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AI Pipeline Uses LangGraph and Git History to Enrich GitHub Issues with Context

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Engineers at large software organizations often spend significant time manually reconstructing context before they can begin solving a technical issue. A newly described AI-driven pipeline addresses this by automatically gathering verified context from GitHub issues, commit histories, and repository diffs. The system uses a vector database built from over 1,500 existing GitHub issues, an LLM for semantic ranking, and LangGraph to orchestrate a series of discrete, stateful processing nodes. Git history and runbooks serve as sources of truth, ensuring the model interprets rather than replaces factual data. The result is a reproducible, step-by-step workflow that reduces the time engineers spend restoring context before tackling a problem.

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AI Pipeline Uses LangGraph and Git History to Enrich GitHub Issues with Context · ShortSingh