HoneyHive is the agent observability platform for tracing, evaluating, monitoring, and improving agents from development through production.
HoneyHive helps you trace, evaluate, monitor, and improve AI agents from development through production. Start with the tracing quickstart to capture your first session, then run an offline experiment to measure quality before you deploy.
Start Tracing
Instrument your first agent and capture traces in 5 minutes.
Run Your First Evaluation
Set up an experiment and evaluate your agent programmatically.
HoneyHive follows an Evaluation-Driven Development (EDD) workflow, where tracing and continuous evals guide every stage of agent development. Production observability feeds datasets and experiments that drive the next iteration, helping you continuously improve agents over time.
1
Production: Observe and Evaluate
Instrument your application with distributed tracing to capture every interaction. Collect traces, user feedback, and quality metrics from production. Run online evals to surface edge cases at scale, and set up alerts to catch failures or metric drift.
Traces
Agent Graphs
Trajectories
Threads
Timeline View
Dashboard
Alerts
Inspect every LLM call, tool invocation, and chain step in a structured execution log.
Visualize agentic workflows as interactive graphs showing how components connect and where execution flows.
Spot loops, stuck steps, and outliers in long agent sessions as bubbles sized by duration, cost, metrics, feedback, or metadata values.
Follow a session across multiple sub-agents in a single chronological thread, including internal messages and context propagation.
Identify latency bottlenecks with a chronological breakdown of every operation in a trace.
Track cost, latency, and success rates with customizable charts and filters.
Get notified when quality drops or errors spike so you can respond before users are affected.
2
Testing: Curate Datasets & Run Experiments
Turn failing production traces into curated test datasets. Run experiments to measure the impact of your changes, track regressions over time, and gate releases in CI.
Experiments
Datasets
Regression Tests
LLM Evaluators
Code Evaluators
Annotation Queues
Compare prompts, models, or configurations side-by-side to see which changes improve performance.
Build test sets from production failures and edge cases to cover real-world scenarios.
Verify that new changes don’t break existing behavior by running evaluations on every update.
Use AI to assess response quality, accuracy, and safety at scale without manual review.
Write custom Python evaluation logic for domain-specific metrics that LLMs can’t judge reliably.
Collect expert judgments on agent outputs to build ground truth labels and improve automated evaluators.
3
Development: Iterate on Prompts
Use evaluation results to guide changes. Iterate on prompts, test new models, and optimize your application based on what the data shows. Validate changes against curated datasets before deploying.
Playground
Prompt Management
Test prompt variations and model configurations with instant feedback before committing to code.
Version and deploy prompts centrally so your team can iterate without code changes or redeployments.
4
Repeat: Continuous Improvement
Deploy improvements and continue the cycle. Each iteration builds on production data, creating a flywheel of improvement that makes your AI systems more reliable over time.
HoneyHive is built on OpenTelemetry, so it works across models, frameworks, and runtimes with no vendor lock-in. See tracing concepts for how sessions, events, and OTel fit together.
Model Agnostic
Works with OpenAI, Anthropic, Bedrock, open-source models, and more.
Framework Agnostic
Native support for LangChain, CrewAI, Google ADK, AWS Strands, and more.
Runtime Agnostic
Trace any runtime - Lambdas, Kubernetes, Bedrock AgentCore, and more.
Bring Your Own Instrumentor
HoneyHive supports official OTEL GenAI, OpenLLMetry, and OpenInference semantic conventions.
Add teammates and configure role-based access control.
Integrations
Connect with OpenAI, Anthropic, LangChain, and more.
What's New
Product updates, feature launches, and platform improvements.
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Validate changes against curated datasets before deploying.`}),_jsxs(Tabs,{children:[_jsxs(Tab,{title:`Playground`,id:`playground`,children:[_jsx(_components.p,{children:`Test prompt variations and model configurations with instant feedback before committing to code.`}),_jsx(Frame,{children:_jsx(OptimizedImage,{src:`https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-playground.png?fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=7b8d091a5c4e34b26be775f11270a9da`,\"data-og-width\":`3024`,width:`3024`,\"data-og-height\":`1562`,height:`1562`,\"data-path\":`images/product-playground.png`,\"data-optimize\":`true`,\"data-opv\":`3`,srcset:`https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-playground.png?w=280\u0026fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=1b9fa4cc34da87934d4b7e81314f14dd 280w, https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-playground.png?w=560\u0026fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=483256940659c682b85d3912ea798ee7 560w, https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-playground.png?w=840\u0026fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=4b1e0561be5201f5a85940b7a942bbeb 840w, https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-playground.png?w=1100\u0026fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=b9852ed0d996b3c6b19d33f97c9743ae 1100w, https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-playground.png?w=1650\u0026fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=687c7dc5a76befc4188a36c07449036b 1650w, https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-playground.png?w=2500\u0026fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=564f938082bf4834e4402c81d1e23a44 2500w`})})]}),_jsxs(Tab,{title:`Prompt Management`,id:`prompt-management`,children:[_jsx(_components.p,{children:`Version and deploy prompts centrally so your team can iterate without code changes or redeployments.`}),_jsx(Frame,{children:_jsx(OptimizedImage,{src:`https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-prompts.png?fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=f69662f3ea6bc1c742e416c698695d24`,\"data-og-width\":`3024`,width:`3024`,\"data-og-height\":`1562`,height:`1562`,\"data-path\":`images/product-prompts.png`,\"data-optimize\":`true`,\"data-opv\":`3`,srcset:`https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-prompts.png?w=280\u0026fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=bbdc0493ecdd1d153ea041e165c0cf4f 280w, https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-prompts.png?w=560\u0026fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=a6e9fca44a0778d9aec2820d3446149d 560w, https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-prompts.png?w=840\u0026fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=7233496f106b5cd235e11423bade4fb7 840w, https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-prompts.png?w=1100\u0026fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=94e0674db1d44bcb920a0587c2946d46 1100w, https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-prompts.png?w=1650\u0026fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=266b0b4e055ab1bda7eb9438f5a06568 1650w, https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-prompts.png?w=2500\u0026fit=max\u0026auto=format\u0026n=EWG3R5yYrwNnHjQ7\u0026q=85\u0026s=a41296d9bf45ff6af9558faee4026569 2500w`})})]})]})]}),_jsxs(Step,{title:`Repeat: Continuous Improvement`,children:[_jsxs(_components.p,{children:[`Deploy improvements and continue the cycle. 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