piLoci MCP is a self-hosted memory server for AI agents that exposes project-scoped memory storage and retrieval through the Model Context Protocol. Built to run on Raspberry Pi 5, it provides semantic recall, project listing, and user identity tools. Teams connect Claude Desktop, Codex, and other MCP clients to share persistent context without sending memory data to cloud services.
Use cases
- AI agent retrieves relevant project memories before starting a new task
- Team maintains shared context across multiple AI-assisted coding sessions
- Research agent searches historical project knowledge for relevant precedents
- Onboarding agent accesses project documentation and architecture decisions
- Cross-project agent queries only memories from authorized project scopes
Key features
- Claude Desktop
- Codex
- Cursor
Frequently Asked Questions
- What MCP tools does piLoci expose?
- The server exposes four main tools: memory (store new information), recall (semantic search), listProjects (enumerate accessible projects), and whoAmI (check current user identity).
- Does piLoci MCP require Raspberry Pi hardware?
- No, while optimized for Raspberry Pi 5, piLoci MCP runs on any Linux server with Node.js. It requires approximately 4GB RAM and 2GB storage.
- How does project isolation work?
- Each project has strict memory isolation. Users can only recall from projects they are authorized to access, preventing cross-project information leakage in multi-tenant team environments.
Related
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3 Indexed items
Mem0 MCP Server
Mem0 documents a hosted Model Context Protocol server at https://mcp.mem0.ai/mcp that exposes Platform memory tools (`add_memory`, `search_memories`, `get_memories`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`, `list_events`, `get_event_status`) to Claude, Claude Code, Codex, Cursor, Windsurf, VS Code, and OpenCode. Setup uses `npx mcp-add` with HTTP transport or manual JSON/TOML client configs; Codex requires `MEM0_API_KEY` as bearer token per docs.mem0.ai/platform/mem0-mcp. The cloud server needs a Mem0 Platform API key from the dashboard and Node.js for the installer—no local vector database required for the hosted path.
Postgres MCP
pg-mcp-server is a Model Context Protocol server implementation that connects AI agents to PostgreSQL databases. It registers database schemas as MCP resource templates and exposes SQL execution as an MCP tool. Agents can introspect table structures, run parameterized queries, and manage transactions without leaving the chat interface. Designed as a reference implementation for database MCP integrations.
Qdrant MCP Server
Official Qdrant MCP server implementation that gives AI agents a semantic memory layer backed by Qdrant vector search. It exposes MCP tools for storing information and retrieving relevant context, so assistants can persist and recall facts across sessions instead of relying only on short chat history.