--- title: "agentset vs deep-research" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentset-ai-agentset-vs-dzhng-deep-research" tools: ["agentset-ai-agentset", "dzhng-deep-research"] --- # agentset vs deep-research *GraphCanon updated Aug 19, 2026* ## Verdict Pick agentset if agentSet is a Retrieval-Augmented Generation (RAG) platform emphasizing built-in citations and support for deep research. It's designed to handle diverse file formats while ensuring effective memory management; 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. [agentset](https://agentset.ai) reports 2.0k GitHub stars, 183 forks, and 13 open issues, last pushed Jul 16, 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 [agentset's repository](https://github.com/agentset-ai/agentset) and [deep-research's repository](https://github.com/dzhng/deep-research). | | [agentset](/tools/agentset-ai-agentset.md) | [deep-research](/tools/dzhng-deep-research.md) | | --- | --- | --- | | Tagline | The open-source RAG platform with built-in citations and support for deep research | An AI-powered research assistant that refines its topic focus over time using search engines, web scraping, and large language models. | | Stars | 2,035 | 19,571 | | Forks | 183 | 1,993 | | Open issues | 13 | 93 | | Language | TypeScript | TypeScript | | Adopt for | AgentSet is a Retrieval-Augmented Generation (RAG) platform emphasizing built-in citations and support for deep research. It's designed to handle diverse file formats while ensuring effective memory management. | 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 | AgentSet operates under the MIT License, allowing for broad usage and modification rights. | MIT | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentset](/tools/agentset-ai-agentset.md) | [deep-research](/tools/dzhng-deep-research.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 6d | 129d | | Open issues (now) | 13 | 93 | | Stars delta | Unknown | +195 (30d) | | Open issues delta | Unknown | +3 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/agentset-ai-agentset/trust.md) | [trust report](/tools/dzhng-deep-research/trust.md) | ## Decision facts: agentset - **Pricing:** freemium - Free to use as it is open-source. - **Requirements:** Primarily developed in TypeScript.; Best used with an understanding of Retrieval-Augmented Generation and AI agent functionalities. - **Adopt for:** AgentSet is a Retrieval-Augmented Generation (RAG) platform emphasizing built-in citations and support for deep research. It's designed to handle diverse file formats while ensuring effective memory management. - **License detail:** AgentSet operates under the MIT License, allowing for broad usage and modification rights. ## 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 agentset if… - Pricing: Free to use as it is open-source.. - Requirements: Primarily developed in TypeScript.; Best used with an understanding of Retrieval-Augmented Generation and AI agent functionalities.. - Tags unique to agentset: agentic-rag, ai-agents, embeddings, memory-management. - - Use AgentSet when you require deep integration with multiple file types including over 22 supported formats. ### Choose deep-research if… - Requirements: Requires Docker. - Tags unique to deep-research: agent, ai, gpt, o3-mini. - 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 agentset - - Avoid selecting AgentSet if your application does not benefit from or necessitate support for a wide array of file types, as its complexity might overwhelm simpler use-cases. - - If seamless integration with third-party citation services is more preferred, another tool might be better suited since AgentSet focuses on built-in citation capabilities. ## 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 agentset and deep-research? agentset: The open-source RAG platform with built-in citations and support for deep research. 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 agentset over deep-research? Choose agentset over deep-research when Pricing: Free to use as it is open-source.; Requirements: Primarily developed in TypeScript.; Best used with an understanding of Retrieval-Augmented Generation and AI agent functionalities.; Tags unique to agentset: agentic-rag, ai-agents, embeddings, memory-management; - Use AgentSet when you require deep integration with multiple file types including over 22 supported formats. ### When should I choose deep-research over agentset? Choose deep-research over agentset when Requirements: Requires Docker; Tags unique to deep-research: agent, ai, gpt, o3-mini; 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 agentset? - Avoid selecting AgentSet if your application does not benefit from or necessitate support for a wide array of file types, as its complexity might overwhelm simpler use-cases. - If seamless integration with third-party citation services is more preferred, another tool might be better suited since AgentSet focuses on built-in citation capabilities. ### 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 agentset or deep-research more popular on GitHub? deep-research has more GitHub stars (19,571 vs 2,035). Stars measure visibility, not whether either tool fits your constraints. ### Are agentset and deep-research open source? Yes - both are open-source projects on GitHub (agentset: MIT, deep-research: MIT). ### Where can I find alternatives to agentset or deep-research? GraphCanon lists graph-backed alternatives at [agentset alternatives](/tools/agentset-ai-agentset/alternatives) and [deep-research alternatives](/tools/dzhng-deep-research/alternatives) ([agentset markdown twin](/tools/agentset-ai-agentset/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/agentset-ai-agentset-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, agentset or deep-research? agentset: 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 agentset and deep-research? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentset trust report](/tools/agentset-ai-agentset/trust); [deep-research trust report](/tools/dzhng-deep-research/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agentset-ai-agentset`](/api/graphcanon/graph?tool=agentset-ai-agentset) - 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/_