Building an Autonomous CI/CD Log Triage & Self-Healing PR Engine with Python and DuckDB

Description and use

An autonomous CI/CD pipeline using DuckDB and an LLM automatically parses large logs, generates patches, and opens PRs to fix routine failures without manual intervention.

Problem

CI pipelines produce large, cryptic logs that developers ignore, causing recurring failures such as broken lockfiles, misconfigured test fixtures, missing environment variables, or dependency drift.

Outcome

The pipeline reduces manual log triage time, automatically fixes routine failures, and eliminates network egress and vector DB overhead.

Workflow

• Parse raw logs with DuckDB using regex queries
• Filter error frames and extract line numbers
• Cluster repeated log lines by tokenized signature
• Isolate stack trace window around the error line
• Locate offending source file and extract code context
• Prompt LLM with context to generate unified diff
• Validate diff locally, create branch, commit patch, and open PR via GitHub REST API

Evidence

Every growing engineering team hits the same wall: CI pipelines fail, developers ignore cryptic 200MB console outputs, and senior engineers spend 15+ hours every week parsing raw text to fix the same 4 recurring issues—broken lockfiles, misconfigured test fixtures, missing environment variables, or dependency drift.

Source

It parses multi-gigabyte build artifacts using DuckDB, clusters stack traces deterministically without bloated vector stores, prompts an LLM with compact context, and automatically submits self-healing Pull Requests via the GitHub REST API.

Source