Engine across the lifecycle
For each issue, Engine surfaces the contributing traces, proposes a fix, keeps the issue current by attaching new traces that match the same failure pattern, and creates ground truth dataset examples from the production trace inputs.Build: Open a pull request
Apply the proposed fix by opening a pull request in your connected repository. Engine can propose code changes to agents built with Deep Agents, LangChain, and LangGraph.
Test: Generate datasets
Create ground truth dataset examples from production traces for offline evaluation, so you can verify a fix before it ships.
Monitor: Track recurring issues
Scan your tracing projects on a schedule to surface, prioritize, and diagnose recurring issues, and add new matching traces to each issue as they appear.
How Engine runs
Engine scans each connected tracing project on a dynamic schedule tuned to balance cost and performance, clustering and prioritizing issues by severity. It uses LangChain-managed inference and charges in LangChain Standard Units (LSUs). For setup, costs, and the full issue workflow, see Find and fix your agent’s issues. For how Engine handles your data, its GitHub and model subprocessor controls, and its compliance posture, see Engine security. For how Engine runs in a self-hosted deployment, see Engine on self-hosted. To test a deployment with synthetic requests before failures reach production, see Proactively detect issues with Red Teaming.Get started
Set up Engine
Enable Engine for your organization and configure it for a tracing project or agent environment.
Engine notifications
Send detected issues to Slack or to your incident-management, paging, or chat tools through webhooks.
Connect these docs to your agent of choice via MCP for real-time answers.