langfuse
Official vendor OpenAPI document · agent-readyAI/MLMl Inferencebasic86 EndpointsREST
For Agents
Record LLM traces, manage prompts and datasets, and run evaluations on Langfuse to observe and debug agent and model behaviour.
Use for: I need to log a trace for an LLM call, Get the active prompt named 'support-reply' from Langfuse, Create a dataset item for an evaluation run, Score a generation against a custom evaluator
Not supported: Does not generate model responses, host models, or vectorise text - use for LLM tracing, evaluation, and prompt management only.
Langfuse is an open-source LLM observability and evaluation platform. The public API records traces, observations, generations, scores, and sessions for LLM-powered applications, and exposes endpoints to run evaluations, manage prompts, datasets, annotation queues, and project-level resources. Teams use it to debug agent runs, measure quality across model versions, and track cost and latency for production LLM workloads. The spec covers 86 operations across observability, prompt management, datasets, evaluations, and project administration.
Install Jentic One Beta
Connect the langfuse to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the langfuse, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Flangfuse.com%2Flangfuse" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Flangfuse.com%2Flangfuse" | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with langfuse API.
Record traces, observations, generations, and scores for an LLM-powered application
Manage versioned prompts and pull the active prompt by name from the prompt registry
Create and update datasets and dataset items used to evaluate model outputs
Run evaluations and read evaluator results against dataset items
Manage annotation queues and queue items for human-in-the-loop review
Query project, comment, and integration resources for organisation-level admin
Use Cases
Patterns agents use langfuse API for, with concrete tasks.
★ LLM Observability for Production Agents
An engineering team logs every LLM call from a production agent into Langfuse, capturing the prompt, model, latency, cost, and final response so they can replay and debug failures. The API accepts traces, observations, and generations under a single trace_id so a multi-step agent run is reconstructable end to end. Teams use this to triage incidents and spot regressions after model swaps.
Create a Langfuse trace for the current agent run, log a generation observation with the model, prompt, output, and token usage, then attach a quality score before closing the trace.
Versioned Prompt Management
Product and ML teams keep prompts in Langfuse rather than scattered across code, fetching the active version of a named prompt at runtime. The API supports versioning, labels, and rollback so a prompt can be changed without a redeploy and historical versions remain auditable. This decouples prompt iteration from application releases.
Fetch the active 'support-reply' prompt from Langfuse, render it with the current ticket context, and submit the call to the model - log the full exchange back to Langfuse.
Dataset-Driven Evaluation
ML engineers maintain an evaluation dataset in Langfuse, run model outputs against it, and compare scores between candidate prompts or models. The API exposes datasets, dataset items, and evaluation runs so a CI pipeline can grade a release before it ships. Useful for catching regressions before production rollout.
Run the candidate prompt against the 'tickets-eval' dataset in Langfuse, collect scores per item, and report whether the new prompt beats the current production prompt.
AI Agent Self-Observation
An AI agent built on Jentic logs its own runs to Langfuse for later inspection by engineers and other agents. The agent searches Jentic for trace logging, loads the operation schema, and writes traces directly without manually wiring Langfuse client setup. This makes agent runs auditable and replayable across the team.
Use Jentic to call Langfuse and write a trace for the current agent run, including each tool call as a span, then attach a final score after the user confirms the result.
Key Endpoints
86 endpoints — langfuse is an open-source llm observability and evaluation platform.
METHOD
PATH
DESCRIPTION
GET
/api/public/annotation-queues
List annotation queues.
GET
/api/public/annotation-queues/{queueId}/items
List items waiting in an annotation queue.
GET
/api/public/comments
List comments attached to traces or observations.
GET
/api/public/dataset-items
List dataset items used in evaluation runs.
GET
/api/public/annotation-queues
List annotation queues.
GET
/api/public/annotation-queues/{queueId}/items
List items waiting in an annotation queue.
GET
/api/public/comments
List comments attached to traces or observations.
GET
/api/public/dataset-items
List dataset items used in evaluation runs.
Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring Langfuse by hand means setting up its basic auth from a public and secret key pair, targeting your Langfuse host, and pointing the SDK at the right endpoints yourself. Through Jentic you install once, import Langfuse from the API Directory, store the key pair once, and your agent calls it.
Permission scoping
You choose which Langfuse operations the agent may call, so you can limit it to the ones it needs, such as listing annotation queues, reading comments, or fetching dataset items. Granting read operations lets the agent inspect traces and evaluations without gaining the ability to write scores or change prompts.
Credential isolation
Your Langfuse public and secret keys are stored once, encrypted, by your own Jentic One instance and injected at execution time. They never enter the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'log an LLM trace' or 'list annotation queue items', and Jentic returns the matching Langfuse operation with its input schema so the agent calls the right endpoint without wiring the Langfuse SDK.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using langfuse API through Jentic.
What authentication does the Langfuse API use?
Langfuse uses HTTP Basic auth with a public key as the username and a secret key as the password, both issued per project. Through Jentic, both keys live in the encrypted vault and are injected at execution time so they never appear in the agent context.
Can I log LLM traces with the Langfuse API?
Yes. Create a trace, attach observations and generations under the same trace_id, then post scores to grade the run. The endpoints under /api/public cover the full ingest path used by the official SDKs.
Can I manage prompts with the Langfuse API?
Yes. The API exposes versioned prompts with labels so you can fetch the active 'production' version of a named prompt at runtime, push a new version, or roll back to a prior one without a code redeploy.
What are the rate limits for the Langfuse API?
Langfuse Cloud applies plan-based rate limits that the OpenAPI spec does not enumerate. Self-hosted deployments have no fixed limits beyond the underlying database. Check the Langfuse plan dashboard for current Cloud limits.
How do I run an evaluation through Jentic?
Search Jentic for 'run llm evaluation', load the dataset and run operations, then POST dataset run items pointing at your candidate prompt. Scores attached to each run are returned for comparison.
Is the Langfuse API free?
Langfuse is open source and self-hostable for free. Langfuse Cloud has a free tier with paid plans for higher trace volume and longer retention.
Can I limit what my agent is allowed to do with the Langfuse API?
Yes. Because you run Jentic One yourself, your own rules decide which Langfuse operations and credentials the agent can use, so you can restrict it to just the endpoints it needs, such as listing annotation queues, reading comments, or fetching dataset items. Granting only read operations lets the agent inspect traces and evaluations while withholding the ability to write scores or change prompts. The public and secret key pair is stored by your own instance and injected at execution time, so the agent never handles the raw credentials.
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