HoneyHive

Founded 2022

Trace, evaluate, and monitor AI agents in production and debug execution steps

You can use HoneyHive to trace, evaluate, and debug artificial intelligence agents throughout their lifecycle. Inspect complete execution paths step by step, including tool calls, prompts, and multi-turn sub-agent interactions to identify errors. Run automated evaluations using custom code, LLM judges, or human annotation queues to measure accuracy and groundedness against real production traffic. HoneyHive also integrates with continuous integration pipelines to compare version regressions before release and exports telemetry datasets directly to cloud data warehouses for compliance and reporting needs across enterprise engineering teams.

What HoneyHive does

Trace multi-step agent decisions and tool callsScore outputs with LLM and custom code evaluatorsInspect session replays and trajectory graphsRoute flagged traces to human review queuesCompare prompt versions in a test playgroundRun automated regression evaluations in CI pipelinesExport telemetry data to external cloud warehousesOpenTelemetry-native distributed tracing for multi-turn AI agentsStep-by-step session replays with visual agent trajectory graphsAutomated evaluations using LLM-as-a-judge, custom code, or human feedbackProduction failure detection with automated dataset generation for testingRegression detection and comparative release benchmarking in CI pipelinesPrompt version control and side-by-side comparison playgroundDirect telemetry streaming to cloud data warehouses like Snowflake and BigQueryGranular role-based access control with SOC 2, HIPAA, and GDPR compliance