CopilotKit is the open-source framework for integrating powerful AI Copilots into any application. Easily implement custom AI Chatbots, AI Agents, AI Textareas, and more.
# Documentation
To get started with CopilotKit, please check out the [documentation](https://docs.copilotkit.ai).
## LangFuse Logging Integration
CopilotKit now supports LangFuse logging integration to help you monitor, analyze, and debug your LLM requests and responses.
### Setup
To enable LangFuse logging, you can configure it when initializing the CopilotRuntime:
```typescript
import { CopilotRuntime, OpenAIAdapter } from "@copilotkit/runtime";
import { LangfuseClient } from "langfuse";
// Initialize your LangFuse client
const langfuse = new LangfuseClient({
publicKey: process.env.LANGFUSE_PUBLIC_KEY!,
secretKey: process.env.LANGFUSE_SECRET_KEY!,
baseUrl: process.env.LANGFUSE_BASE_URL,
});
// Create a CopilotRuntime with LangFuse logging enabled
const runtime = new CopilotRuntime({
adapter: new OpenAIAdapter({ apiKey: process.env.OPENAI_API_KEY }),
logging: {
enabled: true,
progressive: true, // Set to false for buffered logging
logger: {
logRequest: (data) => langfuse.trace({ name: "LLM Request", input: data }),
logResponse: (data) => langfuse.trace({ name: "LLM Response", output: data }),
logError: (errorData) => langfuse.trace({ name: "LLM Error", metadata: errorData }),
},
},
});
```
### Configuration Options
The logging configuration accepts the following options:
- `enabled` (boolean): Enable or disable logging (default: false)
- `progressive` (boolean): When true, logs each chunk as it's streamed. When false, logs the complete response (default: true)
- `logger` (object): Contains callback functions for logging:
- `logRequest`: Called when an LLM request is made
- `logResponse`: Called when an LLM response is received
- `logError`: Called when an error occurs during an LLM request
### Custom Logging Integrations
You can integrate with any logging service by implementing the logger interface:
```typescript
const runtime = new CopilotRuntime({
adapter: new OpenAIAdapter({ apiKey: "YOUR_API_KEY" }),
logging: {
enabled: true,
progressive: false,
logger: {
logRequest: (data) => {
// Implement your custom logging logic
console.log("LLM Request:", JSON.stringify(data));
},
logResponse: (data) => {
// Implement your custom logging logic
console.log("LLM Response:", JSON.stringify(data));
},
logError: (error) => {
// Implement your custom error logging
console.error("LLM Error:", error);
},
},
},
});
```
This allows you to send your logs to any system or service that you prefer.
