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Automated model-profile refresh PRs (e.g. #38210) ship a static template body, so a reviewer has to open *Files changed* and read large blocks of generated data to learn what actually moved. Because the underlying profile data is fully structured, we can describe the changes deterministically — no LLM, no hallucination risk. This adds a `langchain-profiles summarize` subcommand that compares the working-tree `_profiles.py` files against a git ref and renders a skimmable Markdown summary: models added (with a short capability descriptor), models removed, and per-field capability changes (context/output tokens, modalities, tool calling, reasoning, etc.), grouped by provider and capped so huge refreshes stay readable. Profiles are read with `ast.literal_eval` rather than imported, so the generated data file is never executed. Example output for a refresh that adds a model and bumps an output limit: ``` ## Summary of changes **1 added · 0 removed · 1 changed** across 1 provider(s). ### openai **➕ 1 added** - `gpt-6-preview` — 1,000,000 ctx, 128,000 out, text+image+audio in, reasoning, tools **✏️ 1 changed** - `gpt-3.5-turbo`: max output tokens 4,096 → 16,384 ``` Made by [Open SWE](https://openswe.vercel.app/agents/9bcbf182-effc-ba9b-0df3-afac620ad152) --------- Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>