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Create custom benchmarks for your application to measure improvements and regressions as your iterate, and debug failures and edge cases with HoneyHive tracing.
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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.
Evaluation runs can be logged programmatically and integrated into your CI/CD workflows via our SDK, allowing you to check for regressions.
Get detailed visibility into your entire LLM pipeline across your run, helping you pinpoint sources of regressions in your pipeline as you run experiments.
Save, version, and compare evaluation runs to create a single source of truth for all experiments and artifacts, accessible to your entire team.
Capture underperforming test cases from production and add corrections to curate golden datasets for continuous testing.
We automatically parallelize requests and metric computation to speed up large evaluation runs spanning thousands of test cases.
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
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LLM apps fail due to issues in either the prompt, model, or your data retrieval pipeline. With full visibility into the entire chain of events, you can quickly pinpoint errors and iterate with confidence.
Debug chains, agents, tools and RAG pipelines
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Root cause errors with AI-assisted RCA
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Integrates with leading orchestration frameworks
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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
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Every use-case is unique. HoneyHive allows you to build your own LLM evaluators and validate them within the evaluator console.
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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
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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 RAGAS.
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Divyansh Garg
Co-Founder and CEO
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Rex Harris
Head of AI/ML