hippmem-mcp 0.1.0

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

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

Copy .env.example to .env and configure:

Variable	Default	Description
HIPPMEM_EMBEDDING_PROVIDER	(empty = deterministic)	Embedding backend: openai-compatible or leave empty for offline fallback
HIPPMEM_EMBEDDING_BASE_URL	https://api.openai.com/v1	API endpoint when using remote embeddings
HIPPMEM_EMBEDDING_MODEL	text-embedding-3-small	Model name for embeddings
OPENAI_API_KEY	(none)	API key for OpenAI-compatible embedding services

The deterministic fallback backend requires no API key, no GPU, and no network connection.

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

Tool	Description
write_memory	Write a memory. The engine automatically discovers associations with existing memories. Supports content_type (Decision, Preference, ProjectKnowledge, TaskState, Correction, Event, Reflection) and importance (0.0–1.0).
retrieve_memories	Cross-session associative recall via multi-channel seed retrieval + spreading activation. Returns scored results with dimensions explaining why each memory was recalled. Supports top_k and max_hops tuning.

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

• 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 [e-mail].