Comparison
gpt-researcher vs storm
gpt-researcher (An autonomous agent that conducts deep research on any data using any LLM providers) vs storm (An LLM-powered knowledge curation system that researches a topic and generates full-length reports with citations.) - live GitHub stats and typed graph relationships, not marketing.
Markdown twin · gpt-researcher alternatives · storm alternatives
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Tagline
- 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.
Stars
- gpt-researcher
- 28k
- storm
- 30k
Forks
- gpt-researcher
- 3.8k
- storm
- 2.8k
Open issues
- gpt-researcher
- 210
- storm
- 123
Language
- gpt-researcher
- Python
- storm
- Python
Adopt for
- gpt-researcher
- 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
- STORM is an LLM-powered knowledge curation system that uses agentic-RAG for deep research to generate full-length reports with citations.
Persona
- gpt-researcher
- -
- storm
- -
Runtime
- gpt-researcher
- -
- storm
- -
License
- gpt-researcher
- Apache-2.0
- storm
- MIT
Last pushed
- gpt-researcher
- Jul 5, 2026
- storm
- Sep 30, 2025
Categories
- gpt-researcher
- AI Agents
- storm
- Data & Retrieval, LLM Frameworks
Trust and health
Maintenance
- gpt-researcher
- Very active (96%)
- storm
- Slowing (36%)
Days since push
- gpt-researcher
- 2d
- storm
- 281d
Open issues (now)
- gpt-researcher
- 210
- storm
- 123
Owner type
- gpt-researcher
- User
- storm
- Organization
Security scan
- gpt-researcher
- 62 low (62 low)
- storm
- No criticals
Full report
- gpt-researcher
- Trust report
- storm
- Trust report
Typed relationship
gpt-researcher alternative stormBoth GPT Researcher and STORM aim to conduct large-scale research tasks, generate detailed reports with citations, using LLMs for deep inquiry into a topic.
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.
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.
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 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.
Explore
gpt-researcher trust report →storm trust report →AI Agents category →Data & Retrieval category →LLM Frameworks category →All comparisonsStack workflowsTrending tools
Related comparisons
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.