> ## 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.
# HoneyHive Overview
> Modern AI Observability and Evaluation
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).
## 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.
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.
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.
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.
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.
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## Platform Capabilities
Explore the core features that power your AI development lifecycle:
Capture and visualize every step of your AI application with distributed tracing.
Test changes with offline experiments and curated datasets before production.
Track metrics with dashboards and get instant alerts when quality degrades.
Run automated evals on traces to monitor quality and catch issues early.
Collect expert feedback and turn qualitative insights into labeled datasets.
Centrally manage and version prompts across UI and code.
## 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.
Works with any LLM—OpenAI, Anthropic, Bedrock, open-source, and more.
Native support for LangChain, CrewAI, Google ADK, AWS Strands, and more.
Deploy on AWS, GCP, Azure, or on-premises—works anywhere.
OpenTelemetry-native for interoperability and future-proof infrastructure.
## Deployment Options
Fully-managed, multi-tenant platform. Get started in minutes.
Private, single-tenant environment managed by our team.
Deploy in your VPC for complete control and compliance.
## Quickstart Guides
Instrument your first application and capture traces in 5 minutes.
Set up experiments and evaluate your AI agents programmatically.
## Additional Resources
Complete REST API documentation for custom integrations.
Python and TypeScript SDK guides for advanced use cases.
Add teammates and configure role-based access control.
Connect with OpenAI, Anthropic, LangChain, and more.