langsmith-observability

# LangSmith - LLM Observability Platform Development platform for debugging, evaluating, and monitoring language models and AI applications. ## When to use LangSmith **Use LangSmith when:** - Debugging LLM application issues (prompts, chains, agents) - Evaluating model outputs systematically against datasets - Monitoring production LLM systems - Building regression testing for AI features - Analyzing latency, token usage, and costs - Collaborating on prompt engineering **Key features:** - **Tracing**: Capture inputs, outputs, latency for all LLM calls - **Evaluation**: Systematic testing with built-in and custom evaluators - **Datasets**: Create test sets from production traces or manually - **Monitoring**: Track metrics, errors, and costs in production - **Integrations**: Works with OpenAI, Anthropic, LangChain, LlamaIndex **Use alternatives instead:** - **Weights & Biases**: Deep learning experiment tracking, model training - **MLflow**: General ML lifecycle, model registry focus - **Arize/WhyLabs**: ML monitoring, data drift detection ## Quick start ### Installation ```bash pip install langsmith # Set environment variables export LANGSMITH_API_KEY="your-api-key" export LANGSMITH_TRACING=true ``` ### Basic tracing with @traceable ```python from langsmith import traceable from openai import OpenAI client = OpenAI() @traceable def generate_response(prompt: str) -> str: response = client.chat.completions.create( model="gpt-4o", messages=[{"role...