Invisible loops
Your agent retried 11 times before you noticed. Traces show each step.
Agent observability
Traces, token cost, and tool calls for every session. Locally, in the IDE.
Reimagined for agents: ask your data analyst agent, plot charts, build dashboards — all locally — and share them with your team.
Built to speed up innovation. Fully reproducible, with one-click publish and deploy the entire agent.
Fastest tool to build agentic software: author agent packs and skills, exercise them on any agent engine in the UI or CLI, then hand off — teammates install in one click from your library or the store.
Store catalog pulls curated agent packs your org can adopt without rebuilding the same glue.
Your agent retried 11 times before you noticed. Traces show each step.
Token cost per session, per step. Know before the invoice arrives.
Tool calls, prompt sizes, model choices. Visible, not guessed.
Every tool call, every model response, every token. Locally stored via OTEL.
Ask for charts in plain language. DuckDB queries on your trace data.
Claude, Codex, ChatGPT, Cursor, LiteLLM. Same traces format across all.
Traces stay on your machine. Your data, your storage, no third-party pipeline.
Visibility tells you the loop burned $300. Control stops it at $5. In governed deployments, every agent carries a hard budget enforced before the model call — the request past the cap is refused, and every dollar attributes to the user who asked. That's the agent control plane: the same trace data you debug with, promoted to enforcement.
Agents are ML models. They drift, costs change, providers update. Monitoring is not a one-time setup.
Get notified when agent spend exceeds your threshold.
Shared cost and trace visibility across your whole team. See who's spending what, on which agents.
Priority help when agents behave unexpectedly or costs spike.