Ollama

Ollama runs open models locally and exposes an OpenAI-compatible API, so you can point LibreChat at your own machine. Download models with ollama run <model> and browse what's available in the Ollama Library.

Configuration

Ollama ignores the API key but still expects the field to be present, so set it to any placeholder. Point baseURL at your Ollama server. Add the endpoint under endpoints.custom in your librechat.yaml:

    - name: "Ollama"
      apiKey: "ollama"
      # use 'host.docker.internal' instead of localhost if running LibreChat in a docker container
      baseURL: "http://localhost:11434/v1/" 
      models:
        default: [
          "llama2",
          "mistral",
          "codellama",
          "dolphin-mixtral",
          "mistral-openorca"
          ]
        # fetching list of models is supported but the `name` field must start
        # with `ollama` (case-insensitive), as it does in this example.
        fetch: true
      titleConvo: true
      titleModel: "current_model"
      summarize: false
      summaryModel: "current_model"
      modelDisplayLabel: "Ollama"

Notes

Set titleModel to "current_model" so title generation reuses the conversation's model instead of loading a second one. This keeps Ollama to a single loaded model per conversation.
The default array above is a sample list of popular models. With fetch: true, LibreChat pulls the full list from your server.

Ollama -> llama3

Once stop was removed from the default parameters, the issue below should no longer occur.

If llama3 keeps generating without stopping, add an addParams block with the stop sequences:

    - name: "Ollama"
      apiKey: "ollama"
      baseURL: "http://host.docker.internal:11434/v1/"
      models:
        default: [
          "llama3"
        ]
        fetch: false # pinned to the list above; set true to discover models from the server
      titleConvo: true
      titleModel: "current_model"
      summarize: false
      summaryModel: "current_model"
      modelDisplayLabel: "Ollama"
      addParams:
          "stop": [
              "<|start_header_id|>",
              "<|end_header_id|>",
              "<|eot_id|>",
              "<|reserved_special_token"
          ]

If you only run llama3 with Ollama, setting stop at the config level via addParams is fine. When you run several models, add stop sequences from the frontend through conversation parameters and presets instead, and omit addParams:

    - name: "Ollama"
      apiKey: "ollama"
      baseURL: "http://host.docker.internal:11434/v1/" 
      models:
        default: [
          "llama3:latest",
          "mistral"
          ]
        fetch: false # pinned to the list above; set true to discover models from the server
      titleConvo: true
      titleModel: "current_model"
      modelDisplayLabel: "Ollama"

Set the stop sequences in conversation parameters (and save them as a preset). Open a conversation on the Ollama endpoint, open the right-hand parameters panel, and add each sequence under Stop Sequences:

LibreChat conversation parameters panel with four llama3 stop sequences entered in the Stop Sequences field

Troubleshooting

Ollama does not appear, or the model list is empty

Work through these in order:

Check the endpoint is reachable from LibreChat, not from your shell. If LibreChat runs in Docker, localhost is the API container itself, not your host. Use http://host.docker.internal:11434/v1/ on Docker Desktop, or the host's LAN address on Linux where host.docker.internal may be unavailable. Running LibreChat outside Docker is the only case where http://localhost:11434/v1/ is correct.
Confirm Ollama is listening beyond loopback. By default Ollama binds to 127.0.0.1, which a container cannot reach. Set OLLAMA_HOST=0.0.0.0 in Ollama's own environment and restart it.

Binding to 0.0.0.0 exposes Ollama on every interface
Ollama's API is unauthenticated. Binding it to 0.0.0.0 on a machine with a LAN or public interface hands model access, and the ability to pull and delete models, to anyone who can reach port 11434. Prefer binding to just the address the LibreChat container actually reaches, which is the gateway of its compose network (docker network inspect <network> reports it), and firewall port 11434 so nothing else can reach it.
Know what the endpoint name changes. LibreChat reaches for Ollama's native /api/tags only when the endpoint name starts with ollama, case-insensitively. Any other name, or a failure of that native call, falls through to the generic OpenAI-compatible /v1/models request. Current Ollama versions answer that one too at the /v1/ base URL above, so a renamed endpoint usually still returns a model list. The prefix matters when you specifically need the native tags route, and a hosted proxy that serves /v1/models but not /api/tags is better off without it.
Set apiKey to any non-empty placeholder. Ollama ignores the value, but a custom endpoint with no apiKey is dropped at config load.
Read the API logs. docker compose logs api reports the connection error and the URL it actually tried.

Using a remote or hosted Ollama server

Nothing is local-specific except the URL: point baseURL at the remote server's OpenAI-compatible path and put the credential in apiKey instead of the placeholder. Name it whatever you like: model fetching falls back to the OpenAI-compatible /v1/models route, which is usually what a hosted proxy exposes.

apiKey is only ever sent as Authorization: Bearer <key>, and only when your headers block has not already set an Authorization header. If your hosted proxy expects a different scheme, such as X-API-Key or Basic auth, put the real credential in headers: an Authorization entry there replaces the Bearer fallback, and any other header is sent alongside it. apiKey still has to be non-empty either way, because an endpoint without one is dropped at config load.

- name: "Ollama"
  apiKey: "unused"
  baseURL: "https://ollama.example.com/v1/"
  headers:
    X-API-Key: "${OLLAMA_PROXY_KEY}"
  models:
    default: ["llama3:latest"]
    fetch: true