How do I deploy AutoMem - AI Memory Service on Railway?

You can deploy AutoMem - AI Memory Service on Railway by clicking the "Deploy Now" button on this page. Railway will automatically set up all the necessary services and configurations for you.

What are the system requirements?

Railway handles all the infrastructure requirements. You only need a web browser to deploy and manage your application.

Is this template free to use?

Yes, this template is free to use within your existing Railway account.

Can I customize this template?

Yes, you can fully customize this template. After deployment, you will have full control of what you've deployed in your Railway project.

How do I get support for this template?

You can get support through our community forum: auto-mem-ai-memory-service-3c2415d6, or click "Get template help" for this template for more assistance.

Deploy AutoMem - AI Memory Service on Railway

AutoMem is a graph-vector hybrid memory service that gives AI agents persistent, human-like long-term memory. It combines FalkorDB (graph) and Qdrant (vector) for sub-second hybrid recall with semantic search, temporal awareness, and 11 typed relationship edges.

Neuroscience-inspired consolidation cycles automatically strengthen important memories and fade noise — like human sleep. Built on principles from HippoRAG 2, A-MEM, MELODI, and ReadAgent research.

About Deploying AutoMem

This template deploys four services with one click:

memory-service — Core AutoMem Flask API with background enrichment workers for entity extraction, pattern detection, and relationship building
falkordb — Redis-compatible graph database with persistent volume for durable storage
qdrant — Vector database for semantic search and similarity detection
mcp-sse-server — Remote MCP bridge (Streamable HTTP + SSE) that connects cloud AI platforms like ChatGPT, Claude.ai, and ElevenLabs to your memories over HTTPS

After deployment, connect your local AI tools (Cursor, Claude Desktop, Claude Code) via the official MCP bridge package (@verygoodplugins/mcp-automem). The API handles concurrent writes with automatic retry logic, graceful degradation, and health monitoring endpoints. Typical cost: ~$0.50-1/month on Railway.

Common Use Cases

Cross-Platform AI Memory — Give ChatGPT, Claude, Cursor, Claude Code, Warp Terminal, OpenAI Codex, and GitHub Copilot a shared memory that persists across sessions and tools
Context-Aware Development — IDEs and coding agents that recall architectural decisions, bug patterns, debugging history, and team conventions
Knowledge Graph Construction — Automatically build interconnected knowledge bases from unstructured text with entity extraction, typed relationships, and clustering
Research & Documentation — Organize knowledge with Zettelkasten-inspired principles, automatic pattern detection, and temporal relationships

Dependencies

FalkorDB (included in template) — Graph database for canonical memory storage, relationships, and consolidation
Qdrant (included in template) — Vector database for semantic search and similarity detection
Embedding Provider — Voyage AI is the default (API key required). Also supports OpenAI, FastEmbed (local, no API key), and a placeholder fallback for testing

Resources

AutoMem GitHub Repository — Source code and deployment configs
MCP Bridge Package — Official MCP integration for Claude Desktop, Cursor, Claude Code, Warp, OpenAI Codex, and more
Documentation — Setup guides and API reference
Discord — Community chat and support
automem.ai — Official website

Why Deploy AutoMem 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 get a production-ready memory service with persistent storage, private networking between services, and HTTPS endpoints for cloud AI platforms — all for under $1/month.