langfuse

Name: langfuse API
Brand: langfuse
Availability: InStock

✓ Official Vendor SpecAI/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.

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.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic 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

GET STARTED

Start building with langfuse API

Explore with Jentic

View OpenAPI Document

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.

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?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Langfuse public and secret keys are stored encrypted in the Jentic vault. Agents get scoped tokens at execution time so the basic-auth credentials never enter the agent context.

Intent-based discovery

Agents search by intent (e.g., 'log an llm trace') and Jentic returns the matching Langfuse operation with its input schema, so the agent can write the trace without wiring the Langfuse SDK.

Time to first call

Direct Langfuse integration: a few hours for SDK install, env vars, and trace flushing. Through Jentic: under 30 minutes — search, load schema, execute.

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.