cozo-memory

CozoDB Memory MCP Server

Local-first memory for Claude & AI agents with hybrid search, Graph-RAG, and time-travel – all in a single binary, no cloud, no Docker.

Table of Contents

• Quick Start
• Key Features
• Positioning & Comparison
• Installation
• Integration
• Documentation
• Troubleshooting

Quick Start

Option 1: Install via npm (Recommended)

# Install globally
npm install -g cozo-memory

# Or run directly with npx (no installation needed)
npx cozo-memory

Option 2: Build from Source

git clone https://github.com/tobs-code/cozo-memory
cd cozo-memory
npm install && npm run build
npm run start

Now add the server to your MCP client (e.g. Claude Desktop) – see Integration below.

Key Features

🔍 Hybrid Search - Combines semantic (HNSW), full-text (FTS), and graph signals via Reciprocal Rank Fusion for intelligent retrieval

🧠 Agentic Retrieval - Auto-routing engine analyzes query intent via local LLM to select optimal search strategy (Vector, Graph, or Community)

⏱️ Time-Travel Queries - Version all changes via CozoDB Validity; query any point in history with full audit trails

🎯 GraphRAG-R1 Adaptive Retrieval - Intelligent system with Progressive Retrieval Attenuation (PRA) and Cost-Aware F1 (CAF) scoring that learns from usage

⏳ Temporal Conflict Resolution - Automatic detection and resolution of contradictory observations with semantic analysis and audit preservation

🏠 100% Local - Embeddings via ONNX/Transformers; no external services, no cloud, complete data ownership

🧠 Multi-Hop Reasoning - Logic-aware graph traversal with vector pivots for deep relational reasoning

🗂️ Hierarchical Memory - Multi-level architecture (L0-L3) with intelligent compression and LLM-backed summarization

→ See all features | Version History

Positioning & Comparison

Most "Memory" MCP servers fall into two categories:

• Simple Knowledge Graphs: CRUD operations on triples, often only text search
• Pure Vector Stores: Semantic search (RAG), but little understanding of complex relationships

This server fills the gap in between ("Sweet Spot"): A local, database-backed memory engine combining vector, graph, and keyword signals.

Comparison with other solutions

Feature	CozoDB Memory (This Project)	Official Reference (@modelcontextprotocol/server-memory)	mcp-memory-service (Community)	Database Adapters (Qdrant/Neo4j)
Backend	CozoDB (Graph + Vector + Relational)	JSON file (memory.jsonl)	SQLite / Cloudflare	Specialized DB (only Vector or Graph)
Search Logic	Agentic (Auto-Route): Hybrid + Graph + Summaries	Keyword only / Exact Graph Match	Vector + Keyword	Mostly only one dimension
Inference	Yes: Built-in engine for implicit knowledge	No	No ("Dreaming" is consolidation)	No (Retrieval only)
Community	Yes: Hierarchical Community Summaries	No	No	Only clustering (no summary)
Time-Travel	Yes: Queries at any point in time (Validity)	No (current state only)	History available, no native DB feature	No
Maintenance	Janitor: LLM-backed cleanup	Manual	Automatic consolidation	Mostly manual
Deployment	Local (Node.js + Embedded DB)	Local (Docker/NPX)	Local or Cloud	Often requires external DB server

The core advantage is Intelligence and Traceability: By combining an Agentic Retrieval Layer with Hierarchical GraphRAG, the system can answer both specific factual questions and broad thematic queries with much higher accuracy than pure vector stores.

Installation

Prerequisites

• Node.js 20+ (recommended)
• RAM: 1.7 GB minimum (for default bge-m3 model)
  • Model download: ~600 MB
  • Runtime memory: ~1.1 GB
  • For lower-spec machines, see Embedding Model Options below
• CozoDB native dependency is installed via cozo-node

Via npm (Easiest)

# Install globally
npm install -g cozo-memory

# Or use npx without installation
npx cozo-memory

From Source

git clone https://github.com/tobs-code/cozo-memory
cd cozo-memory
npm install
npm run build

Windows Quickstart

npm install
npm run build
npm run start

Notes:

• On first start, @xenova/transformers downloads the embedding model (may take time)
• Embeddings are processed on the CPU

Embedding Model Options

CozoDB Memory supports multiple embedding models via the EMBEDDING_MODEL environment variable:

Model	Size	RAM	Dimensions	Best For
Xenova/bge-m3 (default)	~600 MB	~1.7 GB	1024	High accuracy, production use
Xenova/all-MiniLM-L6-v2	~80 MB	~400 MB	384	Low-spec machines, development
Xenova/bge-small-en-v1.5	~130 MB	~600 MB	384	Balanced performance

Configuration Options:

Option 1: Using .env file (Easiest for beginners)

# Copy the example file
cp .env.example .env

# Edit .env and set your preferred model
EMBEDDING_MODEL=Xenova/all-MiniLM-L6-v2

Option 2: MCP Server Config (For Claude Desktop / Kiro)

{
  "mcpServers": {
    "cozo-memory": {
      "command": "npx",
      "args": ["cozo-memory"],
      "env": {
        "EMBEDDING_MODEL": "Xenova/all-MiniLM-L6-v2"
      }
    }
  }
}

Option 3: Command Line

# Use lightweight model for development
EMBEDDING_MODEL=Xenova/all-MiniLM-L6-v2 npm run start

Download Model First (Recommended):

# Set model in .env or via command line, then:
EMBEDDING_MODEL=Xenova/all-MiniLM-L6-v2 npm run download-model

Note: Changing models requires re-embedding existing data. The model is downloaded once on first use.

Integration

Claude Desktop

Using npx (Recommended)

{
  "mcpServers": {
    "cozo-memory": {
      "command": "npx",
      "args": ["cozo-memory"]
    }
  }
}

Using global installation

{
  "mcpServers": {
    "cozo-memory": {
      "command": "cozo-memory"
    }
  }
}

Using local build

{
  "mcpServers": {
    "cozo-memory": {
      "command": "node",
      "args": ["C:/Path/to/cozo-memory/dist/index.js"]
    }
  }
}

Framework Adapters

Official adapters for seamless integration with popular AI frameworks:

🦜 LangChain Adapter

npm install @cozo-memory/langchain @cozo-memory/adapters-core

import { CozoMemoryChatHistory, CozoMemoryRetriever } from '@cozo-memory/langchain';

const chatHistory = new CozoMemoryChatHistory({ sessionName: 'user-123' });
const retriever = new CozoMemoryRetriever({ useGraphRAG: true, graphRAGDepth: 2 });

🦙 LlamaIndex Adapter

npm install @cozo-memory/llamaindex @cozo-memory/adapters-core

import { CozoVectorStore } from '@cozo-memory/llamaindex';

const vectorStore = new CozoVectorStore({ useGraphRAG: true });

Documentation: See adapters/README.md for complete examples and API reference.

CLI & TUI

CLI Tool

Full-featured CLI for all operations:

# System operations
cozo-memory system health
cozo-memory system metrics

# Entity operations
cozo-memory entity create -n "MyEntity" -t "person"
cozo-memory entity get -i <entity-id>

# Search
cozo-memory search query -q "search term" -l 10
cozo-memory search agentic -q "agentic query"

# Graph operations
cozo-memory graph pagerank
cozo-memory graph communities

# Export/Import
cozo-memory export json -o backup.json
cozo-memory import file -i data.json -f cozo

# All commands support -f json or -f pretty for output formatting

See CLI help for complete command reference: cozo-memory --help

TUI (Terminal User Interface)

Interactive TUI with mouse support powered by Python Textual:

# Install Python dependencies (one-time)
pip install textual

# Launch TUI
npm run tui
# or directly:
cozo-memory-tui

TUI Features:

• 🖱️ Full mouse support (click buttons, scroll, select inputs)
• ⌨️ Keyboard shortcuts (q=quit, h=help, r=refresh)
• 📊 Interactive menus for all operations
• 🎨 Rich terminal UI with colors and animations

Architecture Overview

graph TB
    Client[MCP Client<br/>Claude Desktop, etc.]
    Server[MCP Server<br/>FastMCP + Zod Schemas]
    Services[Memory Services]
    Embeddings[Embeddings<br/>ONNX Runtime]
    Search[Hybrid Search<br/>RRF Fusion]
    Cache[Semantic Cache<br/>L1 + L2]
    Inference[Inference Engine<br/>Multi-Strategy]
    DB[(CozoDB SQLite<br/>Relations + Validity<br/>HNSW Indices<br/>Datalog/Graph)]
    
    Client -->|stdio| Server
    Server --> Services
    Services --> Embeddings
    Services --> Search
    Services --> Cache
    Services --> Inference
    Services --> DB
    
    style Client fill:#e1f5ff,color:#000
    style Server fill:#fff4e1,color:#000
    style Services fill:#f0e1ff,color:#000
    style DB fill:#e1ffe1,color:#000

See docs/ARCHITECTURE.md for detailed architecture documentation

MCP Tools Overview

The interface is reduced to 4 consolidated tools:

Tool	Purpose	Key Actions
mutate_memory	Write operations	create_entity, update_entity, delete_entity, add_observation, create_relation, transactions, sessions, tasks
query_memory	Read operations	search, advancedSearch, context, graph_rag, graph_walking, agentic_search, adaptive_retrieval
analyze_graph	Graph analysis	explore, communities, pagerank, betweenness, hits, shortest_path, semantic_walk
manage_system	Maintenance	health, metrics, export, import, cleanup, defrag, reflect, snapshots

See docs/API.md for complete API reference with all parameters and examples

Troubleshooting

Common Issues

First Start Takes Long

• The embedding model download takes 30-90 seconds on first start (Transformers loads ~500MB of artifacts)
• This is normal and only happens once
• Subsequent starts are fast (< 2 seconds)

Cleanup/Reflect Requires Ollama

• If using cleanup or reflect actions, an Ollama service must be running locally
• Install Ollama from https://ollama.ai
• Pull the desired model: ollama pull demyagent-4b-i1:Q6_K (or your preferred model)

Windows-Specific

• Embeddings are processed on CPU for maximum compatibility
• RocksDB backend requires Visual C++ Redistributable if using that option

Performance Issues

• First query after restart is slower (cold cache)
• Use health action to check cache hit rates
• Consider RocksDB backend for datasets > 100k entities

See docs/BENCHMARKS.md for performance optimization tips

Documentation

• docs/API.md - Complete MCP tools reference with all parameters and examples
• docs/ARCHITECTURE.md - System architecture, data model, and technical details
• docs/BENCHMARKS.md - Performance metrics, evaluation results, and optimization tips
• docs/FEATURES.md - Detailed feature documentation with usage examples
• docs/USER-PROFILING.md - User preference profiling and personalization
• CHANGELOG.md - Version history and release notes
• CONTRIBUTING.md - Development guidelines

Development

Structure

• src/index.ts: MCP Server + Tool Registration
• src/memory-service.ts: Core business logic
• src/db-service.ts: Database operations
• src/embedding-service.ts: Embedding Pipeline + Cache
• src/hybrid-search.ts: Search Strategies + RRF
• src/inference-engine.ts: Inference Strategies
• src/api_bridge.ts: Express API Bridge (optional)

Scripts

npm run build        # TypeScript Build
npm run dev          # ts-node Start of MCP Server
npm run start        # Starts dist/index.js (stdio)
npm run bridge       # Build + Start of API Bridge
npm run benchmark    # Runs performance tests
npm run eval         # Runs evaluation suite

Roadmap

Near-Term (v1.x)

• GPU Acceleration - CUDA support for embedding generation (10-50x faster)
• Streaming Ingestion - Real-time data ingestion from logs, APIs, webhooks
• Advanced Chunking - Semantic chunking for ingest_file (paragraph-aware splitting)
• Query Optimization - Automatic query plan optimization for complex graph traversals
• Additional Export Formats - Notion, Roam Research, Logseq compatibility

Mid-Term (v2.x)

• Multi-Modal Embeddings - Support for images, audio, code
• Distributed Memory - Sharding and replication for large-scale deployments
• Advanced Inference - Neural-symbolic reasoning, causal inference
• Real-Time Sync - WebSocket-based real-time updates
• Web UI - Browser-based management interface

Long-Term (v3.x)

• Federated Learning - Privacy-preserving collaborative learning
• Quantum-Inspired Algorithms - Advanced graph algorithms
• Multi-Agent Coordination - Shared memory across multiple agents

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

Apache 2.0 - See LICENSE for details.

Acknowledgments

Built with:

• CozoDB - Embedded graph database
• ONNX Runtime - Local embedding generation
• Transformers.js - Xenova/bge-m3 model
• FastMCP - MCP server framework

Research foundations:

• GraphRAG-R1 (Yu et al., WWW 2026)
• HopRAG (ACL 2025)
• T-GRAG (Li et al., 2025)
• FEEG Framework (Samuel et al., 2026)
• Allan-Poe (arXiv:2511.00855)