HoneyHive

At its core, HoneyHive provides distributed tracing capabilities, allowing you to see inside any agent, on any framework, anywhere. By instrumenting agents with OpenTelemetry, it creates a shared source of truth for debugging agent behavior, working across over 100 LLMs and agent frameworks. Online evaluation features enable live detection of failures across agents, while session replays in the Playground allow for detailed review of chat interactions. Advanced filtering, grouping, graph, and timeline views help debug complex multi-agent systems, complemented by user feedback capture for implicit and explicit signals.

Monitoring and alerts are continuously managed at scale. HoneyHive evaluates live traces, monitors real-world user feedback, and alerts on critical failure modes. Online evaluation can leverage LLM-as-a-judge or custom code. Alerts and drift detection proactively identify agent failures before users do, with automations to add failing prompts to datasets or trigger human review. Custom dashboards provide quick insights into key metrics, and exploratory analytics help discover patterns across millions of traces. Root-cause analysis is enhanced with AI, giving coding agents context to fix issues.

Confidence in shipping changes is boosted by offline evaluations. Production failures can be transformed into test suites, new changes compared against baselines, and regressions caught before every release. Experiments allow offline testing against large datasets, with centralized dataset management and custom evaluators. Human review by domain experts is facilitated, and regression detection identifies critical issues during iteration. CI/CD integration enables automated test suites over every commit.

Shaping agent quality is achieved through annotation queues, bringing domain experts into the loop to review edge cases and define quality. Queue automation routes flagged traces to the right reviewers, and human review is conducted in a user-friendly interface. Custom rubrics standardize review with business-specific criteria, and dataset curation offers Git-native versioning. An audit trail captures expert feedback alongside trace context, and evaluator alignment uses feedback to refine LLM evaluators with subject matter experts.

HoneyHive is built on open standards and an open ecosystem, being OpenTelemetry-native and framework-agnostic. It integrates with any model, framework, or agent runtime supporting OTel instrumentation. Engineered for scale and privacy by default, it handles sensitive AI trace I/O. HoneyHive is trusted by Fortune 500 enterprises for scaling AI agents responsibly. The platform offers virtual data planes for logical isolation, with SaaS, Hybrid, and Self-hosted deployment options. Security features include granular RBAC, SSO & SAML integration, and compliance with SOC 2, GDPR, and HIPAA. Audit logging ensures every action is trackable.