Comparison
deep-research vs storm
deep-research (An AI-powered research assistant that performs iterative, deep research on any topic) 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 · deep-research alternatives · storm alternatives
GraphCanon updated today
vs
Tagline
- deep-research
- An AI-powered research assistant that performs iterative, deep research on any topic
- storm
- An LLM-powered knowledge curation system that researches a topic and generates full-length reports with citations.
Stars
- deep-research
- 19k
- storm
- 30k
Forks
- deep-research
- 2.0k
- storm
- 2.8k
Open issues
- deep-research
- 90
- storm
- 123
Language
- deep-research
- TypeScript
- storm
- Python
Adopt for
- deep-research
- Deep Research is an AI-powered research assistant that efficiently dives into any topic by leveraging search engines, web scraping, and large language models. It iteratively refines its research direction over time to go
- storm
- STORM is an LLM-powered knowledge curation system that uses agentic-RAG for deep research to generate full-length reports with citations.
Persona
- deep-research
- -
- storm
- -
Runtime
- deep-research
- -
- storm
- -
License
- deep-research
- MIT
- storm
- MIT
Last pushed
- deep-research
- Apr 11, 2026
- storm
- Sep 30, 2025
Categories
- deep-research
- AI Agents, Data & Retrieval
- storm
- Data & Retrieval, LLM Frameworks
Trust and health
Maintenance
- deep-research
- Steady (60%)
- storm
- Slowing (36%)
Days since push
- deep-research
- 87d
- storm
- 281d
Open issues (now)
- deep-research
- 90
- storm
- 123
Owner type
- deep-research
- User
- storm
- Organization
Security scan
- deep-research
- 39 low (39 low)
- storm
- No criticals
Full report
- deep-research
- Trust report
- storm
- Trust report
Typed relationship
deep-research alternative stormBoth aim at performing iterative and deep research using AI-powered methods, indicating a competitive relationship in the domain of AI-assisted research.
Choose deep-research if…
- deep-research is primarily TypeScript; storm is Python.
- Requirements: Min 2 GB RAM; Requires Docker; Requires Node.js environment setup.; Needs specific API keys for third-party web search (Firecrawl) and language model services (OpenAI)..
- Both aim at performing iterative and deep research using AI-powered methods, indicating a competitive relationship in the domain of AI-assisted research.
- Tags unique to deep-research: research, ai, o3-mini, gpt.
- Also covers AI Agents.
- deep-research ships Docker support for self-hosted deployment.
- You require a detailed and comprehensive report on a specific topic where traditional manual or less refined automated tools are too basic.
When NOT to use deep-research
- If your project requires proprietary or classified data analysis because Deep Research relies on public web scraping and search engines, which limits access to non-public content.
- You are looking for a tool that operates solely offline; since the tool needs internet access to perform its tasks through API calls to services like Firecrawl and OpenAI.
Choose storm if…
- storm is primarily Python; deep-research is TypeScript.
- Both aim at performing iterative and deep research using AI-powered methods, indicating a competitive relationship in the domain of AI-assisted research.
- 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 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
deep-research 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 deep-research and storm?
- deep-research: An AI-powered research assistant that performs iterative, deep research on any topic. 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 deep-research over storm?
- Choose deep-research over storm when deep-research is primarily TypeScript; storm is Python; Requirements: Min 2 GB RAM; Requires Docker; Requires Node.js environment setup.; Needs specific API keys for third-party web search (Firecrawl) and language model services (OpenAI).; Both aim at performing iterative and deep research using AI-powered methods, indicating a competitive relationship in the domain of AI-assisted research; Tags unique to deep-research: research, ai, o3-mini, gpt; Also covers AI Agents; deep-research ships Docker support for self-hosted deployment; You require a detailed and comprehensive report on a specific topic where traditional manual or less refined automated tools are too basic.
- When should I choose storm over deep-research?
- Choose storm over deep-research when storm is primarily Python; deep-research is TypeScript; Both aim at performing iterative and deep research using AI-powered methods, indicating a competitive relationship in the domain of AI-assisted research; 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 avoid deep-research?
- If your project requires proprietary or classified data analysis because Deep Research relies on public web scraping and search engines, which limits access to non-public content. You are looking for a tool that operates solely offline; since the tool needs internet access to perform its tasks through API calls to services like Firecrawl and OpenAI.
- 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 deep-research or storm more popular on GitHub?
- storm has more GitHub stars (29,951 vs 19,312). Stars measure visibility, not whether either tool fits your constraints.
- Are deep-research and storm open source?
- Yes - both are open-source projects on GitHub (deep-research: MIT, storm: MIT).
- Where can I find alternatives to deep-research or storm?
- GraphCanon lists graph-backed alternatives at /tools/dzhng-deep-research/alternatives and /tools/stanford-oval-storm/alternatives (/tools/dzhng-deep-research/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/dzhng-deep-research-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, deep-research or storm?
- deep-research: Steady. 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 deep-research and storm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: deep-research: /tools/dzhng-deep-research/trust; storm: /tools/stanford-oval-storm/trust.