Langfuse

Technical Use Cases

Langfuse is purpose-built for AI/ML engineers and teams working on LLM applications and agents. Common technical use cases include:

• Agent tracing & debugging — Capture multi-step agent execution traces (tool calls, LLM calls, sub-agents) to diagnose failures and latency bottlenecks across frameworks like CrewAI, OpenAI Agents SDK, AutoGen, and LangChain
• LLM observability in production — Monitor token usage, latency, cost, and error rates across model providers (OpenAI, Anthropic, Bedrock, Gemini, etc.) with OTel-native instrumentation
• Prompt engineering & versioning — Manage prompt templates with version control, run A/B experiments, and link prompt versions directly to production trace data
• Automated evaluation pipelines — Implement LLM-as-a-judge scoring at scale; integrate with Ragas, Promptfoo, or custom scoring logic via the API
• Human annotation workflows — Route traces to human reviewers for structured labeling and quality assessment
• RAG pipeline monitoring — Trace retrieval and generation steps in RAG systems; integrates with LlamaIndex, RAGflow, and LiteLLM
• Self-hosted enterprise deployments — Run Langfuse on-premises with ClickHouse for high-throughput analytics; SOC2 & ISO27001 compliance available on Pro and Enterprise plans