--- title: "agentset vs vault-ai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentset-ai-agentset-vs-pashpashpash-vault-ai" tools: ["agentset-ai-agentset", "pashpashpash-vault-ai"] --- # agentset vs vault-ai *GraphCanon updated Aug 23, 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 vault-ai if vault-ai is a tool that gives long-term memory capabilities to ChatGPT by integrating Pinecone Vector Database with an easy-to-use React frontend for uploading various types of files. [agentset](https://agentset.ai) reports 2.1k GitHub stars, 185 forks, and 14 open issues, last pushed Jul 16, 2026. [vault-ai](https://vault.pash.city) has 3.4k stars, 296 forks, and 50 open issues, last pushed Jul 8, 2025. Figures are from public GitHub metadata via [agentset's repository](https://github.com/agentset-ai/agentset) and [vault-ai's repository](https://github.com/pashpashpash/vault-ai). | | [agentset](/tools/agentset-ai-agentset.md) | [vault-ai](/tools/pashpashpash-vault-ai.md) | | --- | --- | --- | | Tagline | The open-source RAG platform with built-in citations and support for deep research | Enhances ChatGPT with long-term memory using Pinecone Vector Database and React frontend for custom knowledge base uploads. | | Stars | 2,066 | 3,387 | | Forks | 185 | 296 | | Open issues | 14 | 50 | | Language | TypeScript | JavaScript | | 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. | vault-ai is a tool that gives long-term memory capabilities to ChatGPT by integrating Pinecone Vector Database with an easy-to-use React frontend for uploading various types of files into the system. | | Persona | - | - | | Runtime | - | - | | License | AgentSet operates under the MIT License, allowing for broad usage and modification rights. | MIT | | Categories | AI Agents, Data & Retrieval | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentset](/tools/agentset-ai-agentset.md) | [vault-ai](/tools/pashpashpash-vault-ai.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 36d | 410d | | Open issues (now) | 14 | 50 | | Stars delta | +31 (30d) | 0 (30d) | | Open issues delta | +1 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/agentset-ai-agentset/trust.md) | [trust report](/tools/pashpashpash-vault-ai/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: vault-ai - **Adopt for:** vault-ai is a tool that gives long-term memory capabilities to ChatGPT by integrating Pinecone Vector Database with an easy-to-use React frontend for uploading various types of files into the system. ## Choose when ### Choose agentset if… - agentset is primarily TypeScript; vault-ai is JavaScript. - 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 AI Agents. - - Use AgentSet when you require deep integration with multiple file types including over 22 supported formats. ### Choose vault-ai if… - vault-ai is primarily JavaScript; agentset is TypeScript. - Tags unique to vault-ai: ai, artificial-intelligence, chatgpt, generative. - Also covers Vector Databases. - When you need a custom knowledge base that can be queried using a generative AI model, such as extending ChatGPT with historical context from uploaded documents in formats like PDFs or txt. ## 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 vault-ai - When your requirements do not include uploading custom content for the AI to learn from, as vault-ai focuses on integrating a knowledge base with ChatGPT. - If you prefer using other vector search databases such as Qdrant instead of Pinecone, as vault-ai is specifically designed around Pinecone. ## Common questions ### What is the difference between agentset and vault-ai? agentset: The open-source RAG platform with built-in citations and support for deep research. vault-ai: Enhances ChatGPT with long-term memory using Pinecone Vector Database and React frontend for custom knowledge base uploads.. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentset over vault-ai? Choose agentset over vault-ai when agentset is primarily TypeScript; vault-ai is JavaScript; 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 AI Agents; - Use AgentSet when you require deep integration with multiple file types including over 22 supported formats. ### When should I choose vault-ai over agentset? Choose vault-ai over agentset when vault-ai is primarily JavaScript; agentset is TypeScript; Tags unique to vault-ai: ai, artificial-intelligence, chatgpt, generative; Also covers Vector Databases; When you need a custom knowledge base that can be queried using a generative AI model, such as extending ChatGPT with historical context from uploaded documents in formats like PDFs or txt. ### 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 vault-ai? When your requirements do not include uploading custom content for the AI to learn from, as vault-ai focuses on integrating a knowledge base with ChatGPT. If you prefer using other vector search databases such as Qdrant instead of Pinecone, as vault-ai is specifically designed around Pinecone. ### Is agentset or vault-ai more popular on GitHub? vault-ai has more GitHub stars (3,387 vs 2,066). Stars measure visibility, not whether either tool fits your constraints. ### Are agentset and vault-ai open source? Yes - both are open-source projects on GitHub (agentset: MIT, vault-ai: MIT). ### Where can I find alternatives to agentset or vault-ai? GraphCanon lists graph-backed alternatives at [agentset alternatives](/tools/agentset-ai-agentset/alternatives) and [vault-ai alternatives](/tools/pashpashpash-vault-ai/alternatives) ([agentset markdown twin](/tools/agentset-ai-agentset/alternatives.md), [vault-ai markdown twin](/tools/pashpashpash-vault-ai/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-pashpashpash-vault-ai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentset or vault-ai? agentset: Steady. vault-ai: Dormant. 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 vault-ai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentset trust report](/tools/agentset-ai-agentset/trust); [vault-ai trust report](/tools/pashpashpash-vault-ai/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/_