HoneyHive

Что такое HoneyHive?

HoneyHive is an observability and evaluation platform for production AI agents. It combines distributed tracing, live evaluation, experimentation, and review workflows so teams can inspect how agents behave, identify failures, and improve them in a repeatable loop.

The site positions the product as OpenTelemetry-native and framework-agnostic, with support for instrumenting agents on any stack. It is aimed at teams that want one shared source of truth for debugging agent behavior, monitoring live systems, and turning production issues into test cases for the next iteration.

Что умеет HoneyHive?

Distributed tracing

Instrument agents with OpenTelemetry to trace behavior across frameworks, models, and runtimes, then use a shared view for debugging and analysis.

Online evaluation

Run live evaluations on traces to detect failures, score outputs, and combine automated checks with human review.

Monitoring and alerts

Monitor real-world behavior with alerts, drift detection, and custom dashboards so teams can catch issues before users do.

Session replay and trajectory views

Replay chat sessions and inspect trajectories, graphs, and timelines to understand how multi-step agent workflows unfold.

Experiments and regression tracking

Turn production failures into offline test suites, compare against baselines, and detect regressions before release.

Annotation and dataset workflows

Route flagged traces into annotation queues, apply custom rubrics, and use expert feedback to curate datasets and align evaluators.

Варианты использования

“Debug production agent behavior”

Instrument production agents to see traces, trajectories, and session replays when debugging unexpected behavior across a live system.

“Track quality in production”

Monitor live traffic with online evaluation, alerts, drift detection, and dashboards to catch quality issues as they emerge.

“Prevent regressions before release”

Build offline regression suites from real failures, compare changes against baselines, and run checks in CI/CD before shipping updates.

“Human-in-the-loop review”

Use annotation queues and custom rubrics to let domain experts review edge cases and align evaluators with business standards.

“Enterprise deployment and control”

Adopt self-hosted or hybrid deployment when data isolation, governance, or enterprise controls are required.

Часто задаваемые вопросы

What types of evaluations does HoneyHive support?

HoneyHive supports both automated evaluations and human evaluations. Automated evaluations can use code or LLM-as-a-judge scoring, while human evaluations let domain experts review outputs with custom rubrics and annotation queues.

Can HoneyHive be self-hosted?

Yes. The Enterprise plan supports self-hosting, and the pricing page also lists hybrid options with a HoneyHive-managed control plane and a self-hosted data plane.

How do teams integrate applications with HoneyHive?

The pricing page says HoneyHive provides Python and TypeScript SDKs with native OpenTelemetry support, plus automatic instrumentation for 50+ libraries such as LangChain, LangGraph, AWS Strands, Google ADK, and OpenAI Agents SDK.

What is HoneyHive used for?

HoneyHive is designed for AI agent observability and evaluation. The site positions it for production agents, distributed tracing, online evaluation, experiments, alerts, annotations, and review workflows.

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