--- title: "DeepResearch vs storm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alibaba-nlp-deepresearch-vs-stanford-oval-storm" tools: ["alibaba-nlp-deepresearch", "stanford-oval-storm"] --- # DeepResearch vs storm Neutral, constraint-first comparison with live GitHub stats. | | [DeepResearch](/tools/alibaba-nlp-deepresearch.md) | [storm](/tools/stanford-oval-storm.md) | | --- | --- | --- | | Tagline | Tongyi Deep Research, the Leading Open-source Deep Research Agent | An LLM-powered knowledge curation system that researches a topic and generates full-length reports with citations. | | Stars | 19,621 | 29,951 | | Forks | 1,500 | 2,802 | | Open issues | 91 | 123 | | Language | Python | Python | | Adopt for | DeepResearch is an agentic large language model designed with a focus on long-horizon, deep information-seeking tasks, making it particularly suitable for users needing advanced search capabilities. It comes with 30.5B+3 | STORM is an LLM-powered knowledge curation system that uses agentic-RAG for deep research to generate full-length reports with citations. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks, AI Agents | Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepResearch](/tools/alibaba-nlp-deepresearch.md) | [storm](/tools/stanford-oval-storm.md) | | --- | --- | --- | | Days since push | 130d | 281d | | Open issues (now) | 91 | 123 | | Security scan | 104 low (104 low) | No criticals | | Full report | [trust report](/tools/alibaba-nlp-deepresearch/trust.md) | [trust report](/tools/stanford-oval-storm/trust.md) | **Typed relationship:** DeepResearch _(alternative)_ storm Both platforms are built for deep research using LLMs, with similar goals in mind but likely differing in implementation and features. ## Decision facts: DeepResearch - **Pricing:** unknown - No specific pricing info provided; deployment could vary depending on your cloud service provider or if you self-host on local servers. - **Requirements:** Min 16 GB RAM; Requires substantial computational resources due to its size and complexity.; Recommended for users with access to robust hardware infrastructure, either through cloud services like Aliyun's Bailian or local deployment. - **Adopt for:** DeepResearch is an agentic large language model designed with a focus on long-horizon, deep information-seeking tasks, making it particularly suitable for users needing advanced search capabilities. It comes with 30.5B+3 ## 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. ## Choose when ### Choose DeepResearch if… - License: DeepResearch is Apache-2.0, storm is MIT. - Pricing: No specific pricing info provided; deployment could vary depending on your cloud service provider or if you self-host on local servers.. - Requirements: Min 16 GB RAM; Requires substantial computational resources due to its size and complexity.; Recommended for users with access to robust hardware infrastructure, either through cloud services like Aliyun's Bailian or local deployment.. - Both platforms are built for deep research using LLMs, with similar goals in mind but likely differing in implementation and features. - Tags unique to DeepResearch: llm, artificial-intelligence, alibaba, web-agent. - Also covers AI Agents. - When you need to perform sophisticated long-term horizon tasks that require in-depth information seeking. ### Choose storm if… - License: storm is MIT, DeepResearch is Apache-2.0. - Both platforms are built for deep research using LLMs, with similar goals in mind but likely differing in implementation and features. - Tags unique to storm: large-language-models, report-generation, retrieval-augmented-generation, knowledge-curation. - Also covers Data & Retrieval. - When you need a tool capable of generating comprehensive and cited reports based on deep research. ## When NOT to use DeepResearch - Avoid using it for tasks requiring quick responses as it might suffer from slower response times due to its complex architecture designed for deep information-seeking. - Not suitable for less demanding or simpler tasks where smaller and more efficient models can perform adequately without the overhead of DeepResearch's extensive capability. ## 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. ## Common questions ### What is the difference between DeepResearch and storm? DeepResearch: Tongyi Deep Research, the Leading Open-source Deep Research Agent. storm: An LLM-powered knowledge curation system that researches a topic and generates full-length reports with citations.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepResearch over storm? Choose DeepResearch over storm when License: DeepResearch is Apache-2.0, storm is MIT; Pricing: No specific pricing info provided; deployment could vary depending on your cloud service provider or if you self-host on local servers.; Requirements: Min 16 GB RAM; Requires substantial computational resources due to its size and complexity.; Recommended for users with access to robust hardware infrastructure, either through cloud services like Aliyun's Bailian or local deployment.; Both platforms are built for deep research using LLMs, with similar goals in mind but likely differing in implementation and features; Tags unique to DeepResearch: llm, artificial-intelligence, alibaba, web-agent; Also covers AI Agents; When you need to perform sophisticated long-term horizon tasks that require in-depth information seeking. ### When should I choose storm over DeepResearch? Choose storm over DeepResearch when License: storm is MIT, DeepResearch is Apache-2.0; Both platforms are built for deep research using LLMs, with similar goals in mind but likely differing in implementation and features; Tags unique to storm: large-language-models, report-generation, retrieval-augmented-generation, knowledge-curation; Also covers Data & Retrieval; When you need a tool capable of generating comprehensive and cited reports based on deep research. ### When should I avoid DeepResearch? Avoid using it for tasks requiring quick responses as it might suffer from slower response times due to its complex architecture designed for deep information-seeking. Not suitable for less demanding or simpler tasks where smaller and more efficient models can perform adequately without the overhead of DeepResearch's extensive capability. ### 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. ### Is DeepResearch or storm more popular on GitHub? storm has more GitHub stars (29,951 vs 19,621). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepResearch and storm open source? Yes - both are open-source projects on GitHub (DeepResearch: Apache-2.0, storm: MIT). ### Where can I find alternatives to DeepResearch or storm? GraphCanon lists graph-backed alternatives at /tools/alibaba-nlp-deepresearch/alternatives and /tools/stanford-oval-storm/alternatives (/tools/alibaba-nlp-deepresearch/alternatives.md, /tools/stanford-oval-storm/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/alibaba-nlp-deepresearch-vs-stanford-oval-storm.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepResearch or storm? DeepResearch: Slowing. storm: 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 DeepResearch and storm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepResearch: /tools/alibaba-nlp-deepresearch/trust; storm: /tools/stanford-oval-storm/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alibaba-nlp-deepresearch`](/api/graphcanon/graph?tool=alibaba-nlp-deepresearch) - 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/_