Focxle vs LangSmith

LangSmith is LangChain's tracing, evaluation and monitoring platform. It records the structure of a run, step by step, so you can see what a chain or agent did, debug it, and evaluate output quality over datasets.

Said first and in full, because a comparison that never concedes anything is marketing, and you would be right to stop reading.

• Step-by-step traces of a chain or agent, which is the right tool when you are debugging behaviour rather than cost.
• Evaluation over datasets, regression testing on prompts, and human feedback collection. We do none of that.
• Deep integration with LangChain and LangGraph specifically.
• A collaborative UI built for teams reviewing runs together.

• Framework-agnostic, because we instrument the HTTP layer rather than one framework. The same two lines work for LlamaIndex, CrewAI, plain requests or code you wrote yourself.
• Non-LLM spend is in the same total. Serper, Firecrawl and embedding calls appear on a different bill and no LLM tracer counts them.
• No account or network call required, so it works on the first run with nothing configured.
• Shows what a spending cap would have stopped, before you turn one on.

They solve different problems and using both is reasonable. LangSmith tells you why your agent did something. We tell you what it cost you, across everything, per agent.

pip install focxle

import focxle
focxle.init()

Free forever to watch, no account, and it takes about a minute to find out whether the number surprises you.