$ loading_
Analyze AI agent traces to diagnose failures and recommend actionable improvements.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "agent-trace-intelligence" yet — see the docs or source repo.
Analyze this AI agent trace, identify the failed steps and root causes, and provide prioritized fixes: {trace_data}A report with failed step identification, root-cause analysis, and a prioritized fix list.
Evaluate the agent using this set of traces, including success rate, common error patterns, and measurable optimization opportunities: {trace_dataset}A performance summary with scoring, key failure patterns, and trackable improvement metrics.
Compare these agent traces from before and after the fix, determine whether the issue improved, and summarize remaining risks: {before_traces} {after_traces}A before-and-after comparison showing fix impact, remaining issues, and next-step recommendations.
Analyze local-first AI agent CLI usage patterns and invocation data.
Track AI agent market intelligence, rankings, trust signals, and liveness.
Fetch and analyze LangSmith traces to debug LangChain and LangGraph agents.
Profile AI agents locally to trace costs, behavior issues, and config fixes.
Create tamper-evident audit trails and observability records for AI agents.
Systematically debug failing AI agents with capture, diagnosis, recovery, and reports.