Ship evaluation-driven fixes
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SPIRITT
LangfuseLangfuse
Turn AI evidence into better delivery
Give SPIRITT your Langfuse mission. Build evaluation and reliability workflows from Langfuse observations, prompt versions, experiments, and scores.
What you can do with Langfuse.
Measure actual behavior
Inspect observations and aggregate metrics to connect quality, latency and cost to specific application behavior.
Close the evaluation loop
Read test datasets, compare experiments and attach scores to the exact trace, session or dataset run evaluated.
Build an AI quality control room
Create a release evidence dashboard and regression workflow that turns Langfuse signals into tested engineering changes.
Works with your other apps.
Get started in three steps.
01
Open your workspace
Start a SPIRITT workspace where your agent can build systems and work with observations, prompts, datasets, experiments, and scores.
02
Connect Langfuse
Connect Langfuse so your agent can work with your observations, prompts, datasets, experiments, and scores.
03
Hand over a mission
Name the outcome. SPIRITT executes the work, coordinates connected apps, and keeps the mission moving.
Hand over a mission.
Choose a mission. Make it yours.
Put Langfuse to work.
Turn AI evidence into better delivery
Questions
Langfuse, with SPIRITT.
01What can SPIRITT do with Langfuse?+
Build evaluation and reliability workflows from Langfuse observations, prompt versions, experiments, and scores. Inspect observations and aggregate metrics to connect quality, latency and cost to specific application behavior.
02Can SPIRITT build a custom system around Langfuse?+
Create a release evidence dashboard and regression workflow that turns Langfuse signals into tested engineering changes. SPIRITT builds the application alongside Langfuse and keeps source records linked to the work it executes.
03Can SPIRITT edit prompts or train models through Langfuse?+
SPIRITT retrieves prompt versions, observations, datasets and evaluation metadata, and can create scores on supported subjects. Prompt editing, model training and application changes are separate engineering work, not native actions supplied by this connection.
04How are evaluation scores connected to the behavior reviewed?+
A score targets one trace, session or dataset run; a trace score can be narrowed to an observation. SPIRITT retains the rubric, source references and rationale in its evidence ledger so a release decision can be traced to the behavior actually evaluated.
05Can SPIRITT keep this Langfuse workflow running?+
Yes. Give SPIRITT a checking schedule and an outcome to own. It can revisit observations, prompts, datasets, experiments, and scores, persist progress between runs, and follow unresolved work through your connected apps. Metric queries and observation checks can run on a SPIRITT schedule, with evaluation results tied to their actual source subjects.
06How do I get started with Langfuse?+
Open a SPIRITT workspace, connect Langfuse, and hand over a mission. Name the records to work with, the outcome, and the companion apps you want involved.