I built an AI team that ships real work — and shows you the conversation

A developer released Agent Team, an open-source MIT-licensed local-first runtime in which a Master agent plans work and Researcher, Builder and Reviewer agents execute it, emitting real artifacts plus an auditable log of bot-to-bot messages, timelines and verification bound to each artifact revision. In a 2026-09-13 run through the local Claude Code CLI, the system produced a Japanese launch page and three social-post drafts with four deliverables, 10 passing programmatic checks and a 6/6 reviewer verdict across 39 model turns and 35 tool calls at a $1.66 list-price equivalent. The project supports the Claude API, any OpenAI-compatible endpoint and local Ollama per bot, and the developer is seeking feedback on the review-revise-re-review loop and on limiting workers to task, artifact refs and inbox rather than full history.

Most "multi-agent" demos are bots narrating to each other. The log is the product; the work is secondary. I wanted the opposite: one request in, real files out, and a trail I could audit afterwards. Agent Team is an open-source MIT , local-first runtime where a Master plans, a Researcher / Builder / Reviewer actually do the work, and you get the artifacts plus the real bot-to-bot messages, a timeline, and verification bound to each artifact revision. Repo: https://github.com/FORIFOR/Multibot https://github.com/FORIFOR/Multibot · Site + 59s intro: https://forifor.github.io/Multibot/ https://forifor.github.io/Multibot/ message.sent events — messages that were really delivered to another bot's mailbox. A question from the Builder wakes the Researcher, which answers with reply to . Acknowledgements never wake a model. The default connection runs every agent session through the local claude -p . Claude Code owns the loop for one session; the team's tools send message , publish artifact , run check , … are exposed to it as MCP tools through a small stdio proxy that forwards each call to the runtime's ToolGateway. Policy, budget and the event log are identical to the API path; Claude Code's own built-in tools are disabled for these sessions. Cost and usage are read from the CLI's JSON result, and the model it actually used comes from modelUsage , never from the model's own claims. Claude API official SDK , any OpenAI-compatible chat endpoint and local Ollama work the same way, per bot. On 2026-09-13 I ran this request through the local Claude Code CLI: "From this product description, build a Japanese launch page and three social-post drafts. Record assumptions for anything missing. Stop before publishing. Have the Reviewer verify." | Model reported by the provider | claude-opus-5 | | Plan | Master chose 1 builder task + 1 reviewer task and skipped the researcher; 8 recorded assumptions no prices, no invented numbers, placeholder URLs only | | Deliverables | index.html single-file page , posts.md , HANDOFF.md , final-report.md | | Verification | 10 programmatic checks → all pass; reviewer verdict 6/6, plus 4 optional findings sent back as a real message | | Usage | 39 model turns · 35 tool calls · $1.66 list-price equivalent · 18 min 37 s | | Status | completed | The generated files, the final report and the 77-event JSONL log are committed under docs/evidence/ without edits. Four runs total: run 1 finished partial and exposed a bug a reviewer verifying two tasks had only its last verdict applied — fixed , runs 2–3 hit limits that were then tuned. One request is not a benchmark, and the prompts are original seeds. The demo video on the site drives the real UI and runtime with a scripted test provider labelled on screen so it is deterministic and free to reproduce. Stack: Python 3.12 / FastAPI / SQLite WAL, append-only events, SSE , React + TypeScript. macOS seatbelt sandbox for builder commands no network, writes only inside the task workspace ; elsewhere a plain subprocess that says so in every result. git clone https://github.com/FORIFOR/Multibot && cd Multibot cd backend && uv venv .venv --python 3.12 && uv pip install --python .venv/bin/python -e '. dev ' cd ../frontend && pnpm install && pnpm build && cd ../backend .venv/bin/agentteam probe real capability check through claude -p .venv/bin/agentteam serve http://127.0.0.1:8787 I'd love feedback on two things: whether the review → revise → re-review loop bound to revisions holds up on your tasks, and where "fewer messages, more evidence" workers get their task, artifact refs and inbox — not the history breaks down.