HoneyHive

HoneyHive is a prompt fixer tool. Agent observability and evaluation platform for teams running AI agents in production: full trace replay, LLM and code evaluators, live monitors with drift alerts, human review queues, prompt versioning, and an MCP server plus CLI that let coding agents root-cause failures and gate releases.

HoneyHive is an agent observability and evaluation platform for teams running AI agents in production. Its central promise is that agents improve by learning from the real world, so the product is built around a loop of observing live behavior, scoring it against explicit standards, and feeding what was learned back into the agent, its prompts and its test suites. The dashboard visualizes complete traces: every span, tool call, prompt and decision in the order it happened, including long-running trajectories that stretch over hours or days with retries, loops and handoffs between sub-agents. Evaluation is the second half of the story. HoneyHive compares agent versions on the same dataset, turns production failures into reusable test suites, and scores outputs, tool calls and full agent-to-agent interactions with LLM judges, custom code or composite evaluators. Monitors run on live traffic with sampling and thresholds, and alerts fire by email or webhook when a score drifts. Traces can be routed to annotation queues where domain experts rate answers with numeric, binary, categorical or free-form criteria. Policies let an organization define evaluators and monitors once and apply them across every workspace and agent. In practice the workflow extends past diagnosis. HoneyHive ships an MCP server, a CLI and purpose-built skills so a coding agent such as Claude Code can pull failing sessions, cluster spans, write a root-cause report, propose a prompt fix, re-run the eval suite and gate the release from CI through GitHub Actions or PyTest. Screenshots on the site show exactly this loop: an alert, a root cause, a prompt edit, a re-eval, an iteration with claimed quality gains. The target buyer is an enterprise platform or AI engineering team with a deployed agent, not someone prototyping a single prompt. HoneyHive positions itself against generic observability suites and against model-provider dashboards, and it leans on shared, policy-driven workflows for sensitive data environments and multi-team stacks. That makes it a heavier commitment than lightweight tracing tools, and the payoff depends on having real evaluation criteria worth enforcing. Coding-assistant users should note the homepage markets this as developer infrastructure; there is no creative or content-generation function here despite directory categorization noise.