langfuse-hello-world

# Langfuse Hello World ## Overview Minimal working example demonstrating core Langfuse tracing functionality. ## Prerequisites - Completed `langfuse-install-auth` setup - Valid API credentials configured - Development environment ready ## Instructions ### Step 1: Create Entry File Create a new file for your hello world trace. ### Step 2: Import and Initialize Client ```typescript import { Langfuse } from "langfuse"; const langfuse = new Langfuse({ publicKey: process.env.LANGFUSE_PUBLIC_KEY!, secretKey: process.env.LANGFUSE_SECRET_KEY!, baseUrl: process.env.LANGFUSE_HOST, }); ``` ### Step 3: Create Your First Trace ```typescript async function helloLangfuse() { // Create a trace (top-level operation) const trace = langfuse.trace({ name: "hello-world", userId: "demo-user", metadata: { source: "hello-world-example" }, tags: ["demo", "getting-started"], }); // Add a span (child operation) const span = trace.span({ name: "process-input", input: { message: "Hello, Langfuse!" }, }); // Simulate some processing await new Promise((resolve) => setTimeout(resolve, 100)); // End the span with output span.end({ output: { result: "Processed successfully!" }, }); // Add a generation (LLM call tracking) trace.generation({ name: "llm-response", model: "gpt-4", input: [{ role: "user", content: "Say hello" }], output: { content: "Hello! How can I help you today?" }, usage: { promptTokens: 5, completionTokens: 10, totalTokens: 15, }, }); // Flush to ensure data is sent await langfuse.flushAsync(); console.log("Trace created! View at:", trace.getTraceUrl()); } helloLangfuse().catch(console.error); ``` ## Output - Working code file with Langfuse client initialization - A trace visible in Langfuse dashboard containing: - One span with input/output - One generation with mock LLM data - Console output showing: ``` Trace created! View at: https://cloud.langfuse.com/trace/abc123... ``` ## Error Handling | Error | Cause | Solution | |-------|-------|----------| | Import Error | SDK not installed | Verify with `npm list langfuse` | | Auth Error | Invalid credentials | Check environment variables are set | | Trace not appearing | Data not flushed | Ensure `flushAsync()` is called | | Network Error | Host unreachable | Verify LANGFUSE_HOST URL | ## Examples ### TypeScript Complete Example ```typescript import { Langfuse } from "langfuse"; const langfuse = new Langfuse(); async function main() { // Create trace const trace = langfuse.trace({ name: "hello-world", input: { query: "What is Langfuse?" }, }); // Simulate LLM call const generation = trace.generation({ name: "answer-query", model: "gpt-4", modelParameters: { temperature: 0.7 }, input: [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: "What is Langfuse?" }, ], }); // Simulate response await new Promise((r) => setTimeout(r, 500)); // End generation with output generation.end({ output: "Langfuse is an open-source LLM observability platform...", usage: { promptTokens: 25, completionTokens: 50 }, }); // Update trace with final output trace.update({ output: { answer: "Langfuse is an LLM observability platform." }, }); // Flush and get URL await langfuse.flushAsync(); console.log("View trace:", trace.getTraceUrl()); } main(); ``` ### Python Complete Example ```python from langfuse import Langfuse import time langfuse = Langfuse() def main(): # Create trace trace = langfuse.trace( name="hello-world", input={"query": "What is Langfuse?"}, user_id="demo-user", ) # Add a span for processing span = trace.span( name="process-query", input={"query": "What is Langfuse?"}, ) # Simulate processing time.sleep(0.1) span.end(output={"processed": True}) # Add LLM generation generation = trace.generation( name="answer-query", model="gpt-4", model_parameters={"temperature": 0.7}, input=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What is Langfuse?"}, ], ) # Simulate LLM response time.sleep(0.5) generation.end( output="Langfuse is an open-source LLM observability platform...", usage={"prompt_tokens": 25, "completion_tokens": 50}, ) # Update trace with final output trace.update( output={"answer": "Langfuse is an LLM observability platform."} ) # Flush data langfuse.flush() print(f"View trace: {trace.get_trace_url()}") if __name__ == "__main__": main() ``` ### With Decorators (Python) ```python from langfuse.decorators import observe, langfuse_context @observe() def process_query(query: str) -> str: # This function is automatically traced return f"Processed: {query}" @observe(as_type="generation") def generate_response(messages: list) -> str: # This is tracked as an LLM generation langfuse_context.update_current_observation( model="gpt-4", usage={"prompt_tokens": 10, "completion_tokens": 20}, ) return "Hello from Langfuse!" @observe() def main(): result = process_query("Hello!") response = generate_response([{"role": "user", "content": "Hi"}]) return response main() ``` ## Resources - [Langfuse Tracing Concepts](https://langfuse.com/docs/tracing) - [Langfuse SDK Reference](https://langfuse.com/docs/sdk) - [Langfuse Examples](https://langfuse.com/docs/get-started) ## Next Steps Proceed to `langfuse-local-dev-loop` for development workflow setup.