--- title: "storm vs deep-searcher" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/stanford-oval-storm-vs-zilliztech-deep-searcher" tools: ["stanford-oval-storm", "zilliztech-deep-searcher"] --- # storm vs deep-searcher Neutral, constraint-first comparison with live GitHub stats. | | [storm](/tools/stanford-oval-storm.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Tagline | An LLM-powered knowledge curation system that researches a topic and generates full-length reports with citations. | Open Source Deep Research Alternative to Reason and Search on Private Data | | Stars | 29,951 | 7,934 | | Forks | 2,802 | 767 | | Open issues | 123 | 53 | | Language | Python | Python | | Adopt for | STORM is an LLM-powered knowledge curation system that uses agentic-RAG for deep research to generate full-length reports with citations. | DeepSearcher is an open-source tool that combines advanced large language models (LLMs) and vector databases to perform search, evaluation, and reasoning based on private data. It provides enterprise knowledge management | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Data & Retrieval, LLM Frameworks | Data & Retrieval, Vector Databases, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [storm](/tools/stanford-oval-storm.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Days since push | 281d | 231d | | Open issues (now) | 123 | 53 | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/stanford-oval-storm/trust.md) | [trust report](/tools/zilliztech-deep-searcher/trust.md) | **Typed relationship:** storm _(alternative)_ deep-searcher These are both open-source deep research tools that aim to offer RAG capabilities, indicating they serve similar purposes with potentially varying functionalities. ## Shared compatibility - **Python**: [storm](/tools/stanford-oval-storm.md) - Python runtime; [deep-searcher](/tools/zilliztech-deep-searcher.md) - Python runtime ## Decision facts: storm - **Adopt for:** STORM is an LLM-powered knowledge curation system that uses agentic-RAG for deep research to generate full-length reports with citations. ## Decision facts: deep-searcher - **Adopt for:** DeepSearcher is an open-source tool that combines advanced large language models (LLMs) and vector databases to perform search, evaluation, and reasoning based on private data. It provides enterprise knowledge management ## Choose when ### Choose storm if… - License: storm is MIT, deep-searcher is Apache-2.0. - These are both open-source deep research tools that aim to offer RAG capabilities, indicating they serve similar purposes with potentially varying functionalities. - Tags unique to storm: large-language-models, report-generation, retrieval-augmented-generation, knowledge-curation. - Also covers LLM Frameworks. - When you need a tool capable of generating comprehensive and cited reports based on deep research. ### Choose deep-searcher if… - License: deep-searcher is Apache-2.0, storm is MIT. - These are both open-source deep research tools that aim to offer RAG capabilities, indicating they serve similar purposes with potentially varying functionalities. - Tags unique to deep-searcher: llm, openai, claude, milvus. - Also covers Vector Databases, AI Agents. - deep-searcher ships Docker support for self-hosted deployment. - - **When you need a flexible embedding option**: DeepSearcher supports multiple embedding models like Milvus for optimal selection. ## When NOT to use storm - Avoid STORM if cost optimization is critical as it may involve using multiple different models to balance between quality and expense. - Do not choose STORM if you require a tool that does not modify its behavior through agentic-RAG processes, which are central to this system’s operation. ## When NOT to use deep-searcher - - **If you require real-time web content integration only**: DeepSearcher primarily focuses on local/private data. Online content integration is possible but not its core functionality. - - **When strict API dependency avoidance is needed**: DeepSearcher often relies on specific APIs (e.g., OpenAI) for LLM services, which might be a constraint in environments strictly avoiding third-党 ## Common questions ### What is the difference between storm and deep-searcher? storm: An LLM-powered knowledge curation system that researches a topic and generates full-length reports with citations.. 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 storm over deep-searcher? Choose storm over deep-searcher when License: storm is MIT, deep-searcher is Apache-2.0; These are both open-source deep research tools that aim to offer RAG capabilities, indicating they serve similar purposes with potentially varying functionalities; Tags unique to storm: large-language-models, report-generation, retrieval-augmented-generation, knowledge-curation; Also covers LLM Frameworks; When you need a tool capable of generating comprehensive and cited reports based on deep research. ### When should I choose deep-searcher over storm? Choose deep-searcher over storm when License: deep-searcher is Apache-2.0, storm is MIT; These are both open-source deep research tools that aim to offer RAG capabilities, indicating they serve similar purposes with potentially varying functionalities; Tags unique to deep-searcher: llm, openai, claude, milvus; Also covers Vector Databases, AI Agents; deep-searcher ships Docker support for self-hosted deployment; - **When you need a flexible embedding option**: DeepSearcher supports multiple embedding models like Milvus for optimal selection. ### When should I avoid storm? Avoid STORM if cost optimization is critical as it may involve using multiple different models to balance between quality and expense. Do not choose STORM if you require a tool that does not modify its behavior through agentic-RAG processes, which are central to this system’s operation. ### When should I avoid deep-searcher? - **If you require real-time web content integration only**: DeepSearcher primarily focuses on local/private data. Online content integration is possible but not its core functionality. - **When strict API dependency avoidance is needed**: DeepSearcher often relies on specific APIs (e.g., OpenAI) for LLM services, which might be a constraint in environments strictly avoiding third-党 ### Is storm or deep-searcher more popular on GitHub? storm has more GitHub stars (29,951 vs 7,934). Stars measure visibility, not whether either tool fits your constraints. ### Are storm and deep-searcher open source? Yes - both are open-source projects on GitHub (storm: MIT, deep-searcher: Apache-2.0). ### Where can I find alternatives to storm or deep-searcher? GraphCanon lists graph-backed alternatives at /tools/stanford-oval-storm/alternatives and /tools/zilliztech-deep-searcher/alternatives (/tools/stanford-oval-storm/alternatives.md, /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 /compare/stanford-oval-storm-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, storm or deep-searcher? storm: Slowing. 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 storm and deep-searcher? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: storm: /tools/stanford-oval-storm/trust; deep-searcher: /tools/zilliztech-deep-searcher/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=stanford-oval-storm`](/api/graphcanon/graph?tool=stanford-oval-storm) - 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/_