HoneyHive Docs

HoneyHive is the Modern AI Observability and Evaluation Platform for developers and domain experts to collaboratively build reliable AI applications faster. HoneyHive enables modern AI teams to:

Trace: Log all AI application data using OpenTelemetry to debug execution steps.
Evaluate Offline: Evaluate application, prompt, and component-level quality against a dataset to quantify improvements and regressions.
Evaluate Online: Run evals asynchronously on traces and spans to monitor usage, performance, and quality metrics in production.
Annotate Traces: Involve internal SMEs to annotate logs in the UI, or collect feedback from end-users.
Manage Artifacts: Manage and version prompts, tools, datasets, and evaluators in the cloud, synced between UI and code.

HoneyHive streamlines the AI app development process by bringing together disparate, broken workflows into a unified platform for faster iteration, better visibility, and collaboration. By enabling evaluation before & after production, we promote a Evaluation-Driven Development (EDD) workflow similar to TDD in software engineering, ensuring that your AI applications are reliable by design. Our goal is to replace the current whack-a-mole AI development process with a unified workflow that enables faster iteration for cross-functional teams and promotes quality and reliability at every step.