honeyhive.ai

HoneyHive offers a range of features for agent observability and evaluation. Distributed tracing provides graph and timeline views to debug complex multi-agent systems. Online evaluation uses LLM-as-a-judge or custom code to assess live traces. Monitoring and alerts detect failures and drift before users are affected. Experiments allow offline testing against datasets to catch regressions before release. Annotation queues bring domain experts into the loop for human review and custom rubrics. The platform also includes a CLI, MCP server, and ready-made skills for coding agents, enabling autonomous setup and management.

HoneyHive is used for monitoring and improving AI agents in production. Teams use it to debug agent behavior across any framework, run live evals to detect failures, and replay chat sessions for analysis. It supports continuous improvement by turning production failures into test suites and running offline experiments. Domain experts can review edge cases and define quality standards. The platform also integrates with CI/CD pipelines to run automated test suites on every commit. It is suitable for organizations that need to scale AI agents responsibly, with features like virtual data planes and granular RBAC for security.

The page does not provide specific pricing details. It mentions a 'Start for free' option and a 'Get a demo' call-to-action, but no pricing tiers or costs are listed. For pricing information, users are likely directed to contact the sales team or sign up for a free trial.