--- title: "rag-fusion vs deep-searcher" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/raudaschl-rag-fusion-vs-zilliztech-deep-searcher" tools: ["raudaschl-rag-fusion", "zilliztech-deep-searcher"] --- # rag-fusion vs deep-searcher *GraphCanon updated Jul 12, 2026* ## Verdict Pick rag-fusion when license: rag-fusion is MIT, deep-searcher is Apache-2.0; pick deep-searcher when license: deep-searcher is Apache-2.0, rag-fusion is MIT. [rag-fusion](https://github.com/Raudaschl/rag-fusion) reports 946 GitHub stars, 113 forks, and 0 open issues, last pushed Apr 26, 2026. [deep-searcher](https://zilliztech.github.io/deep-searcher/) has 7.9k stars, 768 forks, and 53 open issues, last pushed Nov 19, 2025. Figures are from public GitHub metadata via [rag-fusion's repository](https://github.com/Raudaschl/rag-fusion) and [deep-searcher's repository](https://github.com/zilliztech/deep-searcher). | | [rag-fusion](/tools/raudaschl-rag-fusion.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Tagline | RAG-Fusion: multi-query generation + Reciprocal Rank Fusion for better retrieval-augmented generation. Includes evaluation harness with NFCorpus/BEIR. | Open Source Deep Research Alternative to Reason and Search on Private Data. | | Stars | 946 | 7,941 | | Forks | 113 | 768 | | Open issues | 0 | 53 | | Language | Python | Python | | Adopt for | - | DeepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Data & Retrieval, LLM Frameworks, Vector Databases | AI Agents, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [rag-fusion](/tools/raudaschl-rag-fusion.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 75d | 234d | | Open issues (now) | 0 | 53 | | Owner type | User | Organization | | Full report | [trust report](/tools/raudaschl-rag-fusion/trust.md) | [trust report](/tools/zilliztech-deep-searcher/trust.md) | ## Decision facts: deep-searcher - **Adopt for:** DeepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license. ## Choose when ### Choose rag-fusion if… - License: rag-fusion is MIT, deep-searcher is Apache-2.0. - Tags unique to rag-fusion: chromadb, information-retrieval, openai, python. - Also covers Data & Retrieval. ### Choose deep-searcher if… - License: deep-searcher is Apache-2.0, rag-fusion is MIT. - Tags unique to deep-searcher: agent, agentic-rag, deep-research, llm. - Also covers AI Agents. - deep-searcher ships Docker support for self-hosted deployment. - When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3. ## When NOT to use rag-fusion - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - 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 deep-searcher - Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems. - Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily. ## Common questions ### What is the difference between rag-fusion and deep-searcher? rag-fusion: RAG-Fusion: multi-query generation + Reciprocal Rank Fusion for better retrieval-augmented generation. Includes evaluation harness with NFCorpus/BEIR.. deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data.. See the comparison table for live GitHub stats and shared categories. ### When should I choose rag-fusion over deep-searcher? Choose rag-fusion over deep-searcher when License: rag-fusion is MIT, deep-searcher is Apache-2.0; Tags unique to rag-fusion: chromadb, information-retrieval, openai, python; Also covers Data & Retrieval. ### When should I choose deep-searcher over rag-fusion? Choose deep-searcher over rag-fusion when License: deep-searcher is Apache-2.0, rag-fusion is MIT; Tags unique to deep-searcher: agent, agentic-rag, deep-research, llm; Also covers AI Agents; deep-searcher ships Docker support for self-hosted deployment; When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3. ### When should I avoid rag-fusion? Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. 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 deep-searcher? Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems. Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily. ### Is rag-fusion or deep-searcher more popular on GitHub? deep-searcher has more GitHub stars (7,941 vs 946). Stars measure visibility, not whether either tool fits your constraints. ### Are rag-fusion and deep-searcher open source? Yes - both are open-source projects on GitHub (rag-fusion: MIT, deep-searcher: Apache-2.0). ### Where can I find alternatives to rag-fusion or deep-searcher? GraphCanon lists graph-backed alternatives at [rag-fusion alternatives](/tools/raudaschl-rag-fusion/alternatives) and [deep-searcher alternatives](/tools/zilliztech-deep-searcher/alternatives) ([rag-fusion markdown twin](/tools/raudaschl-rag-fusion/alternatives.md), [deep-searcher markdown twin](/tools/zilliztech-deep-searcher/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-zilliztech-deep-searcher.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rag-fusion or deep-searcher? rag-fusion: Steady. deep-searcher: 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 rag-fusion and deep-searcher? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rag-fusion trust report](/tools/raudaschl-rag-fusion/trust); [deep-searcher trust report](/tools/zilliztech-deep-searcher/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/_