---
title: "Awesome-AIGC-Tutorials vs LLMSurvey"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/luban-agi-awesome-aigc-tutorials-vs-rucaibox-llmsurvey"
tools: ["luban-agi-awesome-aigc-tutorials", "rucaibox-llmsurvey"]
---

# Awesome-AIGC-Tutorials vs LLMSurvey

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick Awesome-AIGC-Tutorials if awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry; pick LLMSurvey if lLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning.

[Awesome-AIGC-Tutorials](https://github.com/luban-agi/Awesome-AIGC-Tutorials) reports 4.5k GitHub stars, 298 forks, and 10 open issues, last pushed Mar 31, 2024. [LLMSurvey](https://arxiv.org/abs/2303.18223) has 12k stars, 930 forks, and 30 open issues, last pushed Mar 11, 2025. Figures are from public GitHub metadata via [Awesome-AIGC-Tutorials's repository](https://github.com/luban-agi/Awesome-AIGC-Tutorials) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey).

| | [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) |
| --- | --- | --- |
| Tagline | Curated tutorials and resources for Large Language Models, AI Painting, and more | A collection of papers and resources related to Large Language Models |
| Stars | 4,547 | 12,216 |
| Forks | 298 | 930 |
| Open issues | 10 | 30 |
| Language | - | Python |
| Adopt for | Awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry. | LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT license allows for free use in both open-source and proprietary products, with attribution required to the authors. | The license information for LLMSurvey is unknown. |
| Categories | Developer Tools, LLM Frameworks, Model Training | LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) |
| --- | --- | --- |
| Days since push | 902d | 555d |
| Open issues (now) | 10 | 30 |
| Stars delta | +25 (30d) | +11 (30d) |
| Full report | [trust report](/tools/luban-agi-awesome-aigc-tutorials/trust.md) | [trust report](/tools/rucaibox-llmsurvey/trust.md) |

## Shared compatibility

- **ChatGPT**: [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) - Works with ChatGPT; [LLMSurvey](/tools/rucaibox-llmsurvey.md) - Works with ChatGPT

## Decision facts: Awesome-AIGC-Tutorials

- **Requirements:** No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial.
- **Adopt for:** Awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry.
- **License detail:** MIT license allows for free use in both open-source and proprietary products, with attribution required to the authors.

## Decision facts: LLMSurvey

- **Pricing:** freemium - The repository is free to access, but the Chinese book is available for purchase.
- **Requirements:** Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.
- **Adopt for:** LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning.
- **License detail:** The license information for LLMSurvey is unknown.

## Choose when

### Choose Awesome-AIGC-Tutorials if…

- Requirements: No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial..
- Tags unique to Awesome-AIGC-Tutorials: ai, aigc, deep-learning, midjourney.
- Also covers Developer Tools.
- If you aim to deepen your understanding of prompt engineering for models like MidJourney or Stable Diffusion, this repository offers focused tutorials and resources.

### Choose LLMSurvey if…

- Pricing: The repository is free to access, but the Chinese book is available for purchase..
- Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book..
- Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, large-language-models.
- Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning.

## When NOT to use Awesome-AIGC-Tutorials

- Avoid if you are looking for a one-stop-shop coding platform, as Awesome-AIGC-Tutorials provides theoretical knowledge and tutorials rather than practical code samples.
- Not suitable if your focus is solely on the commercial deployment of large language models; this repository does not cover market-specific insights or competitive analysis.

## When NOT to use LLMSurvey

- Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys.
- Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources.

## Common questions

### What is the difference between Awesome-AIGC-Tutorials and LLMSurvey?

Awesome-AIGC-Tutorials: Curated tutorials and resources for Large Language Models, AI Painting, and more. LLMSurvey: A collection of papers and resources related to Large Language Models. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-AIGC-Tutorials over LLMSurvey?

Choose Awesome-AIGC-Tutorials over LLMSurvey when Requirements: No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial.; Tags unique to Awesome-AIGC-Tutorials: ai, aigc, deep-learning, midjourney; Also covers Developer Tools; If you aim to deepen your understanding of prompt engineering for models like MidJourney or Stable Diffusion, this repository offers focused tutorials and resources.

### When should I choose LLMSurvey over Awesome-AIGC-Tutorials?

Choose LLMSurvey over Awesome-AIGC-Tutorials when Pricing: The repository is free to access, but the Chinese book is available for purchase.; Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.; Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, large-language-models; Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning.

### When should I avoid Awesome-AIGC-Tutorials?

Avoid if you are looking for a one-stop-shop coding platform, as Awesome-AIGC-Tutorials provides theoretical knowledge and tutorials rather than practical code samples. Not suitable if your focus is solely on the commercial deployment of large language models; this repository does not cover market-specific insights or competitive analysis.

### When should I avoid LLMSurvey?

Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys. Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources.

### Is Awesome-AIGC-Tutorials or LLMSurvey more popular on GitHub?

LLMSurvey has more GitHub stars (12,216 vs 4,547). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-AIGC-Tutorials and LLMSurvey open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to Awesome-AIGC-Tutorials or LLMSurvey?

GraphCanon lists graph-backed alternatives at [Awesome-AIGC-Tutorials alternatives](/tools/luban-agi-awesome-aigc-tutorials/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([Awesome-AIGC-Tutorials markdown twin](/tools/luban-agi-awesome-aigc-tutorials/alternatives.md), [LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/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/luban-agi-awesome-aigc-tutorials-vs-rucaibox-llmsurvey.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Awesome-AIGC-Tutorials or LLMSurvey?

Awesome-AIGC-Tutorials: Dormant. LLMSurvey: 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-AIGC-Tutorials and LLMSurvey?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AIGC-Tutorials trust report](/tools/luban-agi-awesome-aigc-tutorials/trust); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=luban-agi-awesome-aigc-tutorials`](/api/graphcanon/graph?tool=luban-agi-awesome-aigc-tutorials)
- 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/_