Langfuse provides observability tools for capturing complete traces of LLM applications and agents using OpenTelemetry standards. Developers use these traces to inspect failures, build evaluation datasets, and debug complex AI workflows. The platform offers SDKs for Python and JavaScript/TypeScript, supporting self-hosting or cloud deployment.
Funding
$4.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Debugging, analyzing, and optimizing large language model (LLM) applications presents unique challenges due to their complexity and reliance on external APIs. Developers often lack the tools to effectively trace LLM interactions, manage prompts, evaluate performance, and track key metrics, hindering the development and deployment of reliable LLM systems.
Solution
Langfuse offers an open-source LLM engineering platform designed to address these challenges by providing developers with the tools to trace LLM interactions, manage prompts, evaluate performance, and track metrics. The platform features native integrations with popular frameworks and SDKs, streamlining the development workflow. By offering comprehensive observability into LLM applications, Langfuse enables developers to collaboratively debug, analyze, and iterate on their systems, ensuring secure and compliant deployment. The platform's open APIs facilitate the creation of downstream use cases, extending its functionality and adaptability.
Target Audience
Langfuse primarily targets teams building complex LLM applications, including developers, machine learning engineers, and data scientists.
Features
- LLM tracing capabilities to monitor interactions and identify bottlenecks
- Prompt management tools for versioning, collaboration, and deployment
- Evaluation frameworks for dataset experiments, LLM-as-judge evaluations, and prompt testing
- Interactive playground for prompt iteration and simulation of tool use
- Support for manual annotation and creation of datasets for fine-tuning
- Comprehensive metrics tracking for cost, latency, and quality
- Custom dashboards to visualize and analyze LLM application data
- Integrations with Python, JS/TS SDKs, OpenAI SDK, Langchain, Llama-Index, and other frameworks