Run automated evaluations to ship with confidence

Systematically test and evaluate your LLM pipelines pre-production with Python validators, LLM evaluators, and human feedback.

Evaluation

End-to-end testing and evaluation for your AI applications

Programmatic evaluation

Evaluation runs can be logged programmatically and integrated into your CI/CD workflows via our SDK, making it easier than ever to test and evaluate your LLM apps.

Trace and span-level visibility

Get detailed visibility into your entire LLM pipeline across your run, helping you pinpoint sources of regressions in your pipeline as you iterate.

Evaluators and guardrails

Define your own Python validators and LLM evaluators to automatically test your AI pipelines against your own criteria and guardrails.

Experiment reports

Save and version all evaluation runs to create a single source of truth for all experiments and artifacts, accessible to your entire team.

Dataset management

Capture underperforming test cases from production and add corrections to curate golden datasets for continuous testing.

Optimized infrastructure

We automatically parallelize requests and metric computation to speed up large evaluation runs spanning thousands of test cases.

Compare prompts, agents & RAG pipelines side-by-side

HoneyHive enables you to test AI applications just like you test traditional software, eliminating guesswork and manual effort.

Evaluate prompts, agents, or retrieval strategies programmatically

Invite domain experts to provide human feedback

Collaborate and share learnings with your team

Curate golden datasets from underperforming test cases

HoneyHive enables you to filter and label underperforming data from production to curate "golden" evaluation datasets for continuous testing and iteration.

Filter and add underperforming test cases from production

Invite domain experts to annotate and provide ground truth labels

Manage and version evaluation datasets across your project

Use pre-built evaluators to test your responses

Context Relevance

Answer Relevance

Answer Faithfulness

Agent Trajectory Validity

PII Detection

Toxicity

JSON Validity

Cosine Similarity

Create your own evaluators for your unique use-case

Every use-case is unique. HoneyHive allows you to define your own evaluators and guardrails to build custom test suites for your app.

Evaluate faithfulness and context relevance across RAG pipelines

Write assertions to validate JSON structures or SQL schemas

Implement moderation filters to detect PII leakage and unsafe responses

Semantically analyze text for topic, tone, and sentiment

Calculate NLP metrics such as ROUGE-L or METEOR

Log evaluations runs programmatically via the SDK

Simple SDK. Easy to integrate with your existing codebase and pipeline logic.

CI/CD integration. Allows you to integrate HoneyHive into your existing CI workflow using GitHub Actions or Jenkins.

Customizable evaluators. Use our out-of-the-box evaluators, define your own, or use 3rd party eval libraries like OpenAI Evals.

Ship LLM apps to production with confidence.