Documentation Index
Fetch the complete documentation index at: https://docs.tracelit.io/llms.txt
Use this file to discover all available pages before exploring further.
AI resolution turns production errors into proposed fixes automatically. When something breaks, Tracelit captures full context (stack trace, impacted users, and linked replay), analyzes your code, writes a minimal patch, and opens a PR for review.
How it works
Detection
Tracelit captures the error, stack trace, affected users, and linked session replay.
Analysis
AI reads the relevant code paths and pinpoints the likely root cause.
The fix
Tracelit generates the smallest safe diff needed to resolve the issue.
Pull request
A PR opens with root-cause summary, exact diff, confidence score, and replay link.
Auto-merge (optional)
If CI passes and confidence meets your threshold, Tracelit can auto-merge; otherwise it waits for your review.
Tracelit does not push directly to main by default. It opens a PR on a fresh branch first.
Why this is useful for vibe coders
If you ship fast with tools like Lovable, Cursor, Bolt, Replit, or v0, debugging prod regressions can eat your time. AI resolution removes that loop by turning incidents into ready-to-review PRs quickly.
Use this AI prompt
Set up Tracelit AI resolution for my project.
Requirements:
1) Connect error tracking to GitHub PR workflow.
2) Keep branch protection and CI checks enforced.
3) Configure confidence threshold and auto-merge policy (default: manual review).
4) Ensure every generated PR includes root cause summary + replay link.
5) Show the exact setup steps and files changed.
6) Provide a test plan: trigger one error and verify Detection → Analysis → PR.
Learn more
For product details and examples of the full Detection → Analysis → Fix → PR flow, see the AI resolutions page: Tracelit AI resolutions.
