Personal knowledge graph with 16 MCP tools. Auto-links, deduplicates, tracks themes, synthesizes.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"open-brain": {
"url": "https://YOUR_REF.supabase.co/functions/v1/open-brain-mcp/mcp",
"type": "http",
"headers": {
"x-brain-key": "${MCP_ACCESS_KEY}"
}
}
}
}Are you the author?
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Graph-structured MCP memory. 37.2% on LongMemEval baseline — a benchmark most memory systems don't publish.
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Graph-structured MCP memory. 37.2% on LongMemEval baseline — 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.
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]
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.
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).
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.
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.
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.
Choose how you want to run Open Brain:
| Supabase (hosted) | Docker Compose (self-hosted) | |
|---|---|---|
| Setup | Link project + run scripts | cp .env.example .env + ./start.sh |
| Infrastructure | Managed by Supabase | Runs on your machine/server |
| Scheduling | GitHub Actions | Built-in cron container |
| Cost | Supabase free tier + OpenRouter | Just OpenRouter |
| Guide | Continue below | Docker guide |