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

# rag-fusion vs filco

*GraphCanon updated Sep 20, 2026*

## Verdict

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; pick filco if filco is a Python-based project that focuses on dialog generation, fact verification, and question answering using models like Flan-T5 and LLaMA2, with Dense Passage Retriever for retrieving relevant Wikipedia passages.

[rag-fusion](https://github.com/Raudaschl/rag-fusion) reports 958 GitHub stars, 114 forks, and 0 open issues, last pushed Apr 26, 2026. [filco](https://arxiv.org/pdf/2311.08377.pdf) has 199 stars, 20 forks, and 2 open issues, last pushed Apr 6, 2024. Figures are from public GitHub metadata via [rag-fusion's repository](https://github.com/Raudaschl/rag-fusion) and [filco's repository](https://github.com/zorazrw/filco).

| | [rag-fusion](/tools/raudaschl-rag-fusion.md) | [filco](/tools/zorazrw-filco.md) |
| --- | --- | --- |
| Tagline | multi-query generation + Reciprocal Rank Fusion for retrieval-augmented generation | Learning to Filter Context for Retrieval-Augmented Generation |
| Stars | 958 | 199 |
| Forks | 114 | 20 |
| Open issues | 0 | 2 |
| Language | Python | Python |
| Adopt for | RAG-Fusion leverages multi-query generation and Reciprocal Rank Fusion for enhanced retrieval-augmented generation tasks, supporting evaluations via NFCorpus/BEIR. | Filco is a Python-based project that focuses on dialog generation, fact verification, and question answering using models like Flan-T5 and LLaMA2, with Dense Passage Retriever for retrieving relevant Wikipedia passages. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | CC-BY-SA-4.0 |
| Categories | Data & Retrieval, Evaluation & Observability | Data & Retrieval, Model Training |

## Trust and health

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

| | [rag-fusion](/tools/raudaschl-rag-fusion.md) | [filco](/tools/zorazrw-filco.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 146d | 894d |
| Open issues (now) | 0 | 2 |
| Stars delta | +12 (30d) | +1 (30d) |
| Full report | [trust report](/tools/raudaschl-rag-fusion/trust.md) | [trust report](/tools/zorazrw-filco/trust.md) |

## Shared compatibility

- **Python**: [rag-fusion](/tools/raudaschl-rag-fusion.md) - Python runtime; [filco](/tools/zorazrw-filco.md) - Python runtime

## 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.

## Decision facts: filco

- **Adopt for:** Filco is a Python-based project that focuses on dialog generation, fact verification, and question answering using models like Flan-T5 and LLaMA2, with Dense Passage Retriever for retrieving relevant Wikipedia passages.

## Choose when

### Choose rag-fusion if…

- License: rag-fusion is MIT, filco is CC-BY-SA-4.0.
- Tags unique to rag-fusion: chromadb, information retrieval, openai, python.
- Also covers Evaluation & Observability.
- For enhancing precision in retrieval-augmented generation tasks needing complex query processing

### Choose filco if…

- License: filco is CC-BY-SA-4.0, rag-fusion is MIT.
- Tags unique to filco: dialog-generation, fact-verification, flan-t5, llama2.
- Also covers Model Training.
- When you need to generate dialogues that require fact verification and question answering capabilities, leveraging models like Flan-T5 and LLaMA2.

## 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

## When NOT to use filco

- If your project does not require the use of Flan-T5 or LLaMA2 models for dialog generation, fact verification, or question answering.
- When you do not need to retrieve relevant Wikipedia passages using Dense Passage Retriever.
- If your project does not involve the use of preprocessed datasets with top-5 retrieved passages from Wikipedia.

## Common questions

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

rag-fusion: multi-query generation + Reciprocal Rank Fusion for retrieval-augmented generation. filco: Learning to Filter Context for Retrieval-Augmented Generation. See the comparison table for live GitHub stats and shared categories.

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

Choose rag-fusion over filco when License: rag-fusion is MIT, filco is CC-BY-SA-4.0; Tags unique to rag-fusion: chromadb, information retrieval, openai, python; Also covers Evaluation & Observability; For enhancing precision in retrieval-augmented generation tasks needing complex query processing.

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

Choose filco over rag-fusion when License: filco is CC-BY-SA-4.0, rag-fusion is MIT; Tags unique to filco: dialog-generation, fact-verification, flan-t5, llama2; Also covers Model Training; When you need to generate dialogues that require fact verification and question answering capabilities, leveraging models like Flan-T5 and LLaMA2.

### 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

### When should I avoid filco?

If your project does not require the use of Flan-T5 or LLaMA2 models for dialog generation, fact verification, or question answering. When you do not need to retrieve relevant Wikipedia passages using Dense Passage Retriever. If your project does not involve the use of preprocessed datasets with top-5 retrieved passages from Wikipedia.

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

rag-fusion has more GitHub stars (958 vs 199). Stars measure visibility, not whether either tool fits your constraints.

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

Yes - both are open-source projects on GitHub (rag-fusion: MIT, filco: CC-BY-SA-4.0).

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

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

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

rag-fusion: Slowing. filco: 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 rag-fusion and filco?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=raudaschl-rag-fusion`](/api/graphcanon/graph?tool=raudaschl-rag-fusion)
- 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/_