Deploy and Host AutoMem - AI Memory Service on Railway

AutoMem is a research-validated graph-vector memory service that gives AI assistants human-like long-term memory. It combines FalkorDB (graph) and Qdrant (vector) for sub-second hybrid recall with semantic search, temporal awareness, and 11 relationship types. Built on principles from HippoRAG 2, A-MEM, and MELODI research papers.

About Hosting AutoMem - AI Memory Service

Deploying AutoMem requires running a Flask API service with background enrichment workers, FalkorDB for graph-based knowledge storage, and optionally Qdrant for semantic vector search. The service automatically extracts entities, creates relationships, and consolidates memories over time. Railway's persistent volumes are essential for FalkorDB data durability, while private networking enables secure inter-service communication. The API handles concurrent writes with automatic retry logic and graceful degradation. Health monitoring endpoints ensure database connectivity and memory consistency across restarts.

Common Use Cases

• AI Assistants with Persistent Memory - Claude, ChatGPT, or custom agents that remember user preferences, decisions, and conversation history across sessions
• Knowledge Graph Construction - Automatically build interconnected knowledge bases from unstructured text with entity extraction and relationship detection
• Context-Aware Development Tools - IDEs and coding assistants that recall architectural decisions, bug patterns, and team conventions
• Research & Documentation Systems - Organize notes with Zettelkasten-inspired principles, automatic clustering, and temporal relationships

Dependencies for AutoMem - AI Memory Service

• FalkorDB (Redis-compatible graph database) - Canonical memory storage, relationships, and consolidation engine
• Qdrant (vector database) - Optional but recommended for semantic search and similarity detection
• OpenAI API - For generating embeddings and entity extraction (falls back to deterministic embeddings if unavailable)

Deployment Dependencies

• FalkorDB Docker Image - Latest stable version with persistent volume mounted at /data
• Qdrant Cloud - Free tier (1GB) or self-hosted via Docker
• AutoMem GitHub Repository - Source code and deployment configs
• MCP Protocol Integration - Official bridge for Claude Desktop, Cursor, and Claude Code

Why Deploy AutoMem - AI Memory Service on Railway?

Railway is a singular platform to deploy your infrastructure stack. Railway will host your infrastructure so you don't have to deal with configuration, while allowing you to vertically and horizontally scale it. By deploying AutoMem on Railway, you are one step closer to supporting a complete full-stack application with minimal burden. Host your servers, databases, AI agents, and more on Railway.