---
title: "awesome-embedding-models vs awesome-LLM-resources"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/hironsan-awesome-embedding-models-vs-wangrongsheng-awesome-llm-resources"
tools: ["hironsan-awesome-embedding-models", "wangrongsheng-awesome-llm-resources"]
---

# awesome-embedding-models vs awesome-LLM-resources

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick awesome-embedding-models when license: awesome-embedding-models is MIT, awesome-LLM-resources is Apache-2.0; pick awesome-LLM-resources when license: awesome-LLM-resources is Apache-2.0, awesome-embedding-models is MIT.

[awesome-embedding-models](https://github.com/Hironsan/awesome-embedding-models) reports 1.8k GitHub stars, 249 forks, and 3 open issues, last pushed Apr 7, 2019. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.7k stars, 924 forks, and 39 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [awesome-embedding-models's repository](https://github.com/Hironsan/awesome-embedding-models) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources).

| | [awesome-embedding-models](/tools/hironsan-awesome-embedding-models.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) |
| --- | --- | --- |
| Tagline | A curated list of awesome embedding models tutorials, projects and communities. | 🧑🚀 全世界最好的LLM资料总结（多模态生成、Agent、辅助编程、AI审稿、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型） | Summary of the world's best LLM resources. |
| Stars | 1,843 | 8,668 |
| Forks | 249 | 924 |
| Open issues | 3 | 39 |
| Language | Jupyter Notebook | - |
| Adopt for | - | awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | Apache-2.0 |
| Categories | Vector Databases | Vector Databases, LLM Frameworks, AI Agents |

## Trust and health

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

| | [awesome-embedding-models](/tools/hironsan-awesome-embedding-models.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 2651d | 1d |
| Open issues (now) | 3 | 39 |
| Full report | [trust report](/tools/hironsan-awesome-embedding-models/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) |

## Decision facts: awesome-LLM-resources

- **Adopt for:** awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a

## Choose when

### Choose awesome-embedding-models if…

- License: awesome-embedding-models is MIT, awesome-LLM-resources is Apache-2.0.
- Tags unique to awesome-embedding-models: embedding-models, awesome, embeddings, machine-learning.
- Leaner open-issue backlog (3).

### Choose awesome-LLM-resources if…

- License: awesome-LLM-resources is Apache-2.0, awesome-embedding-models is MIT.
- Tags unique to awesome-LLM-resources: llama, mistral, llm, course.
- Also covers LLM Frameworks, AI Agents.
- - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.

## When NOT to use awesome-embedding-models

- Last GitHub push was 2652 days ago (dormant maintenance, Apr 7, 2019). Validate activity before betting a new project on awesome-embedding-models.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

## When NOT to use awesome-LLM-resources

- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage.
- - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.

## Common questions

### What is the difference between awesome-embedding-models and awesome-LLM-resources?

awesome-embedding-models: A curated list of awesome embedding models tutorials, projects and communities.. awesome-LLM-resources: 🧑🚀 全世界最好的LLM资料总结（多模态生成、Agent、辅助编程、AI审稿、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型） | Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.

### When should I choose awesome-embedding-models over awesome-LLM-resources?

Choose awesome-embedding-models over awesome-LLM-resources when License: awesome-embedding-models is MIT, awesome-LLM-resources is Apache-2.0; Tags unique to awesome-embedding-models: embedding-models, awesome, embeddings, machine-learning; Leaner open-issue backlog (3).

### When should I choose awesome-LLM-resources over awesome-embedding-models?

Choose awesome-LLM-resources over awesome-embedding-models when License: awesome-LLM-resources is Apache-2.0, awesome-embedding-models is MIT; Tags unique to awesome-LLM-resources: llama, mistral, llm, course; Also covers LLM Frameworks, AI Agents; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.

### When should I avoid awesome-embedding-models?

Last GitHub push was 2652 days ago (dormant maintenance, Apr 7, 2019). Validate activity before betting a new project on awesome-embedding-models. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

### When should I avoid awesome-LLM-resources?

- Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.

### Is awesome-embedding-models or awesome-LLM-resources more popular on GitHub?

awesome-LLM-resources has more GitHub stars (8,668 vs 1,843). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-embedding-models and awesome-LLM-resources open source?

Yes - both are open-source projects on GitHub (awesome-embedding-models: MIT, awesome-LLM-resources: Apache-2.0).

### Where can I find alternatives to awesome-embedding-models or awesome-LLM-resources?

GraphCanon lists graph-backed alternatives at [awesome-embedding-models alternatives](/tools/hironsan-awesome-embedding-models/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([awesome-embedding-models markdown twin](/tools/hironsan-awesome-embedding-models/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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/hironsan-awesome-embedding-models-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, awesome-embedding-models or awesome-LLM-resources?

awesome-embedding-models: Dormant. awesome-LLM-resources: Very active. 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-embedding-models and awesome-LLM-resources?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-embedding-models trust report](/tools/hironsan-awesome-embedding-models/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=hironsan-awesome-embedding-models`](/api/graphcanon/graph?tool=hironsan-awesome-embedding-models)
- 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/_