Langfuse is an open source platform designed to help teams develop, monitor, evaluate, and debug large language model (LLM) applications. Built by Y Combinator W23 company Langfuse, it addresses the operational challenges of LLM engineering by providing integrated tools for tracing LLM calls, managing prompts, running evaluations, and testing in a playground—all with full observability. Unlike generic monitoring tools, Langfuse is purpose-built for the unique needs of LLM workflows, including context-aware tracing, prompt versioning, and structured evaluation pipelines. It is used by developers, ML engineers, and DevOps teams who need to improve LLM reliability, reduce latency in iterations, and maintain control over their AI stack through self-hosted deployment.
Langfuse supports both cloud and self-hosted deployments, making it suitable for organizations with strict data governance requirements or those seeking to avoid vendor lock-in. Its integrations with popular LLM frameworks like LangChain, LlamaIndex, OpenAI SDK, and LiteLLM allow for seamless adoption without rewriting existing code. With typed SDKs for Python and JavaScript/TypeScript, a full OpenAPI specification, and pre-built instrumentation hooks, Langfuse enables teams to instrument their applications in minutes and start gaining insights into LLM behavior at scale.
What You Get
- LLM Application Observability - Trace LLM calls, embeddings, retrievals, and agent actions end-to-end with detailed session logs. View user interactions, latency metrics, token usage, and prompt/response pairs in a unified interface to debug failures and optimize performance.
- Prompt Management - Centralize, version-control, and collaboratively iterate on prompts with server-side and client-side caching to avoid latency penalties. Track prompt performance across deployments and roll back changes safely.
- Evaluations - Run LLM-as-a-judge evaluations, collect user feedback, manually label outputs, and build custom evaluation pipelines using APIs or SDKs to quantify model performance without manual review.
- Datasets - Create and manage test sets for benchmarking LLM applications. Support structured experiments, pre-deployment validation, and continuous improvement by linking datasets to evaluation runs and tracing data.
- LLM Playground - Test prompts and model configurations interactively with real-time feedback. Jump directly from tracing results to the playground to iterate on problematic prompts without leaving the UI.
- Comprehensive API - Programmatically ingest traces, manage prompts, run evaluations, and fetch metrics via a full OpenAPI spec. Use typed Python and TypeScript SDKs to integrate Langfuse into CI/CD pipelines or custom analytics tools.
Common Use Cases
- Building a multi-tenant SaaS dashboard with real-time analytics - Teams use Langfuse to trace LLM calls per user session, measure prompt performance across tenants, and automatically flag low-quality responses using custom evaluations—all while maintaining data isolation via self-hosted deployment.
- Creating a mobile-first e-commerce platform with 10k+ SKUs - Engineers use Langfuse to evaluate product descriptions generated by LLMs against ground truth, track prompt variations for different regions, and optimize latency using dataset-backed A/B testing.
- Problem: Inconsistent LLM outputs in production → Solution: Langfuse traces every prompt, logs metadata, and allows teams to compare outputs across versions. By linking failed requests to specific prompts and models, engineers reduce debugging time from hours to minutes.
- DevOps teams managing microservices across multiple cloud providers - Langfuse’s self-hosted Kubernetes Helm charts and Terraform templates enable consistent LLM observability across AWS, Azure, and GCP environments. Teams instrument LiteLLM proxies to monitor all LLM providers in one dashboard.
Under The Hood
Langfuse is a comprehensive observability platform tailored for large language model (LLM) applications, offering trace visualization, prompt management, and evaluation scoring in a unified system. It is built with a monorepo structure using TypeScript and modern web frameworks, emphasizing reusable components and enterprise-grade features through an EE module.
Architecture
Langfuse adopts a modular monorepo architecture that promotes separation of concerns and extensibility.
- The codebase is organized into distinct modules, with a shared core library and an EE (Enterprise Edition) for enterprise features
- Clear boundaries between frontend, backend, and data processing layers support scalable development
- The architecture leverages Turborepo for build management and maintains a consistent component structure across services
Tech Stack
The project is built using TypeScript and modern web technologies, integrating a variety of backend systems for data handling.
- The primary tech stack includes React, Next.js, and TypeScript for frontend and full-stack development
- Backend services rely on ClickHouse, PostgreSQL, Redis, and MinIO for scalable data storage and processing
- Linting, formatting, and CI/CD configurations are consistently applied across the codebase for maintainability
Code Quality
Langfuse demonstrates a mature and structured approach to code quality with robust testing and error handling.
- Extensive end-to-end and server-side tests ensure reliability across authentication, API interactions, and system behavior
- Error handling follows consistent try/catch patterns with clear propagation of validation and authentication errors
- Code consistency is maintained through standardized naming and architectural practices, though some conditional logic for cloud behavior introduces technical debt
What Makes It Unique
Langfuse distinguishes itself in the LLM observability space through its integrated approach and data handling capabilities.
- It uniquely combines trace visualization, prompt management, and evaluation scoring into a single platform for LLM developers
- The integration of ClickHouse enables efficient handling of large-scale LLM data, setting it apart from traditional logging tools
- Modular architecture and enterprise features make it adaptable to complex deployment scenarios