> ## Documentation Index
> Fetch the complete documentation index at: https://docs.honeyhive.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Introducing HoneyHive

> 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](https://docs.honeyhive.ai/v2/introduction/tracing-quickstart.md) to capture your first session, then run an [offline experiment](https://docs.honeyhive.ai/v2/introduction/experiments-quickstart.md) to measure quality before you deploy.

![HoneyHive brand banner: Production AI starts with observability.](https://mintcdn.com/honeyhiveai/f5MSV0vr1F1LoeRi/images/intro-banner.png?fit=max\&auto=format\&n=f5MSV0vr1F1LoeRi\&q=85\&s=15bf5a8ecdd227b46e58b103ef790971)

> [card] [Start Tracing](https://docs.honeyhive.ai/v2/introduction/tracing-quickstart.md)
>
> Instrument your first agent and capture traces in 5 minutes.

> [card] [Run Your First Evaluation](https://docs.honeyhive.ai/v2/introduction/experiments-quickstart.md)
>
> Set up an experiment and evaluate your agent programmatically.

***

## How does the HoneyHive workflow work?

HoneyHive follows an **Evaluation-Driven Development (EDD)** workflow, where tracing and continuous evals guide every stage of agent development. Production [observability](https://docs.honeyhive.ai/v2/tracing/introduction.md) feeds [datasets](https://docs.honeyhive.ai/v2/datasets/introduction.md) and [experiments](https://docs.honeyhive.ai/v2/evaluation/introduction.md) 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.

> [tab] Traces
>
> Inspect every LLM call, tool invocation, and chain step in a structured execution log.
>
> ![](https://mintcdn.com/honeyhiveai/sFOpWw98R-jnkhpC/images/product-traces.png?fit=max\&auto=format\&n=sFOpWw98R-jnkhpC\&q=85\&s=99d66a25e27f2188b49cc0185ec8b607)

> [tab] Agent Graphs
>
> Visualize agentic workflows as interactive graphs showing how components connect and where execution flows.
>
> ![](https://mintcdn.com/honeyhiveai/81DpusKRfAED9ab1/images/NewGraphView.png?fit=max\&auto=format\&n=81DpusKRfAED9ab1\&q=85\&s=2ca201663a04093a65acf7b5fe0ff0f6)

> [tab] Trajectories
>
> Spot loops, stuck steps, and outliers in long agent sessions as bubbles sized by duration, cost, metrics, feedback, or metadata values.
>
> ![](https://mintcdn.com/honeyhiveai/sFOpWw98R-jnkhpC/images/trajectory-view.png?fit=max\&auto=format\&n=sFOpWw98R-jnkhpC\&q=85\&s=562a952c38c289a2ac3d789e74d6f168)

> [tab] Threads
>
> Follow a session across multiple sub-agents in a single chronological thread, including internal messages and context propagation.
>
> ![](https://mintcdn.com/honeyhiveai/81DpusKRfAED9ab1/images/ThreadView.png?fit=max\&auto=format\&n=81DpusKRfAED9ab1\&q=85\&s=4b17866633b3fcad1098e57b3a813341)

> [tab] Timeline View
>
> Identify latency bottlenecks with a chronological breakdown of every operation in a trace.
>
> ![](https://mintcdn.com/honeyhiveai/sFOpWw98R-jnkhpC/images/timeline-view.png?fit=max\&auto=format\&n=sFOpWw98R-jnkhpC\&q=85\&s=4afe8f25efea30423253c21c6946baf7)

> [tab] Dashboard
>
> Track cost, latency, and success rates with customizable charts and filters.
>
> ![](https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-dashboard.png?fit=max\&auto=format\&n=EWG3R5yYrwNnHjQ7\&q=85\&s=a96b23c318597e7650d96ca6151e1892)

> [tab] Alerts
>
> Get notified when quality drops or errors spike so you can respond before users are affected.
>
> ![](https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-alerts.png?fit=max\&auto=format\&n=EWG3R5yYrwNnHjQ7\&q=85\&s=75d3ba2822f5d3f747ab9d03243943ac)

**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.

> [tab] Experiments
>
> Compare prompts, models, or configurations side-by-side to see which changes improve performance.
>
> ![](https://mintcdn.com/honeyhiveai/81DpusKRfAED9ab1/images/NewExperiments.png?fit=max\&auto=format\&n=81DpusKRfAED9ab1\&q=85\&s=7cf07761ca0454aeeb67d45cb58dda45)

> [tab] Datasets
>
> Build test sets from production failures and edge cases to cover real-world scenarios.
>
> ![](https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-datapoint.png?fit=max\&auto=format\&n=EWG3R5yYrwNnHjQ7\&q=85\&s=76b7381517d0c5be5c634f0e03a3e194)

> [tab] Regression Tests
>
> Verify that new changes don't break existing behavior by running evaluations on every update.
>
> ![](https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-regressions.png?fit=max\&auto=format\&n=EWG3R5yYrwNnHjQ7\&q=85\&s=23932c9a312f4408b289b87b36c0d8ae)

> [tab] LLM Evaluators
>
> Use AI to assess response quality, accuracy, and safety at scale without manual review.
>
> ![](https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-llm.png?fit=max\&auto=format\&n=EWG3R5yYrwNnHjQ7\&q=85\&s=01cc471bc2e45ffa063e5ab7a8fe26b9)

> [tab] Code Evaluators
>
> Write custom Python evaluation logic for domain-specific metrics that LLMs can't judge reliably.
>
> ![](https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-code.png?fit=max\&auto=format\&n=EWG3R5yYrwNnHjQ7\&q=85\&s=ba616b85f884ec63d70847e3d62fc0b5)

> [tab] Annotation Queues
>
> Collect expert judgments on agent outputs to build ground truth labels and improve automated evaluators.
>
> ![](https://mintcdn.com/honeyhiveai/81DpusKRfAED9ab1/images/AnnotationQueues.png?fit=max\&auto=format\&n=81DpusKRfAED9ab1\&q=85\&s=69951687bdb69d9874f5fe4c6256aed4)

**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.

> [tab] Playground
>
> Test prompt variations and model configurations with instant feedback before committing to code.
>
> ![](https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-playground.png?fit=max\&auto=format\&n=EWG3R5yYrwNnHjQ7\&q=85\&s=7b8d091a5c4e34b26be775f11270a9da)

> [tab] Prompt Management
>
> Version and deploy prompts centrally so your team can iterate without code changes or redeployments.
>
> ![](https://mintcdn.com/honeyhiveai/EWG3R5yYrwNnHjQ7/images/product-prompts.png?fit=max\&auto=format\&n=EWG3R5yYrwNnHjQ7\&q=85\&s=f69662f3ea6bc1c742e416c698695d24)

**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.

![](https://cdn.prod.website-files.com/65483e3a00a324531a626c4e/673b754f7eb65e18df13f812_image%20\(363\).png)

The Agent Development Lifecycle (ADLC)

***

## What can you do with HoneyHive?

Core features across the development lifecycle:

> [card] [Tracing](https://docs.honeyhive.ai/v2/tracing/introduction.md)
>
> Capture and visualize every step of your AI application with distributed tracing.

> [card] [Experiments & Datasets](https://docs.honeyhive.ai/v2/evaluation/introduction.md)
>
> Test changes with offline experiments and curated datasets before deploying.

> [card] [Monitoring & Alerting](https://docs.honeyhive.ai/v2/monitoring/overview.md)
>
> Track metrics with dashboards and get alerts when quality degrades.

> [card] [Online Evaluations](https://docs.honeyhive.ai/v2/evaluators/introduction.md)
>
> Run automated evals on production traces to catch issues early.

> [card] [Annotation Queues](https://docs.honeyhive.ai/v2/evaluators/human.md)
>
> Collect expert feedback and turn it into labeled datasets.

> [card] [Prompt Management](https://docs.honeyhive.ai/v2/prompts/overview.md)
>
> Version and manage prompts across UI and code.

***

## Why is HoneyHive built on OpenTelemetry?

HoneyHive is built on **OpenTelemetry**, so it works across models, frameworks, and runtimes with no vendor lock-in. See [tracing concepts](https://docs.honeyhive.ai/v2/tracing/concepts.md) for how sessions, events, and OTel fit together.

![HoneyHive Ecosystem](https://mintcdn.com/honeyhiveai/W7aN2oGPMTbOQxZv/images/integrations.png?fit=max\&auto=format\&n=W7aN2oGPMTbOQxZv\&q=85\&s=cf54dd0823a45251353273cdef8a2c99)

> [card] Model Agnostic
>
> Works with OpenAI, Anthropic, Bedrock, open-source models, and more.

> [card] Framework Agnostic
>
> Native support for LangChain, CrewAI, Google ADK, AWS Strands, and more.

> [card] Runtime Agnostic
>
> Trace any runtime - Lambdas, Kubernetes, Bedrock AgentCore, and more.

> [card] Bring Your Own Instrumentor
>
> HoneyHive supports official OTEL GenAI, OpenLLMetry, and OpenInference semantic conventions.

***

## What hosting options are available?

> [card] [Multi-Tenant SaaS](https://docs.honeyhive.ai/v2/setup/managed.md)
>
> Fully managed. Get started in minutes.

> [card] [Dedicated Cloud](https://docs.honeyhive.ai/v2/setup/dedicated.md)
>
> Single-tenant environment managed by our team.

> [card] [Self-Hosted](https://docs.honeyhive.ai/v2/setup/self-hosted.md)
>
> Deploy in your VPC for full control and compliance.

***

## Where can you find more resources?

> [card] [API Reference](https://docs.honeyhive.ai/v2/api-reference-autogen/sessions/start-a-new-session.md)
>
> REST API documentation for custom integrations.

> [card] [SDK Documentation](https://docs.honeyhive.ai/v2/sdk-reference/overview.md)
>
> Python SDK guides for advanced use cases.

> [card] [Invite Your Team](https://docs.honeyhive.ai/v2/workspace/inviting-teammates.md)
>
> Add teammates and configure role-based access control.

> [card] [Integrations](https://docs.honeyhive.ai/v2/integrations/google-adk.md)
>
> Connect with OpenAI, Anthropic, LangChain, and more.

> [card] [What's New](https://docs.honeyhive.ai/v2/changelog/product.md)
>
> Product updates, feature launches, and platform improvements.


## Related topics

- [HoneyHive Tracing Concepts](https://docs.honeyhive.ai/v2/tracing/concepts.md)
- [HoneyHive Platform Architecture](https://docs.honeyhive.ai/v2/platform-architecture.md)
- [HoneyHive Platform Concepts](https://docs.honeyhive.ai/v2/concepts.md)


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