HoneyHive Overview

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
• 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.

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.

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.

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.

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.