Deploy Mem0 v2 AI Memory Layer

Deploy and Host Mem0 with Railway

Mem0 is an open-source memory layer for AI agents and assistants. It extracts durable facts from conversations, stores them as embeddings in Postgres with pgvector, and returns the relevant ones for each user, agent or session. This template deploys Mem0's self-hosted REST API, its web dashboard and a pgvector database, ready for your agents to call.

About Hosting Mem0

The self-hosted Mem0 server is a FastAPI application with authentication, per-user API keys and a request log, plus a Next.js dashboard. It needs Postgres with the pgvector extension, a second database for users and settings, migrations before start, an LLM and embedding provider, and periodic cleanup of request logs. This community template builds the API and dashboard from the pinned Mem0 v2.2.1 release, creates both databases and runs migrations before each deploy, creates the dashboard admin from generated credentials so no one else can claim the setup page, and adds a daily cron job for log retention. It also installs Mem0's recommended NLP extra, which enables hybrid keyword, semantic and entity search.

Common Use Cases

• Long-term memory for chatbots and support agents that remember user preferences across sessions
• Shared memory for multi-agent systems, scoped by user_id, agent_id and run_id
• Personalising LLM apps with facts recalled through POST /search
• Auditing what your agents store and recall through the dashboard's Memories, Entities and Requests views

Dependencies for Mem0 Hosting

• An OpenAI API key, or a key for any OpenAI-compatible endpoint set through OPENAI_BASE_URL (required)
• Postgres with pgvector (included)
• Optional: Anthropic or Google keys to switch models in the dashboard's Configuration page

Deployment Dependencies

• Mem0 repository: https://github.com/mem0ai/mem0
• Self-hosted server README (v2.2.1): https://github.com/mem0ai/mem0/tree/v2.2.1/server
• REST API documentation: https://docs.mem0.ai/open-source/features/rest-api
• pgvector: https://github.com/pgvector/pgvector

Implementation Details

First login

1. Paste your OPENAI_API_KEY when deploying.
2. Open the Mem0 Dashboard URL and sign in with MEM0_ADMIN_EMAIL (default admin@example.com) and MEM0_ADMIN_PASSWORD from the Mem0 API service's Variables tab. Change both under Settings.
3. Create an API key under API Keys, or use ADMIN_API_KEY from the API service.
4. Store a memory: curl -X POST https://{api-domain}/memories -H "X-API-Key: {key}" -H "Content-Type: application/json" -d '{"messages": [{"role": "user", "content": "I prefer Python."}], "user_id": "alex"}'. Recall it with POST /search and {"query": "language preference", "filters": {"user_id": "alex"}}. Interactive docs are at https://{api-domain}/docs.

MCP: Mem0's official MCP server is a hosted service for the Mem0 Platform; the self-hosted OpenMemory MCP server was removed upstream in 2026. Connect agents to this deployment through the REST API or the Mem0 SDKs.

Scaling: the API keeps memory history in SQLite on its volume, so it runs as one replica; give it more RAM or CPU if extraction traffic grows. The dashboard is stateless. Postgres can be resized vertically.

Pinning and upgrades: the Mem0 release and its source checksum are build arguments in both Dockerfiles. Change them together, back up the Postgres and history volumes, and redeploy; migrations run automatically before the new version starts.

Why Deploy Mem0 on Railway?

Railway runs the API, dashboard, database and cron job together with private networking, persistent volumes and HTTPS, so your agents get a memory service you own within minutes. You pay for the small amount of RAM and CPU it uses, and your memories stay in your own database.