- Teams operating LLM or agent applications
- Builders who need traces, prompt management, and datasets
- Developers comparing open observability options for AI products
See repository · Tools
Langfuse
LLM engineering platform for observability, evals, prompt management, datasets, and traces.
28K stars 2.9K forks See repository license 2026-06-02 verified
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# Langfuse$
pip install langfuse$
npx langfuse --help Overview
What is Langfuse?
Langfuse is a source-available LLM engineering platform that helps teams observe, evaluate, and improve LLM and agent applications with traces, metrics, prompts, datasets, and integrations.
Workflow
Langfuse surfaces workflow as a core capability in its published project metadata and source links.
This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs.State
Langfuse surfaces state as a core capability in its published project metadata and source links.
This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs. Use cases
What teams use it for
Ecosystem
Tags & capabilities
Comparison
How it stacks up
When to choose Langfuse
Compare it with nearby tools by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need.
FAQ
Questions
What should I check before using Langfuse?
Add one regression case to a real prompt or RAG workflow, then verify the result can run again in CI or review.
Is Langfuse open source?
Langfuse is listed on OpenAgent.bot with See repository based on the current resource metadata. Re-check the official repository, docs, and license before production use.
Decision brief
JSON
Should you use Langfuse?
- Tiny prototypes that do not need observability yet
- Teams that want a purely local CLI-only evaluation workflow
- Verified 2026-06-02
- License: See repository
- Repo: langfuse/langfuse
- Open-source status needs review
Deployment
self hosted, cloud
Permission surface
shell/files
Decision signals
No extra signals recorded
Agent packet
Structured decision data for Langfuse
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
workflow, state
Constraints
source available
Deployment
self hosted, cloud
Permission surface
shell/files
Recommended workflows
Coding agent workflow, Evaluation and observability, Local or private AI stack, Reusable skill workflow
Overview
What Langfuse does
What it is
Langfuse is listed on OpenAgent.bot as a tools resource for open AI builders.
Why it matters
Agent products need more than a model call. They need traces, evaluations, prompt versions, and production feedback loops. Langfuse is one of the most visible open LLM observability stacks for that layer.
How to evaluate it
Start from the official source links, then validate the project against your deployment needs, license requirements, and maintenance expectations.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type tool
Category Tools
Maturity active
Difficulty Unknown
License See repository
Pricing free
Verified 2026-06-02
Source confidence high
Risk level moderate
Fit matrix
Where Langfuse fits in an agent stack
strong
Coding agent workflow
Langfuse has multiple signals for coding agent workflow, including matching tags, capabilities, category, or positioning.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Evaluation and observability
Langfuse has multiple signals for evaluation and observability, including matching tags, capabilities, category, or positioning.
- Add one repeatable test case and confirm results can run again in review or CI.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Local or private AI stack
Langfuse has multiple signals for local or private ai stack, including matching tags, capabilities, category, or positioning.
- Verify hardware requirements, data path, storage, and whether all calls stay in your environment.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Reusable skill workflow
Langfuse has multiple signals for reusable skill workflow, including matching tags, capabilities, category, or positioning.
- Run one skill end to end and check whether it produces evidence or structured output.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Browser automation
Langfuse has at least one signal for browser automation, but should be checked against a real task before adoption.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Connector or protocol layer
Langfuse has at least one signal for connector or protocol layer, but should be checked against a real task before adoption.
- Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Repositories, files, issues, terminal output, and test results
- Official setup instructions and a small real workflow
Likely outputs
- Diffs, commits, explanations, test results, or review notes
- Scores, traces, regression results, dashboards, or failure cases
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
GitHub github
Repository source for code, license, issues, releases, and implementation details.
Homepage homepageOfficial or project-controlled source for this resource profile.
Langfuse is not currently marked as open source in OpenAgent metadata.
License metadata: See repositoryLangfuse has a recorded GitHub repository: langfuse/langfuse.
Resource facts and GitHub source link.Langfuse supports these recorded deployment modes: self hosted, cloud.
OpenAgent decision signal metadata.Langfuse is tagged with workflow, state capabilities.
OpenAgent capability taxonomy.- Dedicated docs link is missing.
- Repository freshness has not been recorded.
Next action
How to start evaluating Langfuse
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open sourceOpen Homepage
Start from the official source before adopting third-party instructions.
Open source Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about Langfuse
What is Langfuse used for?
Langfuse is used as a tool for tools workflows. The most relevant recorded capabilities are workflow, state.
Is Langfuse open source?
Langfuse is not currently marked as open source in OpenAgent metadata. Check official links for current licensing.
Can agents use Langfuse directly?
Langfuse has recorded interfaces such as repo, docs. Agents should prefer the JSON or Markdown profile first, then follow official docs for real execution.
What should I check before production use?
Check source confidence (high), risk level (moderate), license, maintenance freshness, permission surface, required credentials, and whether the first workflow succeeds in a sandbox.