Langfuse: Complete Platform Profile

Langfuse is an open-source observability and analytics platform purpose-built for LLM applications and AI agents. Founded in 2023 by Clemens Rawert, Marc Klingen, and Max Deichmann, it addresses one of the most pressing challenges in production AI development: understanding what your agents are actually doing, why they are failing, and how to improve them systematically. By providing detailed execution traces, evaluation scoring, prompt versioning, and dataset management in a single platform, Langfuse gives development teams the visibility they need to operate AI applications with the same confidence they bring to traditional software systems.

Review the AI agent tracing concepts in the glossary for background on why observability is critical for AI agents. Explore the AI agent tools directory to understand how Langfuse fits into the broader AI infrastructure stack.

Overview#

Langfuse was born from the founders' direct experience trying to debug and improve LLM applications in production. The core problem they identified was that AI applications lack the observability primitives that developers take for granted in traditional software: structured logs, distributed traces, error tracking, and performance metrics. When an LLM application gives a wrong answer or a poor user experience, there is typically no clear audit trail showing why — which prompt was used, what context was retrieved, how the model responded, and where the reasoning went wrong.

The Langfuse team built the platform they wished had existed when they were operating their own LLM applications. The result is a tool that captures the full execution trace of every AI operation — every prompt, every model call, every tool invocation, every retrieval step — and presents it in a UI designed for the specific patterns of AI debugging.

The project has grown rapidly, with over 10,000 GitHub stars and an active community of users spanning startups and enterprise teams. The combination of a generous open-source license (MIT), straightforward self-hosting, and a polished cloud offering has made it the most popular independent LLM observability solution — "independent" being significant because it is not tied to a specific framework vendor the way LangSmith is tied to LangChain.

Langfuse integrates with every major LLM framework through native SDKs, OpenTelemetry support, and LangChain callbacks. This framework-agnostic approach means teams can use Langfuse regardless of whether they are building with OpenAI Agents SDK, LlamaIndex, custom code, or any other stack.

Core Features#

Distributed Tracing#

Langfuse's tracing system captures hierarchical execution trees that mirror the structure of complex LLM applications. A trace represents a single end-to-end operation — a user query, an automated pipeline run, a batch processing job. Within each trace, spans capture individual operations: model calls, tool invocations, retrieval steps, function executions, and any other steps the developer chooses to instrument.

The trace viewer presents these hierarchical spans as a timeline, making it immediately clear how time was distributed across an operation and where bottlenecks or errors occurred. For multi-agent systems, traces capture handoffs between agents, showing which agent handled which portion of a task and what context was passed at each handoff.

Traces include full input and output content for every span by default, enabling developers to see exactly what prompt was sent, what context was included, and what the model returned at every step. This level of detail is essential for debugging issues where the problem is not a code error but a reasoning error: the model had the wrong context, the retrieval returned irrelevant documents, or the prompt was ambiguous for the specific input.

The tracing system supports nested spans of arbitrary depth, custom metadata attributes, user and session identifiers, and trace tags. This flexibility allows teams to organize traces in ways that reflect their specific application architecture and debugging needs. See the AI agent glossary for context on how complex agents generate these trace structures.

Evaluation and Scoring#

Langfuse's evaluation system allows teams to score traces and individual spans with both automated and human-in-the-loop evaluation methods. Scores can be numeric (0–1 quality scores, thumbs up/down, Likert scales) or categorical, and they can be attached to any level of the trace hierarchy.

Automated evaluation can be performed by running LLM-based judges against stored traces. Langfuse provides built-in evaluator templates for common quality dimensions — answer relevance, faithfulness, context precision — and supports custom evaluators defined as Python functions or LLM prompts. Automated evaluations can run on all new traces, on sampled subsets for cost management, or on targeted samples (traces with error scores, traces from specific user segments).

Human evaluation is supported through a review queue interface where annotators can score traces, add comments, and flag issues. This is valuable for building evaluation datasets and for calibrating automated evaluators against human judgment.

The combination of automated and human evaluation enables a continuous evaluation loop: new traces are automatically scored, human reviewers investigate edge cases and ambiguous outputs, and the evaluation data feeds back into prompt improvements and fine-tuning decisions.

Prompt Management#

Langfuse includes a prompt management system that versions, tests, and deploys prompts as managed artifacts rather than hardcoded strings. Prompts are stored in Langfuse with version history, can be edited through the web interface, and are fetched by the application at runtime.

This decoupling of prompts from code has significant operational benefits. When a prompt needs to be improved, it can be updated in Langfuse without a code deployment. A/B testing different prompt versions is built into the system, with the platform automatically routing traffic between prompt versions and collecting performance data.

Prompt linking in traces means that every trace records which exact prompt version was used to generate it. This makes it possible to analyze the impact of prompt changes retrospectively: comparing the distribution of evaluation scores before and after a prompt update, or identifying which prompt version performs best on a specific category of queries.

Dataset and Fine-Tuning Support#

Langfuse's dataset functionality allows teams to build curated evaluation and fine-tuning datasets directly from production traces. A dataset is a collection of input/output pairs, optionally annotated with expected outputs and quality scores.

Building datasets from production traces is a significant workflow advantage. Rather than constructing synthetic evaluation examples, teams can select representative real-world examples — including the edge cases and failure modes that matter most — and build datasets that reflect the actual distribution of inputs their application encounters.

Datasets can be exported in formats suitable for fine-tuning with major model providers. This closes the loop from observability to improvement: identify failures through traces, annotate examples, build a fine-tuning dataset, train an improved model, and observe the improvement through updated evaluation scores.

Pricing and Plans#

Langfuse offers both a self-hosted open-source deployment and a managed cloud service.

The self-hosted deployment is free and MIT-licensed. It requires running a PostgreSQL database, a ClickHouse database for analytics, and the Langfuse application itself. Docker Compose and Kubernetes Helm chart configurations are provided. For teams with the infrastructure capability, self-hosting provides full data control, no usage limits, and zero platform costs beyond infrastructure.

The cloud service offers a Hobby tier (free, with usage limits suitable for development), a Pro tier ($59/month for small teams with generous usage allowances), and a Team tier ($499/month with higher limits and priority support). Enterprise pricing is available for large organizations with custom requirements.

Strengths#

Framework-agnostic by design. Langfuse works with any LLM framework or custom implementation. This is the platform's most strategically important property: teams are not locked into a framework vendor to use the observability tool.

Self-hostable with a genuine open-source commitment. The MIT license and high-quality deployment documentation mean that organizations with data sovereignty requirements can operate Langfuse entirely within their own infrastructure. No data leaves the organization.

Evaluation is deeply integrated with traces. The tight coupling between trace collection and evaluation scoring enables workflows that are difficult to implement with separate observability and evaluation tools. The evaluation data is co-located with the traces it refers to, enabling efficient review and analysis.

Active development and responsive maintainers. The Langfuse team is known for rapid feature development and responsive engagement with community issues and pull requests. The project has added significant functionality each quarter since its launch.

Limitations#

Self-hosting has non-trivial infrastructure requirements. Running Langfuse self-hosted requires managing PostgreSQL and ClickHouse in addition to the application. For teams without dedicated infrastructure, this adds operational overhead.

Less tightly integrated with LangChain than LangSmith. LangSmith benefits from being built by the LangChain team, with deep integration that Langfuse, as a third-party tool, cannot fully replicate. Teams whose entire stack is LangChain may find LangSmith's native integration more convenient.

Evaluation workflow requires setup. Getting maximum value from Langfuse's evaluation system requires defining evaluators, setting up scoring workflows, and building review processes. This is not plug-and-play — it requires deliberate configuration and process design.

Ideal Use Cases#

• Multi-framework AI stacks: Organizations using multiple AI frameworks — some LangChain, some custom, some other frameworks — benefit from Langfuse's framework-agnostic observability layer.
• Teams with data sovereignty requirements: Organizations in regulated industries or jurisdictions where data must remain within specific boundaries can self-host Langfuse to eliminate external data transmission.
• Systematic prompt engineering: Teams doing rigorous prompt A/B testing and quality measurement benefit from Langfuse's prompt management and evaluation systems working together.
• Production agent debugging: Teams operating complex multi-agent systems where understanding the full execution trace is essential for diagnosing failures and regressions.

Getting Started#

Install the Langfuse Python SDK:

pip install langfuse

Set your credentials (from the Langfuse cloud UI or your self-hosted instance):

export LANGFUSE_PUBLIC_KEY="pk-lf-..."
export LANGFUSE_SECRET_KEY="sk-lf-..."
export LANGFUSE_HOST="https://cloud.langfuse.com"  # or your self-hosted URL

Instrument a simple LLM call:

from langfuse import Langfuse
from langfuse.decorators import observe, langfuse_context

langfuse = Langfuse()

@observe()
def generate_answer(question: str) -> str:
    """This function will be automatically traced."""
    import openai
    client = openai.OpenAI()

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": question}]
    )
    return response.choices[0].message.content

answer = generate_answer("What is the capital of France?")

The @observe() decorator automatically creates a trace for the function call. OpenAI calls within the function are captured through Langfuse's OpenAI SDK wrapper. The trace appears in the Langfuse UI within seconds.

For LangChain integration:

from langfuse.callback import CallbackHandler

handler = CallbackHandler()  # Picks up credentials from env vars
# Pass handler to any LangChain chain or agent
chain.invoke({"input": "..."}, config={"callbacks": [handler]})

How It Compares#

Langfuse vs LangSmith: LangSmith is LangChain's observability platform, deeply integrated with the LangChain framework. Langfuse is framework-agnostic and self-hostable. Teams fully invested in LangChain may find LangSmith's native integration more convenient. Teams using other frameworks or with data sovereignty requirements will prefer Langfuse. See both profiles side by side in the profiles directory.

Langfuse vs Arize Phoenix: Arize Phoenix is another open-source LLM observability tool with a strong evaluation focus. Langfuse generally has broader framework integration and more active development. Phoenix has strengths in model explainability inherited from Arize's ML observability background.

Langfuse vs OpenTelemetry-based observability: Langfuse supports OpenTelemetry, so it can work alongside standard observability stacks. The difference is that Langfuse's UI is purpose-built for LLM applications — it understands traces, prompts, and evaluation scores in ways that general-purpose observability tools like Jaeger or Honeycomb do not.

For a broader look at the AI infrastructure ecosystem, browse the AI agent tools directory.

Bottom Line#

Langfuse has established itself as the leading independent LLM observability platform through a combination of technical quality, framework neutrality, and a genuine open-source commitment. For teams that cannot or do not want to be tied to a specific framework vendor's observability offering, Langfuse is the most capable and well-supported alternative.

The evaluation system is a standout feature — the integration of automated scoring, human review, and dataset creation within the same platform creates workflows that would otherwise require multiple separate tools. For teams serious about systematic quality improvement, this integrated approach is a significant productivity advantage.

Best for: Engineering teams operating AI applications in production who need deep observability, systematic evaluation capabilities, and a framework-agnostic or self-hostable solution.

Frequently Asked Questions#

Can Langfuse be used without LangChain?

Yes. This is one of Langfuse's primary selling points. It supports native SDKs for Python and JavaScript/TypeScript, OpenTelemetry integration, and callbacks for LangChain and LlamaIndex. Any application can be instrumented without using LangChain. The framework-agnostic design is why many teams choose Langfuse over LangSmith for non-LangChain stacks.

How does Langfuse handle sensitive data in traces?

Langfuse stores full input and output content by default. For sensitive applications, it provides masking and filtering capabilities that can redact PII from traces before storage. The self-hosted deployment eliminates the data transmission concern for organizations with strict data handling requirements, as all trace data stays within the organization's own infrastructure.

What databases does Langfuse require?

Langfuse uses PostgreSQL for transactional data (trace metadata, scores, prompts, users) and ClickHouse for analytics queries. The ClickHouse requirement exists because LLM trace analytics — filtering billions of trace events, aggregating scores, comparing prompt versions — requires a column-oriented analytics database that PostgreSQL cannot efficiently handle at scale.

Does Langfuse support real-time alerting?

As of early 2026, Langfuse focuses on analytical observability rather than real-time alerting. For real-time alerts on production failures, teams typically combine Langfuse's trace data with standard application monitoring tools (PagerDuty, Grafana) through webhooks or custom integrations. The Langfuse team has indicated alerting capabilities are on their roadmap.

How does Langfuse's prompt management compare to dedicated prompt engineering tools?

Langfuse's prompt management is designed for teams that want versioning and A/B testing integrated with their observability workflow. Dedicated prompt engineering tools may offer richer collaborative editing features. For most production teams, Langfuse's integrated approach provides sufficient prompt management capability alongside observability without requiring a separate tool. The key advantage is that prompt version data is directly linked to trace and evaluation data, enabling impact analysis that separate tools cannot provide.