LangSmith
Best for Traces, evaluates, and debugs LLM chains and agents with full experiment history.
When not LangChain-focused. some features limited for non-LangChain stacks.
A developer platform from LangChain for building, debugging, testing, and monitoring LLM applications in production. LangSmith provides full observability into every LLM call inside an application: input prompts, model responses, latency, token counts, and the full execution trace of multi-step agent workflows. A Dataset and Evaluation module lets developers build test datasets and run automated evaluations to measure output quality as models or prompts are updated. A Prompt Hub stores and versions prompts, enabling teams to track changes and A/B test variations systematically. The Playground allows prompt iteration with full trace visibility. LangSmith works with any LLM framework including LangChain, LlamaIndex, OpenAI SDK, and raw API calls. A free tier covers 5,000 traces per month; paid plans start at $39/month for higher volumes. Used by AI engineers and development teams building production LLM applications who need visibility into what is happening inside their AI pipeline.
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On these task shortlists
- Run ML experiments and track modelsbest overall
Log training runs, compare model performance, and manage datasets and checkpoints across the ML lifecycle.
- Build a RAG pipeline or LLM appbest for teams
Connect a large language model to private data sources for retrieval-augmented generation and document Q&A.
Best for Monitors and evaluates RAG pipeline performance with detailed trace logging.
When not Best value when already using LangChain or LangGraph.
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