Looking for the latest HoneyHive docs? See HoneyHive v2.
HoneyHive is the AI Observability and Evaluation Platform that empowers developers and domain experts to collaborate and build reliable AI agents faster. We provide a unified platform for tracing, evaluating, and monitoring AI agents throughout the entire Agent Development Lifecycle (ADLC).
Traditional AI development is reactive—you build, deploy, and hope for the best. HoneyHive enables a systematic Evaluation-Driven Development (EDD) approach, similar to Test-Driven Development in software engineering, where evaluation guides every stage of the Agent Development Lifecycle.
1
Production: Observe and Evaluate Agents
Deploy your AI application with distributed tracing to capture every interaction. Collect real-world traces, user feedback, and quality metrics from production. Run online evals to identify edge cases and evaluate quality at scale. Set up alerts to monitor critical failures or metric drift over time.
Traces
Agent Graphs
Threads
Timeline View
Dashboard
Alerts
2
Testing: Curate Datasets & Run Experiments
Transform failing traces from production into curated datasets. Run comprehensive experiments to quantify performance and track regressions as you change prompts, models, tools, and more.
Experiments
Datasets
Regression Tests
LLM Evaluators
Code Evaluators
Annotation Queues
3
Development: Iterate & Refine Prompts
Use evaluation results to guide improvements. Iterate on prompts, test new models, and optimize your AI application based on data-driven insights. Test changes against your curated datasets before deploying to production.
Playground
Prompt Management
4
Repeat: Continuous Improvement
Deploy improvements to production and continue the cycle. Each iteration builds on data-driven insights, creating a flywheel of continuous improvement that ensures your AI systems become more reliable over time.
HoneyHive is natively built on OpenTelemetry, making it fully agnostic across models, frameworks, and clouds. Integrate seamlessly with your existing AI stack with no vendor lock-in.
Model Agnostic
Works with any LLM—OpenAI, Anthropic, Bedrock, open-source, and more.
Framework Agnostic
Native support for LangChain, CrewAI, Google ADK, AWS Strands, and more.
Cloud Agnostic
Deploy on AWS, GCP, Azure, or on-premises—works anywhere.
Built on Open Standards
OpenTelemetry-native for interoperability and future-proof infrastructure.
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