@jasondostal
基本信息
Persistent memory for agents and humans. Semantic, episodic and frictionless via an automated three tier capture.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"cairn": {
"type": "http",
"url": "http://localhost:8000/mcp"
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
点击上方按钮,直接连接服务器端点实时获取工具列表。
Cairn is a self-hosted memory and orchestration layer for AI agents and humans. It persists memories across sessions and projects, using PostgreSQL 16 with pgvector for hybrid search and optionally Neo4j for a knowledge graph. It is built for systems-oriented, t-shaped engineers who need a memory that works across everything.
Deploy with a single command: curl -O https://raw.githubusercontent.com/jasondostal/cairn-mcp/main/docker-compose.yml && docker compose up -d. Then add the MCP server to your IDE config (e.g., Claude Code, Cursor) with { "mcpServers": { "cairn": { "type": "http", "url": "http://localhost:8000/mcp" } } }. Use tools like store, search, recall, orient, work_items, code_index, and arch_check to save and retrieve memories, manage work, and analyze code.
No. Store, search, recall, and rules work without an LLM. You only lose auto-enrichment (summaries, tags, importance), knowledge extraction, and chat. When needed, Cairn supports Ollama, AWS Bedrock, Google Gemini, and any OpenAI-compatible backend.
Memories and embeddings are stored in PostgreSQL 16 with pgvector. The optional knowledge graph lives in Neo4j 5. Both run as containers alongside the Cairn server and UI. All data is self-hosted; no external services are required.
Cairn exposes an MCP server (HTTP on port 8000) and a REST API on the same port. API key authentication can be enabled via the `CAIRN_AUTH_ENABLED` and `CAIRN_API_KEY` environment variables. Client configuration expects an HTTP MCP URL.
On the LoCoMo long-conversation memory benchmark (1,986 questions), Cairn scores 81.6% with Llama-3.3-70B, compared to a human baseline of 87.9%, Letta/MemGPT at 74.0%, and Mem0 at 66.9%.
Cairn runs in four Docker containers: the server, a web UI, a PostgreSQL 16 database with pgvector, and an optional Neo4j 5 knowledge graph. For LLM enrichment, a local Ollama installation or cloud provider credentials are required. A GPU is not needed for the default local embedding model (MiniLM, 384-dim).
Serve your docs to AI agents over SSH and MCP. Agents use ls, cat, grep, find, and pipes against a virtual filesystem backed by your documentation. Identity-scoped access, governed writing with human approval, and portab
AI agents start every session with amnesia — you re-explain the project, repeat your preferences, and correct the same mistakes over and over. PLUR gives them a memory that persists. Your agent's corrections, preference
Model Context Protocol Servers
Memlord is a remote MCP server available at https://app.memlord.com.
The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs
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