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@@ -39,7 +39,10 @@ Run from the **repo root**:
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uv sync
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```
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`uv sync` creates a virtual environment at `.venv/`, installs all dependencies, and registers the CLI entry points (`plan-execute`, `*-mcp-server`). You can either prefix commands with `uv run` (no activation needed) or activate the venv once for your shell session:
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`uv sync` creates a virtual environment at `.venv/`, installs all dependencies,
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and registers the CLI entry points (`plan-execute`, `*-agent`, `*-mcp-server`).
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You can either prefix commands with `uv run` (no activation needed) or activate
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the venv once for your shell session:
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```bash
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source .venv/bin/activate # macOS / Linux
@@ -92,25 +95,34 @@ See [MCP Servers](#mcp-servers) for available tools and [docs/mcp-servers.md](do
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|`WO_DBNAME`|`workorder`| Work order database name |
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|`VIBRATION_DBNAME`|`vibration`| Vibration sensor database name |
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**WatsonX** — plan-execute runner (when `--model-id` starts with `watsonx/`)
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**WatsonX** — plan-execute runner and WatsonX-backed agent routes
**LiteLLM proxy** — used by every runner whenever `--model-id` carries the `litellm_proxy/` prefix (the default for claude-agent, openai-agent, deep-agent)
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**LiteLLM proxy** — used by every runner whenever `--model-id` carries the `litellm_proxy/` prefix
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## Agents
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Seven runners are available as CLIs registered by `uv sync`; six use MCP tools, while `direct-llm-agent` is a model-only baseline that makes a direct LiteLLM call without MCP tools, planning, retrieval, or code execution. Each is a CLI registered by `uv sync` that takes a single positional `question` argument and spawns the MCP servers as stdio subprocesses on demand.
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Seven runners are available as CLIs registered by `uv sync`; six use MCP tools,
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while `direct-llm-agent` is a model-only baseline that makes a direct LiteLLM
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call without MCP tools, planning, retrieval, or code execution. Each takes a
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single positional `question` argument and spawns the MCP servers as stdio
|`deep-agent`|`src/agent/deep_agent/`|[LangChain deep-agents](https://docs.langchain.com/oss/python/deepagents/overview) (LangGraph), MCP bridged via `langchain-mcp-adapters`|`litellm_proxy/aws/claude-opus-4-6`|
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|`stirrup-agent`|`src/agent/stirrup_agent/`|[Stirrup](https://github.com/ArtificialAnalysis/Stirrup) agent loop (in-process), MCP via its `MCPToolProvider`; **code-capable** (writes/runs Python) |`watsonx/meta-llama/llama-4-maverick-17b-128e-instruct-fp8`|
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|`opencode-agent`|`src/agent/opencode_agent/`|[OpenCode](https://opencode.ai/docs/) CLI agent loop, MCP via generated OpenCode config; web/bash/edit denied by default |`opencode/gpt-5.1-codex`|
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|`opencode-agent`|`src/agent/opencode_agent/`|[OpenCode](https://opencode.ai/docs/) CLI agent loop, MCP via generated OpenCode config; MCP-only by default with optional CLI workspace mode|`opencode/gpt-5.1-codex`|
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|`direct-llm-agent`|`src/agent/direct_llm_agent/`| Single direct LLM call, no MCP tools, planning, retrieval, or code execution |`litellm_proxy/Azure/gpt-5-mini-2025-08-07`|
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-[Agents](#agents) — Stirrup specifics in [docs/stirrup-agent.md](docs/stirrup-agent.md); OpenCode specifics in [docs/opencode-agent.md](docs/opencode-agent.md)
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|`--max-tokens N`| stirrup-agent | Max output tokens per call; keep under provider limit (default 16384) |
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