Langfuse: The Open-Source LLM Engineering Platform Powering Smarter AI Development

Langfuse, an open-source LLM engineering platform (8.4K GitHub stars), streamlines the development of production-grade AI applications. Trusted by developers with 1M+ monthly SDK installs and Docker pulls, it offers an integrated toolkit for debugging, optimizing, and scaling LLM workflows.

Key features include Tracing for granular debugging, Prompt Management with collaborative version control, and Playground for testing prompts and models. Developers can collect user feedback, build datasets from production data, and track cost, latency, and quality metrics. Langfuse supports Python, JS/TS, and integrates natively with OpenAI, LangChain, Amazon Bedrock, Gemini, and 20+ frameworks.

Security-focused teams benefit from SOC 2 Type II, ISO 27001, and GDPR compliance, with enterprise options for SSO, RBAC, and audit logs. The free Hobby tier (50k observations/month) suits small projects, while Pro ($59/month) and Team ($499/month) plans scale for growing needs. Recent updates like Gemini 2.0 support and JSONPath evaluations highlight rapid iteration.

Self-hostable and backed by Y Combinator, Langfuse balances flexibility with enterprise-grade reliability. Explore interactive demos or deploy via Docker—all documentation and community support are openly accessible.

Integrated Observability

Langfuse provides comprehensive observability through detailed production tracing and performance metrics. The tracing feature captures granular data points across LLM calls, including inputs, outputs, latency, token usage, and errors, enabling developers to debug complex workflows. Metrics track cost, latency, and quality trends over time, offering actionable insights for optimization. This dual functionality allows teams to correlate technical performance (e.g., high latency in specific prompts) with business outcomes (e.g., user satisfaction scores). By centralizing observability data, Langfuse eliminates the need for fragmented monitoring tools and provides a unified view of LLM application health.

Collaborative Prompt Development

The platform streamlines prompt engineering with version-controlled prompt templates and a testing playground. Teams can collaboratively manage prompts through Git-like workflows, including branching, comparisons, and low-latency deployments to production. The integrated playground lets users test prompts against multiple models (OpenAI, Gemini, Ollama, etc.) while simulating variables like temperature parameters. This closed-loop system connects experimentation directly to production deployment, ensuring tested prompts can be implemented within seconds. Version history maintains audit trails for compliance, while A/B testing capabilities enable performance validation across prompt iterations.

Data-Driven Evaluation Framework

Langfuse automates LLM evaluation through customizable metrics and dataset creation tools. Users define quality scores using automated checks (e.g., output structure validation via JSONPath), human feedback, or hybrid approaches. Production data is automatically aggregated into datasets, preserving full context chains for model retraining. The system supports bulk annotation and integrates with third-party tools like PostHog for behavioral analysis. This feature transforms raw interaction data into structured evaluation pipelines, enabling continuous model improvement while maintaining data provenance from initial user input to final model output.

Open Ecosystem Integration

Built as an extensible platform, Langfuse offers native SDKs (Python/JS/TS) and prebuilt integrations with 20+ LLM frameworks including LangChain, LlamaIndex, and AWS Bedrock. The architecture supports hybrid deployments—teams can self-host while maintaining compatibility with managed cloud services. Security integrations enforce SSO, RBAC, and audit logging without requiring custom code. This open approach allows developers to embed Langfuse into existing ML pipelines with <100 lines of code while maintaining interoperability across cloud providers and on-premise systems.