Langfuse
Open source LLM observability, tracing, and evaluation platform
About Langfuse
Langfuse is an open-source LLM engineering and observability platform built for teams developing production-grade generative AI applications and autonomous multi-agent pipelines. It captures granular traces across token usage, prompt versions, latency bottlenecks, and retrieval accuracy, giving developers complete visibility into model behavior at runtime. By integrating seamlessly with major AI frameworks such as LangChain, LlamaIndex, LiteLLM, and the OpenAI SDK, Langfuse eliminates the guesswork from debugging complex agent execution trees. Developers can monitor production cost metrics, identify hallucinated responses, and run rigorous continuous evaluation suites on live traffic.
The platform architecture features asynchronous tracing hooks that introduce negligible latency overhead to live user interactions. Teams can set up human-in-the-loop scoring, programmatic assertion checks, and automated LLM-as-a-judge evaluations to benchmark prompt iterations against golden test datasets. Langfuse is fully open-source with MIT licensing, allowing organizations with strict data governance policies to self-host the complete observability stack on private Kubernetes clusters or AWS VPCs while maintaining identical enterprise dashboard ergonomics.
Install the Langfuse SDK in your Python or TypeScript application codebase.
Wrap your LLM calls, vector database retrievals, or agent execution steps with the Langfuse tracing decorator.
Run your application to stream latency, token costs, and prompt inputs to the Langfuse dashboard in real time.
Configure automated evaluation metrics to assess response ground truth, relevance, and toxicity scores.
Iterate on prompt templates and model hyperparameters using side-by-side comparative analytics.
Capabilities & Features
Common Use Cases
llm-observability
agent-tracing
prompt-evaluation
cost-tracking
rag-debugging
Frequently Asked Questions
What is Langfuse and who is it built for?
Langfuse is an open-source observability and evaluation platform designed for AI engineers, data scientists, and developers building LLM applications and agentic workflows.
Is Langfuse open source and free to self-host?
Yes, Langfuse is fully open source under the MIT license and can be self-hosted for free using Docker or Kubernetes without any telemetry limits.
Does Langfuse add latency to LLM application calls?
No, Langfuse utilizes background asynchronous queuing to transmit telemetry data, ensuring that user request latencies remain completely unaffected.
What frameworks does Langfuse support?
Langfuse provides first-class native SDKs for Python and TypeScript with out-of-the-box support for LangChain, LlamaIndex, LiteLLM, Vercel AI SDK, and raw API calls.
Free Plan
Generous free cloud tier with 50k traces/month and unlimited self-hosting via Docker
Paid Plan
Pro from $59/mo and Enterprise for custom SLAs, team RBAC, and data retention
Pros & Cons
100% open source with complete self-hosting freedom via Docker and Helm
Native integrations with LangChain, LlamaIndex, LiteLLM, and OpenAI
Granular cost tracking and per-user token consumption breakdowns
Comprehensive LLM-as-a-judge and human scoring workflows
Asynchronous telemetry with near-zero latency overhead
Self-hosting requires maintaining PostgreSQL and ClickHouse storage backends
Advanced multi-tenant team RBAC is restricted to enterprise tiers
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