HoneyHive

OpenTelemetry-native observability and evaluation platform for LLM agents in production.

Freemium· Free tier available; paid/enterprise tiers via salesEvaluationMulti-model

Best for

Pick HoneyHive if you're running real LLM agents in production and need tracing, evals, and human review under one OTel-native platform.

Skip if

Skip it if you're prototyping a single prompt, want a self-hostable open-source stack, or need transparent published pricing before talking to sales.

HoneyHive is an observability and evaluation layer built specifically for teams shipping LLM agents to production. It combines distributed tracing (OpenTelemetry-native, with instrumentation for 100+ models and frameworks), online evaluation with LLM-as-a-judge, offline experiments against datasets, drift/failure alerts, and human annotation queues into a single workflow. The pitch is that you can trace an agent from user turn down to individual tool calls, replay sessions in a playground, and wire the same evaluators into CI so regressions get caught before deploy.

It targets AI-native startups and Fortune 500 engineering teams building non-trivial agents, and its differentiation is the tight coupling of tracing, eval, and human review under one roof rather than stitching together LangSmith, Arize, and a spreadsheet. Pricing is not published on the homepage: there is a self-serve free tier ("Start for free") and a sales-led path for larger deployments, so real budgets require a call.

It's framework-agnostic thanks to OTel, ships a CLI and an MCP server for IDE integration, and exposes a documented API for programmatic dataset and trace management. Not open source, and the evaluation-heavy workflow will feel like overkill if you're still prototyping a single prompt.

Editor's take

HoneyHive is one of the more coherent answers to the "we shipped an agent, now what?" problem, and going all-in on OpenTelemetry is the right bet for portability. The unified tracing-plus-eval-plus-annotation loop is genuinely useful, but the opaque pricing and closed-source posture mean you should benchmark it against LangSmith and Arize Phoenix before committing.

— The AI Tool Bible editorial team