--- title: "Deploy LibreChat | Open Source ChatGPT Alternative" description: "5-service LibreChat with search and RAG. ~1.5 GB RAM, about $25/month" category: "AI/ML" url: https://railway.com/deploy/librechat-ai-chat --- # Deploy LibreChat | Open Source ChatGPT Alternative 5-service LibreChat with search and RAG. ~1.5 GB RAM, about $25/month **[Deploy LibreChat | Open Source ChatGPT Alternative on Railway](https://railway.com/template/librechat-ai-chat)** - **Creator:** katsuba.dev - **Category:** AI/ML ## Template content ### RAG https://cdn.jsdelivr.net/gh/homarr-labs/dashboard-icons/svg/librechat.svg - **Image:** ghcr.io/danny-avila/librechat-rag-api-dev-lite:v0.9.0 ### MongoDB https://cdn.jsdelivr.net/gh/homarr-labs/dashboard-icons/svg/mongodb.svg - **Image:** mongo:8.0.28 - **Start command:** `docker-entrypoint.sh mongod --ipv6 --bind_ip ::,0.0.0.0` ### VectorDB https://cdn.jsdelivr.net/gh/homarr-labs/dashboard-icons/svg/postgresql.svg - **Image:** pgvector/pgvector:0.8.6-pg17-trixie ### Meilisearch https://cdn.jsdelivr.net/gh/homarr-labs/dashboard-icons/svg/meilisearch.svg - **Image:** getmeili/meilisearch:v1.35.1 ### LibreChat https://cdn.jsdelivr.net/gh/homarr-labs/dashboard-icons/svg/librechat.svg - **Image:** ghcr.io/danny-avila/librechat:v0.8.7 - **Start command:** `sh -c 'mkdir -p /data/uploads /data/images && rm -rf /app/uploads /app/client/public/images && ln -sfn /data/uploads /app/uploads && ln -sfn /data/images /app/client/public/images && exec npm run backend'` - **Health check:** /health - **Public domain:** Yes ## Documentation # Deploy and Host LibreChat on Railway LibreChat is an open-source chat interface for every major model provider at once — OpenAI, Anthropic, Google, and two dozen more — with conversations, agents, file search and full-text history in one place. This template deploys the whole stack, configured and working. ## About Hosting LibreChat Five services, all from official upstream images: - **LibreChat** — the web interface and API. Public domain, healthcheck, and a volume for uploads and generated images. - **MongoDB** — conversations, messages, accounts and agents, on a volume. - **Meilisearch** — full-text search across your conversation history, on a volume. - **RAG** — the file-search sidecar that reads uploaded documents and answers from them. - **VectorDB** — PostgreSQL with pgvector, holding the document embeddings, on a volume. All 106 variables are filled in and described on the deploy screen. There is nothing you have to type to get a working deployment; sign in and it works. Adding your own model provider key is one variable. Four things this template does that are worth knowing about: **File search actually reaches the RAG service.** That sidecar is a Python server, and Python servers bind IPv4 by default while Railway's private network is IPv6 — so left alone it is simply unreachable from the chat container, and file search fails without an obvious reason. Here it binds `::` and the two talk over the private network, which the deploy log confirms in as many words: `RAG API is running and reachable`. The alternative other deployments settle on is giving the RAG service a public domain, which puts your document store on the open internet. **Uploads and generated images survive a deploy.** LibreChat writes avatars, uploaded files and generated images to disk. Without a volume they are gone after the next restart — the database still lists them, and every one of them 404s. The app container has a volume here, with both directories on it, and it is verified: upload, redeploy, the file is still byte-identical. **It pins released versions.** The image is `librechat:v0.8.7`, the newest release. That is a different image from the `librechat-dev:latest` that upstream's compose file and the existing deployments use — `dev` is built from the main branch and `latest` moves under you. Meilisearch is on the version upstream currently pins, twenty-four minor releases ahead of what the other templates ship, and Meilisearch changes its on-disk index format between minors. **Boot logs stay readable.** LibreChat prints its whole resolved configuration at info level on every start, and the bundled model catalogue makes that about 114 KB — enough to blow through Railway's per-deployment log limit and drop the one line that explains a failed boot. The console level is not configurable, so this template uses the structured log transport, which is the one that truncates. A restart now costs about forty log lines instead of six hundred. ## Common Use Cases - **One interface for every provider**: switch between GPT, Claude, Gemini, Llama and the rest inside a single conversation, paying each provider directly instead of a subscription per tool. - **A private team assistant**: your own domain, your own accounts, your own database — no conversation leaves your deployment except to the model provider you chose. - **Chatting with your documents**: upload PDFs, spreadsheets and text, and have answers grounded in them. Add an OpenAI key to the RAG service to turn it on. - **Agents and tools**: build assistants with instructions, tools and file collections, and share them with the other people on your deployment. ## Dependencies for LibreChat Hosting ### Deployment Dependencies - [LibreChat](https://github.com/danny-avila/LibreChat) — the upstream project and its official release image - [rag_api](https://github.com/danny-avila/rag_api) — the file-search sidecar, versioned separately - MongoDB for conversations and accounts, Meilisearch for search, PostgreSQL with pgvector for embeddings ### Implementation Details - **The first account to register becomes the administrator.** Registration is open so you can create it; turn `ALLOW_REGISTRATION` off once you have, because the domain is public. - **Model keys default to `user_provided`** — each person enters their own in the interface. Put a real key in the matching variable instead and the whole deployment uses it. - **File search needs one key of its own.** `RAG_OPENAI_API_KEY` on the RAG service starts as a placeholder rather than an empty value, because an empty key crashes that container in a loop instead of merely disabling the feature. Replace it and uploads become searchable. - **`CREDS_KEY` and `CREDS_IV` encrypt the keys people paste in.** They are generated for you at the correct lengths. Changing either one makes every stored key unreadable, and nothing validates them at startup — a wrong value surfaces later as an unrelated-looking error. - **Ban rules are on, and they are strict.** A request with no browser user-agent scores an instant ban, and the ban lives in MongoDB, so a restart will not clear it. Set `NON_BROWSER_VIOLATION_SCORE` to 0 before driving the REST API from a script. - **The model and endpoint catalogue is fetched from LibreChat's own repository** and regenerated daily, which is why `CONFIG_PATH` points at a branch: pinning it would freeze the model lists on the day you deployed. Point it at your own file to take control. - **Nothing but the app has a public address.** Mongo, Meilisearch, pgvector and the RAG service are reachable only from inside the project. ### Resource Usage and Cost Five services idle at roughly **1.2–2 GB of RAM**, plus 7 GB of volume — about **$20–35/month**. The chat container is the bulk of it; usage on top of that is mostly what you pay your model provider, not Railway. ## Why Deploy LibreChat on Railway? LibreChat is a five-container application with three different databases, and the interesting failures are all in the wiring between them: the search index, the vector store, the file sidecar. Railway gives it a private network, a volume per stateful service, a domain with TLS, and one deploy screen where every one of those connections is already filled in. The alternative is a compose file on a VM, where the same wiring is yours to get right — starting with the one that has no error message: the file service that answers on the wrong address family and simply never replies. ## Similar templates - [Chat Chat](https://railway.com/deploy/-WWW5r) — Chat Chat, your own unified chat and search to AI platform. - [stella](https://railway.com/deploy/stella) — Self-host stella with web, API, Postgres, Redis, and object storage. - [Hermes Agent | OpenClaw Alternative with Dashboard](https://railway.com/deploy/hermes-agent-or-openclaw-alternative-wit) — Self-Hosted Hermes AI Agent for Telegram, Discord & Slack Open this page in a browser: https://railway.com/deploy/librechat-ai-chat