HoneyHive
Agent-native observability and evaluation — traces, online evals, and experiments in one loop.
About
HoneyHive is an OpenTelemetry-native observability and evaluation platform built for AI agents. Drop its SDK into LangChain, LangGraph, CrewAI, or OpenAI Agents and every model call, tool call, and retrieval streams in as traces rendered as execution DAGs, with cost, latency, and quality side by side. Online evaluators — faithfulness, PII detection, tool-misuse, looping — run continuously on live traffic; failing traces get promoted into regression datasets with one click; and the experiment framework A/B-tests prompt or model changes before they ship. It runs in production at CBA across agents serving 17M+ consumers.
The honest picture: observability is a crowded category — LangSmith, Langfuse, and Helicone all want this seat — and HoneyHive's value compounds only once you have real production traffic to observe. Scale-tier pricing is not public, so enterprise cost is a sales conversation.
What it can do
- OpenTelemetry-native agent tracing
- Online evaluators on live traffic
- Failure-to-dataset curation loop
- Offline experiments and A/B comparison
- Alerts on accuracy, latency, and cost drift
Setup
Sign up at honeyhive.ai and drop the OpenTelemetry-native SDK into your agent code.
Prerequisites
Running AI agents with code paths you can instrument.
Use cases
Works with
Trust score — 7/10
Scored 7/10 (metrics tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 51; Trending score 70/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.