This Langflow flow implements an issue triage and deduplication pipeline for bug reports.

The workflow starts by ingesting a new bug report (title, description, steps, environment, and optional logs). The system normalizes the text, extracts key signals (component area, suspected root cause, error messages, reproduction steps), and produces a structured representation that is comparable across reports.

A retrieval stage searches an indexed bug backlog (tickets, incident notes, and resolved issues) to find the most similar items. Similarity can be driven by embeddings and semantic search so the system can match reports even when wording differs.

A deduplication decision layer scores candidate matches and classifies the report as duplicate, related, or novel. For duplicates, it recommends linking to the canonical ticket and suggests metadata updates (labels, affected versions, severity signals). For related issues, it creates linkage recommendations to preserve context and prevent fragmented investigations.

For fix proposals, the flow retrieves resolution notes from similar closed bugs and synthesizes a suggested fix approach. The output is grounded in retrieved historical context, so recommendations reflect what previously worked and highlight when evidence is missing.

Finally, structured output components format the results into a consistent triage payload that can be pasted into issue trackers or used as an automated comment: matched ticket links, confidence rationale, recommended actions, and a proposed fix summary.

The flow can be extended to integrate directly with your issue tracker and raise the quality of triage decisions. Add connectors for Jira, Linear, or GitHub Issues to fetch backlogs, write comments, auto-link duplicates, and apply labels. Store structured ticket metadata (component, platform, version, stack trace hashes) to improve matching precision. For stronger governance, add thresholds and review gates so only high-confidence duplicates are auto-closed, while medium-confidence matches are routed to humans. You can also implement an incremental indexing pipeline to keep embeddings fresh and include release notes or changelog context to detect regressions and cluster bugs by release.