Langfuse
Open source LLM observability, tracing, and evaluation platform
About Langfuse
Langfuse is an open-source LLM engineering and observability platform built for teams developing production-grade generative AI applications and autonomous multi-agent pipelines. It captures granular traces across token usage, prompt versions, latency bottlenecks, and retrieval accuracy, giving developers complete visibility into model behavior at runtime. By integrating seamlessly with major AI frameworks such as LangChain, LlamaIndex, LiteLLM, and the OpenAI SDK, Langfuse eliminates the guesswork from debugging complex agent execution trees. Developers can monitor production cost metrics, identify hallucinated responses, and run rigorous continuous evaluation suites on live traffic.
The platform architecture features asynchronous tracing hooks that introduce negligible latency overhead to live user interactions. Teams can set up human-in-the-loop scoring, programmatic assertion checks, and automated LLM-as-a-judge evaluations to benchmark prompt iterations against golden test datasets. Langfuse is fully open-source with MIT licensing, allowing organizations with strict data governance policies to self-host the complete observability stack on private Kubernetes clusters or AWS VPCs while maintaining identical enterprise dashboard ergonomics.
Install the Langfuse SDK in your Python or TypeScript application codebase.
Wrap your LLM calls, vector database retrievals, or agent execution steps with the Langfuse tracing decorator.
Run your application to stream latency, token costs, and prompt inputs to the Langfuse dashboard in real time.
Configure automated evaluation metrics to assess response ground truth, relevance, and toxicity scores.
Iterate on prompt templates and model hyperparameters using side-by-side comparative analytics.
Capabilities & Features
Common Use Cases
llm-observability
agent-tracing
prompt-evaluation
cost-tracking
rag-debugging
Frequently Asked Questions
What is Langfuse and who is it built for?
Langfuse is an open-source observability and evaluation platform designed for AI engineers, data scientists, and developers building LLM applications and agentic workflows.
Is Langfuse open source and free to self-host?
Yes, Langfuse is fully open source under the MIT license and can be self-hosted for free using Docker or Kubernetes without any telemetry limits.
Does Langfuse add latency to LLM application calls?
No, Langfuse utilizes background asynchronous queuing to transmit telemetry data, ensuring that user request latencies remain completely unaffected.
What frameworks does Langfuse support?
Langfuse provides first-class native SDKs for Python and TypeScript with out-of-the-box support for LangChain, LlamaIndex, LiteLLM, Vercel AI SDK, and raw API calls.
Free Plan
Generous free cloud tier with 50k traces/month and unlimited self-hosting via Docker
Paid Plan
Pro from $59/mo and Enterprise for custom SLAs, team RBAC, and data retention
Direct link · Verified & reader-supported
Pros & Cons
100% open source with complete self-hosting freedom via Docker and Helm
Native integrations with LangChain, LlamaIndex, LiteLLM, and OpenAI
Granular cost tracking and per-user token consumption breakdowns
Comprehensive LLM-as-a-judge and human scoring workflows
Asynchronous telemetry with near-zero latency overhead
Self-hosting requires maintaining PostgreSQL and ClickHouse storage backends
Advanced multi-tenant team RBAC is restricted to enterprise tiers
Alternatives
View allPortkey
Production AI gateway, load-balancing, and LLMOps control plane
Portkey is an enterprise-grade AI Gateway and LLMOps control plane designed to make production AI applications fast, reliable, and cost-efficient. By acting as a unified proxy between your applications and 250+ LLMs, Portkey handles automated provider fallbacks, load balancing, rate-limiting, and semantic caching with zero code changes. Engineering teams use Portkey to eliminate single-provider downtime risks (e.g. automatic failover from OpenAI to Anthropic during outages) while cutting inference latency and API costs by up to 40% through intelligent semantic caching.
Braintrust
Enterprise AI evaluation, prompt playground, and continuous LLM monitoring
Braintrust is an enterprise-grade AI evaluation, prompt engineering, and LLM observability platform built to help software teams safely iterate and deploy generative AI features to production. It bridges the gap between ad-hoc prompt tweaking and rigorous software engineering CI/CD workflows. With Braintrust, teams run automated evaluation benchmarks on every prompt change, comparing output quality, hallucination rates, and latency across multiple LLM versions before committing changes to production codebases.
Browser Use
Open-source web browsing AI agent for Python & LangChain
Browser Use is an open-source Python library that connects LLMs to browser automation pipelines, enabling AI agents to navigate websites, interact with dynamic DOM elements, bypass multi-step forms, and extract structured data autonomously. Built on top of Playwright and LangChain, it provides vision-augmented element detection and deterministic state tracking. Unlike traditional headless scrapers, Browser Use feeds DOM tree snapshots and viewport screenshots to multimodal models like Claude 3.7 Sonnet or GPT-4o, allowing agents to understand complex UI layouts, handle popups, solve interactive workflows, and execute sequential tasks in plain English.
LiveKit Agents
Open-source real-time WebRTC infrastructure for building ultra-low-latency voice and multimodal AI agents
LiveKit Agents is an open-source real-time communication framework engineered to build conversational voice, video, and multimodal AI agents with sub-500ms latency. Leveraging WebRTC, it connects speech-to-text (Deepgram, Whisper), LLMs (OpenAI, Anthropic), and text-to-speech (Cartesia, ElevenLabs) in a tightly synchronized bidirectional stream. From customer service avatars to interactive language tutors and hands-free coding copilots, LiveKit Agents provides the enterprise infrastructure for real-time human-AI interaction.
Cosine Genie
Autonomous AI software engineer for solving real-world GitHub issues
Cosine Genie is a next-generation autonomous software engineering agent built to tackle complex software bugs, refactors, and feature requests. Operating with deep semantic understanding of massive codebases, Genie analyzes repository architectures, creates comprehensive execution plans, and generates multi-file diffs that pass existing continuous integration test suites. Designed to bridge the gap between AI code completion and full software development lifecycle automation, Genie mimics human engineering workflows by navigating dependency graphs, testing assumptions in sandboxed environments, and autonomously self-correcting logic errors before opening pull requests.
Smolagents
Lightweight, code-first multi-agent framework by Hugging Face
Smolagents is an ultra-lightweight, code-first Python framework created by Hugging Face for building, orchestrating, and executing autonomous AI agents in minimal lines of code. Rejecting the bloated, multi-layered abstractions of legacy agent libraries, Smolagents emphasizes 'Code Agents'—agents that express their reasoning and tool actions directly in executable Python code rather than rigid JSON string payloads. By letting LLMs write executable Python logic, Smolagents achieves vastly superior composability for data manipulation, mathematical operations, and complex loops while cutting prompt token overhead by up to 30%.
Compare Langfuse with Alternatives
Side-by-side feature, pricing, and pros & cons breakdowns
Featured in In-Depth Guides & Comparisons
Read our hands-on technical evaluations and workflow guides mentioning Langfuse
