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tainguyen07/agent-workflow-mcp

Multi-agent workflow orchestrator with MCP tool servers: planner/executor agents, tool-use loop, MCP client/server, tracing and durable runs.

15 stars
0 forks
Python
momentum ▲ 30.0
created 2026-08-20
on radar since 2026-08-21
agentsgenerative-aillmmcpmodel-context-protocolmulti-agentorchestrationtool-use
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About agent-workflow-mcp

Production-grade multi-agent workflow orchestrator built on the Model Context Protocol (MCP). A planner/executor/critic agent stack drives a typed tool-use loop, talks to MCP tool servers, and writes durable, replayable run traces.

Most agent frameworks stop at a chat loop. agent-workflow-mcp goes further: deterministic planning, structured tool calls, MCP-native tool discovery, retries with backoff, durable run state, and a trace log you can replay end-to-end. Designed to run unattended for hours and pick up where it left off after a crash.

- Planner / Executor / Critic agents that decompose a goal into a typed plan, dispatch tool calls, and critique each step before committing. - MCP client + server transport over stdio and WebSocket, with full JSON-RPC 2.0 protocol support and capability negotiation. - Tool-use loop with bounded retries, exponential backoff, schema validation, and a stop-on-criteria hook so loops cannot run away. - Durable run state: every step, tool call, and intermediate message is appended to an event log that can be replayed or resumed. - OpenTelemetry-style tracing with span IDs, parent links, token accounting, and latency histograms per agent role. - Typed config via Pydantic v2 with profile-based overrides (default, dev, prod). - Pluggable providers: built-in Anthropic adapter with a clean Provider protocol for OpenAI, Bedrock, or local backends. - CLI with serve, run, replay, trace subcommands and JSON output for scripting. - 92% test coverage, property-based tests for the retry and replay logic.

From the project README.

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