OpenAI - Compatible Providers

OpenAI-Compatible Providers

Because the component speaks the OpenAI API, you do not need a separate component to use third-party gateways or local model servers that expose an OpenAI-compatible API. Point baseUrl at the provider, set apiKey if it requires one, and use the same operations and options as with OpenAI.

Ensure the provider supports the chat completions and/or embeddings endpoint format you use. Authentication requirements and minor API variations differ between providers.
Provider baseUrl Notes

OpenAI

https://api.openai.com/v1

Default; baseUrl can be omitted

OpenRouter

https://openrouter.ai/api/v1

Multi-model gateway with provider routing and fallbacks

Ollama

http://localhost:11434/v1

Local LLM server

LM Studio

http://localhost:1234/v1

Local model runner

vLLM

http://localhost:8000/v1

High-throughput, self-hosted serving engine

Ollama (local)

Ollama runs models locally and does not require an API key. Install Ollama and pull the model used in the example below: 

ollama run llama3.2

  • Java

  • YAML 

from("direct:chat")
    .setBody(constant("What is Apache Camel?"))
    .to("openai:chat-completion?baseUrl=http://localhost:11434/v1&model=llama3.2");
- to:
    uri: openai:chat-completion
    parameters:
      baseUrl: http://localhost:11434/v1
      model: llama3.2
      userMessage: What is Apache Camel?

For local embeddings, use an embedding model such as nomic-embed-text (see the Embedding Models by Provider table below).

LM Studio (local)

LM Studio serves the model currently loaded in the app. Set model to the identifier shown in its UI.

  • Java

  • XML

  • YAML 

from("direct:chat")
    .to("openai:chat-completion?baseUrl=http://localhost:1234/v1&model=local-model");
<route>
  <from uri="direct:chat"/>
  <to uri="openai:chat-completion?baseUrl=http://localhost:1234/v1&amp;model=local-model"/>
</route>
- route:
    from:
      uri: direct:chat
      steps:
        - to:
            uri: openai:chat-completion
            parameters:
              baseUrl: http://localhost:1234/v1
              model: local-model

vLLM (self-hosted)

vLLM is a high-throughput LLM serving engine. Install it with pip install vllm and start the model used in the example below: 

vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000

  • Java

  • XML

  • YAML 

from("direct:chat")
    .to("openai:chat-completion?baseUrl=http://localhost:8000/v1"
        + "&model=meta-llama/Llama-3.1-8B-Instruct");
<route>
  <from uri="direct:chat"/>
  <to uri="openai:chat-completion?baseUrl=http://localhost:8000/v1&amp;model=meta-llama/Llama-3.1-8B-Instruct"/>
</route>
- route:
    from:
      uri: direct:chat
      steps:
        - to:
            uri: openai:chat-completion
            parameters:
              baseUrl: http://localhost:8000/v1
              model: meta-llama/Llama-3.1-8B-Instruct

If vLLM was started with --api-key , pass the same value via the apiKey option.

On Apple Silicon, the vllm-mlx variant uses Apple’s MLX framework and runs the same way: 

vllm-mlx serve mlx-community/Qwen2.5-7B-Instruct-4bit --port 8000

OpenRouter

OpenRouter is an OpenAI-compatible gateway that routes requests across many model providers. Set baseUrl to its endpoint and select a model with a cross-provider identifier:

  • Java

  • XML

  • YAML 

from("direct:chat")
    .to("openai:chat-completion?baseUrl=https://openrouter.ai/api/v1"
        + "&apiKey={{openrouter.api.key}}"
        + "&model=anthropic/claude-sonnet-4-20250514");
<route>
  <from uri="direct:chat"/>
  <to uri="openai:chat-completion?baseUrl=https://openrouter.ai/api/v1&amp;apiKey={{openrouter.api.key}}&amp;model=anthropic/claude-sonnet-4-20250514"/>
</route>
- route:
    from:
      uri: direct:chat
      steps:
        - to:
            uri: openai:chat-completion
            parameters:
              baseUrl: https://openrouter.ai/api/v1
              apiKey: "{{openrouter.api.key}}"
              model: anthropic/claude-sonnet-4-20250514

Provider Routing

OpenRouter accepts a provider object in the request body to control routing order and fallbacks. Pass it through the additionalBodyProperty option as a JSON value — the component parses JSON-valued properties and adds them to the request body: 

- to:
    uri: openai:chat-completion
    parameters:
      baseUrl: https://openrouter.ai/api/v1
      apiKey: "{{openrouter.api.key}}"
      model: anthropic/claude-sonnet-4-20250514
      additionalBodyProperty.provider: '{"order":["anthropic","google"],"allow_fallbacks":false}'

OpenRouter’s optional attribution headers ( HTTP-Referer and X-Title , which identify your app on the OpenRouter rankings) are sent as HTTP request headers, not body properties. The component does not currently expose a way to set custom HTTP request headers, so they cannot be configured today. They are optional and do not affect chat completions.

Embedding Models by Provider

Provider Recommended Model Dimensions

OpenAI

text-embedding-3-small

1536 (reducible to 256, 512, 1024)

OpenAI

text-embedding-3-large

3072 (reducible)

Ollama

nomic-embed-text

768

Ollama

mxbai-embed-large

1024

Mistral

mistral-embed

1024

Example using Ollama for local embeddings: 
- to:
    uri: openai:embeddings
    parameters:
      baseUrl: http://localhost:11434/v1
      embeddingModel: nomic-embed-text