hippmem-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@hippmem-mcpretrieve memories about React hooks"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
Related MCP server: Recall
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 installthen one JSON block in your MCP client configuration; deterministic fallback backend works offlineWrite-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
dimensionsandmatched_dimensionsSingle-file storage — one redb file + Tantivy full-text index + HNSW vector index; no external database
Install
pip install hippmem-mcpRequires 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 |
| (empty = deterministic) | Embedding backend: |
|
| API endpoint when using remote embeddings |
|
| Model name for embeddings |
| (none) | API key for OpenAI-compatible embedding services |
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:
Use retrieve_memories to check what we know about this project before starting.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 a memory. The engine automatically discovers associations with existing memories. Supports |
| Cross-session associative recall via multi-channel seed retrieval + spreading activation. Returns scored results with |
Development
git clone https://github.com/hippmem/hippmem-mcp.git
cd hippmem-mcp
pip install -e ".[dev]"
pytestSee 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
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.
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