REPOGEO REPORT · LITE
codefuse-ai/CodeFuse-CGM
Default branch main · commit 2c12754a · scanned 6/6/2026, 10:03:41 AM
GitHub: 533 stars · 56 forks
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface codefuse-ai/CodeFuse-CGM, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- hightopics#1Add relevant topics to the repository
Why:
COPY-PASTE FIX["llm", "graph-neural-networks", "software-engineering", "code-generation", "code-understanding", "large-language-models", "ai-framework", "neurips"]
- highreadme#2Update README H1 and Introduction's first paragraph to clarify positioning
Why:
COPY-PASTE FIX# CGM: Code Graph LLM - A Graph-Integrated Framework for Repository-Level Software Engineering Tasks ## Contents - [News](#news) - [Introduction](#introduction) - [Installation](#installation) - [Examples](#examples) - [Rewriter](#rewriter) - [Retriever](#retriever) - [Reranker](#reranker) - [Reader](#reader) - [Contributing](#contributing) - [Citation](#citation) - [Join Us](#join-us) ## News 🔥🔥🔥 [2025/09/19] Our paper Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks has been accepted to NeurIPS 2025! 🔥🔥🔥 [2025/01/15] We are pleased to announce the updated version of the CGM-72B-V1.2. The model further achieves a remarkable 44.00% resolve rate on the SWE-Bench-Lite leaderboard. 🔥🔥🔥 [2024/12/28] We are pleased to announce the updated version of the CGM-72B-V1.1. The model further achieves a remarkable 41.67% resolve rate on the SWE-Bench-Lite leaderboard. 🔥🔥🔥 [2024/10/28] We are pleased to announce that CGM-72B achieves a remarkable 35.67% resolve rate on the SWE-Bench-Lite leaderboard. 🔥🔥🔥 [2024/10/28] We released **CGM**, mainly for repository-level coding tasks. - 📜 **Paper**: Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks - 🤖 **Model**: codefuse-ai/CodeFuse-CGM-72B - 📊 **Data**: codefuse-ai/CodeGraph ## Introduction CGM is a cutting-edge **graph-integrated LLM framework** specifically engineered for complex, repository-level software engineering (SE) tasks. It uniquely constructs a comprehensive code graph to provide rich context, enabling advanced capabilities for code generation, rewriting, retrieval, and understanding.
- highlicense#3Add a LICENSE file to the repository
Why:
COPY-PASTE FIXCreate a LICENSE file in the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) and adding its SPDX identifier to the repository's "About" section.
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- AutoGPT · recommended 1×
- MetaGPT · recommended 1×
- LangChain · recommended 1×
- LlamaIndex · recommended 1×
- CrewAI · recommended 1×
- CATEGORY QUERYWhat LLM frameworks are available for automating complex repository-level software engineering tasks?you: not recommendedAI recommended (in order):
- AutoGPT
- MetaGPT
- LangChain
- LlamaIndex
- CrewAI
- OpenDevin
AI recommended 6 alternatives but never named codefuse-ai/CodeFuse-CGM. This is the gap to close.
Show full AI answer
- CATEGORY QUERYSeeking a large language model that leverages graph structures for advanced code understanding and generation.you: not recommendedAI recommended (in order):
- Code Llama
- Neo4j
- ArangoDB
- GPT-4
- Joern
- CodeQL
- DeepMind's AlphaCode 2
- Google's Gemini
- Hugging Face Transformers
- Salesforce/codegen-350M-mono
- bigcode/starcoder
- PyTorch Geometric
- DGL
AI recommended 13 alternatives but never named codefuse-ai/CodeFuse-CGM. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenessfail
Suggestion:
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of codefuse-ai/CodeFuse-CGM?passAI named codefuse-ai/CodeFuse-CGM explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts codefuse-ai/CodeFuse-CGM in production, what risks or prerequisites should they evaluate first?passAI named codefuse-ai/CodeFuse-CGM explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo codefuse-ai/CodeFuse-CGM solve, and who is the primary audience?passAI named codefuse-ai/CodeFuse-CGM explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
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codefuse-ai/CodeFuse-CGM — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite