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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).

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Evaluation-Driven Development Workflow

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.
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.
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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.
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.

The Modern Agent Development Lifecycle (ADLC)

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Platform Capabilities

Explore the core features that power your AI development lifecycle:

Tracing

Capture and visualize every step of your AI application with distributed tracing.

Experiments & Datasets

Test changes with offline experiments and curated datasets before production.

Monitoring & Alerting

Track metrics with dashboards and get instant alerts when quality degrades.

Online Evaluations

Run automated evals on traces to monitor quality and catch issues early.

Annotation Queues

Collect expert feedback and turn qualitative insights into labeled datasets.

Prompt Management

Centrally manage and version prompts across UI and code.

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Open Standards, Open Ecosystem

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.
HoneyHive Ecosystem

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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Deployment Options

SaaS Cloud

Fully-managed, multi-tenant platform. Get started in minutes.

Dedicated Cloud

Private, single-tenant environment managed by our team.

Self-Hosted

Deploy in your VPC for complete control and compliance.

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Quickstart Guides

Start Tracing

Instrument your first application and capture traces in 5 minutes.

Run Your First Evaluation

Set up experiments and evaluate your AI agents programmatically.

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Additional Resources

API Reference

Complete REST API documentation for custom integrations.

SDK Documentation

Python and TypeScript SDK guides for advanced use cases.

Invite Your Team

Add teammates and configure role-based access control.

Integrations

Connect with OpenAI, Anthropic, LangChain, and more.