Lightweight MCP orchestrator: parallel tool calls, local filtering, token trimming, and safe sandboxed flow scripts for AI agents.
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# mcp-flow **Lightweight MCP orchestrator for AI agents** — parallel tool calls, local filtering, token trimming, and a safe sandbox for LLM-generated control flow. [](https://pypi.org/project/mcp-flow/) [](https://pypi.org/project/mcp-flow/) [](LICENSE) [](https://github.com/mano7onam/mcp-flow/actions/workflows/test.yml) ```bash pip install mcp-flow # or uv add mcp-flow ``` --- ## The problem vs the solution ``` STANDARD TOOL-CALLING (sequential, slow, expensive) ────────────────────────────────────────────────── LLM ──► tool A ──► wait ──► LLM ──► tool B ──► wait ──► LLM ──► tool C │ │ │ └──── full JSON into context every hop ─┘ latency ≈ sum(A+B+C) tokens ≈ 3× raw payloads mcp-flow (parallel, local filter, token-bounded) ──────────────────────────────────────────────── LLM ──► Flow.parallel(A, B, C) ──► local filter/trim ──► LLM │ │ │ └──asyncio.gather──┘ latency ≈ max(A,B,C) tokens ≤ limit_tokens(N) ``` | | Sequential MCP | **mcp-flow** | |--|----------------|--------------| | Latency (3 tools @ 200ms) | ~600ms | **~200ms** | | Round-trips to LLM | 3–4 | **1** | | Context tokens | full raw JSON | **capped** (`limit_tokens`) | | Dependencies | frameworks | **`mcp` + `pydantic` only** | | LLM-written scripts | unsafe `exec` | **AST sandbox** | --- ## Quickstart (≈10 lines) ```python import asyncio from mcp_flow import Flow, MCPClientPool async def main(): async with MCPClientPool() as pool: await pool.add_stdio( "fs", "npx", ["-y", "@modelcontextprotocol/server-filesystem", "."], ) await pool.add_http("api", "http://127.0.0.1:8000/mc…
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