Deploy LibreChat Private AI Chat (MongoDB + Meilisearch)

Deploy and Host LibreChat with Railway

LibreChat is an open-source AI chat platform with one interface for OpenAI, Anthropic, Google, OpenRouter and many other model providers, plus agents, file chat (RAG), conversation search and multi-user accounts. This community template runs LibreChat v0.8.8 with MongoDB, Meilisearch and a RAG API on Postgres with pgvector.

About Hosting LibreChat

LibreChat is a Node.js application. It keeps users, conversations, agents and settings in MongoDB, indexes messages in Meilisearch for full-text search, and sends uploaded documents to its RAG API, which stores embeddings in Postgres with pgvector. This template pins every image to a version and digest, generates all secrets, and creates the administrator account on first start, so the instance is never open for a stranger to claim. Uploads and avatars live on a Railway volume. No model keys are needed to deploy: by default every user adds their own API key in the chat interface, and you can switch to shared keys at any time.

Common Use Cases

• A private ChatGPT-style workspace for a team, with conversations kept in your own database.
• One interface for several providers (OpenAI, Claude, Gemini, OpenRouter models) with per-user keys.
• Agents with tools and MCP servers, shared inside the team.
• Chat with PDFs and other documents through the built-in RAG API.
• A controlled AI entry point for a company, with accounts managed by an administrator.

Dependencies for LibreChat Hosting

• LibreChat (ghcr.io/librechat-ai/librechat:v0.8.8)
• MongoDB 8.0
• Meilisearch 1.56
• LibreChat RAG API (lite) v0.9.0 with Postgres 18 + pgvector
• Model provider API keys (OpenAI, Anthropic, Google, OpenRouter, ...), added by users or by you

Deployment Dependencies

• LibreChat documentation: https://www.librechat.ai/docs
• Environment variables: https://www.librechat.ai/docs/configuration/dotenv
• librechat.yaml reference: https://www.librechat.ai/docs/configuration/librechat_yaml
• RAG API: https://www.librechat.ai/docs/configuration/rag_api
• LibreChat source: https://github.com/LibreChat-AI/LibreChat
• Meilisearch: https://www.meilisearch.com/docs

Implementation Details

First login. Open the LibreChat domain and sign in with LIBRECHAT_ADMIN_EMAIL (default admin@example.com) and the generated LIBRECHAT_ADMIN_PASSWORD, both on the LibreChat service's Variables tab. The account has the ADMIN role. To use another email, change LIBRECHAT_ADMIN_EMAIL before deploying or later (a redeploy creates the new admin). Changing LIBRECHAT_ADMIN_PASSWORD and redeploying resets the admin password.

Accounts. Public sign-up is off (ALLOW_REGISTRATION=false). To onboard a team, set it to true, let people register, then set it back to false. While sign-up is open, anyone who finds the URL can create an account, so do not leave it open together with shared API keys.

Model keys. OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_KEY and OPENROUTER_KEY default to user_provided: each user enters their own key from the model menu, and LibreChat stores it encrypted with CREDS_KEY. Replace a value with a real key to share it with all users (their usage is then billed to you).

File chat (RAG). Embeddings need an OpenAI key on the server: set RAG_OPENAI_API_KEY on rag-api, or put a real OPENAI_API_KEY on LibreChat (the RAG API uses it). Until then, chat works but uploading a file for file search returns an error. Other embedding providers (Azure, Ollama, Hugging Face TEI, Bedrock) are set with EMBEDDINGS_PROVIDER and their variables.

Configuration. A small librechat.yaml is built in (OpenRouter endpoint, welcome text). To use your own, set CONFIG_PATH on LibreChat to a raw file URL and redeploy.

Plans. Five services, about 1.3 GB RAM in total when idle (MongoDB uses less when its limit is lower). LibreChat and MongoDB were tested with a 0.5 GB limit, as on Railway's Free plan, and stay under it at idle; the other three services idle below 0.2 GB. Trial allows 1 GB per service. For busy teams or large uploads, Hobby or Pro is recommended.

Scaling. LibreChat keeps uploads on its volume, so run one replica and give it more memory and CPU as usage grows. MongoDB, Meilisearch and the RAG API scale vertically.

Pinning and upgrades. The LibreChat version is the FROM line in services/librechat/Dockerfile of the template repository; the other images are pinned in the service settings. Read the LibreChat changelog before upgrading. Never change CREDS_KEY or CREDS_IV after first start, or stored user keys can no longer be decrypted.

Why Deploy LibreChat on Railway?

Railway runs LibreChat, its databases, search and RAG services together on a private network with HTTPS, volumes and usage-based pricing. You get a private, multi-provider AI chat for your team in one deploy, keep conversations in your own infrastructure, and can grow each service independently.