--- title: "DocsGPT vs deep-research" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/arc53-docsgpt-vs-dzhng-deep-research" tools: ["arc53-docsgpt", "dzhng-deep-research"] --- # DocsGPT vs deep-research *GraphCanon updated Aug 19, 2026* ## Verdict Pick DocsGPT if docsGPT is a private AI platform tailored for building agents, conducting deep research, and enabling enterprise search capabilities; pick deep-research if deep-research is an AI-powered research assistant that leverages search engines, web scraping, and large language models to conduct iterative and in-depth exploration of topics. [DocsGPT](https://app.docsgpt.cloud/) reports 18k GitHub stars, 2.1k forks, and 96 open issues, last pushed Aug 14, 2026. [deep-research](https://github.com/dzhng/deep-research) has 20k stars, 2.0k forks, and 93 open issues, last pushed Apr 11, 2026. Figures are from public GitHub metadata via [DocsGPT's repository](https://github.com/arc53/DocsGPT) and [deep-research's repository](https://github.com/dzhng/deep-research). | | [DocsGPT](/tools/arc53-docsgpt.md) | [deep-research](/tools/dzhng-deep-research.md) | | --- | --- | --- | | Tagline | Private AI platform for agents, assistants and enterprise search. | An AI-powered research assistant that refines its topic focus over time using search engines, web scraping, and large language models. | | Stars | 18,216 | 19,571 | | Forks | 2,122 | 1,993 | | Open issues | 96 | 93 | | Language | Python | TypeScript | | Adopt for | DocsGPT is a private AI platform tailored for building agents, conducting deep research, and enabling enterprise search capabilities. | Deep-research is an AI-powered research assistant that leverages search engines, web scraping, and large language models to conduct iterative and in-depth exploration of topics. | | Persona | - | - | | Runtime | - | - | | License | MIT License - Permits free use for commercial or non-commercial purposes but requires you to include the license text if distributing source code. | MIT | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DocsGPT](/tools/arc53-docsgpt.md) | [deep-research](/tools/dzhng-deep-research.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 129d | | Open issues (now) | 96 | 93 | | Stars delta | +223 (30d) | +195 (30d) | | Open issues delta | +4 (30d) | +3 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/arc53-docsgpt/trust.md) | [trust report](/tools/dzhng-deep-research/trust.md) | ## Decision facts: DocsGPT - **Requirements:** DocsGPT is built using Python and PyTorch, requiring familiarity with these technologies. It supports a wide variety of models but may need customization based. - **Adopt for:** DocsGPT is a private AI platform tailored for building agents, conducting deep research, and enabling enterprise search capabilities. - **License detail:** MIT License - Permits free use for commercial or non-commercial purposes but requires you to include the license text if distributing source code. ## Decision facts: deep-research - **Requirements:** Requires Docker - **Adopt for:** Deep-research is an AI-powered research assistant that leverages search engines, web scraping, and large language models to conduct iterative and in-depth exploration of topics. ## Choose when ### Choose DocsGPT if… - DocsGPT is primarily Python; deep-research is TypeScript. - Requirements: DocsGPT is built using Python and PyTorch, requiring familiarity with these technologies. It supports a wide variety of models but may need customization based.. - Tags unique to DocsGPT: agent-builder, agents, chatgpt, docsgpt. - When you need to build custom AI agents with built-in agent builder capability ### Choose deep-research if… - deep-research is primarily TypeScript; DocsGPT is Python. - Requirements: Requires Docker. - Tags unique to deep-research: agent, gpt, o3-mini, research. - deep-research ships Docker support for self-hosted deployment. - When you need a tool that can refine its topic focus over time through repeated iterations. ## When NOT to use DocsGPT - If your project relies on open-source tools that are not compatible with the specific ecosystem of DocsGPT - When you specifically require real-time collaboration features directly integrated into the tool, as DocsGPT focuses more on agent building and research capabilities rather than live collaborative AI - For projects where a significant emphasis is placed on user-facing search interfaces, as DocsGPT's strength lies more in backend integration and deep research functionalities ## When NOT to use deep-research - When you prefer a language other than TypeScript, as deep-research specifically requires a Node.js environment. - If your use case does not necessitate the use of both Firecrawl and OpenAI APIs, preferring instead solutions with more API flexibility or that do not require API keys. ## Common questions ### What is the difference between DocsGPT and deep-research? DocsGPT: Private AI platform for agents, assistants and enterprise search.. deep-research: An AI-powered research assistant that refines its topic focus over time using search engines, web scraping, and large language models.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DocsGPT over deep-research? Choose DocsGPT over deep-research when DocsGPT is primarily Python; deep-research is TypeScript; Requirements: DocsGPT is built using Python and PyTorch, requiring familiarity with these technologies. It supports a wide variety of models but may need customization based.; Tags unique to DocsGPT: agent-builder, agents, chatgpt, docsgpt; When you need to build custom AI agents with built-in agent builder capability. ### When should I choose deep-research over DocsGPT? Choose deep-research over DocsGPT when deep-research is primarily TypeScript; DocsGPT is Python; Requirements: Requires Docker; Tags unique to deep-research: agent, gpt, o3-mini, research; deep-research ships Docker support for self-hosted deployment; When you need a tool that can refine its topic focus over time through repeated iterations. ### When should I avoid DocsGPT? If your project relies on open-source tools that are not compatible with the specific ecosystem of DocsGPT When you specifically require real-time collaboration features directly integrated into the tool, as DocsGPT focuses more on agent building and research capabilities rather than live collaborative AI For projects where a significant emphasis is placed on user-facing search interfaces, as DocsGPT's strength lies more in backend integration and deep research functionalities ### When should I avoid deep-research? When you prefer a language other than TypeScript, as deep-research specifically requires a Node.js environment. If your use case does not necessitate the use of both Firecrawl and OpenAI APIs, preferring instead solutions with more API flexibility or that do not require API keys. ### Is DocsGPT or deep-research more popular on GitHub? deep-research has more GitHub stars (19,571 vs 18,216). Stars measure visibility, not whether either tool fits your constraints. ### Are DocsGPT and deep-research open source? Yes - both are open-source projects on GitHub (DocsGPT: MIT, deep-research: MIT). ### Where can I find alternatives to DocsGPT or deep-research? GraphCanon lists graph-backed alternatives at [DocsGPT alternatives](/tools/arc53-docsgpt/alternatives) and [deep-research alternatives](/tools/dzhng-deep-research/alternatives) ([DocsGPT markdown twin](/tools/arc53-docsgpt/alternatives.md), [deep-research markdown twin](/tools/dzhng-deep-research/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 [this comparison](/compare/arc53-docsgpt-vs-dzhng-deep-research.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DocsGPT or deep-research? DocsGPT: Very active. deep-research: 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 DocsGPT and deep-research? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DocsGPT trust report](/tools/arc53-docsgpt/trust); [deep-research trust report](/tools/dzhng-deep-research/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=arc53-docsgpt`](/api/graphcanon/graph?tool=arc53-docsgpt) - 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/_