Swarmmesh MCP server

<!-- mcp-name: io.github.RudrenduPaul/swarmmesh -->

SwarmMesh

Install • Quickstart • Features • CLI reference • Compare • FAQ

Shared context and memory for swarms of parallel AI agents, over a small protocol both Python and Node speak the same way.

Spin up ten coding agents on the same task and they cannot see what each other found. One agent rediscovers a bug another already fixed. Two agents overwrite the same file because neither knew the other touched it. SwarmMesh is a small server that sits alongside your existing agent framework and gives every agent process, in any language that can speak HTTP, a shared place to publish context and search memory.

It is not an orchestration framework. It does not schedule tasks, define agent roles, or route work between agents. Your existing framework (or your own code) keeps doing that. SwarmMesh only answers one question: how do independent agent processes read and write the same shared state.

Install

pip install swarmmesh-cli
# or
npm install -g swarmmesh-cli

Either gives you a swarmmesh command on your PATH.

See it work

This is a real terminal session, not a mockup: a Python-run mesh, a Node agent writing to it, and a Python agent reading back what the Node agent wrote. Two different languages, one shared mesh.

# Terminal 1: start a mesh (Python implementation, but either works)
$ swarmmesh serve --port 8420
INFO: Uvicorn running on http://127.0.0.1:8420

# Terminal 2: a Node agent joins and writes
$ swarmmesh agent register node-agent-1 researcher --port 8420 --json
{ "agent_id": "node-agent-1", "role": "researcher", ... }

$ swarmmesh context set interop-demo status '"investigating flaky test"' \
    --agent-id node-agent-1 --port 8420 --json
{ "namespace": "interop-demo", "key": "status", "value": "investigating flaky test", ... }

$ swarmmesh memory write interop-demo \
    "found a race condition in the retry loop" --agent-id node-agent-1 --port 8420 --json
{ "namespace": "interop-demo", "text": "found a race condition in the retry loop", ... }

# Terminal 3: a Python agent joins the same mesh and reads it back
$ swarmmesh context get interop-demo status --port 8420 --json
{ "value": "investigating flaky test", "updated_by": "node-agent-1", ... }

$ swarmmesh memory query interop-demo "race condition" --port 8420 --json
{ "results": [{ "entry": { "text": "found a race condition in the retry loop" }, "score": 0.575 }] }

Every command above was re-run for real against both CLIs while writing this README: the Node CLI registered an agent and wrote context and memory against a Python-hosted mesh, and the Python CLI read it straight back, in the same run, over the real HTTP API, with the score above (0.575) reproduced exactly. No shared filesystem, no shared process, no translation layer. Just the protocol.

Quickstart

# Start a mesh (in-memory by default; add --persist ./mesh.db for SQLite storage)
swarmmesh serve --host 127.0.0.1 --port 8420

# From another terminal: register an agent
swarmmesh agent register agent-1 researcher

Shortened. The full README is on GitHub.