--- title: "agentset vs DeepResearch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentset-ai-agentset-vs-alibaba-nlp-deepresearch" tools: ["agentset-ai-agentset", "alibaba-nlp-deepresearch"] --- # agentset vs DeepResearch *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 DeepResearch if deepResearch is an open-source AI research agent specialized for information-seeking tasks, built with Python and licensed under Apache-2.0. [agentset](https://agentset.ai) reports 2.0k GitHub stars, 183 forks, and 13 open issues, last pushed Jul 16, 2026. [DeepResearch](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/) has 20k stars, 1.5k forks, and 92 open issues, last pushed Feb 27, 2026. Figures are from public GitHub metadata via [agentset's repository](https://github.com/agentset-ai/agentset) and [DeepResearch's repository](https://github.com/Alibaba-NLP/DeepResearch). | | [agentset](/tools/agentset-ai-agentset.md) | [DeepResearch](/tools/alibaba-nlp-deepresearch.md) | | --- | --- | --- | | Tagline | The open-source RAG platform with built-in citations and support for deep research | Tongyi Deep Research, the Leading Open-source Deep Research Agent | | Stars | 2,035 | 19,846 | | Forks | 183 | 1,510 | | Open issues | 13 | 92 | | Language | TypeScript | Python | | 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. | DeepResearch is an open-source AI research agent specialized for information-seeking tasks, built with Python and licensed under Apache-2.0. | | Persona | - | - | | Runtime | - | - | | License | AgentSet operates under the MIT License, allowing for broad usage and modification rights. | Apache-2.0 | | Categories | AI Agents, Data & Retrieval | AI Agents, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentset](/tools/agentset-ai-agentset.md) | [DeepResearch](/tools/alibaba-nlp-deepresearch.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 6d | 172d | | Open issues (now) | 13 | 92 | | Stars delta | Unknown | +161 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/agentset-ai-agentset/trust.md) | [trust report](/tools/alibaba-nlp-deepresearch/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: DeepResearch - **Adopt for:** DeepResearch is an open-source AI research agent specialized for information-seeking tasks, built with Python and licensed under Apache-2.0. ## Choose when ### Choose agentset if… - agentset is primarily TypeScript; DeepResearch is Python. - License: agentset is MIT, DeepResearch is Apache-2.0. - 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. - Also covers Data & Retrieval. - - Use AgentSet when you require deep integration with multiple file types including over 22 supported formats. ### Choose DeepResearch if… - DeepResearch is primarily Python; agentset is TypeScript. - License: DeepResearch is Apache-2.0, agentset is MIT. - Tags unique to DeepResearch: agent, alibaba, artificial-intelligence, deep-research. - Also covers Inference & Serving. - When you need a tool backed by Alibaba’s ecosystem that offers a robust environment for conducting deep research using AI techniques. ## 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 DeepResearch - Avoid DeepResearch if you require specialized features that are not covered under its deep research and information-seeking scope. - Do not use this tool if your project necessitates proprietary solutions, as the open-source nature might be a constraint in such scenarios. ## Common questions ### What is the difference between agentset and DeepResearch? agentset: The open-source RAG platform with built-in citations and support for deep research. DeepResearch: Tongyi Deep Research, the Leading Open-source Deep Research Agent. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentset over DeepResearch? Choose agentset over DeepResearch when agentset is primarily TypeScript; DeepResearch is Python; License: agentset is MIT, DeepResearch is Apache-2.0; 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; Also covers Data & Retrieval; - Use AgentSet when you require deep integration with multiple file types including over 22 supported formats. ### When should I choose DeepResearch over agentset? Choose DeepResearch over agentset when DeepResearch is primarily Python; agentset is TypeScript; License: DeepResearch is Apache-2.0, agentset is MIT; Tags unique to DeepResearch: agent, alibaba, artificial-intelligence, deep-research; Also covers Inference & Serving; When you need a tool backed by Alibaba’s ecosystem that offers a robust environment for conducting deep research using AI techniques. ### 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 DeepResearch? Avoid DeepResearch if you require specialized features that are not covered under its deep research and information-seeking scope. Do not use this tool if your project necessitates proprietary solutions, as the open-source nature might be a constraint in such scenarios. ### Is agentset or DeepResearch more popular on GitHub? DeepResearch has more GitHub stars (19,846 vs 2,035). Stars measure visibility, not whether either tool fits your constraints. ### Are agentset and DeepResearch open source? Yes - both are open-source projects on GitHub (agentset: MIT, DeepResearch: Apache-2.0). ### Where can I find alternatives to agentset or DeepResearch? GraphCanon lists graph-backed alternatives at [agentset alternatives](/tools/agentset-ai-agentset/alternatives) and [DeepResearch alternatives](/tools/alibaba-nlp-deepresearch/alternatives) ([agentset markdown twin](/tools/agentset-ai-agentset/alternatives.md), [DeepResearch markdown twin](/tools/alibaba-nlp-deepresearch/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-alibaba-nlp-deepresearch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentset or DeepResearch? agentset: Very active. DeepResearch: 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 DeepResearch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentset trust report](/tools/agentset-ai-agentset/trust); [DeepResearch trust report](/tools/alibaba-nlp-deepresearch/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/_