--- title: "gpt-researcher vs storm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/assafelovic-gpt-researcher-vs-stanford-oval-storm" tools: ["assafelovic-gpt-researcher", "stanford-oval-storm"] --- # gpt-researcher vs storm Neutral, constraint-first comparison with live GitHub stats. | | [gpt-researcher](/tools/assafelovic-gpt-researcher.md) | [storm](/tools/stanford-oval-storm.md) | | --- | --- | --- | | Tagline | An autonomous agent that conducts deep research on any data using any LLM providers | An LLM-powered knowledge curation system that researches a topic and generates full-length reports with citations. | | Stars | 28,146 | 29,951 | | Forks | 3,803 | 2,802 | | Open issues | 210 | 123 | | Language | Python | Python | | Adopt for | GPT Researcher is an open-source deep research agent that conducts thorough and unbiased web or local document analysis, producing comprehensive reports with inline images and detailed citations. It uses a 'planner' and | 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 | AI Agents | Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gpt-researcher](/tools/assafelovic-gpt-researcher.md) | [storm](/tools/stanford-oval-storm.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 2d | 281d | | Open issues (now) | 210 | 123 | | Owner type | User | Organization | | Security scan | 62 low (62 low) | No criticals | | Full report | [trust report](/tools/assafelovic-gpt-researcher/trust.md) | [trust report](/tools/stanford-oval-storm/trust.md) | **Typed relationship:** gpt-researcher _(alternative)_ storm Both GPT Researcher and STORM aim to conduct large-scale research tasks, generate detailed reports with citations, using LLMs for deep inquiry into a topic. ## Decision facts: gpt-researcher - **Requirements:** Min 4 GB RAM; - A Python environment needs to be set up.; - Google Gemini (Nano Banana) integration for AI-generated images requires specific setup and keys. - **Adopt for:** GPT Researcher is an open-source deep research agent that conducts thorough and unbiased web or local document analysis, producing comprehensive reports with inline images and detailed citations. It uses a 'planner' and - **License detail:** Apache-2.0 ## 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 gpt-researcher if… - License: gpt-researcher is Apache-2.0, storm is MIT. - Requirements: Min 4 GB RAM; - A Python environment needs to be set up.; - Google Gemini (Nano Banana) integration for AI-generated images requires specific setup and keys.. - Both GPT Researcher and STORM aim to conduct large-scale research tasks, generate detailed reports with citations, using LLMs for deep inquiry into a topic. - Tags unique to gpt-researcher: llms, deepresearch, ai, python. - Also covers AI Agents. - gpt-researcher ships Docker support for self-hosted deployment. - - You need to generate objective and detailed research reports beyond 2,000 words using both web sources and local documents. ### Choose storm if… - License: storm is MIT, gpt-researcher is Apache-2.0. - Both GPT Researcher and STORM aim to conduct large-scale research tasks, generate detailed reports with citations, using LLMs for deep inquiry into a topic. - Tags unique to storm: large-language-models, report-generation, retrieval-augmented-generation, knowledge-curation. - Also covers Data & Retrieval, LLM Frameworks. - When you need a tool capable of generating comprehensive and cited reports based on deep research. ## When NOT to use gpt-researcher - - Your project requires real-time or interactive research with immediate feedback, as GPT Researcher focuses on in-depth analysis rather than quick responses. - - You are working within a restricted network environment where web scraping is not permitted, since the tool relies heavily on online sources for data gathering. ## 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 gpt-researcher and storm? gpt-researcher: An autonomous agent that conducts deep research on any data using any LLM providers. 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 gpt-researcher over storm? Choose gpt-researcher over storm when License: gpt-researcher is Apache-2.0, storm is MIT; Requirements: Min 4 GB RAM; - A Python environment needs to be set up.; - Google Gemini (Nano Banana) integration for AI-generated images requires specific setup and keys.; Both GPT Researcher and STORM aim to conduct large-scale research tasks, generate detailed reports with citations, using LLMs for deep inquiry into a topic; Tags unique to gpt-researcher: llms, deepresearch, ai, python; Also covers AI Agents; gpt-researcher ships Docker support for self-hosted deployment; - You need to generate objective and detailed research reports beyond 2,000 words using both web sources and local documents. ### When should I choose storm over gpt-researcher? Choose storm over gpt-researcher when License: storm is MIT, gpt-researcher is Apache-2.0; Both GPT Researcher and STORM aim to conduct large-scale research tasks, generate detailed reports with citations, using LLMs for deep inquiry into a topic; Tags unique to storm: large-language-models, report-generation, retrieval-augmented-generation, knowledge-curation; Also covers Data & Retrieval, LLM Frameworks; When you need a tool capable of generating comprehensive and cited reports based on deep research. ### When should I avoid gpt-researcher? - Your project requires real-time or interactive research with immediate feedback, as GPT Researcher focuses on in-depth analysis rather than quick responses. - You are working within a restricted network environment where web scraping is not permitted, since the tool relies heavily on online sources for data gathering. ### 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 gpt-researcher or storm more popular on GitHub? storm has more GitHub stars (29,951 vs 28,146). Stars measure visibility, not whether either tool fits your constraints. ### Are gpt-researcher and storm open source? Yes - both are open-source projects on GitHub (gpt-researcher: Apache-2.0, storm: MIT). ### Where can I find alternatives to gpt-researcher or storm? GraphCanon lists graph-backed alternatives at /tools/assafelovic-gpt-researcher/alternatives and /tools/stanford-oval-storm/alternatives (/tools/assafelovic-gpt-researcher/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/assafelovic-gpt-researcher-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, gpt-researcher or storm? gpt-researcher: Very active. 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 gpt-researcher and storm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpt-researcher: /tools/assafelovic-gpt-researcher/trust; storm: /tools/stanford-oval-storm/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=assafelovic-gpt-researcher`](/api/graphcanon/graph?tool=assafelovic-gpt-researcher) - 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/_