A-MEM: Agentic Memory System

An agentic memory system for LLM agents based on the Zettelkasten principle.

Based on: "A-Mem: Agentic Memory for LLM Agents"
by Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, Yongfeng Zhang
Rutgers University, Independent Researcher, AIOS Foundation

🚀 Features

Core Features

• ✅ Note Construction: Automatic extraction of keywords, tags, and contextual summary
• ✅ Link Generation: Automatic linking of similar memories
• ✅ Memory Evolution: Dynamic updating of existing memories
• ✅ Semantic Retrieval: Intelligent search with graph traversal
• ✅ Multi-Provider Support: Ollama (local) or OpenRouter (cloud)
• ✅ Environment Variables: Configuration via .env file
• ✅ Graph Backend Selection: Choose between NetworkX (default), RustworkX (3x-100x faster), or FalkorDB (experimental, not fully tested)
• ✅ Parameter Validation: Automatic validation of all MCP tool parameters
• ✅ Safe Graph Wrapper: Edge case handling and data sanitization for robust operations

Advanced Features (New)

• ✅ Type Classification: Automatic classification of notes into 6 types (rule, procedure, concept, tool, reference, integration)
• ✅ Priority Scoring: On-the-fly priority calculation based on type, age, usage, and edge count for better search rankings
• ✅ Event Logging: Append-only JSONL event log for all critical operations (NOTE_CREATED, RELATION_CREATED, MEMORY_EVOLVED)
• ✅ Memory Enzymes: Autonomous background processes for graph maintenance
  • Link Pruner: Removes old/weak edges automatically
  • Relation Suggester: Finds new semantic connections between notes
  • Dead-End Node Auto-Linker: Automatically creates out-connections for dead-end nodes with intelligent filtering
  • Summary Digester: Compresses overcrowded nodes with many children
• ✅ Automatic Scheduler: Runs memory enzymes every hour in the background
• ✅ Metadata Field: Experimental fields support without schema changes
• ✅ Researcher Agent: Deep web research for low-confidence queries (JIT context optimization)
  • Automatic triggering when retrieval confidence < threshold
  • Manual research via research_and_store MCP tool
  • Hybrid approach: MCP tools (if available) or HTTP-based fallbacks (Google Search API, DuckDuckGo, Jina Reader)
• ✅ Local Jina Reader: Support for local Docker-based Jina Reader instance (fallback to cloud API)
• ✅ Unstructured PDF Extraction: Automatic PDF extraction using Unstructured (library or API)

🔄 Relationship to Original Implementation

This implementation was developed independently based on the research paper "A-Mem: Agentic Memory for LLM Agents". The original authors' production-ready system (A-mem-sys) was discovered after this implementation was completed.

Key Differences:

This implementation focuses on MCP Server integration for IDE environments (Cursor, VSCode), providing:

• Direct IDE integration via MCP protocol
• Explicit graph-based memory linking using NetworkX (DiGraph) with typed edges, reasoning, and weights
• File import with automatic chunking
• Memory reset and management tools
• Modern TUI benchmarking tool for Ollama model speed testing

The original A-mem-sys repository provides a pip-installable Python library with:

• Multiple LLM backend support (OpenAI, Ollama, OpenRouter, SGLang)
• Library-based integration for Python applications
• Comprehensive API for programmatic usage
• Implicit linking via ChromaDB embeddings (no explicit graph structure)

Technical Architecture Difference:

• This implementation: Dual-storage architecture
  • ChromaDB for vector

... View full README on GitHub