Langfuse is an open source platform designed for teams building and operating large language model (LLM) applications. It solves the challenge of debugging and monitoring non-deterministic AI workflows by providing end-to-end observability, prompt versioning, and automated evaluation — all in a single integrated system. Built for developers and ML engineers, it eliminates tool fragmentation by unifying tracing, prompt management, and evaluation into one workflow.
Langfuse is built with TypeScript and uses ClickHouse as its underlying database. It offers native SDKs for Python and JavaScript/TypeScript, integrates with OpenTelemetry, LangChain, LlamaIndex, LiteLLM, and Vercel AI SDK, and supports deployment via Docker Compose, Kubernetes (Helm), or cloud providers using Terraform. Its API-first architecture ensures extensibility and enterprise-grade security.
What You Get
- LLM Application Observability - Tracks LLM calls, retrieval, embeddings, and agent actions with full trace context, including multi-turn conversations and user sessions, powered by OpenTelemetry standards.
- Prompt Management - Centralized version control, collaborative editing, and deployment of prompts via UI or API with environment labels, enabling zero-downtime updates without code changes.
- LLM Playground - Interactive environment to test and iterate on prompts and model configurations in real time, with direct links from trace failures to prompt experiments.
- Evaluations - Supports LLM-as-a-judge, manual labeling, user feedback, and custom evaluation pipelines via APIs, enabling quantitative measurement of output quality across environments.
- Datasets - Create and manage test sets and benchmarks for pre-deployment testing, enabling structured experiments and continuous improvement of LLM applications with LangChain and LlamaIndex integration.
- Comprehensive API & SDKs - OpenAPI spec, typed Python and JS/TS SDKs, and Postman collection for building custom LLMOps workflows, with support for automated instrumentation via OpenAI, LiteLLM, and Vercel AI SDK.
Common Use Cases
- Debugging production LLM failures - A ML engineer uses Langfuse traces to inspect failed LLM calls, identify slow retrievals, and correlate them with specific prompt versions to fix hallucinations in real time.
- Running A/B tests on prompts - A product team deploys two prompt versions using Langfuse’s versioning and evaluates their performance against a dataset to choose the highest-quality output before full rollout.
- Monitoring LLM costs and latency at scale - A startup tracks per-user cost and latency metrics across 10k+ LLM calls daily using Langfuse’s dashboard and user tagging to optimize spending and performance.
- Building multi-agent systems - A research team visualizes agent workflows as graphs in Langfuse to understand decision flows, trace failures between agents, and improve coordination in autonomous AI systems.
Under The Hood
Architecture
- Monorepo structure cleanly separates frontend, background workers, and shared utilities, enabling independent deployment and testing
- Prisma ORM centralizes data access with clear service-layer isolation, avoiding business logic contamination
- Dependency injection via TypeScript interfaces supports flexible service resolution and environment-aware implementations
- Event-driven design decouples analytics and storage systems using queues, with ClickHouse and MinIO handling offload tasks
- Multi-environment configurations managed through Docker Compose ensure consistent behavior across dev, cloud, and test setups
- Turbo orchestrates build and test pipelines uniformly across packages, reinforcing modularity and reducing cross-package coupling
Tech Stack
- Next.js frontend with TypeScript and NextAuth for authentication, integrated with Prisma for PostgreSQL data access
- Worker service built on Node.js with ClickHouse for analytics, Redis for queuing and caching, and MinIO for S3-compatible storage
- Docker Compose enables multi-environment orchestration with health checks, volume persistence, and cluster-aware configurations
- Monorepo tooling unifies ESLint, TypeScript, and Prisma schema definitions across all packages for consistency
- End-to-end testing via Playwright, CI/CD automation with release-it, and targeted patches to authentication libraries for custom needs
Code Quality
- Comprehensive test coverage spans unit, integration, and end-to-end scenarios with robust assertions across API, database, and transformation layers
- Strong type safety enforced through Zod schemas and precise type guards, ensuring data integrity at boundaries
- Clear separation of test concerns with reusable utilities for mocking contexts, validating responses, and isolating database dependencies
- Consistent naming and modular test structures mirror production code, enhancing maintainability and readability
- Explicit error handling with domain-specific types and structured messages improves debugging and user feedback
- Extensive edge case testing for invalid inputs, authentication failures, and data anomalies using reusable setup utilities
What Makes It Unique
- Native trace-level observability with dynamic score analytics enables real-time comparison of human and model evaluations without external tools
- Intelligent dataset mapping engine auto-generates key-value configurations from JSON schemas, reducing manual annotation burden
- Context-aware ChatML renderer preserves semantic structure of LLM interactions with intelligent JSON toggling and tool-call visualization
- Built-in statistical analysis for inter-annotator agreement (Cohen’s Kappa, F1) is tightly integrated into scoring workflows
- Unified trace-to-dataset pipeline retains full observation context during labeling, enabling reproducible evaluation from raw LLM outputs
- Semantic highlighting and redaction in chat interfaces enhance auditability of model reasoning while protecting sensitive internals