Langfuse

Quick answer: Langfuse is trace, evaluate, and improve AI agents with one open platform. Use production data to understand behavior, collaborate on fixes, and ship better quality at lower cost and latency.

Listed 2026-08-29 · Request removal

Definition: Langfuse is an open-source AI engineering platform for teams that build and operate LLM applications and AI agents. It brings together tracing, evaluation, observability, prompt management, and production analysis so teams can understand how their AI systems behave, investigate issues, and make informed improvements.

What does Langfuse do for AI agent teams?

Langfuse is designed to support the full operational cycle of an AI application after it moves beyond an early prototype. Teams can use it to trace requests and agent activity, monitor behavior in production, assess output quality, review prompts, and analyze the resource use associated with LLM interactions.

For an AI agent, a single user request may involve multiple model calls, tools, retrieval steps, prompt variants, and intermediate decisions. Langfuse provides a way to inspect this activity in context. That makes it more practical to investigate unexpected outputs, review execution paths, and identify areas where an application may need adjustments.

The platform also focuses on using production data as an input to iteration. Rather than relying only on isolated test examples, AI engineers and product teams can examine real application behavior and collaborate around production issues. This can help teams connect observed problems with possible fixes across prompts, agent workflows, evaluations, or application logic.

• Trace LLM application and agent activity.
• Monitor production behavior and investigate issues.
• Evaluate outputs and support quality improvement work.
• Track tokens and costs associated with usage.
• Manage prompts as part of an AI engineering workflow.
• Collaborate on fixes informed by production data.

How does tracing in Langfuse help debug agent behavior?

Tracing is central to Langfuse’s approach to AI observability. It gives teams a record of how an LLM application or agent handled a request, enabling them to look at the activity that occurred during an execution. This is especially useful when an agent’s response is incorrect, incomplete, unexpectedly expensive, or slower than expected.

AI agent behavior can be difficult to diagnose because results depend on more than one static code path. Prompts, model responses, tools, retrieved context, and multi-step workflows may all affect the final output. By reviewing traces, teams can use production evidence to understand the sequence of events around a result and narrow down where a problem may have appeared.

Langfuse is relevant both for debugging one-off incidents and for finding recurring patterns. For example, teams operating an LLM application can compare problematic interactions with successful ones, then use what they learn to guide further evaluation or prompt work. The goal is not merely to collect telemetry, but to make that information useful for improving an AI system over time.

Which evaluation and prompt workflows does Langfuse support?

Langfuse includes evaluation capabilities alongside tracing and observability. Evaluation gives teams a structured way to assess LLM or agent output as they work to improve application quality. This is useful for teams that need a repeatable process for reviewing behavior rather than relying solely on ad hoc inspection of individual responses.

Prompt management is another part of the platform. Prompts are an important operational component of many LLM applications, and changes to prompts can affect output quality, costs, latency, and agent behavior. Keeping prompt work connected with tracing and evaluation can help teams make changes with more context about how an application performs.

The collaboration focus is relevant for organizations where AI engineering, ML, and product teams all participate in operating an AI feature. A production issue may be identified through observability data, investigated through traces, assessed through evaluation, and addressed through changes in prompts or application workflows. Langfuse aims to provide one platform for these connected activities.

• Use evaluations to assess AI application outputs.
• Manage prompts within the broader AI engineering process.
• Review production behavior when prioritizing improvements.
• Coordinate investigation and remediation work across teams.
• Iterate on quality, latency, and cost using observed data.

Can Langfuse monitor tokens, costs, quality, and latency?

Langfuse provides observability features for LLM and AI agent applications, including token and cost tracking. This can be important for teams that need visibility into how model usage changes as traffic grows, prompts evolve, or agent workflows become more complex.

Cost is only one operational concern. Teams may also seek to improve output quality and latency. Langfuse is positioned to help teams analyze production behavior and use that information when iterating on their systems. In practice, a change that improves one area may influence another, so having tracing, evaluation, and usage visibility in the same platform can support more informed decisions.

Organizations building customer-facing AI features may use this kind of monitoring to identify behavior worth investigating. AI engineers can focus on technical execution details, while product teams can use production findings to understand how the application is performing for users. Langfuse supports this shared view of operating an AI system in production.

What integrations and deployment options does Langfuse offer?

Langfuse is available for web-based use and cloud deployment, and it also supports self-hosting. The self-hosted option may be relevant to teams that want to run the platform in their own environment. As an open-source product, Langfuse also appeals to organizations that value access to an open platform for AI observability and evaluation workflows.

The listed integrations include LangChain, LangGraph, and the Vercel AI SDK. These integrations make Langfuse relevant to teams already using popular frameworks and tools for building LLM applications and agent workflows. Compatibility with an existing engineering stack can reduce the effort needed to introduce tracing and production analysis into a project.

How much does Langfuse cost?

Langfuse offers a Hobby free tier. Its listed paid cloud plans are Core at $29 per month, Pro at $199 per month, and Enterprise at $2,499 per month. Paid cloud plans can also include usage-based overage charges, so total spending may depend on a team’s use of the service.

The free Hobby tier gives prospective users a way to assess Langfuse before moving to a paid plan. Teams comparing paid options should consider their expected usage as well as the platform capabilities they need. In particular, organizations requiring advanced enterprise functionality should review the relevant plan because those capabilities are associated with higher tiers.

Pricing is only one part of the selection process for an AI observability platform. A team may also weigh open-source availability, self-hosting needs, integrations, evaluation requirements, and the degree to which it wants tracing and prompt workflows in the same product.

What are the limitations of Langfuse?

Langfuse is focused on observability, tracing, evaluation, and iteration for LLM applications and AI agents. Teams should confirm that its workflow and integrations fit their particular architecture, engineering practices, and deployment requirements before standardizing on it.

Its paid cloud offerings may incur usage-based overages, which means costs can vary with usage. Advanced enterprise features require higher pricing tiers. Teams with strict budget controls should evaluate expected cloud usage, while teams considering self-hosting should assess the operational responsibilities involved in running their own deployment.

Finally, Langfuse is one option in a broader AI observability and evaluation category. Alternatives named for comparison include LangSmith, Helicone, Weights & Biases, and OpenTelemetry. The best fit depends on whether a team prioritizes open-source software, self-hosting, specific framework integrations, prompt workflows, or the operational analysis of production AI agents.

FAQ

What is Langfuse used for?

Langfuse is used to trace, evaluate, monitor, and improve LLM applications and AI agents. It helps teams use production data to debug behavior and collaborate on changes.

Is Langfuse open source?

Yes. Langfuse is an open-source AI engineering platform and also supports self-hosting.

Does Langfuse have a free plan?

Yes. Langfuse offers a Hobby free tier, alongside paid cloud plans with potential usage-based overages.

What tools integrate with Langfuse?

Langfuse lists integrations with LangChain, LangGraph, and the Vercel AI SDK. These can help teams add observability to existing AI application workflows.

What is AI observability?

AI observability is the practice of examining how LLM applications and agents operate, including their requests, outputs, usage, and behavior. Langfuse provides AI observability through tracing and production analysis.

How do teams monitor AI agents in production?

Teams commonly use tracing, evaluations, and usage monitoring to review AI agent activity in production. Langfuse combines these capabilities to support investigation and iteration.

Why is LLM token and cost tracking important?

Token and cost tracking helps teams understand the resource use of LLM applications as usage and workflows change. Langfuse includes token and cost tracking as part of its observability offering.