HoneyHive Overview

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

View detailed execution logs of every LLM call, tool invocation, and chain step to understand exactly what your agent did.

Visualize complex agentic workflows as interactive graphs showing how components connect and where execution flows.

View a single session across multiple subagents in a chronological thread, showing internal messages and context propagation throughout multi-agent systems.

Analyze latency and identify performance bottlenecks with a chronological view of all operations in a trace.

Monitor key metrics like cost, latency, and success rates at a glance with customizable charts and filters.

Get notified instantly when quality degrades or errors spike so you can fix issues before they impact users.

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

Compare different prompts, models, or configurations side-by-side to measure which changes actually improve performance.

Build curated test sets from production failures or edge cases to ensure your agent handles real-world scenarios.

Automatically verify that new changes don’t break existing behavior by running evaluations on every update.

Use AI to automatically 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 create ground truth labels and improve automated evaluators.

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

Rapidly test prompt variations and model configurations with instant feedback before committing changes to code.

Version and deploy prompts centrally so your team can iterate without code changes or redeployments.

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.