---
title: "raft vs llama_index"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/nvidia-raft-vs-run-llama-llama-index"
tools: ["nvidia-raft", "run-llama-llama-index"]
---

# raft vs llama_index

*GraphCanon updated Sep 30, 2026*

## Verdict

Pick raft if rAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications; pick llama_index if llamaIndex is an open-source framework for building AI-powered document processing applications, including RAG and agent applications, with a focus on document parsing and extraction. It offers over 300 integration packs.

[raft](https://docs.rapids.ai/api/raft/stable/) reports 1.0k GitHub stars, 251 forks, and 439 open issues, last pushed Sep 18, 2026. [llama_index](https://developers.llamaindex.ai) has 52k stars, 8.2k forks, and 802 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [raft's repository](https://github.com/NVIDIA/raft) and [llama_index's repository](https://github.com/run-llama/llama_index).

| | [raft](/tools/nvidia-raft.md) | [llama_index](/tools/run-llama-llama-index.md) |
| --- | --- | --- |
| Tagline | A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications. | Document processing platform for AI |
| Stars | 1,042 | 52,207 |
| Forks | 251 | 8,167 |
| Open issues | 439 | 802 |
| Language | Cuda | Python |
| Adopt for | RAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications. | LlamaIndex is an open-source framework for building AI-powered document processing applications, including RAG and agent applications, with a focus on document parsing and extraction. It offers over 300 integration packs |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | MIT |
| Categories | Data & Retrieval, Model Training | AI Agents, Data & Retrieval, Developer Tools, Model Training |

## Trust and health

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

| | [raft](/tools/nvidia-raft.md) | [llama_index](/tools/run-llama-llama-index.md) |
| --- | --- | --- |
| Days since push | 1d | 0d |
| Open issues (now) | 439 | 802 |
| Stars delta | +11 (30d) | +765 (30d) |
| Open issues delta | -5 (30d) | +187 (30d) |
| Full report | [trust report](/tools/nvidia-raft/trust.md) | [trust report](/tools/run-llama-llama-index/trust.md) |

## Shared compatibility

- **Python**: [raft](/tools/nvidia-raft.md) - Python runtime; [llama_index](/tools/run-llama-llama-index.md) - Python runtime

## Decision facts: raft

- **Requirements:** Ensure access to NVIDIA GPUs; Compatibility with CUDA is essential for utilizing the RAFT algorithms effectively.; The user must have familiarity or develop understanding of CUDA programming to optimize their application integration with RAFT.
- **Adopt for:** RAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications.

## Decision facts: llama_index

- **Adopt for:** LlamaIndex is an open-source framework for building AI-powered document processing applications, including RAG and agent applications, with a focus on document parsing and extraction. It offers over 300 integration packs

## Choose when

### Choose raft if…

- raft is primarily Cuda; llama_index is Python.
- License: raft is Apache-2.0, llama_index is MIT.
- Requirements: Ensure access to NVIDIA GPUs; Compatibility with CUDA is essential for utilizing the RAFT algorithms effectively.; The user must have familiarity or develop understanding of CUDA programming to optimize their application integration with RAFT..
- Tags unique to raft: anns, building-blocks, clustering, cuda.
- - You are developing on an NVIDIA GPU architecture and require optimized, CUDA-accelerated primitives.

### Choose llama_index if…

- llama_index is primarily Python; raft is Cuda.
- License: llama_index is MIT, raft is Apache-2.0.
- Tags unique to llama_index: agents, application, data, fine-tuning.
- Also covers AI Agents, Developer Tools.
- When you need a framework that focuses on document parsing and extraction, as LlamaIndex has evolved to prioritize these capabilities

## When NOT to use raft

- - Your application does not have access to NVIDIA GPUs, as RAFT's algorithms leverage CUDA specifically for performance gains.
- - If your workload requires more generalized machine learning libraries without a dependency on GPU-accelerated primitives and you are working in a multi-platform or cross-vendor environment.

## When NOT to use llama_index

- If your project requires enterprise-level features such as agentic OCR, as LlamaIndex's primary focus has shifted towards LlamaParse, an enterprise platform
- When you need a tool that is actively developed and maintained as the primary focus of the company has shifted towards LlamaParse and LiteParse, and the OSS framework is now a secondary focus

## Common questions

### What is the difference between raft and llama_index?

raft: A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications.. llama_index: Document processing platform for AI. See the comparison table for live GitHub stats and shared categories.

### When should I choose raft over llama_index?

Choose raft over llama_index when raft is primarily Cuda; llama_index is Python; License: raft is Apache-2.0, llama_index is MIT; Requirements: Ensure access to NVIDIA GPUs; Compatibility with CUDA is essential for utilizing the RAFT algorithms effectively.; The user must have familiarity or develop understanding of CUDA programming to optimize their application integration with RAFT.; Tags unique to raft: anns, building-blocks, clustering, cuda; - You are developing on an NVIDIA GPU architecture and require optimized, CUDA-accelerated primitives.

### When should I choose llama_index over raft?

Choose llama_index over raft when llama_index is primarily Python; raft is Cuda; License: llama_index is MIT, raft is Apache-2.0; Tags unique to llama_index: agents, application, data, fine-tuning; Also covers AI Agents, Developer Tools; When you need a framework that focuses on document parsing and extraction, as LlamaIndex has evolved to prioritize these capabilities.

### When should I avoid raft?

- Your application does not have access to NVIDIA GPUs, as RAFT's algorithms leverage CUDA specifically for performance gains. - If your workload requires more generalized machine learning libraries without a dependency on GPU-accelerated primitives and you are working in a multi-platform or cross-vendor environment.

### When should I avoid llama_index?

If your project requires enterprise-level features such as agentic OCR, as LlamaIndex's primary focus has shifted towards LlamaParse, an enterprise platform When you need a tool that is actively developed and maintained as the primary focus of the company has shifted towards LlamaParse and LiteParse, and the OSS framework is now a secondary focus

### Is raft or llama_index more popular on GitHub?

llama_index has more GitHub stars (52,207 vs 1,042). Stars measure visibility, not whether either tool fits your constraints.

### Are raft and llama_index open source?

Yes - both are open-source projects on GitHub (raft: Apache-2.0, llama_index: MIT).

### Where can I find alternatives to raft or llama_index?

GraphCanon lists graph-backed alternatives at [raft alternatives](/tools/nvidia-raft/alternatives) and [llama_index alternatives](/tools/run-llama-llama-index/alternatives) ([raft markdown twin](/tools/nvidia-raft/alternatives.md), [llama_index markdown twin](/tools/run-llama-llama-index/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/nvidia-raft-vs-run-llama-llama-index.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, raft or llama_index?

raft: Very active. llama_index: 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 raft and llama_index?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [raft trust report](/tools/nvidia-raft/trust); [llama_index trust report](/tools/run-llama-llama-index/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=nvidia-raft`](/api/graphcanon/graph?tool=nvidia-raft)
- 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/_