Tracing
Both trace whole agent runs. Lemma records one trace per execution with its conversation, span tree, generations and tool calls, and links turns with a thread ID. HoneyHive traces prompts, tool calls, outputs and handoffs, follows long-running trajectories across retries and sub-agents, and is OpenTelemetry-native with a single semantic convention across custom, coding-agent and no-code platforms.
If your agents live inside ServiceNow, Copilot Studio or Agentforce as well as your own code, HoneyHive's coverage is wider.
Evaluation approach
HoneyHive's detection is evaluator-driven. You pick or write evaluators (LLM judges, code, human criteria), sample live traffic, and watch scores. Offline, you run experiments on datasets and compare versions, and evals can run in CI.
Lemma doesn't ask you to choose evaluators first. It analyzes each completed trace against what the agent was told to do and records evidence of behavior that hurt the task. Behavior monitors you describe in words add targeted checks. It has no offline experiments; its evals run on production usage, including an online eval created after a fix.
Alerting
HoneyHive alerts on aggregate thresholds and drift in scores, by email or webhook. That tells you a number moved; you open the traces behind it to learn why.
Lemma alerts follow the issue lifecycle in Slack, with optional Issue Briefs, and through signed webhooks. The alert names the failure pattern and links the traces that support it.
Workflow
HoneyHive is built for a cross-functional organization: annotation queues for domain experts, custom roles, policies applied across workspaces, config-as-code and audit logging. Lemma is built for the engineers who own the agent: issues with status and priority, Linear tickets, and an MCP server that hands a coding agent the evidence.