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Context layer: qwen3-repo

GitHub | PyPI

Ingests a GitHub repository into Qwen3.6's context window with dependency graph ordering, importance ranking, multimodal asset detection, and benchmark parity against Qwen's NL2Repo numbers.

Why ordering matters for linear attention

Gated DeltaNet processes tokens sequentially with a fixed-size recurrent state. Putting high-dependency files late in context (after their dependents) means the model sees call sites before definitions. Nobody has published an ordering strategy for Qwen3.6's hybrid attention architecture. qwen3-repo orders files by dependency graph so definitions come before their callers.

Install

pip install qwen3-repo

Usage

from qwen3_repo import ingest_repo

context_pack, files, budget = ingest_repo(
    "https://github.com/user/repo",
    model="Qwen3-32B",
)
print(f"{len(files)} files, ~{budget.pack_budget:,} tokens")

What it does

  • Dependency graph ingestion: git clone, parse imports, topological sort
  • Importance ranking: recently changed files, import count, test coverage
  • Vision asset detection: scans for images/mockups, sets --language-model-only accordingly
  • Agentic scaffold: OpenAI-compatible loop with bash + file-edit tools
  • Eval runners: NL2Repo and SWE-bench Verified reproducers against Qwen's published numbers

Context on benchmarks

Qwen3.6's NL2Repo evaluations were run via Claude Code with temp=1.0, top_p=0.95, max_turns=900. Both the 27B and 35B-A3B model cards still note: "NL2Repo: others are evaluated via Claude Code." No neutral third-party scaffold exists for running these evals independently. qwen3-repo is that scaffold.