---
title: "aquila vs rag-fusion"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/aquila-network-aquila-vs-raudaschl-rag-fusion"
tools: ["aquila-network-aquila", "raudaschl-rag-fusion"]
---

# aquila vs rag-fusion

*GraphCanon updated Aug 23, 2026*

## Verdict

Pick aquila if aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches; pick rag-fusion if rAG-Fusion leverages multi-query generation and Reciprocal Rank Fusion for enhanced retrieval-augmented generation tasks, supporting evaluations via NFCorpus/BEIR.

[aquila](https://aquila.network) reports 379 GitHub stars, 26 forks, and 13 open issues, last pushed May 6, 2024. [rag-fusion](https://github.com/Raudaschl/rag-fusion) has 952 stars, 115 forks, and 0 open issues, last pushed Apr 26, 2026. Figures are from public GitHub metadata via [aquila's repository](https://github.com/Aquila-Network/aquila) and [rag-fusion's repository](https://github.com/Raudaschl/rag-fusion).

| | [aquila](/tools/aquila-network-aquila.md) | [rag-fusion](/tools/raudaschl-rag-fusion.md) |
| --- | --- | --- |
| Tagline | Efficient Neural Search Engine | multi-query generation + Reciprocal Rank Fusion for retrieval-augmented generation |
| Stars | 379 | 952 |
| Forks | 26 | 115 |
| Open issues | 13 | 0 |
| Language | HTML | Python |
| Adopt for | Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. | RAG-Fusion leverages multi-query generation and Reciprocal Rank Fusion for enhanced retrieval-augmented generation tasks, supporting evaluations via NFCorpus/BEIR. |
| Persona | - | - |
| Runtime | - | - |
| License | - | MIT |
| Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Evaluation & Observability |

## Trust and health

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

| | [aquila](/tools/aquila-network-aquila.md) | [rag-fusion](/tools/raudaschl-rag-fusion.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 817d | 118d |
| Open issues (now) | 13 | 0 |
| Stars delta | Unknown | +6 (30d) |
| Open issues delta | Unknown | 0 (30d) |
| Owner type | Organization | User |
| Full report | [trust report](/tools/aquila-network-aquila/trust.md) | [trust report](/tools/raudaschl-rag-fusion/trust.md) |

## Decision facts: aquila

- **Adopt for:** Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches.

## Decision facts: rag-fusion

- **Adopt for:** RAG-Fusion leverages multi-query generation and Reciprocal Rank Fusion for enhanced retrieval-augmented generation tasks, supporting evaluations via NFCorpus/BEIR.

## Choose when

### Choose aquila if…

- aquila is primarily HTML; rag-fusion is Python.
- Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors.
- Also covers Vector Databases.
- When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary

### Choose rag-fusion if…

- rag-fusion is primarily Python; aquila is HTML.
- Tags unique to rag-fusion: chromadb, openai, python, rag-fusion.
- Also covers Evaluation & Observability.
- For enhancing precision in retrieval-augmented generation tasks needing complex query processing

## When NOT to use aquila

- If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration
- In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide

## When NOT to use rag-fusion

- If you require real-time performance, as multi-query generation may introduce latency
- In scenarios where only simple keyword-based search suffices without the need for advanced fusion techniques

## Common questions

### What is the difference between aquila and rag-fusion?

aquila: Efficient Neural Search Engine. rag-fusion: multi-query generation + Reciprocal Rank Fusion for retrieval-augmented generation. See the comparison table for live GitHub stats and shared categories.

### When should I choose aquila over rag-fusion?

Choose aquila over rag-fusion when aquila is primarily HTML; rag-fusion is Python; Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors; Also covers Vector Databases; When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary.

### When should I choose rag-fusion over aquila?

Choose rag-fusion over aquila when rag-fusion is primarily Python; aquila is HTML; Tags unique to rag-fusion: chromadb, openai, python, rag-fusion; Also covers Evaluation & Observability; For enhancing precision in retrieval-augmented generation tasks needing complex query processing.

### When should I avoid aquila?

If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide

### When should I avoid rag-fusion?

If you require real-time performance, as multi-query generation may introduce latency In scenarios where only simple keyword-based search suffices without the need for advanced fusion techniques

### Is aquila or rag-fusion more popular on GitHub?

rag-fusion has more GitHub stars (952 vs 379). Stars measure visibility, not whether either tool fits your constraints.

### Are aquila and rag-fusion open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to aquila or rag-fusion?

GraphCanon lists graph-backed alternatives at [aquila alternatives](/tools/aquila-network-aquila/alternatives) and [rag-fusion alternatives](/tools/raudaschl-rag-fusion/alternatives) ([aquila markdown twin](/tools/aquila-network-aquila/alternatives.md), [rag-fusion markdown twin](/tools/raudaschl-rag-fusion/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/aquila-network-aquila-vs-raudaschl-rag-fusion.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, aquila or rag-fusion?

aquila: Dormant. rag-fusion: Slowing. 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 aquila and rag-fusion?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aquila trust report](/tools/aquila-network-aquila/trust); [rag-fusion trust report](/tools/raudaschl-rag-fusion/trust).

---

**Machine-readable endpoints**

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