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View Full Features →Both do LLM tracing and prompt management. Langfuse is MIT-licensed with a self-hosted option — meaningful for regulated industries. LangSmith is deeper on LangChain-specific ergonomics and integrates tightly with LangGraph. Many teams evaluate both before committing.
No. Langfuse works with raw OpenAI/Anthropic SDKs, LlamaIndex, Vercel AI SDK, LiteLLM, Haystack, and generic OpenTelemetry. LangChain is one of many supported integrations.
Yes — the docker-compose and Helm chart run the full stack (app + ClickHouse + Postgres + Redis). Many enterprises use the self-hosted version to keep prompts and traces inside their own VPC.
You create versioned prompts in the Langfuse UI and label them (`production`, `staging`). Your app calls `langfuse.get_prompt('name', label='production')`, which returns the current version — swapping a prompt requires no redeploy.
An observation is a single logged unit — typically an LLM generation, a tool call, or a retrieval span. A single user request that fires 5 LLM calls plus 3 tool calls generates 8 observations.
AI builders and operators use Langfuse to streamline their workflow.
Try Langfuse Now →LangSmith helps developers trace, evaluate, test, and monitor applications built with language models and agent workflows.
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