #ff6f61 !important;\n box-shadow: 0 10px 30px rgba(0, 0, 0, 0.07),\n inset 0 1px 2px rgba(0, 0, 0, 0.01),\n 0 0 10px rgba(255, 111, 97, 0.15);\n }\n}\n\n/* Existing animation for haiku lines */\n@keyframes fade-slide-in {\n from {\n opacity: 0;\n transform: translateX(-15px);\n }\n to {\n opacity: 1;\n transform: translateX(0);\n }\n}\n\n.animated-fade-in {\n /* Use the new pop-in animation */\n animation: pop-in 0.6s ease-out forwards;\n}\n\n.haiku-card {\n /* Subtle animated gradient background */\n background: linear-gradient(120deg, #ffffff 0%, #fdfdfd 50%, #ffffff 100%);\n background-size: 200% 200%;\n animation: animated-gradient 10s ease infinite;\n\n /* === Explicit Border Override Attempt === */\n /* 1. Set the default grey border for all sides */\n border: 1px solid #dee2e6;\n\n /* 2. Explicitly override the top border immediately after */\n border-top: 10px solid #ff6f61 !important; /* Orange top - Added !important */\n /* === End Explicit Border Override Attempt === */\n\n padding: 2.5rem 3rem;\n border-radius: 20px;\n\n /* Default glow intensity */\n box-shadow: 0 10px 30px rgba(0, 0, 0, 0.07),\n inset 0 1px 2px rgba(0, 0, 0, 0.01),\n 0 0 15px rgba(255, 111, 97, 0.25);\n text-align: left;\n max-width: 745px;\n margin: 3rem auto;\n min-width: 600px;\n\n /* Transition */\n transition: transform 0.35s ease, box-shadow 0.35s ease, border-top-width 0.35s ease, border-top-color 0.35s ease;\n}\n\n.haiku-card:hover {\n transform: translateY(-8px) scale(1.03);\n /* Enhanced shadow + Glow */\n box-shadow: 0 15px 35px rgba(0, 0, 0, 0.1),\n inset 0 1px 2px rgba(0, 0, 0, 0.01),\n 0 0 25px rgba(255, 91, 74, 0.5);\n /* Modify only top border properties */\n border-top-width: 14px !important; /* Added !important */\n border-top-color: #ff5b4a !important; /* Added !important */\n}\n\n.haiku-card .flex {\n margin-bottom: 1.5rem;\n}\n\n.haiku-card .flex.haiku-line { /* Target the lines specifically */\n margin-bottom: 1.5rem;\n opacity: 0; /* Start hidden for animation */\n animation: fade-slide-in 0.5s ease-out forwards;\n /* animation-delay is set inline in page.tsx */\n}\n\n/* Remove previous explicit color overrides - rely on Tailwind */\n/* .haiku-card p.text-4xl {\n color: #212529;\n}\n\n.haiku-card p.text-base {\n color: #495057;\n} */\n\n.haiku-card.applied-flash {\n /* Apply the flash animation once */\n /* Note: animation itself has !important on border-top-color */\n animation: flash-border-glow 0.6s ease-out forwards;\n}\n\n/* Styling for images within the main haiku card */\n.haiku-card-image {\n width: 9.5rem; /* Increased size (approx w-48) */\n height: 9.5rem; /* Increased size (approx h-48) */\n object-fit: cover;\n border-radius: 1.5rem; /* rounded-xl */\n border: 1px solid #e5e7eb;\n /* Enhanced shadow with subtle orange hint */\n box-shadow: 0 8px 15px rgba(0, 0, 0, 0.1),\n 0 3px 6px rgba(0, 0, 0, 0.08),\n 0 0 10px rgba(255, 111, 97, 0.2);\n /* Inherit animation delay from inline style */\n animation-name: fadeIn;\n animation-duration: 0.5s;\n animation-fill-mode: both;\n}\n\n/* Styling for images within the suggestion card */\n.suggestion-card-image {\n width: 6.5rem; /* Increased slightly (w-20) */\n height: 6.5rem; /* Increased slightly (h-20) */\n object-fit: cover;\n border-radius: 1rem; /* Equivalent to rounded-md */\n border: 1px solid #d1d5db; /* Equivalent to border (using Tailwind gray-300) */\n margin-top: 0.5rem;\n /* Added shadow for suggestion images */\n box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1),\n 0 2px 4px rgba(0, 0, 0, 0.06);\n transition: all 0.2s ease-in-out; /* Added for smooth deselection */\n}\n\n/* Styling for the focused suggestion card image */\n.suggestion-card-image-focus {\n width: 6.5rem;\n height: 6.5rem;\n object-fit: cover;\n border-radius: 1rem;\n margin-top: 0.5rem;\n /* Highlight styles */\n border: 2px solid #ff6f61; /* Thicker, themed border */\n box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1), /* Base shadow for depth */\n 0 0 12px rgba(255, 111, 97, 0.6); /* Orange glow */\n transform: scale(1.05); /* Slightly scale up */\n transition: all 0.2s ease-in-out; /* Smooth transition for focus */\n}\n\n/* Styling for the suggestion card container in the sidebar */\n.suggestion-card {\n border: 1px solid #dee2e6; /* Same default border as haiku-card */\n border-top: 10px solid #ff6f61; /* Same orange top border */\n border-radius: 0.375rem; /* Default rounded-md */\n /* Note: background-color is set by Tailwind bg-gray-100 */\n /* Other styles like padding, margin, flex are handled by Tailwind */\n}\n\n.suggestion-image-container {\n display: flex;\n gap: 1rem;\n justify-content: space-between;\n width: 100%;\n height: 6.5rem;\n}\n\n/* Mobile responsive styles - matches useMobileView hook breakpoint */\n@media (max-width: 767px) {\n .haiku-card {\n padding: 1rem 1.5rem; /* Reduced from 2.5rem 3rem */\n min-width: auto; /* Remove min-width constraint */\n max-width: 100%; /* Full width on mobile */\n margin: 1rem auto; /* Reduced margin */\n }\n\n .haiku-card-image {\n width: 5.625rem; /* 90px - smaller on mobile */\n height: 5.625rem; /* 90px - smaller on mobile */\n }\n\n .suggestion-card-image {\n width: 5rem; /* Slightly smaller on mobile */\n height: 5rem; /* Slightly smaller on mobile */\n }\n\n .suggestion-card-image-focus {\n width: 5rem; /* Slightly smaller on mobile */\n height: 5rem; /* Slightly smaller on mobile */\n }\n}\n", + "language": "css", + "type": "file" + }, + { + "name": "README.mdx", + "content": "# ๐Ÿชถ Tool-Based Generative UI Haiku Creator\n\n## What This Demo Shows\n\nThis demo showcases CopilotKit's **tool-based generative UI** capabilities:\n\n1. **Frontend Rendering of Tool Calls**: Backend tool calls are automatically\n rendered in the UI\n2. **Dynamic UI Generation**: The UI updates in real-time as the agent generates\n content\n3. **Elegant Content Presentation**: Complex structured data (haikus) are\n beautifully displayed\n\n## How to Interact\n\nChat with your Copilot and ask for haikus about different topics:\n\n- \"Create a haiku about nature\"\n- \"Write a haiku about technology\"\n- \"Generate a haiku about the changing seasons\"\n- \"Make a humorous haiku about programming\"\n\nEach request will trigger the agent to generate a haiku and display it in a\nvisually appealing card format in the UI.\n\n## โœจ Tool-Based Generative UI in Action\n\n**What's happening technically:**\n\n- The agent processes your request and determines it should create a haiku\n- It calls a backend tool that returns structured haiku data\n- CopilotKit automatically renders this tool call in the frontend\n- The rendering is handled by the registered tool component in your React app\n- No manual state management is required to display the results\n\n**What you'll see in this demo:**\n\n- As you request a haiku, a beautifully formatted card appears in the UI\n- The haiku follows the traditional 5-7-5 syllable structure\n- Each haiku is presented with consistent styling\n- Multiple haikus can be generated in sequence\n- The UI adapts to display each new piece of content\n\nThis pattern of tool-based generative UI can be extended to create any kind of\ndynamic content - from data visualizations to interactive components, all driven\nby your Copilot's tool calls!\n", + "language": "markdown", + "type": "file" + }, + { + "name": "tool_based_generative_ui.py", + "content": "\"\"\"Tool-based generative UI โ€” frontend tool renders the content.\n\nLike :mod:`human_in_the_loop`, the tool (``generate_haiku``) is *client*-owned:\nit arrives per-request in ``RunAgentInput.tools``, gets wrapped into an SDK\n``FunctionTool`` proxy, and ``StopAtTools`` ends the run the moment the model\ncalls it. The difference is intent: here the tool call *is* the deliverable โ€”\nthe frontend renders the haiku card from the streamed ``TOOL_CALL_ARGS``,\nno approval round-trip expected.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom agents import Agent, StopAtTools\nfrom fastapi import FastAPI\n\nfrom ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint\nfrom .constants import DEFAULT_MODEL\n\n# Must match the AG-UI client tool name the frontend declares in\n# RunAgentInput.tools for this demo.\nFRONTEND_TOOL_NAME = \"generate_haiku\"\n\nINSTRUCTIONS = \"\"\"You are a creative writing assistant that renders haikus with a UI component.\n\nWhen the user asks for a haiku:\n1. Immediately call the `generate_haiku` tool with the haiku data\n (Japanese lines, English lines, and any other fields the tool declares).\n2. Do NOT write the haiku as plain text โ€” the frontend renders it.\n\nFor non-creative requests, respond normally without the tool.\n\"\"\"\n\n\ndef create_tool_based_generative_ui_agent() -> Agent:\n return Agent(\n name=\"haiku_assistant\",\n model=DEFAULT_MODEL,\n instructions=INSTRUCTIONS,\n tool_use_behavior=StopAtTools(stop_at_tool_names=[FRONTEND_TOOL_NAME]),\n )\n\n\nagent = OpenAIAgentsAgent(\n create_tool_based_generative_ui_agent(), name=\"tool_based_generative_ui\"\n)\napp = FastAPI(title=\"Tool-based generative UI AG-UI demo\")\nadd_openai_agents_fastapi_endpoint(app, agent, \"/\")\n", + "language": "python", + "type": "file" + } + ], + "openai-agents-python::subagents": [ + { + "name": "page.tsx", + "content": "\"use client\";\nimport React, { useCallback, useEffect, useState } from \"react\";\nimport \"@copilotkit/react-core/v2/styles.css\";\nimport { CopilotChat, useConfigureSuggestions, useRenderTool } from \"@copilotkit/react-core/v2\";\nimport { CopilotKit } from \"@copilotkit/react-core\";\nimport { Badge } from \"@/components/ui/badge\";\nimport { z } from \"zod\";\n\ninterface SubagentsProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ntype Role = \"research\" | \"writer\" | \"critic\";\ntype Status = \"inProgress\" | \"executing\" | \"complete\";\n\nconst ROLES: { role: Role; label: string; emoji: string }[] = [\n { role: \"research\", label: \"Researcher\", emoji: \"๐Ÿ”Ž\" },\n { role: \"writer\", label: \"Writer\", emoji: \"โœ๏ธ\" },\n { role: \"critic\", label: \"Critic\", emoji: \"๐Ÿง\" },\n];\n\ninterface DelegationEntry {\n id: string;\n role: Role;\n status: Status;\n text: string;\n}\n\nconst Subagents: React.FC = ({ params }) => {\n const { integrationId } = React.use(params);\n\n return (\n \n \n \n );\n};\n\nconst SubagentsView = () => {\n const [active, setActive] = useState>({\n research: null,\n writer: null,\n critic: null,\n });\n const [log, setLog] = useState([]);\n\n // Called by each DelegationTracker instance as its tool call's status\n // changes. Same shared state feeds both the role chips (who's working\n // right now) and the log (what happened, in order).\n const track = useCallback((entry: DelegationEntry) => {\n setActive((prev) => ({\n ...prev,\n [entry.role]: entry.status === \"complete\" ? null : entry.status,\n }));\n setLog((prev) => {\n const idx = prev.findIndex((e) => e.id === entry.id);\n if (idx === -1) return [...prev, entry];\n const next = [...prev];\n next[idx] = entry;\n return next;\n });\n }, []);\n\n useConfigureSuggestions({\n suggestions: [\n {\n title: \"History of AI\",\n message: \"Write a short article about the history of AI\",\n },\n {\n title: \"History of programming languages\",\n message: \"Write a short piece about the history of programming languages\",\n },\n ],\n available: \"always\",\n });\n\n useRenderTool({\n name: \"research_topic\",\n agentId: \"subagents\",\n parameters: z.object({ input: z.string().optional() }),\n render: ({ toolCallId, status, args, result }: any) => (\n \n ),\n });\n\n useRenderTool({\n name: \"write_prose\",\n agentId: \"subagents\",\n parameters: z.object({ input: z.string().optional() }),\n render: ({ toolCallId, status, args, result }: any) => (\n \n ),\n });\n\n useRenderTool({\n name: \"critique_draft\",\n agentId: \"subagents\",\n parameters: z.object({ input: z.string().optional() }),\n render: ({ toolCallId, status, args, result }: any) => (\n \n ),\n });\n\n return (\n
\n
\n

Supervisor's team

\n
\n {ROLES.map(({ role, label, emoji }) => {\n const status = active[role];\n return (\n \n {emoji}\n {label}\n {status && (\n \n {status === \"executing\" ? \"working\" : \"starting\"}\n \n )}\n
\n );\n })}\n
\n\n

\n Delegation log\n

\n
\n {log.length === 0 && (\n
No delegations yet.
\n )}\n {log.map((entry) => {\n const roleInfo = ROLES.find((r) => r.role === entry.role);\n return (\n \n
\n \n {roleInfo?.emoji} {roleInfo?.label}\n \n \n {entry.status}\n \n
\n {entry.text && (\n
\n {entry.text}\n
\n )}\n
\n );\n })}\n
\n
\n\n
\n
\n \n
\n
\n \n );\n};\n\n// Each tool call renders its own instance of this. `render` is invoked as a\n// real component (not a plain function), so effects are legal here โ€” this\n// is what reports status upstream into the shared chip/log state on every\n// inProgress โ†’ executing โ†’ complete transition, in addition to rendering\n// its own inline card in the chat transcript.\nfunction DelegationTracker({\n role,\n toolCallId,\n status,\n preview,\n result,\n onUpdate,\n}: {\n role: Role;\n toolCallId: string;\n status: Status;\n preview?: string;\n result?: string;\n onUpdate: (entry: DelegationEntry) => void;\n}) {\n const text = status === \"complete\" ? (result ?? \"\") : (preview ?? \"\");\n\n useEffect(() => {\n onUpdate({ id: toolCallId, role, status, text });\n // eslint-disable-next-line react-hooks/exhaustive-deps\n }, [toolCallId, role, status, text]);\n\n const roleInfo = ROLES.find((r) => r.role === role)!;\n const label =\n status === \"complete\"\n ? `${roleInfo.label} finished`\n : status === \"executing\"\n ? `${roleInfo.label} workingโ€ฆ`\n : `Calling ${roleInfo.label.toLowerCase()}โ€ฆ`;\n\n return (\n
\n {roleInfo.emoji} {label}\n
\n );\n}\n\nexport default Subagents;\n", + "language": "typescript", + "type": "file" + }, + { + "name": "README.mdx", + "content": "# ๐Ÿงญ Subagents\n\n## What This Demo Shows\n\nA supervisor agent delegates a writing task to three specialist agents:\nresearcher, writer, and critic. The supervisor remains responsible for the\nconversation, calls the specialists as tools, and produces the final response.\n\nThe sidebar shows which specialist is working and records every delegation in\norder. Simple questions can be answered directly without calling the team.\n\n## How to Interact\n\n- Ask: \"Write a short piece about the history of coffee.\" The supervisor\n calls the researcher, writer, and critic before returning the final draft.\n- Ask: \"What is 2 + 2?\" The supervisor answers directly without delegating.\n\n## Technical Details\n\n- The OpenAI Agents SDK exposes each specialist through `Agent.as_tool()`.\n This is call-and-return delegation; it is not a handoff of the conversation.\n- Each delegation becomes an AG-UI tool-call sequence followed by a\n `TOOL_CALL_RESULT` event.\n- A `useRenderTool` renderer for each specialist updates the role indicators\n and delegation log.\n- The specialists' internal model turns remain inside the OpenAI Agents SDK;\n the client receives one supervisor-owned conversation.\n", + "language": "markdown", + "type": "file" + }, + { + "name": "subagents.py", + "content": "\"\"\"Subagents โ€” multi-agent via the SDK's agents-as-tools pattern.\n\nUnlike a handoff (control *transfers* to the specialist, triage agent exits\nthe conversation), the supervisor here stays in charge and *calls*\nspecialists as tools (``Agent.as_tool()``), possibly several in one turn,\nthen synthesizes their outputs itself โ€” a call-and-return delegation, not a\nhandoff. Same shape as the LangGraph \"subagents\" showcase demo (a supervisor\ncalling child agents as tools and getting results back), just built with the\nSDK's own ``as_tool()`` instead of a routing graph.\n\nOn the AG-UI side each specialist invocation is an ordinary\n``TOOL_CALL_START/ARGS/END`` + ``TOOL_CALL_RESULT`` sequence โ€” the nested\nagent's own model turns stay internal to the SDK and are not streamed as\nseparate messages, so the client sees one coherent supervisor transcript.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom agents import Agent\nfrom fastapi import FastAPI\n\nfrom ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint\nfrom .constants import DEFAULT_MODEL\n\nresearch_agent = Agent(\n name=\"research_agent\",\n model=DEFAULT_MODEL,\n instructions=(\n \"You research a topic and return the key facts as a short bullet \"\n \"list. Facts only โ€” no fluff, no conclusions.\"\n ),\n)\n\nwriter_agent = Agent(\n name=\"writer_agent\",\n model=DEFAULT_MODEL,\n instructions=(\n \"You turn bullet-point facts into short, engaging prose. One tight \"\n \"paragraph unless asked otherwise.\"\n ),\n)\n\ncritic_agent = Agent(\n name=\"critic_agent\",\n model=DEFAULT_MODEL,\n instructions=(\n \"You review a draft and return concrete improvement suggestions as a \"\n \"numbered list. Max 3 suggestions, be specific.\"\n ),\n)\n\n\ndef create_subagents_agent() -> Agent:\n return Agent(\n name=\"orchestrator\",\n model=DEFAULT_MODEL,\n instructions=(\n \"You orchestrate a small content team. For writing requests: \"\n \"call research_topic for the facts, then write_prose to draft, \"\n \"then critique_draft to review, then produce the final version \"\n \"yourself incorporating the critique. For simple questions, \"\n \"answer directly without the team.\"\n ),\n tools=[\n research_agent.as_tool(\n tool_name=\"research_topic\",\n tool_description=\"Research a topic and return key facts as bullets.\",\n ),\n writer_agent.as_tool(\n tool_name=\"write_prose\",\n tool_description=\"Turn bullet-point facts into short prose.\",\n ),\n critic_agent.as_tool(\n tool_name=\"critique_draft\",\n tool_description=\"Review a draft and suggest up to 3 improvements.\",\n ),\n ],\n )\n\n\nagent = OpenAIAgentsAgent(create_subagents_agent(), name=\"subagents\")\napp = FastAPI(title=\"Subagents AG-UI demo\")\nadd_openai_agents_fastapi_endpoint(app, agent, \"/\")\n", + "language": "python", + "type": "file" + } + ], + "openai-agents-python::custom_lifecycle_events": [ + { + "name": "page.tsx", + "content": "\"use client\";\n\nimport React, { useEffect, useMemo, useRef, useSyncExternalStore } from \"react\";\nimport \"@copilotkit/react-core/v2/styles.css\";\nimport {\n CopilotChat,\n CopilotKitProvider,\n useAgent,\n useConfigureSuggestions,\n} from \"@copilotkit/react-core/v2\";\n\ninterface CustomLifecycleEventsProps {\n params: Promise<{ integrationId: string }>;\n}\n\ninterface RunUsage {\n inputTokens: number;\n outputTokens: number;\n}\n\nconst usageByMessageId = new Map();\nconst usageListeners = new Set<() => void>();\n\nfunction setMessageUsage(messageId: string, usage: RunUsage) {\n usageByMessageId.set(messageId, usage);\n usageListeners.forEach((listener) => listener());\n}\n\nfunction subscribeUsage(listener: () => void) {\n usageListeners.add(listener);\n return () => usageListeners.delete(listener);\n}\n\nfunction UsageLabel({\n message,\n position,\n}: {\n message: { id: string; role: string };\n position: \"before\" | \"after\";\n}) {\n const usage = useSyncExternalStore(\n subscribeUsage,\n () => usageByMessageId.get(message.id),\n () => undefined,\n );\n if (message.role !== \"assistant\" || position !== \"after\" || !usage) {\n return null;\n }\n\n return (\n \n โฌ‡๏ธ {usage.inputTokens} input tokens ยท โฌ†๏ธ {usage.outputTokens} output\n tokens\n \n );\n}\n\nconst CustomLifecycleEvents: React.FC = ({\n params,\n}) => {\n const { integrationId } = React.use(params);\n const renderCustomMessages = useMemo(() => [{ render: UsageLabel }], []);\n\n return (\n \n \n \n );\n};\n\nconst Chat = () => {\n const { agent } = useAgent({ agentId: \"custom_lifecycle_events\" });\n const completedMessageId = useRef(null);\n\n useConfigureSuggestions({\n suggestions: [\n { title: \"Say hi\", message: \"Say hi in one sentence.\" },\n {\n title: \"Tell me a fact about AI\",\n message: \"Tell me one interesting fact about AI.\",\n },\n ],\n available: \"before-first-message\",\n consumerAgentId: \"custom_lifecycle_events\",\n });\n\n useEffect(() => {\n const subscription = agent.subscribe({\n onRunStartedEvent: () => {\n completedMessageId.current = null;\n },\n onMessagesSnapshotEvent: ({ event }) => {\n const assistant = [...event.messages]\n .reverse()\n .find((message) => message.role === \"assistant\");\n completedMessageId.current = assistant?.id ?? null;\n },\n onCustomEvent: ({ event }) => {\n if (event.name !== \"run_usage\") return;\n if (!completedMessageId.current) return;\n\n const value = event.value as {\n input_tokens: number;\n output_tokens: number;\n };\n setMessageUsage(completedMessageId.current, {\n inputTokens: value.input_tokens,\n outputTokens: value.output_tokens,\n });\n },\n });\n return () => subscription.unsubscribe();\n }, [agent]);\n\n return (\n
\n
\n \n
\n
\n );\n};\n\nexport default CustomLifecycleEvents;\n", + "language": "typescript", + "type": "file" + }, + { + "name": "README.mdx", + "content": "# ๐Ÿช Custom Lifecycle Events\n\n## What This Demo Shows\n\nA custom AG-UI event can carry application-specific information alongside the\nstandard run events. This example emits a `run_usage` event containing the\nactual input and output token counts, then displays those counts beneath the\ncompleted assistant message.\n\nThe usage is emitted at the end of the run because final token totals are only\navailable after the OpenAI Agents SDK stream has completed.\n\n## How to Interact\n\n- Ask: \"Say hi in one sentence.\"\n- Ask: \"Tell me one interesting fact about AI.\"\n- After the response completes, check the input and output token counts shown\n directly below it.\n\n## Technical Details\n\n- The backend uses `AGUITranslator` directly so it can access the\n `RunResultStreaming` object and its final usage totals.\n- It passes a `CustomEvent` named `run_usage` through the translator's\n `end_custom_event` option. The event is emitted immediately before\n `RUN_FINISHED`.\n- The event value is resolved after the stream finishes, when\n `context_wrapper.usage` contains the actual totals.\n- The frontend subscribes to `run_usage`, associates it with the completed\n assistant message, and renders a compact usage label.\n", + "language": "markdown", + "type": "file" + }, + { + "name": "custom_lifecycle_events.py", + "content": "\"\"\"Custom lifecycle events โ€” a real usage summary right before RUN_FINISHED.\n\nPlain chat agent, same as agentic_chat โ€” the point isn't the agent, it's the\nCUSTOM event at the end. The SDK only knows real input/output token counts\nonce the run has finished, so this demo composes the translator directly\n(rather than the OpenAIAgentsAgent wrapper) to read them off the run result\nafter the stream completes and before to_agui() emits RUN_FINISHED.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom agents import Agent, Runner\nfrom fastapi import FastAPI, Request\nfrom fastapi.responses import StreamingResponse\n\nfrom ag_ui.core import CustomEvent, EventType, RunAgentInput\nfrom ag_ui.encoder import EventEncoder\nfrom ag_ui_openai_agents import AGUITranslator\nfrom .constants import DEFAULT_MODEL\n\nagent = Agent(\n name=\"assistant\",\n model=DEFAULT_MODEL,\n instructions=\"You are a helpful assistant. Be concise.\",\n)\n\napp = FastAPI(title=\"Custom lifecycle events AG-UI demo\")\ntranslator = AGUITranslator()\n\n\n@app.post(\"/\")\nasync def run_custom_lifecycle_events(\n body: RunAgentInput, request: Request\n) -> StreamingResponse:\n \"\"\"Translate one AG-UI request into an SDK run, then report real token usage.\"\"\"\n encoder = EventEncoder(accept=request.headers.get(\"accept\"))\n\n async def stream():\n # AGUI input -> OpenAI SDK\n translated_input = translator.to_openai(body)\n result = Runner.run_streamed(\n agent,\n input=translated_input.messages,\n context=translated_input.context,\n )\n\n def usage_value() -> dict[str, int]:\n # Resolved after the stream finishes, so these are the run's\n # actual totals, not an estimate made up before anything ran.\n usage = result.context_wrapper.usage\n return {\n \"input_tokens\": usage.input_tokens,\n \"output_tokens\": usage.output_tokens,\n }\n\n end_custom_event = CustomEvent(\n type=EventType.CUSTOM, name=\"run_usage\", value=usage_value\n )\n\n # OpenAI SDK -> AGUI events\n async for event in translator.to_agui(\n result, body, end_custom_event=end_custom_event\n ):\n yield encoder.encode(event)\n\n return StreamingResponse(stream(), media_type=encoder.get_content_type())\n", + "language": "python", + "type": "file" + } + ], + "openai-agents-python::dynamic_system_prompt": [ + { + "name": "page.tsx", + "content": "\"use client\";\nimport React, { useState } from \"react\";\nimport \"@copilotkit/react-core/v2/styles.css\";\nimport { CopilotChat, useAgentContext } from \"@copilotkit/react-core/v2\";\nimport { CopilotKit } from \"@copilotkit/react-core\";\n\ninterface DynamicSystemPromptProps {\n params: Promise<{\n integrationId: string;\n }>;\n}\n\ntype Language = \"English\" | \"Arabic\" | \"German\";\n\nconst LANGUAGES: { value: Language; label: string; flag: string }[] = [\n { value: \"English\", label: \"English\", flag: \"๐Ÿ‡ฌ๐Ÿ‡ง\" },\n { value: \"Arabic\", label: \"Arabic\", flag: \"๐Ÿ‡ธ๐Ÿ‡ฆ\" },\n { value: \"German\", label: \"German\", flag: \"๐Ÿ‡ฉ๐Ÿ‡ช\" },\n];\n\nconst DynamicSystemPrompt: React.FC = ({ params }) => {\n const { integrationId } = React.use(params);\n\n return (\n \n \n \n );\n};\n\nconst DynamicSystemPromptView = () => {\n const [language, setLanguage] = useState(\"English\");\n\n // The only thing this demo sends: one context item the agent's\n // instructions callable reads on every turn to pick the reply language.\n // No tool, no state โ€” just the AG-UI context channel.\n useAgentContext({\n description: \"Reply language\",\n value: language,\n });\n\n return (\n
\n
\n

Reply language

\n
\n {LANGUAGES.map(({ value, label, flag }) => (\n setLanguage(value)}\n className={`flex items-center gap-2 rounded-lg border px-3 py-2 text-sm text-left transition-colors ${\n language === value\n ? \"border-blue-400 bg-blue-50 dark:bg-blue-950/40\"\n : \"border-black/10 dark:border-white/10\"\n }`}\n >\n {flag}\n {label}\n \n ))}\n
\n

\n Switching language updates the AG-UI context sent with the next\n message โ€” the agent's system prompt is rebuilt every turn to\n match.\n

\n
\n\n
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\n );\n};\n\nexport default DynamicSystemPrompt;\n", + "language": "typescript", + "type": "file" + }, + { + "name": "README.mdx", + "content": "# ๐ŸŒ Dynamic System Prompt\n\n## What This Demo Shows\n\nThe frontend sends the selected reply language through the AG-UI context\nchannel. The backend reads that context on every run and uses the OpenAI Agents\nSDK dynamic-instructions API to rebuild the agent's system prompt.\n\nChanging the language affects the next response without restarting the server\nor storing the selection in agent state.\n\n## How to Interact\n\n- Select English, Arabic, or German in the sidebar, then ask a question.\n- Change the language and send another message. The next response follows the\n new selection immediately.\n\n## Technical Details\n\n- `useAgentContext` adds `{ description: \"Reply language\", value: language }`\n to `RunAgentInput.context` on every request.\n- `OpenAIAgentsAgent` forwards the translated context to\n `Runner.run_streamed(..., context=...)`.\n- The agent's `instructions` callable reads `RunContextWrapper.context` and\n returns a new system prompt for that run.\n- Context is read-only request information in this example; no shared-state\n bridge is required.\n", + "language": "markdown", + "type": "file" + }, + { + "name": "dynamic_system_prompt.py", + "content": "\"\"\"Dynamic system prompt โ€” reply language driven by the AG-UI ``context`` channel.\n\nThe frontend sends a language choice in ``RunAgentInput.context`` and the SDK\nrebuilds its instructions for that request.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom agents import Agent, RunContextWrapper\nfrom fastapi import FastAPI\n\nfrom ag_ui.core import Context\nfrom ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint\nfrom .constants import DEFAULT_MODEL\n\nBASE_INSTRUCTIONS = (\n \"You are a helpful, concise assistant. Answer the user's questions directly.\"\n)\n\n# Fallback when the frontend hasn't picked a language yet.\nDEFAULT_LANGUAGE = \"English\"\n\n\ndef _read_language(ctx: RunContextWrapper[list[Context]]) -> str:\n \"\"\"Pull the reply language out of the AG-UI context list.\n\n ``ctx.context`` here IS the raw ``list[Context]`` the client sent โ€”\n each item a ``{description, value}`` pair, nothing wrapping it. We match\n the item whose description mentions \"language\" and use its value.\n \"\"\"\n items = ctx.context or []\n for item in items:\n if \"language\" in (item.description or \"\").lower():\n return item.value or DEFAULT_LANGUAGE\n return DEFAULT_LANGUAGE\n\n\ndef dynamic_instructions(ctx: RunContextWrapper[list[Context]], agent: Agent) -> str:\n \"\"\"Native SDK dynamic-instructions hook: build the prompt fresh each turn,\n baking in whatever language the frontend currently has selected.\"\"\"\n language = _read_language(ctx)\n return (\n f\"{BASE_INSTRUCTIONS}\\n\"\n f\"Always reply in {language}, no matter what language the user writes in. \"\n f\"Every word of your response must be in {language}.\"\n )\n\n\ndef create_dynamic_system_prompt_agent() -> Agent:\n return Agent(\n name=\"multilingual_assistant\",\n model=DEFAULT_MODEL,\n instructions=dynamic_instructions,\n )\n\n\nagent = OpenAIAgentsAgent(create_dynamic_system_prompt_agent(), name=\"dynamic_system_prompt\")\napp = FastAPI(title=\"Dynamic system prompt AG-UI demo\")\nadd_openai_agents_fastapi_endpoint(app, agent, \"/\")\n", + "language": "python", + "type": "file" + } ] } \ No newline at end of file diff --git a/apps/dojo/src/menu.ts b/apps/dojo/src/menu.ts index f7cef402a2..abd5b78c6f 100644 --- a/apps/dojo/src/menu.ts +++ b/apps/dojo/src/menu.ts @@ -392,4 +392,19 @@ export const menuIntegrations = [ name: "IBM watsonx orchestrate", features: ["agentic_chat", "v1_agentic_chat"], }, + { + id: "openai-agents-python", + name: "OpenAI Agents SDK (Python)", + features: [ + "ag_ui_docs_copilot", + "agentic_chat", + "backend_tool_rendering", + "human_in_the_loop", + "human_in_the_loop_approval", + "tool_based_generative_ui", + "subagents", + "custom_lifecycle_events", + "dynamic_system_prompt", + ], + }, ] as const satisfies MenuIntegrationConfig[]; diff --git a/apps/dojo/src/types/integration.ts b/apps/dojo/src/types/integration.ts index 6be810e9d6..2c29e97561 100644 --- a/apps/dojo/src/types/integration.ts +++ b/apps/dojo/src/types/integration.ts @@ -1,9 +1,11 @@ import type { menuIntegrations } from "../menu"; export type Feature = + | "ag_ui_docs_copilot" | "agentic_chat" | "agentic_generative_ui" | "human_in_the_loop" + | "human_in_the_loop_approval" | "interrupt" | "predictive_state_updates" | "shared_state" @@ -20,6 +22,9 @@ export type Feature = | "a2ui_advanced" | "a2ui_recovery" | "crew_chat" + | "subagents" + | "custom_lifecycle_events" + | "dynamic_system_prompt" | "error_flow" | "background_agents" | "observational_memory"; diff --git a/docs/integrations.mdx b/docs/integrations.mdx index 5eb3f18897..3caa17632d 100644 --- a/docs/integrations.mdx +++ b/docs/integrations.mdx @@ -23,6 +23,7 @@ that demonstrate the protocol's capabilities and versatility. - **[CrewAI](https://crewai.com)** - Streamline workflows across industries with powerful AI agents. - **[AG2](https://ag2.ai)** - The Open-Source AgentOS +- **[OpenAI Agents SDK](https://openai.github.io/openai-agents-python/)** - OpenAIโ€™s Python framework for building agentic applications ## Infrastructure / Deployment diff --git a/docs/introduction.mdx b/docs/introduction.mdx index 43b40f2784..3be4079ce5 100644 --- a/docs/introduction.mdx +++ b/docs/introduction.mdx @@ -267,7 +267,7 @@ AG-UI was born from CopilotKit's initial **partnership** with LangGraph and Crew ### Agent Framework - Community | Framework | Status | AG-UI Resources | | :-------- | ------ | --------------- | -| [OpenAI Agent SDK](https://openai.github.io/openai-agents-python/) | In Progress | โ€“ | +| [OpenAI Agents SDK](https://openai.github.io/openai-agents-python/) | In Progress | โ€“ | | [Cloudflare Agents](https://developers.cloudflare.com/agents/) | In Progress | โ€“ | ### Agent Interaction Protocols diff --git a/integrations/openai-agents/python/.gitignore b/integrations/openai-agents/python/.gitignore new file mode 100644 index 0000000000..1749724d93 --- /dev/null +++ b/integrations/openai-agents/python/.gitignore @@ -0,0 +1,226 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[codz] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py.cover +*.lcov +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +# Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +# poetry.lock +# poetry.toml + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. +# https://pdm-project.org/en/latest/usage/project/#working-with-version-control +# pdm.lock +# pdm.toml +.pdm-python +.pdm-build/ + +# pixi +# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. +# pixi.lock +# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one +# in the .venv directory. It is recommended not to include this directory in version control. +.pixi/* +!.pixi/config.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule* +celerybeat.pid + +# Redis +*.rdb +*.aof +*.pid + +# RabbitMQ +mnesia/ +rabbitmq/ +rabbitmq-data/ + +# ActiveMQ +activemq-data/ + +# SageMath parsed files +*.sage.py + +# Environments +.env +.envrc +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. + .idea/ + .junie/ + +# Abstra +# Abstra is an AI-powered process automation framework. +# Ignore directories containing user credentials, local state, and settings. +# Learn more at https://abstra.io/docs +.abstra/ + +# Visual Studio Code +# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore +# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore +# and can be added to the global gitignore or merged into this file. However, if you prefer, +# you could uncomment the following to ignore the entire vscode folder +# .vscode/ +# Temporary file for partial code execution +tempCodeRunnerFile.py + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc + +# Marimo +marimo/_static/ +marimo/_lsp/ +__marimo__/ + +# Streamlit +.streamlit/secrets.toml + +# Dev +.dev/ +CLAUDE.md +AGENTS.md diff --git a/integrations/openai-agents/python/LICENSE b/integrations/openai-agents/python/LICENSE new file mode 100644 index 0000000000..b77bf2ab72 --- /dev/null +++ b/integrations/openai-agents/python/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/integrations/openai-agents/python/README.md b/integrations/openai-agents/python/README.md new file mode 100644 index 0000000000..9793662730 --- /dev/null +++ b/integrations/openai-agents/python/README.md @@ -0,0 +1,785 @@ +# AG-UI ร— OpenAI Agents SDK + +Connect an [OpenAI Agents SDK](https://openai.github.io/openai-agents-python/) +agent to any [AG-UI](https://github.com/ag-ui-protocol/ag-ui) client. The package +translates AG-UI requests into OpenAI Agents SDK input and translates the OpenAI Agents SDK's +stream back into ordered AG-UI events. + +You keep your existing OpenAI agent, tools, handoffs, guardrails, model settings, +and server architecture. This integration owns only the protocol boundary. + +```text +AG-UI RunAgentInput + โ”‚ + โ–ผ +AGUITranslator.to_openai() + โ”‚ OpenAI Agents SDK messages, client-tool proxies, request metadata + โ–ผ +Runner.run_streamed(...) + โ”‚ OpenAI Agents SDK stream + โ–ผ +AGUITranslator.to_agui() + โ”‚ + โ–ผ +AG-UI lifecycle, text, tool, reasoning, step, state, and snapshot events +``` + +## Requirements + +- Python 3.10 or newer +- `ag-ui-protocol >= 0.1.19` +- `openai-agents >= 0.8.4` +- An `OPENAI_API_KEY` for live OpenAI runs + +## Installation + +With [uv](https://docs.astral.sh/uv/): + +```bash +uv add ag-ui-openai-agents +``` + +Or with pip: + +```bash +pip install ag-ui-openai-agents +``` + +## Configuration + +Set the OpenAI API key in the environment where your server runs: + +```bash +export OPENAI_API_KEY="sk-..." +``` + +`AGUITranslator` does not read environment variables or change OpenAI Agents +SDK settings. With the recommended translator integration, your application +owns the OpenAI Agents SDK runner and its tracing configuration. + +If you use the `OpenAIAgentsAgent` wrapper or +`add_openai_agents_fastapi_endpoint`, you can optionally set +`OPENAI_AGENTS_DISABLE_TRACING=true` to disable OpenAI Agents SDK tracing. It +controls tracing only; it does not override the agent, model, or `RunConfig`. + +| OpenAI Agents SDK variable | Required | Purpose | +|---|---:|---| +| `OPENAI_API_KEY` | For the default OpenAI provider | Authenticates OpenAI requests. A custom provider may use different credentials. | +| `OPENAI_AGENTS_DISABLE_TRACING` | No | Optional when using the wrapper; set to `true` to disable OpenAI Agents SDK tracing. | + +Model selection, retries, and other run behavior remain OpenAI Agents SDK +settings. Pass a model to `Agent(...)` and, when needed, pass a `RunConfig` to +`OpenAIAgentsAgent` or `Runner.run_streamed(...)`. + +The bundled example server additionally reads: + +| Variable | Required | Purpose | +|---|---:|---| +| `OPENAI_DEFAULT_MODEL` | No | Overrides the model used by all examples. | +| `HOST` | No | Example server bind address; defaults to `0.0.0.0`. | +| `PORT` | No | Example server port; defaults to `8024`. | + +Keep secrets in your deployment secret manager or an uncommitted `.env` file. + +## Choose an integration level + +| Need | Use | +|---|---| +| Existing agent and custom server logic | `AGUITranslator` (recommended) | +| Fixed agent with less orchestration code | `OpenAIAgentsAgent` | +| Ready-made FastAPI SSE route | `OpenAIAgentsAgent` + `add_openai_agents_fastapi_endpoint` | +| Change one message or event mapping | Custom inbound/outbound engine subclass | + +Start with `AGUITranslator`. It leaves the OpenAI run and HTTP transport under +your control. Use the wrapper only when its standard behavior fits your server. + +## Quick start: use the translator (recommended) + +This is the same small architecture used by the `ag_ui_docs_copilot` example: +the agent owns its instructions and tools, while the endpoint only translates +the request, runs the OpenAI Agents SDK, and encodes the AG-UI stream. It accepts an AG-UI +request, runs a normal streaming OpenAI agent, and returns Server-Sent Events +(SSE). + +```python +from agents import Agent, Runner +from ag_ui.core import RunAgentInput +from ag_ui.encoder import EventEncoder +from ag_ui_openai_agents import AGUITranslator +from fastapi import FastAPI, Request +from fastapi.responses import StreamingResponse + +app = FastAPI(title="AG-UI Docs Copilot") + +copilot_agent = Agent( + name="AG-UI Docs Copilot", + instructions="Answer AG-UI developer questions clearly and concisely.", +) +translator = AGUITranslator() # Reusable across requests. + + +@app.post("/") +async def run_ag_ui_docs_copilot( + body: RunAgentInput, request: Request +) -> StreamingResponse: + encoder = EventEncoder(accept=request.headers.get("accept")) + + async def stream(): + translated = translator.to_openai(body) + + result = Runner.run_streamed( + copilot_agent, + input=translated.messages, + context=translated.context, + ) + + async for event in translator.to_agui(result, body): + yield encoder.encode(event) + + return StreamingResponse(stream(), media_type=encoder.get_content_type()) +``` + +The complete implementation adds the two documentation lookup tools to +`copilot_agent`; each tool reads one README section on demand. The full agent +and its endpoint are in +[`examples/agents_examples/ag_ui_docs_copilot.py`](examples/agents_examples/ag_ui_docs_copilot.py). +That file is intentionally linear: `RunAgentInput โ†’ to_openai โ†’ +Runner.run_streamed โ†’ to_agui โ†’ EventEncoder`. + +Run the app, then send an AG-UI request: + +```bash +curl -N -X POST http://localhost:8000/ \ + -H 'Content-Type: application/json' \ + -d '{ + "thread_id": "thread-1", + "run_id": "run-1", + "messages": [ + {"id": "user-1", "role": "user", "content": "Say hello in one sentence."} + ], + "tools": [], + "state": {}, + "context": [], + "forwarded_props": null + }' +``` + +A text-only run normally produces: + +```text +RUN_STARTED +TEXT_MESSAGE_START +TEXT_MESSAGE_CONTENT ... +TEXT_MESSAGE_END +MESSAGES_SNAPSHOT +RUN_FINISHED +``` + +## Quick start: use the FastAPI shortcut + +`OpenAIAgentsAgent` performs the same `to_openai โ†’ run_streamed โ†’ to_agui` +flow. The endpoint helper adds an AG-UI POST route, SSE encoding, content +negotiation, and a health route. + +```python +from agents import Agent +from ag_ui_openai_agents import ( + OpenAIAgentsAgent, + add_openai_agents_fastapi_endpoint, +) +from fastapi import FastAPI + +app = FastAPI() + +agent = OpenAIAgentsAgent( + Agent(name="assistant", instructions="Be helpful and concise."), +) + +add_openai_agents_fastapi_endpoint(app, agent, "/agent") +``` + +This registers: + +- `POST /agent` โ€” accepts `RunAgentInput` and streams encoded AG-UI events. +- `GET /agent/health` โ€” returns the wrapped agent's name and health status. + +Pass `include_health=False` if your application already owns health checks. +The helper adds routes; it does not start a server, store sessions, or manage +authentication. + +## Public API + +```python +from ag_ui_openai_agents import ( + AGUITranslator, + OpenAIAgentsAgent, + TranslatedInput, + add_openai_agents_fastapi_endpoint, +) +``` + +### `AGUITranslator` + +The main, stateless public API. One instance can serve concurrent requests. + +```python +translator = AGUITranslator() +``` + +#### `to_openai(run_input)` + +Converts one `RunAgentInput` into `TranslatedInput`: + +| Field | Behavior | +|---|---| +| `messages` | Converted to the OpenAI Agents SDK's input format. | +| `tools` | Converted to request-scoped OpenAI Agents SDK `FunctionTool` proxies. | +| `thread_id`, `run_id`, `parent_run_id` | Preserved unchanged. | +| `state`, `context`, `forwarded_props`, `resume` | Preserved for application orchestration. | + +`to_openai` does not run the agent. It also does not automatically put `state`, +`context`, or `forwarded_props` into the model prompt. + +#### `to_agui(events, run_input, ...)` + +Accepts either the OpenAI Agents SDK's `RunResultStreaming` or its `stream_events()` async +iterator and yields AG-UI `BaseEvent` objects. + +```python +async for event in translator.to_agui(result, run_input): + ... +``` + +| Option | Default | Purpose | +|---|---:|---| +| `start_custom_event` | `None` | Emit a `CustomEvent` after `RUN_STARTED`. | +| `initial_state` | `None` | Emit an initial `STATE_SNAPSHOT`. | +| `final_state` | `None` | Emit a final `STATE_SNAPSHOT` after streaming. | +| `emit_messages_snapshot` | `True` | Emit the complete message history before finishing. | +| `end_custom_event` | `None` | Emit a `CustomEvent` immediately before `RUN_FINISHED`. | +| `emit_run_error` | `True` | Emit `RUN_ERROR` before re-raising ordinary errors. | +| `run_error_message` | `None` | Replace exception text with a client-safe message. | + +State sources and custom-event values may be static values, zero-argument +functions, or zero-argument async functions. They are resolved at their +documented lifecycle position. `None` skips a state snapshot; `{}` emits one. + +### `OpenAIAgentsAgent` + +A convenience wrapper around a fixed OpenAI Agents SDK `Agent`: + +```python +wrapped = OpenAIAgentsAgent( + agent, + name="public-name", + description="Optional description", + run_config=run_config, + initial_state=lambda: load_state(), + final_state=lambda: load_state(), + run_error_message="Agent run failed", +) + +async for event in wrapped.run_streamed(run_input): + ... +``` + +The wrapper: + +- translates the request; +- adds non-conflicting client tools to a per-request agent clone; +- passes `translated.context` to `Runner.run_streamed(context=...)`; +- runs the OpenAI Agents SDK in streaming mode; +- translates the result and emits lifecycle/snapshot events. + +The wrapper exposes the same lifecycle controls as `AGUITranslator.to_agui()`: + +| Option | Purpose | +|---|---| +| `start_custom_event` | Emit a custom event after `RUN_STARTED`. | +| `initial_state` | Emit the initial `STATE_SNAPSHOT`. | +| `final_state` | Emit the final `STATE_SNAPSHOT`. | +| `emit_messages_snapshot` | Enable or disable `MESSAGES_SNAPSHOT`. | +| `end_custom_event` | Emit a custom event before `RUN_FINISHED`. | +| `emit_run_error` | Enable or disable `RUN_ERROR`. | +| `run_error_message` | Set the client-visible `RUN_ERROR.message`. | + +It does not expose the `RunResultStreaming` object. Use the translator directly +when a custom event must inspect the run result, or when you need custom +orchestration. + +The shared agent is never mutated. If a client tool has the same name as a +server tool, the server tool wins and the client tool is ignored with a warning. + +Use `AGUITranslator` directly when you need the `RunResultStreaming`, custom +branching, resume logic, a different transport, or per-request OpenAI Agents SDK options. + +### `add_openai_agents_fastapi_endpoint` + +```python +add_openai_agents_fastapi_endpoint( + app, + wrapped_agent, + path="/agent", + include_health=True, +) +``` + +The helper respects the request's `Accept` header through AG-UI's +`EventEncoder` and returns the matching streaming content type. + +## What happens during a run + +1. `to_openai` translates message history and client-declared tools. +2. Your codeโ€”or `OpenAIAgentsAgent`โ€”starts `Runner.run_streamed`. +3. A fresh outbound engine is created for that run. +4. `RUN_STARTED` is emitted using the request's IDs. +5. OpenAI Agents SDK events are correlated into AG-UI text, reasoning, tool, and step windows. +6. Any open windows are closed when the OpenAI Agents SDK stream ends. +7. Optional final state and the default `MESSAGES_SNAPSHOT` are emitted. +8. The run ends with `RUN_FINISHED`, or `RUN_ERROR` for an ordinary failure. + +Successful lifecycle order: + +```text +RUN_STARTED +start_custom_event optional +STATE_SNAPSHOT optional initial state +... STEP / REASONING / TEXT / TOOL events ... +STATE_SNAPSHOT optional final state +MESSAGES_SNAPSHOT enabled by default +end_custom_event optional +RUN_FINISHED +``` + +On an ordinary error, open event windows are closed, `RUN_ERROR` is emitted +when enabled, and the original exception is re-raised for server logging. +Final state, messages snapshot, end custom event, and `RUN_FINISHED` are not +emitted on the error path. + +The client-visible error text is configurable with `run_error_message`: + +```python +async for event in translator.to_agui( + result, + run_input, + run_error_message="The agent could not complete this request.", +): + yield encoder.encode(event) +``` + +This changes `RUN_ERROR.message` only. The original exception is still raised +after the event so the server can log the detailed failure. Use +`run_error_message` in production to keep internal exception details out of the +client response while preserving the original exception for server logs. + +If a consumer stops reading early and passed a `RunResultStreaming`, the + translator cancels that owned OpenAI Agents SDK run. If the caller passes only a bare async +iterator, cancellation remains the caller's responsibility. + +## Feature support + +| Feature | Support | Notes | +|---|---:|---| +| Streaming assistant text and refusals | Yes | Emits one ordered text window per OpenAI Agents SDK message item. | +| Backend function tools | Yes | Tool call arguments and results are streamed. | +| Hosted tools | Yes | OpenAI Agents SDK hosted tools are surfaced as tool calls; some do not expose streamed arguments. | +| Frontend/client-owned tools | Yes | Request tools become OpenAI Agents SDK `FunctionTool` proxies and complete in a later AG-UI request. | +| Handoffs | Yes | Handoff calls/results map to tools; agent changes map to steps. | +| Agents as tools | Yes | Nested specialist calls appear as ordinary tool calls/results. | +| Reasoning | Yes | Summary/text streams; encrypted content supports replay. | +| Guardrails | Yes | OpenAI Agents SDK tripwire exceptions surface through `RUN_ERROR`. | +| State snapshots | Yes | Application supplies initial/final state sources. | +| Messages snapshot | Yes | Enabled by default with stable streamed IDs. | +| Custom lifecycle events | Yes | Start/end `CustomEvent` hooks. | +| Multimodal input | Partial | Text, images, documents, and some audio; see below. | +| Multimodal output | No | AG-UI currently has no matching image/audio output event. | +| Backend approval | Example | Requires application-owned `RunState` persistence and resume logic. | +| MCP approval request | Yes | Emitted as `CUSTOM` named `mcp_approval_request`. | + +## Message mapping: AG-UI โ†’ OpenAI + +`to_openai` keeps message order. One AG-UI message may produce multiple OpenAI Agents SDK +items, especially an assistant message containing tool calls. + +| AG-UI input | OpenAI Agents SDK input | ID behavior | +|---|---|---| +| `SystemMessage` | `message`, role `system` | AG-UI message ID is not sent. | +| `DeveloperMessage` | `message`, role `developer` | AG-UI message ID is not sent. | +| `UserMessage` | `message`, role `user`, with translated content parts | AG-UI message ID is not sent. | +| `AssistantMessage.content` | Assistant input message | AG-UI message ID is not sent; empty text is omitted. | +| `AssistantMessage.tool_calls[]` | One `function_call` per tool call | `ToolCall.id โ†’ call_id` unchanged. | +| `ToolMessage` | `function_call_output` | `tool_call_id โ†’ call_id` unchanged. | +| `ReasoningMessage` | `reasoning` item | `message.id โ†’ reasoning.id`; emitted only with `encrypted_value`. | +| `ActivityMessage` | Not mapped | Dropped with a debug log. | +| `RunAgentInput.tools[]` | OpenAI Agents SDK `FunctionTool` proxies | Tool name and JSON Schema are preserved. | + +The Responses input format has no `tool` role. Tool history is represented by +`function_call` and `function_call_output` items joined by the same `call_id`. + +Unknown message types are skipped rather than failing the entire request. + +## Event mapping: OpenAI โ†’ AG-UI + +| OpenAI Agents SDK source | AG-UI output | +|---|---| +| Message item plus text/refusal deltas | `TEXT_MESSAGE_START โ†’ TEXT_MESSAGE_CONTENT* โ†’ TEXT_MESSAGE_END` | +| Function, hosted-tool, or handoff call | `TOOL_CALL_START โ†’ TOOL_CALL_ARGS* โ†’ TOOL_CALL_END` | +| Function/handoff output | `TOOL_CALL_RESULT` | +| Reasoning summary/text | `REASONING_START โ†’ REASONING_MESSAGE_START โ†’ REASONING_MESSAGE_CONTENT* โ†’ REASONING_MESSAGE_END โ†’ REASONING_END` | +| Reasoning encrypted content | `REASONING_ENCRYPTED_VALUE` | +| Agent update/handoff target | `STEP_FINISHED` for the previous agent, then `STEP_STARTED` | +| MCP approval request | `CUSTOM` with name `mcp_approval_request` | +| MCP tool listing/approval response | Not emitted; server bookkeeping only. | +| Run start/success/failure | `RUN_STARTED`, `RUN_FINISHED`, or `RUN_ERROR` | + +`response.output_text.done` closes one text part, not necessarily the complete +message item. The translator therefore closes a text window on the item-level +done event, the completed run item, or finalization. This keeps multi-part text +under one AG-UI message ID. + +## ID mapping and guarantees + +| Source | AG-UI ID | +|---|---| +| OpenAI Agents SDK message item `id` | `message_id` | +| Function/hosted tool `call_id` | `tool_call_id` | +| Hosted tool without `call_id` | Item `id`, then generated `call_...` fallback | +| Tool result `call_id` | `tool_call_id`; result `message_id` is `-result` | +| Reasoning item `id` | Reasoning phase `message_id` | +| Additional reasoning parts | `-1`, `-2`, ... | +| AG-UI request `thread_id` / `run_id` | Same lifecycle event fields, unchanged | + +Rules: + +- A real OpenAI Agents SDK ID always wins. +- If the OpenAI Agents SDK omits an ID or uses its placeholder ID, the integration generates + a stable ID for that item (`msg_...`, `call_...`, or `rs_...`). +- One resolved ID is reused for every start/content/end event in that window. +- The same IDs are reused in `MESSAGES_SNAPSHOT`, preventing duplicate client + messages when streamed output is reconciled with final history. +- Existing input history keeps its original AG-UI IDs in the snapshot. +- Reasoning is intentionally excluded from `MESSAGES_SNAPSHOT`; encrypted + reasoning is carried by `REASONING_ENCRYPTED_VALUE` instead. +- Internal output-index correlation keys never appear on AG-UI events. + +## Messages and state snapshots + +`MESSAGES_SNAPSHOT` is emitted before `RUN_FINISHED` by default. It contains: + +1. the original `RunAgentInput.messages` with their existing IDs; and +2. assistant messages, tool calls, and tool results produced during this run, + using the same IDs already streamed to the client. + +Disable it only when your application emits its own authoritative history: + +```python +async for event in translator.to_agui( + result, + run_input, + emit_messages_snapshot=False, +): + ... +``` + +State remains application-owned. Supply snapshots explicitly: + +```python +state = dict(run_input.state or {}) +initial_state = dict(state) + +result = Runner.run_streamed( + agent, + input=translated.messages, + context=state, +) + +async for event in translator.to_agui( + result, + run_input, + initial_state=initial_state, + final_state=lambda: dict(state), +): + ... +``` + +The final factory runs after streaming, so it can observe changes made by tools +or hooks. + +## Context and forwarded properties + +`state`, `context`, `forwarded_props`, and `resume` pass through on +`TranslatedInput`; their meaning remains application-specific. + +Be aware of two uses of โ€œcontextโ€: + +- `RunAgentInput.context` is the AG-UI list sent by the client. +- `Runner.run_streamed(context=...)` is the OpenAI Agents SDK dependency object + available through `RunContextWrapper`. + +The convenience wrapper passes the AG-UI context list into the OpenAI Agents SDK context +slot. This enables dynamic instructions or tools to read the list, as shown by +the `dynamic_system_prompt` example. With the direct translator, you may pass +that list, transform it into prompt text, or replace it with your own dependency +object. + +The inbound engine's `translate_context()` method renders AG-UI context as +`description: value` lines when you want to add it to instructions. + +## Tools and human-in-the-loop + +### Backend tools + +Normal OpenAI Agents SDK `@function_tool` tools stay on the server. Their calls and results +become AG-UI tool events without special integration code. + +### Frontend-owned tools + +Tools declared in `RunAgentInput.tools` belong to the client. `to_openai` +creates OpenAI Agents SDK `FunctionTool` proxies so the model can call them, but the frontend +must execute them. + +Recommended flow: + +1. Merge translated client tools onto a per-request agent clone. +2. Configure OpenAI Agents SDK `StopAtTools` for those names when the run should stop as soon + as the model calls one. +3. Render/execute the tool in the client. +4. Send its result as a `ToolMessage` in the next AG-UI request. +5. `to_openai` converts that result to `function_call_output`, preserving the + `call_id`, and the agent continues from the conversation history. + +```python +from agents import Agent, StopAtTools + +agent = Agent( + name="planner", + instructions="Use generate_steps when asked to make a plan.", + tool_use_behavior=StopAtTools(stop_at_tool_names=["generate_steps"]), +) +``` + +This pattern covers frontend tool rendering, tool-based generative UI, and +client-owned human approval. + +### Backend tool approval + +OpenAI Agents SDK tools marked `needs_approval=True` are different: real backend +code exists, but the OpenAI Agents SDK must pause before executing it. + +```python +from agents import function_tool + + +@function_tool(needs_approval=True) +def issue_refund(order_id: str) -> str: + return f"Refund issued for {order_id}" +``` + +`result.interruptions` is known after draining the OpenAI Agents SDK stream. A production +server must: + +1. call the OpenAI Agents SDK's `result.to_state()` method and store the + returned paused `RunState` in durable storage, keyed by your own thread/run + identity. This method is provided by the OpenAI Agents SDK; this AG-UI + integration only calls it. +2. emit an approval request before `RUN_FINISHED` (for example through + `end_custom_event`); +3. authenticate and validate the next approval decision; +4. claim the stored state exactly once; +5. call the OpenAI Agents SDK's `state.approve(item)` or `state.reject(item)`; + and +6. resume the OpenAI Agents SDK run with `Runner.run_streamed(agent, state)`. + +The AG-UI-specific part is the approval notification and transport: + +```python +state = result.to_state() # OpenAI Agents SDK +store[run_input.thread_id] = state + +approval_event = CustomEvent( + name="approval_request", + value=[{"call_id": item.raw_item.call_id, "tool_name": item.tool_name}], +) # AG-UI event, emitted through to_agui(..., end_custom_event=approval_event) + +# On the next request, after validating the frontend decision: +state.approve(item) # or state.reject(item) โ€” OpenAI Agents SDK +resumed = Runner.run_streamed(agent, state) # OpenAI Agents SDK +``` + +The included demo uses an in-memory store for clarity. Replace it with durable, +atomic storage before production use. See the complete +[`human_in_the_loop_approval.py`](examples/agents_examples/human_in_the_loop_approval.py) +example for the request routing, state store, custom event, and resume flow. + +## Reasoning + +Visible reasoning summary/text maps to AG-UI reasoning events. Replay requires +encrypted reasoning content; plaintext reasoning alone cannot restore model +reasoning state. + +Request encrypted content through normal OpenAI Agents SDK model settings: + +```python +from agents import ModelSettings + +settings = ModelSettings( + response_include=["reasoning.encrypted_content"], +) +``` + +When present, encrypted content is emitted once as +`REASONING_ENCRYPTED_VALUE` and can return later through an AG-UI +`ReasoningMessage.encrypted_value`. + +## Multimodal input + +| AG-UI content | OpenAI input | Notes | +|---|---|---| +| Text | `input_text` | Supported. | +| Image URL or data | `input_image` | Data becomes a base64 data URL; detail is `auto`. | +| Document URL or data | `input_file` | Uses `file_url` or base64 `file_data`. | +| Audio data | `input_audio` | WAV/MP3 only; requires a compatible Chat Completions model. | +| Audio URL | Not mapped | Dropped. | +| Video | Not mapped | OpenAI agent model input has no matching video part. | +| Legacy `BinaryInputContent` | Image, audio, or file by MIME type | Best-effort compatibility path. | + +Unsupported parts are skipped. If every part of a user message is unsupported, +the complete message is skipped so the OpenAI Agents SDK never receives empty content. + +The integration currently emits text/tool/reasoning output only. It does not +translate generated image or audio output into AG-UI events. + +## Custom mappings + +The public translator delegates to two engine classes: + +- `AGUIToOpenAITranslator` โ€” stateless inbound request mapping, reused. +- `OpenAIToAGUITranslator` โ€” stateful outbound stream mapping, created per run. + +Their `translate_*` methods are override points. Subclass only the side you +need and inject the class into `AGUITranslator`: + +```python +from ag_ui_openai_agents import AGUITranslator +from ag_ui_openai_agents.engine import OpenAIToAGUITranslator + + +class MyOutboundTranslator(OpenAIToAGUITranslator): + def translate_mcp_approval_request_item(self, item): + return [] # Application-specific behavior. + + +translator = AGUITranslator(outbound_cls=MyOutboundTranslator) +``` + +Avoid sharing an outbound engine instance: it tracks open text, tool, +reasoning, step, and snapshot state for one run. `AGUITranslator` creates the +correct fresh instance automatically. + +## Examples + +The [`examples/`](examples/) directory contains one runnable app per supported +Dojo feature: + +| Example | Demonstrates | +|---|---| +| `ag_ui_docs_copilot` | Agent using the integration and AG-UI documentation. | +| `agentic_chat` | Basic streaming conversation. | +| `backend_tool_rendering` | Server function call and result rendering. | +| `human_in_the_loop` | Frontend-owned tool plus user interaction. | +| `human_in_the_loop_approval` | OpenAI Agents SDK backend approval, persisted state, and resume. | +| `tool_based_generative_ui` | Frontend renders UI from tool arguments. | +| `subagents` | Agents-as-tools orchestration. | +| `custom_lifecycle_events` | Dynamic custom events using run data. | +| `dynamic_system_prompt` | Instructions derived from AG-UI context. | + +Run all examples: + +```bash +cd examples +uv sync +cp .env.example .env +# Add OPENAI_API_KEY to .env +uv run python server.py +``` + +The server listens on `http://localhost:8024` by default. `PORT` and `HOST` +override the defaults. `GET /health` lists the mounted demos. See the +[examples guide](examples/README.md) for routes and suggested prompts. + +## Production responsibilities + +This package translates the protocol. Your application still owns: + +- authentication and authorization; +- API-key and secret management; +- session and message persistence; +- idempotency and concurrency control; +- approval-state storage and expiry; +- rate limits, retries, logging, and observability; +- model choice, guardrails, tools, handoffs, and OpenAI Agents SDK `RunConfig`; +- HTTP/WebSocket deployment and scaling. + +## Testing and local development + +Clone the AG-UI repository, then work from +`integrations/openai-agents/python`: + +```bash +uv sync +uv run python -c "import ag_ui_openai_agents" +uv run pytest +uv build +``` + +`uv sync` installs the package and its development dependencies. The import +command is a quick installation smoke test, `pytest` runs the package unit +suite, and `uv build` verifies that the source distribution and wheel can be +created. No lint command is documented because this package does not currently +define a package-specific linter. + +The test suite covers the public translator, wrapper, FastAPI endpoint, +message/content mapping, event windows, snapshots, IDs, cancellation, tools, +reasoning, and OpenAI Agents SDK discriminator drift. Unit tests do not require a live API +key. + +The runnable examples have a separate test environment and test suite: + +```bash +cd examples +uv sync +uv run pytest +``` + +Package tests do not automatically collect example tests. Run both suites +before opening a pull request that changes integration behavior or examples. + +After changing `openai-agents` or `openai`, always run the full suite. The drift +tests compare the event and item discriminators used by this integration with +the installed OpenAI Agents SDK so renamed or newly introduced wire types are caught before +release. + +## Troubleshooting + +| Problem | Check | +|---|---| +| Authentication fails | Confirm `OPENAI_API_KEY` is set in the server process, not only in another terminal or frontend environment. | +| The client receives no stream | Return `StreamingResponse` with `EventEncoder.get_content_type()` and yield every encoded event from `translator.to_agui(...)`. | +| A frontend tool never appears | Include its definition in `RunAgentInput.tools`; frontend-owned tools are created from the request, not from the backend agent. | +| A backend approval cannot resume | Persist the OpenAI Agents SDK `RunState`, return the matching `call_id`, apply `state.approve()` or `state.reject()`, and resume with that state. The example's in-memory store is not production storage. | +| IDs do not match across events | Preserve the incoming AG-UI message and tool-call IDs; see [ID mapping and guarantees](#id-mapping-and-guarantees). | +| A run fails without useful client details | Set `run_error_message` for a safe client-facing message and inspect the original re-raised exception in server logs. | + +When reporting a bug, include the package versions, a minimal `RunAgentInput`, +the observed AG-UI event sequence, and a small reproducer. Remove API keys and +sensitive message content first. + +## Resources + +- [OpenAI Agents SDK documentation](https://openai.github.io/openai-agents-python/) +- [OpenAI Agents guide](https://developers.openai.com/api/docs/guides/agents) +- [AG-UI Protocol](https://github.com/ag-ui-protocol/ag-ui) +- [Integration source](https://github.com/ag-ui-protocol/ag-ui/tree/main/integrations/openai-agents) +- [Examples](examples/) +- [Dojo demos](https://dojo.ag-ui.com/openai-agents-python/feature/agentic_chat) +- [MIT License](LICENSE) diff --git a/integrations/openai-agents/python/examples/.env.example b/integrations/openai-agents/python/examples/.env.example new file mode 100644 index 0000000000..e26b21383b --- /dev/null +++ b/integrations/openai-agents/python/examples/.env.example @@ -0,0 +1,9 @@ +# OpenAI API Key (required) +OPENAI_API_KEY=sk-your-key-here + +# Optional: override the model used by every example agent +# OPENAI_DEFAULT_MODEL=gpt-4.1-mini + +# Optional: OPENAI_AGENTS_DISABLE_TRACING is the OpenAI Agents SDK's own env +# var for tracing. SDK default is "false" (tracing on). Set to "true" to disable tracing. +# OPENAI_AGENTS_DISABLE_TRACING=false diff --git a/integrations/openai-agents/python/examples/.gitignore b/integrations/openai-agents/python/examples/.gitignore new file mode 100644 index 0000000000..cff554307a --- /dev/null +++ b/integrations/openai-agents/python/examples/.gitignore @@ -0,0 +1,3 @@ +.env +__pycache__/ +*.pyc diff --git a/integrations/openai-agents/python/examples/README.md b/integrations/openai-agents/python/examples/README.md new file mode 100644 index 0000000000..d6342bff7e --- /dev/null +++ b/integrations/openai-agents/python/examples/README.md @@ -0,0 +1,292 @@ +# OpenAI Agents (Python) examples + +Runnable demos for `ag_ui_openai_agents`, one mounted FastAPI app per agent. +The aggregate server mounts each example application: + +- **`ag_ui_docs_copilot`** handles normal conversation directly and reads + local README sections on demand via `read_ag_ui_openai_agents_docs` and + `read_ag_ui_protocol_docs` tools. +- The remaining focused feature apps use **`OpenAIAgentsAgent`** and + `add_openai_agents_fastapi_endpoint` where their run does not require custom + control. + +Model provider is **native OpenAI** (`OPENAI_API_KEY`). These examples exercise +the direct OpenAI path deliberately, since that is the reference setup for the +library. + +## Prerequisites + +- Python 3.10 or newer +- [`uv`](https://docs.astral.sh/uv/) +- An OpenAI API key + +## Setup and configuration + +```bash +cd integrations/openai-agents/python/examples +uv sync --no-dev +cp .env.example .env +``` + +Add your API key to `.env`. The example server recognizes: + +| Variable | Required | Purpose | +|---|---:|---| +| `OPENAI_API_KEY` | Yes | Authenticates OpenAI requests. | +| `OPENAI_DEFAULT_MODEL` | No | Overrides the model shared by the examples. | +| `OPENAI_AGENTS_DISABLE_TRACING` | No | Set to `true` to disable OpenAI Agents SDK tracing. | +| `HOST` | No | Bind address; defaults to `0.0.0.0`. | +| `PORT` | No | Server port; defaults to `8024`. | +| `RELOAD` | No | Set to a non-empty value to restart the server after local code changes. | + +These examples use the OpenAI provider directly. Provider selection is not an +AG-UI translator setting; applications that use another OpenAI Agents SDK +model provider configure that provider when constructing their agents and +runner. + +## Running the server + +```bash +uv run --env-file .env --no-dev python server.py +``` + +Server runs on **http://localhost:8024** (the port the AG-UI Dojo expects; +override with `PORT`). Tracing follows the SDK's own `OPENAI_AGENTS_DISABLE_TRACING` +env var and default (`false` โ€” tracing on); set it to `true` to disable +tracing. + +## Available endpoints + +| Endpoint | Demonstrates | +|---|---| +| `/ag_ui_docs_copilot/` | Documentation retrieval through backend tools | +| `/agentic_chat/` | Basic streamed conversation | +| `/backend_tool_rendering/` | Backend function tools and rendered results | +| `/human_in_the_loop/` | Frontend-owned tool execution | +| `/human_in_the_loop_approval/` | Approval before a backend tool runs | +| `/tool_based_generative_ui/` | UI rendered from frontend tool arguments | +| `/subagents/` | OpenAI Agents SDK agents-as-tools orchestration | +| `/custom_lifecycle_events/` | Custom events around a run | +| `/dynamic_system_prompt/` | Instructions derived from AG-UI context | +| `/health` | Server status and registered demo names | + +All demo endpoints accept AG-UI `RunAgentInput` requests and return an SSE +event stream. + +## Testing with Dojo + +Start the example server, then run the Dojo from the repository root in a +second terminal: + +```bash +cd apps/dojo +pnpm dev +``` + +Open `http://localhost:3000`, select **OpenAI Agents (Python)**, and choose a +feature. The Dojo uses `http://localhost:8024` by default. Set +`OPENAI_AGENTS_PYTHON_URL` in the Dojo environment if the backend uses another +address. + +## Automated tests + +The examples have an independent test suite. From this directory, install the +development dependencies and run it with: + +```bash +uv sync +uv run pytest +``` + +Running `uv run pytest` from the main SDK directory tests only the SDK suite; +it does not collect these example tests. + +## Manual smoke test + +```bash +curl -N -X POST http://localhost:8024/agentic_chat/ \ + -H 'Content-Type: application/json' \ + -d '{ + "thread_id": "t1", + "run_id": "r1", + "messages": [{"id":"m1","role":"user","content":"Say hi in one sentence."}], + "tools": [], + "state": {}, + "context": [], + "forwarded_props": null + }' +``` + +Swap the path to hit a different demo. `GET /health` on the aggregate server +lists every registered agent. Demos map 1:1 onto the AG-UI Dojo feature pages. + +## Agents + +### `ag_ui_docs_copilot` + +One Copilot agent handles normal conversation directly. Its instructions carry +only the heading list (table of contents) for two local READMEs; for a +documentation or code question it picks a heading and calls +`read_ag_ui_openai_agents_docs` or `read_ag_ui_protocol_docs` to read that one +section into context. The retrieval is deterministic and adds no vector +database, embedding request, network dependency, or extra agent hop โ€” the +whole document never enters the prompt, and neither does a second model's +paraphrase of it. The endpoint keeps the direct `to_openai` โ†’ +`Runner.run_streamed` โ†’ `to_agui` flow visible. + +**Try:** `"Explain how to connect my existing OpenAI Agents SDK agent to AG-UI, +then give me the smallest FastAPI streaming endpoint."` + +### `agentic_chat` + +Plain conversation, no tools. Exercises `TEXT_MESSAGE_START/CONTENT/END` +only โ€” the smallest possible smoke test. + +**Try:** `"Say hi in one sentence."` + +### `backend_tool_rendering` + +A server-side `@function_tool` (`get_weather`) the SDK executes itself. +Exercises `TOOL_CALL_START/ARGS/END` + `TOOL_CALL_RESULT`. + +**Try:** `"What's the weather in Berlin?"` + +### `human_in_the_loop` + +A _frontend_-owned tool (`generate_task_steps`) declared by the client in +`RunAgentInput.tools`, not the server. Uses the SDK's built-in +`tool_use_behavior=StopAtTools(...)` so the run ends the moment the model +calls it โ€” the tool body never executes server-side. The frontend renders +the steps, gets user approval, and sends the result back as an ordinary +`ToolMessage` in the next request; no custom pause/resume state needed. + +**Try:** `"Plan a birthday party."` โ€” requires a client that actually sends +a `generate_task_steps` tool definition in `RunAgentInput.tools` (e.g. the +AG-UI Dojo's human-in-the-loop page, which uses the same tool name). + +> **vs. `human_in_the_loop_approval` (below):** here the tool has _no server +> implementation_ โ€” only the browser can run it, so there's nothing to gate. +> `human_in_the_loop_approval` is the opposite case: a real backend tool, +> paused by the SDK's own approval API before _its_ body runs. Different +> problem, different mechanism โ€” see the comparison table under +> `human_in_the_loop_approval`. + +### `tool_based_generative_ui` + +Another frontend-owned tool (`generate_haiku`), but here the tool call _is_ +the deliverable: the frontend renders the haiku card straight from the +streamed `TOOL_CALL_ARGS` โ€” no approval round-trip. Same `StopAtTools` +mechanics as `human_in_the_loop`. + +**Try:** `"Write me a haiku about the ocean."` โ€” needs a client that sends a +`generate_haiku` tool definition (Dojo's tool-based generative UI page). + +### `human_in_the_loop_approval` + +A _backend_-owned tool (`issue_refund`) that requires approval before it +runs โ€” the SDK's `needs_approval=True` (`agents.tool.function_tool`), not an +AG-UI concept. Unlike `human_in_the_loop`, the tool has a real server-side +implementation; the SDK itself pauses the run and reports a +`ToolApprovalItem` on `result.interruptions` before the body ever executes. +That's only known once the stream is fully drained, so this demo is +hand-routed in its own application instead of running +through the shared loop: + +1. First request runs normally; if `result.interruptions` is non-empty after + the stream ends, the server serializes the paused run + (`result.to_state()`), keeps it in memory keyed by `thread_id`, and sends + a `CustomEvent(name="approval_request")` as `to_agui()`'s + `end_custom_event` โ€” right before `RUN_FINISHED`, not after (the client + drops anything that arrives once a run is marked finished). +2. The client's decision comes back on the _next_ request as + `RunAgentInput.forwarded_props["approval"]` (`{"call_id", "approve"}`). + `resolve_approval()` claims the stored state, calls `state.approve()` / + `state.reject()`, and resumes with `Runner.run_streamed(agent, state)` + instead of starting over from `translated.messages`. + +`resolve_approval()` takes the paused run out of the store before deciding +anything, so a double-clicked Approve can't resume the same run twice โ€” the +second request finds nothing to claim. A request that _isn't_ a matching +decision (a plain message, a stale `call_id`, an `approve` that isn't a real +bool โ€” `"false"` is a truthy string) abandons the paused run and answers +normally rather than refusing: the user moved on, and a thread whose approval +card got lost on a page refresh should still be usable. + +Both servers drain the SDK stream by hand here, which puts that part outside +`to_agui`'s error handling. A failure mid-drain is kept and re-raised from the +replay instead of reported on the spot, so it lands back inside `to_agui` and +the route behaves like every other one: whatever already streamed still +reaches the client, open sequences close, and `RUN_ERROR` goes out last. + +**Try:** `"I'd like a refund for ORD-1001."` โ€” requires a client that renders +`approval_request` custom events and sends the decision back via +`forwarded_props` (the AG-UI Dojo's Human in the Loop (Backend Approval) +page does this). + +**`human_in_the_loop_approval` vs. `human_in_the_loop` โ€” same goal, different mechanism:** + +| | `human_in_the_loop` | `human_in_the_loop_approval` | +| --------------------------- | --------------------------------------------------------------------- | ---------------------------------------------------------------------------- | +| Who owns the tool | Frontend โ€” no server implementation exists | Backend โ€” real `@function_tool` code | +| What pauses the run | `StopAtTools`, the instant the model calls it | The SDK's own `needs_approval` gate, before the body runs | +| How the pause surfaces | Normal `TOOL_CALL_*` events, mid-stream | `result.interruptions`, only known after the stream ends | +| How the decision comes back | An ordinary `ToolMessage` in the next request's `messages` | `RunAgentInput.forwarded_props["approval"]`, resumed via a stored `RunState` | +| Why | The action can _only_ happen client-side (render UI, wait on a click) | The backend _can_ do it, but shouldn't without sign-off first | + +### `subagents` + +Multi-agent via the SDK's **agents-as-tools** pattern (`Agent.as_tool()`) โ€” +control never transfers, the supervisor calls `research_agent`, +`writer_agent`, and `critic_agent` as tools and synthesizes the final answer +itself. Each specialist invocation appears to the client as a normal +`TOOL_CALL_*` + `TOOL_CALL_RESULT` sequence; the nested agents' inner turns +stay internal to the SDK. + +**Try:** `"Write a short piece about the history of coffee."` + +### `custom_lifecycle_events` + +Adds application-defined lifecycle events around the normal translated OpenAI +Agents SDK stream. This demonstrates how an application can extend a run with +AG-UI `CUSTOM` events without changing the translator's built-in mappings. + +### `dynamic_system_prompt` + +Builds OpenAI Agents SDK instructions from AG-UI context supplied with the +request. This demonstrates application-owned prompt construction while the +translator continues to handle messages and streamed events. + +## How the examples work + +- `server.py` mounts every example FastAPI application under its demo name. +- Each module in `agents_examples/` owns its agent and endpoint behavior. +- Most demos use `OpenAIAgentsAgent` and + `add_openai_agents_fastapi_endpoint`. +- Demos that need custom lifecycle or pause/resume behavior use + `AGUITranslator` directly around `Runner.run_streamed(...)`. +- The package dependency points to the parent integration in editable mode, so + local integration changes are used without publishing a wheel. + +## Adding an example + +1. Add a module under `agents_examples/` containing the agent and FastAPI app. +2. Register its agent and app in `DEMOS` and `DEMO_APPS` in `server.py`. +3. Add the endpoint and behavior to this README. +4. Add focused tests under `tests/` and, when it is a Dojo feature, its Dojo + registration and end-to-end test. + +## Troubleshooting + +| Problem | Check | +|---|---| +| Server exits immediately | Set `OPENAI_API_KEY` in `examples/.env`. | +| Dojo cannot connect | Confirm the server is on port `8024`, or set `OPENAI_AGENTS_PYTHON_URL` for Dojo. | +| Endpoint returns 404 | Include the mounted demo path and its trailing slash, such as `/agentic_chat/`. | +| Changes are not picked up | Set `RELOAD=1` or restart the example server. | +| Example tests are not running | Run `uv run pytest` from the `examples` directory; the package suite does not collect them. | + +## References + +- [AG-UI Protocol documentation](https://docs.ag-ui.com/) +- [OpenAI Agents SDK documentation](https://openai.github.io/openai-agents-python/) +- [OpenAI Agents integration README](../README.md) diff --git a/integrations/openai-agents/python/examples/agents_examples/__init__.py b/integrations/openai-agents/python/examples/agents_examples/__init__.py new file mode 100644 index 0000000000..483ed985e2 --- /dev/null +++ b/integrations/openai-agents/python/examples/agents_examples/__init__.py @@ -0,0 +1 @@ +"""OpenAI Agents SDK example applications.""" diff --git a/integrations/openai-agents/python/examples/agents_examples/ag_ui_docs_copilot.py b/integrations/openai-agents/python/examples/agents_examples/ag_ui_docs_copilot.py new file mode 100644 index 0000000000..1ea39f3c5c --- /dev/null +++ b/integrations/openai-agents/python/examples/agents_examples/ag_ui_docs_copilot.py @@ -0,0 +1,159 @@ +"""AG-UI documentation assistant that reads local README sections on demand.""" + +from __future__ import annotations + +import re +from importlib.metadata import metadata + +from agents import Agent, Runner, function_tool +from fastapi import FastAPI, Request +from fastapi.responses import StreamingResponse + +from ag_ui.core import RunAgentInput +from ag_ui.encoder import EventEncoder +from ag_ui_openai_agents import AGUITranslator +from .constants import DEFAULT_MODEL + +_HEADING_RE = re.compile(r"^#{1,6}\s+(.+)$") + + +def _load_distribution_readme(distribution: str) -> str: + """Load the installed package README from its distribution metadata.""" + document = metadata(distribution).get_payload() + if not isinstance(document, str) or not document.strip(): + raise RuntimeError(f"{distribution} has no README in its package metadata") + return document + + +class MarkdownSections: + """A Markdown document split by heading, read one section at a time. + + The Copilot sees only the heading list in its instructions and fetches + the sections it needs on demand, so the full document never sits in a + prompt. + """ + + def __init__(self, document: str) -> None: + sections: dict[str, list[str]] = {} + heading = "Overview" + for line in document.splitlines(): + match = _HEADING_RE.match(line) + if match: + heading = match.group(1).strip() + sections.setdefault(heading, []).append(line) + self._sections = { + name: content + for name, lines in sections.items() + if (content := "\n".join(lines).strip()) + } + + @property + def headings(self) -> str: + """The table of contents: one heading per line.""" + return "\n".join(f"- {name}" for name in self._sections) + + def read(self, heading: str) -> str: + """Return one section by heading (case-insensitive, substring ok).""" + wanted = heading.strip().lstrip("#").strip().lower() + for name, content in self._sections.items(): + if name.lower() == wanted: + return content + for name, content in self._sections.items(): + if wanted in name.lower(): + return content + return f"No section matches {heading!r}. Available headings:\n{self.headings}" + + +# ag-ui-protocol docs: the core Python SDK README. +AG_UI_PROTOCOL_DOCS = _load_distribution_readme("ag-ui-protocol") +_AG_UI_PROTOCOL_SECTIONS = MarkdownSections(AG_UI_PROTOCOL_DOCS) + + +@function_tool +def read_ag_ui_protocol_docs(heading: str) -> str: + """Read one AG-UI Protocol Python documentation section by its heading. + + Args: + heading: A heading from the "AG-UI Protocol docs" table of contents + in your instructions. + """ + return _AG_UI_PROTOCOL_SECTIONS.read(heading) + + +# ag-ui-openai-agents docs: this integration's README. +AG_UI_OPENAI_AGENTS_DOCS = _load_distribution_readme("ag-ui-openai-agents") +_AG_UI_OPENAI_AGENTS_SECTIONS = MarkdownSections(AG_UI_OPENAI_AGENTS_DOCS) + + +@function_tool +def read_ag_ui_openai_agents_docs(heading: str) -> str: + """Read one AG-UI OpenAI Agents integration documentation section. + + Args: + heading: A heading from the "AG-UI OpenAI Agents docs" table of + contents in your instructions. + """ + return _AG_UI_OPENAI_AGENTS_SECTIONS.read(heading) + + +copilot_instructions = f"""You are the developer-facing Copilot for an AG-UI +application. Answer only what the user asks. Be clear, practical, and concise; +do not add a tutorial, unrelated details, alternatives, or follow-up work +unless requested. Handle ordinary conversation directly. + +For any question about AG-UI, the OpenAI Agents SDK, translator APIs, FastAPI +endpoints, streaming, tools, client tools, state, lifecycle events, errors, +tests, or Python implementation, pick the most relevant heading from the +matching table of contents below and call the matching tool before answering. +Use read_ag_ui_openai_agents_docs for OpenAI Agents SDK integration questions +and read_ag_ui_protocol_docs for ag_ui.core protocol types and EventEncoder +questions. Treat the returned section as your only source of truth. Read +another section if the first one is not enough. + +Use only the part of the section that answers the user's question. Do not +invent behavior or claim details the documentation did not provide. For code +requests, provide only the smallest complete, production-readable Python +snippet needed, followed by a short explanation. Use documented APIs only. + +AG-UI Protocol docs table of contents: +{_AG_UI_PROTOCOL_SECTIONS.headings} + +AG-UI OpenAI Agents docs table of contents: +{_AG_UI_OPENAI_AGENTS_SECTIONS.headings} +""" + +copilot_agent = Agent( + name="AG-UI Docs Copilot", + model=DEFAULT_MODEL, + instructions=copilot_instructions, + tools=[read_ag_ui_protocol_docs, read_ag_ui_openai_agents_docs], +) + +# AGUI Translator Integration +app = FastAPI(title="AG-UI Docs Copilot") +translator = AGUITranslator() + + +@app.post("/") +async def run_ag_ui_docs_copilot( + body: RunAgentInput, request: Request +) -> StreamingResponse: + """Translate one AG-UI request into an SDK run and stream it back.""" + encoder = EventEncoder(accept=request.headers.get("accept")) + + async def stream(): + # AGUI input -> OpenAI SDK + translated_input = translator.to_openai(body) + + # normal OpenAI SDK streaming run + result = Runner.run_streamed( + copilot_agent, + input=translated_input.messages, + context=translated_input.context, + ) + + # OpenAI SDK -> AGUI events + async for event in translator.to_agui(result, body): + yield encoder.encode(event) + + return StreamingResponse(stream(), media_type=encoder.get_content_type()) diff --git a/integrations/openai-agents/python/examples/agents_examples/agentic_chat.py b/integrations/openai-agents/python/examples/agents_examples/agentic_chat.py new file mode 100644 index 0000000000..b688e0a366 --- /dev/null +++ b/integrations/openai-agents/python/examples/agents_examples/agentic_chat.py @@ -0,0 +1,22 @@ +"""Agentic chat โ€” plain conversation, no tools.""" + +from __future__ import annotations + +from agents import Agent +from fastapi import FastAPI + +from ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint +from .constants import DEFAULT_MODEL + + +def create_agentic_chat_agent() -> Agent: + return Agent( + name="assistant", + model=DEFAULT_MODEL, + instructions="You are a helpful assistant. Be concise.", + ) + + +agent = OpenAIAgentsAgent(create_agentic_chat_agent(), name="agentic_chat") +app = FastAPI(title="Agentic chat AG-UI demo") +add_openai_agents_fastapi_endpoint(app, agent, "/") diff --git a/integrations/openai-agents/python/examples/agents_examples/backend_tool_rendering.py b/integrations/openai-agents/python/examples/agents_examples/backend_tool_rendering.py new file mode 100644 index 0000000000..eabc2ceb56 --- /dev/null +++ b/integrations/openai-agents/python/examples/agents_examples/backend_tool_rendering.py @@ -0,0 +1,49 @@ +"""Backend tool rendering โ€” a server-side ``@function_tool``. + +Exercises ``TOOL_CALL_START/ARGS/END`` + ``TOOL_CALL_RESULT`` for a tool the +*backend* owns and executes (as opposed to :mod:`human_in_the_loop`, where the +frontend owns execution). The SDK runs the tool body itself; the translator +just reports the call and its result as they stream past. + +The dojo's weather card (apps/dojo .../backend_tool_rendering/page.tsx) reads +the tool result as a JSON object โ€” temperature/conditions/humidity/ +wind_speed/feels_like โ€” and the argument as `location`, matching every other +integration's version of this demo. A plain sentence string or a `city` +param renders as a blank/all-zero card, since the frontend has nothing to +parse. The fixed return value (same for every location) matches how most +other integrations' versions of this demo work too โ€” the point of the demo +is the tool-call plumbing, not real weather data. +""" + +from __future__ import annotations + +from agents import Agent, function_tool +from fastapi import FastAPI + +from ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint +from .constants import DEFAULT_MODEL + +_WEATHER = {"temperature": 20, "conditions": "sunny", "humidity": 50, "wind_speed": 10, "feels_like": 20} + + +@function_tool +def get_weather(location: str) -> dict: + """Get the current weather for a location.""" + return _WEATHER + + +def create_backend_tool_agent() -> Agent: + return Agent( + name="weather_assistant", + model=DEFAULT_MODEL, + instructions=( + "You are a helpful weather assistant. Use the get_weather tool " + "whenever the user asks about weather in a specific location." + ), + tools=[get_weather], + ) + + +agent = OpenAIAgentsAgent(create_backend_tool_agent(), name="backend_tool_rendering") +app = FastAPI(title="Backend tool rendering AG-UI demo") +add_openai_agents_fastapi_endpoint(app, agent, "/") diff --git a/integrations/openai-agents/python/examples/agents_examples/constants.py b/integrations/openai-agents/python/examples/agents_examples/constants.py new file mode 100644 index 0000000000..80b615ad32 --- /dev/null +++ b/integrations/openai-agents/python/examples/agents_examples/constants.py @@ -0,0 +1,13 @@ +"""Shared constants for the example agents. + +These examples target **native OpenAI** as the model provider. The reference +examples exercise the straightforward OpenAI path: real ids and orderly +``output_item.done`` events. Point ``DEFAULT_MODEL`` at any Responses-API model +your ``OPENAI_API_KEY`` has access to. +""" + +from __future__ import annotations + +import os + +DEFAULT_MODEL = os.getenv("OPENAI_DEFAULT_MODEL", "gpt-5.5") diff --git a/integrations/openai-agents/python/examples/agents_examples/custom_lifecycle_events.py b/integrations/openai-agents/python/examples/agents_examples/custom_lifecycle_events.py new file mode 100644 index 0000000000..f96994698e --- /dev/null +++ b/integrations/openai-agents/python/examples/agents_examples/custom_lifecycle_events.py @@ -0,0 +1,66 @@ +"""Custom lifecycle events โ€” a real usage summary right before RUN_FINISHED. + +Plain chat agent, same as agentic_chat โ€” the point isn't the agent, it's the +CUSTOM event at the end. The SDK only knows real input/output token counts +once the run has finished, so this demo composes the translator directly +(rather than the OpenAIAgentsAgent wrapper) to read them off the run result +after the stream completes and before to_agui() emits RUN_FINISHED. +""" + +from __future__ import annotations + +from agents import Agent, Runner +from fastapi import FastAPI, Request +from fastapi.responses import StreamingResponse + +from ag_ui.core import CustomEvent, EventType, RunAgentInput +from ag_ui.encoder import EventEncoder +from ag_ui_openai_agents import AGUITranslator +from .constants import DEFAULT_MODEL + +agent = Agent( + name="assistant", + model=DEFAULT_MODEL, + instructions="You are a helpful assistant. Be concise.", +) + +app = FastAPI(title="Custom lifecycle events AG-UI demo") +translator = AGUITranslator() + + +@app.post("/") +async def run_custom_lifecycle_events( + body: RunAgentInput, request: Request +) -> StreamingResponse: + """Translate one AG-UI request into an SDK run, then report real token usage.""" + encoder = EventEncoder(accept=request.headers.get("accept")) + + async def stream(): + # AGUI input -> OpenAI SDK + translated_input = translator.to_openai(body) + result = Runner.run_streamed( + agent, + input=translated_input.messages, + context=translated_input.context, + ) + + def usage_value() -> dict[str, int]: + # Resolved after the stream finishes, so these are the run's + # actual totals, not an estimate made up before anything ran. + usage = result.context_wrapper.usage + return { + "input_tokens": usage.input_tokens, + "output_tokens": usage.output_tokens, + } + + end_custom_event = CustomEvent( + type=EventType.CUSTOM, name="run_usage", value=usage_value + ) + + # OpenAI SDK -> AGUI events + async for event in translator.to_agui( + result, body, end_custom_event=end_custom_event + ): + yield encoder.encode(event) + + return StreamingResponse(stream(), media_type=encoder.get_content_type()) diff --git a/integrations/openai-agents/python/examples/agents_examples/dynamic_system_prompt.py b/integrations/openai-agents/python/examples/agents_examples/dynamic_system_prompt.py new file mode 100644 index 0000000000..da750444ff --- /dev/null +++ b/integrations/openai-agents/python/examples/agents_examples/dynamic_system_prompt.py @@ -0,0 +1,59 @@ +"""Dynamic system prompt โ€” reply language driven by the AG-UI ``context`` channel. + +The frontend sends a language choice in ``RunAgentInput.context`` and the SDK +rebuilds its instructions for that request. +""" + +from __future__ import annotations + +from agents import Agent, RunContextWrapper +from fastapi import FastAPI + +from ag_ui.core import Context +from ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint +from .constants import DEFAULT_MODEL + +BASE_INSTRUCTIONS = ( + "You are a helpful, concise assistant. Answer the user's questions directly." +) + +# Fallback when the frontend hasn't picked a language yet. +DEFAULT_LANGUAGE = "English" + + +def _read_language(ctx: RunContextWrapper[list[Context]]) -> str: + """Pull the reply language out of the AG-UI context list. + + ``ctx.context`` here IS the raw ``list[Context]`` the client sent โ€” + each item a ``{description, value}`` pair, nothing wrapping it. We match + the item whose description mentions "language" and use its value. + """ + items = ctx.context or [] + for item in items: + if "language" in (item.description or "").lower(): + return item.value or DEFAULT_LANGUAGE + return DEFAULT_LANGUAGE + + +def dynamic_instructions(ctx: RunContextWrapper[list[Context]], agent: Agent) -> str: + """Native SDK dynamic-instructions hook: build the prompt fresh each turn, + baking in whatever language the frontend currently has selected.""" + language = _read_language(ctx) + return ( + f"{BASE_INSTRUCTIONS}\n" + f"Always reply in {language}, no matter what language the user writes in. " + f"Every word of your response must be in {language}." + ) + + +def create_dynamic_system_prompt_agent() -> Agent: + return Agent( + name="multilingual_assistant", + model=DEFAULT_MODEL, + instructions=dynamic_instructions, + ) + + +agent = OpenAIAgentsAgent(create_dynamic_system_prompt_agent(), name="dynamic_system_prompt") +app = FastAPI(title="Dynamic system prompt AG-UI demo") +add_openai_agents_fastapi_endpoint(app, agent, "/") diff --git a/integrations/openai-agents/python/examples/agents_examples/human_in_the_loop.py b/integrations/openai-agents/python/examples/agents_examples/human_in_the_loop.py new file mode 100644 index 0000000000..a219316785 --- /dev/null +++ b/integrations/openai-agents/python/examples/agents_examples/human_in_the_loop.py @@ -0,0 +1,51 @@ +"""Human-in-the-loop โ€” a *frontend*-owned tool, approved before execution. + +Unlike :mod:`backend_tool_rendering`, ``generate_task_steps`` has no server +implementation โ€” it arrives per-request as an AG-UI client tool +(``RunAgentInput.tools``), gets wrapped into an SDK ``FunctionTool`` proxy by +``AGUIToOpenAITranslator.translate_tools()``, and is merged onto this agent by +the run loop (see ``server.py``). + +``tool_use_behavior=StopAtTools(...)`` is the SDK's built-in for "the model +may only *call* this tool, never execute it": the run ends the moment the +model emits the call, before the (dead-code) proxy body would ever run. The +frontend renders the steps for user approval, then sends the result back as +an AG-UI ``ToolMessage`` in the *next* request โ€” ordinary multi-turn history, +no custom pause/resume machinery needed. +""" + +from __future__ import annotations + +from agents import Agent, StopAtTools +from fastapi import FastAPI + +from ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint +from .constants import DEFAULT_MODEL + +# Must match the AG-UI client tool name the frontend declares in +# RunAgentInput.tools for this demo. +FRONTEND_TOOL_NAME = "generate_task_steps" + +INSTRUCTIONS = """You are a task planning assistant that breaks work into clear, actionable steps. + +When the user asks for help with a task: +1. Immediately call the `generate_task_steps` tool with an array of steps. + Each step is an object: {"description": "...", "status": "enabled"}. +2. Do not restate the steps as plain text โ€” the frontend renders them. +3. After the call, wait for the user to approve/select steps; do not call + the tool again until they respond. +""" + + +def create_human_in_the_loop_agent() -> Agent: + return Agent( + name="task_planner", + model=DEFAULT_MODEL, + instructions=INSTRUCTIONS, + tool_use_behavior=StopAtTools(stop_at_tool_names=[FRONTEND_TOOL_NAME]), + ) + + +agent = OpenAIAgentsAgent(create_human_in_the_loop_agent(), name="human_in_the_loop") +app = FastAPI(title="Human in the loop AG-UI demo") +add_openai_agents_fastapi_endpoint(app, agent, "/") diff --git a/integrations/openai-agents/python/examples/agents_examples/human_in_the_loop_approval.py b/integrations/openai-agents/python/examples/agents_examples/human_in_the_loop_approval.py new file mode 100644 index 0000000000..8591ef82e2 --- /dev/null +++ b/integrations/openai-agents/python/examples/agents_examples/human_in_the_loop_approval.py @@ -0,0 +1,215 @@ +"""Tool approval โ€” a *backend*-owned tool gated by the SDK's own approval API. + +Unlike :mod:`human_in_the_loop` (a frontend-only tool with no server +implementation) and :mod:`backend_tool_rendering` (a server tool that always +runs), ``issue_refund`` here is real server-side logic that only runs after a +human approves it โ€” the SDK's ``needs_approval=True`` mechanism +(``agents.tool.function_tool``), not an AG-UI concept. + +Mechanically: when the model calls ``issue_refund``, the SDK stops the run +*before* the tool body executes and surfaces a ``ToolApprovalItem`` on +``result.interruptions``. That only becomes known once the stream is fully +drained โ€” there is no mid-stream event for it โ€” so it can't go through the +normal per-item translator dispatch the way ``MCPApprovalRequestItem`` does. +The example's run loop checks +``result.interruptions`` right after ``to_agui()`` finishes, and if any are +pending: + +1. Serializes the paused run via ``result.to_state()`` and keeps it + server-side, keyed by ``thread_id`` (an in-memory dict here โ€” a real app + would use a session store; this survives one process, not a restart). +2. Emits one ``CustomEvent(name="approval_request")`` carrying every + interruption, as ``to_agui()``'s ``end_custom_event`` โ€” right before + ``RUN_FINISHED``, not after it. The client drops anything that arrives + once a run is marked finished, so this has to land before that event, + which means draining the raw SDK stream by hand first (interruptions + aren't known until it's fully drained) instead of handing ``result`` + straight to ``to_agui()``. + +The frontend renders Approve/Reject; either choice comes back as the next +``RunAgentInput.forwarded_props["approval"]`` (``{"call_id", "approve"}``). +The aggregate server looks up the stored state, calls ``state.approve()`` / +``state.reject()``, and resumes with ``Runner.run_streamed(agent, state)`` +instead of starting fresh from ``translated.messages``. +""" + +from __future__ import annotations + +import logging +from typing import Any + +from agents import Agent, Runner, function_tool +from fastapi import FastAPI +from fastapi.responses import StreamingResponse + +from ag_ui.core import CustomEvent, EventType, RunAgentInput +from ag_ui.encoder import EventEncoder +from ag_ui_openai_agents import AGUITranslator +from ag_ui_openai_agents.engine import ClientToolPending +from .constants import DEFAULT_MODEL + +logger = logging.getLogger(__name__) + +# Fake order book โ€” good enough to make "approved" visibly do something. +_ORDERS: dict[str, dict] = { + "ORD-1001": {"amount": 49.99, "status": "paid"}, + "ORD-1002": {"amount": 129.50, "status": "paid"}, +} + + +@function_tool(needs_approval=True) +def issue_refund(order_id: str) -> str: + """Issue a full refund for an order. Requires human approval before running.""" + order = _ORDERS.get(order_id) + if order is None: + return f"No such order: {order_id}" + order["status"] = "refunded" + return f"Refunded ${order['amount']:.2f} for {order_id}." + + +def create_human_in_the_loop_approval_agent() -> Agent: + return Agent( + name="refund_assistant", + model=DEFAULT_MODEL, + instructions=( + "You are a customer support assistant. When the user asks for a " + "refund on an order, call issue_refund with that order id. " + "Known orders: ORD-1001 ($49.99), ORD-1002 ($129.50). Don't ask " + "for confirmation yourself โ€” the approval happens outside the " + "conversation before the tool runs." + ), + tools=[issue_refund], + ) + + +agent = create_human_in_the_loop_approval_agent() +app = FastAPI(title="Human in the loop approval AG-UI demo") +_translator = AGUITranslator() +_encoder = EventEncoder() +_pending_approvals: dict[str, object] = {} + + +def resolve_approval( + store: dict[str, Any], + thread_id: str, + forwarded_props: Any, +) -> tuple[Any, Any, bool]: + """Claim the paused run for this request when a matching decision arrived. + + Pops first: whoever gets here wins, so a double-clicked Approve can't + resume the same run twice. Anything other than a decision that matches a + pending interruption abandons the paused run and starts a fresh turn โ€” + the user moved on, and a thread should never be stuck waiting forever. + + ``approve`` has to be a real bool. Truthiness would read the string + "false" as approval and run a refund the user declined, so a malformed + decision is treated as no decision at all. + + Args: + store: thread_id -> paused RunState. + thread_id: The thread this request belongs to. + forwarded_props: The request's forwarded_props. + + Returns: + (pending_state, item, approve) to resume, or (None, None, False) to + run the request fresh. + """ + pending_state = store.pop(thread_id, None) + if pending_state is None: + return None, None, False + + decision = None + if isinstance(forwarded_props, dict): + decision = forwarded_props.get("approval") + if not isinstance(decision, dict): + return None, None, False + + approve = decision.get("approve") + if not isinstance(approve, bool): + return None, None, False + + item = next( + ( + item + for item in pending_state.get_interruptions() + if getattr(item.raw_item, "call_id", None) == decision.get("call_id") + ), + None, + ) + if item is None: + return None, None, False + return pending_state, item, approve + + +@app.post("/") +async def run(body: RunAgentInput) -> StreamingResponse: + """Run or resume the approval-gated agent.""" + + pending_state, item, approve = resolve_approval( + _pending_approvals, body.thread_id, body.forwarded_props + ) + + async def stream(): + if item is not None: + if approve: + pending_state.approve(item) + else: + pending_state.reject(item) + result = Runner.run_streamed(agent, pending_state) + else: + translated = _translator.to_openai(body) + run_agent = agent + if translated.tools: + run_agent = run_agent.clone(tools=[*agent.tools, *translated.tools]) + result = Runner.run_streamed( + run_agent, input=translated.messages, context=translated.context + ) + + # Collect as we go rather than in one comprehension: whatever streamed + # before a mid-drain stop still has to reach the client. A client-owned + # tool ends the run cleanly here; a real failure is kept and re-raised + # from replay() below, so to_agui sees it and handles it the same way + # it would on any other route. + raw_events = [] + stream_error = None + try: + async for event in result.stream_events(): + raw_events.append(event) + except ClientToolPending: + pass + except Exception as exc: + stream_error = exc + + end_custom_event = None + if stream_error is None and result.interruptions: + _pending_approvals[body.thread_id] = result.to_state() + end_custom_event = CustomEvent( + type=EventType.CUSTOM, + name="approval_request", + value=[ + { + "call_id": getattr(item.raw_item, "call_id", None), + "tool_name": item.tool_name, + "arguments": getattr(item.raw_item, "arguments", None), + } + for item in result.interruptions + ], + ) + + async def replay(): + for event in raw_events: + yield event + if stream_error is not None: + raise stream_error + + try: + async for event in _translator.to_agui( + replay(), body, end_custom_event=end_custom_event + ): + yield _encoder.encode(event) + except Exception: + # to_agui already sent RUN_ERROR before re-raising; log the real + # traceback here rather than let it escape the response. + logger.exception("Agent run failed") + + return StreamingResponse(stream(), media_type=_encoder.get_content_type()) diff --git a/integrations/openai-agents/python/examples/agents_examples/subagents.py b/integrations/openai-agents/python/examples/agents_examples/subagents.py new file mode 100644 index 0000000000..4f8f8dc741 --- /dev/null +++ b/integrations/openai-agents/python/examples/agents_examples/subagents.py @@ -0,0 +1,83 @@ +"""Subagents โ€” multi-agent via the SDK's agents-as-tools pattern. + +Unlike a handoff (control *transfers* to the specialist, triage agent exits +the conversation), the supervisor here stays in charge and *calls* +specialists as tools (``Agent.as_tool()``), possibly several in one turn, +then synthesizes their outputs itself โ€” a call-and-return delegation, not a +handoff. Same shape as the LangGraph "subagents" showcase demo (a supervisor +calling child agents as tools and getting results back), just built with the +SDK's own ``as_tool()`` instead of a routing graph. + +On the AG-UI side each specialist invocation is an ordinary +``TOOL_CALL_START/ARGS/END`` + ``TOOL_CALL_RESULT`` sequence โ€” the nested +agent's own model turns stay internal to the SDK and are not streamed as +separate messages, so the client sees one coherent supervisor transcript. +""" + +from __future__ import annotations + +from agents import Agent +from fastapi import FastAPI + +from ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint +from .constants import DEFAULT_MODEL + +research_agent = Agent( + name="research_agent", + model=DEFAULT_MODEL, + instructions=( + "You research a topic and return the key facts as a short bullet " + "list. Facts only โ€” no fluff, no conclusions." + ), +) + +writer_agent = Agent( + name="writer_agent", + model=DEFAULT_MODEL, + instructions=( + "You turn bullet-point facts into short, engaging prose. One tight " + "paragraph unless asked otherwise." + ), +) + +critic_agent = Agent( + name="critic_agent", + model=DEFAULT_MODEL, + instructions=( + "You review a draft and return concrete improvement suggestions as a " + "numbered list. Max 3 suggestions, be specific." + ), +) + + +def create_subagents_agent() -> Agent: + return Agent( + name="orchestrator", + model=DEFAULT_MODEL, + instructions=( + "You orchestrate a small content team. For writing requests: " + "call research_topic for the facts, then write_prose to draft, " + "then critique_draft to review, then produce the final version " + "yourself incorporating the critique. For simple questions, " + "answer directly without the team." + ), + tools=[ + research_agent.as_tool( + tool_name="research_topic", + tool_description="Research a topic and return key facts as bullets.", + ), + writer_agent.as_tool( + tool_name="write_prose", + tool_description="Turn bullet-point facts into short prose.", + ), + critic_agent.as_tool( + tool_name="critique_draft", + tool_description="Review a draft and suggest up to 3 improvements.", + ), + ], + ) + + +agent = OpenAIAgentsAgent(create_subagents_agent(), name="subagents") +app = FastAPI(title="Subagents AG-UI demo") +add_openai_agents_fastapi_endpoint(app, agent, "/") diff --git a/integrations/openai-agents/python/examples/agents_examples/tool_based_generative_ui.py b/integrations/openai-agents/python/examples/agents_examples/tool_based_generative_ui.py new file mode 100644 index 0000000000..0f5fff7f62 --- /dev/null +++ b/integrations/openai-agents/python/examples/agents_examples/tool_based_generative_ui.py @@ -0,0 +1,47 @@ +"""Tool-based generative UI โ€” frontend tool renders the content. + +Like :mod:`human_in_the_loop`, the tool (``generate_haiku``) is *client*-owned: +it arrives per-request in ``RunAgentInput.tools``, gets wrapped into an SDK +``FunctionTool`` proxy, and ``StopAtTools`` ends the run the moment the model +calls it. The difference is intent: here the tool call *is* the deliverable โ€” +the frontend renders the haiku card from the streamed ``TOOL_CALL_ARGS``, +no approval round-trip expected. +""" + +from __future__ import annotations + +from agents import Agent, StopAtTools +from fastapi import FastAPI + +from ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint +from .constants import DEFAULT_MODEL + +# Must match the AG-UI client tool name the frontend declares in +# RunAgentInput.tools for this demo. +FRONTEND_TOOL_NAME = "generate_haiku" + +INSTRUCTIONS = """You are a creative writing assistant that renders haikus with a UI component. + +When the user asks for a haiku: +1. Immediately call the `generate_haiku` tool with the haiku data + (Japanese lines, English lines, and any other fields the tool declares). +2. Do NOT write the haiku as plain text โ€” the frontend renders it. + +For non-creative requests, respond normally without the tool. +""" + + +def create_tool_based_generative_ui_agent() -> Agent: + return Agent( + name="haiku_assistant", + model=DEFAULT_MODEL, + instructions=INSTRUCTIONS, + tool_use_behavior=StopAtTools(stop_at_tool_names=[FRONTEND_TOOL_NAME]), + ) + + +agent = OpenAIAgentsAgent( + create_tool_based_generative_ui_agent(), name="tool_based_generative_ui" +) +app = FastAPI(title="Tool-based generative UI AG-UI demo") +add_openai_agents_fastapi_endpoint(app, agent, "/") diff --git a/integrations/openai-agents/python/examples/pyproject.toml b/integrations/openai-agents/python/examples/pyproject.toml new file mode 100644 index 0000000000..87498c6c7c --- /dev/null +++ b/integrations/openai-agents/python/examples/pyproject.toml @@ -0,0 +1,34 @@ +[project] +name = "ag-ui-openai-agents-examples" +version = "0.1.0" +description = "Example usage of the AG-UI ร— OpenAI Agents SDK integration" +license = "MIT" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "ag-ui-openai-agents", + "ag-ui-protocol>=0.1.19", + "fastapi>=0.115.2", + "uvicorn[standard]>=0.35.0", +] + +[dependency-groups] +dev = [ + "pytest>=8.0", +] + +[tool.pytest.ini_options] +testpaths = ["tests"] + +[tool.uv.sources] +ag-ui-openai-agents = { path = "../", editable = true } + +[project.scripts] +dev = "server:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["agents_examples"] diff --git a/integrations/openai-agents/python/examples/server.py b/integrations/openai-agents/python/examples/server.py new file mode 100644 index 0000000000..f485815b42 --- /dev/null +++ b/integrations/openai-agents/python/examples/server.py @@ -0,0 +1,87 @@ +"""Aggregate FastAPI server for the OpenAI Agents SDK Dojo demos. + +Each demo owns a small FastAPI app. This server mounts those apps under the +Dojo feature paths and serves them together on port 8024. +""" + +from __future__ import annotations + +import os +from pathlib import Path + +import uvicorn +from fastapi import FastAPI + +from agents_examples import ( + ag_ui_docs_copilot, + agentic_chat, + backend_tool_rendering, + custom_lifecycle_events, + dynamic_system_prompt, + human_in_the_loop, + human_in_the_loop_approval, + subagents, + tool_based_generative_ui, +) + +# Tracing follows the SDK's own OPENAI_AGENTS_DISABLE_TRACING env var and its +# own default (tracing on) โ€” nothing to set up here. + +DEMOS = { + "ag_ui_docs_copilot": ag_ui_docs_copilot.copilot_agent, + "agentic_chat": agentic_chat.agent, + "backend_tool_rendering": backend_tool_rendering.agent, + "human_in_the_loop": human_in_the_loop.agent, + "human_in_the_loop_approval": human_in_the_loop_approval.agent, + "tool_based_generative_ui": tool_based_generative_ui.agent, + "subagents": subagents.agent, + "custom_lifecycle_events": custom_lifecycle_events.agent, + "dynamic_system_prompt": dynamic_system_prompt.agent, +} + +DEMO_APPS = { + "ag_ui_docs_copilot": ag_ui_docs_copilot.app, + "agentic_chat": agentic_chat.app, + "backend_tool_rendering": backend_tool_rendering.app, + "human_in_the_loop": human_in_the_loop.app, + "human_in_the_loop_approval": human_in_the_loop_approval.app, + "tool_based_generative_ui": tool_based_generative_ui.app, + "subagents": subagents.app, + "custom_lifecycle_events": custom_lifecycle_events.app, + "dynamic_system_prompt": dynamic_system_prompt.app, +} + +app = FastAPI(title="AG-UI ร— OpenAI Agents SDK examples") + +for demo_name, demo_app in DEMO_APPS.items(): + app.mount(f"/{demo_name}", demo_app, name=demo_name) + + +@app.get("/health") +async def health() -> dict: + return {"status": "ok", "agents": list(DEMOS)} + + +def main() -> int: + if not os.getenv("OPENAI_API_KEY"): + print("Error: OPENAI_API_KEY required") + return 1 + port = int(os.getenv("PORT", "8024")) + host = os.getenv("HOST", "0.0.0.0") + print(f"Starting server on port {port} โ€” agents: {list(DEMOS)}") + if os.getenv("RELOAD"): + uvicorn.run( + "server:app", + host=host, + port=port, + reload=True, + reload_dirs=[str(Path(__file__).parent), str(Path(__file__).parent.parent / "src")], + log_level="info", + ) + else: + uvicorn.run(app, host=host, port=port, log_level="info") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/integrations/openai-agents/python/examples/tests/test_ag_ui_docs_copilot.py b/integrations/openai-agents/python/examples/tests/test_ag_ui_docs_copilot.py new file mode 100644 index 0000000000..9350bed238 --- /dev/null +++ b/integrations/openai-agents/python/examples/tests/test_ag_ui_docs_copilot.py @@ -0,0 +1,70 @@ +"""Tests for the AG-UI Docs Copilot example.""" + +from __future__ import annotations + +import inspect +import sys +from pathlib import Path + +EXAMPLES = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(EXAMPLES)) + +from agents_examples import ag_ui_docs_copilot # noqa: E402 +from agents_examples.ag_ui_docs_copilot import MarkdownSections # noqa: E402 + + +def test_docs_copilot_loads_the_integration_readme() -> None: + assert "AG-UI ร— OpenAI Agents SDK" in ag_ui_docs_copilot.AG_UI_OPENAI_AGENTS_DOCS + assert "AGUITranslator" in ag_ui_docs_copilot.AG_UI_OPENAI_AGENTS_DOCS + assert "ag-ui-protocol" in ag_ui_docs_copilot.AG_UI_PROTOCOL_DOCS + assert "EventEncoder" in ag_ui_docs_copilot.AG_UI_PROTOCOL_DOCS + + +def test_docs_copilot_reads_both_doc_sources_directly() -> None: + assert ag_ui_docs_copilot.copilot_agent.name == "AG-UI Docs Copilot" + assert {tool.name for tool in ag_ui_docs_copilot.copilot_agent.tools} == { + "read_ag_ui_openai_agents_docs", + "read_ag_ui_protocol_docs", + } + + +def test_docs_copilot_instructions_carry_both_tocs_but_not_the_docs() -> None: + instructions = ag_ui_docs_copilot.copilot_instructions + assert ag_ui_docs_copilot._AG_UI_PROTOCOL_SECTIONS.headings in instructions + assert ag_ui_docs_copilot._AG_UI_OPENAI_AGENTS_SECTIONS.headings in instructions + assert ag_ui_docs_copilot.AG_UI_OPENAI_AGENTS_DOCS not in instructions + assert ag_ui_docs_copilot.AG_UI_PROTOCOL_DOCS not in instructions + + +def test_docs_copilot_reads_one_section_by_heading() -> None: + section = ag_ui_docs_copilot._AG_UI_OPENAI_AGENTS_SECTIONS.read( + "Backend tool approval" + ) + assert "needs_approval" in section + assert len(section) < len(ag_ui_docs_copilot.AG_UI_OPENAI_AGENTS_DOCS) + + +def test_markdown_sections_splits_and_matches_loosely() -> None: + sections = MarkdownSections( + "intro line\n\n## Setup\npip install it\n\n## Usage Notes\ncall run()\n" + ) + assert sections.headings == "- Overview\n- Setup\n- Usage Notes" + assert sections.read("Setup") == "## Setup\npip install it" + assert sections.read("## usage notes") == "## Usage Notes\ncall run()" + assert sections.read("usage") == "## Usage Notes\ncall run()" + assert "Available headings" in sections.read("missing") + + +def test_markdown_sections_handles_empty_documents() -> None: + for document in ("", " \n\t"): + sections = MarkdownSections(document) + assert sections.headings == "" + assert "Available headings" in sections.read("AGUITranslator") + + +def test_docs_copilot_keeps_the_direct_translator_flow_visible() -> None: + assert "/" in {route.path for route in ag_ui_docs_copilot.app.routes} + source = inspect.getsource(ag_ui_docs_copilot.run_ag_ui_docs_copilot) + assert "translator.to_openai(body)" in source + assert "Runner.run_streamed(" in source + assert "translator.to_agui(result, body)" in source diff --git a/integrations/openai-agents/python/examples/tests/test_human_in_the_loop_approval.py b/integrations/openai-agents/python/examples/tests/test_human_in_the_loop_approval.py new file mode 100644 index 0000000000..a741bb28a9 --- /dev/null +++ b/integrations/openai-agents/python/examples/tests/test_human_in_the_loop_approval.py @@ -0,0 +1,91 @@ +"""Tests for the approval demo's decision handling. + +resolve_approval is the gate in front of a paused run: it decides whether a +request resumes that run or starts a fresh turn. No model, no network โ€” just +the decision logic, driven with a stub RunState. +""" + +from __future__ import annotations + +import sys +from pathlib import Path +from types import SimpleNamespace + +import pytest + +EXAMPLES = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(EXAMPLES)) + +from agents_examples.human_in_the_loop_approval import resolve_approval # noqa: E402 + + +class _StubState: + """Stands in for a paused RunState carrying one interruption.""" + + def __init__(self, call_id: str = "call_1") -> None: + self.item = SimpleNamespace(raw_item=SimpleNamespace(call_id=call_id)) + + def get_interruptions(self) -> list: + return [self.item] + + +def _decision(call_id: str = "call_1", approve: bool = True) -> dict: + return {"approval": {"call_id": call_id, "approve": approve}} + + +def test_matching_decision_resumes_and_claims_the_paused_run(): + state = _StubState() + store = {"t1": state} + + pending_state, item, approve = resolve_approval(store, "t1", _decision()) + + assert pending_state is state + assert item is state.item + assert approve is True + assert store == {}, "the claimed run must not stay behind for a second request" + + +def test_reject_decision_resumes_with_approve_false(): + state = _StubState() + _, item, approve = resolve_approval({"t1": state}, "t1", _decision(approve=False)) + assert item is state.item + assert approve is False + + +def test_second_concurrent_decision_finds_nothing_to_resume(): + # Both requests validate before either runs, so the store is the only thing + # keeping a double-clicked Approve from resuming the same run twice. + store = {"t1": _StubState()} + first = resolve_approval(store, "t1", _decision()) + second = resolve_approval(store, "t1", _decision()) + + assert first[1] is not None + assert second == (None, None, False) + + +def test_plain_message_starts_a_fresh_turn_instead_of_wedging_the_thread(): + # The user typed something else instead of deciding. That abandons the + # paused run rather than blocking every later message on the thread. + store = {"t1": _StubState()} + + assert resolve_approval(store, "t1", None) == (None, None, False) + assert store == {} + + +@pytest.mark.parametrize("approve", ["false", "true", 0, 1, None, "", "yes"]) +def test_a_non_boolean_approve_never_resumes_the_run(approve): + # "false" is a truthy string โ€” reading it as approval would refund an order + # the user declined. Only a real bool decides anything. + store = {"t1": _StubState()} + assert resolve_approval( + store, "t1", {"approval": {"call_id": "call_1", "approve": approve}} + ) == (None, None, False) + + +def test_decision_for_an_unknown_call_id_starts_fresh(): + store = {"t1": _StubState(call_id="call_1")} + assert resolve_approval(store, "t1", _decision(call_id="other")) == (None, None, False) + + +def test_decision_with_no_pending_run_starts_fresh(): + assert resolve_approval({}, "t1", _decision()) == (None, None, False) diff --git a/integrations/openai-agents/python/examples/uv.lock b/integrations/openai-agents/python/examples/uv.lock new file mode 100644 index 0000000000..3282c5a6e2 --- /dev/null +++ b/integrations/openai-agents/python/examples/uv.lock @@ -0,0 +1,1730 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", + 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OpenAI Agents SDK" +license = "MIT" +license-files = ["LICENSE"] +readme = "README.md" +authors = [ + { name = "Abdelrahman Abozied", email = "me@abdelrahman.ai" }, +] +requires-python = ">=3.10" +dependencies = [ + "ag-ui-protocol>=0.1.19", + "openai-agents>=0.8.4", + "pydantic>=2.13.4", + "fastapi>=0.115.12", + "uvicorn>=0.51.0", +] + +[dependency-groups] +dev = [ + "pytest>=8.0", +] + +[tool.pytest.ini_options] +testpaths = ["tests"] + +[tool.ag-ui.scripts] +test = "python -m pytest" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/ag_ui_openai_agents"] diff --git a/integrations/openai-agents/python/src/ag_ui_openai_agents/__init__.py b/integrations/openai-agents/python/src/ag_ui_openai_agents/__init__.py new file mode 100644 index 0000000000..0ca55540ba --- /dev/null +++ b/integrations/openai-agents/python/src/ag_ui_openai_agents/__init__.py @@ -0,0 +1,20 @@ +"""OpenAI Agents SDK ร— AG-UI Protocol integration. + +Compose it yourself (recommended, full control of the agent and server): AGUITranslator, TranslatedInput. +Serve a fixed agent instead (less code, less control): OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint. +Need a custom mapping? Subclass a translator in ag_ui_openai_agents.engine. +""" + +from .agent import OpenAIAgentsAgent +from .endpoint import add_openai_agents_fastapi_endpoint +from .engine import TranslatedInput +from .translator import AGUITranslator + +__version__ = "0.1.0" + +__all__ = [ + "add_openai_agents_fastapi_endpoint", + "AGUITranslator", + "OpenAIAgentsAgent", + "TranslatedInput", +] diff --git a/integrations/openai-agents/python/src/ag_ui_openai_agents/agent.py b/integrations/openai-agents/python/src/ag_ui_openai_agents/agent.py new file mode 100644 index 0000000000..f97b748431 --- /dev/null +++ b/integrations/openai-agents/python/src/ag_ui_openai_agents/agent.py @@ -0,0 +1,168 @@ +"""Agent wrapper โ€” an OpenAI Agents SDK Agent that speaks AG-UI.""" + +import logging +from typing import Any, AsyncIterator + +from agents import Agent, RunConfig, Runner + +from ag_ui.core import BaseEvent, CustomEvent, RunAgentInput +from .translator import AGUITranslator + +logger = logging.getLogger(__name__) + +# Sentinel: distinguishes "caller passed context=None" (a real SDK context of +# None) from "caller said nothing" (fall back to the AG-UI context list). +_UNSET_CONTEXT: Any = object() + + +class OpenAIAgentsAgent: + """Wrap an OpenAI Agents SDK Agent so it speaks the AG-UI protocol. + + The wrapper is reusable across requests. Non-conflicting client tools are + added to a per-request agent clone, and output uses fresh per-run state. + + This shortcut hides the run loop. Use ``AGUITranslator`` directly when + application logic needs access to ``RunResultStreaming``. + + Example: + from agents import Agent + from ag_ui_openai_agents import OpenAIAgentsAgent + + sdk_agent = Agent(name="assistant", instructions="You are helpful.") + agent = OpenAIAgentsAgent(sdk_agent) + + async for event in agent.run_streamed(run_input): + ... + """ + + def __init__( + self, + agent: Agent, + *, + name: str | None = None, + description: str = "", + translator: AGUITranslator | None = None, + run_config: RunConfig | None = None, + context: Any = _UNSET_CONTEXT, + start_custom_event: CustomEvent | None = None, + initial_state: Any = None, + final_state: Any = None, + emit_messages_snapshot: bool = True, + end_custom_event: CustomEvent | None = None, + emit_run_error: bool = True, + run_error_message: str | None = None, + ) -> None: + """Configure an OpenAI Agents SDK Agent for AG-UI requests. + + Args: + agent: The OpenAI Agents SDK Agent to serve. + name: Public name for health/introspection. Defaults to agent.name. + description: Optional human-readable description. + translator: Reusable translator. Provide one to customize mapping + through engine subclasses. + run_config: Run-wide OpenAI Agents SDK configuration. None uses the + SDK defaults. + context: The SDK run context (``TContext``) handed to tools, hooks, + and dynamic instructions via ``RunContextWrapper.context``. When + omitted, the AG-UI request's ambient ``context`` list is passed + through instead. Provide this when the wrapped agent's tools + expect an application-specific context object. + start_custom_event: CustomEvent emitted after RUN_STARTED. Its value + may be static or a zero-argument sync/async factory. + initial_state: Passed to AGUITranslator.to_agui unchanged. + final_state: Passed to AGUITranslator.to_agui unchanged. + emit_messages_snapshot: Whether to emit MESSAGES_SNAPSHOT before + RUN_FINISHED. Defaults to True. + end_custom_event: CustomEvent emitted before the terminal event. + Its value may be static or a zero-argument sync/async factory. + Use the translator directly when its value needs the run result. + emit_run_error: Whether to emit RUN_ERROR for ordinary lifecycle + errors. + Defaults to True. + run_error_message: Fixed RUN_ERROR message. None sends str(exc). + """ + self.agent = agent + self.name = name or agent.name + self.description = description + self._translator = translator or AGUITranslator() + self._run_config = run_config + self._context = context + self._start_custom_event = start_custom_event + self._initial_state = initial_state + self._final_state = final_state + self._emit_messages_snapshot = emit_messages_snapshot + self._end_custom_event = end_custom_event + self._emit_run_error = emit_run_error + self._run_error_message = run_error_message + + async def run_streamed(self, input: RunAgentInput) -> AsyncIterator[BaseEvent]: + """Translate one AG-UI request, run the agent, and yield AG-UI events. + + Client tools are added to a per-request agent clone. The translator + handles input and output mapping, lifecycle events, and snapshots. + + Args: + input: The incoming AG-UI RunAgentInput. + + Yields: + AG-UI BaseEvent instances, ready to encode. + """ + translated = self._translator.to_openai(input) + + run_agent = self.agent + if translated.tools: + server_tool_names = { + name + for tool in self.agent.tools + if (name := getattr(tool, "name", None)) is not None + } + client_tools = [] + seen_client_names: set[str] = set() + for tool in translated.tools: + if tool.name in server_tool_names: + logger.warning( + "Ignoring client tool %r because a server tool already uses this name", + tool.name, + ) + continue + if tool.name in seen_client_names: + logger.warning( + "Ignoring duplicate client tool %r; the SDK cannot resolve two tools with one name", + tool.name, + ) + continue + seen_client_names.add(tool.name) + client_tools.append(tool) + + if client_tools: + run_agent = self.agent.clone( + tools=[*self.agent.tools, *client_tools] + ) + + # Fall back to the AG-UI ambient context; normalize an empty list to + # None so a context-less request matches the SDK's own default (None), + # not [] โ€” agents that branch on `ctx.context is None` rely on this. + context = ( + self._context + if self._context is not _UNSET_CONTEXT + else (translated.context or None) + ) + result = Runner.run_streamed( + run_agent, + input=translated.messages, + run_config=self._run_config, + context=context, + ) + + async for event in self._translator.to_agui( + result, + input, + start_custom_event=self._start_custom_event, + initial_state=self._initial_state, + final_state=self._final_state, + emit_messages_snapshot=self._emit_messages_snapshot, + end_custom_event=self._end_custom_event, + emit_run_error=self._emit_run_error, + run_error_message=self._run_error_message, + ): + yield event diff --git a/integrations/openai-agents/python/src/ag_ui_openai_agents/endpoint.py b/integrations/openai-agents/python/src/ag_ui_openai_agents/endpoint.py new file mode 100644 index 0000000000..2cfe2963ed --- /dev/null +++ b/integrations/openai-agents/python/src/ag_ui_openai_agents/endpoint.py @@ -0,0 +1,116 @@ +"""FastAPI endpoint helper for serving an OpenAI Agents SDK agent over AG-UI.""" + +import logging +import re +from typing import AsyncIterator + +from fastapi import FastAPI, Request +from fastapi.responses import StreamingResponse + +from ag_ui.core import EventType, RunAgentInput, RunErrorEvent, RunStartedEvent +from ag_ui.encoder import EventEncoder +from .agent import OpenAIAgentsAgent + +logger = logging.getLogger(__name__) + + +def add_openai_agents_fastapi_endpoint( + app: FastAPI, + agent: OpenAIAgentsAgent, + path: str = "/", + *, + include_health: bool = True, +) -> None: + """Add an OpenAI Agents SDK agent endpoint to a FastAPI app. + + Args: + app: The FastAPI application to register the route on. + agent: The wrapped agent to serve. + path: The POST path for the agent. Defaults to "/". + include_health: Whether to register a GET health check alongside the + agent endpoint. Defaults to True. + """ + # Unique per-route names/operation_ids so mounting several agents on one app + # does not collide (FastAPI derives both from the handler __name__ otherwise, + # producing duplicate operation ids and an ambiguous OpenAPI schema). + slug = re.sub(r"\W+", "_", path).strip("_") or "root" + # Guarantee uniqueness even if two mount paths normalize to the same slug + # (e.g. "/a/b" and "/a_b"), so route names / operation ids never collide. + used_slugs = getattr(app.state, "_openai_agents_slugs", None) + if used_slugs is None: + used_slugs = set() + app.state._openai_agents_slugs = used_slugs + base_slug, suffix = slug, 1 + while slug in used_slugs: + slug = f"{base_slug}_{suffix}" + suffix += 1 + used_slugs.add(slug) + + @app.post(path, name=f"openai_agents_run_{slug}", operation_id=f"openai_agents_run_{slug}") + async def openai_agents_endpoint( + input_data: RunAgentInput, + request: Request, + ) -> StreamingResponse: + """Run the agent and stream AG-UI events back to the client.""" + logger.info( + "Starting agent run: agent=%s thread_id=%s run_id=%s", + agent.name, + input_data.thread_id, + input_data.run_id, + ) + accept_header = request.headers.get("accept") + encoder = EventEncoder(accept=accept_header) + + async def event_generator() -> AsyncIterator[str]: + started = False + try: + async for event in agent.run_streamed(input_data): + if event.type == EventType.RUN_STARTED: + started = True + yield encoder.encode(event) + except Exception: + # If the run never emitted RUN_STARTED it failed in setup (input + # translation / clone / Runner.run_streamed) before to_agui ran, so + # no lifecycle reached the client โ€” an empty 200 otherwise. Emit a + # minimal well-formed RUN_STARTED + RUN_ERROR โ€” deliberately even + # when emit_run_error=False, since a silent empty 200 the client + # can't distinguish from success is worse than a terminal error at + # the transport boundary. If the run DID start, to_agui already + # handled the error per its emit_run_error config (possibly + # deliberately silent), so add nothing โ€” just log. Either way, + # never sever the stream with an ASGI-level traceback. + if not started: + yield encoder.encode( + RunStartedEvent( + type=EventType.RUN_STARTED, + thread_id=input_data.thread_id, + run_id=input_data.run_id, + # Preserve parent linkage for nested/branched runs, + # matching the normal to_agui RUN_STARTED path. + parent_run_id=getattr(input_data, "parent_run_id", None), + ) + ) + yield encoder.encode( + RunErrorEvent(type=EventType.RUN_ERROR, message="Agent run failed") + ) + logger.exception( + "Agent run failed: agent=%s run_id=%s", + agent.name, + input_data.run_id, + ) + + return StreamingResponse( + event_generator(), + media_type=encoder.get_content_type(), + ) + + if include_health: + + @app.get( + path.rstrip("/") + "/health", + name=f"openai_agents_health_{slug}", + operation_id=f"openai_agents_health_{slug}", + ) + def health(): + """Health check.""" + return {"status": "ok", "agent": {"name": agent.name}} diff --git a/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/__init__.py b/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/__init__.py new file mode 100644 index 0000000000..4ce722ca92 --- /dev/null +++ b/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/__init__.py @@ -0,0 +1,40 @@ +"""Engine layer โ€” the per-direction translation logic. + +Advanced use only: subclass an engine to customize a single mapping and +inject it into the public translator via inbound_cls / outbound_cls. For +everything else, import AGUITranslator from the package root instead. +""" + +from .agui_to_openai import AGUIToOpenAITranslator +from .helpers import ( + new_message_id, + new_tool_call_id, + new_tool_result_id, + read_attr, + to_string, +) +from .openai_to_agui import OpenAIToAGUITranslator +from .types import ( + ClientToolPending, + HOSTED_TOOL_CALL_TYPES, + OpenAIItemType, + OpenAIRawResponseEventType, + OpenAIStreamEventType, + TranslatedInput, +) + +__all__ = [ + "AGUIToOpenAITranslator", + "ClientToolPending", + "HOSTED_TOOL_CALL_TYPES", + "new_message_id", + "new_tool_call_id", + "new_tool_result_id", + "OpenAIItemType", + "OpenAIRawResponseEventType", + "OpenAIStreamEventType", + "OpenAIToAGUITranslator", + "read_attr", + "to_string", + "TranslatedInput", +] diff --git a/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/agui_to_openai.py b/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/agui_to_openai.py new file mode 100644 index 0000000000..f245f4f662 --- /dev/null +++ b/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/agui_to_openai.py @@ -0,0 +1,769 @@ +"""Translate AG-UI request data into OpenAI Agents SDK input types.""" + +import logging +from typing import Any, Iterable + +from agents import FunctionTool, RunContextWrapper, TResponseInputItem + +from ag_ui.core import ( + ActivityMessage, + AssistantMessage, + AudioInputContent, + BinaryInputContent, + Context, + DeveloperMessage, + DocumentInputContent, + ImageInputContent, + Message, + ReasoningMessage, + RunAgentInput, + SystemMessage, + TextInputContent, + Tool as AGUITool, + ToolMessage, + UserMessage, + VideoInputContent, +) +from .helpers import read_attr, to_string +from .types import ClientToolPending, TranslatedInput + +logger = logging.getLogger(__name__) + + +class AGUIToOpenAITranslator: + """Map AG-UI request data to OpenAI Agents SDK input types. + + This stateless inbound engine powers ``AGUITranslator.to_openai()``. + Its public ``translate_*`` methods are mapping-level override points for + applications that need to customize individual conversions. + + Example: + Public translator: + + translated_input = AGUITranslator().to_openai(run_input) + result = Runner.run_streamed( + agent.clone(tools=agent.tools + translated_input.tools), + input=translated_input.messages, + ) + + Advanced per-item mapping: + + translator = AGUIToOpenAITranslator() + translated_message = translator.translate_user_message(message) + """ + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 1 โ€” Complete request translation + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + def translate(self, run_input: RunAgentInput) -> TranslatedInput: + """Translate a complete AG-UI request for the OpenAI Agents SDK. + + Converts message history and client-declared tools while preserving + the request identifiers, state, context, forwarded props, and resume + entries in a ``TranslatedInput`` result. + + Context and resume entries pass through unchanged. Context is not + automatically added to model input; use ``translate_context()`` when + the model should receive it. + + Args: + run_input: The incoming AG-UI RunAgentInput. + + Returns: + OpenAI Agents SDK inputs and preserved AG-UI request metadata. + """ + # Read defensively โ€” older RunAgentInput versions lack the field, the + # same reason parent_run_id below uses getattr (see types.py resume doc). + resume = getattr(run_input, "resume", None) + return TranslatedInput( + thread_id=run_input.thread_id, + run_id=run_input.run_id, + parent_run_id=getattr(run_input, "parent_run_id", None), + messages=self.translate_messages(run_input.messages or []), + tools=self.translate_tools(run_input.tools or []), + state=run_input.state, + context=list(run_input.context or []), + forwarded_props=run_input.forwarded_props, + resume=list(resume) if resume else None, + ) + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 2 โ€” Collection translation + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + def translate_messages( + self, + messages: Iterable[Message], + ) -> list[TResponseInputItem]: + """Translate message history into OpenAI Agents SDK input items. + + Args: + messages: AG-UI messages to translate. + + Returns: + Flattened SDK input-item list in the original history order. + """ + items = [] + for message in messages: + items.extend(self.translate_message(message)) + return items + + def translate_tools( + self, + tools: Iterable[AGUITool], + ) -> list[FunctionTool]: + """Translate tools declared in ``RunAgentInput.tools``. + + Each client-declared definition becomes an OpenAI Agents SDK ``FunctionTool`` proxy. + These are request tools, not historical ``ToolMessage`` instances. + + Args: + tools: Client-declared tools from the AG-UI request. + + Returns: + One SDK ``FunctionTool`` proxy per request tool. + """ + return [self.translate_tool(tool) for tool in tools] + + def translate_context( + self, + items: Iterable[Context], + ) -> str: + """Render AG-UI context as text that callers may add to model input. + + This method does not inject context automatically. Each non-empty + item is rendered as one ``description: value`` line. + + Example: + prompt = "You are helpful." + ctx = translator.translate_context(translated_input.context) + if ctx: + prompt = f"{prompt}\\n\\nContext:\\n{ctx}" + + Args: + items: AG-UI context items. + + Returns: + Rendered text block, or an empty string when input is empty. + """ + lines = [ + f"{item.description}: {item.value}" + for item in items + if item.description and item.value + ] + return "\n".join(lines) + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 3a โ€” Message translation + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + def translate_message(self, message: Message) -> list[dict[str, Any]]: + """Dispatch one AG-UI message to its type-specific translator. + + One message may produce multiple SDK input items; for example, an + ``AssistantMessage`` may contain text and multiple tool calls. + + Args: + message: An AG-UI message of any supported type. + + Returns: + Zero or more OpenAI Agents SDK input items. + """ + if isinstance(message, SystemMessage): + return [self.translate_system_message(message)] + if isinstance(message, DeveloperMessage): + return [self.translate_developer_message(message)] + if isinstance(message, UserMessage): + item = self.translate_user_message(message) + return [item] if item is not None else [] + if isinstance(message, ReasoningMessage): + item = self.translate_reasoning_message(message) + return [item] if item is not None else [] + if isinstance(message, AssistantMessage): + return self.translate_assistant_message(message) + if isinstance(message, ToolMessage): + return [self.translate_tool_message(message)] + if isinstance(message, ActivityMessage): + item = self.translate_activity_message(message) + return [item] if item is not None else [] + logger.warning("Unknown AG-UI message type: %s", type(message).__name__) + return [] + + def translate_system_message(self, message: SystemMessage) -> dict[str, Any]: + """Translate an AG-UI system prompt into an SDK input item. + + Args: + message: The AG-UI system message. + + Returns: + {"type": "message", "role": "system", ...} + """ + return { + "type": "message", + "role": "system", + "content": [{"type": "input_text", "text": message.content or ""}], + } + + def translate_developer_message(self, message: DeveloperMessage) -> dict[str, Any]: + """Translate an AG-UI developer prompt into an SDK input item. + + Args: + message: The AG-UI developer message. + + Returns: + {"type": "message", "role": "developer", ...} + """ + return { + "type": "message", + "role": "developer", + "content": [{"type": "input_text", "text": message.content or ""}], + } + + def translate_user_message(self, message: UserMessage) -> dict[str, Any] | None: + """Translate an AG-UI user turn into an SDK input item. + + Content may be text or a list of typed multimodal parts. A message + whose parts are all unsupported (e.g. video-only) is dropped whole โ€” + sending a message with empty content would be rejected by the API. + + Args: + message: The AG-UI user message. + + Returns: + {"type": "message", "role": "user", "content": [...]}, or None + when nothing in the message could be translated. + """ + content = self.translate_content(message.content) + if not content: + logger.debug( + "Dropping UserMessage id=%s: no translatable content parts", + message.id, + ) + return None + return { + "type": "message", + "role": "user", + "content": content, + } + + def translate_reasoning_message( + self, + message: ReasoningMessage, + ) -> dict[str, Any] | None: + """Translate replayable AG-UI reasoning into an SDK input item. + + Reasoning is replayed only when ``encrypted_value`` is available. + Plaintext-only reasoning is dropped because it cannot restore the + model's reasoning state. + + Args: + message: The AG-UI reasoning message. + + Returns: + A reasoning input item, or ``None`` when replay is unavailable. + """ + if not message.encrypted_value: + logger.debug( + "Dropping ReasoningMessage id=%s: no encrypted_value to re-ingest", + message.id, + ) + return None + return { + "type": "reasoning", + "id": message.id, + "encrypted_content": message.encrypted_value, + # Omit the summary entry entirely when there's no text โ€” some backends + # reject a summary_text with an empty string. + "summary": ( + [{"type": "summary_text", "text": message.content}] + if message.content + else [] + ), + } + + def translate_assistant_message( + self, + message: AssistantMessage, + ) -> list[dict[str, Any]]: + """Translate an assistant turn into SDK message and function-call items. + + Assistant text becomes an input message, while each requested tool + invocation becomes a separate function-call item. + + Args: + message: The AG-UI assistant message. + + Returns: + Optional text message item followed by one function_call + item per tool call. + """ + items: list[dict[str, Any]] = [] + text = message.content or "" + if text: + # Keep prior assistant text as an EasyInputMessageParam (role+content). + # Do NOT add type="message" here: that reroutes it through the SDK's + # output-message converter instead of the EasyInputMessage path. + items.append( + { + "role": "assistant", + "content": text, + } + ) + for tool_call in message.tool_calls or []: + items.append( + { + "type": "function_call", + "call_id": tool_call.id, + "name": tool_call.function.name, + "arguments": tool_call.function.arguments or "", + } + ) + return items + + def translate_tool_message(self, message: ToolMessage) -> dict[str, Any]: + """Translate an AG-UI tool result into an SDK function-call output. + + Args: + message: The AG-UI tool message. + + Returns: + {"type": "function_call_output", ...} + """ + # Surface an error-only tool result: a ToolMessage may report failure in + # `error` with empty content (read defensively โ€” the field is newer). + # Without this the model receives a blank output and cannot recover. + return { + "type": "function_call_output", + "call_id": message.tool_call_id, + "output": message.content or getattr(message, "error", None) or "", + } + + def translate_activity_message( + self, + message: ActivityMessage, + ) -> dict[str, Any] | None: + """Drop an AG-UI activity message by default. + + Neither Responses nor Chat Completions has an equivalent model-input + item. Subclasses may override this mapping for application-specific + activity that should be included in model input. + + Args: + message: The AG-UI activity message. + + Returns: + Always ``None`` in the default implementation. + """ + logger.debug( + "Dropping ActivityMessage id=%s activity_type=%s", + message.id, + message.activity_type, + ) + return None + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 3b โ€” Client tool translation + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + def translate_tool(self, tool: AGUITool) -> FunctionTool: + """Wrap a client-declared AG-UI tool as an SDK ``FunctionTool``. + + The proxy's invocation handler raises ClientToolPending so the + outer run loop can pause the SDK and hand control back to the + client. Client schemas remain non-strict because they are supplied + externally and may not follow the OpenAI strict-schema subset. + + Args: + tool: The AG-UI client-declared tool. + + Returns: + An SDK FunctionTool proxy for the tool. + """ + schema = self._ensure_object_schema(tool.parameters) + + async def on_invoke_tool( + ctx: RunContextWrapper[Any], + arguments_json: str, + ) -> str: + call_id = getattr(ctx, "tool_call_id", None) or "" + raise ClientToolPending( + tool_name=tool.name, + tool_call_id=call_id, + arguments=arguments_json or "", + ) + + return FunctionTool( + name=tool.name, + description=tool.description or "", + params_json_schema=schema, + on_invoke_tool=on_invoke_tool, + strict_json_schema=False, + ) + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 3c โ€” Content-part translation (multimodal) + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + def translate_content(self, content: Any) -> list[dict[str, Any]]: + """Translate a message content field (str or list of parts) to input parts. + + A string is wrapped as a single input_text part. A list has each part + passed through translate_content_part; parts that translate to None + are dropped, so an all-unsupported list (e.g. video-only) comes back + empty rather than stringified. Anything else is best-effort + stringified. + + Args: + content: The message's content field. + + Returns: + List of Responses-API input parts; empty when every part was + unsupported. + """ + if isinstance(content, str): + # Empty string carries nothing: return no parts so translate_user_message + # drops the turn instead of sending an empty content part the API rejects. + return [{"type": "input_text", "text": content}] if content else [] + if isinstance(content, list): + parts: list[dict[str, Any]] = [] + for part in content: + converted = self.translate_content_part(part) + if converted is not None: + parts.append(converted) + return parts + text = to_string(content) + return [{"type": "input_text", "text": text}] if text else [] + + def translate_content_part(self, part: Any) -> dict[str, Any] | None: + """Dispatch one content part to its per-type translator. + + Args: + part: A single typed or dict-shaped content part. + + Returns: + The translated input part, or None if unsupported. + """ + if isinstance(part, TextInputContent): + return self.translate_text_content(part) + if isinstance(part, ImageInputContent): + return self.translate_image_content(part) + if isinstance(part, AudioInputContent): + return self.translate_audio_content(part) + if isinstance(part, VideoInputContent): + return self.translate_video_content(part) + if isinstance(part, DocumentInputContent): + return self.translate_document_content(part) + if isinstance(part, BinaryInputContent): + return self.translate_binary_content(part) + # Support dict-shaped parts passed directly to the mapping API. + return self._dispatch_dict_content_part(part) + + def translate_text_content(self, part: TextInputContent) -> dict[str, Any]: + """Translate a TextInputContent part. + + Args: + part: The AG-UI text content part. + + Returns: + {"type": "input_text", "text": ...} + """ + return {"type": "input_text", "text": part.text or ""} + + def translate_image_content( + self, + part: ImageInputContent, + ) -> dict[str, Any] | None: + """Translate an ImageInputContent part. + + URL sources pass through unchanged. Data sources become base64 + data URLs. OpenAI requires a detail level, so AG-UI images use + ``auto``. + + Args: + part: The AG-UI image content part. + + Returns: + {"type": "input_image", "image_url": ..., "detail": "auto"}, + or None if the source has no usable value. + """ + url = self._data_source_to_url(part.source) + if url is None: + return None + return self._image_input(url) + + def translate_audio_content( + self, + part: AudioInputContent, + ) -> dict[str, Any] | None: + """Translate an AudioInputContent part. + + OpenAI accepts base64 audio through Chat Completions with an + audio-capable model; Responses does not accept audio input. URL audio + sources are unsupported and are dropped. + + Args: + part: The AG-UI audio content part. + + Returns: + {"type": "input_audio", "input_audio": {...}}, or None if + unsupported. + """ + source_type = read_attr(part.source, "type") + value = read_attr(part.source, "value") + mime = read_attr(part.source, "mime_type") + if not value or source_type != "data": + logger.debug("Dropping audio part: only data sources are supported") + return None + audio_format = self._audio_format_from_mime(mime) + if audio_format is None: + logger.debug("Dropping audio part: unsupported format %s", mime) + return None + return { + "type": "input_audio", + "input_audio": { + "data": value, + "format": audio_format, + }, + } + + def translate_video_content( + self, + part: VideoInputContent, + ) -> dict[str, Any] | None: + """Translate a VideoInputContent part โ€” dropped by default. + + Neither Responses nor Chat Completions has a native video input part. + Override this in a subclass to use a placeholder or extract frames. + + Args: + part: The AG-UI video content part. + + Returns: + Always None. + """ + logger.debug("Dropping video part: OpenAI model inputs do not accept video") + return None + + def translate_document_content( + self, + part: DocumentInputContent, + ) -> dict[str, Any] | None: + """Translate a DocumentInputContent part. + + URL sources use file_url; data sources use file_data (base64). + + Args: + part: The AG-UI document content part. + + Returns: + {"type": "input_file", ...}, or None if no usable value. + """ + source_type = read_attr(part.source, "type") + value = read_attr(part.source, "value") + mime = read_attr(part.source, "mime_type") + if not value: + return None + if source_type == "url": + return {"type": "input_file", "file_url": value} + if source_type == "data": + # The Responses API requires a filename alongside base64 file_data; + # DocumentInputContent carries none, so synthesize one from the mime + # subtype (mirrors _binary_as_file). + subtype = (mime or "application/octet-stream").split("/")[-1] or "bin" + return { + "type": "input_file", + "filename": f"document.{subtype}", + "file_data": f"data:{mime or 'application/octet-stream'};base64,{value}", + } + return None + + def translate_binary_content( + self, + part: BinaryInputContent, + ) -> dict[str, Any] | None: + """Translate the deprecated BinaryInputContent catch-all by mime type. + + This legacy AG-UI type predates dedicated image, audio, video, and + document parts. Image and audio mime types use their matching SDK + shapes; all remaining mime types are represented as files. + + Args: + part: The AG-UI binary content part. + + Returns: + The translated input part, or None if unsupported. + """ + mime = part.mime_type or "application/octet-stream" + + if mime.startswith("image/"): + return self._binary_as_image(part) + if mime.startswith("audio/"): + return self._binary_as_audio(part, mime) + # Treat anything else (pdf, text, application/*) as a file. + return self._binary_as_file(part, mime) + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 4 โ€” Internal helpers + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + @staticmethod + def _ensure_object_schema(parameters: Any) -> dict[str, Any]: + """Normalize a possibly-empty tool parameter spec into a JSON Schema object. + + The OpenAI Agents SDK expects every function tool's schema to be a + JSON Schema object. AG-UI tools sometimes ship parameter-less + specs as None or {}; those get coerced to an empty-but-valid + object. A spec that declares real fields but omits the optional + top-level "type" keeps its fields โ€” only the missing key is added. + + Args: + parameters: The tool's raw parameter spec. + + Returns: + A valid JSON Schema object. + """ + if not isinstance(parameters, dict) or not parameters: + return {"type": "object", "properties": {}, "additionalProperties": True} + if "type" not in parameters: + return {**parameters, "type": "object"} + return parameters + + @staticmethod + def _data_source_to_url(source: Any) -> str | None: + """Render an image source as the URL string used by the Agents SDK. + + Both SDK transports accept URL sources and base64 data URLs. + + Args: + source: The content part's source object. + + Returns: + The resolved URL string, or None if there's no usable value. + """ + if source is None: + return None + source_type = read_attr(source, "type") + value = read_attr(source, "value") + if not value: + return None + if source_type == "url": + return value + if source_type == "data": + mime = read_attr(source, "mime_type") or "application/octet-stream" + return f"data:{mime};base64,{value}" + return None + + @staticmethod + def _audio_format_from_mime(mime: str | None) -> str | None: + """Map a mime type to the format expected for Chat Completions audio. + + Chat Completions only accepts "wav" and "mp3" for input audio, so + anything else maps to None and the caller drops the part โ€” sending + e.g. "ogg" would get the whole request rejected. + + Args: + mime: The audio mime type, or None. + + Returns: + "wav" or "mp3", or None for unsupported formats. A missing mime + defaults to "wav". + """ + if not mime: + return "wav" + # Strip any parameters (e.g. "audio/wav; codecs=1") and whitespace before + # matching, so a parameterized-but-valid content type isn't dropped. + subtype = mime.split("/", 1)[-1].split(";", 1)[0].strip().lower() + # Same audio, lots of names for it โ€” fold the common ones together. + if subtype in ("mpeg", "mpeg3", "mp3"): + return "mp3" + if subtype in ("x-wav", "wav", "wave"): + return "wav" + return None + + def _dispatch_dict_content_part( + self, + part: Any, + ) -> dict[str, Any] | None: + """Best-effort dispatch for dict-shaped content parts (loose typing). + + Sometimes content arrives as raw dicts rather than pydantic + objects (e.g. when callers hand-craft inputs). The type field + is sniffed and routed. + + Args: + part: A dict-shaped (or duck-typed) content part. + + Returns: + The translated input part, or None if unsupported. + """ + part_type = read_attr(part, "type") + if part_type == "text": + text = read_attr(part, "text") + if text is None: + return None + return {"type": "input_text", "text": text} + if part_type == "image": + url = self._data_source_to_url(read_attr(part, "source")) + return self._image_input(url) if url else None + # Don't recognize it โ€” skip it rather than guess. + logger.warning("Ignoring unsupported input content type: %s", part_type) + return None + + # -- Helpers for translate_binary_content, split out to keep it readable + + def _binary_as_image(self, part: BinaryInputContent) -> dict[str, Any] | None: + if part.url: + return self._image_input(part.url) + if part.data: + mime = part.mime_type or "application/octet-stream" + return self._image_input(f"data:{mime};base64,{part.data}") + return None + + @staticmethod + def _image_input(url: str) -> dict[str, Any]: + """Build an OpenAI image input with the required detail level.""" + return {"type": "input_image", "image_url": url, "detail": "auto"} + + def _binary_as_audio( + self, + part: BinaryInputContent, + mime: str, + ) -> dict[str, Any] | None: + if not part.data: + logger.debug("Dropping binary audio: URL-only audio not supported") + return None + audio_format = self._audio_format_from_mime(mime) + if audio_format is None: + logger.debug("Dropping binary audio: unsupported format %s", mime) + return None + return { + "type": "input_audio", + "input_audio": { + "data": part.data, + "format": audio_format, + }, + } + + def _binary_as_file( + self, + part: BinaryInputContent, + mime: str, + ) -> dict[str, Any] | None: + if part.url: + return {"type": "input_file", "file_url": part.url} + if part.data: + # The Responses API requires a filename alongside base64 file_data; + # synthesize one from the mime subtype when the part carries none. + subtype = mime.split("/")[-1] or "bin" + return { + "type": "input_file", + "filename": part.filename or f"file.{subtype}", + "file_data": f"data:{mime};base64,{part.data}", + } + return None + + +__all__ = [ + "AGUIToOpenAITranslator", +] diff --git a/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/helpers.py b/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/helpers.py new file mode 100644 index 0000000000..77ec024962 --- /dev/null +++ b/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/helpers.py @@ -0,0 +1,109 @@ +"""Internal helpers shared by both translator directions. + +These are deliberately tiny, side-effect-free, and dependency-light so they +can be reused by either translator without coupling them. +""" + +import json +import logging +import uuid +from typing import Any + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# ID generation +# --------------------------------------------------------------------------- + +def new_message_id() -> str: + """Generate a fresh AG-UI message id (msg_).""" + return f"msg_{uuid.uuid4().hex}" + + +def new_reasoning_id() -> str: + """Generate a fresh reasoning id (rs_, matching the wire prefix).""" + return f"rs_{uuid.uuid4().hex}" + + +def new_tool_call_id() -> str: + """Generate a fresh tool call id (call_).""" + return f"call_{uuid.uuid4().hex}" + + +def new_tool_result_id(call_id: str) -> str: + """Derive the tool result message id (-result). + + Deterministic โ€” the wire has no id on function_call_output, so the + result id is derived from the call_id it answers. Hyphen never + appears in wire ids, marking the suffix as ours. + + Args: + call_id: The tool call id being answered. + + Returns: + The derived result message id. + """ + return f"{call_id}-result" + + +# --------------------------------------------------------------------------- +# Polymorphic attribute / value reading +# --------------------------------------------------------------------------- + +def read_attr(obj: Any, name: str) -> Any: + """Read a field from a pydantic model or a dict โ€” whichever we got. + + The SDK and AG-UI both hand us a mix of dicts and pydantic objects + depending on whether something has been through JSON serialization + yet. This shields callers from caring. + + Args: + obj: A pydantic model, a dict, or None. + name: The field/key name to read. + + Returns: + The field value, or None if obj is None or the field is missing. + """ + if obj is None: + return None + if isinstance(obj, dict): + return obj.get(name) + return getattr(obj, name, None) + + +def to_string(value: Any) -> str: + """Best-effort stringification for tool outputs and content payloads. + + Args: + value: Any value โ€” string, None, or JSON-serializable object. + + Returns: + A string representation, empty string for None. + """ + if value is None: + return "" + if isinstance(value, str): + return value + # Pydantic models are a common tool return type; serialize their fields + # rather than falling through to an opaque quoted repr via default=str. + model_dump = getattr(value, "model_dump", None) + if callable(model_dump): + try: + value = model_dump() + except Exception as exc: # noqa: BLE001 โ€” never let stringification raise + logger.debug("model_dump() failed during stringification: %s", exc) + try: + return json.dumps(value, default=str) + except (TypeError, ValueError): + return str(value) + + +__all__ = [ + "new_message_id", + "new_reasoning_id", + "new_tool_call_id", + "new_tool_result_id", + "read_attr", + "to_string", +] diff --git a/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/openai_to_agui.py b/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/openai_to_agui.py new file mode 100644 index 0000000000..ab2c4dc2d0 --- /dev/null +++ b/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/openai_to_agui.py @@ -0,0 +1,1197 @@ +"""Translate OpenAI Agents SDK stream events into AG-UI events.""" + +import logging +from typing import Any, Sequence + +from agents import ( + HandoffCallItem, + HandoffOutputItem, + ItemHelpers, + MCPApprovalRequestItem, + MCPApprovalResponseItem, + MessageOutputItem, + ReasoningItem, + RunItem, + ToolCallItem, + ToolCallOutputItem, +) +from agents.items import MCPListToolsItem # not re-exported at package top level +from agents.models.fake_id import FAKE_RESPONSES_ID + +from ag_ui.core import ( + AssistantMessage, + BaseEvent, + CustomEvent, + EventType, + FunctionCall, + Message, + MessagesSnapshotEvent, + ReasoningEncryptedValueEvent, + ReasoningEndEvent, + ReasoningMessageContentEvent, + ReasoningMessageEndEvent, + ReasoningMessageStartEvent, + ReasoningStartEvent, + RunAgentInput, + StepFinishedEvent, + StepStartedEvent, + TextMessageContentEvent, + TextMessageEndEvent, + TextMessageStartEvent, + ToolCall, + ToolCallArgsEvent, + ToolCallEndEvent, + ToolCallResultEvent, + ToolCallStartEvent, + ToolMessage, +) +from .helpers import ( + new_message_id, + new_reasoning_id, + new_tool_call_id, + new_tool_result_id, + read_attr, + to_string, +) +from .types import ( + HOSTED_TOOL_CALL_TYPES, + OpenAIItemType, + OpenAIRawResponseEventType, + OpenAIStreamEventType, +) + +logger = logging.getLogger(__name__) + + +class OpenAIToAGUITranslator: + """Translate one OpenAI Agents SDK run into AG-UI events. + + This translator is stateful and must be created once per run. It tracks + open text, tool-call, reasoning, and agent-step sequences so each AG-UI + sequence follows START, content, and END ordering. + + Raw response events provide incremental content. Completed run items close + streamed sequences or emit a complete sequence when no deltas arrived. + ``finalize()`` closes any sequence still open when the stream ends. + + OpenAI Agents SDK item IDs are reused when available. Missing or placeholder + IDs are replaced with generated AG-UI IDs, while internal correlation keys + ensure later deltas and completion events update the same sequence. + + ``AGUITranslator`` owns run-level lifecycle and snapshot events, including + ``RUN_STARTED``, ``RUN_FINISHED``, ``RUN_ERROR``, and message/state snapshots. + """ + + # Prefix marking internal placeholder window keys minted by _window_key. + # These are correlation keys only and must never be emitted as AG-UI ids + # (unlike real wire item ids, which _is_real_id also reports truthy). + _PLACEHOLDER_PREFIX = "__idx_" + + def __init__(self) -> None: + # Text sequences: defer empty messages and reconcile streamed commits. + self._open_texts: dict[str, str] = {} # key -> message_id + self._pending_text_ids: dict[str, str] = {} # key -> deferred message_id + self._closed_text_ids: list[str] = [] # closed message_ids + + # Tool-call sequences: keep streamed arguments and commits deduplicated. + self._open_tool_calls: dict[str, str] = {} # key -> tool_call_id + self._seen_call_ids: set[str] = set() # emitted tool_call_ids + # Args that streamed before a START (provider skipped output_item.added): + # buffered by window key until the real call_id is known, so we never + # emit a call under a throwaway id the later commit can't reconcile. + self._pending_tool_args: dict[str, str] = {} # key -> buffered args + + # Reasoning sequences: track phases, parts, replay data, and reconciliation. + self._open_reasonings: dict[str, str] = {} # key -> phase message_id + self._open_reasoning_parts: dict[str, str] = {} # key -> part message_id + self._closed_reasoning_ids: list[str] = [] # closed phase message_ids + self._emitted_encrypted_keys: set[str] = set() # emitted keys + self._reasoning_part_seq: dict[str, int] = {} # key -> next part index + self._reasoning_phase_ids: dict[str, str] = {} # key -> phase message_id + + # Active placeholder IDs: output_index -> unique internal key. + self._placeholder_window_keys: dict[Any, str] = {} + self._placeholder_window_seq = 0 + + # Agent-step state for ordered STEP_FINISHED and STEP_STARTED events. + self._current_step: str | None = None # active step name + + # Completed messages using the same IDs as their streamed events. + self._snapshot_messages: list[Message] = [] # completed AG-UI messages + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 1 โ€” Stream lifecycle + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + def translate(self, openai_event: Any) -> list[BaseEvent]: + """Translate one OpenAI Agents SDK stream event into ordered AG-UI events. + + Raw response events stream START, content, and END sequences. Run-item + events close those sequences or emit them whole when no deltas arrived. + Agent updates emit STEP_FINISHED before STEP_STARTED. + + Across a run, ``AGUITranslator.to_agui()`` emits RUN_STARTED, optional + initial events, translated stream sequences, finalized closing events, + optional snapshots, and RUN_FINISHED. It emits RUN_ERROR on failure. + + Args: + openai_event: One event from ``result.stream_events()``. + + Returns: + Ordered AG-UI events, or an empty list if nothing is translatable. + """ + event_type = read_attr(openai_event, "type") + if event_type == OpenAIStreamEventType.RAW_RESPONSE: + return self.translate_raw_response_event(openai_event) + if event_type == OpenAIStreamEventType.RUN_ITEM: + return self.translate_run_item_event(openai_event) + if event_type == OpenAIStreamEventType.AGENT_UPDATED: + return self.translate_agent_updated_event(openai_event) + logger.debug("Unknown OpenAI Agents SDK stream event type: %s", event_type) + return [] + + def finalize(self) -> list[BaseEvent]: + """Emit pending *_END markers when the OpenAI Agents SDK stream terminates. + + Always call this after the stream ends โ€” even on error โ€” so the + AG-UI client never sees a window that opened but never closed. + + Returns: + Closing events, or an empty list if no windows or agent step remain open. + """ + events: list[BaseEvent] = [] + for key in list(self._open_texts): + events.extend(self._close_text(key)) + for key in list(self._open_tool_calls): + events.extend(self._close_tool_call(key)) + # Args that streamed before a START and never got one (no output_item.added + # and no run-item commit) describe a tool call the SDK never committed โ€” + # it is incomplete and has no known name/call_id, so drop it rather than + # surface a phantom empty-name call under a synthesized id. + for key in list(self._pending_tool_args): + delta = self._pending_tool_args.pop(key) + logger.debug( + "Dropping %d buffered arg chars for uncommitted tool call (key=%s)", + len(delta), + key, + ) + for key in list(self._open_reasonings): + events.extend(self._close_reasoning(key)) + if self._current_step is not None: + events.append( + StepFinishedEvent( + type=EventType.STEP_FINISHED, + step_name=self._current_step, + ) + ) + self._current_step = None + return events + + def build_messages_snapshot( + self, + run_input: RunAgentInput | Sequence[Message] | None = None, + ) -> MessagesSnapshotEvent: + """Build the end-of-run MESSAGES_SNAPSHOT event. + + The snapshot contains the prior AG-UI message history followed by + messages completed during this run. Prior messages retain their AG-UI + IDs, and completed messages reuse the IDs emitted by their streaming + events, allowing clients to merge the snapshot without duplicates. + + Prior history comes from ``run_input.messages``, not + ``result.to_input_list()``. The OpenAI Agents SDK represents model input + as Responses-shaped items even when the underlying model uses Chat + Completions. Round-tripping prior AG-UI messages through that format + does not preserve their AG-UI IDs. + + Callers should remove messages that must not be echoed to the client, + such as server-only system instructions, before passing ``run_input``. + + Args: + run_input: The original run input, a message sequence, or ``None`` + when there is no prior history. + + Returns: + A ``MESSAGES_SNAPSHOT`` containing prior and completed messages. + """ + if run_input is None: + prior: list[Message] = [] + elif isinstance(run_input, RunAgentInput): + prior = list(run_input.messages or []) + else: + prior = list(run_input) + return MessagesSnapshotEvent( + type=EventType.MESSAGES_SNAPSHOT, + messages=[*prior, *self._snapshot_messages], + ) + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 2 โ€” Event-family translation + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + def translate_raw_response_event(self, event: Any) -> list[BaseEvent]: + """Handle a RawResponsesStreamEvent (token-level Responses deltas). + + Args: + event: The OpenAI Agents SDK's RawResponsesStreamEvent. + + Returns: + Translated AG-UI events, or an empty list if ignored or invalid. + """ + data = read_attr(event, "data") + if data is None: + return [] + kind = read_attr(data, "type") + if kind == OpenAIRawResponseEventType.OUTPUT_ITEM_ADDED: + return self.translate_output_item_added(data) + if kind == OpenAIRawResponseEventType.OUTPUT_ITEM_DONE: + return self.translate_output_item_done(data) + if kind == OpenAIRawResponseEventType.TEXT_DELTA: + return self.translate_text_delta(data) + if kind == OpenAIRawResponseEventType.TEXT_DONE: + return self.translate_text_done(data) + if kind == OpenAIRawResponseEventType.REFUSAL_DELTA: + return self.translate_refusal_delta(data) + if kind == OpenAIRawResponseEventType.FUNCTION_CALL_ARGUMENTS_DELTA: + return self.translate_function_call_arguments_delta(data) + if kind in ( + OpenAIRawResponseEventType.REASONING_SUMMARY_DELTA, + OpenAIRawResponseEventType.REASONING_TEXT_DELTA, + ): + return self.translate_reasoning_delta(data) + if kind in ( + OpenAIRawResponseEventType.REASONING_SUMMARY_PART_DONE, + OpenAIRawResponseEventType.REASONING_TEXT_DONE, + ): + return self.translate_reasoning_part_done(data) + # Everything else (response.created/.completed, content_part chatter, + # audio) has nothing to show on the AG-UI side, so we drop it. + return [] + + def translate_run_item_event(self, event: Any) -> list[BaseEvent]: + """Handle a RunItemStreamEvent (semantic commit signals). + + Args: + event: The OpenAI Agents SDK's RunItemStreamEvent. + + Returns: + Translated AG-UI events, or an empty list if the event has no output. + """ + item = read_attr(event, "item") + if item is None: + return [] + return self.translate_item(item) + + def translate_agent_updated_event(self, event: Any) -> list[BaseEvent]: + """Translate an AgentUpdatedStreamEvent into STEP_FINISHED + STEP_STARTED. + + Each agent (including the first, and each handoff target) is + surfaced as an AG-UI step named after the agent. + + Args: + event: The OpenAI Agents SDK's AgentUpdatedStreamEvent. + + Returns: + STEP_FINISHED for the previous step (if any) followed by + STEP_STARTED for the new one. + """ + new_agent = read_attr(event, "new_agent") + step_name = read_attr(new_agent, "name") or "agent" + events: list[BaseEvent] = [] + if self._current_step is not None: + events.append( + StepFinishedEvent( + type=EventType.STEP_FINISHED, + step_name=self._current_step, + ) + ) + self._current_step = step_name + events.append( + StepStartedEvent(type=EventType.STEP_STARTED, step_name=step_name) + ) + return events + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 3a โ€” Raw Responses event translation + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + def translate_output_item_added(self, data: Any) -> list[BaseEvent]: + """Prepare the AG-UI sequence for a new Responses output item. + + Messages defer their text sequence until content arrives. Function and + hosted tool calls open tool-call sequences; reasoning opens its sequence. + + Args: + data: The raw output_item.added payload. + + Returns: + The opening event(s) for the window, if any. + """ + item = read_attr(data, "item") + item_type = read_attr(item, "type") + item_id = read_attr(item, "id") + key = self._window_key(item_id, read_attr(data, "output_index"), start=True) + + if item_type == OpenAIItemType.MESSAGE: + # Defer text until its first delta to avoid empty messages on tool-only + # turns. Close reasoning now because some providers send its done late. + self._pending_text_ids[key] = self._resolve_id(item_id, new_message_id) + return self._close_all_reasonings() + if item_type == OpenAIItemType.FUNCTION_CALL: + call_id = read_attr(item, "call_id") or self._resolve_id(item_id, new_tool_call_id) + name = read_attr(item, "name") or "" + return self._open_tool_call(key, call_id, name) + if item_type == OpenAIItemType.REASONING: + return self._open_reasoning(key, self._resolve_id(item_id, new_reasoning_id)) + if item_type in HOSTED_TOOL_CALL_TYPES: + # Some hosted calls (computer, shell, custom tools) carry a call_id + # distinct from the item id. The run-item commit reconciles by + # call_id, so open the window under it too โ€” otherwise the same + # call streams twice, once per id, and the tool result attaches to + # the second copy instead of the one the client watched stream. + call_id = read_attr(item, "call_id") or self._resolve_id(item_id, new_tool_call_id) + return self._open_tool_call(key, call_id, item_type) + return [] + + def translate_output_item_done(self, data: Any) -> list[BaseEvent]: + """Translate response.output_item.done by closing the matching AG-UI window. + + For reasoning items this also emits REASONING_ENCRYPTED_VALUE + (subtype "message") when the finished item carries + encrypted_content. + + Args: + data: The raw output_item.done payload. + + Returns: + The closing event(s) for the window, if any. + """ + item = read_attr(data, "item") + item_type = read_attr(item, "type") + item_id = read_attr(item, "id") + output_index = read_attr(data, "output_index") + key = self._window_key(item_id, output_index) + + if item_type == OpenAIItemType.MESSAGE: + # Discard a pending message if no text delta opened it; otherwise + # _close_text closes the active sequence below. + self._pending_text_ids.pop(key, None) + events = self._close_text(key) + elif item_type == OpenAIItemType.FUNCTION_CALL or item_type in HOSTED_TOOL_CALL_TYPES: + events = self._close_tool_call(key) + elif item_type == OpenAIItemType.REASONING: + events = self._emit_encrypted_value(key, item) + events.extend(self._close_reasoning(key)) + else: + events = [] + + if not self._is_real_id(item_id): + self._placeholder_window_keys.pop(output_index, None) + return events + + def translate_text_delta(self, data: Any) -> list[BaseEvent]: + """Translate response.output_text.delta into TEXT_MESSAGE_CONTENT (lazy start). + + Args: + data: The raw output_text.delta payload. + + Returns: + Content event(s), opening the window first if needed. + """ + key = self._window_key(read_attr(data, "item_id"), read_attr(data, "output_index")) + return self._emit_text_content(key, read_attr(data, "delta") or "") + + def translate_text_done(self, data: Any) -> list[BaseEvent]: + """Handle response.output_text.done without closing the message window. + + This signal marks the end of one text content part, not the whole + assistant message โ€” a message can carry several parts (multiple + output_text parts, or text followed by a refusal). Closing here + would split one wire message into several AG-UI messages, the + later ones under generated ids the snapshot can't merge. The + window closes on output_item.done instead, with the run-item + commit and finalize() as fallbacks when a backend skips it. + + Args: + data: The raw output_text.done payload. + + Returns: + Always [] โ€” the window outlives the content part. + """ + return [] + + def translate_refusal_delta(self, data: Any) -> list[BaseEvent]: + """Translate response.refusal.delta into TEXT_MESSAGE_CONTENT. + + Refusal text is user-visible assistant output, so it streams + into the same text window as regular content. + + Args: + data: The raw refusal.delta payload. + + Returns: + Content event(s), opening the window first if needed. + """ + key = self._window_key(read_attr(data, "item_id"), read_attr(data, "output_index")) + return self._emit_text_content(key, read_attr(data, "delta") or "") + + def translate_function_call_arguments_delta(self, data: Any) -> list[BaseEvent]: + """Translate response.function_call_arguments.delta into TOOL_CALL_ARGS. + + Args: + data: The raw function_call_arguments.delta payload. + + Returns: + Args event(s), opening the window first if needed. + """ + delta = read_attr(data, "delta") or "" + if not delta: + return [] + key = self._window_key(read_attr(data, "item_id"), read_attr(data, "output_index")) + if key not in self._open_tool_calls: + # No START yet: the provider streamed args before output_item.added, + # so the real call_id is not known here. Buffer the args; the window + # opens under the correct id at output_item.added or the run-item + # commit, and _open_tool_call flushes what we buffered. Opening under + # a throwaway id here would make the later commit fail to reconcile + # and emit a duplicate TOOL_CALL_START/ARGS/END. + self._pending_tool_args[key] = self._pending_tool_args.get(key, "") + delta + return [] + return [ + ToolCallArgsEvent( + type=EventType.TOOL_CALL_ARGS, + tool_call_id=self._open_tool_calls[key], + delta=delta, + ) + ] + + def translate_reasoning_delta(self, data: Any) -> list[BaseEvent]: + """Translate reasoning deltas into REASONING_MESSAGE_CONTENT (lazy part start). + + Covers both sources โ€” response.reasoning_summary_text.delta + (hosted models: model-written summaries) and + response.reasoning_text.delta (open-weight models: full chain + of thought). Whichever arrives streams; each part becomes its + own REASONING_MESSAGE_* window inside one REASONING_START/END + phase, matching the other AG-UI integrations. + + Args: + data: The raw reasoning delta payload. + + Returns: + Content event(s), opening the phase/part first if needed. + """ + delta = read_attr(data, "delta") or "" + if not delta: + return [] + key = self._window_key(read_attr(data, "item_id"), read_attr(data, "output_index")) + events: list[BaseEvent] = [] + if key not in self._open_reasonings: + # Reuse the real wire id when present (mirrors output_item.added), so + # the phase/part ids and REASONING_ENCRYPTED_VALUE entity_id round-trip. + events.extend( + self._open_reasoning( + key, self._resolve_id(read_attr(data, "item_id"), new_reasoning_id) + ) + ) + if key not in self._open_reasoning_parts: + events.extend(self._open_reasoning_part(key)) + events.append( + ReasoningMessageContentEvent( + type=EventType.REASONING_MESSAGE_CONTENT, + message_id=self._open_reasoning_parts[key], + delta=delta, + ) + ) + return events + + def translate_reasoning_part_done(self, data: Any) -> list[BaseEvent]: + """Translate a summary-part / reasoning-text done signal into REASONING_MESSAGE_END. + + Args: + data: The raw part-done payload. + + Returns: + The closing event, or [] if already closed. + """ + key = self._window_key(read_attr(data, "item_id"), read_attr(data, "output_index")) + return self._close_reasoning_part(key) + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 3b โ€” Run-item translation + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + def translate_item(self, item: RunItem) -> list[BaseEvent]: + """Dispatch one OpenAI Agents SDK RunItem to its per-type translator. + + Usually these act as safety-net closers (raw events already + streamed the content); when no deltas were ever streamed for the + item, they emit its full AG-UI sequence instead. + + Args: + item: A finished OpenAI Agents SDK RunItem. + + Returns: + Translated AG-UI events, or an empty list if the item has no output. + """ + if isinstance(item, MessageOutputItem): + return self.translate_message_output_item(item) + if isinstance(item, HandoffCallItem): + return self.translate_handoff_call_item(item) + if isinstance(item, HandoffOutputItem): + return self.translate_handoff_output_item(item) + if isinstance(item, ToolCallItem): + return self.translate_tool_call_item(item) + if isinstance(item, ToolCallOutputItem): + return self.translate_tool_call_output_item(item) + if isinstance(item, ReasoningItem): + return self.translate_reasoning_item(item) + if isinstance(item, MCPApprovalRequestItem): + return self.translate_mcp_approval_request_item(item) + if isinstance(item, MCPListToolsItem): + return self.translate_mcp_list_tools_item(item) + if isinstance(item, MCPApprovalResponseItem): + return self.translate_mcp_approval_response_item(item) + logger.warning("Unknown OpenAI Agents SDK run item type: %s", type(item).__name__) + return [] + + def translate_message_output_item(self, item: MessageOutputItem) -> list[BaseEvent]: + """Handle an assistant message commit by closing its window or emitting the full triplet. + + Streamed: the raw deltas already carried the text โ€” just close. + No deltas seen: emit TEXT_MESSAGE_START/CONTENT/END from the + finished item, using the OpenAI Agents SDK's ItemHelpers extractors (text + first, refusal as fallback โ€” both are user-visible). + + Args: + item: The finished MessageOutputItem. + + Returns: + Closing event, or the full START/CONTENT/END triplet. + """ + raw = item.raw_item + raw_id = read_attr(raw, "id") + # Concatenate text and refusal rather than falling back: extract_text + # ignores refusals and returns truthy whenever any output_text part + # exists, so `text or refusal` would drop a refusal that streamed + # (translate_refusal_delta feeds the same window) from the snapshot. + text = "".join( + part + for part in (ItemHelpers.extract_text(raw), ItemHelpers.extract_refusal(raw)) + if part + ) + action, key = self._reconcile(raw_id, self._open_texts, self._closed_text_ids) + if action == "close": + # key is still open at this point โ€” read its id before _close_text pops it. + message_id = self._open_texts[key] + events = self._close_text(key) + self._record_text(message_id, text) + return events + if action == "skip": + # Already closed via a raw event before this commit arrived โ€” + # no BaseEvents to emit, but the snapshot still needs this + # message, under the exact id that earlier close already used. + self._record_text(key, text) + return [] + # "new": nothing was ever streamed for this item. A real id also + # supersedes any deferred window we were holding for it. + self._pending_text_ids.pop(raw_id, None) + if not text: + # An empty assistant commit โ€” emit no window (START+END with no + # content is exactly the empty-bubble artifact) and keep it out of + # the snapshot. + return [] + message_id = self._resolve_id(raw_id, new_message_id) + events = self._open_text(message_id, message_id) + events.append( + TextMessageContentEvent( + type=EventType.TEXT_MESSAGE_CONTENT, + message_id=message_id, + delta=text, + ) + ) + events.extend(self._close_text(message_id)) + self._record_text(message_id, text) + return events + + def translate_tool_call_item(self, item: ToolCallItem) -> list[BaseEvent]: + """Handle a tool call commit by closing its window or emitting START/[ARGS]/END. + + Reconciled by call_id (present and real regardless of backend), + using the OpenAI Agents SDK's built-in ToolCallItem.call_id / .tool_name + properties where available โ€” getattr fallbacks because + HandoffCallItem routes through here too and lacks them. + + Args: + item: The finished ToolCallItem (or a HandoffCallItem). + + Returns: + Closing event, or the full START/[ARGS]/END sequence. + """ + raw = item.raw_item + call_id = ( + getattr(item, "call_id", None) + or read_attr(raw, "call_id") + or self._resolve_id(read_attr(raw, "id"), new_tool_call_id) + ) + name = ( + getattr(item, "tool_name", None) + or read_attr(raw, "name") + or read_attr(raw, "type") + or "" + ) + arguments = read_attr(raw, "arguments") or "" + for key, open_call_id in self._open_tool_calls.items(): + if open_call_id == call_id: + events = self._close_tool_call(key) + self._record_tool_call(call_id, name, arguments) + return events + if call_id in self._seen_call_ids: + # Already closed via a raw event before this commit arrived โ€” + # no BaseEvents to emit, but the snapshot still needs this call. + self._record_tool_call(call_id, name, arguments) + return [] + # "new": never streamed as an open window. Any args buffered before a + # START are superseded by raw.arguments below. Clear a buffer we can + # positively correlate by real item id; a placeholder buffer we cannot + # address by id is harmlessly dropped (never re-emitted) by finalize, so + # we must NOT blind-FIFO-drop here โ€” that could evict a *different* + # still-streaming call's buffered args. + self._pending_tool_args.pop(read_attr(raw, "id"), None) + events = self._open_tool_call(call_id, call_id, name) + if arguments: + events.append( + ToolCallArgsEvent( + type=EventType.TOOL_CALL_ARGS, + tool_call_id=call_id, + delta=arguments, + ) + ) + events.extend(self._close_tool_call(call_id)) + self._record_tool_call(call_id, name, arguments) + return events + + def translate_tool_call_output_item(self, item: ToolCallOutputItem) -> list[BaseEvent]: + """Translate a tool result into TOOL_CALL_RESULT. + + Args: + item: The finished ToolCallOutputItem. + + Returns: + A single TOOL_CALL_RESULT event, or [] if call_id is missing. + """ + # call_id is a property only on newer SDK ToolCallOutputItem; read it + # defensively (as translate_tool_call_item does) to support the declared + # openai-agents floor, falling back to the raw item. + call_id = getattr(item, "call_id", None) or read_attr(item.raw_item, "call_id") + if not call_id: + logger.debug("Tool output without call_id; skipping TOOL_CALL_RESULT") + return [] + content = to_string(item.output) + result_id = self._record_result(call_id, content) + return [ + ToolCallResultEvent( + type=EventType.TOOL_CALL_RESULT, + message_id=result_id, + tool_call_id=call_id, + content=content, + ) + ] + + def translate_reasoning_item(self, item: ReasoningItem) -> list[BaseEvent]: + """Handle a reasoning commit by closing its phase or emitting the full sequence. + + Non-streamed emission walks the finished ResponseReasoningItem: + one REASONING_MESSAGE_* window per summary/content entry, then + REASONING_ENCRYPTED_VALUE (when present), then REASONING_END. + + Args: + item: The finished ReasoningItem. + + Returns: + Closing event(s), or the full reasoning sequence. + """ + raw = item.raw_item + raw_id = read_attr(raw, "id") + action, key = self._reconcile(raw_id, self._open_reasonings, self._closed_reasoning_ids) + if action == "close": + events = self._emit_encrypted_value(key, raw) + events.extend(self._close_reasoning(key)) + return events + if action == "skip": + # The phase already closed via a raw event, but the commit may be the + # only carrier of encrypted_content (some providers omit it on the raw + # done). Surface it so reasoning replay is not lost โ€” _emit_encrypted_value + # guards against a double-emit. It arrives after REASONING_END in this + # path because the value is unknown until the commit; late beats dropped. + return self._emit_encrypted_value(key, raw) + phase_id = self._resolve_id(raw_id, new_reasoning_id) + events = self._open_reasoning(phase_id, phase_id) + parts = [read_attr(entry, "text") for entry in (read_attr(raw, "summary") or [])] + parts += [read_attr(entry, "text") for entry in (read_attr(raw, "content") or [])] + for text in parts: + if not text: + continue + events.extend(self._open_reasoning_part(phase_id)) + events.append( + ReasoningMessageContentEvent( + type=EventType.REASONING_MESSAGE_CONTENT, + message_id=self._open_reasoning_parts[phase_id], + delta=text, + ) + ) + events.extend(self._close_reasoning_part(phase_id)) + events.extend(self._emit_encrypted_value(phase_id, raw)) + events.extend(self._close_reasoning(phase_id)) + return events + + def translate_handoff_call_item(self, item: HandoffCallItem) -> list[BaseEvent]: + """Surface a handoff request as a tool call (it is a function call). + + The same underlying item also arrives through the raw-event + path, so the call-id reconciliation in translate_tool_call_item + prevents a duplicate TOOL_CALL_START. + + Args: + item: The finished HandoffCallItem. + + Returns: + Closing event, or the full START/[ARGS]/END sequence. + """ + return self.translate_tool_call_item(item) # type: ignore[arg-type] + + def translate_handoff_output_item(self, item: HandoffOutputItem) -> list[BaseEvent]: + """Translate a handoff completion into TOOL_CALL_RESULT. + + Args: + item: The finished HandoffOutputItem. + + Returns: + A single TOOL_CALL_RESULT event, or [] if call_id is missing. + """ + raw = item.raw_item + call_id = read_attr(raw, "call_id") + if not call_id: + logger.debug("Handoff output without call_id; skipping TOOL_CALL_RESULT") + return [] + target = read_attr(item.target_agent, "name") or "" + output = read_attr(raw, "output") or f"Handed off to {target}" + content = to_string(output) + result_id = self._record_result(call_id, content) + return [ + ToolCallResultEvent( + type=EventType.TOOL_CALL_RESULT, + message_id=result_id, + tool_call_id=call_id, + content=content, + ) + ] + + def translate_mcp_approval_request_item( + self, + item: MCPApprovalRequestItem, + ) -> list[BaseEvent]: + """Translate an MCP approval request into a CUSTOM event (name="mcp_approval_request"). + + AG-UI has no native approval shape yet; forwarding the raw + request lets a frontend implement approval UI without protocol + changes. + + Args: + item: The finished MCPApprovalRequestItem. + + Returns: + A single CUSTOM event carrying the raw request. + """ + raw = item.raw_item + value = raw.model_dump() if hasattr(raw, "model_dump") else raw + return [ + CustomEvent( + type=EventType.CUSTOM, + name="mcp_approval_request", + value=value, + ) + ] + + def translate_mcp_list_tools_item(self, item: MCPListToolsItem) -> list[BaseEvent]: + """Drop an MCP tool listing item (server-side bookkeeping). Overridable. + + Args: + item: The finished MCPListToolsItem. + + Returns: + Always []. + """ + logger.debug("Dropping MCPListToolsItem id=%s", read_attr(item.raw_item, "id")) + return [] + + def translate_mcp_approval_response_item( + self, + item: MCPApprovalResponseItem, + ) -> list[BaseEvent]: + """Drop an MCP approval response item (echo of client input). Overridable. + + Args: + item: The finished MCPApprovalResponseItem. + + Returns: + Always []. + """ + logger.debug("Dropping MCPApprovalResponseItem") + return [] + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 4a โ€” ID handling + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + @staticmethod + def _is_real_id(item_id: Any) -> bool: + """Check whether an id is usable on the wire (not empty, not a placeholder). + + Some backends stamp every item with the OpenAI Agents SDK's + FAKE_RESPONSES_ID + sentinel โ€” sharing it across AG-UI events would collide every + message of the run. Checked against the OpenAI Agents SDK's constant, so + it is a no-op on native OpenAI and correct for any backend that + does not use placeholder ids. + + Args: + item_id: The item's wire id, or None. + + Returns: + True if the id is real and usable. + """ + return bool(item_id) and item_id != FAKE_RESPONSES_ID + + @classmethod + def _is_placeholder_key(cls, key: Any) -> bool: + """True for internal placeholder window keys (``__idx_*``). + + ``_is_real_id`` reports these truthy because they are neither empty nor + ``FAKE_RESPONSES_ID``, so a window-key reuse decision must also exclude + placeholders explicitly before treating the key as a wire id. + """ + return isinstance(key, str) and key.startswith(cls._PLACEHOLDER_PREFIX) + + @classmethod + def _resolve_id(cls, item_id: Any, generate: Any) -> str: + """Return the id if real, else a freshly generated one. + + Args: + item_id: The item's wire id, or None. + generate: A zero-arg callable producing a fresh id. + + Returns: + The real id, or a freshly generated one. + """ + return item_id if cls._is_real_id(item_id) else generate() + + def _window_key(self, item_id: Any, output_index: Any, *, start: bool = False) -> str: + """Return the internal key that correlates one output item's raw events. + + This key indexes open text, tool-call, and reasoning sequences; it is + never emitted as an AG-UI ID. A real OpenAI item ID is used when + available. Placeholder IDs use ``output_index`` only while that item + is active, then receive a new internal key when the index is reused. + + Args: + item_id: The item's wire id, or None. + output_index: The item's position in the response. + start: Whether this event starts a new output item. + + Returns: + The OpenAI item ID, or a unique internal placeholder key. + """ + if self._is_real_id(item_id): + return item_id + key = self._placeholder_window_keys.get(output_index) + active = key is not None and ( + key in self._open_texts + or key in self._pending_text_ids + or key in self._open_tool_calls + or key in self._pending_tool_args + or key in self._open_reasonings + ) + if key is None or (start and not active): + key = f"__idx_{output_index}_{self._placeholder_window_seq}" + self._placeholder_window_seq += 1 + self._placeholder_window_keys[output_index] = key + return key + + def _reconcile( + self, + raw_id: Any, + open_windows: dict[str, str], + closed_ids: list[str], + ) -> tuple[str, str | None]: + """Match a run-item commit against streamed window state. + + With a real id the match is exact. With a placeholder id run + items arrive in stream order, so the oldest open window (or one + queued closed id) is consumed instead. + + Args: + raw_id: The run item's raw wire id, or None. + open_windows: Currently open windows for this category. + closed_ids: Ids already closed via streaming, oldest first. + + Returns: + A tuple[str, str | None] containing one of: + - ("close", key): Close the matching open sequence. + - ("skip", resolved_id): Reuse an already closed sequence's ID. + - ("new", None): Emit the complete item; no sequence exists. + """ + if self._is_real_id(raw_id): + if raw_id in open_windows: + return ("close", raw_id) + if raw_id in closed_ids: + return ("skip", raw_id) + return ("new", None) + if open_windows: + return ("close", next(iter(open_windows))) + if closed_ids: + resolved_id = closed_ids.pop(0) + return ("skip", resolved_id) + return ("new", None) + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 4b โ€” Sequence management + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + def _open_text(self, key: str, message_id: str) -> list[BaseEvent]: + """Open a text sequence, closing active reasoning first.""" + if key in self._open_texts: + return [] + # Close reasoning before assistant text; its done event may arrive late. + events = self._close_all_reasonings() + self._open_texts[key] = message_id + events.append( + TextMessageStartEvent( + type=EventType.TEXT_MESSAGE_START, + message_id=message_id, + role="assistant", + ) + ) + return events + + def _emit_text_content(self, key: str, delta: str) -> list[BaseEvent]: + """Emit text content, opening its sequence when needed.""" + if not delta: + return [] + events: list[BaseEvent] = [] + if key not in self._open_texts: + # Open deferred text on its first delta. Reuse the real item id when + # the window key is one (honors "IDs are reused when available"); only + # synthesize an id when the key is an internal placeholder. + events.extend( + self._open_text( + key, + self._pending_text_ids.pop(key, None) + or ( + key + if self._is_real_id(key) and not self._is_placeholder_key(key) + else new_message_id() + ), + ) + ) + events.append( + TextMessageContentEvent( + type=EventType.TEXT_MESSAGE_CONTENT, + message_id=self._open_texts[key], + delta=delta, + ) + ) + return events + + def _close_text(self, key: str) -> list[BaseEvent]: + """Close a text sequence and remember its message ID.""" + message_id = self._open_texts.pop(key, None) + if message_id is None: + return [] + self._closed_text_ids.append(message_id) + return [ + TextMessageEndEvent( + type=EventType.TEXT_MESSAGE_END, + message_id=message_id, + ) + ] + + def _open_tool_call(self, key: str, call_id: str, name: str) -> list[BaseEvent]: + """Open a tool-call sequence, closing active reasoning first.""" + if key in self._open_tool_calls: + return [] + events = self._close_all_reasonings() # see _open_text + self._open_tool_calls[key] = call_id + self._seen_call_ids.add(call_id) + events.append( + ToolCallStartEvent( + type=EventType.TOOL_CALL_START, + tool_call_id=call_id, + tool_call_name=name, + ) + ) + # Flush any args that streamed before this START (see _pending_tool_args). + buffered = self._pending_tool_args.pop(key, None) + if buffered: + events.append( + ToolCallArgsEvent( + type=EventType.TOOL_CALL_ARGS, + tool_call_id=call_id, + delta=buffered, + ) + ) + return events + + def _close_tool_call(self, key: str) -> list[BaseEvent]: + """Close an active tool-call sequence.""" + call_id = self._open_tool_calls.pop(key, None) + if call_id is None: + return [] + return [ + ToolCallEndEvent( + type=EventType.TOOL_CALL_END, + tool_call_id=call_id, + ) + ] + + def _open_reasoning(self, key: str, phase_id: str) -> list[BaseEvent]: + """Open a reasoning sequence.""" + if key in self._open_reasonings: + return [] + self._open_reasonings[key] = phase_id + self._reasoning_phase_ids[key] = phase_id + return [ + ReasoningStartEvent( + type=EventType.REASONING_START, + message_id=phase_id, + ) + ] + + def _open_reasoning_part(self, key: str) -> list[BaseEvent]: + """Open the next part of a reasoning sequence.""" + if key in self._open_reasoning_parts: + return [] + seq = self._reasoning_part_seq.get(key, 0) + self._reasoning_part_seq[key] = seq + 1 + # The first part just reuses the phase id (keeps round-trips clean); + # extra parts get a -1, -2, ... suffix. Wire ids never contain a + # hyphen, so anything with one is clearly something we made up. + phase_id = self._open_reasonings.get(key, key) + message_id = phase_id if seq == 0 else f"{phase_id}-{seq}" + self._open_reasoning_parts[key] = message_id + return [ + ReasoningMessageStartEvent( + type=EventType.REASONING_MESSAGE_START, + message_id=message_id, + role="reasoning", + ) + ] + + def _close_reasoning_part(self, key: str) -> list[BaseEvent]: + """Close an active reasoning part.""" + message_id = self._open_reasoning_parts.pop(key, None) + if message_id is None: + return [] + return [ + ReasoningMessageEndEvent( + type=EventType.REASONING_MESSAGE_END, + message_id=message_id, + ) + ] + + def _close_reasoning(self, key: str) -> list[BaseEvent]: + """Close a reasoning part and its parent sequence.""" + phase_id = self._open_reasonings.pop(key, None) + if phase_id is None: + return [] + events = self._close_reasoning_part(key) + self._closed_reasoning_ids.append(phase_id) + events.append( + ReasoningEndEvent( + type=EventType.REASONING_END, + message_id=phase_id, + ) + ) + return events + + def _close_all_reasonings(self) -> list[BaseEvent]: + """Close every active reasoning sequence.""" + events: list[BaseEvent] = [] + for key in list(self._open_reasonings): + events.extend(self._close_reasoning(key)) + return events + + def _emit_encrypted_value(self, key: str, item: Any) -> list[BaseEvent]: + """Emit REASONING_ENCRYPTED_VALUE once per reasoning item, if present. + + Args: + key: The reasoning item's window key. + item: The raw reasoning item. + + Returns: + A single REASONING_ENCRYPTED_VALUE event, or [] if absent + or already emitted. + """ + encrypted = read_attr(item, "encrypted_content") + # Dedup on the stable phase id, not the caller's key: the raw-done path + # passes a window key (a placeholder for FAKE-id backends) while the + # run-item skip path passes the resolved phase id. Both map to the same + # phase id here, so keying the guard on it catches the cross-path dup. + entity_id = self._reasoning_phase_ids.get(key, key) + if not encrypted or entity_id in self._emitted_encrypted_keys: + return [] + self._emitted_encrypted_keys.add(entity_id) + return [ + ReasoningEncryptedValueEvent( + type=EventType.REASONING_ENCRYPTED_VALUE, + subtype="message", + entity_id=entity_id, + encrypted_value=encrypted, + ) + ] + + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # LEVEL 4c โ€” Snapshot message builders + # โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + # Snapshot messages reuse streamed IDs so clients can merge without duplicates. + # Reasoning is omitted from the snapshot: clients persist replayable reasoning + # from the streamed REASONING_ENCRYPTED_VALUE events, not from this list. + + def _record_text(self, message_id: str, text: str) -> None: + """Add an assistant text message to the snapshot.""" + self._snapshot_messages.append( + AssistantMessage(id=message_id, role="assistant", content=text) + ) + + def _record_tool_call(self, call_id: str, name: str, arguments: str) -> None: + """Add an assistant tool call to the snapshot.""" + # The streamed TOOL_CALL_START carried no parent message id, so the + # client keyed the bubble by the tool call id โ€” mirror that here. + self._snapshot_messages.append( + AssistantMessage( + id=call_id, + role="assistant", + tool_calls=[ + ToolCall( + id=call_id, + type="function", + function=FunctionCall(name=name, arguments=arguments), + ) + ], + ) + ) + + def _record_result(self, call_id: str, content: str) -> str: + """Add a tool result and return its derived message ID.""" + # Returns the derived result id so the caller reuses the same value + # for its TOOL_CALL_RESULT event โ€” snapshot and stream stay in sync. + result_id = new_tool_result_id(call_id) + self._snapshot_messages.append( + ToolMessage( + id=result_id, role="tool", tool_call_id=call_id, content=content + ) + ) + return result_id + + +__all__ = ["OpenAIToAGUITranslator"] diff --git a/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/types.py b/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/types.py new file mode 100644 index 0000000000..b2ce67866b --- /dev/null +++ b/integrations/openai-agents/python/src/ag_ui_openai_agents/engine/types.py @@ -0,0 +1,220 @@ +"""Type definitions for the OpenAI Agents integration. + +Result containers the translators hand back (``TranslatedInput``), plus the +OpenAI wire-level discriminator values the outbound translator dispatches on +(``OpenAIItemType``, ``OpenAIRawResponseEventType``, ``OpenAIStreamEventType``, +``HOSTED_TOOL_CALL_TYPES``). Members of the ``(str, Enum)`` classes compare +equal to the raw wire strings (``StrEnum`` needs Python 3.11; this package +supports 3.10+). The SDK's own ``Literal[...]`` annotations are the source of +truth for those โ€” see ``tests/engine/test_types_drift.py``. Translator +behavior lives in ``agui_to_openai.py`` and ``openai_to_agui.py``; this module +only describes shapes and constants. +""" + +from __future__ import annotations + +from enum import Enum +from typing import Any + +from agents import FunctionTool, TResponseInputItem +from agents.exceptions import AgentsException +from pydantic import BaseModel, ConfigDict, SkipValidation + +from ag_ui.core import Context + + +# โ”€โ”€ ag_ui_openai_agents result types โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + +class TranslatedInput(BaseModel): + """OpenAI Agents SDK-ready bundle produced by translating an AG-UI RunAgentInput. + + Returned by ``AGUITranslator.to_openai()`` (or + ``AGUIToOpenAITranslator.translate()`` directly, for advanced/per-mapping + use). Fields mirror ``ag_ui.core.RunAgentInput`` by name, so if you know the + wire format you already know this shape. (``parent_run_id`` and ``resume`` + are the newest core fields and are read defensively, so an older client may + not carry them.) Only ``messages`` and ``tools`` are actually + translated into OpenAI Agents SDK types; everything else passes through + unchanged for you to use however your app needs. + + Example: + translated_input = translator.to_openai(run_input) + result = Runner.run_streamed( + agent, + input=translated_input.messages, + context=translated_input.context, + ) + + Attributes: + thread_id: Conversation key. Stays the same across turns of one thread. + run_id: Id for this single run. Goes on the RUN_STARTED / RUN_FINISHED events. + parent_run_id: Set when this run was kicked off by another run + (nested/branched); None for a top-level run. + messages: Responses-API input items, ready to pass to + ``Runner.run*(input=...)``. + tools: FunctionTool proxies for the client's tools. Merge these with + your agent's own tools before running. + state: Whatever the client sent as state. Any, since AG-UI doesn't + pin a shape. + context: Ambient ``{description, value}`` items from the frontend. + Nothing folds these into the prompt for you. + forwarded_props: Grab-bag of extra client props (model overrides, + temperature, flags, ...) that don't have a dedicated field. + resume: Resume entries when continuing from an interrupt, else None. + """ + + # โ”€โ”€ Identity โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + thread_id: str + """Conversation key. Stays the same across turns of one thread.""" + + run_id: str + """Id for this single run. Goes on the RUN_STARTED / RUN_FINISHED events.""" + + parent_run_id: str | None = None + """Set when this run was kicked off by another run (nested/branched); + None for a top-level run.""" + + # โ”€โ”€ Translated payload (the actual work the translator does) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + messages: SkipValidation[list[TResponseInputItem]] + """Responses-API input items, ready to pass to Runner.run*(input=...). + + Validation is skipped (like ``tools``). Two reasons: (1) audio parts use the + Chat-Completions ``input_audio`` shape, which is a valid model input for an + audio-capable agent but is not a member of the Responses input-item union, so + strict validation would reject an otherwise-usable request; (2) validating a + reasoning item's ``summary`` (typed ``Iterable`` in the SDK) materializes it as + a single-use ``ValidatorIterator`` that empties after one read. The SDK + validates the shape it actually consumes at run time.""" + + tools: SkipValidation[list[FunctionTool]] = [] + """FunctionTool proxies for the client's tools. Merge these with your + agent's own tools before running. The public translators fill this in; if + you call the inbound engine directly, call translate_tools yourself. + Validation skipped for the same forward-ref reason as messages.""" + + # โ”€โ”€ Passthroughs (raw from the wire) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + state: Any + """Whatever the client sent as state. Any, since AG-UI doesn't pin a shape.""" + + context: list[Context] + """Ambient {description, value} items from the frontend. Nothing folds these + into the prompt for you โ€” run them through translate_context if you want the + model to see them.""" + + forwarded_props: Any + """Grab-bag of extra client props (model overrides, temperature, flags, ...) + that don't have a dedicated field.""" + + resume: list[Any] | None = None + """Resume entries when continuing from an interrupt, else None. Read + defensively since not every RunAgentInput version carries this field yet.""" + + # FunctionTool isn't a Pydantic model, so Pydantic won't accept it unless + # we explicitly allow arbitrary types. + model_config = ConfigDict(arbitrary_types_allowed=True) + + +# FunctionTool's schema points at AgentBase, which Pydantic can't see from +# here. Pull it into scope and rebuild the model so the `tools` field resolves. +from agents.agent import AgentBase # noqa: E402,F401 + +TranslatedInput.model_rebuild() + + +# โ”€โ”€ OpenAI wire discriminators (raw events openai-agents emits) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + +class OpenAIStreamEventType(str, Enum): + """Top-level StreamEvent.type values yielded by Runner.run_streamed.""" + + RAW_RESPONSE = "raw_response_event" + RUN_ITEM = "run_item_stream_event" + AGENT_UPDATED = "agent_updated_stream_event" + + +class OpenAIRawResponseEventType(str, Enum): + """Raw Responses-API delta type values the outbound translator consumes. + + Non-semantic bookkeeping kinds (response.created / .completed, + content_part.*, audio) are deliberately absent โ€” they translate to + nothing. + """ + + OUTPUT_ITEM_ADDED = "response.output_item.added" + OUTPUT_ITEM_DONE = "response.output_item.done" + TEXT_DELTA = "response.output_text.delta" + TEXT_DONE = "response.output_text.done" + REFUSAL_DELTA = "response.refusal.delta" + FUNCTION_CALL_ARGUMENTS_DELTA = "response.function_call_arguments.delta" + REASONING_SUMMARY_DELTA = "response.reasoning_summary_text.delta" + REASONING_SUMMARY_PART_DONE = "response.reasoning_summary_part.done" + REASONING_TEXT_DELTA = "response.reasoning_text.delta" + REASONING_TEXT_DONE = "response.reasoning_text.done" + + +class OpenAIItemType(str, Enum): + """Output-item type values carried by output_item.added / .done.""" + + MESSAGE = "message" + FUNCTION_CALL = "function_call" + REASONING = "reasoning" + + +# Tools that run on OpenAI's side. We still show them as AG-UI tool calls, but +# the API doesn't stream their arguments, so at the raw level all we can emit is +# START/END. The run-item layer fills in the rest when it has it. +HOSTED_TOOL_CALL_TYPES: frozenset[str] = frozenset( + { + "web_search_call", + "file_search_call", + "code_interpreter_call", + "image_generation_call", + "computer_call", + "local_shell_call", + "shell_call", + "apply_patch_call", + "mcp_call", + "custom_tool_call", + "tool_search_call", + } +) + + +# โ”€โ”€ Sentinel exception used by client-tool proxies โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + +class ClientToolPending(AgentsException): + """Raised by a client-tool proxy to signal "stop, the UI owns this call". + + The AG-UI run closes normally after forwarding the tool call. The client + executes it and returns the result in a later request. + + Subclasses AgentsException deliberately: the SDK's own tool executor + (agents.run_internal.tool_execution._run_single_tool) special-cases + `isinstance(e, AgentsException)` to re-raise it as-is โ€” anything else + gets wrapped in a generic UserError first, which would hide this from + the outer run loop's `except ClientToolPending` and turn every + client-owned tool call into a hard run failure instead of a clean + hand-off. + + Args: + tool_name: Name of the client-owned tool that was called. + tool_call_id: The SDK's call_id for this invocation. + arguments: Raw JSON arguments string the model produced. + """ + + def __init__(self, tool_name: str, tool_call_id: str, arguments: str) -> None: + super().__init__( + f"Client tool '{tool_name}' (call_id={tool_call_id}) pending UI execution" + ) + self.tool_name = tool_name + self.tool_call_id = tool_call_id + self.arguments = arguments + + +__all__ = [ + "ClientToolPending", + "HOSTED_TOOL_CALL_TYPES", + "OpenAIItemType", + "OpenAIRawResponseEventType", + "OpenAIStreamEventType", + "TranslatedInput", +] diff --git a/integrations/openai-agents/python/src/ag_ui_openai_agents/translator.py b/integrations/openai-agents/python/src/ag_ui_openai_agents/translator.py new file mode 100644 index 0000000000..ac41d1057c --- /dev/null +++ b/integrations/openai-agents/python/src/ag_ui_openai_agents/translator.py @@ -0,0 +1,228 @@ +"""Translate between AG-UI requests and OpenAI Agents SDK streams.""" + +import inspect +from typing import Any, AsyncIterator + +from agents.result import RunResultStreaming +from agents.stream_events import StreamEvent + +from ag_ui.core import ( + BaseEvent, + CustomEvent, + EventType, + RunAgentInput, + RunErrorEvent, + RunFinishedEvent, + RunStartedEvent, + StateSnapshotEvent, +) +from .engine.agui_to_openai import AGUIToOpenAITranslator +from .engine.openai_to_agui import OpenAIToAGUITranslator +from .engine.types import ClientToolPending, TranslatedInput + + +class AGUITranslator: + """Connect an existing streamed OpenAI Agents SDK run to AG-UI. + + Example: + translator = AGUITranslator() + translated_input = translator.to_openai(run_input) + result = Runner.run_streamed(agent, input=translated_input.messages) + async for event in translator.to_agui(result, run_input): + ... # encode or send the AG-UI event + + The translator does not own the SDK agent, server, or session storage. + See the README for the complete API reference and integration patterns. + """ + + def __init__( + self, + *, + inbound_cls: type[AGUIToOpenAITranslator] = AGUIToOpenAITranslator, + outbound_cls: type[OpenAIToAGUITranslator] = OpenAIToAGUITranslator, + ) -> None: + """Create a translator; engine classes are advanced mapping override points. + + The inbound translator is reused because request translation is stateless. + A new outbound translator is created per run because it tracks that + stream's open text, tool, reasoning, and step windows. + + Args: + inbound_cls: Request-to-SDK translator class. + outbound_cls: SDK-stream-to-AG-UI translator class, created per run. + """ + self._inbound = inbound_cls() + self._outbound_cls = outbound_cls + + def to_openai(self, run_input: RunAgentInput) -> TranslatedInput: + """Translate an AG-UI request into OpenAI Agents SDK input and passthrough data. + + The inbound translator performs the complete request conversion, + including client-owned tool proxies. + + Args: + run_input: The incoming AG-UI RunAgentInput. + + Returns: + SDK-ready messages, client-tool proxies, and request passthrough fields. + """ + return self._inbound.translate(run_input) + + async def to_agui( + self, + events: RunResultStreaming | AsyncIterator[StreamEvent], + run_input: RunAgentInput, + *, + start_custom_event: CustomEvent | None = None, + initial_state: Any = None, + final_state: Any = None, + emit_messages_snapshot: bool = True, + end_custom_event: CustomEvent | None = None, + emit_run_error: bool = True, + run_error_message: str | None = None, + ) -> AsyncIterator[BaseEvent]: + """Translate an SDK event stream into ordered AG-UI events. + + The translator supplies the lifecycle and snapshot events; the outbound + engine translates streamed text, tool, reasoning, and step events. + + 1. RUN_STARTED โ€” always first + 2. start_custom_event (optional) โ€” if provided + 3. STATE_SNAPSHOT (initial, optional) + โ€ฆ streamed STEP/TEXT/TOOL/REASONING events โ€ฆ + 4. Finalize open stream windows + 5. STATE_SNAPSHOT (final, optional) + 6. MESSAGES_SNAPSHOT (optional) + 7. end_custom_event (optional) + 8. RUN_FINISHED โ€” always last; RUN_ERROR on failure + + Ordinary errors close open stream windows, yield ``RUN_ERROR`` by + default, and re-raise. Cancellation only stops an owned SDK run. + + Args: + events: A ``RunResultStreaming`` or its ``stream_events()`` iterator. + run_input: The request supplying lifecycle IDs and message history. + start_custom_event: ``CustomEvent`` emitted after ``RUN_STARTED``. + Its value may be static or a zero-argument sync/async factory. + initial_state: Optional source for the first snapshot. Static value + or zero-argument sync/async factory. + final_state: Optional source for the final snapshot. Static value or + zero-argument sync/async factory, resolved after the stream so it + can observe changes the run made. + emit_messages_snapshot: Append ``MESSAGES_SNAPSHOT`` before finishing. + end_custom_event: ``CustomEvent`` emitted before ``RUN_FINISHED``. + Its value may be static or a zero-argument sync/async factory + resolved just before emission. + emit_run_error: Emit ``RUN_ERROR`` before re-raising an ordinary + error. + run_error_message: Safe fixed ``RUN_ERROR`` message; defaults to ``str(exc)``. + + Yields: + AG-UI events ready to encode or send. + + Raises: + TypeError: A custom event is not a CustomEvent instance. + """ + lifecycle_completed = False + outbound = self._outbound_cls() + try: + # 1. RUN_STARTED โ€” always first + yield RunStartedEvent( + type=EventType.RUN_STARTED, + thread_id=run_input.thread_id, + run_id=run_input.run_id, + # Read defensively โ€” older RunAgentInput versions lack the field. + parent_run_id=getattr(run_input, "parent_run_id", None), + ) + + # 2. start_custom_event (optional) + if start_custom_event is not None: + yield await self._resolve_custom_event(start_custom_event) + + # 3. STATE_SNAPSHOT (initial) โ€” resolve now ({} emits, None skips) + if initial_state is not None: + snapshot = await self._resolve_lifecycle_value(initial_state) + if snapshot is not None: + yield StateSnapshotEvent( + type=EventType.STATE_SNAPSHOT, + snapshot=snapshot, + ) + + # Accept either the SDK result or an iterator already obtained from it. + stream_events = ( + events.stream_events() + if isinstance(events, RunResultStreaming) + else events + ) + try: + # Streamed STEP / TEXT / TOOL / REASONING events. + async for openai_event in stream_events: + for event in outbound.translate(openai_event): + yield event + except ClientToolPending: + # Client-owned tools complete through the shared path below. + pass + + # 4. Close any text, tool, reasoning, or step window still open. + for event in outbound.finalize(): + yield event + # 5. STATE_SNAPSHOT (final, optional) โ€” resolved after the stream. + if final_state is not None: + snapshot = await self._resolve_lifecycle_value(final_state) + if snapshot is not None: + yield StateSnapshotEvent( + type=EventType.STATE_SNAPSHOT, + snapshot=snapshot, + ) + # 6. MESSAGES_SNAPSHOT (optional) + if emit_messages_snapshot: + yield outbound.build_messages_snapshot(run_input) + # 7. end_custom_event (optional) โ€” resolved just before emitting. + if end_custom_event is not None: + yield await self._resolve_custom_event(end_custom_event) + + # 8. RUN_FINISHED โ€” always last + run_finished_event = RunFinishedEvent( + type=EventType.RUN_FINISHED, + thread_id=run_input.thread_id, + run_id=run_input.run_id, + ) + # Set before yield because a consumer may close at the terminal event. + lifecycle_completed = True + yield run_finished_event + except Exception as exc: + for event in outbound.finalize(): + yield event + if emit_run_error: + yield RunErrorEvent( + type=EventType.RUN_ERROR, + # Only None means "use str(exc)"; an explicit "" is a + # deliberate (empty) message and must be honored verbatim. + message=run_error_message if run_error_message is not None else str(exc), + ) + raise + finally: + # Cancel an owned SDK result if AG-UI consumption stops early; + # callers retain ownership when they pass a bare iterator. + if not lifecycle_completed and isinstance(events, RunResultStreaming): + events.cancel() + + async def _resolve_custom_event( + self, + event: CustomEvent, + ) -> CustomEvent: + """Return a custom event with its value resolved.""" + if not isinstance(event, CustomEvent): + raise TypeError( + f"custom event must be a CustomEvent, got {type(event).__name__}" + ) + value = await self._resolve_lifecycle_value(event.value) + return event.model_copy(update={"value": value}) + + @staticmethod + async def _resolve_lifecycle_value(source: Any) -> Any: + """Resolve a static value or a synchronous/asynchronous factory.""" + value = source() if callable(source) else source + if inspect.isawaitable(value): + value = await value + return value diff --git a/integrations/openai-agents/python/tests/engine/test_agui_to_openai_multimodal.py b/integrations/openai-agents/python/tests/engine/test_agui_to_openai_multimodal.py new file mode 100644 index 0000000000..8abab99d18 --- /dev/null +++ b/integrations/openai-agents/python/tests/engine/test_agui_to_openai_multimodal.py @@ -0,0 +1,631 @@ +""" +Tests for AGUIToOpenAITranslator's multimodal content mapping. + +Pins the wire shape each ``translate_*_content`` method produces (or the +``None``/drop behavior when unsupported), and validates the final input against +the OpenAI Agents SDK types. No network or model is used. +""" + +from __future__ import annotations + +import warnings +from types import SimpleNamespace + +import pytest +from pydantic import ValidationError + +from ag_ui.core import ( + AudioInputContent, + BinaryInputContent, + DocumentInputContent, + ImageInputContent, + InputContentDataSource, + InputContentUrlSource, + RunAgentInput, + TextInputContent, + UserMessage, + VideoInputContent, +) +from ag_ui_openai_agents import AGUITranslator +from ag_ui_openai_agents.engine.agui_to_openai import AGUIToOpenAITranslator +from ag_ui_openai_agents.engine.types import TranslatedInput + +_engine = AGUIToOpenAITranslator() + + +def _binary(**kwargs) -> BinaryInputContent: + """BinaryInputContent is deprecated but still a real inbound shape callers + may send; construct it without polluting test output with the warning.""" + with warnings.catch_warnings(): + warnings.simplefilter("ignore", DeprecationWarning) + return BinaryInputContent(**kwargs) + + +# โ”€โ”€ image โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_image_url_source_passes_through(): + part = ImageInputContent(source=InputContentUrlSource(value="https://x/y.png")) + out = _engine.translate_image_content(part) + assert out == { + "type": "input_image", + "image_url": "https://x/y.png", + "detail": "auto", + } + + +def test_image_data_source_becomes_data_url(): + part = ImageInputContent( + source=InputContentDataSource(value="Zm9v", mime_type="image/png") + ) + out = _engine.translate_image_content(part) + assert out == { + "type": "input_image", + "image_url": "data:image/png;base64,Zm9v", + "detail": "auto", + } + + +def test_image_with_no_usable_source_returns_none(): + part = ImageInputContent(source=InputContentDataSource(value="", mime_type="image/png")) + assert _engine.translate_image_content(part) is None + + +# โ”€โ”€ audio โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_audio_data_source_maps_to_input_audio(): + part = AudioInputContent( + source=InputContentDataSource(value="Zm9v", mime_type="audio/wav") + ) + out = _engine.translate_audio_content(part) + assert out == { + "type": "input_audio", + "input_audio": {"data": "Zm9v", "format": "wav"}, + } + + +def test_audio_url_source_is_dropped(): + # Responses API accepts no URL form for audio input. + part = AudioInputContent(source=InputContentUrlSource(value="https://x/y.wav")) + assert _engine.translate_audio_content(part) is None + + +def test_audio_missing_mime_defaults_to_wav(): + part = AudioInputContent(source=InputContentDataSource(value="Zm9v", mime_type="")) + out = _engine.translate_audio_content(part) + assert out["input_audio"]["format"] == "wav" + + +def test_audio_format_from_mime_variants(): + fmt = _engine._audio_format_from_mime + assert fmt("audio/mpeg") == "mp3" + assert fmt("audio/mp3") == "mp3" + assert fmt("audio/mpeg3") == "mp3" + assert fmt("audio/x-wav") == "wav" + assert fmt("audio/wav") == "wav" + assert fmt("audio/wave") == "wav" + assert fmt(None) == "wav" + # Content types carrying parameters must still match (params stripped). + assert fmt("audio/wav; codecs=1") == "wav" + assert fmt("audio/mpeg; rate=16000") == "mp3" + # Chat Completions only takes wav/mp3 โ€” anything else must map to None + # so the caller drops the part instead of sending a rejectable request. + assert fmt("audio/ogg") is None + assert fmt("audio/flac") is None + + +def test_audio_unsupported_format_is_dropped(): + part = AudioInputContent( + source=InputContentDataSource(value="Zm9v", mime_type="audio/ogg") + ) + assert _engine.translate_audio_content(part) is None + + +# โ”€โ”€ document โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_document_url_source_uses_file_url(): + part = DocumentInputContent(source=InputContentUrlSource(value="https://x/y.pdf")) + out = _engine.translate_document_content(part) + assert out == {"type": "input_file", "file_url": "https://x/y.pdf"} + + +def test_document_data_source_uses_file_data(): + part = DocumentInputContent( + source=InputContentDataSource(value="Zm9v", mime_type="application/pdf") + ) + out = _engine.translate_document_content(part) + assert out == { + "type": "input_file", + "filename": "document.pdf", + "file_data": "data:application/pdf;base64,Zm9v", + } + + +def test_document_with_no_usable_value_returns_none(): + part = DocumentInputContent( + source=InputContentDataSource(value="", mime_type="application/pdf") + ) + assert _engine.translate_document_content(part) is None + + +# โ”€โ”€ video (unsupported by the Responses API) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_video_is_always_dropped(): + part = VideoInputContent(source=InputContentUrlSource(value="https://x/y.mp4")) + assert _engine.translate_video_content(part) is None + + +# โ”€โ”€ binary (mime-sniffed, routed to image/audio/file) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_binary_image_mime_routes_to_image_url(): + part = _binary(mime_type="image/png", url="https://x/y.png") + out = _engine.translate_binary_content(part) + assert out == { + "type": "input_image", + "image_url": "https://x/y.png", + "detail": "auto", + } + + +def test_binary_audio_mime_routes_to_input_audio(): + part = _binary(mime_type="audio/wav", data="Zm9v") + out = _engine.translate_binary_content(part) + assert out == { + "type": "input_audio", + "input_audio": {"data": "Zm9v", "format": "wav"}, + } + + +def test_binary_other_mime_routes_to_file(): + part = _binary(mime_type="application/pdf", data="Zm9v", filename="a.pdf") + out = _engine.translate_binary_content(part) + assert out == { + "type": "input_file", + "file_data": "data:application/pdf;base64,Zm9v", + "filename": "a.pdf", + } + + +def test_binary_as_image_prefers_url_over_data(): + part = _binary(mime_type="image/png", url="https://x/y.png", data="Zm9v") + out = _engine._binary_as_image(part) + assert out == { + "type": "input_image", + "image_url": "https://x/y.png", + "detail": "auto", + } + + +def test_binary_as_image_falls_back_to_data(): + part = _binary(mime_type="image/png", data="Zm9v") + out = _engine._binary_as_image(part) + assert out == { + "type": "input_image", + "image_url": "data:image/png;base64,Zm9v", + "detail": "auto", + } + + +def test_binary_as_image_with_neither_returns_none(): + part = _binary(mime_type="image/png", id="file-123") + assert _engine._binary_as_image(part) is None + + +def test_binary_as_audio_without_data_is_dropped(): + # URL-only audio isn't accepted by the Responses API. + part = _binary(mime_type="audio/wav", url="https://x/y.wav") + assert _engine._binary_as_audio(part, "audio/wav") is None + + +def test_binary_as_audio_unsupported_format_is_dropped(): + part = _binary(mime_type="audio/ogg", data="Zm9v") + assert _engine._binary_as_audio(part, "audio/ogg") is None + + +def test_binary_as_file_url_source(): + part = _binary(mime_type="application/pdf", url="https://x/y.pdf") + out = _engine._binary_as_file(part, "application/pdf") + assert out == {"type": "input_file", "file_url": "https://x/y.pdf"} + + +def test_binary_as_file_data_without_filename_synthesizes_one(): + # Base64 file_data requires a filename (Responses API); synthesize from mime. + part = _binary(mime_type="application/pdf", data="Zm9v") + out = _engine._binary_as_file(part, "application/pdf") + assert out == { + "type": "input_file", + "filename": "file.pdf", + "file_data": "data:application/pdf;base64,Zm9v", + } + + +# โ”€โ”€ source resolution helper โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_data_source_to_url_handles_url_data_and_none(): + fn = _engine._data_source_to_url + assert fn(InputContentUrlSource(value="https://x/y.png")) == "https://x/y.png" + assert ( + fn(InputContentDataSource(value="Zm9v", mime_type="image/png")) + == "data:image/png;base64,Zm9v" + ) + assert fn(None) is None + + +# โ”€โ”€ dict-shaped fallback dispatch โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_dispatch_dict_content_part_text(): + out = _engine._dispatch_dict_content_part({"type": "text", "text": "hi"}) + assert out == {"type": "input_text", "text": "hi"} + + +def test_dispatch_dict_content_part_image(): + out = _engine._dispatch_dict_content_part( + {"type": "image", "source": {"type": "url", "value": "https://x/y.png"}} + ) + assert out == { + "type": "input_image", + "image_url": "https://x/y.png", + "detail": "auto", + } + + +def test_dispatch_dict_content_part_unknown_type_returns_none(): + assert _engine._dispatch_dict_content_part({"type": "carrier_pigeon"}) is None + + +# โ”€โ”€ translate_content_part tier-2 dispatcher โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_translate_content_part_dispatches_by_type(): + text = TextInputContent(text="hi") + image = ImageInputContent(source=InputContentUrlSource(value="https://x/y.png")) + assert _engine.translate_content_part(text) == {"type": "input_text", "text": "hi"} + assert _engine.translate_content_part(image) == { + "type": "input_image", + "image_url": "https://x/y.png", + "detail": "auto", + } + + +# โ”€โ”€ all-unsupported content: drop, never stringify โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_content_list_of_only_unsupported_parts_translates_to_empty(): + # A video-only message must come back empty โ€” not fall through to the + # stringify path and send the parts' repr to the model as input_text. + parts = [VideoInputContent(source=InputContentUrlSource(value="https://x/y.mp4"))] + assert _engine.translate_content(parts) == [] + + +def test_user_message_with_only_unsupported_parts_is_dropped_whole(): + message = UserMessage( + id="u1", + role="user", + content=[VideoInputContent(source=InputContentUrlSource(value="https://x/y.mp4"))], + ) + assert _engine.translate_user_message(message) is None + assert _engine.translate_message(message) == [] + + +def test_user_message_with_invalid_image_and_text_keeps_only_text(): + message = UserMessage( + id="u1", + role="user", + content=[ + TextInputContent(text="look"), + ImageInputContent( + source=InputContentDataSource(value="", mime_type="image/png") + ), + ], + ) + item = _engine.translate_user_message(message) + assert item["content"] == [{"type": "input_text", "text": "look"}] + + +# โ”€โ”€ end-to-end: mixed multimodal user message โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_mixed_multimodal_parts_translate_to_matching_blocks(): + parts = [ + TextInputContent(text="what's this?"), + ImageInputContent(source=InputContentUrlSource(value="https://x/y.png")), + AudioInputContent( + source=InputContentDataSource(value="Zm9v", mime_type="audio/mpeg") + ), + DocumentInputContent(source=InputContentUrlSource(value="https://x/y.pdf")), + VideoInputContent(source=InputContentUrlSource(value="https://x/y.mp4")), + ] + blocks = [_engine.translate_content_part(p) for p in parts] + assert blocks == [ + {"type": "input_text", "text": "what's this?"}, + { + "type": "input_image", + "image_url": "https://x/y.png", + "detail": "auto", + }, + {"type": "input_audio", "input_audio": {"data": "Zm9v", "format": "mp3"}}, + {"type": "input_file", "file_url": "https://x/y.pdf"}, + None, # video: no Responses-API input block + ] + + +def test_image_survives_translated_input_validation(): + run_input = RunAgentInput( + thread_id="t1", + run_id="r1", + messages=[ + UserMessage( + id="u1", + role="user", + content=[ + ImageInputContent( + source=InputContentUrlSource(value="https://x/y.png") + ) + ], + ) + ], + tools=[], + state={}, + context=[], + forwarded_props=None, + ) + + translated = AGUITranslator().to_openai(run_input) + + assert translated.messages[0]["content"][0] == { + "type": "input_image", + "image_url": "https://x/y.png", + "detail": "auto", + } + + +def test_translated_input_skips_message_validation(): + # messages uses SkipValidation, so shapes that are not Responses input-item + # members (e.g. a detail-less image, or an input_audio block) construct + # without a ValidationError; the SDK validates what it consumes at run time. + ti = TranslatedInput( + thread_id="t1", + run_id="r1", + messages=[ + { + "type": "message", + "role": "user", + "content": [{"type": "input_image", "image_url": "https://x/y.png"}], + } + ], + tools=[], + state={}, + context=[], + forwarded_props=None, + ) + assert ti.messages[0]["content"][0] == { + "type": "input_image", + "image_url": "https://x/y.png", + } + + +def test_audio_user_message_survives_to_openai(): + # input_audio is a Chat-Completions shape, not a Responses input-item member; + # with SkipValidation an audio request translates instead of crashing. + run_input = RunAgentInput( + thread_id="t1", + run_id="r1", + messages=[ + UserMessage( + id="u1", + role="user", + content=[ + AudioInputContent( + source=InputContentDataSource(value="Zm9v", mime_type="audio/wav") + ) + ], + ) + ], + tools=[], + state={}, + context=[], + forwarded_props=None, + ) + translated = AGUITranslator().to_openai(run_input) + assert translated.messages[0]["content"][0] == { + "type": "input_audio", + "input_audio": {"data": "Zm9v", "format": "wav"}, + } + + +def test_reasoning_summary_survives_as_a_stable_list_through_to_openai(): + from ag_ui.core import ReasoningMessage + + run_input = RunAgentInput( + thread_id="t1", + run_id="r1", + messages=[ + ReasoningMessage( + id="rs", role="reasoning", content="because", encrypted_value="enc" + ) + ], + tools=[], + state={}, + context=[], + forwarded_props=None, + ) + translated = AGUITranslator().to_openai(run_input) + summary = translated.messages[0]["summary"] + # A real list that survives multiple reads, not a one-shot ValidatorIterator. + assert list(summary) == [{"type": "summary_text", "text": "because"}] + assert list(summary) == [{"type": "summary_text", "text": "because"}] + + +# โ”€โ”€ empty-content dropping โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_empty_string_user_message_is_dropped(): + # translate_content("") yields no parts, so translate_user_message returns + # None and the turn is omitted rather than sent as an empty content part the + # API rejects (matches the method's documented contract). + assert _engine.translate_message(UserMessage(id="u1", role="user", content="")) == [] + + +def test_empty_list_user_message_is_dropped(): + assert _engine.translate_message(UserMessage(id="u1", role="user", content=[])) == [] + + +# โ”€โ”€ defensive reads for newest RunAgentInput fields โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_translate_reads_resume_defensively_when_field_absent(): + # Older RunAgentInput versions lack `resume` (and `parent_run_id`). translate + # must read them via getattr and not raise AttributeError. + run_input = SimpleNamespace( + thread_id="t1", + run_id="r1", + messages=[], + tools=[], + state=None, + context=[], + forwarded_props=None, + ) + result = _engine.translate(run_input) + assert result.resume is None + assert result.parent_run_id is None + assert result.thread_id == "t1" + + +# โ”€โ”€ document data source carries a synthesized filename โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_document_data_source_includes_synthesized_filename(): + # The Responses API requires a filename alongside base64 file_data. + part = DocumentInputContent( + source=InputContentDataSource(value="Zm9v", mime_type="application/pdf") + ) + out = _engine.translate_document_content(part) + assert out == { + "type": "input_file", + "filename": "document.pdf", + "file_data": "data:application/pdf;base64,Zm9v", + } + + +# โ”€โ”€ inbound message-type translation โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_system_and_developer_messages_map_to_role_items(): + from ag_ui.core import DeveloperMessage, SystemMessage + + assert _engine.translate_message( + SystemMessage(id="s1", role="system", content="be nice") + ) == [{"type": "message", "role": "system", "content": [{"type": "input_text", "text": "be nice"}]}] + assert _engine.translate_message( + DeveloperMessage(id="d1", role="developer", content="dev note") + ) == [{"type": "message", "role": "developer", "content": [{"type": "input_text", "text": "dev note"}]}] + + +def test_assistant_message_maps_text_and_tool_calls_without_type_message(): + from ag_ui.core import AssistantMessage, FunctionCall, ToolCall + + msg = AssistantMessage( + id="a1", + role="assistant", + content="hi", + tool_calls=[ + ToolCall(id="call_1", type="function", function=FunctionCall(name="f", arguments='{"x":1}')) + ], + ) + items = _engine.translate_message(msg) + # Prior assistant text stays an EasyInputMessageParam: NO type="message". + assert items[0] == {"role": "assistant", "content": "hi"} + assert "type" not in items[0] + assert items[1] == { + "type": "function_call", + "call_id": "call_1", + "name": "f", + "arguments": '{"x":1}', + } + + +def test_tool_message_surfaces_error_when_content_empty(): + from ag_ui.core import ToolMessage + + ok = _engine.translate_message( + ToolMessage(id="t1", role="tool", tool_call_id="call_1", content="result") + ) + assert ok[0]["output"] == "result" + + err = _engine.translate_message( + ToolMessage(id="t2", role="tool", tool_call_id="call_2", content="", error="boom") + ) + assert err[0] == {"type": "function_call_output", "call_id": "call_2", "output": "boom"} + + +def test_reasoning_message_replays_only_with_encrypted_value(): + from ag_ui.core import ReasoningMessage + + assert ( + _engine.translate_message(ReasoningMessage(id="r1", role="reasoning", content="thinking")) + == [] + ) + items = _engine.translate_message( + ReasoningMessage(id="r2", role="reasoning", content="sum", encrypted_value="enc") + ) + assert items == [ + { + "type": "reasoning", + "id": "r2", + "encrypted_content": "enc", + "summary": [{"type": "summary_text", "text": "sum"}], + } + ] + + +def test_activity_message_is_dropped(): + from ag_ui.core import ActivityMessage + + assert _engine.translate_message( + ActivityMessage(id="act1", role="activity", activity_type="typing", content={}) + ) == [] + + +def test_translate_context_renders_nonempty_items(): + from ag_ui.core import Context + + rendered = _engine.translate_context( + [ + Context(description="Language", value="German"), + Context(description="", value=""), + ] + ) + assert rendered == "Language: German" + + +# โ”€โ”€ CR4 fixes: filename synthesis, context filtering, reasoning summary โ”€โ”€โ”€ + + +def test_translate_context_drops_items_missing_either_field(): + from ag_ui.core import Context + + rendered = _engine.translate_context( + [ + Context(description="Language", value="German"), + Context(description="LabelOnly", value=""), + Context(description="", value="ValueOnly"), + ] + ) + assert rendered == "Language: German" + + +def test_reasoning_message_with_empty_content_omits_summary_entry(): + from ag_ui.core import ReasoningMessage + + items = _engine.translate_message( + ReasoningMessage(id="r3", role="reasoning", content="", encrypted_value="enc") + ) + assert items == [ + {"type": "reasoning", "id": "r3", "encrypted_content": "enc", "summary": []} + ] diff --git a/integrations/openai-agents/python/tests/engine/test_openai_to_agui.py b/integrations/openai-agents/python/tests/engine/test_openai_to_agui.py new file mode 100644 index 0000000000..bf918d658e --- /dev/null +++ b/integrations/openai-agents/python/tests/engine/test_openai_to_agui.py @@ -0,0 +1,818 @@ +""" +Tests for OpenAIToAGUITranslator's streaming event mapping. + +The rest of the suite pins IDs (test_openai_to_agui_snapshot) and wire strings +(test_types_drift); this one pins the actual translation: feed the +engine the SDK stream events a real run emits โ€” raw Responses deltas, +run-item commits, agent-updated signals โ€” and assert the exact AG-UI event +sequence it yields. No network, no model; just the mapping. + +Events are driven through ``translate`` (the tier-1 dispatcher ``to_agui`` +calls per event), so raw dispatch, family dispatch, and the per-type methods +are all on the path. Raw deltas are duck-typed SimpleNamespaces (the engine +reads them via ``read_attr``, exactly as it does the real payloads); run +items use the SDK's own item classes so the isinstance dispatch is real. +""" + +from __future__ import annotations + +from types import SimpleNamespace + +from agents import ( + Agent, + HandoffCallItem, + HandoffOutputItem, + MCPApprovalRequestItem, + ReasoningItem, +) +from agents.items import MessageOutputItem, ToolCallItem, ToolCallOutputItem +from agents.models.fake_id import FAKE_RESPONSES_ID +from openai.types.responses import ( + ResponseFunctionToolCall, + ResponseOutputMessage, + ResponseOutputRefusal, + ResponseOutputText, + ResponseReasoningItem, +) +from pydantic import BaseModel +from openai.types.responses.response_output_item import McpApprovalRequest + +from ag_ui.core import EventType +from ag_ui_openai_agents.engine import OpenAIToAGUITranslator +from ag_ui_openai_agents.engine.types import ( + OpenAIItemType, + OpenAIRawResponseEventType, + OpenAIStreamEventType, +) + +_AGENT = Agent(name="test-agent") + + +# โ”€โ”€ event builders โ€” shaped like the SDK's real stream events โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def _raw(kind: OpenAIRawResponseEventType, **data) -> SimpleNamespace: + """A RawResponsesStreamEvent wrapping one Responses payload.""" + return SimpleNamespace( + type=OpenAIStreamEventType.RAW_RESPONSE, + data=SimpleNamespace(type=kind, **data), + ) + + +def _added(item_type: OpenAIItemType, output_index: int = 0, **item) -> SimpleNamespace: + return _raw( + OpenAIRawResponseEventType.OUTPUT_ITEM_ADDED, + item=SimpleNamespace(type=item_type, **item), + output_index=output_index, + ) + + +def _done(item_type: OpenAIItemType, output_index: int = 0, **item) -> SimpleNamespace: + return _raw( + OpenAIRawResponseEventType.OUTPUT_ITEM_DONE, + item=SimpleNamespace(type=item_type, **item), + output_index=output_index, + ) + + +def _run_item(item) -> SimpleNamespace: + return SimpleNamespace(type=OpenAIStreamEventType.RUN_ITEM, name="x", item=item) + + +def _message_item(item_id: str, text: str) -> MessageOutputItem: + return MessageOutputItem( + agent=_AGENT, + raw_item=ResponseOutputMessage( + id=item_id, + type="message", + role="assistant", + status="completed", + content=[ResponseOutputText(type="output_text", text=text, annotations=[])], + ), + ) + + +def _agent_updated(name: str) -> SimpleNamespace: + return SimpleNamespace( + type=OpenAIStreamEventType.AGENT_UPDATED, + new_agent=SimpleNamespace(name=name), + ) + + +def _drive(engine: OpenAIToAGUITranslator, *events) -> list: + out: list = [] + for event in events: + out.extend(engine.translate(event)) + return out + + +def _types(events) -> list[EventType]: + return [e.type for e in events] + + +# โ”€โ”€ text streaming โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_text_streams_start_content_end_under_one_id(): + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _added(OpenAIItemType.MESSAGE, id="msg_1"), + _raw(OpenAIRawResponseEventType.TEXT_DELTA, item_id="msg_1", output_index=0, delta="Hel"), + _raw(OpenAIRawResponseEventType.TEXT_DELTA, item_id="msg_1", output_index=0, delta="lo"), + _done(OpenAIItemType.MESSAGE, id="msg_1"), + ) + assert _types(events) == [ + EventType.TEXT_MESSAGE_START, + EventType.TEXT_MESSAGE_CONTENT, + EventType.TEXT_MESSAGE_CONTENT, + EventType.TEXT_MESSAGE_END, + ] + ids = {e.message_id for e in events} + assert ids == {"msg_1"}, "the whole window must carry the real item id" + assert events[0].role == "assistant" + assert [e.delta for e in events if e.type == EventType.TEXT_MESSAGE_CONTENT] == [ + "Hel", + "lo", + ] + + +def test_text_delta_lazily_opens_window_without_output_item_added(): + # Some backends jump straight to deltas with no output_item.added first. + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _raw(OpenAIRawResponseEventType.TEXT_DELTA, item_id="msg_1", output_index=0, delta="hi"), + ) + assert _types(events) == [EventType.TEXT_MESSAGE_START, EventType.TEXT_MESSAGE_CONTENT] + # The real item id must be reused even on the lazy path โ€” a generated id here + # would make a later MessageOutputItem commit fail to reconcile and duplicate. + assert {e.message_id for e in events} == {"msg_1"} + + +def test_text_done_does_not_close_the_message_window(): + # output_text.done ends one content part, not the message โ€” the window + # must stay open for output_item.done to close. + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _added(OpenAIItemType.MESSAGE, id="msg_1"), + _raw(OpenAIRawResponseEventType.TEXT_DELTA, item_id="msg_1", output_index=0, delta="hi"), + _raw(OpenAIRawResponseEventType.TEXT_DONE, item_id="msg_1", output_index=0), + ) + assert EventType.TEXT_MESSAGE_END not in _types(events) + events = _drive(engine, _done(OpenAIItemType.MESSAGE, id="msg_1")) + assert _types(events) == [EventType.TEXT_MESSAGE_END] + assert events[0].message_id == "msg_1" + + +def test_two_text_parts_stream_as_one_message(): + # A message can carry several content parts, each with its own + # output_text.done. All of them belong to one AG-UI message id. + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _added(OpenAIItemType.MESSAGE, id="msg_1"), + _raw(OpenAIRawResponseEventType.TEXT_DELTA, item_id="msg_1", output_index=0, delta="First"), + _raw(OpenAIRawResponseEventType.TEXT_DONE, item_id="msg_1", output_index=0), + _raw( + OpenAIRawResponseEventType.TEXT_DELTA, item_id="msg_1", output_index=0, delta="Second" + ), + _raw(OpenAIRawResponseEventType.TEXT_DONE, item_id="msg_1", output_index=0), + _done(OpenAIItemType.MESSAGE, id="msg_1"), + ) + assert _types(events) == [ + EventType.TEXT_MESSAGE_START, + EventType.TEXT_MESSAGE_CONTENT, + EventType.TEXT_MESSAGE_CONTENT, + EventType.TEXT_MESSAGE_END, + ] + assert {e.message_id for e in events} == {"msg_1"} + + +def test_run_item_commit_closes_text_when_output_item_done_is_skipped(): + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _added(OpenAIItemType.MESSAGE, id="msg_1"), + _raw(OpenAIRawResponseEventType.TEXT_DELTA, item_id="msg_1", output_index=0, delta="hi"), + _raw(OpenAIRawResponseEventType.TEXT_DONE, item_id="msg_1", output_index=0), + _run_item(_message_item("msg_1", "hi")), + ) + assert _types(events)[-1] == EventType.TEXT_MESSAGE_END + assert events[-1].message_id == "msg_1" + + +def test_finalize_closes_text_left_open_after_text_done(): + engine = OpenAIToAGUITranslator() + _drive( + engine, + _added(OpenAIItemType.MESSAGE, id="msg_1"), + _raw(OpenAIRawResponseEventType.TEXT_DELTA, item_id="msg_1", output_index=0, delta="hi"), + _raw(OpenAIRawResponseEventType.TEXT_DONE, item_id="msg_1", output_index=0), + ) + events = engine.finalize() + assert _types(events) == [EventType.TEXT_MESSAGE_END] + assert events[0].message_id == "msg_1" + + +def test_message_with_text_and_refusal_records_both_in_snapshot(): + # extract_text ignores refusals, so a naive `text or refusal` would drop the + # refusal from the snapshot even though it streamed into the same window. + engine = OpenAIToAGUITranslator() + raw = ResponseOutputMessage( + id="msg_1", + type="message", + role="assistant", + status="completed", + content=[ + ResponseOutputText(type="output_text", text="Here is ", annotations=[]), + ResponseOutputRefusal(type="refusal", refusal="I can't help with that."), + ], + ) + events = _drive(engine, _run_item(MessageOutputItem(agent=_AGENT, raw_item=raw))) + streamed = "".join( + e.delta for e in events if e.type == EventType.TEXT_MESSAGE_CONTENT + ) + assert streamed == "Here is I can't help with that." + assert engine._snapshot_messages[0].content == "Here is I can't help with that." + + +def test_refusal_delta_streams_into_the_text_window(): + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _added(OpenAIItemType.MESSAGE, id="msg_1"), + _raw(OpenAIRawResponseEventType.REFUSAL_DELTA, item_id="msg_1", output_index=0, delta="no"), + ) + assert _types(events) == [EventType.TEXT_MESSAGE_START, EventType.TEXT_MESSAGE_CONTENT] + assert events[1].delta == "no" + + +def test_text_less_message_item_wrapping_a_tool_call_emits_no_window(): + # Some backends can announce a message item even on a pure tool-call turn + # that never carries text: an + # output_item.added(message), the whole tool call, then + # output_item.done(message) โ€” with no text delta in between. The message + # window must never open, so the tool call stays a clean sibling and no + # empty TEXT_MESSAGE_START/END brackets it on the wire. + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _added(OpenAIItemType.MESSAGE, output_index=0, id="msg_1"), + _added( + OpenAIItemType.FUNCTION_CALL, + output_index=1, + id="fc_1", + call_id="call_1", + name="change_background", + ), + _raw( + OpenAIRawResponseEventType.FUNCTION_CALL_ARGUMENTS_DELTA, + item_id="fc_1", + output_index=1, + delta='{"background":"red"}', + ), + _done( + OpenAIItemType.FUNCTION_CALL, + output_index=1, + id="fc_1", + call_id="call_1", + name="change_background", + ), + _done(OpenAIItemType.MESSAGE, output_index=0, id="msg_1"), + ) + assert _types(events) == [ + EventType.TOOL_CALL_START, + EventType.TOOL_CALL_ARGS, + EventType.TOOL_CALL_END, + ] + assert EventType.TEXT_MESSAGE_START not in _types(events) + assert EventType.TEXT_MESSAGE_END not in _types(events) + + +# โ”€โ”€ tool-call streaming โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_function_call_streams_start_args_end_under_the_call_id(): + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _added(OpenAIItemType.FUNCTION_CALL, id="fc_1", call_id="call_1", name="get_weather"), + _raw( + OpenAIRawResponseEventType.FUNCTION_CALL_ARGUMENTS_DELTA, + item_id="fc_1", + output_index=0, + delta='{"city":', + ), + _raw( + OpenAIRawResponseEventType.FUNCTION_CALL_ARGUMENTS_DELTA, + item_id="fc_1", + output_index=0, + delta='"Cairo"}', + ), + _done(OpenAIItemType.FUNCTION_CALL, id="fc_1", call_id="call_1", name="get_weather"), + ) + assert _types(events) == [ + EventType.TOOL_CALL_START, + EventType.TOOL_CALL_ARGS, + EventType.TOOL_CALL_ARGS, + EventType.TOOL_CALL_END, + ] + assert {e.tool_call_id for e in events} == {"call_1"} + assert events[0].tool_call_name == "get_weather" + assert "".join(e.delta for e in events if e.type == EventType.TOOL_CALL_ARGS) == ( + '{"city":"Cairo"}' + ) + + +def test_function_call_args_delta_before_added_is_buffered_then_dropped_if_uncommitted(): + # A provider that streams args with no output_item.added has no call_id to + # emit yet, so the args are buffered (nothing on the wire) rather than opened + # under a throwaway id the later commit could not reconcile. With no + # output_item.added and no run-item commit ever arriving, the call was never + # completed by the SDK, so finalize drops the buffer (no phantom call). + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _raw( + OpenAIRawResponseEventType.FUNCTION_CALL_ARGUMENTS_DELTA, + item_id="fc_1", + output_index=0, + delta="{}", + ), + ) + assert events == [], "premature args must buffer, not open under a throwaway id" + assert engine.finalize() == [] + + +def test_placeholder_id_args_before_added_flush_into_single_call(): + # Chat-Completions-backed (FAKE id) backend streams args before + # output_item.added, then the added arrives. The buffered args must flush into + # the same call the added opens โ€” one call under the real call_id, no split, + # and no internal placeholder key leaked onto the wire. + engine = OpenAIToAGUITranslator() + assert ( + _drive( + engine, + _raw( + OpenAIRawResponseEventType.FUNCTION_CALL_ARGUMENTS_DELTA, + item_id=FAKE_RESPONSES_ID, + output_index=0, + delta='{"a":', + ), + ) + == [] + ) + events = _drive( + engine, + _added( + OpenAIItemType.FUNCTION_CALL, + output_index=0, + id=FAKE_RESPONSES_ID, + call_id="call_1", + name="f", + ), + _raw( + OpenAIRawResponseEventType.FUNCTION_CALL_ARGUMENTS_DELTA, + item_id=FAKE_RESPONSES_ID, + output_index=0, + delta="1}", + ), + _done( + OpenAIItemType.FUNCTION_CALL, + output_index=0, + id=FAKE_RESPONSES_ID, + call_id="call_1", + name="f", + ), + ) + assert _types(events) == [ + EventType.TOOL_CALL_START, + EventType.TOOL_CALL_ARGS, + EventType.TOOL_CALL_ARGS, + EventType.TOOL_CALL_END, + ] + assert {e.tool_call_id for e in events} == {"call_1"} + assert "".join(e.delta for e in events if e.type == EventType.TOOL_CALL_ARGS) == '{"a":1}' + assert engine.finalize() == [] + + +def test_placeholder_id_args_then_commit_emits_no_phantom(): + # FAKE-id backend streams args before any added, then commits via a + # ToolCallItem. The buffer (keyed by a placeholder the commit cannot address + # by id) must be consumed so finalize does not re-emit it as a phantom call. + engine = OpenAIToAGUITranslator() + assert ( + _drive( + engine, + _raw( + OpenAIRawResponseEventType.FUNCTION_CALL_ARGUMENTS_DELTA, + item_id=FAKE_RESPONSES_ID, + output_index=0, + delta='{"a":1}', + ), + ) + == [] + ) + raw = ResponseFunctionToolCall( + id=FAKE_RESPONSES_ID, + type="function_call", + call_id="call_1", + name="f", + arguments='{"a":1}', + ) + commit = _drive(engine, _run_item(ToolCallItem(agent=_AGENT, raw_item=raw))) + assert _types(commit) == [ + EventType.TOOL_CALL_START, + EventType.TOOL_CALL_ARGS, + EventType.TOOL_CALL_END, + ] + assert {e.tool_call_id for e in commit} == {"call_1"} + assert engine.finalize() == [] + + +def test_placeholder_id_text_delta_synthesizes_a_clean_message_id(): + # A FAKE-id text delta with no output_item.added must synthesize a real + # generated id, never leak the internal "__idx_" placeholder window key. + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _raw( + OpenAIRawResponseEventType.TEXT_DELTA, + item_id=FAKE_RESPONSES_ID, + output_index=0, + delta="hi", + ), + ) + assert _types(events) == [EventType.TEXT_MESSAGE_START, EventType.TEXT_MESSAGE_CONTENT] + mid = events[0].message_id + assert mid.startswith("msg_") + assert not mid.startswith("__idx_") and mid != FAKE_RESPONSES_ID + + +def test_reasoning_delta_reuses_real_wire_id(): + # Reasoning that streams deltas before output_item.added must still reuse the + # real wire id for the phase (honors the id-reuse invariant), not synthesize one. + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _raw( + OpenAIRawResponseEventType.REASONING_SUMMARY_DELTA, + item_id="rs_9", + output_index=0, + delta="think", + ), + ) + starts = [e for e in events if e.type == EventType.REASONING_START] + assert starts and starts[0].message_id == "rs_9" + + +def test_args_before_added_then_run_item_commit_emits_no_duplicate(): + # Regression: args stream before output_item.added, then the semantic + # ToolCallItem commit arrives with the real call_id. Must emit exactly one + # START/ARGS/END under the real call_id โ€” not a duplicate (buffered id + + # committed id) as the pre-fix throwaway-id path produced. + engine = OpenAIToAGUITranslator() + buffered = _drive( + engine, + _raw( + OpenAIRawResponseEventType.FUNCTION_CALL_ARGUMENTS_DELTA, + item_id="fc_1", + output_index=0, + delta='{"city":"Cairo"}', + ), + ) + assert buffered == [] + raw = ResponseFunctionToolCall( + id="fc_1", + type="function_call", + call_id="call_1", + name="get_weather", + arguments='{"city":"Cairo"}', + ) + commit = _drive(engine, _run_item(ToolCallItem(agent=_AGENT, raw_item=raw))) + assert _types(commit) == [ + EventType.TOOL_CALL_START, + EventType.TOOL_CALL_ARGS, + EventType.TOOL_CALL_END, + ] + assert {e.tool_call_id for e in commit} == {"call_1"} + # Buffer was consumed by the commit โ€” finalize has nothing left to flush. + assert engine.finalize() == [] + + +def test_hosted_tool_call_with_distinct_call_id_streams_once(): + # Hosted calls like computer_call carry both an item id and a call_id. + # The raw window and the run-item commit must agree on the call_id โ€” + # otherwise the same call streams two full lifecycles under two ids. + engine = OpenAIToAGUITranslator() + raw = SimpleNamespace(type="computer_call", id="comp_1", call_id="call_1") + events = _drive( + engine, + _added("computer_call", id="comp_1", call_id="call_1"), + _done("computer_call", id="comp_1", call_id="call_1"), + _run_item(ToolCallItem(agent=_AGENT, raw_item=raw)), + ) + assert _types(events) == [EventType.TOOL_CALL_START, EventType.TOOL_CALL_END] + assert {e.tool_call_id for e in events} == {"call_1"} + + +def test_hosted_tool_call_without_call_id_falls_back_to_item_id(): + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _added("web_search_call", id="ws_1"), + _done("web_search_call", id="ws_1"), + ) + assert _types(events) == [EventType.TOOL_CALL_START, EventType.TOOL_CALL_END] + assert {e.tool_call_id for e in events} == {"ws_1"} + + +def test_tool_output_item_emits_tool_call_result(): + engine = OpenAIToAGUITranslator() + item = ToolCallOutputItem( + agent=_AGENT, + raw_item={"type": "function_call_output", "call_id": "call_1", "output": "sunny"}, + output="sunny", + ) + events = _drive(engine, _run_item(item)) + assert _types(events) == [EventType.TOOL_CALL_RESULT] + assert events[0].tool_call_id == "call_1" + assert events[0].content == "sunny" + + +def test_tool_output_call_id_falls_back_to_raw_item_when_property_absent(): + # Older SDK ToolCallOutputItem has no `call_id` property; the translator must + # fall back to the raw item rather than raise AttributeError. + engine = OpenAIToAGUITranslator() + item = SimpleNamespace( + raw_item={"type": "function_call_output", "call_id": "call_1", "output": "sunny"}, + output="sunny", + ) + events = engine.translate_tool_call_output_item(item) + assert _types(events) == [EventType.TOOL_CALL_RESULT] + assert events[0].tool_call_id == "call_1" + assert events[0].content == "sunny" + + +# โ”€โ”€ reasoning streaming โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_reasoning_summary_delta_opens_phase_and_part(): + engine = OpenAIToAGUITranslator() + events = _drive( + engine, + _raw( + OpenAIRawResponseEventType.REASONING_SUMMARY_DELTA, + item_id="rs_1", + output_index=0, + delta="thinking", + ), + _raw(OpenAIRawResponseEventType.REASONING_SUMMARY_PART_DONE, item_id="rs_1", output_index=0), + ) + assert _types(events) == [ + EventType.REASONING_START, + EventType.REASONING_MESSAGE_START, + EventType.REASONING_MESSAGE_CONTENT, + EventType.REASONING_MESSAGE_END, + ] + assert events[2].delta == "thinking" + # The phase stays open until finalize (only the part closed above). + assert _types(engine.finalize()) == [EventType.REASONING_END] + + +def test_fake_reasoning_id_starts_fresh_when_output_index_is_reused(): + engine = OpenAIToAGUITranslator() + events = [] + + for encrypted in ("first", "second"): + events.extend( + _drive( + engine, + _added(OpenAIItemType.REASONING, id=FAKE_RESPONSES_ID), + _raw( + OpenAIRawResponseEventType.REASONING_TEXT_DELTA, + item_id=FAKE_RESPONSES_ID, + output_index=0, + delta="thinking", + ), + _raw( + OpenAIRawResponseEventType.REASONING_TEXT_DONE, + item_id=FAKE_RESPONSES_ID, + output_index=0, + ), + _done( + OpenAIItemType.REASONING, + id=FAKE_RESPONSES_ID, + encrypted_content=encrypted, + ), + ) + ) + + # A new item must also reset the key when the previous provider turn + # omitted output_item.done but another output already closed its reasoning. + events.extend( + _drive( + engine, + _added(OpenAIItemType.REASONING, id=FAKE_RESPONSES_ID), + _raw( + OpenAIRawResponseEventType.REASONING_TEXT_DELTA, + item_id=FAKE_RESPONSES_ID, + output_index=0, + delta="thinking", + ), + _raw( + OpenAIRawResponseEventType.REASONING_TEXT_DONE, + item_id=FAKE_RESPONSES_ID, + output_index=0, + ), + _added(OpenAIItemType.MESSAGE, output_index=1, id="msg_1"), + _added(OpenAIItemType.REASONING, id=FAKE_RESPONSES_ID), + _raw( + OpenAIRawResponseEventType.REASONING_TEXT_DELTA, + item_id=FAKE_RESPONSES_ID, + output_index=0, + delta="thinking again", + ), + ) + ) + + phase_ids = [ + event.message_id for event in events if event.type == EventType.REASONING_START + ] + part_ids = [ + event.message_id + for event in events + if event.type == EventType.REASONING_MESSAGE_START + ] + encrypted_values = [ + event.encrypted_value + for event in events + if event.type == EventType.REASONING_ENCRYPTED_VALUE + ] + + assert part_ids == phase_ids + assert encrypted_values == ["first", "second"] + + +def test_reasoning_commit_surfaces_encrypted_value_on_skip_path(): + # Reasoning streams and closes via raw events carrying no encrypted_content; + # the ReasoningItem run-item commit is then the only carrier of it. The skip + # path must still surface REASONING_ENCRYPTED_VALUE so replay data is not lost. + engine = OpenAIToAGUITranslator() + _drive( + engine, + _added(OpenAIItemType.REASONING, id="rs_1"), + _raw( + OpenAIRawResponseEventType.REASONING_TEXT_DELTA, + item_id="rs_1", + output_index=0, + delta="hmm", + ), + _raw(OpenAIRawResponseEventType.REASONING_TEXT_DONE, item_id="rs_1", output_index=0), + _done(OpenAIItemType.REASONING, id="rs_1"), # no encrypted_content here + ) + item = ReasoningItem( + agent=_AGENT, + raw_item=ResponseReasoningItem( + id="rs_1", type="reasoning", summary=[], encrypted_content="secret" + ), + ) + events = _drive(engine, _run_item(item)) + assert _types(events) == [EventType.REASONING_ENCRYPTED_VALUE] + assert events[0].encrypted_value == "secret" + + +def test_tool_output_pydantic_model_serializes_its_fields(): + # to_string should serialize a pydantic tool output to its field dict rather + # than an opaque quoted repr. + class Weather(BaseModel): + city: str + temp: int + + engine = OpenAIToAGUITranslator() + item = ToolCallOutputItem( + agent=_AGENT, + raw_item={"type": "function_call_output", "call_id": "call_1", "output": "ignored"}, + output=Weather(city="Cairo", temp=30), + ) + events = _drive(engine, _run_item(item)) + assert _types(events) == [EventType.TOOL_CALL_RESULT] + assert __import__("json").loads(events[0].content) == {"city": "Cairo", "temp": 30} + + +def test_reasoning_auto_closes_when_text_output_starts(): + # Once real output begins, any open reasoning must be closed first โ€” + # reasoning must never bleed into the answer window. + engine = OpenAIToAGUITranslator() + _drive( + engine, + _raw( + OpenAIRawResponseEventType.REASONING_TEXT_DELTA, + item_id="rs_1", + output_index=0, + delta="hmm", + ), + ) + # output_item.added closes reasoning right away (output has begun), but + # holds back TEXT_MESSAGE_START โ€” that waits for a real delta so a + # text-less item can't emit an empty window. + events = _drive(engine, _added(OpenAIItemType.MESSAGE, id="msg_1")) + assert _types(events) == [ + EventType.REASONING_MESSAGE_END, + EventType.REASONING_END, + ] + events = _drive( + engine, + _raw(OpenAIRawResponseEventType.TEXT_DELTA, item_id="msg_1", output_index=0, delta="ok"), + ) + assert _types(events) == [EventType.TEXT_MESSAGE_START, EventType.TEXT_MESSAGE_CONTENT] + assert events[0].message_id == "msg_1", "deferred START must carry the reserved id" + + +# โ”€โ”€ multi-agent steps โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_agent_updated_opens_a_step_finalize_closes_it(): + engine = OpenAIToAGUITranslator() + events = _drive(engine, _agent_updated("triage_agent")) + assert _types(events) == [EventType.STEP_STARTED] + assert events[0].step_name == "triage_agent" + closing = engine.finalize() + assert _types(closing) == [EventType.STEP_FINISHED] + assert closing[0].step_name == "triage_agent" + + +def test_handoff_pairs_step_finished_with_the_next_step_started(): + engine = OpenAIToAGUITranslator() + _drive(engine, _agent_updated("triage_agent")) + events = _drive(engine, _agent_updated("billing_agent")) + assert _types(events) == [EventType.STEP_FINISHED, EventType.STEP_STARTED] + assert events[0].step_name == "triage_agent" + assert events[1].step_name == "billing_agent" + + +# โ”€โ”€ handoff items (surface as a tool call + result) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_handoff_call_and_output_items_map_to_tool_call_and_result(): + engine = OpenAIToAGUITranslator() + call = HandoffCallItem( + agent=_AGENT, + raw_item=ResponseFunctionToolCall( + id="fc_h", + type="function_call", + call_id="call_h", + name="transfer_to_billing", + arguments="{}", + ), + ) + output = HandoffOutputItem( + agent=_AGENT, + raw_item={"type": "function_call_output", "call_id": "call_h", "output": "done"}, + source_agent=_AGENT, + target_agent=Agent(name="billing_agent"), + ) + call_events = _drive(engine, _run_item(call)) + assert _types(call_events) == [ + EventType.TOOL_CALL_START, + EventType.TOOL_CALL_ARGS, + EventType.TOOL_CALL_END, + ] + assert {e.tool_call_id for e in call_events} == {"call_h"} + assert call_events[0].tool_call_name == "transfer_to_billing" + + result_events = _drive(engine, _run_item(output)) + assert _types(result_events) == [EventType.TOOL_CALL_RESULT] + assert result_events[0].tool_call_id == "call_h" + assert result_events[0].content == "done" + + +# โ”€โ”€ MCP approval โ†’ CUSTOM (no native AG-UI shape yet) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_mcp_approval_request_maps_to_a_custom_event(): + engine = OpenAIToAGUITranslator() + item = MCPApprovalRequestItem( + agent=_AGENT, + raw_item=McpApprovalRequest( + id="mcpr_1", + type="mcp_approval_request", + name="do_it", + arguments="{}", + server_label="srv", + ), + ) + events = _drive(engine, _run_item(item)) + assert _types(events) == [EventType.CUSTOM] + assert events[0].name == "mcp_approval_request" + assert events[0].value["name"] == "do_it" + + +# โ”€โ”€ graceful degradation โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_unknown_stream_event_type_translates_to_nothing(): + engine = OpenAIToAGUITranslator() + assert engine.translate(SimpleNamespace(type="something_new", data=None)) == [] diff --git a/integrations/openai-agents/python/tests/engine/test_openai_to_agui_snapshot.py b/integrations/openai-agents/python/tests/engine/test_openai_to_agui_snapshot.py new file mode 100644 index 0000000000..fdc8d10666 --- /dev/null +++ b/integrations/openai-agents/python/tests/engine/test_openai_to_agui_snapshot.py @@ -0,0 +1,290 @@ +""" +Tests for OpenAIToAGUITranslator.build_messages_snapshot. + +The invariant that matters: every id in the snapshot equals the id the +streamed events already used โ€” guaranteed by construction, since the +engine resolves each item's id once and hands it to both the streamed +event and the accumulated snapshot message. See the snapshot-message +builders in engine/openai_to_agui.py. +""" + +from __future__ import annotations + +from agents import Agent +from agents.items import ( + MessageOutputItem, + ReasoningItem, + ToolCallItem, + ToolCallOutputItem, +) +from openai.types.responses import ( + ResponseFunctionToolCall, + ResponseOutputMessage, + ResponseOutputText, + ResponseReasoningItem, +) + +from ag_ui.core import ( + AssistantMessage, + EventType, + RunAgentInput, + ToolMessage, + UserMessage, +) +from ag_ui_openai_agents.engine import OpenAIToAGUITranslator + +_AGENT = Agent(name="test-agent") + + +def _text_item(item_id: str = "msg_abc", text: str = "hello") -> MessageOutputItem: + return MessageOutputItem( + agent=_AGENT, + raw_item=ResponseOutputMessage( + id=item_id, + type="message", + role="assistant", + status="completed", + content=[ + ResponseOutputText(type="output_text", text=text, annotations=[]) + ], + ), + ) + + +def _tool_call_item(call_id: str = "call_1", name: str = "get_weather") -> ToolCallItem: + return ToolCallItem( + agent=_AGENT, + raw_item=ResponseFunctionToolCall( + id="fc_1", + type="function_call", + call_id=call_id, + name=name, + arguments='{"city":"Cairo"}', + ), + ) + + +def _tool_output_item(call_id: str = "call_1", output: str = "sunny") -> ToolCallOutputItem: + return ToolCallOutputItem( + agent=_AGENT, + raw_item={ + "type": "function_call_output", + "call_id": call_id, + "output": output, + }, + output=output, + ) + + +def _reasoning_item(item_id: str = "rs_1") -> ReasoningItem: + return ReasoningItem( + agent=_AGENT, + raw_item=ResponseReasoningItem(id=item_id, type="reasoning", summary=[]), + ) + + +# โ”€โ”€ id consistency: snapshot ids == streamed event ids โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_snapshot_ids_match_streamed_event_ids(): + engine = OpenAIToAGUITranslator() + items = [_text_item(), _tool_call_item(), _tool_output_item()] + streamed = [] + for item in items: + streamed.extend(engine.translate_item(item)) + streamed.extend(engine.finalize()) + + streamed_text_ids = { + e.message_id for e in streamed if e.type == EventType.TEXT_MESSAGE_START + } + streamed_call_ids = { + e.tool_call_id for e in streamed if e.type == EventType.TOOL_CALL_START + } + streamed_result_ids = { + e.message_id for e in streamed if e.type == EventType.TOOL_CALL_RESULT + } + + messages = engine._snapshot_messages + text_ids = { + m.id for m in messages + if isinstance(m, AssistantMessage) and not m.tool_calls + } + call_ids = { + call.id + for m in messages + if isinstance(m, AssistantMessage) and m.tool_calls + for call in m.tool_calls + } + result_ids = {m.id for m in messages if isinstance(m, ToolMessage)} + + assert text_ids == streamed_text_ids == {"msg_abc"} + assert call_ids == streamed_call_ids == {"call_1"} + assert result_ids == streamed_result_ids == {"call_1-result"} + + # Guard against duplicate emission that the set comparisons above would + # silently collapse: each id must appear on the wire exactly once. + start_ids = [e.message_id for e in streamed if e.type == EventType.TEXT_MESSAGE_START] + call_start_ids = [e.tool_call_id for e in streamed if e.type == EventType.TOOL_CALL_START] + result_event_ids = [e.message_id for e in streamed if e.type == EventType.TOOL_CALL_RESULT] + assert len(start_ids) == len(set(start_ids)) + assert len(call_start_ids) == len(set(call_start_ids)) + assert len(result_event_ids) == len(set(result_event_ids)) + + +# โ”€โ”€ tool call + result round-trip โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_tool_call_and_result_round_trip(): + engine = OpenAIToAGUITranslator() + engine.translate_item(_tool_call_item()) + engine.translate_item(_tool_output_item()) + + call_message, result_message = engine._snapshot_messages + assert isinstance(call_message, AssistantMessage) + assert call_message.id == "call_1" + assert call_message.tool_calls[0].function.name == "get_weather" + assert call_message.tool_calls[0].function.arguments == '{"city":"Cairo"}' + + assert isinstance(result_message, ToolMessage) + assert result_message.tool_call_id == "call_1" + assert result_message.id == "call_1-result" + assert result_message.content == "sunny" + + +# โ”€โ”€ multi-turn history: prior messages pass through untouched โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_snapshot_prepends_input_history_with_original_ids(): + run_input = RunAgentInput( + thread_id="t1", + run_id="r1", + messages=[ + UserMessage(id="m1", role="user", content="hi"), + AssistantMessage(id="msg_prev", role="assistant", content="hello"), + UserMessage(id="m2", role="user", content="and again"), + ], + tools=[], + state={}, + context=[], + forwarded_props=None, + ) + engine = OpenAIToAGUITranslator() + engine.translate_item(_text_item("msg_new", "hi again")) + + event = engine.build_messages_snapshot(run_input) + + assert event.type == EventType.MESSAGES_SNAPSHOT + assert [m.id for m in event.messages] == ["m1", "msg_prev", "m2", "msg_new"] + # Prior messages are the same objects, not copies with fresh ids. + assert event.messages[0] is run_input.messages[0] + + +def test_snapshot_accepts_message_list_and_none_history(): + engine = OpenAIToAGUITranslator() + engine.translate_item(_text_item()) + + prior = [UserMessage(id="m1", role="user", content="hi")] + assert [m.id for m in engine.build_messages_snapshot(prior).messages] == [ + "m1", + "msg_abc", + ] + assert [m.id for m in engine.build_messages_snapshot(None).messages] == ["msg_abc"] + assert [m.id for m in engine.build_messages_snapshot().messages] == ["msg_abc"] + + +# โ”€โ”€ graceful degradation โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_reasoning_items_are_skipped_from_snapshot(): + engine = OpenAIToAGUITranslator() + engine.translate_item(_reasoning_item()) + engine.translate_item(_text_item()) + + assert [m.id for m in engine._snapshot_messages] == ["msg_abc"] + + +def test_unknown_item_types_are_skipped_not_raised(): + from types import SimpleNamespace + + engine = OpenAIToAGUITranslator() + unknown = SimpleNamespace(raw_item={"type": "something_else"}) + + events = engine.translate_item(unknown) + + assert events == [] + assert engine._snapshot_messages == [] + + +# โ”€โ”€ raw close beats run-item commit: the real regression โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ +# +# Some backends emit response.output_item.done (closing the window via the +# raw-event path) before the RunItemStreamEvent commit for the same item +# arrives. translate_message_output_item/translate_tool_call_item then hit +# their "skip" branch (nothing left to close) โ€” that branch used to return +# [] without ever recording a snapshot message, so the item streamed fully +# but never made it into MESSAGES_SNAPSHOT. Observed on a real run. + + +def test_text_message_closed_by_raw_event_before_commit_still_snapshots(): + from types import SimpleNamespace + + engine = OpenAIToAGUITranslator() + + # response.output_item.added then .done โ€” closes the window via the + # raw-event path, same as a real streaming run. + added = SimpleNamespace( + item=SimpleNamespace(type="message", id="msg_real"), output_index=0 + ) + done = SimpleNamespace( + item=SimpleNamespace(type="message", id="msg_real"), output_index=0 + ) + engine.translate_output_item_added(added) + # A text delta must actually open the (deferred) message window, so that + # output_item.done closes it and appends msg_real to _closed_text_ids โ€” + # otherwise the commit reconciles as "new" and this never exercises the + # "skip" branch the regression is about. + engine.translate_text_delta( + SimpleNamespace(item_id="msg_real", output_index=0, delta="hello") + ) + engine.translate_output_item_done(done) + assert engine._snapshot_messages == [] # streamed close alone never records + + # The run-item commit arrives after โ€” must still record, under the + # same id the raw close already used. + engine.translate_item(_text_item("msg_real", "hello")) + + assert len(engine._snapshot_messages) == 1 + message = engine._snapshot_messages[0] + assert isinstance(message, AssistantMessage) + assert message.id == "msg_real" + assert message.content == "hello" + + +def test_tool_call_closed_by_raw_event_before_commit_still_snapshots(): + from types import SimpleNamespace + + engine = OpenAIToAGUITranslator() + + added = SimpleNamespace( + item=SimpleNamespace( + type="function_call", id="fc_1", call_id="call_real", name="get_weather" + ), + output_index=0, + ) + done = SimpleNamespace( + item=SimpleNamespace( + type="function_call", id="fc_1", call_id="call_real", name="get_weather" + ), + output_index=0, + ) + engine.translate_output_item_added(added) + engine.translate_output_item_done(done) + assert engine._snapshot_messages == [] + + engine.translate_item(_tool_call_item(call_id="call_real")) + + assert len(engine._snapshot_messages) == 1 + message = engine._snapshot_messages[0] + assert isinstance(message, AssistantMessage) + assert message.tool_calls[0].id == "call_real" + assert message.tool_calls[0].function.name == "get_weather" diff --git a/integrations/openai-agents/python/tests/engine/test_types_drift.py b/integrations/openai-agents/python/tests/engine/test_types_drift.py new file mode 100644 index 0000000000..cb6216d4f7 --- /dev/null +++ b/integrations/openai-agents/python/tests/engine/test_types_drift.py @@ -0,0 +1,143 @@ +""" +Drift guard: our wire strings must match the installed SDK's own types. + +``types.py`` hardcodes the discriminator strings the translators +dispatch on. The SDK declares the same strings as ``Literal[...]`` annotations +on its event/item classes. Nothing compares the two at runtime โ€” if the SDK +renamed a wire value, dispatch would silently stop matching (graceful +degradation swallows unknowns). These tests make that drift a loud CI failure +instead: bump ``openai-agents`` / ``openai``, run pytest, and any renamed or +newly added wire type shows up as an assertion diff pointing at the exact +enum member to update by hand. +""" + +from __future__ import annotations + +import dataclasses +from typing import Union, get_args, get_origin + +import pytest +from agents.stream_events import ( + AgentUpdatedStreamEvent, + RawResponsesStreamEvent, + RunItemStreamEvent, +) +from openai.types.responses import ( + ResponseFunctionCallArgumentsDeltaEvent, + ResponseFunctionToolCall, + ResponseOutputItem, + ResponseOutputItemAddedEvent, + ResponseOutputItemDoneEvent, + ResponseOutputMessage, + ResponseReasoningItem, + ResponseReasoningSummaryPartDoneEvent, + ResponseReasoningSummaryTextDeltaEvent, + ResponseRefusalDeltaEvent, + ResponseTextDeltaEvent, + ResponseTextDoneEvent, +) +from openai.types.responses.response_reasoning_text_delta_event import ( + ResponseReasoningTextDeltaEvent, +) +from openai.types.responses.response_reasoning_text_done_event import ( + ResponseReasoningTextDoneEvent, +) + +from ag_ui_openai_agents.engine import ( + HOSTED_TOOL_CALL_TYPES, + OpenAIItemType, + OpenAIRawResponseEventType, + OpenAIStreamEventType, +) + + +def pydantic_wire_value(model_cls: type) -> str: + """Extract the wire string from a pydantic ``type: Literal['...']`` field.""" + return get_args(model_cls.model_fields["type"].annotation)[0] + + +def dataclass_wire_value(cls: type) -> str: + """Extract the wire string from a dataclass ``type`` field default.""" + for field in dataclasses.fields(cls): + if field.name == "type": + return field.default + raise AssertionError(f"{cls.__name__} has no 'type' field") + + +def output_item_union_members() -> tuple[type, ...]: + """Unwrap ``ResponseOutputItem`` (``Annotated[Union[...], meta]``).""" + inner = get_args(ResponseOutputItem)[0] + assert get_origin(inner) is Union + return get_args(inner) + + +@pytest.mark.parametrize( + ("ours", "sdk_cls"), + [ + (OpenAIStreamEventType.RAW_RESPONSE, RawResponsesStreamEvent), + (OpenAIStreamEventType.RUN_ITEM, RunItemStreamEvent), + (OpenAIStreamEventType.AGENT_UPDATED, AgentUpdatedStreamEvent), + ], +) +def test_stream_event_types_match_sdk(ours: OpenAIStreamEventType, sdk_cls: type) -> None: + assert ours == dataclass_wire_value(sdk_cls) + + +@pytest.mark.parametrize( + ("ours", "sdk_cls"), + [ + (OpenAIRawResponseEventType.OUTPUT_ITEM_ADDED, ResponseOutputItemAddedEvent), + (OpenAIRawResponseEventType.OUTPUT_ITEM_DONE, ResponseOutputItemDoneEvent), + (OpenAIRawResponseEventType.TEXT_DELTA, ResponseTextDeltaEvent), + (OpenAIRawResponseEventType.TEXT_DONE, ResponseTextDoneEvent), + (OpenAIRawResponseEventType.REFUSAL_DELTA, ResponseRefusalDeltaEvent), + ( + OpenAIRawResponseEventType.FUNCTION_CALL_ARGUMENTS_DELTA, + ResponseFunctionCallArgumentsDeltaEvent, + ), + ( + OpenAIRawResponseEventType.REASONING_SUMMARY_DELTA, + ResponseReasoningSummaryTextDeltaEvent, + ), + ( + OpenAIRawResponseEventType.REASONING_SUMMARY_PART_DONE, + ResponseReasoningSummaryPartDoneEvent, + ), + (OpenAIRawResponseEventType.REASONING_TEXT_DELTA, ResponseReasoningTextDeltaEvent), + (OpenAIRawResponseEventType.REASONING_TEXT_DONE, ResponseReasoningTextDoneEvent), + ], +) +def test_raw_response_event_types_match_sdk( + ours: OpenAIRawResponseEventType, sdk_cls: type +) -> None: + assert ours == pydantic_wire_value(sdk_cls) + + +@pytest.mark.parametrize( + ("ours", "sdk_cls"), + [ + (OpenAIItemType.MESSAGE, ResponseOutputMessage), + (OpenAIItemType.FUNCTION_CALL, ResponseFunctionToolCall), + (OpenAIItemType.REASONING, ResponseReasoningItem), + ], +) +def test_item_types_match_sdk(ours: OpenAIItemType, sdk_cls: type) -> None: + assert ours == pydantic_wire_value(sdk_cls) + + +def test_every_sdk_tool_call_item_type_is_known() -> None: + """ + Catch the SDK *adding* hosted tool kinds, not just renaming them. + + Every ``*_call`` member of the ``ResponseOutputItem`` union must be either + ``function_call`` or listed in ``HOSTED_TOOL_CALL_TYPES`` โ€” otherwise a new + hosted tool would silently translate to nothing. + """ + sdk_call_types = { + pydantic_wire_value(member) + for member in output_item_union_members() + if pydantic_wire_value(member).endswith("_call") + } + known = HOSTED_TOOL_CALL_TYPES | {OpenAIItemType.FUNCTION_CALL.value} + unknown = sdk_call_types - known + assert not unknown, f"SDK added tool-call item types we don't map: {unknown}" diff --git a/integrations/openai-agents/python/tests/test_agent.py b/integrations/openai-agents/python/tests/test_agent.py new file mode 100644 index 0000000000..71c937b41e --- /dev/null +++ b/integrations/openai-agents/python/tests/test_agent.py @@ -0,0 +1,328 @@ +""" +Tests for the OpenAIAgentsAgent wrapper. + +Covers orchestration only โ€” the event mapping itself is the translator's job +and has its own coverage: + +- ``run_streamed`` yields the translator-wrapped stream (RUN_STARTED first, + RUN_FINISHED last) for one AG-UI request. +- Client-declared tools are merged onto a per-request ``clone`` โ€” the wrapped + static agent is never mutated; without tools no clone happens. +- ``run_config`` and context pass through to ``Runner.run_streamed``. +- Every ``to_agui`` option is forwarded with the same public name. +- ``name`` defaults to the SDK agent's name. +""" + +from __future__ import annotations + +import asyncio +from unittest.mock import MagicMock + +import pytest +from agents import Agent, RunConfig, function_tool +from agents.result import RunResultStreaming + +import ag_ui_openai_agents.agent as agent_module +from ag_ui.core import ( + CustomEvent, + Context, + EventType, + RunAgentInput, + Tool, + UserMessage, +) +from ag_ui_openai_agents import OpenAIAgentsAgent + + +def _run_input(with_tool: bool = False) -> RunAgentInput: + return RunAgentInput( + thread_id="t1", + run_id="r1", + messages=[UserMessage(id="m1", role="user", content="hi")], + tools=[ + Tool( + name="confirm", + description="Ask the user to confirm.", + parameters={"type": "object", "properties": {}}, + ) + ] + if with_tool + else [], + state={}, + context=[], + forwarded_props=None, + ) + + +async def _empty_stream(): + return + yield # pragma: no cover โ€” makes this an async generator + + +@pytest.fixture +def patched_runner(monkeypatch): + """Replace Runner.run_streamed with a capture that returns an empty stream.""" + calls: list[dict] = [] + + def fake_run_streamed(agent, *, input, run_config=None, **kwargs): + calls.append( + {"agent": agent, "input": input, "run_config": run_config, **kwargs} + ) + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _empty_stream() + return result + + monkeypatch.setattr(agent_module.Runner, "run_streamed", fake_run_streamed) + return calls + + +def _collect(wrapper: OpenAIAgentsAgent, run_input: RunAgentInput) -> list: + async def go(): + return [event async for event in wrapper.run_streamed(run_input)] + + return asyncio.run(go()) + + +def test_run_wraps_stream_with_lifecycle(patched_runner): + wrapper = OpenAIAgentsAgent(Agent(name="assistant", instructions="hi")) + events = _collect(wrapper, _run_input()) + assert events[0].type == EventType.RUN_STARTED + assert events[-1].type == EventType.RUN_FINISHED + assert events[0].thread_id == "t1" + assert events[0].run_id == "r1" + + +def test_run_without_client_tools_uses_static_agent(patched_runner): + sdk_agent = Agent(name="assistant", instructions="hi") + wrapper = OpenAIAgentsAgent(sdk_agent) + _collect(wrapper, _run_input()) + assert patched_runner[0]["agent"] is sdk_agent + + +def test_run_merges_client_tools_onto_clone(patched_runner): + sdk_agent = Agent(name="assistant", instructions="hi") + wrapper = OpenAIAgentsAgent(sdk_agent) + _collect(wrapper, _run_input(with_tool=True)) + ran_agent = patched_runner[0]["agent"] + assert ran_agent is not sdk_agent, "client tools must go on a clone" + assert [t.name for t in ran_agent.tools] == ["confirm"] + assert sdk_agent.tools == [], "the static agent must stay untouched" + + +def test_run_keeps_server_tool_when_client_name_conflicts(patched_runner, caplog): + @function_tool + def confirm() -> str: + """Confirm on the server.""" + return "confirmed" + + sdk_agent = Agent(name="assistant", instructions="hi", tools=[confirm]) + wrapper = OpenAIAgentsAgent(sdk_agent) + + import logging + + with caplog.at_level(logging.WARNING): + _collect(wrapper, _run_input(with_tool=True)) + + assert patched_runner[0]["agent"] is sdk_agent + assert [tool.name for tool in sdk_agent.tools] == ["confirm"] + assert "Ignoring client tool 'confirm'" in caplog.text + + +def test_run_config_passes_through(patched_runner): + run_config = RunConfig() + wrapper = OpenAIAgentsAgent( + Agent(name="assistant", instructions="hi"), run_config=run_config + ) + _collect(wrapper, _run_input()) + assert patched_runner[0]["run_config"] is run_config + + +def test_context_passes_through(patched_runner): + run_input = _run_input() + run_input.context = [Context(description="Response language", value="German")] + wrapper = OpenAIAgentsAgent(Agent(name="assistant", instructions="hi")) + _collect(wrapper, run_input) + assert patched_runner[0]["context"] == run_input.context + + +def test_explicit_context_overrides_agui_context(patched_runner): + # A caller that supplies its own SDK context gets exactly that object, + # not the AG-UI ambient context list. + sentinel = {"app": "context"} + run_input = _run_input() + run_input.context = [Context(description="Response language", value="German")] + wrapper = OpenAIAgentsAgent( + Agent(name="assistant", instructions="hi"), context=sentinel + ) + _collect(wrapper, run_input) + assert patched_runner[0]["context"] is sentinel + + +def test_no_context_defaults_to_none_not_empty_list(patched_runner): + # A context-less request must run with the SDK default (None), not [] โ€” + # agents branching on `ctx.context is None` depend on this. + wrapper = OpenAIAgentsAgent(Agent(name="assistant", instructions="hi")) + _collect(wrapper, _run_input()) # _run_input has context=[] + assert patched_runner[0]["context"] is None + + +def test_explicit_context_none_is_honored(patched_runner): + # context=None is a real SDK context, distinct from "not provided". + run_input = _run_input() + run_input.context = [Context(description="x", value="y")] + wrapper = OpenAIAgentsAgent(Agent(name="assistant", instructions="hi"), context=None) + _collect(wrapper, run_input) + assert patched_runner[0]["context"] is None + + +def test_duplicate_client_tool_names_are_deduped(patched_runner, caplog): + import logging + + run_input = RunAgentInput( + thread_id="t1", + run_id="r1", + messages=[UserMessage(id="m1", role="user", content="hi")], + tools=[ + Tool(name="dup", description="a", parameters={"type": "object", "properties": {}}), + Tool(name="dup", description="b", parameters={"type": "object", "properties": {}}), + ], + state={}, + context=[], + forwarded_props=None, + ) + wrapper = OpenAIAgentsAgent(Agent(name="assistant", instructions="hi")) + with caplog.at_level(logging.WARNING): + _collect(wrapper, run_input) + ran_agent = patched_runner[0]["agent"] + assert [t.name for t in ran_agent.tools] == ["dup"], "duplicate client tool must be dropped" + assert "Ignoring duplicate client tool 'dup'" in caplog.text + + +def test_to_agui_options_pass_through(patched_runner, monkeypatch): + start_value = lambda: {"phase": "start"} + end_value = lambda: {"phase": "end"} + start = CustomEvent(type=EventType.CUSTOM, name="start", value=start_value) + end = CustomEvent(type=EventType.CUSTOM, name="end", value=end_value) + initial_state = lambda: {"phase": "initial"} + final_state = lambda: {"phase": "final"} + translated_calls: list[dict] = [] + wrapper = OpenAIAgentsAgent( + Agent(name="assistant", instructions="hi"), + start_custom_event=start, + initial_state=initial_state, + final_state=final_state, + emit_messages_snapshot=False, + end_custom_event=end, + emit_run_error=False, + run_error_message="safe error", + ) + + original_to_agui = wrapper._translator.to_agui + + async def spy_to_agui(result, run_input, **kwargs): + translated_calls.append(kwargs) + async for event in original_to_agui(result, run_input, **kwargs): + yield event + + monkeypatch.setattr(wrapper._translator, "to_agui", spy_to_agui) + _collect(wrapper, _run_input()) + assert translated_calls == [ + { + "start_custom_event": start, + "initial_state": initial_state, + "final_state": final_state, + "emit_messages_snapshot": False, + "end_custom_event": end, + "emit_run_error": False, + "run_error_message": "safe error", + } + ] + + +def test_custom_event_value_factories_run_once_per_request(patched_runner): + calls = {"start": 0, "end": 0} + + def start_value(): + calls["start"] += 1 + return {"call": calls["start"]} + + def end_value(): + calls["end"] += 1 + return {"call": calls["end"]} + + wrapper = OpenAIAgentsAgent( + Agent(name="assistant", instructions="hi"), + start_custom_event=CustomEvent( + type=EventType.CUSTOM, name="start", value=start_value + ), + end_custom_event=CustomEvent( + type=EventType.CUSTOM, name="end", value=end_value + ), + ) + first = _collect(wrapper, _run_input()) + second = _collect(wrapper, _run_input()) + + assert calls == {"start": 2, "end": 2} + assert [event.value for event in first if isinstance(event, CustomEvent)] == [ + {"call": 1}, + {"call": 1}, + ] + assert [event.value for event in second if isinstance(event, CustomEvent)] == [ + {"call": 2}, + {"call": 2}, + ] + + +@pytest.mark.parametrize("custom_event_arg", ["start_custom_event", "end_custom_event"]) +def test_custom_event_value_factory_failure_emits_run_error_and_cancels( + monkeypatch, + custom_event_arg, +): + def failing_factory(): + raise RuntimeError("event factory failed") + + captured: list = [] + collected: list = [] + + def fake_run_streamed(agent, *, input, run_config=None, **kwargs): + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _empty_stream() + result.new_items = [] + captured.append(result) + return result + + wrapper = OpenAIAgentsAgent( + Agent(name="assistant", instructions="hi"), + **{ + custom_event_arg: CustomEvent( + type=EventType.CUSTOM, + name="failing", + value=failing_factory, + ) + }, + ) + + monkeypatch.setattr(agent_module.Runner, "run_streamed", fake_run_streamed) + + async def collect(): + async for event in wrapper.run_streamed(_run_input()): + collected.append(event) + + with pytest.raises(RuntimeError, match="event factory failed"): + asyncio.run(collect()) + + assert len(captured) == 1, "the run must have started" + captured[0].cancel.assert_called_once() + assert [ + event.type + for event in collected + if event.type in {EventType.RUN_STARTED, EventType.RUN_ERROR} + ] == [EventType.RUN_STARTED, EventType.RUN_ERROR] + + +def test_name_defaults_to_agent_name(): + assert OpenAIAgentsAgent(Agent(name="helper", instructions="hi")).name == "helper" + assert ( + OpenAIAgentsAgent(Agent(name="helper", instructions="hi"), name="api").name + == "api" + ) diff --git a/integrations/openai-agents/python/tests/test_endpoint.py b/integrations/openai-agents/python/tests/test_endpoint.py new file mode 100644 index 0000000000..c1626bcc47 --- /dev/null +++ b/integrations/openai-agents/python/tests/test_endpoint.py @@ -0,0 +1,234 @@ +""" +Tests for add_openai_agents_fastapi_endpoint. + +Wiring only โ€” the wrapper's behavior is covered in test_agent.py: + +- POST on the given path streams the run as SSE frames. +- A GET health check is registered next to it and reports the agent name. +- Custom (non-root) paths get their health check at /health. +- Health registration can be disabled when the application owns that route. +""" + +from __future__ import annotations + +from unittest.mock import MagicMock + +import pytest +from agents import Agent +from agents.result import RunResultStreaming +from fastapi import FastAPI +from fastapi.testclient import TestClient + +import ag_ui_openai_agents.agent as agent_module +from ag_ui.core import CustomEvent, EventType +from ag_ui_openai_agents import OpenAIAgentsAgent, add_openai_agents_fastapi_endpoint + +RUN_INPUT_JSON = { + "thread_id": "t1", + "run_id": "r1", + "messages": [{"id": "m1", "role": "user", "content": "hi"}], + "tools": [], + "state": {}, + "context": [], + "forwarded_props": None, +} + + +async def _empty_stream(): + return + yield # pragma: no cover โ€” makes this an async generator + + +@pytest.fixture +def client(monkeypatch) -> TestClient: + def fake_run_streamed(agent, *, input, run_config=None, **kwargs): + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _empty_stream() + return result + + monkeypatch.setattr(agent_module.Runner, "run_streamed", fake_run_streamed) + + app = FastAPI() + wrapper = OpenAIAgentsAgent(Agent(name="assistant", instructions="hi")) + add_openai_agents_fastapi_endpoint(app, wrapper, "/") + return TestClient(app) + + +def test_post_streams_sse_run(client): + with client.stream("POST", "/", json=RUN_INPUT_JSON) as response: + assert response.status_code == 200 + assert response.headers["content-type"].startswith("text/event-stream") + body = "".join(response.iter_text()) + assert '"RUN_STARTED"' in body + assert '"RUN_FINISHED"' in body + assert body.count("data: ") >= 2, "each event must be its own SSE frame" + + +def test_post_resolves_dynamic_custom_event_values(monkeypatch): + def fake_run_streamed(agent, *, input, run_config=None, **kwargs): + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _empty_stream() + return result + + monkeypatch.setattr(agent_module.Runner, "run_streamed", fake_run_streamed) + + app = FastAPI() + wrapper = OpenAIAgentsAgent( + Agent(name="assistant", instructions="hi"), + start_custom_event=CustomEvent( + type=EventType.CUSTOM, + name="request_metadata", + value=lambda: {"status": "dynamic"}, + ), + ) + add_openai_agents_fastapi_endpoint(app, wrapper, "/") + + with TestClient(app).stream("POST", "/", json=RUN_INPUT_JSON) as response: + body = "".join(response.iter_text()) + + assert response.status_code == 200 + assert '"name":"request_metadata"' in body + assert '"status":"dynamic"' in body + + +def test_health_reports_agent_name(client): + response = client.get("/health") + assert response.status_code == 200 + assert response.json() == {"status": "ok", "agent": {"name": "assistant"}} + + +def test_custom_path_places_health_under_it(monkeypatch): + def fake_run_streamed(agent, *, input, run_config=None, **kwargs): + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _empty_stream() + return result + + monkeypatch.setattr(agent_module.Runner, "run_streamed", fake_run_streamed) + + app = FastAPI() + wrapper = OpenAIAgentsAgent(Agent(name="assistant", instructions="hi")) + add_openai_agents_fastapi_endpoint(app, wrapper, "/my_agent") + client = TestClient(app) + + assert client.get("/my_agent/health").status_code == 200 + with client.stream("POST", "/my_agent", json=RUN_INPUT_JSON) as response: + assert response.status_code == 200 + + +def test_health_can_be_disabled(): + app = FastAPI() + wrapper = OpenAIAgentsAgent(Agent(name="assistant", instructions="hi")) + add_openai_agents_fastapi_endpoint( + app, + wrapper, + "/my_agent", + include_health=False, + ) + + routes = {(route.path, method) for route in app.routes for method in route.methods} + assert ("/my_agent", "POST") in routes + assert ("/my_agent/health", "GET") not in routes + + +def test_multiple_agents_on_one_app_get_unique_operation_ids(monkeypatch): + def fake_run_streamed(agent, *, input, run_config=None, **kwargs): + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _empty_stream() + return result + + monkeypatch.setattr(agent_module.Runner, "run_streamed", fake_run_streamed) + + app = FastAPI() + add_openai_agents_fastapi_endpoint( + app, OpenAIAgentsAgent(Agent(name="one", instructions="hi")), "/one" + ) + add_openai_agents_fastapi_endpoint( + app, OpenAIAgentsAgent(Agent(name="two", instructions="hi")), "/two" + ) + + # Mounting several agents must not collide on FastAPI-derived operation ids. + op_ids = [ + operation["operationId"] + for path in app.openapi()["paths"].values() + for operation in path.values() + ] + assert len(op_ids) == len(set(op_ids)), f"operation ids must be unique: {op_ids}" + + +def test_agent_error_is_logged_not_leaked_through_asgi(monkeypatch, caplog): + import logging + + def fake_run_streamed(agent, *, input, run_config=None, **kwargs): + async def boom(): + raise RuntimeError("kaboom") + yield # pragma: no cover + + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = boom() + return result + + monkeypatch.setattr(agent_module.Runner, "run_streamed", fake_run_streamed) + + app = FastAPI() + wrapper = OpenAIAgentsAgent(Agent(name="assistant", instructions="hi")) + add_openai_agents_fastapi_endpoint(app, wrapper, "/") + + # raise_server_exceptions=True (default): if the re-raise escaped the + # generator it would surface here as a 500/exception. The client must still + # receive its RUN_ERROR frame and the error must be logged, not leaked. + with caplog.at_level(logging.ERROR): + with TestClient(app).stream("POST", "/", json=RUN_INPUT_JSON) as response: + assert response.status_code == 200 + body = "".join(response.iter_text()) + + assert '"RUN_ERROR"' in body + assert "Agent run failed" in caplog.text + + +def test_setup_error_before_streaming_still_emits_run_error(monkeypatch, caplog): + import logging + + # Runner.run_streamed runs inside OpenAIAgentsAgent.run_streamed BEFORE + # to_agui, so a failure here never reaches to_agui's RUN_ERROR path. The + # endpoint must still surface a terminal RUN_ERROR rather than an empty 200. + def boom(agent, *, input, run_config=None, **kwargs): + raise RuntimeError("setup exploded") + + monkeypatch.setattr(agent_module.Runner, "run_streamed", boom) + + app = FastAPI() + wrapper = OpenAIAgentsAgent(Agent(name="assistant", instructions="hi")) + add_openai_agents_fastapi_endpoint(app, wrapper, "/") + + with caplog.at_level(logging.ERROR): + with TestClient(app).stream("POST", "/", json=RUN_INPUT_JSON) as response: + assert response.status_code == 200 + body = "".join(response.iter_text()) + + assert '"RUN_ERROR"' in body + assert "Agent run failed" in caplog.text + + +def test_colliding_path_slugs_get_unique_operation_ids(monkeypatch): + def fake_run_streamed(agent, *, input, run_config=None, **kwargs): + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _empty_stream() + return result + + monkeypatch.setattr(agent_module.Runner, "run_streamed", fake_run_streamed) + + # "/a/b" and "/a_b" both normalize to the slug "a_b"; the uniqueness guard + # must still hand out distinct operation ids. + app = FastAPI() + add_openai_agents_fastapi_endpoint( + app, OpenAIAgentsAgent(Agent(name="one", instructions="hi")), "/a/b" + ) + add_openai_agents_fastapi_endpoint( + app, OpenAIAgentsAgent(Agent(name="two", instructions="hi")), "/a_b" + ) + op_ids = [ + operation["operationId"] + for path in app.openapi()["paths"].values() + for operation in path.values() + ] + assert len(op_ids) == len(set(op_ids)), f"operation ids must be unique: {op_ids}" diff --git a/integrations/openai-agents/python/tests/test_translator.py b/integrations/openai-agents/python/tests/test_translator.py new file mode 100644 index 0000000000..dfca53d340 --- /dev/null +++ b/integrations/openai-agents/python/tests/test_translator.py @@ -0,0 +1,872 @@ +""" +Tests for the two-method translator API. + +Covers the public translator contract only โ€” engine mappings have their own coverage: + +- ``to_openai`` delegates to the inbound engine and populates ``tools``. +- ``AGUITranslator.to_agui`` streams engine output live and appends the + engine flush, with a fresh engine per call (reusable translator). +- ``to_agui`` wraps ordinary runs with RUN_STARTED and a terminal lifecycle + event; cancellation stops an owned SDK run without emitting RUN_ERROR. +- ``to_agui`` appends a MESSAGES_SNAPSHOT by default just before + RUN_FINISHED; snapshot content itself is covered in + ``engine/test_openai_to_agui_snapshot.py``, + this file only checks the wiring (default on, ``emit_messages_snapshot=False`` + opts out โ€” same for bare iterators, since the snapshot no longer depends + on ``result.new_items``). +""" + +from __future__ import annotations + +import asyncio +from unittest.mock import MagicMock + +import pytest +from agents import FunctionTool +from agents.result import RunResultStreaming + +from ag_ui.core import ( + CustomEvent, + EventType, + MessagesSnapshotEvent, + RunAgentInput, + RunErrorEvent, + RunFinishedEvent, + RunStartedEvent, + ResumeEntry, + StateSnapshotEvent, + Tool, + UserMessage, +) +from ag_ui_openai_agents import AGUITranslator +from ag_ui_openai_agents.engine import ClientToolPending + + +def _run_input( + with_tool: bool = False, + resume: list[ResumeEntry] | None = None, +) -> RunAgentInput: + return RunAgentInput( + thread_id="t1", + run_id="r1", + messages=[UserMessage(id="m1", role="user", content="hi")], + tools=[ + Tool( + name="confirm", + description="Ask the user to confirm.", + parameters={"type": "object", "properties": {}}, + ) + ] + if with_tool + else [], + state={}, + context=[], + forwarded_props=None, + resume=resume, + ) + + +def _event(name: str) -> CustomEvent: + return CustomEvent(type=EventType.CUSTOM, name=name, value=None) + + +class _StubOutbound: + """Records lifecycle; one AG-UI event per SDK event + a flush marker.""" + + instances = 0 + + def __init__(self) -> None: + type(self).instances += 1 + self._snapshot_messages: list = [] + + def translate(self, openai_event): + return [_event(f"translated:{openai_event}")] + + def finalize(self): + return [_event("finalized")] + + def build_messages_snapshot(self, run_input=None): + prior = getattr(run_input, "messages", run_input) or [] + return MessagesSnapshotEvent( + type=EventType.MESSAGES_SNAPSHOT, + messages=[*prior, *self._snapshot_messages], + ) + + +async def _fake_stream(*names: str): + for name in names: + yield name + + +# โ”€โ”€ to_openai โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_to_openai_translates_messages_and_tools(): + translator = AGUITranslator() + bundle = translator.to_openai(_run_input(with_tool=True)) + assert bundle.thread_id == "t1" + assert bundle.messages, "user message should be translated into input items" + assert len(bundle.tools) == 1 + assert isinstance(bundle.tools[0], FunctionTool) + assert bundle.tools[0].name == "confirm" + assert bundle.tools[0].strict_json_schema is False + + +def test_to_openai_keeps_a_tool_schema_that_omits_the_optional_type_key(): + # "type": "object" is optional in JSON Schema; dropping the declared + # fields with it would leave the model calling the tool with no arguments. + run_input = _run_input() + run_input.tools = [ + Tool( + name="confirm", + description="Ask the user to confirm.", + parameters={ + "properties": {"reason": {"type": "string"}}, + "required": ["reason"], + }, + ) + ] + schema = AGUITranslator().to_openai(run_input).tools[0].params_json_schema + assert schema["type"] == "object" + assert schema["properties"] == {"reason": {"type": "string"}} + assert schema["required"] == ["reason"] + + +def test_to_openai_without_tools_leaves_bundle_empty(): + bundle = AGUITranslator().to_openai(_run_input()) + assert bundle.tools == [] + + +def test_to_openai_preserves_resume_entries(): + resume = ResumeEntry(interrupt_id="interrupt-1", status="resolved", payload=True) + translated = AGUITranslator().to_openai(_run_input(resume=[resume])) + + assert translated.resume == [resume] + + +# โ”€โ”€ AGUITranslator.to_agui (streaming) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_streaming_to_agui_streams_then_finalizes(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + + async def collect(): + return [ + e + async for e in translator.to_agui( + _fake_stream("a", "b"), + _run_input(), + emit_messages_snapshot=False, + initial_state=None, + final_state=None, + ) + ] + + events = asyncio.run(collect()) + assert isinstance(events[0], RunStartedEvent) + assert isinstance(events[-1], RunFinishedEvent) + assert [e.name for e in events[1:-1]] == [ + "translated:a", + "translated:b", + "finalized", + ] + + +def test_streaming_to_agui_carries_parent_run_id_onto_run_started(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + run_input = _run_input() + nested_input = run_input.model_copy(update={"parent_run_id": "r0"}) + + async def collect(): + return [e async for e in translator.to_agui(_fake_stream(), nested_input)] + + events = asyncio.run(collect()) + assert isinstance(events[0], RunStartedEvent) + assert events[0].parent_run_id == "r0" + + +def test_streaming_translator_is_reusable_fresh_engine_per_run(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + before = _StubOutbound.instances + + async def one_run(): + return [e async for e in translator.to_agui(_fake_stream("x"), _run_input())] + + asyncio.run(one_run()) + asyncio.run(one_run()) + assert _StubOutbound.instances == before + 2 + + +def test_streaming_to_agui_accepts_run_streaming_result(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + + # spec= makes isinstance(result, RunResultStreaming) True, matching what + # to_agui actually checks โ€” a bare duck-typed stand-in would not. + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _fake_stream("sdk") + result.new_items = [] + + async def collect(): + return [ + e + async for e in translator.to_agui( + result, _run_input(), initial_state=None, final_state=None + ) + ] + + events = asyncio.run(collect()) + assert isinstance(events[0], RunStartedEvent) + assert [e.name for e in events[1:3]] == ["translated:sdk", "finalized"] + assert isinstance(events[3], MessagesSnapshotEvent) + assert isinstance(events[4], RunFinishedEvent) + + +# โ”€โ”€ AGUITranslator.to_agui (MESSAGES_SNAPSHOT) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_to_agui_emits_snapshot_by_default(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _fake_stream() + result.new_items = [] + run_input = _run_input() + + async def collect(): + return [e async for e in translator.to_agui(result, run_input)] + + events = asyncio.run(collect()) + assert isinstance(events[-1], RunFinishedEvent) + snapshot = events[-2] + assert isinstance(snapshot, MessagesSnapshotEvent) + assert [m.id for m in snapshot.messages] == ["m1"] + + +def test_to_agui_emit_messages_snapshot_false_opts_out(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _fake_stream() + result.new_items = [] + + async def collect(): + return [ + e + async for e in translator.to_agui( + result, _run_input(), emit_messages_snapshot=False + ) + ] + + events = asyncio.run(collect()) + assert not any(isinstance(e, MessagesSnapshotEvent) for e in events) + + +def test_to_agui_emits_snapshot_for_bare_iterator_too(): + # The snapshot is built from the engine's own accumulator, not + # result.new_items, so a bare stream_events() iterator works the same + # as passing the RunResultStreaming object itself. + translator = AGUITranslator(outbound_cls=_StubOutbound) + + async def collect(): + return [e async for e in translator.to_agui(_fake_stream("a"), _run_input())] + + events = asyncio.run(collect()) + assert isinstance(events[-1], RunFinishedEvent) + assert isinstance(events[-2], MessagesSnapshotEvent) + + +# โ”€โ”€ AGUITranslator.to_agui (lifecycle events) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_to_agui_wraps_stream_with_lifecycle_events(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + + async def collect(): + return [e async for e in translator.to_agui(_fake_stream("a"), _run_input())] + + events = asyncio.run(collect()) + started, finished = events[0], events[-1] + assert isinstance(started, RunStartedEvent) + assert started.thread_id == "t1" + assert started.run_id == "r1" + assert isinstance(finished, RunFinishedEvent) + assert finished.thread_id == "t1" + assert finished.run_id == "r1" + + +def test_to_agui_emits_run_error_and_reraises_on_exception(): + class _ExplodingOutbound(_StubOutbound): + def translate(self, openai_event): + raise RuntimeError("boom") + + translator = AGUITranslator(outbound_cls=_ExplodingOutbound) + collected: list = [] + + async def collect(): + async for e in translator.to_agui(_fake_stream("a"), _run_input()): + collected.append(e) + + with pytest.raises(RuntimeError, match="boom"): + asyncio.run(collect()) + + assert isinstance(collected[0], RunStartedEvent) + assert isinstance(collected[-1], RunErrorEvent) + assert collected[-1].message == "boom" + + +def test_to_agui_emit_run_error_false_suppresses_event_but_still_raises(): + class _ExplodingOutbound(_StubOutbound): + def translate(self, openai_event): + raise RuntimeError("boom") + + translator = AGUITranslator(outbound_cls=_ExplodingOutbound) + collected: list = [] + + async def collect(): + async for e in translator.to_agui( + _fake_stream("a"), _run_input(), emit_run_error=False + ): + collected.append(e) + + with pytest.raises(RuntimeError, match="boom"): + asyncio.run(collect()) + + assert isinstance(collected[0], RunStartedEvent) + assert not any(isinstance(e, RunErrorEvent) for e in collected) + + +def test_to_agui_run_error_message_overrides_exception_text(): + class _ExplodingOutbound(_StubOutbound): + def translate(self, openai_event): + raise RuntimeError("raw internal detail") + + translator = AGUITranslator(outbound_cls=_ExplodingOutbound) + collected: list = [] + + async def collect(): + async for e in translator.to_agui( + _fake_stream("a"), _run_input(), run_error_message="Agent run failed" + ): + collected.append(e) + + with pytest.raises(RuntimeError, match="raw internal detail"): + asyncio.run(collect()) + + error = collected[-1] + assert isinstance(error, RunErrorEvent) + assert error.message == "Agent run failed" + + +def test_to_agui_empty_run_error_message_is_honored_not_treated_as_unset(): + # An explicit "" means "send an empty message" (suppress the raw text), only + # None falls back to str(exc). Guards against leaking exception detail. + class _ExplodingOutbound(_StubOutbound): + def translate(self, openai_event): + raise RuntimeError("raw internal detail") + + translator = AGUITranslator(outbound_cls=_ExplodingOutbound) + collected: list = [] + + async def collect(): + async for e in translator.to_agui( + _fake_stream("a"), _run_input(), run_error_message="" + ): + collected.append(e) + + with pytest.raises(RuntimeError, match="raw internal detail"): + asyncio.run(collect()) + + error = collected[-1] + assert isinstance(error, RunErrorEvent) + assert error.message == "" + assert "raw internal detail" not in error.message + + +def test_to_agui_closes_open_windows_before_run_error(): + # A client keying its teardown on *_END would spin forever on the + # half-streamed message if the error path skipped the flush. + class _ExplodingOutbound(_StubOutbound): + def translate(self, openai_event): + raise RuntimeError("boom") + + translator = AGUITranslator(outbound_cls=_ExplodingOutbound) + collected: list = [] + + async def collect(): + async for e in translator.to_agui(_fake_stream("a"), _run_input()): + collected.append(e) + + with pytest.raises(RuntimeError, match="boom"): + asyncio.run(collect()) + + assert [getattr(e, "name", None) for e in collected if isinstance(e, CustomEvent)] == [ + "finalized" + ] + assert isinstance(collected[-1], RunErrorEvent) + + +def test_to_agui_client_tool_pending_uses_the_normal_completion_path(): + class _ClientToolOutbound(_StubOutbound): + def translate(self, openai_event): + raise ClientToolPending("confirm", "call-1", "{}") + + translator = AGUITranslator(outbound_cls=_ClientToolOutbound) + end = CustomEvent(type=EventType.CUSTOM, name="end", value={}) + + async def collect(): + return [ + event + async for event in translator.to_agui( + _fake_stream("tool"), + _run_input(), + final_state={"status": "pending"}, + end_custom_event=end, + ) + ] + + events = asyncio.run(collect()) + assert any( + isinstance(event, CustomEvent) and event.name == "finalized" + for event in events + ) + assert any(isinstance(event, StateSnapshotEvent) for event in events) + assert any(isinstance(event, MessagesSnapshotEvent) for event in events) + assert end in events + assert isinstance(events[-1], RunFinishedEvent) + assert not any(isinstance(event, RunErrorEvent) for event in events) + + +def test_to_agui_resolves_custom_event_values_at_their_lifecycle_positions(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + state = {"status": "starting"} + start = CustomEvent( + type=EventType.CUSTOM, + name="start", + value={"status": "starting"}, + ) + end = CustomEvent( + type=EventType.CUSTOM, + name="end", + value=lambda: dict(state), + ) + + async def stream(): + state["status"] = "complete" + yield "a" + + async def collect(): + return [ + event + async for event in translator.to_agui( + stream(), + _run_input(), + start_custom_event=start, + end_custom_event=end, + emit_messages_snapshot=False, + ) + ] + + events = asyncio.run(collect()) + lifecycle_events = [ + event for event in events if getattr(event, "name", None) in {"start", "end"} + ] + assert [event.value for event in lifecycle_events] == [ + {"status": "starting"}, + {"status": "complete"}, + ] + assert callable(end.value), "the reusable event must retain its factory" + + +def test_to_agui_awaits_an_async_custom_event_value_factory(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + + async def end_value(): + return {"status": "complete"} + + end = CustomEvent(type=EventType.CUSTOM, name="end", value=end_value) + + async def collect(): + return [ + event + async for event in translator.to_agui( + _fake_stream(), + _run_input(), + end_custom_event=end, + emit_messages_snapshot=False, + ) + ] + + events = asyncio.run(collect()) + emitted = next(event for event in events if getattr(event, "name", None) == "end") + assert emitted.value == {"status": "complete"} + + +@pytest.mark.parametrize( + ("custom_event_arg", "invalid_value"), + [ + ("start_custom_event", "not-a-custom-event"), + ("end_custom_event", False), + ], +) +def test_to_agui_cancels_the_sdk_run_when_custom_event_validation_fails( + custom_event_arg, invalid_value +): + translator = AGUITranslator(outbound_cls=_StubOutbound) + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _fake_stream() + + async def collect(): + async for _ in translator.to_agui( + result, + _run_input(), + **{custom_event_arg: invalid_value}, + ): + pass + + with pytest.raises(TypeError, match="custom event must be a CustomEvent"): + asyncio.run(collect()) + + result.cancel.assert_called_once() + + +@pytest.mark.parametrize("custom_event_arg", ["start_custom_event", "end_custom_event"]) +def test_to_agui_emits_run_error_when_custom_event_value_factory_fails( + custom_event_arg, +): + translator = AGUITranslator(outbound_cls=_StubOutbound) + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _fake_stream() + collected: list = [] + + def failing_value(): + raise RuntimeError("value failed") + + async def collect(): + async for event in translator.to_agui( + result, + _run_input(), + emit_messages_snapshot=False, + **{ + custom_event_arg: CustomEvent( + type=EventType.CUSTOM, + name="failing", + value=failing_value, + ) + }, + ): + collected.append(event) + + with pytest.raises(RuntimeError, match="value failed"): + asyncio.run(collect()) + + assert [ + event.type + for event in collected + if event.type in {EventType.RUN_STARTED, EventType.RUN_ERROR} + ] == [EventType.RUN_STARTED, EventType.RUN_ERROR] + result.cancel.assert_called_once() + + +@pytest.mark.parametrize("state_arg", ["initial_state", "final_state"]) +def test_to_agui_emits_run_error_when_state_factory_fails(state_arg): + translator = AGUITranslator(outbound_cls=_StubOutbound) + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _fake_stream() + collected: list = [] + + def failing_state(): + raise RuntimeError("state failed") + + async def collect(): + async for event in translator.to_agui( + result, + _run_input(), + emit_messages_snapshot=False, + **{state_arg: failing_state}, + ): + collected.append(event) + + with pytest.raises(RuntimeError, match="state failed"): + asyncio.run(collect()) + + assert [ + event.type + for event in collected + if event.type in {EventType.RUN_STARTED, EventType.RUN_ERROR} + ] == [EventType.RUN_STARTED, EventType.RUN_ERROR] + result.cancel.assert_called_once() + + +@pytest.mark.parametrize( + ("events_to_read", "closed_at"), + [ + (1, "RUN_STARTED"), + (2, "the initial STATE_SNAPSHOT"), + (3, "the first streamed event"), + ], +) +def test_to_agui_cancels_the_sdk_run_when_the_consumer_stops_reading( + events_to_read, closed_at +): + # Closing at any yield must stop the SDK run owned by this generator. + translator = AGUITranslator(outbound_cls=_StubOutbound) + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _fake_stream("a", "b") + result.new_items = [] + + async def stop_after(event_count: int): + stream = translator.to_agui(result, _run_input(), initial_state={"k": "v"}) + for _ in range(event_count): + await stream.__anext__() + await stream.aclose() + + asyncio.run(stop_after(events_to_read)) + assert result.cancel.call_count == 1, f"closing at {closed_at} must cancel the run" + + +def test_to_agui_cancels_the_sdk_run_when_a_pending_step_is_cancelled(): + # Cancellation while awaiting initial state must still stop the SDK run. + translator = AGUITranslator(outbound_cls=_StubOutbound) + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _fake_stream("a") + result.new_items = [] + + async def never_resolves(): + await asyncio.Event().wait() + + async def cancel_mid_initial_state(): + stream = translator.to_agui( + result, _run_input(), initial_state=never_resolves + ) + await stream.__anext__() # RUN_STARTED + pending = asyncio.create_task(stream.__anext__()) + await asyncio.sleep(0) # let it reach the await inside initial_state + pending.cancel() + with pytest.raises(asyncio.CancelledError): + await pending + + asyncio.run(cancel_mid_initial_state()) + result.cancel.assert_called_once() + + +def test_to_agui_does_not_cancel_a_run_that_streamed_to_completion(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _fake_stream("a") + result.new_items = [] + + async def collect(): + return [e async for e in translator.to_agui(result, _run_input())] + + asyncio.run(collect()) + result.cancel.assert_not_called() + + +def test_to_agui_does_not_cancel_when_closed_right_after_run_finished(): + # Closing while paused at the terminal yield is normal completion. + translator = AGUITranslator(outbound_cls=_StubOutbound) + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _fake_stream("a") + result.new_items = [] + + async def close_on_run_finished(): + stream = translator.to_agui(result, _run_input()) + async for event in stream: + if isinstance(event, RunFinishedEvent): + break # implicit aclose while paused at the terminal yield + await stream.aclose() + + asyncio.run(close_on_run_finished()) + result.cancel.assert_not_called() + + +def test_to_agui_cancels_without_run_error_on_cancelled_error(): + class _CancelledOutbound(_StubOutbound): + def translate(self, openai_event): + raise asyncio.CancelledError() + + translator = AGUITranslator(outbound_cls=_CancelledOutbound) + result = MagicMock(spec=RunResultStreaming) + result.stream_events.return_value = _fake_stream("a") + collected: list = [] + + async def collect(): + async for e in translator.to_agui(result, _run_input()): + collected.append(e) + + with pytest.raises(asyncio.CancelledError): + asyncio.run(collect()) + + assert isinstance(collected[0], RunStartedEvent) + assert not any(isinstance(event, RunErrorEvent) for event in collected) + result.cancel.assert_called_once() + + +# โ”€โ”€ AGUITranslator.to_agui (state snapshot) โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + +def test_to_agui_echoes_run_input_state_as_snapshot(): + # Non-empty run_input.state is echoed back as a STATE_SNAPSHOT right + # after RUN_STARTED โ€” matching the other integrations with no native SDK + # state. + translator = AGUITranslator(outbound_cls=_StubOutbound) + run_input = _run_input().model_copy(update={"state": {"theme": "dark"}}) + + async def collect(): + return [ + e + async for e in translator.to_agui( + _fake_stream("a"), + run_input, + emit_messages_snapshot=False, + initial_state=run_input.state, + final_state=None, + ) + ] + + events = asyncio.run(collect()) + assert isinstance(events[0], RunStartedEvent) + assert isinstance(events[1], StateSnapshotEvent) + assert events[1].snapshot == {"theme": "dark"} + # final_state=None here isolates the initial echo โ€” just the one. + assert sum(isinstance(e, StateSnapshotEvent) for e in events) == 1 + + +def test_to_agui_empty_run_input_state_still_emits_snapshot(): + # Gated on `is not None`, so an empty {} still echoes as STATE_SNAPSHOT({}) + # โ€” matching aws-strands / claude-agent-sdk. + translator = AGUITranslator(outbound_cls=_StubOutbound) + + async def collect(): + return [ + e + async for e in translator.to_agui( + _fake_stream("a"), + _run_input(), + emit_messages_snapshot=False, + initial_state={}, + final_state=None, + ) + ] + + events = asyncio.run(collect()) + snapshots = [e for e in events if isinstance(e, StateSnapshotEvent)] + assert len(snapshots) == 1 + assert snapshots[0].snapshot == {} + + +def test_to_agui_none_state_sources_suppress_both_snapshots(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + run_input = _run_input().model_copy(update={"state": {"x": 1}}) + + async def collect(): + return [ + e + async for e in translator.to_agui( + _fake_stream("a"), + run_input, + emit_messages_snapshot=False, + initial_state=None, + final_state=None, + ) + ] + + events = asyncio.run(collect()) + assert not any(isinstance(e, StateSnapshotEvent) for e in events) + + +def test_to_agui_emits_final_state_before_messages_snapshot(): + # Pass both sources: state echoes twice โ€” initial right after RUN_STARTED + # and final in the settled-state slot (#5), just before MESSAGES_SNAPSHOT. + translator = AGUITranslator(outbound_cls=_StubOutbound) + run_input = _run_input().model_copy(update={"state": {"theme": "dark"}}) + + async def collect(): + return [ + e + async for e in translator.to_agui( + _fake_stream("a"), + run_input, + initial_state=run_input.state, + final_state=run_input.state, + ) + ] + + events = asyncio.run(collect()) + snapshots = [e for e in events if isinstance(e, StateSnapshotEvent)] + # Two snapshots: initial echo + final settled state, both == run_input.state. + assert len(snapshots) == 2 + assert all(s.snapshot == {"theme": "dark"} for s in snapshots) + # Canonical order #5 โ†’ #6 โ†’ #8: final STATE, then MESSAGES, then RUN_FINISHED. + assert isinstance(events[-1], RunFinishedEvent) + assert isinstance(events[-2], MessagesSnapshotEvent) + assert events[-3] is snapshots[-1] + + +def test_to_agui_accepts_static_and_lazy_state_sources(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + state = {"status": "starting"} + + async def collect(): + async def stream(): + state["status"] = "complete" + yield "a" + + return [ + e + async for e in translator.to_agui( + stream(), + _run_input(), + emit_messages_snapshot=False, + initial_state={"status": "starting"}, + final_state=lambda: dict(state), + ) + ] + + events = asyncio.run(collect()) + snapshots = [e.snapshot for e in events if isinstance(e, StateSnapshotEvent)] + assert snapshots == [{"status": "starting"}, {"status": "complete"}] + + +def test_to_agui_awaits_async_state_source(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + + async def final_state(): + return {"status": "complete"} + + async def collect(): + return [ + e + async for e in translator.to_agui( + _fake_stream("a"), + _run_input(), + emit_messages_snapshot=False, + initial_state=None, + final_state=final_state, + ) + ] + + events = asyncio.run(collect()) + snapshots = [e.snapshot for e in events if isinstance(e, StateSnapshotEvent)] + assert snapshots == [{"status": "complete"}] + + +def test_to_agui_final_state_none_keeps_only_initial(): + translator = AGUITranslator(outbound_cls=_StubOutbound) + run_input = _run_input().model_copy(update={"state": {"x": 1}}) + + async def collect(): + return [ + e + async for e in translator.to_agui( + _fake_stream("a"), + run_input, + emit_messages_snapshot=False, + initial_state=run_input.state, + final_state=None, + ) + ] + + events = asyncio.run(collect()) + snapshots = [e for e in events if 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extensions +.vscode-test + +# yarn v3 +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/sdks +!.yarn/versions + +# Vite files +vite.config.js.timestamp-* +vite.config.ts.timestamp-* +.vite/ diff --git a/integrations/openai-agents/typescript/LICENSE b/integrations/openai-agents/typescript/LICENSE new file mode 100644 index 0000000000..b77bf2ab72 --- /dev/null +++ b/integrations/openai-agents/typescript/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/integrations/openai-agents/typescript/README.md b/integrations/openai-agents/typescript/README.md new file mode 100644 index 0000000000..6e6a814817 --- /dev/null +++ b/integrations/openai-agents/typescript/README.md @@ -0,0 +1,32 @@ +# @ag-ui/openai-agents + +TypeScript client for connecting an AG-UI front end to a server built with +the companion Python integration +([`ag-ui-openai-agents`](../python/README.md)), which bridges the +[OpenAI Agents SDK](https://openai.github.io/openai-agents-python/) and the +AG-UI protocol. + +## Status + +`OpenAIAgentsHttpAgent` is currently an empty subclass of `HttpAgent` โ€” it +adds no behavior beyond what `@ag-ui/client`'s `HttpAgent` already provides, +since the Python side exposes no extra endpoints (e.g. no capability +discovery) for a client to call. It is marked `"private": true` and is not +published to npm. + +## Usage + +```typescript +import { OpenAIAgentsHttpAgent } from "@ag-ui/openai-agents"; + +const agent = new OpenAIAgentsHttpAgent({ + url: "http://localhost:8024/agentic_chat/", +}); + +// Use the agent with AG-UI clients +``` + +## Requirements + +- An `ag-ui-openai-agents` Python server running (see `../python/README.md`) +- AG-UI compatible client diff --git a/integrations/openai-agents/typescript/package.json b/integrations/openai-agents/typescript/package.json new file mode 100644 index 0000000000..7b7981f37a --- /dev/null +++ b/integrations/openai-agents/typescript/package.json @@ -0,0 +1,60 @@ +{ + "name": "@ag-ui/openai-agents", + "//": "private: @ag-ui/openai-agents is currently an empty HttpAgent subclass with no added behavior โ€” intentionally NOT published. Mirrors integrations/langroid/typescript's precedent. See src/index.ts.", + "private": true, + "version": "0.0.1", + "description": "AG-UI integration for the OpenAI Agents SDK โ€” thin TypeScript client for the companion Python integration (ag-ui-openai-agents)", + "main": "./dist/index.js", + "module": "./dist/index.mjs", + "types": "./dist/index.d.ts", + "sideEffects": false, + "files": [ + "dist/**", + "README.md" + ], + "keywords": [ + "ag-ui", + "openai", + "openai-agents", + "agents", + "ai" + ], + "scripts": { + "build": "tsdown", + "dev": "tsdown --watch", + "clean": "git clean -fdX --exclude=\"!.env\"", + "typecheck": "tsc --noEmit", + "test": "vitest run", + "test:coverage": "vitest run --coverage", + "test:watch": "vitest", + "link:global": "pnpm link --global", + "unlink:global": "pnpm unlink --global" + }, + "peerDependencies": { + "@ag-ui/core": ">=0.0.57", + "@ag-ui/client": ">=0.0.57", + "rxjs": "7.8.1" + }, + "devDependencies": { + "@ag-ui/core": "workspace:*", + "@ag-ui/client": "workspace:*", + "@types/node": "^20.11.19", + "tsdown": "^0.20.1", + "typescript": "^5.3.3", + "vitest": "^4.0.18" + }, + "repository": { + "type": "git", + "url": "git+https://github.com/ag-ui-protocol/ag-ui.git", + "directory": "integrations/openai-agents/typescript" + }, + "author": "Abdelrahman Abozied ", + "license": "MIT", + "exports": { + ".": { + "require": "./dist/index.js", + "import": "./dist/index.mjs" + }, + "./package.json": "./package.json" + } +} diff --git a/integrations/openai-agents/typescript/src/index.test.ts b/integrations/openai-agents/typescript/src/index.test.ts new file mode 100644 index 0000000000..a9478ab7d1 --- /dev/null +++ b/integrations/openai-agents/typescript/src/index.test.ts @@ -0,0 +1,19 @@ +import { HttpAgent } from "@ag-ui/client"; +import { describe, expect, it } from "vitest"; + +import { OpenAIAgentsHttpAgent } from "./index"; + +describe("OpenAIAgentsHttpAgent", () => { + it("extends HttpAgent", () => { + expect(OpenAIAgentsHttpAgent.prototype).toBeInstanceOf(HttpAgent); + }); + + it("can be created with a URL", () => { + const agent = new OpenAIAgentsHttpAgent({ + url: "http://localhost:8024/agentic_chat/", + }); + + expect(agent).toBeInstanceOf(OpenAIAgentsHttpAgent); + expect(agent).toBeInstanceOf(HttpAgent); + }); +}); diff --git a/integrations/openai-agents/typescript/src/index.ts b/integrations/openai-agents/typescript/src/index.ts new file mode 100644 index 0000000000..01e2af96ac --- /dev/null +++ b/integrations/openai-agents/typescript/src/index.ts @@ -0,0 +1,3 @@ +import { HttpAgent } from "@ag-ui/client"; + +export class OpenAIAgentsHttpAgent extends HttpAgent {} diff --git a/integrations/openai-agents/typescript/tsconfig.json b/integrations/openai-agents/typescript/tsconfig.json new file mode 100644 index 0000000000..d12ec063d5 --- /dev/null +++ b/integrations/openai-agents/typescript/tsconfig.json @@ -0,0 +1,24 @@ +{ + "compilerOptions": { + "target": "es2017", + "module": "esnext", + "lib": ["dom", "dom.iterable", "esnext"], + "declaration": true, + "declarationMap": true, + "sourceMap": true, + "moduleResolution": "node", + "skipLibCheck": true, + "strict": true, + "jsx": "react-jsx", + "esModuleInterop": true, + "resolveJsonModule": true, + "isolatedModules": true, + "baseUrl": ".", + "paths": { + "@/*": ["./src/*"] + }, + "stripInternal": true + }, + "include": ["src"], + "exclude": ["node_modules", "dist"] +} diff --git a/integrations/openai-agents/typescript/tsdown.config.ts b/integrations/openai-agents/typescript/tsdown.config.ts new file mode 100644 index 0000000000..6f3030ec48 --- /dev/null +++ b/integrations/openai-agents/typescript/tsdown.config.ts @@ -0,0 +1,12 @@ +import { defineConfig } from "tsdown"; + +export default defineConfig({ + entry: ["src/index.ts"], + format: ["cjs", "esm"], + dts: true, + exports: true, + fixedExtension: false, + sourcemap: true, + clean: true, + minify: true, +}); diff --git a/integrations/openai-agents/typescript/vitest.config.ts b/integrations/openai-agents/typescript/vitest.config.ts new file mode 100644 index 0000000000..c85b19a3f1 --- /dev/null +++ b/integrations/openai-agents/typescript/vitest.config.ts @@ -0,0 +1,10 @@ +import { defineConfig } from "vitest/config"; + +export default defineConfig({ + test: { + globals: true, + environment: "node", + include: ["**/*.test.ts"], + passWithNoTests: true, + }, +}); diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml index 10c1172d4f..123c3bd034 100644 --- a/pnpm-lock.yaml +++ b/pnpm-lock.yaml @@ -1013,6 +1013,31 @@ importers: specifier: ^5.9.3 version: 5.9.3 + integrations/openai-agents/typescript: + dependencies: + rxjs: + specifier: 7.8.1 + version: 7.8.1 + devDependencies: + '@ag-ui/client': + specifier: workspace:* + version: link:../../../sdks/typescript/packages/client + '@ag-ui/core': + specifier: workspace:* + version: link:../../../sdks/typescript/packages/core + '@types/node': + specifier: ^20.11.19 + version: 20.19.21 + tsdown: + specifier: ^0.20.1 + version: 0.20.1(publint@0.3.17)(typescript@5.9.3) + typescript: + specifier: ^5.3.3 + version: 5.9.3 + vitest: + specifier: ^4.0.18 + version: 4.0.18(@opentelemetry/api@1.9.0)(@types/node@20.19.21)(jiti@2.6.1)(lightningcss@1.30.1)(tsx@4.20.6)(yaml@2.8.4) + integrations/pydantic-ai/typescript: dependencies: rxjs: