langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill langsmith --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- When to use LangSmith
- Quick start
- Installation
- Basic tracing with @traceable
- OpenAI wrapper (automatic tracing)
- Core concepts
- Runs and traces
- Projects
- Client API
- Datasets and evaluation
- Create dataset
- Run evaluation
- Built-in evaluators
- Advanced tracing
pip install langsmith Set environment variables export LANGSMITH_API_KEY="your-api-key" export LANGSMITH_TRACING=true
What does the langsmith-observability skill do?
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill langsmith --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.