--- title: "awesome-generative-ai vs CodeGeeX" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/steven2358-awesome-generative-ai-vs-zai-org-codegeex" tools: ["steven2358-awesome-generative-ai", "zai-org-codegeex"] --- # awesome-generative-ai vs CodeGeeX *GraphCanon updated Aug 17, 2026* ## Verdict Pick awesome-generative-ai if _awesome-generative-ai_ is a comprehensive resource list focusing on the deployment of Large Language Models (LLMs) locally, aiming to cater to users looking for offline capabilities with feature-rich interfaces; pick CodeGeeX if codeGeeX is an open-source multilingual code generation model, originally developed with MindSpore and compatible with PyTorch through DeepSpeed integration. [awesome-generative-ai](https://github.com/steven2358/awesome-generative-ai) reports 13k GitHub stars, 2.0k forks, and 574 open issues, last pushed Aug 3, 2026. [CodeGeeX](https://codegeex.cn) has 8.8k stars, 688 forks, and 188 open issues, last pushed Aug 13, 2024. Figures are from public GitHub metadata via [awesome-generative-ai's repository](https://github.com/steven2358/awesome-generative-ai) and [CodeGeeX's repository](https://github.com/zai-org/CodeGeeX). | | [awesome-generative-ai](/tools/steven2358-awesome-generative-ai.md) | [CodeGeeX](/tools/zai-org-codegeex.md) | | --- | --- | --- | | Tagline | A curated list of modern Generative Artificial Intelligence projects and services | CodeGeeX is an open multilingual code generation model implemented in Mindspore and available via PyTorch. | | Stars | 12,501 | 8,809 | | Forks | 1,990 | 688 | | Open issues | 574 | 188 | | Language | - | Python | | Adopt for | _awesome-generative-ai_ is a comprehensive resource list focusing on the deployment of Large Language Models (LLMs) locally, aiming to cater to users looking for offline capabilities with feature-rich interfaces. | CodeGeeX is an open-source multilingual code generation model, originally developed with MindSpore and compatible with PyTorch through DeepSpeed integration. | | Persona | - | - | | Runtime | - | - | | License | Licensed under CC0-1.0, which waives all copyright interest in its marked works worldwide. | Apache-2.0 | | Categories | Developer Tools, Inference & Serving, LLM Frameworks | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-generative-ai](/tools/steven2358-awesome-generative-ai.md) | [CodeGeeX](/tools/zai-org-codegeex.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 13d | 719d | | Open issues (now) | 574 | 188 | | Stars delta | +160 (30d) | Unknown | | Open issues delta | +106 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/steven2358-awesome-generative-ai/trust.md) | [trust report](/tools/zai-org-codegeex/trust.md) | ## Shared compatibility - **Python**: [awesome-generative-ai](/tools/steven2358-awesome-generative-ai.md) - Python runtime; [CodeGeeX](/tools/zai-org-codegeex.md) - Python runtime ## Decision facts: awesome-generative-ai - **Requirements:** Min 4 GB RAM - **Adopt for:** _awesome-generative-ai_ is a comprehensive resource list focusing on the deployment of Large Language Models (LLMs) locally, aiming to cater to users looking for offline capabilities with feature-rich interfaces. - **License detail:** Licensed under CC0-1.0, which waives all copyright interest in its marked works worldwide. ## Decision facts: CodeGeeX - **Adopt for:** CodeGeeX is an open-source multilingual code generation model, originally developed with MindSpore and compatible with PyTorch through DeepSpeed integration. ## Choose when ### Choose awesome-generative-ai if… - License: awesome-generative-ai is CC0-1.0, CodeGeeX is Apache-2.0. - Requirements: Min 4 GB RAM. - Tags unique to awesome-generative-ai: ai, artificial-intelligence, awesome-list, generative-ai. - Also covers Developer Tools, Inference & Serving. - - When seeking **offline and comprehensive local deployment options** for large language models that require no internet access ### Choose CodeGeeX if… - License: CodeGeeX is Apache-2.0, awesome-generative-ai is CC0-1.0. - Tags unique to CodeGeeX: ai programming tools, code generation, pretrained-models. - Also covers Model Training. - When you require support for multilingual code generation and your project has a Python-based infrastructure with CUDA GPU availability. ## When NOT to use awesome-generative-ai - - Not recommended if you need real-time online resources and services, as the focus here is on **offline deployment** - - Avoid using it if your project heavily relies on internet-accessible APIs; _awesome-generative-ai_ emphasizes offline operational capabilities ## When NOT to use CodeGeeX - If your development environment lacks the necessary dependencies like Python 3.7+, CUDA 11+, PyTorch 1.10+, and DeepSpeed 0.6+. - In scenarios where an open-source solution is not preferable or when support for exclusively one language's syntax is sufficient. ## Common questions ### What is the difference between awesome-generative-ai and CodeGeeX? awesome-generative-ai: A curated list of modern Generative Artificial Intelligence projects and services. CodeGeeX: CodeGeeX is an open multilingual code generation model implemented in Mindspore and available via PyTorch.. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-generative-ai over CodeGeeX? Choose awesome-generative-ai over CodeGeeX when License: awesome-generative-ai is CC0-1.0, CodeGeeX is Apache-2.0; Requirements: Min 4 GB RAM; Tags unique to awesome-generative-ai: ai, artificial-intelligence, awesome-list, generative-ai; Also covers Developer Tools, Inference & Serving; - When seeking **offline and comprehensive local deployment options** for large language models that require no internet access. ### When should I choose CodeGeeX over awesome-generative-ai? Choose CodeGeeX over awesome-generative-ai when License: CodeGeeX is Apache-2.0, awesome-generative-ai is CC0-1.0; Tags unique to CodeGeeX: ai programming tools, code generation, pretrained-models; Also covers Model Training; When you require support for multilingual code generation and your project has a Python-based infrastructure with CUDA GPU availability. ### When should I avoid awesome-generative-ai? - Not recommended if you need real-time online resources and services, as the focus here is on **offline deployment** - Avoid using it if your project heavily relies on internet-accessible APIs; _awesome-generative-ai_ emphasizes offline operational capabilities ### When should I avoid CodeGeeX? If your development environment lacks the necessary dependencies like Python 3.7+, CUDA 11+, PyTorch 1.10+, and DeepSpeed 0.6+. In scenarios where an open-source solution is not preferable or when support for exclusively one language's syntax is sufficient. ### Is awesome-generative-ai or CodeGeeX more popular on GitHub? awesome-generative-ai has more GitHub stars (12,501 vs 8,809). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-generative-ai and CodeGeeX open source? Yes - both are open-source projects on GitHub (awesome-generative-ai: CC0-1.0, CodeGeeX: Apache-2.0). ### Where can I find alternatives to awesome-generative-ai or CodeGeeX? GraphCanon lists graph-backed alternatives at [awesome-generative-ai alternatives](/tools/steven2358-awesome-generative-ai/alternatives) and [CodeGeeX alternatives](/tools/zai-org-codegeex/alternatives) ([awesome-generative-ai markdown twin](/tools/steven2358-awesome-generative-ai/alternatives.md), [CodeGeeX markdown twin](/tools/zai-org-codegeex/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/steven2358-awesome-generative-ai-vs-zai-org-codegeex.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-generative-ai or CodeGeeX? awesome-generative-ai: Active. CodeGeeX: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for awesome-generative-ai and CodeGeeX? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-generative-ai trust report](/tools/steven2358-awesome-generative-ai/trust); [CodeGeeX trust report](/tools/zai-org-codegeex/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=steven2358-awesome-generative-ai`](/api/graphcanon/graph?tool=steven2358-awesome-generative-ai) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_