diff --git a/README.md b/README.md index e05d542..cb8d29f 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,1105 @@ # Kagenti Workload Harness -This repository contains workload harnesses for driving test and evaluation traffic to Kagenti agents. +Workload harnesses for driving test and evaluation traffic to Kagenti agents. The current implementation is the **Exgentic A2A Runner** (`exgentic_a2a_runner/`) — a standalone Python runner that integrates Exgentic benchmarks with Kagenti agents using the A2A (Agent-to-Agent) protocol. This harness implements the execution model defined in [GitHub Issue #963](https://github.com/kagenti/kagenti/issues/963). -## Current Scope +The harness exists to robustly exercise agents and validate that the Kagenti platform is reliable, scalable, and observable. -- **AppWorld harness** (`appworld_a2a_runner/`) — the first supported workload driver. -- **Exgentic harness** (`exgentic_a2a_runner/`) — drives Exgentic benchmarks (tau2, gsm8k, appworld) against Kagenti agents via A2A protocol. -- Designed to expand to additional workload drivers over time. +## Features -## Purpose +- **Exgentic MCP Integration**: Communicates with Exgentic MCP server for benchmark tasks +- **Parallel session processing**: Configurable concurrency for efficient benchmark execution and stress testing +- **A2A protocol support**: Communicates with remote agents using the A2A protocol via JSON-RPC over HTTP +- **OpenTelemetry instrumentation**: Comprehensive traces, metrics, and logs +- **Strict failure handling**: Any error or timeout marks the session as failed +- **Configurable via environment variables**: Easy deployment and configuration +- **Composable AuthBridge plugin pipeline**: Per-agent selection of inbound/outbound plugins (`jwt-validation`, `token-exchange`, `a2a-parser`, `ibac`, …) with `enforce` / `observe` / `off` policies — see [AuthBridge Plugin Pipeline](#authbridge-plugin-pipeline). -The workload harness exists to robustly exercise agents and validate that the Kagenti platform is: +## Getting Started -- Reliable -- Scalable -- Observable +The minimal end-to-end path: stand up a Kagenti cluster, build the agent + MCP images, then deploy and evaluate. Each block here is self-contained — copy the commands you need. + +```bash +# 1. Stand up the Kagenti cluster (one-time) +git clone git@github.com:kagenti/kagenti.git +cd kagenti +env CONTAINER_ENGINE=podman scripts/kind/setup-kagenti.sh --with-all --preload-images +cd .. + +# 2. Install the runner +git clone git@github.com:kagenti/workload-harness.git +cd workload-harness/exgentic_a2a_runner +uv sync --python 3.12 +source .venv/bin/activate +cp example.env .env # edit as needed (OPENAI_API_KEY, IBAC tunables, etc.) + +# 3a. Plain run — no AuthBridge sidecar +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling + +# 3b. Run with auth + token exchange only +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling \ + --plugin-preset auth-only + +# 3c. Run with IBAC enforcing intent-based access control +export IBAC_JUDGE_ENDPOINT=http://host.docker.internal:11434 +export IBAC_JUDGE_MODEL=llama3.2:3b +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling \ + --plugin-preset ibac-only + +# 3d. Canary IBAC in observe mode (telemetry only, no blocking) +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling \ + --plugin-preset ibac-only --plugin ibac:observe +``` + +The deploy + evaluate steps can also be run separately — see [Usage](#usage). For full plugin pipeline mechanics, see [AuthBridge Plugin Pipeline](#authbridge-plugin-pipeline). + +## Architecture + +The runner follows this execution model for each benchmark session: + +1. **Create Session**: `(session_id, task) = mcp_server.create_session()` +2. **Invoke Agent**: `agent.invoke_agent("{task}")` . Pass session_id as meta_data. +3. **Evaluate Session**: `success = mcp_server.evaluate_session(session_id)` +4. **Close Session**: `mcp_server.close_session(session_id)` +5. **Record Statistics**: Track completion time, success rate, compute costs, tokens. + +## Benchmarks + +The runner currently drives three benchmarks via the Exgentic MCP server. Each is a separate MCP image (`./build.sh ` from `agent-examples/mcp/exgentic_benchmarks`) and is selected at deploy time with `--benchmark `. + +| Benchmark | What it tests | Tool surface | Notes | +|-----------|---------------|--------------|-------| +| `gsm8k` | Grade-school math word problems — single-turn arithmetic reasoning. | Minimal — primarily LLM reasoning, light tool use. | Cheap and fast; good smoke test. The deploy script sets `EXGENTIC_SET_BENCHMARK_RUNNER=direct` for this benchmark. | +| `tau2` | Multi-turn customer-support conversations against a simulated user. Measures whether the agent can complete realistic task flows over several turns. | Domain tools (retail, airline, telecom) plus a user-simulator LLM. | Deploy passes `EXGENTIC_SET_BENCHMARK_USER_SIMULATOR_MODEL` so the simulator runs on the same model as the agent. The IBAC plugin also lands its canonical attack-shape tests against tau-style multi-turn traffic — see [`ibac-benchmarking.md`](exgentic_a2a_runner/ibac-benchmarking.md). | +| `appworld` | Long-horizon, tool-heavy tasks across a simulated app ecosystem (calendar, email, contacts, etc.). Stresses tool selection and planning. | Very wide — hundreds of tools across the simulated apps. | OpenAI models can't handle this tool surface without shortlisting; use `gemini-2.5-pro` or another model with strong tool selection. | + +### Picking a model + +The model name passed via `--model` (or `LLM_MODEL` / `EXGENTIC_SET_AGENT_MODEL`) is consumed by [LiteLLM](https://docs.litellm.ai/) on the agent side, so it follows LiteLLM's `/` routing convention. The default is `Azure/gpt-4.1`. + +**OpenAI-compatible backends** (vLLM, Ollama, llama.cpp, LM Studio, custom proxies, etc.) — prefix the model name with `openai/` to force LiteLLM down its OpenAI-compatible route, and point `OPENAI_API_BASE` at your endpoint: + +```bash +# Custom Azure deployment fronted by an OpenAI-compatible proxy +./deploy-agent.sh --benchmark tau2 --agent tool_calling \ + --model openai/Azure/gpt-4o-mini + +# Local model served via vLLM/Ollama +./deploy-agent.sh --benchmark gsm8k --agent tool_calling \ + --model openai/llama3.1-70b-instruct +``` + +For `appworld`, use a model with strong tool-selection — e.g. `gemini-2.5-pro` — rather than an OpenAI-route model. + +## Installation + +> **⏱️ Estimated Setup Time:** ~15 minutes (excluding container image pulls) + +### Prerequisites + +- Python 3.11 or 3.12 (Python 3.13+ is **not supported** due to dependency compatibility) + - **Note:** The `uv` package manager will automatically use Python 3.12 when you run `uv sync --python 3.12`, regardless of your system Python version +- [uv](https://docs.astral.sh/uv/) package manager +- kubectl v0.6.0 (tested on v0.6.0-rc.2) +- Kagenti cluster running with: + - Kagenti backend in `kagenti-system` namespace + - Keycloak in `keycloak` namespace + - `team1` namespace for deployments + +> **AuthBridge sidecar:** the deploy scripts can attach an AuthBridge +> sidecar with a composable plugin pipeline (auth, token exchange, +> intent-based access control, …) to each agent. See +> [AuthBridge Plugin Pipeline](#authbridge-plugin-pipeline) for the +> full surface; the sidecar is only injected when you pass a plugin +> selector. + +### Install from source + +#### Deploy a kagenti cluster + +```bash +git clone git@github.com:kagenti/kagenti.git +cd kagenti + +env CONTAINER_ENGINE=podman scripts/kind/setup-kagenti.sh --with-all --preload-images + +``` + + +#### Deploy agent and MCP server per benchmark + +```bash +git clone git@github.com:yoavkatz/workload-harness.git +cd workload-harness +git checkout feature/exgentic-a2a-runner +cd exgentic_a2a_runner +uv sync --python 3.12 +source .venv/bin/activate + +# Deploy and configure MCP server using Kagenti Tool API +# This script now combines deployment and configuration in one step +./deploy-benchmark.sh --benchmark tau2 + +# Deploy and configure agent using Kagenti Agent API +# This script now combines deployment and configuration in one step +./deploy-agent.sh --benchmark tau2 --agent tool_calling +``` + +**Note:** All deployment scripts now use named parameters: + +**Benchmark Deployment:** +```bash +# Basic deployment with defaults (model: Azure/gpt-4.1, keycloak: admin/admin) +./deploy-benchmark.sh --benchmark tau2 + +# Deploy with custom model +./deploy-benchmark.sh --benchmark tau2 --model Azure/gpt-4o-mini + +# Deploy with custom Keycloak credentials +./deploy-benchmark.sh --benchmark tau2 --model Azure/gpt-4o-mini --keycloak-user admin --keycloak-pass admin + +# Show help +./deploy-benchmark.sh --help +``` + +**Agent Deployment:** +```bash +# Basic deployment with defaults (model: Azure/gpt-4.1, keycloak: admin/admin) +./deploy-agent.sh --benchmark tau2 --agent tool_calling + +# Deploy with custom model +./deploy-agent.sh --benchmark tau2 --agent tool_calling --model Azure/gpt-4o-mini + +# Deploy with custom Keycloak credentials +./deploy-agent.sh --benchmark tau2 --agent tool_calling --model Azure/gpt-4o-mini --keycloak-user admin --keycloak-pass admin + +# Show help +./deploy-agent.sh --help +``` + +**Agent Naming:** Underscores in agent names are automatically converted to hyphens for Kubernetes compatibility (e.g., `tool_calling` becomes `tool-calling`). + +**Important:** Both deployment scripts now combine deployment and configuration steps: + +**`deploy-benchmark.sh`** will: +1. Deploy the MCP server to the Kagenti cluster +2. Automatically configure secrets before deployment: + - Updates `openai-secret` with OPENAI_API_KEY (if set in environment) + - Creates/updates `hf-secret` with HF_TOKEN (uses dummy token if not set) +3. Configure environment variables (OPENAI_API_BASE, EXGENTIC_SET_BENCHMARK_RUNNER for gsm8k) +4. Set memory limits and model settings +5. Wait for the deployment to be ready + +**`deploy-agent.sh`** will: +1. Deploy the agent to the Kagenti cluster +2. Automatically configure environment variables (OPENAI_API_BASE, OPENAI_API_KEY, LLM_MODEL) +3. Set model settings (LLM_MODEL, EXGENTIC_SET_AGENT_MODEL) +4. Wait for the deployment to be ready + +**Environment Variables for Deployment:** +- `OPENAI_API_KEY`: OpenAI API key (optional, updates openai-secret if set) +- `HF_TOKEN`: HuggingFace token (optional, creates hf-secret with dummy token if not set) +- `OPENAI_API_BASE`: OpenAI API base URL (optional, added to deployment env vars) + +For benchmark fit and model-name conventions (including the `openai/` prefix for OpenAI-compatible backends), see [Benchmarks](#benchmarks). + +## MCP Gateway Support + +The runner can optionally route MCP traffic through an [MCP Gateway](https://github.com/kuadrant/mcp-gateway) instead of connecting directly to the benchmark MCP server. When enabled, the gateway acts as a single entry point that multiplexes access to registered MCP servers and namespaces their tools with a configurable prefix. + +### How It Works + +1. **Benchmark deployment** (`deploy-benchmark.sh --use-mcp-gateway`) creates an `HTTPRoute` and an `MCPServerRegistration` CR that registers the MCP server with the gateway. +2. **Agent deployment** (`deploy-agent.sh --use-mcp-gateway`) points the agent's `MCP_URL` at the gateway service (`mcp-gateway-istio.gateway-system.svc.cluster.local:8080`) instead of the benchmark's MCP service directly. +3. **Evaluation** (`evaluate-benchmark.sh`) port-forwards the gateway service and sets `EXGENTIC_MCP_TOOL_PREFIX` so the runner prepends the gateway-assigned prefix to every tool call (e.g. `list_tasks` becomes `exgentic_list_tasks`). + +### Deploying with the MCP Gateway + +Pass `--use-mcp-gateway` to any deployment or evaluation script: + +```bash +# Individual scripts +./deploy-benchmark.sh --benchmark tau2 --use-mcp-gateway +./deploy-agent.sh --benchmark tau2 --agent tool_calling --use-mcp-gateway + +# All-in-one +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling --use-mcp-gateway +``` + +You can also set the flag in your `.env` file so it applies by default: + +```bash +USE_MCP_GATEWAY=true +``` + +### Tool Prefix + +The MCP Gateway exposes tools under a namespace prefix (default `exgentic_`). The runner reads `EXGENTIC_MCP_TOOL_PREFIX` and prepends it to every MCP tool call. When using the gateway via `evaluate-benchmark.sh`, this variable is set automatically. + +## AuthBridge Plugin Pipeline + +The deploy scripts can attach an **AuthBridge sidecar** to each +deployed agent. AuthBridge is a forward proxy whose behavior is +defined by a composable pipeline of named plugins — `jwt-validation`, +`token-exchange`, `token-broker`, `a2a-parser`, `mcp-parser`, +`inference-parser`, `ibac` — each independently togglable and each +running under an `on_error` policy of `enforce`, `observe`, or `off`. + +This replaces the older opaque `--authbridge` / `--ibac` toggles. The +full design lives in [`AUTHBRIDGE_PIPELINE_SPEC.md`](exgentic_a2a_runner/AUTHBRIDGE_PIPELINE_SPEC.md). + +### Architectural implications + +- **The sidecar is opt-in per deploy.** With no plugin selectors, the + operator does not inject a sidecar at all and the agent runs as + before. Pass any selector (`--plugin-preset`, `--plugin`, + `--no-plugin`, `--plugin-config-file`) and the sidecar is injected + with the resolved pipeline. +- **The pipeline mediates every request the agent sends and receives.** + Inbound plugins run on traffic to the agent (auth validation, A2A + parsing); outbound plugins run on traffic the agent makes to tools + and LLMs (token exchange, MCP/inference parsing, IBAC judging). + Adding a plugin adds a hop on that path — measure latency + accordingly. +- **Plugins share a `Session` object.** Inbound plugins populate fields + (e.g. `a2a-parser` extracts the user's intent into `Session.Intents`) + that outbound plugins read (e.g. `ibac` compares each outbound action + against that intent). Hard runtime dependencies are encoded in the + framework — IBAC fails closed if `a2a-parser` didn't run. +- **The operator's base config enables every plugin by default.** To + disable a plugin, the deploy overlay must explicitly emit + `on_error: off` for it. The script handles this — any plugin not in + your resolved selection is turned off in the overlay, not omitted. +- **Selector resolution is last-write-wins.** A preset seeds the set; + subsequent `--plugin` / `--no-plugin` flags apply in order. This + makes "preset minus one plugin" or "preset, but canary IBAC" easy + to express on the command line. +- **Configuration is delivered via overlay, hot-reloaded.** No operator + changes are required to flip the pipeline shape — the script writes + a merged ConfigMap and AuthBridge picks it up. + +### Flags + +These flags are accepted by `deploy-agent.sh` and forwarded by +`deploy-and-evaluate.sh`: + +| Flag | Description | +|------|-------------| +| `--plugin-preset NAME` | Named bundle. Available: `auth-only`, `ibac-only`, `full`. | +| `--plugin NAME[:POLICY]` | Enable plugin with policy ∈ {`enforce`(default), `observe`, `off`}. Repeatable. | +| `--no-plugin NAME` | Shorthand for `--plugin NAME:off`. Repeatable. | +| `--plugin-config-file PATH` | Flat-map YAML overlay merged after selectors. | + +### Presets + +| Preset | Inbound | Outbound | +|--------|---------|----------| +| `auth-only` | `jwt-validation` | `token-exchange` | +| `ibac-only` | `a2a-parser` | `inference-parser`, `mcp-parser`, `ibac` | +| `full` | `a2a-parser`, `jwt-validation` | `token-exchange`, `inference-parser`, `mcp-parser`, `ibac` | + +### Running with IBAC + +IBAC (Intent-Based Access Control) is an outbound plugin that compares +each agent action against the user's most-recent declared intent and +asks an LLM judge to deny requests that don't align — catching +prompt-injection-driven exfiltration that traditional auth gates miss. +See [`ibac-benchmarking.md`](exgentic_a2a_runner/ibac-benchmarking.md) for the full +benchmarking profile. + +**Prerequisites:** + +- An OpenAI-compatible chat-completion endpoint for the judge (ollama, + OpenAI, vLLM, Azure, etc.). +- The cluster's AuthBridge sidecar image must include the `ibac` + plugin. IBAC landed in `kagenti-extensions` on 2026-05-17 (PR #421); + use sidecar image **`v0.6.0-alpha.7`** or newer. + + > **Caveat — not in the latest stable Kagenti release.** As of this + > writing, the IBAC plugin and the additive plugin-pipeline merge + > behavior the deploy scripts depend on are only available in + > `v0.6.0-alpha.7`, which has not yet been published in a stable + > Kagenti release. Installing from the official `v0.6.0` chart + > release pulls an older alpha that will fail with errors like + > `jwt-validation config: issuer is required` during pipeline + > apply. Until a release containing alpha.7 is cut, install Kagenti + > from `main`: + > + > ```bash + > git clone git@github.com:kagenti/kagenti.git + > cd kagenti # use main, not a release tag + > env CONTAINER_ENGINE=podman scripts/kind/setup-kagenti.sh --with-all --preload-images + > ``` + > + > To verify the sidecar image actually deployed: + > + > ```bash + > kubectl -n kagenti-system get cm kagenti-platform-config \ + > -o jsonpath='{.data.authbridge}' + > ``` + > + > Expect `ghcr.io/kagenti/kagenti-extensions/authbridge:v0.6.0-alpha.7` + > or newer. + +**Configure the judge** in your `.env` (consumed by the IBAC plugin +fragment via envsubst when `ibac` is in the active set): + +```bash +# Judge LLM base URL — OpenAI-compatible (POST /v1/chat/completions) +IBAC_JUDGE_ENDPOINT=http://host.docker.internal:11434 +# Judge model id served by that endpoint +IBAC_JUDGE_MODEL=llama3.2:3b +# Per-judge-call timeout in milliseconds +IBAC_TIMEOUT_MS=15000 +# Hostname of the agent's own LLM endpoint (auto-derived from +# OPENAI_API_BASE if unset). Added to the IBAC bypass list so the +# agent's own reasoning calls aren't recursively judged. +# IBAC_AGENT_LLM_HOST=host.docker.internal +``` + +**Deploy with IBAC enforcing** -- *UNTESTED* --(full pipeline — auth + parsers + IBAC): + +```bash +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling \ + --plugin-preset full +``` + + +**IBAC without inbound auth** — for environments where an upstream +gateway already terminates auth but you still want intent-based +blocking on outbound calls: + +```bash +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling \ + --plugin-preset ibac-only +``` + +When IBAC blocks a request, the agent sees a `403 ibac.blocked` from +the proxy and the `ibac.evaluate` span on that outbound call carries +`verdict=deny`, `reason=blocked`, and the (truncated) intent and +action description. See `ibac-benchmarking.md` for the full set of +metrics IBAC emits. + +### Other compositions + +```bash +# Auth + token exchange only (no IBAC, no parsers). +./deploy-agent.sh --benchmark tau2 --agent tool_calling \ + --plugin-preset auth-only + +# Token-broker instead of token-exchange. +./deploy-agent.sh --benchmark tau2 --agent tool_calling \ + --plugin-preset full \ + --no-plugin token-exchange \ + --plugin token-broker + +# Custom set without a preset. +./deploy-agent.sh --benchmark tau2 --agent tool_calling \ + --plugin jwt-validation --plugin token-exchange --plugin ibac + +# No AuthBridge sidecar at all (omit all plugin flags). +./deploy-agent.sh --benchmark tau2 --agent tool_calling +``` + +### `--plugin-config-file` format + +A flat YAML map keyed by plugin name; values are deep-merged into each +plugin's `config:` block on top of the fragment defaults: + +```yaml +ibac: + judge_model: "llama3.2:8b" + timeout_ms: 30000 +token-exchange: + default_policy: exchange +jwt-validation: + bypass_paths: + - /healthz + - /metrics +``` + +Unknown plugin names in the file are ignored with a WARN to stderr. + +### Sidecar image compatibility + +Every plugin you select must be compiled into the running sidecar +binary. The merge validates the YAML shape, but the sidecar will fail +at Configure with `unknown plugin ""` after reload if a plugin +isn't registered: + +``` +reloader: reload failed error="build: outbound: unknown plugin \"\"" +``` + +The image tag is pinned in `kagenti/charts/kagenti/values.yaml`. To +verify what's running: + +```bash +kubectl -n team1 get pod -l app.kubernetes.io/name= \ + -o jsonpath='{range .items[0].spec.containers[?(@.name=="authbridge")]}{.image}{"\n"}{end}' +``` + +> **Compatibility note.** Newer chart versions tie sidecar image +> versions to operator versions (per-plugin config support, +> jwt-validation field shape). When bumping the sidecar image past +> `v0.5.0-rc.3`, confirm your kagenti-operator is recent enough — +> older operators may emit ConfigMaps the newer sidecar can't parse. + +### Troubleshooting + +- **`unknown plugin ""`** at reload: the sidecar binary doesn't + have that plugin compiled in. Bump the image tag. +- **Mutex error: `token-exchange` and `token-broker`**: both claim + `ClaimAuthorizationHeader`; the script rejects this before any + kubectl call. Disable one with `--no-plugin`. +- **`Reads ... no earlier plugin writes it`**: parser ordering issue; + shouldn't happen with the canonical-position table, but possible if a + malformed `--plugin-config-file` introduces an unknown plugin. See + [`framework-architecture.md` §6](https://github.com/kagenti/kagenti-extensions/blob/main/authbridge/docs/framework-architecture.md) + for the underlying rules. +- **`ibac.no_intent`** in IBAC telemetry: the inbound chain is + misconfigured — `a2a-parser` didn't run, so `Session.Intents` is + empty and IBAC fails closed. Confirm `a2a-parser` is in the active + inbound set (it is for the `ibac-only` and `full` presets). +- **Unknown plugin / preset name**: script-side error; valid names are + listed in the spec §3.1, valid presets in §3.2. + +## Configuration + +### Before Running Evaluations + +**Required:** Create and configure your environment file: + +```bash +cp example.env .env +``` + +Then edit the .env file as needed. + +### Main Configuration + +| Environment Variable | Default | Description | +| --- | --- | --- | +| `MAX_TASKS` | `(none)` | Maximum number of sessions to process. Useful for testing with a subset. | +| `MAX_PARALLEL_SESSIONS` | `1` | Number of sessions to run concurrently. Set higher for parallel execution. | +| `ABORT_ON_FAILURE` | `false` | Stop processing after the first failed session. | + +### Debug Configuration + +| Environment Variable | Default | Description | +| --- | --- | --- | +| `LOG_LEVEL` | `INFO` | Log level for the runner. Set to `DEBUG` for verbose logging with detailed debug information. Options: `DEBUG`, `INFO`, `WARNING`, `ERROR`. | +| `LOG_PROMPT` | `0` | Log prompt payloads for debugging (1 to enable). | +| `LOG_RESPONSE` | `0` | Log response payloads for debugging (1 to enable). | + +### Tracing Configuration (OpenTelemetry) + +| Environment Variable | Default | Description | +| --- | --- | --- | +| `OTEL_EXPORTER_OTLP_ENDPOINT` | `(none)` | OTLP collector endpoint (for this runner, use gRPC such as `http://localhost:4317`). If not set, no traces are exported. | +| `OTEL_SERVICE_NAME` | `exgentic-a2a-runner` | Service name in traces. | +| `OTEL_RESOURCE_ATTRIBUTES` | `(none)` | Additional resource attributes (format: `key1=val1,key2=val2`). | +| `OTEL_INSTRUMENT_REQUESTS` | `true` | Auto-instrument HTTP requests. | +| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | OTLP protocol. The current exporter implementation in [`OTELInstrumentation._initialize_tracing()`](exgentic_a2a_runner/exgentic_a2a_runner/otel.py:80) and [`OTELInstrumentation._initialize_metrics()`](exgentic_a2a_runner/exgentic_a2a_runner/otel.py:114) uses OTLP gRPC. | +| `OTEL_EXPORTER_OTLP_INSECURE` | `true` | Use insecure OTLP connection. | + +### MCP Gateway Configuration + +| Environment Variable | Default | Description | +| --- | --- | --- | +| `USE_MCP_GATEWAY` | `false` | Route MCP traffic through the MCP Gateway instead of connecting directly to the MCP server. | +| `EXGENTIC_MCP_TOOL_PREFIX` | `(empty)` | Prefix prepended to MCP tool names. Set to match the gateway's `MCPServerRegistration.spec.toolPrefix` (e.g. `exgentic_`). | + +### Advanced Configuration + +| Environment Variable | Default | Description | +| --- | --- | --- | +| `EXGENTIC_MCP_TIMEOUT_SECONDS` | `60` | Timeout for MCP operations. | +| `A2A_TIMEOUT_SECONDS` | `300` | Timeout for A2A requests. | +| `A2A_AUTH_TOKEN` | `(none)` | Bearer token for A2A authentication. | +| `A2A_VERIFY_TLS` | `true` | Verify TLS certificates for HTTPS. | +| `A2A_ENDPOINT_PATH` | `/` | Endpoint path for A2A requests. | + +## Usage + +### All-in-One: Deploy and Evaluate + +The `deploy-and-evaluate.sh` script provides a convenient way to deploy both the benchmark and agent, then run the evaluation in a single command: + +```bash +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling +``` + +This script will: +1. Deploy the benchmark MCP server +2. Deploy the agent +3. Run the evaluation + +MLflow tracing via the OTEL Collector is **enabled by default**. Pass `--disable-mlflow` to skip it. + +**Options:** +```bash +# Basic usage with defaults (MLflow tracing enabled) +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling + +# Route MCP traffic through the MCP Gateway +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling --use-mcp-gateway + +# With custom model +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling --model Azure/gpt-4o-mini + +# With custom Keycloak credentials +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling --model Azure/gpt-4o-mini --keycloak-user admin --keycloak-pass admin + +# Dry run mode - print commands without executing them +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling --dry + +# Show help +./deploy-and-evaluate.sh --help +``` + +#### Dry Run Mode + +The `--dry` flag enables dry-run mode, which prints all commands that would be executed without actually running them. This is useful for: +- Verifying command syntax before execution +- Debugging deployment issues +- Understanding what the script will do +- Documentation and training purposes + +```bash +# See what commands would be executed +./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling --dry + +# Dry run with all options +./deploy-and-evaluate.sh --benchmark gsm8k --agent tool_calling \ + --model Azure/gpt-4o-mini --mlflow --use-mcp-gateway --dry +``` + +**Example output:** +``` +======================================== +Deploy and Evaluate Exgentic Benchmark +======================================== +Benchmark: tau2 +Agent: tool_calling +Model: Azure/gpt-4.1 +Keycloak User: admin +MLflow tracing: false +MCP Gateway: false +Dry run: true + +======================================== +Step 1/3: Deploying Benchmark +======================================== +[DRY RUN] Would execute: +./deploy-benchmark.sh --benchmark "tau2" --model "Azure/gpt-4.1" --keycloak-user "admin" --keycloak-pass "admin" + +======================================== +Step 2/3: Deploying Agent +======================================== +[DRY RUN] Would execute: +./deploy-agent.sh --benchmark "tau2" --agent "tool_calling" --model "Azure/gpt-4.1" --keycloak-user "admin" --keycloak-pass "admin" + +======================================== +Step 3/3: Running Evaluation +======================================== +[DRY RUN] Would execute: +./evaluate-benchmark.sh --benchmark "tau2" --agent "tool_calling" --experiment "default" + +======================================== +✓ Dry run completed - no commands executed +======================================== +``` + +### Running Benchmarks + +The `evaluate-benchmark.sh` script automatically: +- Uses HTTP routes to reach services (no port-forwarding for MCP/agent) +- Port-forwards the OTEL Collector (traces → MLflow) on dev laptops — skipped in-cluster +- Waits for services to be ready via HTTP health checks +- Tests connectivity to services +- Runs the benchmark evaluation +- Propagates the current OpenTelemetry trace context into outbound A2A HTTP requests so the agent can continue the same distributed trace when it supports W3C trace headers +- Cleans up port forwards on exit + +```bash +./evaluate-benchmark.sh --benchmark tau2 --agent tool_calling +./evaluate-benchmark.sh --benchmark gsm8k --agent tool_calling +``` + +## Output + +### Console Summary + +At the end of each run, a summary is printed: + +``` +============================================================ +RUN SUMMARY +============================================================ +Sessions Attempted: 100 +Sessions Succeeded: 95 +Sessions Failed: 5 +Evaluation Success: 92.6% +Total Wall Time: 1234.56s +Average Latency: 12345.67ms +P50 Latency: 10000.00ms +P95 Latency: 20000.00ms +============================================================ +``` + +### OpenTelemetry Data + +The runner emits comprehensive telemetry: + +#### Traces + +Each session creates a span (`exgentic_a2a.session`) with: + +**Attributes:** +- `exgentic.session_id`: Session identifier +- `exgentic.mcp_server_url`: MCP server URL +- `exgentic.evaluation_result`: Whether evaluation was successful +- `a2a.base_url`: A2A endpoint URL +- `a2a.timeout_seconds`: Timeout value +- `prompt.chars`: Prompt size in characters +- `response.chars`: Response size in characters +- `session.status`: `success` or `failed` +- `a2a.duration_ms`: End-to-end A2A operation latency in milliseconds + +**Child spans:** +- `exgentic_a2a.prompt.build`: Prompt construction +- `exgentic_a2a.a2a.send_prompt`: End-to-end A2A `send_prompt` call +- `exgentic_a2a.mcp.evaluate_session`: Session evaluation +- `exgentic_a2a.mcp.close_session`: Session cleanup + +**Auto-instrumented HTTP spans:** +- Outbound `requests` spans for agent-card discovery, `message/send`, and `tasks/get` calls + +**Events:** +- `prompt_built`: When prompt is constructed +- `session_failed`: When session fails (includes error details) + +#### Metrics + +**Counters:** +- `exgentic_a2a_sessions_total{status=success|failed}`: Total sessions processed +- `exgentic_a2a_errors_total{error_type=...}`: Total errors by type + +**Histograms:** +- `exgentic_a2a_session_latency_ms`: End-to-end session latency +- `exgentic_a2a_evaluation_latency_ms`: Evaluation operation latency +- `exgentic_a2a_session_creation_latency_ms`: Session creation latency +- `exgentic_a2a_a2a_latency_ms`: A2A request latency +- `exgentic_a2a_prompt_size_chars`: Prompt size distribution +- `exgentic_a2a_response_size_chars`: Response size distribution + +**Gauge:** +- `exgentic_a2a_inflight_sessions`: Current sessions in flight (0 or 1) + +## Execution Flow + +``` +┌─────────────────────────────────────────────────────────┐ +│ For Each Session │ +├─────────────────────────────────────────────────────────┤ +│ │ +│ 1. Create Session │ +│ └─> MCP: create_session() → (session_id, task) │ +│ │ +│ 2. Build Prompt │ +│ └─> Include session_id in instructions │ +│ │ +│ 3. Invoke Agent │ +│ └─> A2A: send_prompt(prompt) → response │ +│ │ +│ 4. Evaluate Session │ +│ └─> MCP: evaluate_session(session_id) → success │ +│ │ +│ 5. Close Session │ +│ └─> MCP: close_session(session_id) │ +│ │ +│ 6. Record Statistics │ +│ └─> Track time, success, evaluation result │ +│ │ +└─────────────────────────────────────────────────────────┘ +``` + +## OpenTelemetry and Observability + +### Using MLflow in the kind Cluster + +The Kagenti cluster exposes an MLflow service in the `kagenti-system` namespace. An OTEL Collector forwards traces to MLflow's `/v1/traces` endpoint with OAuth2 authentication. + +#### 1. Send runner telemetry to MLflow + +MLflow tracing is **enabled by default**. The script automatically port-forwards the OTEL Collector on a developer laptop and configures the required environment variables. To disable it, pass `--disable-mlflow`: + +```bash +# Default — MLflow tracing enabled +env MAX_TASKS=1 MAX_PARALLEL_SESSIONS=1 ./evaluate-benchmark.sh --benchmark gsm8k --agent tool_calling + +# Disable MLflow tracing +env MAX_TASKS=1 MAX_PARALLEL_SESSIONS=1 ./evaluate-benchmark.sh --benchmark gsm8k --agent tool_calling --disable-mlflow +``` + +#### 2. Open the MLflow UI + +Open http://mlflow.localtest.me:8080 in your browser to view traces and experiments. + +### Analyzing Traces with analyze-run.sh + +The [`analyze-run.sh`](exgentic_a2a_runner/analyze-run.sh) script provides comprehensive trace analysis by downloading Agent.Session traces from MLflow and generating detailed performance reports. + +#### Features + +- **Automatic MLflow connectivity**: Connects to MLflow REST API with OAuth2 authentication and optional auto port-forwarding +- **Trace filtering**: Downloads Agent.Session root spans and all child spans +- **Experiment filtering**: Filter or compare traces by experiment name +- **Performance metrics**: Calculates timing statistics (avg, p50, p95, min, max) for: + - Session creation time + - Agent call time (end-to-end agent execution) + - Evaluation time + - LLM call time and token usage + - Tool call time +- **Grouping**: Groups traces by agent, benchmark, model, and parallel session count +- **Detailed reports**: Generates both summary statistics and individual trace details + +#### Usage + +```bash +# Basic usage (assumes MLflow is accessible at http://mlflow.localtest.me:8080) +./analyze-run.sh + +# With custom MLflow URL and limit +./analyze-run.sh --url http://mlflow.localtest.me:8080 --limit 200 + +# Auto port-forward from kind cluster if MLflow is not accessible locally +./analyze-run.sh --forward --limit 50 + +# Filter by experiment name +./analyze-run.sh --experiment baseline + +# Compare two experiments +./analyze-run.sh --compare baseline,test1 +``` + +#### Options + +| Option | Description | Default | +|--------|-------------|---------| +| `-u, --url URL` | MLflow REST API base URL | `http://mlflow.localtest.me:8080` | +| `-l, --limit NUM` | Maximum number of traces to download | `100` | +| `-e, --experiment NAME` | Filter traces by experiment name attribute | (none) | +| `-c, --compare EXP1,EXP2` | Compare two experiments (comma-separated) | (none) | +| `--experiment-id ID` | MLflow experiment ID to query | `0` | +| `-f, --forward` | Auto port-forward MLflow from kind cluster if not accessible | `false` | +| `-h, --help` | Show help message | - | + +#### How It Works + +1. **Connectivity Test**: Attempts to connect to MLflow REST API health endpoint +2. **Auto Port-Forward** (if `--forward` is used): Sets up port-forwarding from kind cluster if MLflow is not accessible +3. **OAuth2 Authentication**: Obtains a bearer token from the cluster's `mlflow-oauth-secret` +4. **Trace Download**: Queries MLflow's trace API for the specified experiment, with pagination +5. **Format Transformation**: Converts MLflow trace format to the analysis input format via [`download_mlflow_traces.py`](exgentic_a2a_runner/download_mlflow_traces.py) +6. **Analysis**: Pipes trace data to [`analyze_traces.py`](exgentic_a2a_runner/analyze_traces.py) for detailed analysis + +#### Report Output + +The script generates two main sections: + +**1. Summary Statistics by Configuration** + +Groups traces by (agent, benchmark, model, parallel sessions) and shows: +- Count of traces +- Average, P50, P95, Min, Max for: + - Session creation time + - Agent call time + - Evaluation time + - LLM call time (with token counts) + - Tool call time +- Time distribution percentages (LLM%, Tool%, Other%) + +**2. Individual Trace Details** + +Lists each trace with: +- Trace ID +- Agent, Benchmark, Model, Parallel sessions +- Session creation time +- Agent call time (with LLM% and Tool% breakdown) +- Evaluation time +- LLM tokens (input/output) +- Tool call count and time + +#### Example Output + +``` +=== MLflow Trace Analysis === +MLflow URL: http://mlflow.localtest.me:8080 +Experiment ID: 0 +Limit: 100 + +✓ Connected to MLflow +✓ OAuth token obtained + +Found 45 Agent.Session traces +Downloading traces... +Downloaded 45 traces + +=== Trace Analysis Report === + +Summary Statistics by Configuration: +┌─────────────┬───────────┬─────────┬──────────┬───────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐ +│ Agent │ Benchmark │ Model │ Parallel │ Count │ Avg Create │ Avg Agent │ Avg Eval │ Avg LLM │ Avg Tool │ +│ │ │ │ │ │ (ms) │ Call (ms) │ (ms) │ (ms) │ (ms) │ +├─────────────┼───────────┼─────────┼──────────┼───────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ +│ tool-calling│ gsm8k │ gpt-4o │ 1 │ 45 │ 125.3 │ 8234.5 │ 45.2 │ 6543.2 │ 1234.5 │ +│ │ │ │ │ │ │ │ │ (79.5%) │ (15.0%) │ +└─────────────┴───────────┴─────────┴──────────┴───────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘ +``` + +#### Prerequisites + +- **jq**: JSON processor for parsing API responses + ```bash + # macOS + brew install jq + + # Ubuntu/Debian + apt-get install jq + ``` +- **Python 3**: For running the download and analysis scripts +- **MLflow**: Running and accessible (either locally or in kind cluster) +- **kubectl**: For port-forwarding and OAuth token retrieval + +#### Troubleshooting + +**Connection refused:** +- Ensure MLflow is running: `kubectl get pods -n kagenti-system -l app=mlflow` +- Use `--forward` flag to auto port-forward from kind cluster +- Manually port-forward: `kubectl port-forward -n kagenti-system svc/mlflow 8080:5000` + +**No traces found:** +- Verify traces exist in MLflow UI: http://mlflow.localtest.me:8080 +- Check that Agent.Session spans are being created by the runner +- MLflow tracing is enabled by default; pass `--disable-mlflow` only if you want to skip it + +**OAuth errors:** +- Ensure the `mlflow-oauth-secret` exists in the `kagenti-system` namespace +- Verify the MLflow pod is running (token acquisition executes inside the pod) + +### What Gets Traced + +When OTEL is enabled, you'll see: + +- **Session spans**: Complete session lifecycle with timing +- **MCP operations**: create_session, evaluate_session, close_session +- **A2A requests**: Agent invocations with request/response sizes +- **HTTP calls**: Auto-instrumented outbound requests +- **Errors**: Failed operations with exception details + +## In-Cluster Execution (Kubernetes Job) + +The runner can execute entirely inside the cluster as a Kubernetes Job — no local machine needed after the image is built. This is the recommended path for CI and automated evaluation runs. + +### Overview + +`k8s/job.yaml` is the launch template. It references secrets for credentials and passes benchmark/agent flags as container `args`. The job container uses cluster-internal DNS to reach the Kagenti API, Keycloak, MCP server, and agent — no port-forwarding is required. MLflow tracing switches automatically to HTTP/protobuf when `KUBERNETES_SERVICE_HOST` is set. + +### Step 1 — Build and push the runner image + +```bash +cd exgentic_a2a_runner +docker build -t ghcr.io/exgentic/runner:latest . +docker push ghcr.io/exgentic/runner:latest +``` + +Replace `ghcr.io/exgentic/runner:latest` with your own registry path if needed, and update `k8s/job.yaml` → `image:` to match. + +### Step 2 — Set up required secrets + +Run this **from your local machine** (not inside the cluster) before submitting the job. `update-secrets.sh` creates/patches the API-key secrets via kubectl: + +```bash +export OPENAI_API_KEY=sk-... +export HF_TOKEN=hf_... # optional; skip if not needed +./update-secrets.sh --namespace team1 +``` + +The `kagenti-test-user` secret (key: `password`) must already exist in `team1` — it is created by the Kagenti cluster setup and holds the Keycloak password. + +> **Note:** The job container itself does not have kubectl RBAC, so `update-secrets.sh` will print harmless warnings if it tries to run inside the cluster. The secrets just need to be present before the job starts. + +### Step 3 — Configure the job + +Edit `k8s/job.yaml` to set: + +| Field | Where | Example | +|-------|-------|---------| +| Benchmark | `args: ["--benchmark", "…"]` | `gsm8k`, `tau2`, `appworld` | +| Agent | `args: ["--agent", "…"]` | `tool_calling` | +| Model | `args: ["--model", "…"]` | `openai/Azure/gpt-4.1` | +| LLM API base | `env: OPENAI_API_BASE` | your LiteLLM proxy URL | + +To disable MLflow tracing, add `"--disable-mlflow"` to `args`. + +### Step 4 — Submit and watch the job + +```bash +# Delete any previous run with the same name first +kubectl delete job exgentic-runner -n team1 --ignore-not-found + +# Submit +kubectl apply -f k8s/job.yaml + +# Stream logs (the runner prints a summary table at the end) +kubectl logs -f job/exgentic-runner -n team1 +``` + +To watch job status separately: + +```bash +kubectl get job exgentic-runner -n team1 -w +``` + +### Step 5 — View results + +**Console output** — the log stream ends with a run summary: + +``` +============================================================ +RUN SUMMARY +============================================================ +Max Parallel Sessions: 1 +Sessions Attempted: 10 +Sessions Succeeded: 9 +Sessions With Error: 1 +Evaluation Success: 90.0% +Total Wall Time: 312.4s + +TIMING BREAKDOWN (average per session) + Session Creation: 0.05s + Agent Processing: 31.2s + Evaluation: 0.02s + +AGENT PROCESSING LATENCY + Average: 31.20s + P50: 28.00s + P95: 58.00s +============================================================ +``` + +**MLflow UI** — if MLflow tracing was enabled (the default), open `http://mlflow.localtest.me:8080` to view traces grouped by experiment. + +### Iterating with a locally-built image + +To test a local image change without pushing to a registry, sync it into the kind cluster first: + +```bash +export REMOTE_IMAGE_NAME=ghcr.io/exgentic/runner:dev +export KIND_CLUSTER_NAME=kagenti +source ./sync-image-to-cluster.sh +``` + +Then set `imagePullPolicy: IfNotPresent` in `k8s/job.yaml` and submit as above. + +## E2E Test Script + +`e2e-test.sh` runs `deploy-and-evaluate.sh` for every benchmark (or a chosen subset) and prints a consolidated results table. + +### Basic usage + +```bash +# Run all three benchmarks sequentially (gsm8k → tau2 → appworld), 1 task each +./e2e-test.sh --agent tool_calling + +# Run only a subset +./e2e-test.sh --agent tool_calling --benchmarks gsm8k,tau2 + +# Run with more tasks per benchmark +./e2e-test.sh --agent tool_calling --tasks 10 + +# Run all benchmarks in parallel (each benchmark gets its own port-forward slots) +./e2e-test.sh --agent tool_calling --parallel-jobs + +# Run all benchmarks as Kubernetes Jobs (in-cluster mode) +./e2e-test.sh --agent tool_calling --in-cluster + +# Dry run — prints all commands without executing them +./e2e-test.sh --agent tool_calling --dry +``` + +Any flag not recognised by `e2e-test.sh` is forwarded verbatim to `deploy-and-evaluate.sh` (e.g. `--model`, `--experiment`, `--disable-mlflow`, `--plugin-preset`). + +### Results table + +After all benchmarks finish, the script prints and writes `e2e-results.md`: + +``` +| Benchmark | Status | Tasks | Parallel Sessions | Eval Success Rate | Avg Latency (s) | Failures | +|-----------|--------|-------|-------------------|-------------------|-----------------|----------| +| gsm8k | PASS | 1 | -- | 100.0% | 4.2s | 0 | +| tau2 | PASS | 1 | -- | 100.0% | 12.8s | 0 | +| appworld | PASS | 1 | -- | 100.0% | 9.1s | 0 | +``` + +- **Status** is `PASS`, `FAIL`, or `SKIP` (skipped benchmarks appear when a sequential run aborts early). When a step fails, the status includes the step name, e.g. `FAIL(deploy-benchmark)`. +- **Parallel jobs mode** (`--parallel-jobs`): all benchmarks run concurrently; each gets unique OTEL-collector and Prometheus ports so local port-forwards don't collide. +- **In-cluster mode** (`--in-cluster`): one Kubernetes Job is created per benchmark from `k8s/job.yaml`; `e2e-test.sh` streams the logs and checks the job's success status. + +## Current Limitations + +- No retry mechanism for failed operations +- No streaming response support +- Tested only with local kind Kagenti installation with Podman (not tested with Docker) + +## Troubleshooting + + +## Development + +### Running Tests + +```bash +uv run pytest +``` + +### Code Formatting + +```bash +uv run black exgentic_a2a_runner/ +``` + +### Type Checking + +```bash +uv run mypy exgentic_a2a_runner/ +``` + +## Contributing + +Contributions are welcome! Please ensure: +- Code follows the existing style +- Tests pass +- Documentation is updated +- Commit messages are clear + +## License + +See LICENSE file in the repository root. + +## Additional Resources + +- **Kagenti UI**: Access at http://kagenti-ui.localtest.me:8080/ to monitor deployments +- **GitHub Issues**: https://github.com/kagenti/workload-harness/issues +- **Related Issue**: https://github.com/kagenti/kagenti/issues/963 + +## Next Steps + +After successful test run: +1. Increase `MAX_TASKS` in `.env` for longer runs +2. Adjust `MAX_PARALLEL_SESSIONS` for different concurrency levels +3. Enable OTLP exporter for telemetry collection +4. Deploy different benchmarks (gsm8k, tau2, appworld) +5. Test with various models via configure script +6. Analyze results and agent performance in Kagenti UI \ No newline at end of file diff --git a/appworld_a2a_runner/.gitignore b/appworld_a2a_runner/.gitignore deleted file mode 100644 index cb9c616..0000000 --- a/appworld_a2a_runner/.gitignore +++ /dev/null @@ -1,138 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -*.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 -.hypothesis/ -.pytest_cache/ - -# 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 -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -Pipfile.lock - -# PEP 582 -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.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/ - -# IDE -.vscode/ -.idea/ -*.swp -*.swo -*~ - -# OS -.DS_Store -Thumbs.db - -# Project specific -results.jsonl -*.log diff --git a/appworld_a2a_runner/README.md b/appworld_a2a_runner/README.md deleted file mode 100644 index 0589242..0000000 --- a/appworld_a2a_runner/README.md +++ /dev/null @@ -1,148 +0,0 @@ -# AppWorld A2A Proxy Runner - -A standalone Python runner that uses AppWorld to enumerate tasks and fetch task text, then calls a remote agent over A2A (Agent-to-Agent) protocol with a plain-text prompt, collects the plain-text response, and emits OpenTelemetry (OTEL) telemetry. - -## Features - -- **Standalone execution**: Not integrated with AppWorld's experiment framework -- **Sequential task processing**: One task at a time for simplicity -- **A2A protocol support**: Communicates with remote agents using the A2A protocol via JSON-RPC over HTTP -- **OpenTelemetry instrumentation**: Comprehensive traces, metrics, and logs -- **Strict failure handling**: Any error or timeout marks the task as failed -- **Configurable via environment variables**: Easy deployment and configuration -- **No SDK dependencies**: Uses plain HTTP requests instead of the A2A SDK to avoid dependency conflicts with AppWorld - -## Installation - -### Prerequisites - -- Python 3.11 or 3.12 (Python 3.13 is **not supported** due to `uvloop` build incompatibility) -- [uv](https://docs.astral.sh/uv/) package manager -- Access to an A2A-compatible agent endpoint such as [Simple Generalist in Kagenti examples](a2a/simple_generalist/src/simple_generalist) -- Access to the AppWorld API server such as [AppWorld Tool in Kagenti](https://github.com/kagenti/agent-examples/tree/main/mcp/appworld_apis) -- Deploy both the agent and the AppWorld API server on Kagenti. Expose them as external services or `port-forward` to their exposed ports. For the AppWorld API server, you will need to access its REST API interface, which is by default exposed on port `8000` - - -### Install from source - -```bash -git clone git@github.com:kagenti/workload-harness.git -cd appworld_a2a_runner -uv sync --python 3.12 -source .venv/bin/activate -``` - -## Configuration - -``` -cp example.env .env -``` -Configure the .env file as needed - -Required Variables -| Environment Variable | Default Setting | Required? | Description | -| --- | --- | --- | --- | -| `A2A_BASE_URL` | `(none)` | Yes | Base URL for the target agent to run the tests against. Must be A2A compatible.| -| `APPWORLD_DATASET` | `test_normal` | Yes | AppWorld dataset to run (for example `test_normal` or `test_challenge`). | -| `APPWORLD_REMOTE_APIS_URL` | `(none)` | Yes | URL for the AppWorld remote APIs server. | - -Optional Variables -| Environment Variable | Default Setting | Required? | Description | -| --- | --- | --- | --- | -| `A2A_TIMEOUT_SECONDS` | `300` | No | Timeout for each A2A request in seconds. | -| `A2A_AUTH_TOKEN` | `(none)` | No | Bearer token sent for A2A endpoint authentication. | -| `A2A_VERIFY_TLS` | `true` | No | Whether TLS certificates are verified for HTTPS requests. | -| `A2A_ENDPOINT_PATH` | `/v1/chat` | No | Endpoint path appended to `A2A_BASE_URL` for requests. | -| `APPWORLD_ROOT` | `(none)` | No | Overrides the AppWorld root directory path. | -| `MAX_TASKS` | `(none)` | No | Maximum number of tasks to process before exiting. | -| `ABORT_ON_FAILURE` | `false` | No | Stops processing after the first failed task when enabled. | -| `OTEL_SERVICE_NAME` | `appworld-a2a-proxy` | No | OpenTelemetry service name reported with traces. | -| `OTEL_EXPORTER_OTLP_ENDPOINT` | `(none)` | No | OTLP collector endpoint used to export telemetry. | -| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | No | OTLP transport protocol (`grpc` or `http/protobuf`). | -| `OTEL_RESOURCE_ATTRIBUTES` | `(none)` | No | Additional OpenTelemetry resource attributes (`key=value`). | -| `OTEL_INSTRUMENT_REQUESTS` | `true` | No | Enables automatic instrumentation for HTTP requests. | -| `LOG_PROMPT` | `0` | No | Enables logging of prompt payloads for debugging. | -| `LOG_RESPONSE` | `0` | No | Enables logging of response payloads for debugging. | - - -## Usage - -### Basic Usage - -```bash -uv run appworld-a2a-runner -``` - -## Output - -### Console Summary - -At the end of each run, a summary is printed: - -``` -============================================================ -RUN SUMMARY -============================================================ -Dataset: test_normal -Tasks Attempted: 100 -Tasks Succeeded: 95 -Tasks Failed: 5 -Total Wall Time: 1234.56s -Average Latency: 12345.67ms -P50 Latency: 10000.00ms -P95 Latency: 20000.00ms -============================================================ -``` - -### OpenTelemetry Data - -The runner emits comprehensive telemetry: - -#### Traces - -Each task creates a span (`a2a_proxy.task`) with: - -**Attributes:** -- `appworld.task_id`: Task identifier -- `appworld.dataset`: Dataset name -- `a2a.base_url`: A2A endpoint URL -- `a2a.timeout_seconds`: Timeout value -- `prompt.chars`: Prompt size in characters -- `response.chars`: Response size in characters -- `task.status`: `success` or `failed` -- `a2a.duration_ms`: End-to-end A2A operation latency in milliseconds - -**Child spans:** -- `a2a_proxy.prompt.build`: Prompt construction -- `a2a_proxy.a2a.send_prompt`: End-to-end A2A `send_prompt` call - -**Auto-instrumented HTTP spans:** -- Outbound `requests` spans for agent-card discovery, `message/send`, and `tasks/get` calls - -**Events:** -- `prompt_built`: When prompt is constructed -- `task_failed`: When task fails (includes error details) - -#### Metrics - -**Counters:** -- `a2a_proxy_tasks_total{status=success|failed}`: Total tasks processed -- `a2a_proxy_errors_total{error_type=...}`: Total errors by type - -**Histograms:** -- `a2a_proxy_task_latency_ms`: End-to-end task latency -- `a2a_proxy_a2a_latency_ms`: A2A request latency -- `a2a_proxy_prompt_size_chars`: Prompt size distribution -- `a2a_proxy_response_size_chars`: Response size distribution - -**Gauge:** -- `a2a_proxy_inflight_tasks`: Current tasks in flight (0 or 1) - - - -## Current Limitations - -- Sequential execution only (no concurrency) -- No retry mechanism -- No streaming response support -- No structured response parsing diff --git a/appworld_a2a_runner/appworld_a2a_runner/__init__.py b/appworld_a2a_runner/appworld_a2a_runner/__init__.py deleted file mode 100644 index 9870bd9..0000000 --- a/appworld_a2a_runner/appworld_a2a_runner/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -"""AppWorld A2A Proxy Runner. - -A standalone Python runner that uses AppWorld to enumerate tasks and fetch task text, -then calls a remote agent over A2A with a plain-text prompt, collects the plain-text -response, and emits OpenTelemetry (OTEL) telemetry. -""" - -__version__ = "0.1.0" - -from .config import Config -from .runner import Runner - -__all__ = ["Config", "Runner", "__version__"] - -# Made with Bob diff --git a/appworld_a2a_runner/appworld_a2a_runner/a2a_client.py b/appworld_a2a_runner/appworld_a2a_runner/a2a_client.py deleted file mode 100644 index 1563df4..0000000 --- a/appworld_a2a_runner/appworld_a2a_runner/a2a_client.py +++ /dev/null @@ -1,334 +0,0 @@ -"""A2A client for communicating with remote agent endpoints. - -Implements the A2A protocol for sending prompts and receiving responses using JSON-RPC. -""" - -import logging -import time -import uuid -from typing import Any, Dict, Optional -from urllib.parse import urlparse - -import requests - -from .config import A2AConfig - -logger = logging.getLogger(__name__) - - -class A2AProxyClient: - """Client for A2A protocol communication using JSON-RPC.""" - - def __init__(self, config: A2AConfig): - """Initialize A2A client. - - Args: - config: A2A configuration - """ - self.config = config - self.session = requests.Session() - - # Set default headers - self.session.headers.update( - { - "Content-Type": "application/json", - "Accept": "application/json", - } - ) - - # Set auth token if provided - if config.auth_token: - self.session.headers["Authorization"] = f"Bearer {config.auth_token}" - - # Fetch agent card and determine RPC URL - self.rpc_url = self._discover_rpc_url() - logger.info(f"A2A client initialized with RPC URL: {self.rpc_url}") - - def _normalize_endpoint_path(self) -> str: - """Normalize configured endpoint path to '/path' format.""" - endpoint_path = (self.config.endpoint_path or "/").strip() - if not endpoint_path: - endpoint_path = "/" - if not endpoint_path.startswith("/"): - endpoint_path = "/" + endpoint_path - return endpoint_path - - def _build_rpc_url(self, base_url: str) -> str: - """Build RPC URL by appending configured endpoint path to a base URL.""" - return base_url.rstrip("/") + self._normalize_endpoint_path() - - def _get_agent_card(self) -> Dict[str, Any]: - """Fetch the agent card from the standard discovery location. - - Returns: - Agent card as dict - - Raises: - requests.RequestException: On network or HTTP errors - """ - card_url = self.config.base_url.rstrip("/") + "/.well-known/agent-card.json" - logger.debug(f"Fetching agent card from {card_url}") - - try: - response = self.session.get( - card_url, - timeout=30, - verify=self.config.verify_tls, - ) - response.raise_for_status() - return response.json() - except requests.RequestException as e: - logger.warning(f"Failed to fetch agent card: {e}") - raise - - def _discover_rpc_url(self) -> str: - """Discover the JSON-RPC endpoint URL from the agent card. - - Returns: - JSON-RPC endpoint URL - """ - try: - card = self._get_agent_card() - service_url = card.get("url") - - # If the card has an explicit non-root path, treat it as the RPC URL. - # Otherwise build URL from base + configured endpoint path. - if service_url: - parsed = urlparse(service_url) - if parsed.path and parsed.path != "/": - rpc_url = service_url.rstrip("/") - else: - rpc_url = self._build_rpc_url(service_url) - else: - rpc_url = self._build_rpc_url(self.config.base_url) - - logger.debug(f"Discovered RPC URL from agent card: {rpc_url}") - return rpc_url - except Exception as e: - # Fallback to configured base_url + endpoint path if card fetch fails - logger.warning(f"Could not fetch agent card, using configured endpoint: {e}") - return self._build_rpc_url(self.config.base_url) - - def _jsonrpc_call( - self, - method: str, - params: Dict[str, Any], - request_id: int, - ) -> Any: - """Make a JSON-RPC call to the agent endpoint. - - Args: - method: JSON-RPC method name (e.g., "message/send", "tasks/get") - params: Method parameters - request_id: Request ID for tracking - - Returns: - Result from the JSON-RPC response - - Raises: - RuntimeError: On JSON-RPC error - requests.RequestException: On network or HTTP errors - """ - payload = { - "jsonrpc": "2.0", - "id": request_id, - "method": method, - "params": params, - } - - logger.debug(f"JSON-RPC call: {method} with request_id={request_id}") - - response = self.session.post( - self.rpc_url, - json=payload, - timeout=self.config.timeout_seconds, - verify=self.config.verify_tls, - ) - response.raise_for_status() - - data = response.json() - if "error" in data: - raise RuntimeError(f"JSON-RPC error: {data['error']}") - - if "result" not in data: - raise RuntimeError(f"JSON-RPC response missing 'result' field: {data}") - - return data["result"] - - def _extract_text_from_message(self, message: Dict[str, Any]) -> str: - """Extract text content from a message object. - - Args: - message: Message object with role and parts - - Returns: - Extracted text content - - Raises: - ValueError: If message format is invalid - """ - # Per spec, a Message has role + parts[{kind:"text", text:"..."}] - if "parts" in message: - parts = message["parts"] - if isinstance(parts, list): - text_parts = [] - for part in parts: - if isinstance(part, dict) and part.get("kind") == "text": - text = part.get("text", "") - if text: - text_parts.append(text) - if text_parts: - return "\n".join(text_parts) - - # Fallback: check for direct content field - if "content" in message: - return str(message["content"]) - - raise ValueError("Could not extract text from message") - - def _extract_text_from_task(self, task: Dict[str, Any]) -> str: - """Extract text content from a completed task. - - Args: - task: Task object - - Returns: - Extracted text content - - Raises: - ValueError: If task format is invalid or task failed - """ - status = task.get("status", {}) - state = status.get("state") - - if state == "failed": - error = status.get("error", "Unknown error") - raise ValueError(f"Task failed: {error}") - - if state == "canceled": - raise ValueError("Task was canceled") - - if state == "rejected": - raise ValueError("Task was rejected") - - # Look for artifacts in task (A2A spec) - if "artifacts" in task: - artifacts = task["artifacts"] - if isinstance(artifacts, list) and len(artifacts) > 0: - artifact = artifacts[0] - if isinstance(artifact, dict) and "parts" in artifact: - parts = artifact["parts"] - if isinstance(parts, list): - text_parts = [] - for part in parts: - if isinstance(part, dict) and part.get("kind") == "text": - text = part.get("text", "") - if text: - text_parts.append(text) - if text_parts: - return "\n".join(text_parts) - - # Look for result in task (fallback) - if "result" in task: - result = task["result"] - if isinstance(result, dict): - # Try to extract message from result - if "message" in result: - return self._extract_text_from_message(result["message"]) - # Try direct text extraction - if "text" in result: - return str(result["text"]) - if "content" in result: - return str(result["content"]) - elif isinstance(result, str): - return result - - raise ValueError("Could not extract text from task result") - - def send_prompt( - self, - prompt: str, - poll_interval_s: float = 0.5, - timeout_s: Optional[float] = None, - ) -> str: - """Send prompt to A2A endpoint and get response. - - Args: - prompt: The prompt text to send - poll_interval_s: Polling interval for task status (default: 0.5s) - timeout_s: Timeout for task completion (default: config timeout) - - Returns: - Plain text response from the agent - - Raises: - requests.RequestException: On network or HTTP errors - ValueError: On invalid response format - TimeoutError: If task doesn't complete in time - Exception: On any other error - """ - if timeout_s is None: - timeout_s = float(self.config.timeout_seconds) - - try: - # Build message per A2A spec: role + parts[{kind:"text", text:"..."}] - message = { - "role": "user", - "parts": [{"kind": "text", "text": prompt}], - "messageId": str(uuid.uuid4()), - } - - logger.debug(f"Sending message to {self.rpc_url}") - - # Send message via JSON-RPC - result = self._jsonrpc_call( - "message/send", - params={"message": message, "metadata": {}}, - request_id=1, - ) - - # The server can return either a Message or a Task - if result.get("kind") != "task": - # It's a Message object - extract text directly - logger.debug("Received direct message response") - return self._extract_text_from_message(result) - - # It's a Task - poll for completion - task_id = result["id"] - logger.debug(f"Received task {task_id}, polling for completion") - - deadline = time.time() + timeout_s - request_id = 2 - - while time.time() < deadline: - task = self._jsonrpc_call( - "tasks/get", - params={"id": task_id}, - request_id=request_id, - ) - request_id += 1 - - logger.debug("Task poll response: %s", task) - - state = task.get("status", {}).get("state") - logger.debug(f"Task {task_id} state: {state}") - - if state in {"completed", "failed", "canceled", "rejected"}: - return self._extract_text_from_task(task) - - time.sleep(poll_interval_s) - - raise TimeoutError(f"Task {task_id} did not finish within {timeout_s}s") - - except requests.Timeout as e: - logger.error(f"A2A request timed out: {e}") - raise - except requests.RequestException as e: - logger.error(f"A2A request failed: {type(e).__name__}: {e}") - raise - except Exception as e: - logger.error(f"A2A request failed: {type(e).__name__}: {e}") - raise - - -# Made with Bob diff --git a/appworld_a2a_runner/appworld_a2a_runner/appworld_adapter.py b/appworld_a2a_runner/appworld_a2a_runner/appworld_adapter.py deleted file mode 100644 index a877c34..0000000 --- a/appworld_a2a_runner/appworld_a2a_runner/appworld_adapter.py +++ /dev/null @@ -1,145 +0,0 @@ -"""AppWorld adapter for task enumeration and data extraction. - -Provides interface to AppWorld dataset without using AppWorld's evaluation system. -""" - -import logging -from typing import Any, Iterator, List - -from appworld import AppWorld, load_task_ids -from appworld.task import Task - -from .config import AppWorldConfig - -logger = logging.getLogger(__name__) - - -class TaskData: - """Container for task data extracted from AppWorld.""" - - def __init__(self, task_id: str, instruction: str, supervisor: Any, app_descriptions: dict[str, str]): - """Initialize task data. - - Args: - task_id: Unique task identifier - instruction: Task instruction text - supervisor: Supervisor data (string, dict, or other) - app_descriptions: High level description of the apps available to interact with - """ - self.task_id = task_id - self.instruction = instruction - self.supervisor = supervisor - self.app_descriptions = app_descriptions - - -class AppWorldAdapter: - """Adapter for accessing AppWorld tasks without evaluation.""" - - def __init__(self, config: AppWorldConfig): - """Initialize AppWorld adapter. - - Args: - config: AppWorld configuration - """ - self.config = config - self._initialized = False - - def initialize(self) -> None: - """Initialize AppWorld adapter.""" - logger.info(f"Initializing AppWorld adapter with dataset: {self.config.dataset}") - if not self.config.remote_apis_url: - raise ValueError("APPWORLD_REMOTE_APIS_URL environment variable is required") - - # Note: AppWorld uses load_task_ids() for dataset enumeration - # Individual tasks are loaded on-demand using Task.load() - # No global AppWorld instance is needed for task enumeration - - self._initialized = True - logger.info("AppWorld adapter initialized successfully") - - def get_task_ids(self) -> List[str]: - """Get list of task IDs for the configured dataset. - - Returns: - List of task IDs - """ - if not self._initialized: - raise RuntimeError("AppWorld adapter not initialized. Call initialize() first.") - - # Load task IDs from AppWorld dataset - logger.info(f"Loading task IDs from dataset: {self.config.dataset}") - task_ids = load_task_ids(dataset_name=self.config.dataset) - - # Apply max_tasks limit if configured - if self.config.max_tasks is not None: - task_ids = task_ids[: self.config.max_tasks] - logger.info(f"Limited to {len(task_ids)} tasks (MAX_TASKS={self.config.max_tasks})") - else: - logger.info(f"Found {len(task_ids)} tasks in dataset") - - return task_ids - - def get_task_data(self, task_id: str) -> TaskData: - """Extract task data from AppWorld. - - Args: - task_id: Task identifier - - Returns: - TaskData containing instruction and supervisor - - Raises: - Exception: If task cannot be loaded or data is missing - """ - if not self._initialized: - raise RuntimeError("AppWorld adapter not initialized. Call initialize() first.") - - logger.debug(f"Loading task data for: {task_id}") - - # Load task using Task.load() - task = Task.load(task_id, load_ground_truth=False) - - # Extract instruction - instruction = task.instruction - if not instruction: - raise ValueError(f"Task {task_id} has no instruction") - - # Extract supervisor (may be string, dict, or None) - supervisor = getattr(task, "supervisor", None) - - logger.debug( - f"Task {task_id}: instruction length={len(instruction)}, supervisor type={type(supervisor).__name__}" - ) - - # Capture app_descriptions before closing to avoid use-after-close - app_descriptions = task.app_descriptions - - # Close the task to free resources - task.close() - return TaskData( - task_id=task_id, - instruction=instruction, - supervisor=supervisor, - app_descriptions=app_descriptions, - ) - - def iterate_tasks(self) -> Iterator[TaskData]: - """Iterate over all tasks in the dataset. - - Yields: - TaskData for each task - """ - task_ids = self.get_task_ids() - - for task_id in task_ids: - try: - with AppWorld(task_id=task_id, remote_apis_url=self.config.remote_apis_url) as world: - yield self.get_task_data(task_id) - world.save_state() - world.save_logs() - except Exception as e: - logger.error(f"Failed to load task {task_id}: {e}") - raise - - -# Made with Bob diff --git a/appworld_a2a_runner/appworld_a2a_runner/config.py b/appworld_a2a_runner/appworld_a2a_runner/config.py deleted file mode 100644 index d2b43de..0000000 --- a/appworld_a2a_runner/appworld_a2a_runner/config.py +++ /dev/null @@ -1,147 +0,0 @@ -"""Configuration management for AppWorld A2A Runner. - -Configuration is loaded from environment variables with optional CLI overrides. -""" - -import os -from dataclasses import dataclass -from typing import Optional - - -def _get_bool(key: str, default: bool = False) -> bool: - """Get boolean value from environment variable.""" - value = os.getenv(key, "").lower() - if value in ("1", "true", "yes", "on"): - return True - elif value in ("0", "false", "no", "off"): - return False - return default - - -def _get_int(key: str, default: Optional[int] = None) -> Optional[int]: - """Get integer value from environment variable.""" - value = os.getenv(key) - if value is None: - return default - try: - return int(value) - except ValueError: - return default - - -@dataclass -class A2AConfig: - """A2A endpoint configuration.""" - - base_url: str - timeout_seconds: int = 300 - auth_token: Optional[str] = None - verify_tls: bool = True - endpoint_path: str = "/v1/chat" - - @classmethod - def from_env(cls) -> "A2AConfig": - """Load A2A configuration from environment variables.""" - base_url = os.getenv("A2A_BASE_URL") - if not base_url: - raise ValueError("A2A_BASE_URL environment variable is required") - - return cls( - base_url=base_url, - timeout_seconds=_get_int("A2A_TIMEOUT_SECONDS", 300) or 300, - auth_token=os.getenv("A2A_AUTH_TOKEN"), - verify_tls=_get_bool("A2A_VERIFY_TLS", True), - endpoint_path=os.getenv("A2A_ENDPOINT_PATH", "/v1/chat"), - ) - - -@dataclass -class AppWorldConfig: - """AppWorld dataset configuration.""" - - dataset: str - remote_apis_url: Optional[str] = None - root: Optional[str] = None - max_tasks: Optional[int] = None - abort_on_failure: bool = False - - @classmethod - def from_env(cls) -> "AppWorldConfig": - """Load AppWorld configuration from environment variables.""" - dataset = os.getenv("APPWORLD_DATASET") - if not dataset: - raise ValueError("APPWORLD_DATASET environment variable is required") - remote_apis_url = os.getenv("APPWORLD_REMOTE_APIS_URL") - if not remote_apis_url: - raise ValueError("APPWORLD_REMOTE_APIS_URL environment variable is required") - - return cls( - dataset=dataset, - remote_apis_url=remote_apis_url, - root=os.getenv("APPWORLD_ROOT"), - max_tasks=_get_int("MAX_TASKS"), - abort_on_failure=_get_bool("ABORT_ON_FAILURE", False), - ) - - -@dataclass -class OTELConfig: - """OpenTelemetry configuration.""" - - service_name: str = "appworld-a2a-proxy" - exporter_endpoint: Optional[str] = None - exporter_protocol: str = "grpc" - resource_attributes: Optional[str] = None - instrument_requests: bool = True - exporter_insecure: bool = True - - @classmethod - def from_env(cls) -> "OTELConfig": - """Load OTEL configuration from environment variables.""" - return cls( - service_name=os.getenv("OTEL_SERVICE_NAME", "appworld-a2a-proxy"), - exporter_endpoint=os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT"), - exporter_protocol=os.getenv("OTEL_EXPORTER_OTLP_PROTOCOL", "grpc"), - resource_attributes=os.getenv("OTEL_RESOURCE_ATTRIBUTES"), - instrument_requests=_get_bool("OTEL_INSTRUMENT_REQUESTS", True), - exporter_insecure=_get_bool("OTEL_EXPORTER_OTLP_INSECURE", True), - ) - - -@dataclass -class DebugConfig: - """Debug and logging configuration.""" - - log_prompt: bool = False - log_response: bool = False - - @classmethod - def from_env(cls) -> "DebugConfig": - """Load debug configuration from environment variables.""" - return cls( - log_prompt=_get_bool("LOG_PROMPT", False), - log_response=_get_bool("LOG_RESPONSE", False), - ) - - -@dataclass -class Config: - """Complete runner configuration.""" - - a2a: A2AConfig - appworld: AppWorldConfig - otel: OTELConfig - debug: DebugConfig - - @classmethod - def from_env(cls) -> "Config": - """Load complete configuration from environment variables.""" - return cls( - a2a=A2AConfig.from_env(), - appworld=AppWorldConfig.from_env(), - otel=OTELConfig.from_env(), - debug=DebugConfig.from_env(), - ) - - -# Made with Bob diff --git a/appworld_a2a_runner/appworld_a2a_runner/otel.py b/appworld_a2a_runner/appworld_a2a_runner/otel.py deleted file mode 100644 index 475a8db..0000000 --- a/appworld_a2a_runner/appworld_a2a_runner/otel.py +++ /dev/null @@ -1,332 +0,0 @@ -"""OpenTelemetry instrumentation for AppWorld A2A Runner. - -Provides traces, metrics, and logs for monitoring task execution. -""" - -import logging -import time -from contextlib import contextmanager -from typing import Iterator, Optional - -from opentelemetry import metrics, trace -from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter -from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter -from opentelemetry.instrumentation.requests import RequestsInstrumentor -from opentelemetry.sdk.metrics import MeterProvider -from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader -from opentelemetry.sdk.resources import Resource -from opentelemetry.sdk.trace import TracerProvider -from opentelemetry.sdk.trace.export import BatchSpanProcessor, ConsoleSpanExporter -from opentelemetry.trace import Status, StatusCode - -from .config import OTELConfig - -logger = logging.getLogger(__name__) - - -class OTELInstrumentation: - """OpenTelemetry instrumentation manager.""" - - def __init__(self, config: OTELConfig): - """Initialize OTEL instrumentation. - - Args: - config: OTEL configuration - """ - self.config = config - self.tracer: Optional[trace.Tracer] = None - self.meter: Optional[metrics.Meter] = None - self._trace_provider: Optional[TracerProvider] = None - self._meter_provider: Optional[MeterProvider] = None - - # Metrics - self.tasks_counter: Optional[metrics.Counter] = None - self.errors_counter: Optional[metrics.Counter] = None - self.task_latency_histogram: Optional[metrics.Histogram] = None - self.a2a_latency_histogram: Optional[metrics.Histogram] = None - self.prompt_size_histogram: Optional[metrics.Histogram] = None - self.response_size_histogram: Optional[metrics.Histogram] = None - self.inflight_gauge: Optional[metrics.UpDownCounter] = None - self._requests_instrumented = False - - def initialize(self) -> None: - """Initialize OTEL providers and instruments.""" - logger.info("Initializing OpenTelemetry instrumentation") - - # Create resource with service name and attributes - resource_attrs = {"service.name": self.config.service_name} - if self.config.resource_attributes: - # Parse resource attributes (format: key1=val1,key2=val2) - for attr in self.config.resource_attributes.split(","): - if "=" in attr: - key, value = attr.split("=", 1) - resource_attrs[key.strip()] = value.strip() - - resource = Resource.create(resource_attrs) - - # Initialize tracing - self._initialize_tracing(resource) - - # Initialize auto-instrumentation - self._initialize_auto_instrumentation() - - # Initialize metrics - self._initialize_metrics(resource) - - logger.info("OpenTelemetry instrumentation initialized") - - def _initialize_tracing(self, resource: Resource) -> None: - """Initialize tracing provider and exporter.""" - trace_provider = TracerProvider(resource=resource) - - if self.config.exporter_endpoint: - # Use OTLP exporter - logger.info(f"Configuring OTLP trace exporter: {self.config.exporter_endpoint}") - span_exporter = OTLPSpanExporter( - endpoint=self.config.exporter_endpoint, - insecure=self.config.exporter_insecure, - ) - else: - # Use console exporter for development - logger.info("Using console trace exporter (no OTLP endpoint configured)") - span_exporter = ConsoleSpanExporter() - - trace_provider.add_span_processor(BatchSpanProcessor(span_exporter)) - trace.set_tracer_provider(trace_provider) - self._trace_provider = trace_provider - - self.tracer = trace.get_tracer(__name__) - - def _initialize_auto_instrumentation(self) -> None: - """Initialize opt-in auto instrumentation.""" - if not self.config.instrument_requests: - logger.info("Requests auto-instrumentation disabled") - return - - if self._requests_instrumented: - return - - RequestsInstrumentor().instrument() - self._requests_instrumented = True - logger.info("Enabled OpenTelemetry requests auto-instrumentation") - - def _initialize_metrics(self, resource: Resource) -> None: - """Initialize metrics provider and instruments.""" - metric_readers = [] - - if self.config.exporter_endpoint: - # Use OTLP exporter - logger.info(f"Configuring OTLP metric exporter: {self.config.exporter_endpoint}") - metric_exporter = OTLPMetricExporter( - endpoint=self.config.exporter_endpoint, - insecure=self.config.exporter_insecure, - ) - metric_reader = PeriodicExportingMetricReader(metric_exporter) - metric_readers.append(metric_reader) - else: - # No exporter configured - metrics will be collected but not exported - logger.info("No OTLP endpoint configured, metrics will not be exported") - - meter_provider = MeterProvider( - resource=resource, - metric_readers=metric_readers, - ) - metrics.set_meter_provider(meter_provider) - self._meter_provider = meter_provider - - self.meter = metrics.get_meter(__name__) - - # Create metric instruments - self.tasks_counter = self.meter.create_counter( - name="a2a_proxy_tasks_total", - description="Total number of tasks processed", - unit="1", - ) - - self.errors_counter = self.meter.create_counter( - name="a2a_proxy_errors_total", - description="Total number of errors", - unit="1", - ) - - self.task_latency_histogram = self.meter.create_histogram( - name="a2a_proxy_task_latency_ms", - description="Task processing latency in milliseconds", - unit="ms", - ) - - self.a2a_latency_histogram = self.meter.create_histogram( - name="a2a_proxy_a2a_latency_ms", - description="A2A request latency in milliseconds", - unit="ms", - ) - - self.prompt_size_histogram = self.meter.create_histogram( - name="a2a_proxy_prompt_size_chars", - description="Prompt size in characters", - unit="chars", - ) - - self.response_size_histogram = self.meter.create_histogram( - name="a2a_proxy_response_size_chars", - description="Response size in characters", - unit="chars", - ) - - self.inflight_gauge = self.meter.create_up_down_counter( - name="a2a_proxy_inflight_tasks", - description="Number of tasks currently in flight", - unit="1", - ) - - def shutdown(self) -> None: - """Shut down OTEL providers to flush pending spans and metrics.""" - logger.info("Shutting down OpenTelemetry providers") - if self._trace_provider: - self._trace_provider.shutdown() - if self._meter_provider: - self._meter_provider.shutdown() - - @contextmanager - def task_span( - self, - task_id: str, - dataset: str, - a2a_base_url: str, - a2a_timeout: int, - ) -> Iterator[trace.Span]: - """Create a span for task processing. - - Args: - task_id: Task identifier - dataset: Dataset name - a2a_base_url: A2A endpoint URL (sanitized) - a2a_timeout: A2A timeout in seconds - - Yields: - Span object for adding events and attributes - """ - if not self.tracer: - raise RuntimeError("OTEL not initialized") - - # Increment inflight gauge - if self.inflight_gauge: - self.inflight_gauge.add(1) - - start_time = time.time() - - with self.tracer.start_as_current_span("a2a_proxy.task") as span: - # Set span attributes - span.set_attribute("appworld.task_id", task_id) - span.set_attribute("appworld.dataset", dataset) - span.set_attribute("a2a.base_url", a2a_base_url) - span.set_attribute("a2a.timeout_seconds", a2a_timeout) - - try: - yield span - finally: - # Decrement inflight gauge - if self.inflight_gauge: - self.inflight_gauge.add(-1) - - # Record task latency - latency_ms = (time.time() - start_time) * 1000 - if self.task_latency_histogram: - self.task_latency_histogram.record(latency_ms) - - @contextmanager - def child_span(self, name: str) -> Iterator[trace.Span]: - """Create a child span under the current context.""" - if not self.tracer: - raise RuntimeError("OTEL not initialized") - - with self.tracer.start_as_current_span(name) as span: - yield span - - def record_prompt(self, span: trace.Span, prompt: str) -> None: - """Record prompt information. - - Args: - span: Current span - prompt: Prompt text - """ - prompt_chars = len(prompt) - span.set_attribute("prompt.chars", prompt_chars) - span.add_event("prompt_built") - - if self.prompt_size_histogram: - self.prompt_size_histogram.record(prompt_chars) - - def record_a2a_request( - self, - span: trace.Span, - duration_ms: float, - ) -> None: - """Record A2A request metrics. - - Args: - span: Current span - duration_ms: Request duration in milliseconds - """ - span.set_attribute("a2a.duration_ms", duration_ms) - - if self.a2a_latency_histogram: - self.a2a_latency_histogram.record(duration_ms) - - def record_response(self, span: trace.Span, response: str) -> None: - """Record response information. - - Args: - span: Current span - response: Response text - """ - response_chars = len(response) - span.set_attribute("response.chars", response_chars) - - if self.response_size_histogram: - self.response_size_histogram.record(response_chars) - - def record_success(self, span: trace.Span) -> None: - """Record successful task completion. - - Args: - span: Current span - """ - span.set_attribute("task.status", "success") - span.set_status(Status(StatusCode.OK)) - - if self.tasks_counter: - self.tasks_counter.add(1, {"status": "success"}) - - def record_failure( - self, - span: trace.Span, - error: Exception, - error_type: str, - ) -> None: - """Record task failure. - - Args: - span: Current span - error: Exception that caused failure - error_type: Error type classification - """ - span.set_attribute("task.status", "failed") - span.add_event( - "task_failed", - attributes={ - "error.type": error_type, - "error.message": str(error), - }, - ) - span.set_status(Status(StatusCode.ERROR, str(error))) - span.record_exception(error) - - if self.tasks_counter: - self.tasks_counter.add(1, {"status": "failed"}) - - if self.errors_counter: - self.errors_counter.add(1, {"error_type": error_type}) - - -# Made with Bob diff --git a/appworld_a2a_runner/appworld_a2a_runner/prompt.py b/appworld_a2a_runner/appworld_a2a_runner/prompt.py deleted file mode 100644 index 8a6c21a..0000000 --- a/appworld_a2a_runner/appworld_a2a_runner/prompt.py +++ /dev/null @@ -1,71 +0,0 @@ -"""Prompt construction for AppWorld A2A Runner. - -Builds prompts according to the exact format specified in requirements. -""" - -import json -from typing import Any, Dict, Union - - -def serialize_supervisor(supervisor: Union[str, Dict[str, Any], None]) -> str: - """Serialize supervisor data to string format. - - Args: - supervisor: Supervisor data (string, dict, or None) - - Returns: - Serialized supervisor string (empty string if None) - """ - if supervisor is None: - return "" - - if isinstance(supervisor, str): - return supervisor - - if isinstance(supervisor, dict): - # Serialize with stable key ordering and minimal pretty-print - return json.dumps(supervisor, sort_keys=True, indent=2) - - # Fallback for other types - return str(supervisor) - - -def build_prompt( - instruction: str, supervisor: Union[str, Dict[str, Any], None], app_descriptions: dict[str, str] -) -> str: - """Build prompt according to exact specification. - - The prompt format is: - - I am your supervisor: - {supervisor_text} - - The task you are to complete is: - {instruction_text} - - The applications available to you to help you complete the task are the following: - {app_descriptions} - - Args: - instruction: Task instruction text - supervisor: Supervisor data (string, dict, or None) - app_descriptions: Mapping of application names to their descriptions - - Returns: - Formatted prompt string - """ - supervisor_text = serialize_supervisor(supervisor) - - prompt = f"""I am your supervisor: -{supervisor_text} - -The task you are to complete is: -{instruction} - -The applications available to you to help you complete the task are the following: -{str(app_descriptions)}""" - - return prompt - - -# Made with Bob diff --git a/appworld_a2a_runner/appworld_a2a_runner/runner.py b/appworld_a2a_runner/appworld_a2a_runner/runner.py deleted file mode 100644 index 8c4c296..0000000 --- a/appworld_a2a_runner/appworld_a2a_runner/runner.py +++ /dev/null @@ -1,310 +0,0 @@ -"""Main runner for AppWorld A2A Proxy. - -Orchestrates task enumeration, A2A calls, and telemetry collection. -""" - -import argparse -import logging -import sys -import time -from typing import List, Optional - -from .a2a_client import A2AProxyClient -from .appworld_adapter import AppWorldAdapter, TaskData -from .config import Config -from .otel import OTELInstrumentation -from .prompt import build_prompt - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", -) -logger = logging.getLogger(__name__) - - -class TaskResult: - """Result of a single task execution.""" - - def __init__( - self, - task_id: str, - success: bool, - latency_ms: float, - error: Optional[str] = None, - response_chars: Optional[int] = None, - ): - self.task_id = task_id - self.success = success - self.latency_ms = latency_ms - self.error = error - self.response_chars = response_chars - - -class RunSummary: - """Summary of the entire run.""" - - def __init__(self, dataset: str): - self.dataset = dataset - self.start_time = time.time() - self.results: List[TaskResult] = [] - - def add_result(self, result: TaskResult) -> None: - """Add a task result.""" - self.results.append(result) - - def get_summary(self) -> dict: - """Get summary statistics.""" - total_time = time.time() - self.start_time - attempted = len(self.results) - succeeded = sum(1 for r in self.results if r.success) - failed = attempted - succeeded - - latencies = [r.latency_ms for r in self.results] - avg_latency = sum(latencies) / len(latencies) if latencies else 0 - - # Calculate percentiles - sorted_latencies = sorted(latencies) - p50 = sorted_latencies[len(sorted_latencies) // 2] if sorted_latencies else 0 - p95_idx = min(int(len(sorted_latencies) * 0.95), len(sorted_latencies) - 1) - p95 = sorted_latencies[p95_idx] if sorted_latencies else 0 - - return { - "dataset": self.dataset, - "tasks_attempted": attempted, - "tasks_succeeded": succeeded, - "tasks_failed": failed, - "total_wall_time_seconds": total_time, - "average_latency_ms": avg_latency, - "p50_latency_ms": p50, - "p95_latency_ms": p95, - } - - def print_summary(self) -> None: - """Print summary to console.""" - summary = self.get_summary() - - print("\n" + "=" * 60) - print("RUN SUMMARY") - print("=" * 60) - print(f"Dataset: {summary['dataset']}") - print(f"Tasks Attempted: {summary['tasks_attempted']}") - print(f"Tasks Succeeded: {summary['tasks_succeeded']}") - print(f"Tasks Failed: {summary['tasks_failed']}") - print(f"Total Wall Time: {summary['total_wall_time_seconds']:.2f}s") - print(f"Average Latency: {summary['average_latency_ms']:.2f}ms") - print(f"P50 Latency: {summary['p50_latency_ms']:.2f}ms") - print(f"P95 Latency: {summary['p95_latency_ms']:.2f}ms") - print("=" * 60 + "\n") - - -class Runner: - """Main runner orchestrating task execution.""" - - def __init__(self, config: Config): - """Initialize runner. - - Args: - config: Complete configuration - """ - self.config = config - self.appworld = AppWorldAdapter(config.appworld) - self.a2a_client = A2AProxyClient(config.a2a) - self.otel = OTELInstrumentation(config.otel) - self.summary = RunSummary(config.appworld.dataset) - - def initialize(self) -> None: - """Initialize all components.""" - logger.info("Initializing runner components") - self.otel.initialize() - self.appworld.initialize() - logger.info("Runner initialization complete") - - def process_task(self, task_data: TaskData) -> TaskResult: - """Process a single task. - - Args: - task_data: Task data from AppWorld - - Returns: - TaskResult with execution details - """ - task_id = task_data.task_id - start_time = time.time() - - logger.info(f"Processing task: {task_id}") - - # Start OTEL span - with self.otel.task_span( - task_id=task_id, - dataset=self.config.appworld.dataset, - a2a_base_url=self.config.a2a.base_url, - a2a_timeout=self.config.a2a.timeout_seconds, - ) as span: - try: - # Build prompt - with self.otel.child_span("a2a_proxy.prompt.build"): - prompt = build_prompt(task_data.instruction, task_data.supervisor, task_data.app_descriptions) - self.otel.record_prompt(span, prompt) - - if self.config.debug.log_prompt: - logger.debug(f"Prompt length: {len(prompt)} chars") - - # Send A2A request - a2a_start = time.time() - with self.otel.child_span("a2a_proxy.a2a.send_prompt"): - response = self.a2a_client.send_prompt(prompt) - a2a_duration_ms = (time.time() - a2a_start) * 1000 - - self.otel.record_a2a_request(span, a2a_duration_ms) - self.otel.record_response(span, response) - - if self.config.debug.log_response: - logger.debug(f"Response length: {len(response)} chars") - - # Record success - self.otel.record_success(span) - - latency_ms = (time.time() - start_time) * 1000 - logger.info(f"Task {task_id} succeeded in {latency_ms:.2f}ms") - - return TaskResult( - task_id=task_id, - success=True, - latency_ms=latency_ms, - response_chars=len(response), - ) - - except Exception as e: - # Classify error type - error_type = type(e).__name__ - error_msg = str(e) - - logger.error(f"Task {task_id} failed: {error_type}: {error_msg}") - - # Record failure - self.otel.record_failure(span, e, error_type) - - latency_ms = (time.time() - start_time) * 1000 - - return TaskResult( - task_id=task_id, - success=False, - latency_ms=latency_ms, - error=f"{error_type}: {error_msg}", - ) - - def run(self) -> int: - """Run the task processing loop. - - Returns: - Exit code (0 for success, 1 for failure) - """ - try: - self.initialize() - - # Process tasks sequentially - for task_data in self.appworld.iterate_tasks(): - result = self.process_task(task_data) - self.summary.add_result(result) - - # Check abort on failure - if not result.success and self.config.appworld.abort_on_failure: - logger.error("Aborting due to task failure (ABORT_ON_FAILURE=true)") - break - - # Print summary - self.summary.print_summary() - - # Return success if at least one task succeeded - if any(r.success for r in self.summary.results): - return 0 - else: - return 1 - - except Exception as e: - logger.exception(f"Fatal error in runner: {e}") - return 1 - finally: - self.otel.shutdown() - - -def parse_args() -> argparse.Namespace: - """Parse command line arguments. - - Returns: - Parsed arguments - """ - parser = argparse.ArgumentParser( - description="AppWorld A2A Proxy Runner", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Environment Variables: - A2A_BASE_URL A2A endpoint base URL (required) - A2A_TIMEOUT_SECONDS Request timeout in seconds (default: 300) - A2A_AUTH_TOKEN Bearer token for authentication - A2A_VERIFY_TLS Verify TLS certificates (default: true) - A2A_ENDPOINT_PATH Endpoint path (default: /v1/chat) - - APPWORLD_ROOT AppWorld root directory - APPWORLD_DATASET Dataset split name (required) - APPWORLD_REMOTE_APIS_URL AppWorld remote APIs base URL (required) - MAX_TASKS Maximum number of tasks to process - ABORT_ON_FAILURE Stop on first failure (default: false) - - OTEL_SERVICE_NAME Service name for telemetry (default: appworld-a2a-proxy) - OTEL_EXPORTER_OTLP_ENDPOINT OTLP exporter endpoint - OTEL_EXPORTER_OTLP_PROTOCOL OTLP protocol (default: grpc) - OTEL_RESOURCE_ATTRIBUTES Additional resource attributes - OTEL_EXPORTER_OTLP_INSECURE Use insecure connection for OTLP (default: true) - - LOG_PROMPT Log prompt details (default: 0) - LOG_RESPONSE Log response details (default: 0) - """, - ) - - parser.add_argument( - "--verbose", - "-v", - action="store_true", - help="Enable verbose logging", - ) - - return parser.parse_args() - - -def main() -> int: - """Main entry point. - - Returns: - Exit code - """ - args = parse_args() - - # Set log level - if args.verbose: - logging.getLogger().setLevel(logging.DEBUG) - - try: - # Load configuration from environment - config = Config.from_env() - - # Create and run - runner = Runner(config) - return runner.run() - - except ValueError as e: - logger.error(f"Configuration error: {e}") - return 1 - except KeyboardInterrupt: - logger.info("Interrupted by user") - return 130 - except Exception as e: - logger.exception(f"Unexpected error: {e}") - return 1 - - -if __name__ == "__main__": - sys.exit(main()) - -# Made with Bob diff --git a/appworld_a2a_runner/example.env b/appworld_a2a_runner/example.env deleted file mode 100644 index e831375..0000000 --- a/appworld_a2a_runner/example.env +++ /dev/null @@ -1,74 +0,0 @@ -# Example Environment Configuration for AppWorld A2A Runner -# Copy this file to .env and customize for your environment - -# ============================================================ -# REQUIRED CONFIGURATION -# ============================================================ - -# A2A endpoint base URL (REQUIRED) -A2A_BASE_URL=http://localhost:8000 - -# AppWorld dataset to use (REQUIRED) -# Options: test_normal, test_challenge, dev, etc. -APPWORLD_DATASET=test_normal - -# ============================================================ -# A2A CONFIGURATION (Optional) -# ============================================================ - -# Request timeout in seconds (default: 300) -A2A_TIMEOUT_SECONDS=300 - -# Bearer token for authentication (if required by your endpoint) -# A2A_AUTH_TOKEN=your-secret-token-here - -# Verify TLS certificates (default: true) -A2A_VERIFY_TLS=true - -# Custom endpoint path (default: /v1/chat) -# A2A_ENDPOINT_PATH=/v1/chat - -# ============================================================ -# APPWORLD CONFIGURATION (Optional) -# ============================================================ - -# AppWorld root directory (if you need to override default) -# APPWORLD_ROOT=/path/to/appworld - -# Maximum number of tasks to process (useful for testing) -MAX_TASKS=10 - -# Stop processing on first failure (default: false) -ABORT_ON_FAILURE=false - -# ============================================================ -# OPENTELEMETRY CONFIGURATION (Optional) -# ============================================================ - -# Service name for telemetry (default: appworld-a2a-proxy) -OTEL_SERVICE_NAME=appworld-a2a-proxy - -# OTLP exporter endpoint (if not set, telemetry won't be exported) -# OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 - -# OTLP protocol (default: grpc) -# Options: grpc, http/protobuf -OTEL_EXPORTER_OTLP_PROTOCOL=grpc - -# Additional resource attributes (comma-separated key=value pairs) -# OTEL_RESOURCE_ATTRIBUTES=env=development,team=research,version=1.0 - -# Enable requests auto-instrumentation for standardized HTTP client spans (default: true) -OTEL_INSTRUMENT_REQUESTS=true - -# ============================================================ -# DEBUG CONFIGURATION (Optional) -# ============================================================ - -# Log prompt details (default: 0) -# WARNING: May log sensitive information -LOG_PROMPT=0 - -# Log response details (default: 0) -# WARNING: May log sensitive information -LOG_RESPONSE=0 diff --git a/appworld_a2a_runner/pyproject.toml b/appworld_a2a_runner/pyproject.toml deleted file mode 100644 index 1b8b490..0000000 --- a/appworld_a2a_runner/pyproject.toml +++ /dev/null @@ -1,33 +0,0 @@ -[build-system] -requires = ["setuptools>=61.0", "wheel"] -build-backend = "setuptools.build_meta" - -[project] -name = "appworld-a2a-runner" -version = "0.1.0" -description = "Standalone AppWorld A2A Proxy Runner" -readme = "README.md" -requires-python = ">=3.11" -dependencies = [ - "appworld>=0.1.0", - "requests>=2.28.0", - "opentelemetry-api>=1.20.0", - "opentelemetry-sdk>=1.20.0", - "opentelemetry-exporter-otlp>=1.20.0", - "opentelemetry-instrumentation-requests>=0.41b0", -] - 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[x] `exgentic_a2a_a2a_latency_ms` histogram - [x] `exgentic_a2a_prompt_size_chars` histogram - [x] `exgentic_a2a_response_size_chars` histogram @@ -111,7 +110,6 @@ - [x] Usage examples - [x] Output format documentation - [x] OpenTelemetry metrics/traces documentation - - [x] Comparison with appworld_a2a_runner - [x] Execution flow diagram - [x] Troubleshooting guide - [x] Example environment file with all variables @@ -167,7 +165,7 @@ 5. **Comprehensive Telemetry**: ✅ Traces, metrics, and logs 6. **Environment Configuration**: ✅ All settings via env vars 7. **Error Handling**: ✅ Graceful degradation and cleanup -8. **Reusable Components**: ✅ A2A client and OTEL base from appworld +8. **Reusable Components**: ✅ A2A client and OTEL base extracted for reuse ## 🔍 Code Quality @@ -177,7 +175,6 @@ - [x] Logging at appropriate levels - [x] Error messages with context - [x] Clean separation of concerns -- [x] Follows appworld_a2a_runner patterns ## 📊 Testing Readiness @@ -202,7 +199,6 @@ All components have been successfully implemented following the requirements fro - ✅ Communicates with Kagenti agents via A2A - ✅ Implements the exact execution model specified - ✅ Provides comprehensive observability -- ✅ Follows the appworld_a2a_runner pattern - ✅ Is fully documented and ready for deployment The implementation is **COMPLETE** and ready for integration testing with actual Exgentic MCP server and Kagenti agents. \ No newline at end of file diff --git a/exgentic_a2a_runner/README.md b/exgentic_a2a_runner/README.md deleted file mode 100644 index 2e70990..0000000 --- a/exgentic_a2a_runner/README.md +++ /dev/null @@ -1,1114 +0,0 @@ -# Exgentic A2A Runner - -A standalone Python runner that integrates Exgentic benchmarks with Kagenti agents using the A2A (Agent-to-Agent) protocol. This harness implements the execution model defined in [GitHub Issue #963](https://github.com/kagenti/kagenti/issues/963). - -## Features - -- **Exgentic MCP Integration**: Communicates with Exgentic MCP server for benchmark tasks -- **Parallel session processing**: Configurable concurrency for efficient benchmark execution and stress testing -- **A2A protocol support**: Communicates with remote agents using the A2A protocol via JSON-RPC over HTTP -- **OpenTelemetry instrumentation**: Comprehensive traces, metrics, and logs -- **Strict failure handling**: Any error or timeout marks the session as failed -- **Configurable via environment variables**: Easy deployment and configuration -- **Composable AuthBridge plugin pipeline**: Per-agent selection of inbound/outbound plugins (`jwt-validation`, `token-exchange`, `a2a-parser`, `ibac`, …) with `enforce` / `observe` / `off` policies — see [AuthBridge Plugin Pipeline](#authbridge-plugin-pipeline). - -## Getting Started - -The minimal end-to-end path: stand up a Kagenti cluster, build the agent + MCP images, then deploy and evaluate. Each block here is self-contained — copy the commands you need. - -```bash -# 1. Stand up the Kagenti cluster (one-time) -git clone git@github.com:kagenti/kagenti.git -cd kagenti -env CONTAINER_ENGINE=podman scripts/kind/setup-kagenti.sh --with-all --preload-images -cd .. - -# 2. Install the runner -git clone git@github.com:kagenti/workload-harness.git -cd workload-harness/exgentic_a2a_runner -uv sync --python 3.12 -source .venv/bin/activate -cp example.env .env # edit as needed (OPENAI_API_KEY, IBAC tunables, etc.) - -# 3a. Plain run — no AuthBridge sidecar -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling - -# 3b. Run with auth + token exchange only -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling \ - --plugin-preset auth-only - -# 3c. Run with IBAC enforcing intent-based access control -export IBAC_JUDGE_ENDPOINT=http://host.docker.internal:11434 -export IBAC_JUDGE_MODEL=llama3.2:3b -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling \ - --plugin-preset ibac-only - -# 3d. Canary IBAC in observe mode (telemetry only, no blocking) -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling \ - --plugin-preset ibac-only --plugin ibac:observe -``` - -The deploy + evaluate steps can also be run separately — see [Usage](#usage). For full plugin pipeline mechanics, see [AuthBridge Plugin Pipeline](#authbridge-plugin-pipeline). - -## Architecture - -The runner follows this execution model for each benchmark session: - -1. **Create Session**: `(session_id, task) = mcp_server.create_session()` -2. **Invoke Agent**: `agent.invoke_agent("{task}")` . Pass session_id as meta_data. -3. **Evaluate Session**: `success = mcp_server.evaluate_session(session_id)` -4. **Close Session**: `mcp_server.close_session(session_id)` -5. **Record Statistics**: Track completion time, success rate, compute costs, tokens. - -## Benchmarks - -The runner currently drives three benchmarks via the Exgentic MCP server. Each is a separate MCP image (`./build.sh ` from `agent-examples/mcp/exgentic_benchmarks`) and is selected at deploy time with `--benchmark `. - -| Benchmark | What it tests | Tool surface | Notes | -|-----------|---------------|--------------|-------| -| `gsm8k` | Grade-school math word problems — single-turn arithmetic reasoning. | Minimal — primarily LLM reasoning, light tool use. | Cheap and fast; good smoke test. The deploy script sets `EXGENTIC_SET_BENCHMARK_RUNNER=direct` for this benchmark. | -| `tau2` | Multi-turn customer-support conversations against a simulated user. Measures whether the agent can complete realistic task flows over several turns. | Domain tools (retail, airline, telecom) plus a user-simulator LLM. | Deploy passes `EXGENTIC_SET_BENCHMARK_USER_SIMULATOR_MODEL` so the simulator runs on the same model as the agent. The IBAC plugin also lands its canonical attack-shape tests against tau-style multi-turn traffic — see [`ibac-benchmarking.md`](ibac-benchmarking.md). | -| `appworld` | Long-horizon, tool-heavy tasks across a simulated app ecosystem (calendar, email, contacts, etc.). Stresses tool selection and planning. | Very wide — hundreds of tools across the simulated apps. | OpenAI models can't handle this tool surface without shortlisting; use `gemini-2.5-pro` or another model with strong tool selection. | - -### Picking a model - -The model name passed via `--model` (or `LLM_MODEL` / `EXGENTIC_SET_AGENT_MODEL`) is consumed by [LiteLLM](https://docs.litellm.ai/) on the agent side, so it follows LiteLLM's `/` routing convention. The default is `Azure/gpt-4.1`. - -**OpenAI-compatible backends** (vLLM, Ollama, llama.cpp, LM Studio, custom proxies, etc.) — prefix the model name with `openai/` to force LiteLLM down its OpenAI-compatible route, and point `OPENAI_API_BASE` at your endpoint: - -```bash -# Custom Azure deployment fronted by an OpenAI-compatible proxy -./deploy-agent.sh --benchmark tau2 --agent tool_calling \ - --model openai/Azure/gpt-4o-mini - -# Local model served via vLLM/Ollama -./deploy-agent.sh --benchmark gsm8k --agent tool_calling \ - --model openai/llama3.1-70b-instruct -``` - -For `appworld`, use a model with strong tool-selection — e.g. `gemini-2.5-pro` — rather than an OpenAI-route model. - -## Installation - -> **⏱️ Estimated Setup Time:** ~15 minutes (excluding container image pulls) - -### Prerequisites - -- Python 3.11 or 3.12 (Python 3.13+ is **not supported** due to dependency compatibility) - - **Note:** The `uv` package manager will automatically use Python 3.12 when you run `uv sync --python 3.12`, regardless of your system Python version -- [uv](https://docs.astral.sh/uv/) package manager -- kubectl v0.6.0 (tested on v0.6.0-rc.2) -- Kagenti cluster running with: - - Kagenti backend in `kagenti-system` namespace - - Keycloak in `keycloak` namespace - - `team1` namespace for deployments - -> **AuthBridge sidecar:** the deploy scripts can attach an AuthBridge -> sidecar with a composable plugin pipeline (auth, token exchange, -> intent-based access control, …) to each agent. See -> [AuthBridge Plugin Pipeline](#authbridge-plugin-pipeline) for the -> full surface; the sidecar is only injected when you pass a plugin -> selector. - -### Install from source - -#### Deploy a kagenti cluster - -```bash -git clone git@github.com:kagenti/kagenti.git -cd kagenti - -env CONTAINER_ENGINE=podman scripts/kind/setup-kagenti.sh --with-all --preload-images - -``` - - -#### Deploy agent and MCP server per benchmark - -```bash -git clone git@github.com:yoavkatz/workload-harness.git -cd workload-harness -git checkout feature/exgentic-a2a-runner -cd exgentic_a2a_runner -uv sync --python 3.12 -source .venv/bin/activate - -# Deploy and configure MCP server using Kagenti Tool API -# This script now combines deployment and configuration in one step -./deploy-benchmark.sh --benchmark tau2 - -# Deploy and configure agent using Kagenti Agent API -# This script now combines deployment and configuration in one step -./deploy-agent.sh --benchmark tau2 --agent tool_calling -``` - -**Note:** All deployment scripts now use named parameters: - -**Benchmark Deployment:** -```bash -# Basic deployment with defaults (model: Azure/gpt-4.1, keycloak: admin/admin) -./deploy-benchmark.sh --benchmark tau2 - -# Deploy with custom model -./deploy-benchmark.sh --benchmark tau2 --model Azure/gpt-4o-mini - -# Deploy with custom Keycloak credentials -./deploy-benchmark.sh --benchmark tau2 --model Azure/gpt-4o-mini --keycloak-user admin --keycloak-pass admin - -# Show help -./deploy-benchmark.sh --help -``` - -**Agent Deployment:** -```bash -# Basic deployment with defaults (model: Azure/gpt-4.1, keycloak: admin/admin) -./deploy-agent.sh --benchmark tau2 --agent tool_calling - -# Deploy with custom model -./deploy-agent.sh --benchmark tau2 --agent tool_calling --model Azure/gpt-4o-mini - -# Deploy with custom Keycloak credentials -./deploy-agent.sh --benchmark tau2 --agent tool_calling --model Azure/gpt-4o-mini --keycloak-user admin --keycloak-pass admin - -# Show help -./deploy-agent.sh --help -``` - -**Agent Naming:** Underscores in agent names are automatically converted to hyphens for Kubernetes compatibility (e.g., `tool_calling` becomes `tool-calling`). - -**Important:** Both deployment scripts now combine deployment and configuration steps: - -**`deploy-benchmark.sh`** will: -1. Deploy the MCP server to the Kagenti cluster -2. Automatically configure secrets before deployment: - - Updates `openai-secret` with OPENAI_API_KEY (if set in environment) - - Creates/updates `hf-secret` with HF_TOKEN (uses dummy token if not set) -3. Configure environment variables (OPENAI_API_BASE, EXGENTIC_SET_BENCHMARK_RUNNER for gsm8k) -4. Set memory limits and model settings -5. Wait for the deployment to be ready - -**`deploy-agent.sh`** will: -1. Deploy the agent to the Kagenti cluster -2. Automatically configure environment variables (OPENAI_API_BASE, OPENAI_API_KEY, LLM_MODEL) -3. Set model settings (LLM_MODEL, EXGENTIC_SET_AGENT_MODEL) -4. Wait for the deployment to be ready - -**Environment Variables for Deployment:** -- `OPENAI_API_KEY`: OpenAI API key (optional, updates openai-secret if set) -- `HF_TOKEN`: HuggingFace token (optional, creates hf-secret with dummy token if not set) -- `OPENAI_API_BASE`: OpenAI API base URL (optional, added to deployment env vars) - -For benchmark fit and model-name conventions (including the `openai/` prefix for OpenAI-compatible backends), see [Benchmarks](#benchmarks). - -## MCP Gateway Support - -The runner can optionally route MCP traffic through an [MCP Gateway](https://github.com/kuadrant/mcp-gateway) instead of connecting directly to the benchmark MCP server. When enabled, the gateway acts as a single entry point that multiplexes access to registered MCP servers and namespaces their tools with a configurable prefix. - -### How It Works - -1. **Benchmark deployment** (`deploy-benchmark.sh --use-mcp-gateway`) creates an `HTTPRoute` and an `MCPServerRegistration` CR that registers the MCP server with the gateway. -2. **Agent deployment** (`deploy-agent.sh --use-mcp-gateway`) points the agent's `MCP_URL` at the gateway service (`mcp-gateway-istio.gateway-system.svc.cluster.local:8080`) instead of the benchmark's MCP service directly. -3. **Evaluation** (`evaluate-benchmark.sh`) port-forwards the gateway service and sets `EXGENTIC_MCP_TOOL_PREFIX` so the runner prepends the gateway-assigned prefix to every tool call (e.g. `list_tasks` becomes `exgentic_list_tasks`). - -### Deploying with the MCP Gateway - -Pass `--use-mcp-gateway` to any deployment or evaluation script: - -```bash -# Individual scripts -./deploy-benchmark.sh --benchmark tau2 --use-mcp-gateway -./deploy-agent.sh --benchmark tau2 --agent tool_calling --use-mcp-gateway - -# All-in-one -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling --use-mcp-gateway -``` - -You can also set the flag in your `.env` file so it applies by default: - -```bash -USE_MCP_GATEWAY=true -``` - -### Tool Prefix - -The MCP Gateway exposes tools under a namespace prefix (default `exgentic_`). The runner reads `EXGENTIC_MCP_TOOL_PREFIX` and prepends it to every MCP tool call. When using the gateway via `evaluate-benchmark.sh`, this variable is set automatically. - -## AuthBridge Plugin Pipeline - -The deploy scripts can attach an **AuthBridge sidecar** to each -deployed agent. AuthBridge is a forward proxy whose behavior is -defined by a composable pipeline of named plugins — `jwt-validation`, -`token-exchange`, `token-broker`, `a2a-parser`, `mcp-parser`, -`inference-parser`, `ibac` — each independently togglable and each -running under an `on_error` policy of `enforce`, `observe`, or `off`. - -This replaces the older opaque `--authbridge` / `--ibac` toggles. The -full design lives in [`AUTHBRIDGE_PIPELINE_SPEC.md`](AUTHBRIDGE_PIPELINE_SPEC.md). - -### Architectural implications - -- **The sidecar is opt-in per deploy.** With no plugin selectors, the - operator does not inject a sidecar at all and the agent runs as - before. Pass any selector (`--plugin-preset`, `--plugin`, - `--no-plugin`, `--plugin-config-file`) and the sidecar is injected - with the resolved pipeline. -- **The pipeline mediates every request the agent sends and receives.** - Inbound plugins run on traffic to the agent (auth validation, A2A - parsing); outbound plugins run on traffic the agent makes to tools - and LLMs (token exchange, MCP/inference parsing, IBAC judging). - Adding a plugin adds a hop on that path — measure latency - accordingly. -- **Plugins share a `Session` object.** Inbound plugins populate fields - (e.g. `a2a-parser` extracts the user's intent into `Session.Intents`) - that outbound plugins read (e.g. `ibac` compares each outbound action - against that intent). Hard runtime dependencies are encoded in the - framework — IBAC fails closed if `a2a-parser` didn't run. -- **The operator's base config enables every plugin by default.** To - disable a plugin, the deploy overlay must explicitly emit - `on_error: off` for it. The script handles this — any plugin not in - your resolved selection is turned off in the overlay, not omitted. -- **Selector resolution is last-write-wins.** A preset seeds the set; - subsequent `--plugin` / `--no-plugin` flags apply in order. This - makes "preset minus one plugin" or "preset, but canary IBAC" easy - to express on the command line. -- **Configuration is delivered via overlay, hot-reloaded.** No operator - changes are required to flip the pipeline shape — the script writes - a merged ConfigMap and AuthBridge picks it up. - -### Flags - -These flags are accepted by `deploy-agent.sh` and forwarded by -`deploy-and-evaluate.sh`: - -| Flag | Description | -|------|-------------| -| `--plugin-preset NAME` | Named bundle. Available: `auth-only`, `ibac-only`, `full`. | -| `--plugin NAME[:POLICY]` | Enable plugin with policy ∈ {`enforce`(default), `observe`, `off`}. Repeatable. | -| `--no-plugin NAME` | Shorthand for `--plugin NAME:off`. Repeatable. | -| `--plugin-config-file PATH` | Flat-map YAML overlay merged after selectors. | - -### Presets - -| Preset | Inbound | Outbound | -|--------|---------|----------| -| `auth-only` | `jwt-validation` | `token-exchange` | -| `ibac-only` | `a2a-parser` | `inference-parser`, `mcp-parser`, `ibac` | -| `full` | `a2a-parser`, `jwt-validation` | `token-exchange`, `inference-parser`, `mcp-parser`, `ibac` | - -### Running with IBAC - -IBAC (Intent-Based Access Control) is an outbound plugin that compares -each agent action against the user's most-recent declared intent and -asks an LLM judge to deny requests that don't align — catching -prompt-injection-driven exfiltration that traditional auth gates miss. -See [`ibac-benchmarking.md`](ibac-benchmarking.md) for the full -benchmarking profile. - -**Prerequisites:** - -- An OpenAI-compatible chat-completion endpoint for the judge (ollama, - OpenAI, vLLM, Azure, etc.). -- The cluster's AuthBridge sidecar image must include the `ibac` - plugin. IBAC landed in `kagenti-extensions` on 2026-05-17 (PR #421); - use sidecar image **`v0.6.0-alpha.7`** or newer. - - > **Caveat — not in the latest stable Kagenti release.** As of this - > writing, the IBAC plugin and the additive plugin-pipeline merge - > behavior the deploy scripts depend on are only available in - > `v0.6.0-alpha.7`, which has not yet been published in a stable - > Kagenti release. Installing from the official `v0.6.0` chart - > release pulls an older alpha that will fail with errors like - > `jwt-validation config: issuer is required` during pipeline - > apply. Until a release containing alpha.7 is cut, install Kagenti - > from `main`: - > - > ```bash - > git clone git@github.com:kagenti/kagenti.git - > cd kagenti # use main, not a release tag - > env CONTAINER_ENGINE=podman scripts/kind/setup-kagenti.sh --with-all --preload-images - > ``` - > - > To verify the sidecar image actually deployed: - > - > ```bash - > kubectl -n kagenti-system get cm kagenti-platform-config \ - > -o jsonpath='{.data.authbridge}' - > ``` - > - > Expect `ghcr.io/kagenti/kagenti-extensions/authbridge:v0.6.0-alpha.7` - > or newer. - -**Configure the judge** in your `.env` (consumed by the IBAC plugin -fragment via envsubst when `ibac` is in the active set): - -```bash -# Judge LLM base URL — OpenAI-compatible (POST /v1/chat/completions) -IBAC_JUDGE_ENDPOINT=http://host.docker.internal:11434 -# Judge model id served by that endpoint -IBAC_JUDGE_MODEL=llama3.2:3b -# Per-judge-call timeout in milliseconds -IBAC_TIMEOUT_MS=15000 -# Hostname of the agent's own LLM endpoint (auto-derived from -# OPENAI_API_BASE if unset). Added to the IBAC bypass list so the -# agent's own reasoning calls aren't recursively judged. -# IBAC_AGENT_LLM_HOST=host.docker.internal -``` - -**Deploy with IBAC enforcing** -- *UNTESTED* --(full pipeline — auth + parsers + IBAC): - -```bash -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling \ - --plugin-preset full -``` - - -**IBAC without inbound auth** — for environments where an upstream -gateway already terminates auth but you still want intent-based -blocking on outbound calls: - -```bash -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling \ - --plugin-preset ibac-only -``` - -When IBAC blocks a request, the agent sees a `403 ibac.blocked` from -the proxy and the `ibac.evaluate` span on that outbound call carries -`verdict=deny`, `reason=blocked`, and the (truncated) intent and -action description. See `ibac-benchmarking.md` for the full set of -metrics IBAC emits. - -### Other compositions - -```bash -# Auth + token exchange only (no IBAC, no parsers). -./deploy-agent.sh --benchmark tau2 --agent tool_calling \ - --plugin-preset auth-only - -# Token-broker instead of token-exchange. -./deploy-agent.sh --benchmark tau2 --agent tool_calling \ - --plugin-preset full \ - --no-plugin token-exchange \ - --plugin token-broker - -# Custom set without a preset. -./deploy-agent.sh --benchmark tau2 --agent tool_calling \ - --plugin jwt-validation --plugin token-exchange --plugin ibac - -# No AuthBridge sidecar at all (omit all plugin flags). -./deploy-agent.sh --benchmark tau2 --agent tool_calling -``` - -### `--plugin-config-file` format - -A flat YAML map keyed by plugin name; values are deep-merged into each -plugin's `config:` block on top of the fragment defaults: - -```yaml -ibac: - judge_model: "llama3.2:8b" - timeout_ms: 30000 -token-exchange: - default_policy: exchange -jwt-validation: - bypass_paths: - - /healthz - - /metrics -``` - -Unknown plugin names in the file are ignored with a WARN to stderr. - -### Sidecar image compatibility - -Every plugin you select must be compiled into the running sidecar -binary. The merge validates the YAML shape, but the sidecar will fail -at Configure with `unknown plugin ""` after reload if a plugin -isn't registered: - -``` -reloader: reload failed error="build: outbound: unknown plugin \"\"" -``` - -The image tag is pinned in `kagenti/charts/kagenti/values.yaml`. To -verify what's running: - -```bash -kubectl -n team1 get pod -l app.kubernetes.io/name= \ - -o jsonpath='{range .items[0].spec.containers[?(@.name=="authbridge")]}{.image}{"\n"}{end}' -``` - -> **Compatibility note.** Newer chart versions tie sidecar image -> versions to operator versions (per-plugin config support, -> jwt-validation field shape). When bumping the sidecar image past -> `v0.5.0-rc.3`, confirm your kagenti-operator is recent enough — -> older operators may emit ConfigMaps the newer sidecar can't parse. - -### Troubleshooting - -- **`unknown plugin ""`** at reload: the sidecar binary doesn't - have that plugin compiled in. Bump the image tag. -- **Mutex error: `token-exchange` and `token-broker`**: both claim - `ClaimAuthorizationHeader`; the script rejects this before any - kubectl call. Disable one with `--no-plugin`. -- **`Reads ... no earlier plugin writes it`**: parser ordering issue; - shouldn't happen with the canonical-position table, but possible if a - malformed `--plugin-config-file` introduces an unknown plugin. See - [`framework-architecture.md` §6](https://github.com/kagenti/kagenti-extensions/blob/main/authbridge/docs/framework-architecture.md) - for the underlying rules. -- **`ibac.no_intent`** in IBAC telemetry: the inbound chain is - misconfigured — `a2a-parser` didn't run, so `Session.Intents` is - empty and IBAC fails closed. Confirm `a2a-parser` is in the active - inbound set (it is for the `ibac-only` and `full` presets). -- **Unknown plugin / preset name**: script-side error; valid names are - listed in the spec §3.1, valid presets in §3.2. - -## Configuration - -### Before Running Evaluations - -**Required:** Create and configure your environment file: - -```bash -cp example.env .env -``` - -Then edit the .env file as needed. - -### Main Configuration - -| Environment Variable | Default | Description | -| --- | --- | --- | -| `MAX_TASKS` | `(none)` | Maximum number of sessions to process. Useful for testing with a subset. | -| `MAX_PARALLEL_SESSIONS` | `1` | Number of sessions to run concurrently. Set higher for parallel execution. | -| `ABORT_ON_FAILURE` | `false` | Stop processing after the first failed session. | - -### Debug Configuration - -| Environment Variable | Default | Description | -| --- | --- | --- | -| `LOG_LEVEL` | `INFO` | Log level for the runner. Set to `DEBUG` for verbose logging with detailed debug information. Options: `DEBUG`, `INFO`, `WARNING`, `ERROR`. | -| `LOG_PROMPT` | `0` | Log prompt payloads for debugging (1 to enable). | -| `LOG_RESPONSE` | `0` | Log response payloads for debugging (1 to enable). | - -### Tracing Configuration (OpenTelemetry) - -| Environment Variable | Default | Description | -| --- | --- | --- | -| `OTEL_EXPORTER_OTLP_ENDPOINT` | `(none)` | OTLP collector endpoint (for this runner, use gRPC such as `http://localhost:4317`). If not set, no traces are exported. | -| `OTEL_SERVICE_NAME` | `exgentic-a2a-runner` | Service name in traces. | -| `OTEL_RESOURCE_ATTRIBUTES` | `(none)` | Additional resource attributes (format: `key1=val1,key2=val2`). | -| `OTEL_INSTRUMENT_REQUESTS` | `true` | Auto-instrument HTTP requests. | -| `OTEL_EXPORTER_OTLP_PROTOCOL` | `grpc` | OTLP protocol. The current exporter implementation in [`OTELInstrumentation._initialize_tracing()`](exgentic_a2a_runner/exgentic_a2a_runner/otel.py:80) and [`OTELInstrumentation._initialize_metrics()`](exgentic_a2a_runner/exgentic_a2a_runner/otel.py:114) uses OTLP gRPC. | -| `OTEL_EXPORTER_OTLP_INSECURE` | `true` | Use insecure OTLP connection. | - -### MCP Gateway Configuration - -| Environment Variable | Default | Description | -| --- | --- | --- | -| `USE_MCP_GATEWAY` | `false` | Route MCP traffic through the MCP Gateway instead of connecting directly to the MCP server. | -| `EXGENTIC_MCP_TOOL_PREFIX` | `(empty)` | Prefix prepended to MCP tool names. Set to match the gateway's `MCPServerRegistration.spec.toolPrefix` (e.g. `exgentic_`). | - -### Advanced Configuration - -| Environment Variable | Default | Description | -| --- | --- | --- | -| `EXGENTIC_MCP_TIMEOUT_SECONDS` | `60` | Timeout for MCP operations. | -| `A2A_TIMEOUT_SECONDS` | `300` | Timeout for A2A requests. | -| `A2A_AUTH_TOKEN` | `(none)` | Bearer token for A2A authentication. | -| `A2A_VERIFY_TLS` | `true` | Verify TLS certificates for HTTPS. | -| `A2A_ENDPOINT_PATH` | `/` | Endpoint path for A2A requests. | - -## Usage - -### All-in-One: Deploy and Evaluate - -The `deploy-and-evaluate.sh` script provides a convenient way to deploy both the benchmark and agent, then run the evaluation in a single command: - -```bash -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling -``` - -This script will: -1. Deploy the benchmark MCP server -2. Deploy the agent -3. Run the evaluation - -MLflow tracing via the OTEL Collector is **enabled by default**. Pass `--disable-mlflow` to skip it. - -**Options:** -```bash -# Basic usage with defaults (MLflow tracing enabled) -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling - -# Route MCP traffic through the MCP Gateway -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling --use-mcp-gateway - -# With custom model -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling --model Azure/gpt-4o-mini - -# With custom Keycloak credentials -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling --model Azure/gpt-4o-mini --keycloak-user admin --keycloak-pass admin - -# Dry run mode - print commands without executing them -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling --dry - -# Show help -./deploy-and-evaluate.sh --help -``` - -#### Dry Run Mode - -The `--dry` flag enables dry-run mode, which prints all commands that would be executed without actually running them. This is useful for: -- Verifying command syntax before execution -- Debugging deployment issues -- Understanding what the script will do -- Documentation and training purposes - -```bash -# See what commands would be executed -./deploy-and-evaluate.sh --benchmark tau2 --agent tool_calling --dry - -# Dry run with all options -./deploy-and-evaluate.sh --benchmark gsm8k --agent tool_calling \ - --model Azure/gpt-4o-mini --mlflow --use-mcp-gateway --dry -``` - -**Example output:** -``` -======================================== -Deploy and Evaluate Exgentic Benchmark -======================================== -Benchmark: tau2 -Agent: tool_calling -Model: Azure/gpt-4.1 -Keycloak User: admin -MLflow tracing: false -MCP Gateway: false -Dry run: true - -======================================== -Step 1/3: Deploying Benchmark -======================================== -[DRY RUN] Would execute: -./deploy-benchmark.sh --benchmark "tau2" --model "Azure/gpt-4.1" --keycloak-user "admin" --keycloak-pass "admin" - -======================================== -Step 2/3: Deploying Agent -======================================== -[DRY RUN] Would execute: -./deploy-agent.sh --benchmark "tau2" --agent "tool_calling" --model "Azure/gpt-4.1" --keycloak-user "admin" --keycloak-pass "admin" - -======================================== -Step 3/3: Running Evaluation -======================================== -[DRY RUN] Would execute: -./evaluate-benchmark.sh --benchmark "tau2" --agent "tool_calling" --experiment "default" - -======================================== -✓ Dry run completed - no commands executed -======================================== -``` - -### Running Benchmarks - -The `evaluate-benchmark.sh` script automatically: -- Uses HTTP routes to reach services (no port-forwarding for MCP/agent) -- Port-forwards the OTEL Collector (traces → MLflow) on dev laptops — skipped in-cluster -- Waits for services to be ready via HTTP health checks -- Tests connectivity to services -- Runs the benchmark evaluation -- Propagates the current OpenTelemetry trace context into outbound A2A HTTP requests so the agent can continue the same distributed trace when it supports W3C trace headers -- Cleans up port forwards on exit - -```bash -./evaluate-benchmark.sh --benchmark tau2 --agent tool_calling -./evaluate-benchmark.sh --benchmark gsm8k --agent tool_calling -``` - -## Output - -### Console Summary - -At the end of each run, a summary is printed: - -``` -============================================================ -RUN SUMMARY -============================================================ -Sessions Attempted: 100 -Sessions Succeeded: 95 -Sessions Failed: 5 -Evaluation Success: 92.6% -Total Wall Time: 1234.56s -Average Latency: 12345.67ms -P50 Latency: 10000.00ms -P95 Latency: 20000.00ms -============================================================ -``` - -### OpenTelemetry Data - -The runner emits comprehensive telemetry: - -#### Traces - -Each session creates a span (`exgentic_a2a.session`) with: - -**Attributes:** -- `exgentic.session_id`: Session identifier -- `exgentic.mcp_server_url`: MCP server URL -- `exgentic.evaluation_result`: Whether evaluation was successful -- `a2a.base_url`: A2A endpoint URL -- `a2a.timeout_seconds`: Timeout value -- `prompt.chars`: Prompt size in characters -- `response.chars`: Response size in characters -- `session.status`: `success` or `failed` -- `a2a.duration_ms`: End-to-end A2A operation latency in milliseconds - -**Child spans:** -- `exgentic_a2a.prompt.build`: Prompt construction -- `exgentic_a2a.a2a.send_prompt`: End-to-end A2A `send_prompt` call -- `exgentic_a2a.mcp.evaluate_session`: Session evaluation -- `exgentic_a2a.mcp.close_session`: Session cleanup - -**Auto-instrumented HTTP spans:** -- Outbound `requests` spans for agent-card discovery, `message/send`, and `tasks/get` calls - -**Events:** -- `prompt_built`: When prompt is constructed -- `session_failed`: When session fails (includes error details) - -#### Metrics - -**Counters:** -- `exgentic_a2a_sessions_total{status=success|failed}`: Total sessions processed -- `exgentic_a2a_errors_total{error_type=...}`: Total errors by type - -**Histograms:** -- `exgentic_a2a_session_latency_ms`: End-to-end session latency -- `exgentic_a2a_evaluation_latency_ms`: Evaluation operation latency -- `exgentic_a2a_session_creation_latency_ms`: Session creation latency -- `exgentic_a2a_a2a_latency_ms`: A2A request latency -- `exgentic_a2a_prompt_size_chars`: Prompt size distribution -- `exgentic_a2a_response_size_chars`: Response size distribution - -**Gauge:** -- `exgentic_a2a_inflight_sessions`: Current sessions in flight (0 or 1) - -## Key Differences from AppWorld Runner - -| Aspect | AppWorld Runner | Exgentic Runner | -|--------|----------------|-----------------| -| **Task Source** | AppWorld dataset enumeration | MCP server `create_session` | -| **Protocol** | Direct AppWorld API | MCP protocol | -| **Session Management** | Implicit (AppWorld context) | Explicit (create/evaluate/close) | -| **Evaluation** | AppWorld evaluation system | MCP `evaluate_session` | -| **Prompt Format** | Task + supervisor + apps | Task + session_id | -| **Dependencies** | `appworld` package | `mcp` package | - -## Execution Flow - -``` -┌─────────────────────────────────────────────────────────┐ -│ For Each Session │ -├─────────────────────────────────────────────────────────┤ -│ │ -│ 1. Create Session │ -│ └─> MCP: create_session() → (session_id, task) │ -│ │ -│ 2. Build Prompt │ -│ └─> Include session_id in instructions │ -│ │ -│ 3. Invoke Agent │ -│ └─> A2A: send_prompt(prompt) → response │ -│ │ -│ 4. Evaluate Session │ -│ └─> MCP: evaluate_session(session_id) → success │ -│ │ -│ 5. Close Session │ -│ └─> MCP: close_session(session_id) │ -│ │ -│ 6. Record Statistics │ -│ └─> Track time, success, evaluation result │ -│ │ -└─────────────────────────────────────────────────────────┘ -``` - -## OpenTelemetry and Observability - -### Using MLflow in the kind Cluster - -The Kagenti cluster exposes an MLflow service in the `kagenti-system` namespace. An OTEL Collector forwards traces to MLflow's `/v1/traces` endpoint with OAuth2 authentication. - -#### 1. Send runner telemetry to MLflow - -MLflow tracing is **enabled by default**. The script automatically port-forwards the OTEL Collector on a developer laptop and configures the required environment variables. To disable it, pass `--disable-mlflow`: - -```bash -# Default — MLflow tracing enabled -env MAX_TASKS=1 MAX_PARALLEL_SESSIONS=1 ./evaluate-benchmark.sh --benchmark gsm8k --agent tool_calling - -# Disable MLflow tracing -env MAX_TASKS=1 MAX_PARALLEL_SESSIONS=1 ./evaluate-benchmark.sh --benchmark gsm8k --agent tool_calling --disable-mlflow -``` - -#### 2. Open the MLflow UI - -Open http://mlflow.localtest.me:8080 in your browser to view traces and experiments. - -### Analyzing Traces with analyze-run.sh - -The [`analyze-run.sh`](analyze-run.sh) script provides comprehensive trace analysis by downloading Agent.Session traces from MLflow and generating detailed performance reports. - -#### Features - -- **Automatic MLflow connectivity**: Connects to MLflow REST API with OAuth2 authentication and optional auto port-forwarding -- **Trace filtering**: Downloads Agent.Session root spans and all child spans -- **Experiment filtering**: Filter or compare traces by experiment name -- **Performance metrics**: Calculates timing statistics (avg, p50, p95, min, max) for: - - Session creation time - - Agent call time (end-to-end agent execution) - - Evaluation time - - LLM call time and token usage - - Tool call time -- **Grouping**: Groups traces by agent, benchmark, model, and parallel session count -- **Detailed reports**: Generates both summary statistics and individual trace details - -#### Usage - -```bash -# Basic usage (assumes MLflow is accessible at http://mlflow.localtest.me:8080) -./analyze-run.sh - -# With custom MLflow URL and limit -./analyze-run.sh --url http://mlflow.localtest.me:8080 --limit 200 - -# Auto port-forward from kind cluster if MLflow is not accessible locally -./analyze-run.sh --forward --limit 50 - -# Filter by experiment name -./analyze-run.sh --experiment baseline - -# Compare two experiments -./analyze-run.sh --compare baseline,test1 -``` - -#### Options - -| Option | Description | Default | -|--------|-------------|---------| -| `-u, --url URL` | MLflow REST API base URL | `http://mlflow.localtest.me:8080` | -| `-l, --limit NUM` | Maximum number of traces to download | `100` | -| `-e, --experiment NAME` | Filter traces by experiment name attribute | (none) | -| `-c, --compare EXP1,EXP2` | Compare two experiments (comma-separated) | (none) | -| `--experiment-id ID` | MLflow experiment ID to query | `0` | -| `-f, --forward` | Auto port-forward MLflow from kind cluster if not accessible | `false` | -| `-h, --help` | Show help message | - | - -#### How It Works - -1. **Connectivity Test**: Attempts to connect to MLflow REST API health endpoint -2. **Auto Port-Forward** (if `--forward` is used): Sets up port-forwarding from kind cluster if MLflow is not accessible -3. **OAuth2 Authentication**: Obtains a bearer token from the cluster's `mlflow-oauth-secret` -4. **Trace Download**: Queries MLflow's trace API for the specified experiment, with pagination -5. **Format Transformation**: Converts MLflow trace format to the analysis input format via [`download_mlflow_traces.py`](download_mlflow_traces.py) -6. **Analysis**: Pipes trace data to [`analyze_traces.py`](analyze_traces.py) for detailed analysis - -#### Report Output - -The script generates two main sections: - -**1. Summary Statistics by Configuration** - -Groups traces by (agent, benchmark, model, parallel sessions) and shows: -- Count of traces -- Average, P50, P95, Min, Max for: - - Session creation time - - Agent call time - - Evaluation time - - LLM call time (with token counts) - - Tool call time -- Time distribution percentages (LLM%, Tool%, Other%) - -**2. Individual Trace Details** - -Lists each trace with: -- Trace ID -- Agent, Benchmark, Model, Parallel sessions -- Session creation time -- Agent call time (with LLM% and Tool% breakdown) -- Evaluation time -- LLM tokens (input/output) -- Tool call count and time - -#### Example Output - -``` -=== MLflow Trace Analysis === -MLflow URL: http://mlflow.localtest.me:8080 -Experiment ID: 0 -Limit: 100 - -✓ Connected to MLflow -✓ OAuth token obtained - -Found 45 Agent.Session traces -Downloading traces... -Downloaded 45 traces - -=== Trace Analysis Report === - -Summary Statistics by Configuration: -┌─────────────┬───────────┬─────────┬──────────┬───────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐ -│ Agent │ Benchmark │ Model │ Parallel │ Count │ Avg Create │ Avg Agent │ Avg Eval │ Avg LLM │ Avg Tool │ -│ │ │ │ │ │ (ms) │ Call (ms) │ (ms) │ (ms) │ (ms) │ -├─────────────┼───────────┼─────────┼──────────┼───────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ -│ tool-calling│ gsm8k │ gpt-4o │ 1 │ 45 │ 125.3 │ 8234.5 │ 45.2 │ 6543.2 │ 1234.5 │ -│ │ │ │ │ │ │ │ │ (79.5%) │ (15.0%) │ -└─────────────┴───────────┴─────────┴──────────┴───────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘ -``` - -#### Prerequisites - -- **jq**: JSON processor for parsing API responses - ```bash - # macOS - brew install jq - - # Ubuntu/Debian - apt-get install jq - ``` -- **Python 3**: For running the download and analysis scripts -- **MLflow**: Running and accessible (either locally or in kind cluster) -- **kubectl**: For port-forwarding and OAuth token retrieval - -#### Troubleshooting - -**Connection refused:** -- Ensure MLflow is running: `kubectl get pods -n kagenti-system -l app=mlflow` -- Use `--forward` flag to auto port-forward from kind cluster -- Manually port-forward: `kubectl port-forward -n kagenti-system svc/mlflow 8080:5000` - -**No traces found:** -- Verify traces exist in MLflow UI: http://mlflow.localtest.me:8080 -- Check that Agent.Session spans are being created by the runner -- MLflow tracing is enabled by default; pass `--disable-mlflow` only if you want to skip it - -**OAuth errors:** -- Ensure the `mlflow-oauth-secret` exists in the `kagenti-system` namespace -- Verify the MLflow pod is running (token acquisition executes inside the pod) - -### What Gets Traced - -When OTEL is enabled, you'll see: - -- **Session spans**: Complete session lifecycle with timing -- **MCP operations**: create_session, evaluate_session, close_session -- **A2A requests**: Agent invocations with request/response sizes -- **HTTP calls**: Auto-instrumented outbound requests -- **Errors**: Failed operations with exception details - -## In-Cluster Execution (Kubernetes Job) - -The runner can execute entirely inside the cluster as a Kubernetes Job — no local machine needed after the image is built. This is the recommended path for CI and automated evaluation runs. - -### Overview - -`k8s/job.yaml` is the launch template. It references secrets for credentials and passes benchmark/agent flags as container `args`. The job container uses cluster-internal DNS to reach the Kagenti API, Keycloak, MCP server, and agent — no port-forwarding is required. MLflow tracing switches automatically to HTTP/protobuf when `KUBERNETES_SERVICE_HOST` is set. - -### Step 1 — Build and push the runner image - -```bash -cd exgentic_a2a_runner -docker build -t ghcr.io/exgentic/runner:latest . -docker push ghcr.io/exgentic/runner:latest -``` - -Replace `ghcr.io/exgentic/runner:latest` with your own registry path if needed, and update `k8s/job.yaml` → `image:` to match. - -### Step 2 — Set up required secrets - -Run this **from your local machine** (not inside the cluster) before submitting the job. `update-secrets.sh` creates/patches the API-key secrets via kubectl: - -```bash -export OPENAI_API_KEY=sk-... -export HF_TOKEN=hf_... # optional; skip if not needed -./update-secrets.sh --namespace team1 -``` - -The `kagenti-test-user` secret (key: `password`) must already exist in `team1` — it is created by the Kagenti cluster setup and holds the Keycloak password. - -> **Note:** The job container itself does not have kubectl RBAC, so `update-secrets.sh` will print harmless warnings if it tries to run inside the cluster. The secrets just need to be present before the job starts. - -### Step 3 — Configure the job - -Edit `k8s/job.yaml` to set: - -| Field | Where | Example | -|-------|-------|---------| -| Benchmark | `args: ["--benchmark", "…"]` | `gsm8k`, `tau2`, `appworld` | -| Agent | `args: ["--agent", "…"]` | `tool_calling` | -| Model | `args: ["--model", "…"]` | `openai/Azure/gpt-4.1` | -| LLM API base | `env: OPENAI_API_BASE` | your LiteLLM proxy URL | - -To disable MLflow tracing, add `"--disable-mlflow"` to `args`. - -### Step 4 — Submit and watch the job - -```bash -# Delete any previous run with the same name first -kubectl delete job exgentic-runner -n team1 --ignore-not-found - -# Submit -kubectl apply -f k8s/job.yaml - -# Stream logs (the runner prints a summary table at the end) -kubectl logs -f job/exgentic-runner -n team1 -``` - -To watch job status separately: - -```bash -kubectl get job exgentic-runner -n team1 -w -``` - -### Step 5 — View results - -**Console output** — the log stream ends with a run summary: - -``` -============================================================ -RUN SUMMARY -============================================================ -Max Parallel Sessions: 1 -Sessions Attempted: 10 -Sessions Succeeded: 9 -Sessions With Error: 1 -Evaluation Success: 90.0% -Total Wall Time: 312.4s - -TIMING BREAKDOWN (average per session) - Session Creation: 0.05s - Agent Processing: 31.2s - Evaluation: 0.02s - -AGENT PROCESSING LATENCY - Average: 31.20s - P50: 28.00s - P95: 58.00s -============================================================ -``` - -**MLflow UI** — if MLflow tracing was enabled (the default), open `http://mlflow.localtest.me:8080` to view traces grouped by experiment. - -### Iterating with a locally-built image - -To test a local image change without pushing to a registry, sync it into the kind cluster first: - -```bash -export REMOTE_IMAGE_NAME=ghcr.io/exgentic/runner:dev -export KIND_CLUSTER_NAME=kagenti -source ./sync-image-to-cluster.sh -``` - -Then set `imagePullPolicy: IfNotPresent` in `k8s/job.yaml` and submit as above. - -## E2E Test Script - -`e2e-test.sh` runs `deploy-and-evaluate.sh` for every benchmark (or a chosen subset) and prints a consolidated results table. - -### Basic usage - -```bash -# Run all three benchmarks sequentially (gsm8k → tau2 → appworld), 1 task each -./e2e-test.sh --agent tool_calling - -# Run only a subset -./e2e-test.sh --agent tool_calling --benchmarks gsm8k,tau2 - -# Run with more tasks per benchmark -./e2e-test.sh --agent tool_calling --tasks 10 - -# Run all benchmarks in parallel (each benchmark gets its own port-forward slots) -./e2e-test.sh --agent tool_calling --parallel-jobs - -# Run all benchmarks as Kubernetes Jobs (in-cluster mode) -./e2e-test.sh --agent tool_calling --in-cluster - -# Dry run — prints all commands without executing them -./e2e-test.sh --agent tool_calling --dry -``` - -Any flag not recognised by `e2e-test.sh` is forwarded verbatim to `deploy-and-evaluate.sh` (e.g. `--model`, `--experiment`, `--disable-mlflow`, `--plugin-preset`). - -### Results table - -After all benchmarks finish, the script prints and writes `e2e-results.md`: - -``` -| Benchmark | Status | Tasks | Parallel Sessions | Eval Success Rate | Avg Latency (s) | Failures | -|-----------|--------|-------|-------------------|-------------------|-----------------|----------| -| gsm8k | PASS | 1 | -- | 100.0% | 4.2s | 0 | -| tau2 | PASS | 1 | -- | 100.0% | 12.8s | 0 | -| appworld | PASS | 1 | -- | 100.0% | 9.1s | 0 | -``` - -- **Status** is `PASS`, `FAIL`, or `SKIP` (skipped benchmarks appear when a sequential run aborts early). When a step fails, the status includes the step name, e.g. `FAIL(deploy-benchmark)`. -- **Parallel jobs mode** (`--parallel-jobs`): all benchmarks run concurrently; each gets unique OTEL-collector and Prometheus ports so local port-forwards don't collide. -- **In-cluster mode** (`--in-cluster`): one Kubernetes Job is created per benchmark from `k8s/job.yaml`; `e2e-test.sh` streams the logs and checks the job's success status. - -## Current Limitations - -- No retry mechanism for failed operations -- No streaming response support -- Tested only with local kind Kagenti installation with Podman (not tested with Docker) - -## Troubleshooting - - -## Development - -### Running Tests - -```bash -uv run pytest -``` - -### Code Formatting - -```bash -uv run black exgentic_a2a_runner/ -``` - -### Type Checking - -```bash -uv run mypy exgentic_a2a_runner/ -``` - -## Contributing - -Contributions are welcome! Please ensure: -- Code follows the existing style -- Tests pass -- Documentation is updated -- Commit messages are clear - -## License - -See LICENSE file in the repository root. - -## Additional Resources - -- **Kagenti UI**: Access at http://kagenti-ui.localtest.me:8080/ to monitor deployments -- **GitHub Issues**: https://github.com/kagenti/workload-harness/issues -- **Related Issue**: https://github.com/kagenti/kagenti/issues/963 - -## Next Steps - -After successful test run: -1. Increase `MAX_TASKS` in `.env` for longer runs -2. Adjust `MAX_PARALLEL_SESSIONS` for different concurrency levels -3. Enable OTLP exporter for telemetry collection -4. Deploy different benchmarks (gsm8k, tau2, appworld) -5. Test with various models via configure script -6. Analyze results and agent performance in Kagenti UI \ No newline at end of file