AutoMem is a production-grade long-term memory system designed to act as a persistent memory layer for AI assistants. Created to solve the limitation of stateless AI, it enables assistants to recall past information, learn from patterns, and maintain context across sessions. The system operates as a Flask-based HTTP API service, connecting to AI platforms via the Model Context Protocol (MCP). It features a sophisticated graph-based architecture using FalkorDB for structured knowledge storage and optional Qdrant for semantic vector search, allowing for complex multi-hop reasoning that goes beyond traditional RAG systems. By building knowledge graphs, AutoMem maps relationships between memories, such as 'LEADS_TO', 'CONTRADICTS', or 'EXEMPLIFIES', while simultaneously performing entity extraction, temporal alignment, and pattern detection to manage information lifecycle.

Some of the key features are:

• Graph-Based Recall: Retrieves information using a hybrid approach combining semantic vector search, keyword matching, and graph relationships.
• Universal Compatibility: Supports integration with Claude Desktop, Cursor, GitHub Copilot, ChatGPT, and other MCP-compliant tools via local stdio or remote SSE/HTTP.
• Graceful Degradation: Maintains system operations in graph-only mode if the vector database becomes unavailable.
• Asynchronous Enrichment: Automatically processes memory content in the background for entity extraction, summarization, and relationship building.
• Memory Consolidation: Implements neuroscience-inspired cycles including decay, clustering, and forgetting to keep memory stores relevant over time.
• Pluggable Embeddings: Supports multiple embedding providers including OpenAI, Voyage AI, and local FastEmbed, with automatic failover capabilities.
• Portable Deployment: Offers flexible hosting options including self-hosted Docker environments, automated Railway templates, and managed InstaPods.

AutoMem functions by intercepting interaction requests from an AI platform through an MCP bridge. When a user stores information, the memory is analyzed by a classifier and stored in the graph database. Background workers then handle the semantic embedding generation and relationship enrichment asynchronously. During retrieval, the system performs a hybrid search to find the most relevant context, which is then fed back to the AI. This process is seamless and allows the assistant to maintain a consistent knowledge base across different IDEs or chat platforms, effectively offloading long-term state management from the AI model itself.

Some common use cases include:

• Coding Assistant Persistence: Storing project-specific rules, architectural decisions, and bug history in Cursor or Claude Code across multiple sessions.
• Conversational Continuity: Allowing ChatGPT or Claude web users to maintain context about personal preferences and historical interactions across months of dialogue.
• Research Management: Linking concepts, sources, and discoveries in a knowledge graph to assist researchers in navigating complex project data.
• Collaborative Team Memory: Providing a shared, queryable knowledge base that different AI tools can access to ensure consistent assistance across a development team.