Polis Protocol

The local-first control plane for coding agents. Run Claude, Codex, Gemini, and Cursor against one repo — every task gets an owner, every handoff carries evidence, and the team measurably stops repeating its own mistakes. Plain markdown, in git, across every vendor.

🌐 Website → polis-protocol.vercel.app  ·  Install  ·  Proof  ·  Plug into your agent (MCP)

Repeat errors: 65% → 7% (−89%)  ·  Collisions: 0, deterministic  ·  reproduce in 5s: polis bench --mode learning
_{Yes — we benchmarked our own tool and published where it loses. That candor is the whole pitch.}

uvx polis-protocol init      # one command · no server · no database · just markdown in your repo

Shared by Claude · Codex · Gemini · Cursor, all reading one _polis/ folder. MIT.
⭐ If the loop below clicks, a star helps other multi-agent builders find this.

The 10-second version

Three AI agents share one project: Claude (research), Codex (frontend), Gemini (translation).

A Spanish-translation task comes in. Who gets it?

Early on, Claude did — it rated itself highly. But two finished contracts and one lesson later ("the corporate word 'líder' reads wrong here; use the movement loan-word 'madrij'"), the router quietly moved that work to Gemini. Nobody reassigned it. The team learned, and the routing followed.

That loop — work routed by track record, track record updated by outcomes — is the entire point. See it yourself in one command, no install, no API keys:

git clone https://github.com/yehudalevy-collab/polis-protocol.git
cd polis-protocol && bash scripts/demo.sh

Score breakdown (sorted by total):
  gemini-translator-pesaj   total=0.688  hist=0.25  self=1.00  cost=1.00  avail=1.00  lessons=+0.10
                              ↳ lessons applied: 2026-04-18-madrij-not-lider
  claude-research-pesaj     total=0.453  hist=0.15  self=0.60  cost=1.00  avail=1.00  lessons=+0.00
  codex-frontend-pesaj      total=0.290  hist=0.00  self=0.20  cost=1.00  avail=1.00  lessons=+0.00

Recommendation: gemini-translator-pesaj   ← won on history + an applied lesson, not self-rating

If that loop is interesting to you, a ⭐ genuinely helps other multi-agent builders find this.

What it is

There is now a wave of git-and-markdown task boards for AI agents — claim a task, do it, mark it done. They're good, and

... View full README on GitHub