hippmem-mcp

MCP server for HIPPMEM — give AI tools long-term associative memory.

What is HIPPMEM?

HIPPMEM is a native associative memory engine for AI agents, written in Rust. Instead of storing text chunks and searching them by vector similarity, it discovers associations between memories at write time and retrieves them via spreading activation at read time — so the AI recalls not just what was said, but how things connect and why.

It runs fully offline with a deterministic fallback backend.

What is hippmem-mcp?

hippmem-mcp wraps HIPPMEM as a Model Context Protocol server. Configure it once in Claude Desktop (or any MCP-compatible tool), and your AI assistant gains persistent, associative memory across sessions — no API key required.

AI Tool (Claude Desktop / VS Code / ...)
        │  MCP protocol (stdio)
        ▼
  hippmem-mcp
        │  Python bindings
        ▼
  hippmem Engine (Rust)
        │
        ▼
  Local storage (redb + Tantivy + HNSW)

Key Features

• Zero config — pip install then one JSON block in your MCP client configuration; deterministic fallback backend works offline
• Write-time association discovery — entities, topics, goals, causal links extracted and scored automatically
• Spreading activation retrieval — multi-channel seed recall (BM25 + entity + semantic + temporal + topic) fused by RRF
• Graph evolution — co-activated connections strengthen (Hebbian learning); stale edges decay
• Explanation traces — every result shows why it was recalled via dimensions and matched_dimensions
• Single-file storage — one redb file + Tantivy full-text index + HNSW vector index; no external database

Install

pip install hippmem-mcp

Requires Python ≥ 3.11.

Configure

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "hippmem": {
      "command": "python",
      "args": ["-m", "hippmem_mcp.server"]
    }
  }
}

Or use the entry point:

{
  "mcpServers": {
    "hippmem": {
      "command": "hippmem-mcp"
    }
  }
}

Environment Variables

The server reads these variables from the environment that launches it — the env block in your MCP client config (e.g. Claude Desktop) or your shell, not from a .env file (none is auto-loaded). .env.example is a reference template for that environment.

With HIPPMEM_EMBEDDER_PROVIDER=neural, all three API variables above are required — startup fails naming the missing ones. The deterministic fallback backend requires no API key, no GPU, and no network connection.

Claude Code

Claude Code supports MCP natively. Add to your Claude Code MCP config (.claude/mcp.json or project .mcp.json):

{
  "mcpServers": {
    "hippmem": {
      "command": "hippmem-mcp"
    }
  }
}

Then in any Claude Code session, retrieval is query-driven — trigger it when a task signal appears (a past decision, a user preference, an unknown constraint), not by bulk-loading at session start:

Before answering, use retrieve_memories to check whether we have relevant
memory about this topic. Query with the specific question, not the whole task.

See Best Practices for the full retrieval/feedback loop.

Other MCP Clients

hippmem-mcp speaks standard MCP over stdio. Configure any MCP-compatible client the same way — point the command to hippmem-mcp or python -m hippmem_mcp.server.

Tools

Development

git clone https://github.com/hippmem/hippmem-mcp.git
cd hippmem-mcp
pip install -e ".[dev]"
pytest

See CONTRIBUTING.md for commit conventions, PR workflow, and DCO requirements.

Documentation

• Best Practices — when to write, how to query, prompt templates
• HIPPMEM main project — engine architecture, concepts, and API reference
• MCP specification — Model Context Protocol
• Changelog
• Security policy

License

Apache 2.0. See LICENSE and COPYRIGHT.

The underlying HIPPMEM engine (hippmem) is AGPL-3.0-only. A commercial license is available — contact hippmem@gmail.com.