langsmith-trace

IMPORTANT: Always check the environment variables or .env file for LANGSMITH_PROJECT before querying or interacting with LangSmith. This tells you which project contains the relevant traces and data. If the LangSmith project is not available, use your best judgement to identify the right one. CLI Tool For LangChain/LangGraph apps, tracing is automatic. Just set environment variables: Optional variables: For non-LangChain apps, if the framework has native OpenTelemetry support, use LangSmith's OpenTelemetry integration. If the app is NOT using a framework, or using one without automatic OTel support, use the traceable decorator/wrapper and wrap your LLM client. client = wrap_openai(OpenAI()) @traceable def my_llm_pipeline(question: str) -> str: resp = client.chat.completions.create( model="gpt-4o-mini", messages=[{"role": "user", "content": question}], ) return resp.choices[0].message.content @traceable def rag_pipeline(question: str) -> str: docs = retrieve_docs(question) return generate_answer(question, docs) @traceable(name="retrieve_docs") def retrieve_docs(query: str) -> list[str]: return docs @traceable(name="generate_answer") def generate_answer(question: str, docs: list[str]) -> str: return client.chat.completions.create(...) Best Practices: Use the langsmith CLI to query trace data. Understanding the difference is critical: Generally, query traces first — they provide...