HoneyHive - Evaluation
Run automated evaluations to ship with confidence
Evaluate AI agents & application to measure performance, catch regressions, simulate tricky scenarios, and ship to production with confidence.
Continuous testing and release gates for your agents
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 performance and spot regressions quickly
HoneyHive enables you to test AI applications just like you test traditional software, eliminating guesswork and manual effort.
Evaluate prompts, agents, etc. against datasets
Scale human annotations with queues and custom criteria
.png)
Compare experiments and spot regressions in CI
Debug what actually went wrong with traces
Agents fail due to due to cascading failures across tool calls, reasoning steps, and more. With full visibility into the entire sequence of actions, you can quickly pinpoint errors and iterate with confidence.
Debug agents with distributed traces across complex agentic systems
.png)
Understand agent structure and critical paths with graphs
.png)
OpenTelemetry-native, integrates with leading frameworks
Curate 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
.png)
Use our pre-built evaluators to test your application
- Context Relevance
- Context Precision
- Answer Relevance
- Answer Faithfulness
- Intent Recognition
- Toxicity
- Tool Misuse
- 20+ more
Build custom 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.
.png)
Test faithfulness and context relevance across RAG pipelines
.png)
Write assertions to validate JSON structures or find keywords
.png)
Implement custom moderation filters to detect unsafe responses
.png)
Use LLMs to critique agent trajectory over multiple steps
Get started with just a few lines of code
.png)
OpenTelemetry-native . Automatically trace LLM requests and agent frameworks using OpenTelemetry.
.png)
Continuous integration . Integrate HoneyHive into your existing CI workflow using GitHub Actions.
.png)
Flexible. Use pre-built evaluators, define your own, or use any 3rd-party evaluators.
.png)
Trusted by Fortune 500 enterprises.
Scaling AI agents responsibly at Australia's largest bank
HoneyHive powers observability and evaluation across mission-critical AI systems at CBA, enabling safe and responsible deployment of AI agents serving 17M+ consumers.
.jpg)