Run automated evaluations to ship with confidence

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

Evaluation

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

Code, AI, and Human Evaluators

Define your own code or LLM evaluators to automatically test your AI pipelines against your custom criteria, or define human evaluation fields to manually grade outputs.

Continuous Integration

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

Distributed Tracing

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

Evaluation Reports

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 experiments 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 to test and evaluate your application.

Curate datasets from production, or synthetically generate using AI

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 application

Context Relevance

Context Precision

Answer Relevance

Answer Faithfulness

ROUGE

Toxicity

Coherence

10+ more

Build your own evaluators for your unique use-case

Every use-case is unique. HoneyHive allows you to build your own LLM evaluators and validate them within the evaluator console.

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

Use LLMs to critique agent trajectory over multiple steps

Seamless integrates with your application logic

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

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.