LangFuse

Langfuse is the 𝗺𝗼𝘀𝘁 𝗽𝗼𝗽𝘂𝗹𝗮𝗿 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗟𝗟𝗠𝗢𝗽𝘀 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺. It helps teams collaboratively develop, monitor, evaluate, and debug AI applications. Langfuse can be 𝘀𝗲𝗹𝗳-𝗵𝗼𝘀𝘁𝗲𝗱 in minutes and is battle-tested and used in production by thousands of users from YC startups to large companies like Khan Academy or Twilio. Langfuse builds on a proven track record of reliability and performance. Developers can trace any Large Language model or framework using our SDKs for Python and JS/TS, our open API or our native integrations (OpenAI, Langchain, Llama-Index, Vercel AI SDK). Beyond tracing, developers use 𝗟𝗮𝗻𝗴𝗳𝘂𝘀𝗲 𝗣𝗿𝗼𝗺𝗽𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁, 𝗶𝘁𝘀 𝗼𝗽𝗲𝗻 𝗔𝗣𝗜𝘀, 𝗮𝗻𝗱 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 to improve the quality of their applications. Product managers can 𝗮𝗻𝗮𝗹𝘆𝘇𝗲, 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲, 𝗮𝗻𝗱 𝗱𝗲𝗯𝘂𝗴 𝗔𝗜 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 by accessing detailed metrics on costs, latencies, and user feedback in the Langfuse Dashboard. They can bring 𝗵𝘂𝗺𝗮𝗻𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽 by setting up annotation workflows for human labelers to score their application. Langfuse can also be used to 𝗺𝗼𝗻𝗶𝘁𝗼𝗿 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗿𝗶𝘀𝗸𝘀 through security framework and evaluation pipelines. Langfuse enables 𝗻𝗼𝗻-𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘁𝗲𝗮𝗺 𝗺𝗲𝗺𝗯𝗲𝗿𝘀 to iterate on prompts and model configurations directly within the Langfuse UI or use the Langfuse Playground for fast prompt testing. Langfuse is 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 and we are proud to have a fantastic community on Github and Discord that provides help and feedback. Do get in touch with us!

Agent Operations

Visit Website

Founded

2022

Location

San Francisco, CA

Employees

Funding

$3M Seed

Langfuse: Open-Source LLM Observability, Evaluation, and Prompt Management

Overview

Langfuse is an open-source LLM engineering platform for tracing, evaluating, and improving LLM applications in production. It unifies the core production loop—observability and tracing, prompt management, evaluations, and analytics—so teams can debug faster, control costs, and iterate safely. It’s framework- and model-agnostic, with SDKs for Python and JS/TS and native integrations across the LLM stack. Explore the [homepage](https://langfuse.com) and [docs](https://langfuse.com/docs).

Run in the cloud or self-host the OSS edition (MIT licensed). See [self-hosting](https://langfuse.com/self-hosting) and [GitHub](https://github.com/langfuse/langfuse).

End-to-end visibility: prompts, model calls, tool usage, retries, cost, and latency with a clean UI for debugging and iteration. Start with the [observability overview](https://langfuse.com/docs/observability/overview).

Built for production feedback loops: version prompts, link to traces, run LLM-as-a-judge and human-in-the-loop evaluations, compare releases, and unify scores for root-cause analysis. See [evaluations](https://langfuse.com/docs/evaluation/overview) and [prompt management](https://langfuse.com/docs/prompt-management/overview).

Key Capabilities

**Full-stack observability for LLM apps**: Trace multi-step agents, RAG pipelines, and tool calls; track latency, errors, and costs per model, route, and release. [Learn more](https://langfuse.com/docs/observability/overview).

**Versioned prompt management**: Centralize, version, and link prompts to real production traces and outcomes. [Guide](https://langfuse.com/docs/prompt-management/overview).

**Unified evaluations**: Combine LLM-as-a-judge, human labels, heuristics, and custom scores; support offline datasets for regression testing. [Overview](https://langfuse.com/docs/evaluation/overview) and [data model](https://langfuse.com/docs/evaluation/evaluation-methods/data-model).

**Analytics for quality, cost, and latency**: Monitor performance by model/provider (e.g., o3, o4-mini), route, and release to inform optimization. See [integrations](https://langfuse.com/integrations).

**Framework/model agnostic**: Works across OpenAI, Anthropic, LangChain, LlamaIndex, Vercel AI SDK, LiteLLM, Dify, and more. [Browse integrations](https://langfuse.com/integrations).

How It Works (Production Loop)

1. Instrument your app with the Langfuse SDKs .

2. Capture traces, spans, prompts, tool calls, and model responses automatically via native integrations.

3. Manage and version prompts; ship changes tied to release tags. [Prompt management](https://langfuse.com/docs/prompt-management/overview).

4. Run evaluations (LLM-as-a-judge, heuristics, human labels, custom metrics) and connect scores to traces for root-cause analysis. [Evaluations overview](https://langfuse.com/docs/evaluation/overview).

5. Monitor dashboards for quality, cost, and latency; compare releases and run offline evals for regression testing. [Evaluation datasets](https://langfuse.com/docs/evaluation/evaluation-methods/data-model).

Integrations and SDKs

**SDKs**: Python and JS/TS with quick-start examples. [Docs](https://langfuse.com/docs).

**Model providers**: OpenAI, Anthropic, and others with cost and latency tracking. See [model providers](https://langfuse.com/integrations) and [Anthropic integration](https://langfuse.com/integrations/model-providers/anthropic).

**Agent/RAG frameworks**: LangChain, LlamaIndex, Vercel AI SDK, LiteLLM, OpenAI Agents SDK (example [guide](https://langfuse.com/guides/cookbook/example_evaluating_openai_agents)).

**No-code/agent builders**: Dify, Flowise, Langflow (see [Dify integration](https://langfuse.com/integrations/no-code/dify)).

**Ecosystem**: OTEL capture patterns and external evaluation pipelines. [Cookbook](https://langfuse.com/guides/cookbook/example_external_evaluation_pipelines).

Deployment, Security, and Compliance

**Deployment options**: Langfuse Cloud or self-host via Docker/Kubernetes. [Self-hosting](https://langfuse.com/self-hosting).

**Open source model**: MIT-licensed core, open-core approach with most features OSS as of mid-2025. See [GitHub](https://github.com/langfuse/langfuse/langfuse) and the [open-sourcing announcement](https://langfuse.com/changelog/2025-06-04-open-sourcing-langfuse).

**Security**: SOC 2 Type II for cloud buyers; detailed security program. See [security](https://langfuse.com/security) and [SOC 2](https://langfuse.com/security/soc2).

Pricing and Free Options

**Cloud pricing**: Tiers listed with standard trials on signup. [Pricing](https://langfuse.com/pricing).

**Startups**: Discounted plans for eligible teams. [Startup program](https://langfuse.com/startups).

**Self-hosting (OSS)**: Free core features under MIT license. See [self-host pricing](https://langfuse.com/pricing-self-host) and [GitHub](https://github.com/langfuse/langfuse).

Ideal Users

Product and platform teams shipping production LLM features.

Agent framework users needing step-level visibility and evals.

Data/AI engineers who prefer open source, self-hosting, and flexible pipelines.

Teams requiring SOC 2-compliant cloud or OSS for data control.

Common Use Cases

Trace and debug multi-step agents and RAG workflows across tools/functions. [Observability](https://langfuse.com/docs/observability/overview).

Manage/version prompts and link them to real outcomes. [Prompt management](https://langfuse.com/docs/prompt-management/overview).

Run production evaluations (LLM-as-a-judge, human labels, heuristics) tied to traces. [Evaluations](https://langfuse.com/docs/evaluation/overview).

Monitor quality, cost, and latency by model, route, and release.

Compare releases; run offline evaluation datasets for regression testing. [Evaluation datasets](https://langfuse.com/docs/evaluation/evaluation-methods/data-model).

Strengths (User Sentiment)

**Robust tracing and debugging** for LLM apps; valuable early visibility into cost and performance. See community perspectives on [Reddit](https://www.reddit.com/r/LocalLLaMA/comments/1i2ycgi/thoughts_on_langfuse/).

**Open source with self-hosting**, often paired with Grafana and existing infra. Discussion [thread](https://www.reddit.com/r/ArtificialInteligence/comments/1en75d3/langfuse_opensource_alternate_for_langsmith/).

**Practical, quick to adopt** vs. building from scratch; widely used for logging/tracing. See [Reddit](https://www.reddit.com/r/LLMDevs/comments/1n0kai9/whats_the_best_way_to_monitor_ai_systems_in/).

**Cost-effective** with OSS path and startup discounts. Community pricing chatter on [Reddit](https://www.reddit.com/r/LLMDevs/comments/1jb1knr/why_the_heck_is_llm_observation_and_management/) and early [Product Hunt reviews](https://www.producthunt.com/products/langfuse/reviews).

Limitations (What to Watch)

Some teams outgrow it for highly complex or OTEL-heavy pipelines and add specialized tools. See feedback on [Reddit](https://www.reddit.com/r/LocalLLaMA/comments/1i2ycgi/thoughts_on_langfuse/).

Category sprawl: teams may stitch multiple tools (use Langfuse for logging/prompt linkage, other platforms for advanced ML observability). Discussion on [Reddit](https://www.reddit.com/r/LLMDevs/comments/1n0kai9/whats_the_best_way_to_monitor_ai_systems_in/).

G2 coverage exists but is relatively thin; evaluate fit for your stack. See [G2 reviews](https://www.g2.com/products/langfuse/reviews).

As fast-moving OSS, occasional integration friction/feature gaps may appear; track [issues](https://github.com/langfuse/langfuse/issues) and [community discussions](https://github.com/orgs/langfuse/discussions/1328).

Competitive Context

Positioning vs. peers:

LangChain’s LangSmith: [Langfuse alternative page](https://langfuse.com/faq/all/langsmith-alternative).

Helicone: [Best Helicone alternative](https://langfuse.com/faq/all/best-helicone-alternative).

Arize Phoenix: [Phoenix/Arize alternatives](https://langfuse.com/faq/all/best-phoenix-arize-alternatives).

Third-party roundups: [TrueFoundry: LLM observability tools](https://www.truefoundry.com/blog/llm-observability-tools), [Langfuse vs Portkey](https://www.truefoundry.com/blog/langfuse-vs-portkey), [ZenML roundup](https://www.zenml.io/blog/best-llm-observability-tools), [Braintrust list](https://www.braintrust.dev/articles/top-10-llm-observability-tools-2025).

Company Snapshot

Category: LLM observability, evaluation, and prompt management .

Founded: 2022; YC W23; $4M seed led by Lightspeed and La Famiglia. [Seed announcement](https://langfuse.com/blog/announcing-our-seed-round).

Customers (select): Khan Academy, Twilio, Samsara, Merck, SumUp, Rocket Money (see [careers](https://langfuse.com/careers) and [pricing](https://langfuse.com/pricing)).

Team/HQ: SF (per LinkedIn), small distributed team; growing community. [LinkedIn](https://www.linkedin.com/company/langfuse).

Notable Links

[Homepage](https://langfuse.com)

[Docs](https://langfuse.com/docs)

[Integrations](https://langfuse.com/integrations)

[Pricing](https://langfuse.com/pricing) and [Self-host pricing](https://langfuse.com/pricing-self-host)

[Self-hosting](https://langfuse.com/self-hosting)

[Security](https://langfuse.com/security) and [SOC 2](https://langfuse.com/security/soc2)

[GitHub](https://github.com/langfuse/langfuse)

[Evaluation data model](https://langfuse.com/docs/evaluation/evaluation-methods/data-model)

SEO Notes (What Langfuse Solves)

LLM observability and tracing for agents and RAG

Prompt management with versioning linked to production outcomes

LLM evaluations (LLM-as-a-judge, human labels, heuristics, custom metrics)

Cost and latency monitoring across providers (OpenAI, Anthropic, etc.)

Open-source, SOC 2–ready alternative to LangSmith and Helicone

If you need a side-by-side feature map against LangSmith or Helicone, or a deeper dive into evaluation workflows, start with the [evaluations overview](https://langfuse.com/docs/evaluation/overview) and the [LangSmith alternative page](https://langfuse.com/faq/all/langsmith-alternative).

Related Companies

Galileo

Galileo is the leading platform for enterprise GenAI evaluation and observability. Our comprehensive suite of products support builders across the new AI development workflow—from fine-tuning LLMs to developing, testing, monitoring, and securing their AI applications. Each product is powered by our research-backed evaluation metrics. Today, Galileo is used by 100s of AI teams from startups to Fortune 50 enterprises, including Twilio, Comcast, and HP.

HoneyHive

HoneyHive is the leading AI observability and evals platform, trusted by next-gen AI startups to Fortune 100 enterprises. We make it easy and repeatable for modern AI teams to debug, evaluate, and monitor AI agents, and deploy them to production with confidence. HoneyHive’s founding team brings AI and infrastructure expertise from Microsoft OpenAI, Amazon, Amplitude, New Relic, and Sisu. The company is based in New York and San Francisco.

Humanloop

Humanloop is the LLM evals platform for enterprises. Teams at Gusto, Vanta and Duolingo use Humanloop to ship reliable AI products. We enable you to adopt best practices for prompt management, evaluation and observability.

LangSmith

LangChain provides the agent engineering platform and open source frameworks developers need to ship reliable agents fast.

Phoenix (Arize AI)

Ship Agents that Work. Arize AI & Agent Engineering Platform. One place for development, observability, and evaluation.

Portkey

AI Gateway, Guardrails, and Governance. Processing 14 Billion+ LLM tokens every day. Backed by Lightspeed.