Open Brain

Open Brain

Graph-structured MCP memory. [37.2% on LongMemEval baseline](BENCHMARK.md) — a benchmark most memory systems don't publish.

A self-hostable memory server for MCP clients (Claude, ChatGPT, any assistant that speaks MCP). Thoughts flow in from Telegram, pipelines, or direct capture, and land in a Newman-IDF weighted entity graph — not a flat document store. An automated Dream cycle runs in the background: deduplicating near-duplicates, tracking theme drift, synthesizing insights across clusters, and archiving stale content. 17 MCP tools. PostgreSQL + pgvector. You own your data.

How It Works

flowchart LR

TG[Telegram Message] --> TGBot[telegram-bot\nEdge Function] MCP[AI Client\nClaude/ChatGPT] --> MCPServer[open-brain-mcp\nEdge Function] Pipeline[RSS/HF Papers/\nEmergent Mind] --> RunPipeline[run-pipeline\nEdge Function] TGBot --> OR1[OpenRouter\nEmbedding + Metadata] MCPServer --> OR2[OpenRouter\nEmbedding + Search] RunPipeline --> OR3[OpenRouter\nTriage + Embed] OR1 --> DB[(Postgres\n+ pgvector)] OR2 --> DB OR3 --> DB MCPServer --> DB TGBot --> TGReply[Telegram Reply\nwith Metadata]

Capture Path

When you send a message to the Telegram bot, the telegram-bot Edge Function picks it up via webhook. It sends the message to OpenRouter in parallel for two things: generating a vector embedding (a numerical representation of meaning) and extracting metadata like topics, people mentioned, action items, theme, quality score, and named entities. The thought is checked for semantic duplicates, stored in your database with auto-linked connections to related thoughts, and the bot replies with a summary of what it captured.

Pipeline Path

The run-pipeline Edge Function automatically ingests ideas from RSS feeds (AI newsletters), Hugging Face daily papers, and Emergent Mind (trending arXiv papers). Each item is triaged for relevance, embedded, deduplicated, and stored. Runs on a schedule via GitHub Actions (Supabase deployment) or a built-in cron container (Docker deployment).

Retrieval Path

Any AI client connected via MCP (Model Context Protocol) can search your thoughts by meaning using semantic search, browse by filters (type, topic, person, time), get aggregate statistics, or request a weekly review of themes. The open-brain-mcp Edge Function handles these requests, authenticated with your personal access key.

Knowledge Graph

Every thought is automatically linked to related thoughts via vector similarity. Connections above 0.80 similarity are classified by an LLM into typed relationships (extends, contradicts, is-evidence-for, supersedes). Named entities (people, tools, projects, organizations) are extracted and resolved into a shared entity graph. Co-occurrence edges track which thoughts are retrieved together over time, strengthening connections based on actual usage patterns.

Storage

Everything lives in Postgres with pgvector for fast similarity search. Thoughts are stored with their embeddings (1536-dimensional vectors), metadata, typed connections, and entity references. You can deploy on Supabase (managed hosting) or self-host with Docker Compose.

Deployment Options

Choose how you want to run Open Brain:

Supabase Deployment

Prerequisites

1. Supabase account -- Supabase is a hosted Postgres database with built-in APIs, authentication, and Edge Functions (serverless code). Create a free account at supabase.com. Create a new project -- you will need the project URL (looks like https://abcdef.supabase.co) and the service role key (a long string found under Settings > API).

1. Supabase CLI -- The command-line tool for managing your Supabase project (applying database migrations, deploying functions, setting secrets).

   npm install -g supabase

1. OpenRouter account -- OpenRouter routes requests to AI models. It is used here for generating embeddings (vector representations of your thoughts) and extracting metadata. Create an account at openrouter.ai and generate an API key from the dashboard.

1. Telegram bot (recommended) -- The primary way to capture thoughts on the go. Create a bot via @BotFather on Telegram and run the setup script (see below). If you only want MCP access, you can skip this.

Quick Start

1. Clone the repository

git clone https://github.com/YOUR_USERNAME/open_brain.git

cd open_brain

2. Link your Supabase project

cd supabase

supabase link --project-ref YOUR_PROJECT_REF cd ..

Tip: Your project ref is the subdomain in your Supabase URL. If your URL is https://abcdef.supabase.co, your project ref is abcdef.

3. Run bootstrap

./scripts/bootstrap.sh

Bootstrap walks you through setting up your environment. It prompts for each secret (Supabase URL, service role key, OpenRouter API key, Telegram tokens, etc.), generates a cryptographic MCP access key automatically, and writes everything to .env.local. If you already have a .env.local, it will show your existing values and let you update specific ones.

4. Run deploy

./scripts/deploy.sh

Deploy applies the database schema (creates the thoughts table with vector search indexes), uploads your secrets to Supabase, and deploys all Edge Functions. It shows a step-by-step checklist as each operation completes. At the end, it prints your MCP connection URL and a ready-to-paste Claude Code command.

5. Run validate

./scripts/validate.sh

Validate runs 8 checks against your live deployment to confirm everything works: database access, RPC functions, Edge Function reachability, authentication, thought capture, semantic search, and thought listing. It prints a checklist with pass/fail for each check and a final summary.

Set up Telegram bot (optional)

Create a bot via @BotFather on Telegram, then run the setup script:

./scripts/setup-telegram.sh YOUR_BOT_TOKEN

The script verifies your token, registers the webhook, sets up command autocomplete, and prints the env vars and secrets to configure. Follow the printed instructions to complete setup.

Connect Your AI Client

Once deployed, connect your AI client to start using Open Brain. You need two values:

• MCP endpoint URL: https://YOUR_REF.supabase.co/functions/v1/open-brain-mcp/mcp (Supabase) or http://localhost:80/functions/v1/open-brain-mcp (Docker)
• MCP access key: The key generated during setup (stored in .env.local or Docker .env)

Tip: The deploy script (Supabase) or start script (Docker) prints the exact connection command with your values filled in.

Claude Code (CLI -- recommended)

claude mcp add --transport http --header "x-brain-key: YOUR_MCP_KEY" open-brain https://YOUR_REF.supabase.co/functions/v1/open-brain-mcp/mcp

This registers Open Brain as an MCP server that Claude Code can use in any conversation. Replace YOUR_MCP_KEY and YOUR_REF with your actual values.

Claude Code (project .mcp.json)

Add this to a .mcp.json file in your project root to share the connection with your team:

{

"mcpServers": { "open-brain": { "type": "http", "url": "https://YOUR_REF.supabase.co/functions/v1/open-brain-mcp/mcp", "headers": { "x-brain-key": "${MCP_ACCESS_KEY}" } } } }

Note: The ${MCP_ACCESS_KEY} syntax uses environment variable expansion so your key stays out of version control. Set the MCP_ACCESS_KEY environment variable on each machine that uses this config.

Claude Desktop

Claude Desktop does not support remote MCP servers via configuration files. Instead:

1. Open Claude Desktop > Settings > Connectors
2. Click Add a new connector
3. Enter the MCP endpoint URL: https://YOUR_REF.supabase.co/functions/v1/open-brain-mcp/mcp
4. Configure the x-brain-key authentication header with your MCP access key

ChatGPT (Pro/Team/Enterprise/Edu)

1. Go to Settings > Connectors > Advanced > Developer Mode
2. Add the MCP server URL: https://YOUR_REF.supabase.co/functions/v1/open-brain-mcp/mcp
3. Configure the x-brain-key authentication header with your MCP access key

Usage Examples

Telegram Capture

Send any message to your bot and Open Brain automatically processes it:

You: Just had a great meeting with Sarah about the Q3 product roadmap.

She wants to prioritize the mobile app redesign.

Bot: Captured! Type: meeting_note Theme: personal Topics: q3-roadmap, mobile-app-redesign Quality: 0.7 People: Sarah Action items: Prioritize mobile app redesign Why: Records a product strategy decision with clear ownership Related: "Product planning session notes..." (82% similar)

Every message is embedded as a vector, enriched with extracted metadata, checked for duplicates, auto-linked to related thoughts, and entities are resolved into a knowledge graph.

Semantic Search

Ask any connected AI client to search your brain:

You: Search my brain for anything about product roadmap discussions

Claude: I found 3 relevant thoughts:

1. (0.89 similarity) Meeting with Sarah about Q3 product roadmap...
2. (0.82 similarity) Product planning session notes...
3. (0.76 similarity) Quarterly goals discussion...

Semantic search finds thoughts by meaning -- even if you used different words. Asking about "product planning" will find thoughts about "roadmap discussions" because the meanings are similar.

Weekly Review

Get an AI-generated summary of your recent thinking:

You: Give me a weekly review of my recent thoughts

Claude: Here's your weekly review: Themes: Product planning, team meetings, technical architecture Open loops: Mobile redesign decision pending, API migration timeline Connections: Sarah mentioned in 3 meetings this week, all about mobile

The weekly review analyzes the past 7 days of thoughts and synthesizes themes, open loops, connections between ideas, and gaps in your thinking.

Available Tools

See docs/cookbook.md for deta