Brainy

Brainy

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.

Why not just a markdown file?

A shared notes file in a Git repo is a great knowledge store — and Brainy keeps that part on purpose: knowledge is plain Markdown in Git, with history, diff and rollback. The problem is everything a bare file doesn't do once more than one agent touches it:

• Two agents overwrite each other. Nothing serializes concurrent writes to the same file. Brainy writes go through optimistic concurrency (expected_git_commit) plus a server-side write lock: exactly one writer commits, the others get a Conflict and re-read — no lost edits.
• No task hand-off. A file can list TODOs, but it can't stop two agents from grabbing the same one. Brainy tasks use an atomic claim (UPDATE ... WHERE status='READY', one row): exactly one agent wins, the rest get a conflict and move on.
• No per-area permissions. A file is all-or-nothing. Brainy has Spaces with per-space ACLs, so an agent only reads/writes the areas you grant it.
• No approval step. Agents can propose changes for a human to approve (web UI or Telegram) instead of writing straight to the source of truth.
• "Who changed what?" beyond git blame. Every action — reads of task state, claims, writes, rejections — lands in an append-only audit log enforced by a DB trigger.

Both guarantees above are covered by a concurrency test (tests/test_concurrency.py): 20 threads racing on one task yield exactly 1 winner and 19 conflicts, and 20 threads writing the same file yield exactly 1 commit and 19 conflicts, with the repo left clean.

How is Brainy different?

Honest comparison with the tools people mention most. Beads is closest to Brainy's task side, and Central Brain to its knowledge side; neither is a one-to-one match. Facts are from each project's own docs (links below); where something isn't documented, it says so rather than guessing.

[^beads]: Beads — https://github.com/steveyegge/beads (MIT, Go; stores issues in Dolt, a version-controlled SQL database; earlier versions used SQLite + JSONL). [^cb]: Central Brain by NeuroAIgent — https://neuroaigent.ai (local-first AI memory, Windows + Apple-silicon macOS, MCP integration; pricing per the site at time of writing: $12/mo solo, $35/mo for 4 licenses).

Features

Quickstart (Docker)

Try it in one command — no clone, no setup. Runs a throwaway demo (temporary database + example knowledge, deleted on exit) as an MCP stdio server:

docker run --rm -i ghcr.io/memokar/brainy:latest --demo

For a real install, run the server (prebuilt image, published on every release):

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

Or build it yourself with Compose:

git clone https://github.com/memokar/brainy.git
cd brainy
docker compose up -d
docker 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)

export BRAINY_DB_PATH=$PWD/data/brainy.db
export BRAINY_KNOWLEDGE_ROOT=$PWD/data/knowledge
export 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 "$BRAINY_DB_PATH" --with-token   # prints ADMIN token once
python3 scripts/init_prod_db.py "$BRAINY_DB_PATH"             # seeds default spaces
python3 scripts/serve.py

Connecting an AI

• Claude Code:

  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:

  {"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.

Example: two coding agents in one repo

Two agents working the same repository can step on each other's files. With resource claims each one reserves the area it's about to touch — a free-form key per space — and the other backs off until it's released or the lease expires:

// Agent A, before editing the auth code:
claim_resource   { "space": "shared", "resource_key": "repo:app/src/auth/**", "lease_seconds": 1800 }
// -> { "claim_token": "…", "lease_until": "…" }   A now owns that area

// Agent B, about to touch the same area:
claim_resource   { "space": "shared", "resource_key": "repo:app/src/auth/**" }
// -> Conflict: already claimed  → B picks a different area (e.g. "repo:app/src/api/**")

renew_resource   { "space": "shared", "resource_key": "repo:app/src/auth/**", "claim_token": "…" }   // A keeps working
release_resource { "space": "shared", "resource_key": "repo:app/src/auth/**", "claim_token": "…" }   // A done → free again
list_resource_claims { "space": "shared" }   // who holds what right now

Same guarantee as task claims — exactly one holder at a time, expired leases free up automatically (see tests/test_resources.py). Resource claims reuse the task permissions/scopes (can_claim_tasks / brainy:tasks:write), so any agent that can claim tasks can claim resources.

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

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.