---
title: "graphrag-rs vs all-in-rag"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/automataia-graphrag-rs-vs-datawhalechina-all-in-rag"
tools: ["automataia-graphrag-rs", "datawhalechina-all-in-rag"]
---

# graphrag-rs vs all-in-rag

*GraphCanon updated Aug 18, 2026*

## Verdict

Pick graphrag-rs if graphRAG-rs creates knowledge graphs from documents, enables natural language querying with customizable entity extraction and local LLM support, written in Rust; pick all-in-rag if all-in-rag is a comprehensive guide for developers to learn about and implement RAG (Retrieval-Augmented Generation) technology, with a focus on end-to-end practical applications and multi-modal support. It provides an体系.

[graphrag-rs](https://automataia.github.io/graphrag-rs/) reports 522 GitHub stars, 48 forks, and 0 open issues, last pushed Jun 2, 2026. [all-in-rag](https://datawhalechina.github.io/all-in-rag/) has 10k stars, 5.2k forks, and 23 open issues, last pushed Jul 29, 2026. Figures are from public GitHub metadata via [graphrag-rs's repository](https://github.com/automataIA/graphrag-rs) and [all-in-rag's repository](https://github.com/datawhalechina/all-in-rag).

| | [graphrag-rs](/tools/automataia-graphrag-rs.md) | [all-in-rag](/tools/datawhalechina-all-in-rag.md) |
| --- | --- | --- |
| Tagline | GraphRAG-rs implements Graph-based Retrieval Augmented Generation for knowledge graph creation and natural language querying with entity extraction and LLM integration. | 🔍 检索增强生成 (RAG) 技术全栈指南 |
| Stars | 522 | 10,437 |
| Forks | 48 | 5,170 |
| Open issues | 0 | 23 |
| Language | Rust | Python |
| Adopt for | GraphRAG-rs creates knowledge graphs from documents, enables natural language querying with customizable entity extraction and local LLM support, written in Rust. | all-in-rag is a comprehensive guide for developers to learn about and implement RAG (Retrieval-Augmented Generation) technology, with a focus on end-to-end practical applications and multi-modal support. It provides an体系 |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | - |
| Categories | Data & Retrieval, LLM Frameworks | Data & Retrieval, LLM Frameworks |

## Trust and health

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

| | [graphrag-rs](/tools/automataia-graphrag-rs.md) | [all-in-rag](/tools/datawhalechina-all-in-rag.md) |
| --- | --- | --- |
| Maintenance | Steady (60%) | Active (82%) |
| Days since push | 50d | 20d |
| Open issues (now) | 0 | 23 |
| Stars delta | Unknown | +815 (30d) |
| Open issues delta | Unknown | +3 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/automataia-graphrag-rs/trust.md) | [trust report](/tools/datawhalechina-all-in-rag/trust.md) |

## Decision facts: graphrag-rs

- **Adopt for:** GraphRAG-rs creates knowledge graphs from documents, enables natural language querying with customizable entity extraction and local LLM support, written in Rust.

## Decision facts: all-in-rag

- **Adopt for:** all-in-rag is a comprehensive guide for developers to learn about and implement RAG (Retrieval-Augmented Generation) technology, with a focus on end-to-end practical applications and multi-modal support. It provides an体系

## Choose when

### Choose graphrag-rs if…

- graphrag-rs is primarily Rust; all-in-rag is Python.
- Tags unique to graphrag-rs: embeddings, entity-extraction, graphrag, knowledge-graph.
- Need Rust-based implementation for integration into existing Rust projects

### Choose all-in-rag if…

- all-in-rag is primarily Python; graphrag-rs is Rust.
- Tags unique to all-in-rag: embedding, langchain, milvus, multimodal.
- - When you want a comprehensive resource that covers both the theoretical foundations and practical application of RAG.

## When NOT to use graphrag-rs

- Seeking solutions that offer cloud-hosted machine learning services directly
- Projects that demand Python libraries due to ecosystem dependencies

## When NOT to use all-in-rag

- - Avoid if you are looking for a solution that only focuses on theoretical aspects without practical implementation guidance.
- - If your project does not require multi-modal support or is solely focused on text-based applications, more specialized tools might provide better optimization.
- - Not suitable if you're seeking quick prototyping or a light-weight framework; all-in-rag emphasizes comprehensive learning and production-ready practices.

## Common questions

### What is the difference between graphrag-rs and all-in-rag?

graphrag-rs: GraphRAG-rs implements Graph-based Retrieval Augmented Generation for knowledge graph creation and natural language querying with entity extraction and LLM integration.. all-in-rag: 🔍 检索增强生成 (RAG) 技术全栈指南. See the comparison table for live GitHub stats and shared categories.

### When should I choose graphrag-rs over all-in-rag?

Choose graphrag-rs over all-in-rag when graphrag-rs is primarily Rust; all-in-rag is Python; Tags unique to graphrag-rs: embeddings, entity-extraction, graphrag, knowledge-graph; Need Rust-based implementation for integration into existing Rust projects.

### When should I choose all-in-rag over graphrag-rs?

Choose all-in-rag over graphrag-rs when all-in-rag is primarily Python; graphrag-rs is Rust; Tags unique to all-in-rag: embedding, langchain, milvus, multimodal; - When you want a comprehensive resource that covers both the theoretical foundations and practical application of RAG.

### When should I avoid graphrag-rs?

Seeking solutions that offer cloud-hosted machine learning services directly Projects that demand Python libraries due to ecosystem dependencies

### When should I avoid all-in-rag?

- Avoid if you are looking for a solution that only focuses on theoretical aspects without practical implementation guidance. - If your project does not require multi-modal support or is solely focused on text-based applications, more specialized tools might provide better optimization. - Not suitable if you're seeking quick prototyping or a light-weight framework; all-in-rag emphasizes comprehensive learning and production-ready practices.

### Is graphrag-rs or all-in-rag more popular on GitHub?

all-in-rag has more GitHub stars (10,437 vs 522). Stars measure visibility, not whether either tool fits your constraints.

### Are graphrag-rs and all-in-rag open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to graphrag-rs or all-in-rag?

GraphCanon lists graph-backed alternatives at [graphrag-rs alternatives](/tools/automataia-graphrag-rs/alternatives) and [all-in-rag alternatives](/tools/datawhalechina-all-in-rag/alternatives) ([graphrag-rs markdown twin](/tools/automataia-graphrag-rs/alternatives.md), [all-in-rag markdown twin](/tools/datawhalechina-all-in-rag/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/automataia-graphrag-rs-vs-datawhalechina-all-in-rag.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, graphrag-rs or all-in-rag?

graphrag-rs: Steady. all-in-rag: 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 graphrag-rs and all-in-rag?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [graphrag-rs trust report](/tools/automataia-graphrag-rs/trust); [all-in-rag trust report](/tools/datawhalechina-all-in-rag/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=automataia-graphrag-rs`](/api/graphcanon/graph?tool=automataia-graphrag-rs)
- 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/_