Run automated evaluations to ship with confidence

Set up test suites to automatically test and evaluate your LLM application pre-production.


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

Code and LLM evaluators

Define your own code and LLM evaluators to automatically test your AI pipelines against your custom criteria.

Continuous integration

Evaluation runs can be logged programmatically and integrated into your CI/CD workflows via our SDK, allowing you to check for regressions and maintain reliability.

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 run experiments.

Reports & benchmarking

Save, version, and compare 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.

Benchmark and compare evaluation runs 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 for every scenario

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 our pre-built evaluators to test your responses

Context Relevance

Context Correctness

Answer Relevance

Answer Faithfulness

PII Detection



and more

Customize your own evaluators for your unique use-case

Every use-case is unique. HoneyHive allows you to configure your own LLM and code evaluators that can be used with our Evaluate API.

Test faithfulness and context relevance across RAG pipelines

Write assertions to validate JSON structures or find keywords

Implement custom moderation filters to detect unsafe responses

Semantically analyze text for topic, tone, and sentiment

Calculate NLP metrics such as ROUGE-L or BLEU

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.