OpenRouter
LLM service implementation using OpenRouter’s API with OpenAI-compatible interface
Overview
OpenRouterLLMService
provides access to OpenRouter’s language models through an OpenAI-compatible interface. It inherits from OpenAILLMService
and supports streaming responses, function calling, and context management.
API Reference
Complete API documentation and method details
OpenRouter Docs
Official OpenRouter API documentation and features
Example Code
Working example with function calling
Installation
To use OpenRouterLLMService
, install the required dependencies:
You’ll also need to set up your OpenRouter API key as an environment variable: OPENROUTER_API_KEY
.
Get your API key from OpenRouter. Free tier includes $1 of credits.
Frames
Input
OpenAILLMContextFrame
- Conversation context and historyLLMMessagesFrame
- Direct message listVisionImageRawFrame
- Images for vision processing (select models)LLMUpdateSettingsFrame
- Runtime parameter updates
Output
LLMFullResponseStartFrame
/LLMFullResponseEndFrame
- Response boundariesLLMTextFrame
- Streamed completion chunksFunctionCallInProgressFrame
/FunctionCallResultFrame
- Function call lifecycleErrorFrame
- API or processing errors
Function Calling
Function Calling Guide
Learn how to implement function calling with standardized schemas, register handlers, manage context properly, and control execution flow in your conversational AI applications.
Context Management
Context Management Guide
Learn how to manage conversation context, handle message history, and integrate context aggregators for consistent conversational experiences.
Usage Example
Metrics
Inherits all OpenAI metrics capabilities:
- Time to First Byte (TTFB) - Response latency measurement
- Processing Duration - Total request processing time
- Token Usage - Prompt tokens, completion tokens, and totals
Enable with:
Additional Notes
- Model Variety: Access 70+ models from OpenAI, Anthropic, Meta, Google, and more
- OpenAI Compatibility: Full compatibility with existing OpenAI code
- Easy Switching: Change models with a single parameter update
- Fallback Support: Built-in model fallbacks for high availability