One request. The deliverable, and how it was checked.
An AI team drafts, reviews and revises the work, and keeps who checked what next to the deliverable.

_{Product name: Agent Team · repository: FORIFOR/Multibot · MIT}

Website: a real work record you can click through · Reachmade Lab product page · Quickstart · How it works · What's verified · 日本語

A real run, on the site: forifor.github.io/Multibot shows the record of a real claude-opus-5 run — request → first draft → three review findings → the changed passages → re-verification — where clicking a finding jumps to the changed part of the deliverable, plus a 29-second replay of the same run. Files: docs/evidence/scenarios/research2/.

▶ 実モデルの作業記録・日本語解説（32秒）. Edited replay of a real run, with synthesized Japanese narration. The run ended partial, with the unfinished source check disclosed. No scripted provider is used in this video.

Benchmark audit (2026-09-14, updated): latest attempts across 50 tasks × 3 repetitions: team 69 completed / 5 partial / 76 failed; single agent 150 completed. The team has 79 provider-blocked pairs. All 252 team attempts remain accounted for, including earlier failures. These are constructed evaluation inputs, not customer workflows; no team quality advantage is established. Metrics and Reviewer audit.

Production work: Authenticated dedicated deployments now include role checks, per-run access, audit records and offline backup/restore. SSO, distributed recovery and production acceptance remain in progress. Controls and deployment · Acceptance plan.

Local evaluation: Ollama trials exposed review, tool-recovery, source-input, artifact-ownership and completion-state bugs. The current checkout passes 107 deterministic tests (1 skipped). The legacy 300-run comparison is preserved and stopped because its inputs contain fictional products and synthetic business data; it is not acceptance evidence. The fixed-series test using the actual repository documents and a local Qwen model was stopped after two v18 runs exposed translation drift. A v20 retry with explicit repair examples still failed its first current-contract check, so no local run is counted as accepted. Fixes · v18 evidence · v20 evidence.

Enterprise readiness: demonstration material is available, but the required ten repeated runs of one workflow are not yet verified. The latest local-Qwen retry failed the current delivery contract on its first run. L2 customer-data PoC and L3 production readiness are not claimed. L1 / L2 / L3 acceptance criteria.

You type one request. Attach the source files the team should use, then a Master plans the deliverables, a Researcher, a Builder and a Reviewer actually do the work. You get the files plus the real bot-to-bot messages, a timeline, and verification bound to each artifact revision. While a run is active, a human direction is recorded as an event and handed to the next task session; the existing plan is not silently rewritten.

• No scripted chat. The chat panel is a projection of message.sent events — messages that were really delivered to another bot's mailbox. A question wakes the other bot to answer.
• Evidence, not vibes. Checks and review verdicts are events bound to an artifact revision hash. The final report is compiled from the event log; a model summary cannot upgrade “started” to “done”.
• Runtime-enforced limits. Tool scope, write scope, budget reservation, approvals and cancellation are enforced in code, not by prompt wording.
• Per-bot configuration. Each bot inherits a default connection and model and can override endpoint, model, effort and system prompt (lockable). Configured vs. provider-reported model are both shown. No silent fallbacks.
• Replay, resume, fork, export. Replay never calls a model. Fork from a checkpoint with a different model for one bot and compare. Export the whole run as JSONL.
• Real inputs and handoff. Attach .txt, .md or .csv material (up to 512KB per file in the UI). Send a change, question or edit from the team chat and keep the received direction in the run timeline.

One command if you have Claude Code installed and logged in (no API key), plus uv:

uvx --from "git+https://github.com/FORIFOR/Multibot#subdirectory=backend" agentteam quickstart

quickstart makes one small real call through claude -p to confirm tool calling and JSON-schema output, then serves the UI at http://127.0.0.1:8787 and opens it. From a checkout:

git clone https://github.com/FORIFOR/Multibot && cd Multibot/backend
uv venv .venv --python 3.12 && uv pip install --python .venv/bin/python -e '.[dev]'
.venv/bin/agentteam quickstart          # the built UI ships inside the package; `cd ../frontend && pnpm build` only if you change it

Other providers: set defaults.connection_id in data/agents.yaml (or in Settings) to anthropic with ANTHROPIC_API_KEY, to an OpenAI-compatible endpoint (verified live with gpt-4.1-mini), or to local Ollama (driver: ollama, http://localhost:11434/v1). Every connection must pass the probe before a run can start; placeholder models and unknown prices refuse to start with a structured reason.

Sandbox. Builder/Reviewer commands run in Docker when a daemon answers (--network none, read-only root, host uid, CPU/memory/pid limits; Docker Desktop, Colima or any engine), otherwise in the macOS seatbelt; with neither, commands are refused rather than run unsandboxed (set AGENTTEAM_SANDBOX=subprocess to opt out knowingly).

Language. The app UI has an EN/JA toggle in the header (defaults to your browser language).

Everything is written to an append-only event store (SQLite WAL, per-run seq, UTC timestamps, redaction before persistence). Chat, timeline and report are projections of it. The event types are the blueprint's 11 plus runtime extensions, all validated against backend/agentteam/schemas/event.schema.json.

backend/agentteam/
  contracts.py    TeamPlan · Task · Event · Message · Artifact · Approval · Run
  config/         YAML + JSON Schema · secret references (env: / keychain: / file:)
  store/          EventStore · ArtifactStore · RunStore
  providers/      anthropic_messages (official SDK) · openai_compatible_chat / ollama (httpx) · fake (tests only)
  runtime/        PolicyEngine · MessageBus · ToolGateway · Sandbox · Checks · Web tools · AgentRunner · Planner · Scheduler · RunManager
  api/            REST + SSE + static UI
frontend/         React + TypeScript (request · run · settings)
docs/             website, blueprint (spec, security, references), status
evals/            40-case acceptance plan and coverage map

Real runs through the local Claude Code CLI (claude-opus-5, provider-reported). Artifacts, reports and full event logs are committed unedited under docs/evidence/.

Reproducibility, 2026-09-13 afternoon — the same four requests, three runs each, nothing filtered (docs/evidence/scenarios/rerun-2026-09-13/, budget $6 and 120 model calls per run):

I re-ran the generated unit tests myself for all three code runs (18 / 19 / 17 tests, OK) and checked the three generated sites for broken internal links (none). The launch-page run that ended partial had both planned tasks accepted at $1.29; the Master then added two rounds of wording polish and the last task hit the model-call limit. All three research runs had their planned research.md accepted, then ended partial for one structural reason: the default reviewer had no web_fetch, so it could not check claims against their sources as the request asked, and the Master's workaround tasks exhausted the budget. Both are fixed after this evaluation (reviewer gets web_fetch; a milestone replan is skipped and recorded when less than one agent session of budget or model calls remains; the Master is told not to add polish once every deliverable is accepted; partial runs carry a reason). The fixes are covered by deterministic tests; one post-fix research run completed (reviewer fetched all three sources, 5/5 review pass, $2.05, 8m44s, research-postfix/) — one run, not a new 3-run series.

Earlier single runs (v0.2.0, 2026-09-13 morning): launch page completed ($1.66, 18 min); code completed ($1.87, 16 min); research failed on a scheduler deadlock, then partial after the fix ($6.07, 24 min); docs site completed ($3.03, 15 min). Other providers on the launch-page request: OpenAI-compatible gpt-4.1-mini completed in 29 s for $0.02; local Ollama qwen2.5:7b reached partial; qwen2.5:3b could not produce a valid plan.

Also verified: 107 deterministic tests, 1 skipped (DAG validation, real delivery with a question/answer round trip, review → revise → re-review, milestone replanning and its budget gate, budget reservation, approvals with hash/nonce, cancel → resume, fork, redaction, SSRF guard, Docker and seatbelt sandbox confinement, SSE cursor replay, event-schema conformance), and in CI on every push: the Docker isolation test on Linux and a headless-Chrome UI smoke of the bundled UI in English and Japanese (start a request → run view → final report → settings, exits non-zero on any error).

Still open: three runs per request is a record, not a benchmark; plans vary because the Master decides; research-heavy requests are the most expensive (about $6 at list price) and the least reliable; local 7B models complete the mechanics but not the review protocol; no one outside the author has used it yet. Full matrix and the list of bugs found by real runs: docs/STATUS.md. Security boundaries: SECURITY.md.

• dev.to: I built an AI team that ships real work — and shows you the conversation
• Zenn（日本語）: Claude Codeで調査・レビュー・修正を回すOSSを作った。3件直っても「未完了」だった
• Launch threads on X: English · 日本語

The product was built from a written blueprint (docs/blueprint/): spec, implementation brief, security requirements, references, and 40 acceptance cases. Deliberate deviations (no Deep Agents/LangGraph dependency, extended event enum, refusal fallback off by default) are recorded in docs/IMPLEMENTATION_PLAN.md. Contributions: CONTRIBUTING.md. Changes by version: CHANGELOG.md.

依頼は一度。成果物も、検証の経緯も。 AI チームが作成・レビュー・修正を進め、誰が何を確かめたかまで、成果物と一緒に残します。（製品名 Agent Team、リポジトリ FORIFOR/Multibot、MIT）

Master が成果物と完了条件を決め、必要な Bot（Researcher / Builder / Reviewer）だけが実際に作業します。成果物、Bot 間の実メッセージ、時系列、最終報告は同じ実行記録（append-only Event Store）から表示され、台本の会話や固定の成功ログは使いません。

• 成果物を開くと、誰が作り、何を引き継ぎ、どの検証を通ったかまで辿れる
• 成果物の版間差分を確認して採用版を明示でき、成果物・最終報告・イベントログを run 単位の ZIP で取得できる
• 各 Bot の接続先・モデル・システムプロンプトを個別に上書きできる（共通設定を継承、手動固定あり）
• 停止・再開・分岐（Bot のモデルを変えて別案）・再生（LLM 呼出なし）・JSONL エクスポート
• 権限・予算・回数上限・承認はプロンプトではなく Runtime が強制

上の画像と32秒の映像は実モデルの実行記録です。映像は記録の再生を編集し、日本語合成音声を付けています。この実行は予算上限で部分完了し、未検証の出典照合を明示しています。企業紹介用のデモ・導入設計相談は可能ですが、有償PoCや本番運用への適合は別途検証が必要です。

• The "thinking" orb in the app header is a standalone export of the Liquid Orb Editor (MIT, © 2026 LerSent001), vendored under frontend/src/assets/ with its license and parameter snapshot.

MIT. Bundled prompts and skills are original to this project (see docs/blueprint/REFERENCES.md).