Brainy

io.github.memokarv0.1.0更新于 Oct 6, 2026

Shared knowledge base + task queue so Claude, ChatGPT and other agents can work together safely.

概览

AI 生成的概览

自托管的共享知识库与任务队列,让多个 AI 代理读取同一份 Markdown 知识并通过任务协作。

功能
Brainy 为代理提供一个受控的 MCP 端点,用于共享知识和任务。知识工具包括 list_documents、get_document、search_knowledge、write_document、append_document 和 propose_write,写入会提交到 Git 仓库。任务工具包括 create_task、claim_task、renew_claim、complete_task、fail_task 和 release_task,支持原子认领、租约、依赖和审核。空间、角色、按空间的 ACL 以及只追加的审计日志控制访问。
适用场景
当 Claude、ChatGPT 或自定义机器人等多个 AI 客户端需要共享同一知识库和任务列表,而不是各自在独立聊天中工作时适用。也适合希望对代理提出的知识变更进行人工审核并保留代理操作审计记录的团队。
运行要求
作为本地进程运行,通常使用已发布的容器镜像,或从源码运行,需要 Python 3.10 或更高版本和 SQLite。需要数据目录和知识根目录,以及通过 Authorization 头发送的服务令牌;管理员令牌在首次启动时打印一次。远程连接器需要 HTTPS 和 OAuth 2.1 配置。
安装前请注意
Authorization 头携带 Brainy 服务令牌,管理员令牌在首次启动时仅显示一次,请妥善保存。代理可以写入和追加知识文档,并创建、认领、完成或标记任务失败,提出的写入可能需要人工批准。应为每个代理分配独立主体,并只授予其所需的空间。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Brainy,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

Brainy

[CI] [License: AGPL-3.0] [Dependencies: none]

A shared brain for your AI agents — and for the humans who work with them.

Brainy is a self-hosted knowledge and task backbone that Claude, ChatGPT and any other MCP-capable AI connect to through one controlled endpoint. Every agent reads the same knowledge, works on the same task list and leaves an audit trail — so your AIs can hand work to each other instead of living in separate chat silos.

   ChatGPT ─┐                                   ┌─ Git-versioned knowledge (Markdown)   Claude  ─┼──►  MCP endpoint  ──►  Brainy  ───┼─ Tasks with atomic claim/lease   Your bot ┘     (OAuth / tokens)   ACL+Audit  └─ Spaces, roles, append-only audit log                                       ▲                          Humans: web admin UI (+ optional Telegram approvals)

Why Brainy?

  • AIs that collaborate. ChatGPT creates a task, Claude claims it, does the work and writes the result back; a human reviews. Coordination happens through tasks — no hidden agent-to-agent magic.
  • One source of truth. Knowledge lives as plain Markdown in a Git repository. Every write is a commit, so you get history, diffs and rollback for free.
  • Safe by default. No shell, no filesystem, no eval over MCP. Path allowlist, per-space ACLs, secret detection on writes, rate limits, hashed tokens and an append-only audit log.
  • No double work. Atomic task claims with leases and claim tokens guarantee that two agents never process the same task at the same time.
  • Human in the loop. Agents can propose knowledge changes; a human approves or rejects them (web UI or Telegram). Tasks can require review/approval before they count as done.
  • Zero dependencies. Pure Python standard library + SQLite. No pip install, tiny attack surface.

Features

AreaWhat you get
Knowledgelist_documents, get_document, search_knowledge, write_document (optimistic concurrency via Git commit), append_document, propose_write
Taskscreate_task, claim_task, renew_claim, complete_task, fail_task, release_task, dependencies, priorities, review/approve/reject
AccessSpaces (tenants/areas), roles ADMIN / EDITOR / AGENT / READER, per-space ACL, service tokens, OAuth 2.1 (for Claude/ChatGPT remote connectors)
OperationsWeb admin UI, audit log, agent registry + dispatcher framework, backup & verified restore scripts

Quickstart (Docker)

Prebuilt image (published on every release):

bash
docker run -d --name brainy -p 127.0.0.1:8765:8765 -v brainy-data:/data ghcr.io/memokar/brainy:latestdocker logs brainy   # prints your one-time ADMIN token on first start

Or build it yourself with Compose:

bash
git clone https://github.com/memokar/brainy.gitcd brainydocker compose up -ddocker compose logs brainy   # prints your one-time ADMIN token on first start

Brainy now listens on http://127.0.0.1:8765 (MCP endpoint: /mcp, admin UI: /admin — log in with the token). For remote AI connectors put it behind HTTPS (see deploy/nginx-brainy.conf.example) and set BRAINY_PUBLIC_BASE_URL.

Quickstart (bare metal, Linux, Python ≥ 3.10)

bash
export BRAINY_DB_PATH=$PWD/data/brainy.dbexport BRAINY_KNOWLEDGE_ROOT=$PWD/data/knowledgeexport BRAINY_WEB_SESSION_KEY=$PWD/data/web_session.key
cp -r examples/knowledge "$BRAINY_KNOWLEDGE_ROOT"git -C "$BRAINY_KNOWLEDGE_ROOT" init -q && git -C "$BRAINY_KNOWLEDGE_ROOT" add -A \  && git -C "$BRAINY_KNOWLEDGE_ROOT" commit -qm "initial knowledge"
python3 scripts/bootstrap.py "$BRAINY_DB_PATH" --with-token   # prints ADMIN token oncepython3 scripts/init_prod_db.py "$BRAINY_DB_PATH"             # seeds default spacespython3 scripts/serve.py

Connecting an AI

  • Claude Code:
    bash
    claude mcp add --transport http brainy http://127.0.0.1:8765/mcp --header "Authorization: Bearer <token>"
  • Local stdio clients (e.g. Claude Desktop): run Brainy as a subprocess:
    json
    {"mcpServers": {"brainy": {"command": "python3", "args": ["/opt/brainy/scripts/stdio.py"],  "env": {"BRAINY_DB_PATH": "/var/lib/brainy/brainy.db",          "BRAINY_KNOWLEDGE_ROOT": "/opt/brainy-knowledge", "BRAINY_TOKEN": "<token>"}}}}
    Try it without any setup: python3 scripts/stdio.py --demo (temporary data, deleted on exit).
  • Any other MCP client with custom headers: endpoint https://<your-host>/mcp, header Authorization: Bearer <service token>.
  • Claude.ai / ChatGPT remote connectors: use the OAuth 2.1 flow (discovery at /.well-known/oauth-authorization-server). Set BRAINY_PUBLIC_BASE_URL to your HTTPS URL.

Brainy is listed in the official MCP Registry as io.github.memokar/brainy.

Give each AI its own principal (e.g. claude, chatgpt) with role AGENT and only the spaces it needs. Every action then shows up in the audit log under that name.

Configuration

All configuration comes from environment variables — see .env.example. Secrets (tokens, keys) are never stored in the repository or the knowledge base.

Extensions

The core ships a worker plugin interface and a deterministic MockWorker. Real workers that let agents execute tasks autonomously (e.g. Claude Code, Codex) are separate extensions loaded via BRAINY_WORKER_PLUGINS. See docs/extensions.md.

Running the tests

bash
for t in tests/test_*.py; do python3 "$t" || exit 1; done

Status & roadmap

Brainy runs in production for its author. Current limitations:

  • Code comments are still partly German; all user-facing text is English.
  • Single-node design (SQLite). PostgreSQL only if real multi-writer load appears.

License

Copyright (C) 2026 Mehmet Karakolcu

Brainy is dual-licensed:

  • Open source: GNU AGPL-3.0. Free for everyone, including companies — but if you modify Brainy and offer it to others (also as a network service), you must publish your changes under the AGPL.
  • Commercial license: for companies that want to use or embed Brainy without AGPL obligations. See COMMERCIAL.md.

Contributions require agreeing to the Contributor License Agreement.

来源:README.md,提交 79e0ec7

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版本历史

1
  1. v0.1.0最新Oct 6, 2026