--- title: "deep-chat vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ovidijusparsiunas-deep-chat-vs-pguso-agents-from-scratch" tools: ["ovidijusparsiunas-deep-chat", "pguso-agents-from-scratch"] --- # deep-chat vs agents-from-scratch *GraphCanon updated Sep 20, 2026* ## Verdict Pick deep-chat if deep-chat is a TypeScript-based component for developing customizable AI chatbots that work with multiple frontend frameworks and integrate various backend models such as HuggingFace and OpenAI; pick agents-from-scratch if agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks. [deep-chat](https://deepchat.dev) reports 3.7k GitHub stars, 455 forks, and 39 open issues, last pushed Sep 18, 2026. [agents-from-scratch](https://github.com/pguso/agents-from-scratch) has 1.0k stars, 251 forks, and 4 open issues, last pushed Jul 25, 2026. Figures are from public GitHub metadata via [deep-chat's repository](https://github.com/OvidijusParsiunas/deep-chat) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [deep-chat](/tools/ovidijusparsiunas-deep-chat.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | Fully customizable AI chatbot component for website integration | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 3,716 | 1,017 | | Forks | 455 | 251 | | Open issues | 39 | 4 | | Language | TypeScript | Python | | Adopt for | deep-chat is a TypeScript-based component for developing customizable AI chatbots that work with multiple frontend frameworks and integrate various backend models such as HuggingFace and OpenAI. | agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. | | Categories | AI Agents | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deep-chat](/tools/ovidijusparsiunas-deep-chat.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 1d | 56d | | Open issues (now) | 39 | 4 | | Stars delta | +17 (30d) | +63 (30d) | | Open issues delta | +3 (30d) | +1 (30d) | | Full report | [trust report](/tools/ovidijusparsiunas-deep-chat/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Decision facts: deep-chat - **Adopt for:** deep-chat is a TypeScript-based component for developing customizable AI chatbots that work with multiple frontend frameworks and integrate various backend models such as HuggingFace and OpenAI. ## Decision facts: agents-from-scratch - **Requirements:** Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs. - **Adopt for:** agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. - **License detail:** MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. ## Choose when ### Choose deep-chat if… - deep-chat is primarily TypeScript; agents-from-scratch is Python. - Tags unique to deep-chat: ai-chatbot, chatgpt, claude, gemini. - Use deep-chat if you are working on a project where front end flexibility is important, given its support for React, Vue, Svelte, and more. ### Choose agents-from-scratch if… - agents-from-scratch is primarily Python; deep-chat is TypeScript. - Requirements: Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs.. - Tags unique to agents-from-scratch: agent-architecture, ai-agents, llm, local-llm. - Also covers Developer Tools. - You plan to teach yourself or others about the fundamentals of creating AI agents from ground zero with complete transparency into each layer of architecture. ## When NOT to use deep-chat - Avoid using deep-chat if project requirements dictate exclusive use of a non-TypeScript backend setup or frontend framework not supported by deep-chat. - Do not use deep-chat when your team lacks TypeScript experience and cannot dedicate time to learn it, as this may slow down development. ## When NOT to use agents-from-scratch - You are working on an application that needs to be deployed quickly. The tool's approach from first principles can be time-consuming compared to using established frameworks. - If you need scalability or cloud capabilities such as easy scaling with demand, this tool will not provide these features since it strictly operates on local infrastructure. ## Common questions ### What is the difference between deep-chat and agents-from-scratch? deep-chat: Fully customizable AI chatbot component for website integration. agents-from-scratch: Build AI agents locally without relying on frameworks or cloud APIs.. See the comparison table for live GitHub stats and shared categories. ### When should I choose deep-chat over agents-from-scratch? Choose deep-chat over agents-from-scratch when deep-chat is primarily TypeScript; agents-from-scratch is Python; Tags unique to deep-chat: ai-chatbot, chatgpt, claude, gemini; Use deep-chat if you are working on a project where front end flexibility is important, given its support for React, Vue, Svelte, and more. ### When should I choose agents-from-scratch over deep-chat? Choose agents-from-scratch over deep-chat when agents-from-scratch is primarily Python; deep-chat is TypeScript; Requirements: Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs.; Tags unique to agents-from-scratch: agent-architecture, ai-agents, llm, local-llm; Also covers Developer Tools; You plan to teach yourself or others about the fundamentals of creating AI agents from ground zero with complete transparency into each layer of architecture. ### When should I avoid deep-chat? Avoid using deep-chat if project requirements dictate exclusive use of a non-TypeScript backend setup or frontend framework not supported by deep-chat. Do not use deep-chat when your team lacks TypeScript experience and cannot dedicate time to learn it, as this may slow down development. ### When should I avoid agents-from-scratch? You are working on an application that needs to be deployed quickly. The tool's approach from first principles can be time-consuming compared to using established frameworks. If you need scalability or cloud capabilities such as easy scaling with demand, this tool will not provide these features since it strictly operates on local infrastructure. ### Is deep-chat or agents-from-scratch more popular on GitHub? deep-chat has more GitHub stars (3,716 vs 1,017). Stars measure visibility, not whether either tool fits your constraints. ### Are deep-chat and agents-from-scratch open source? Yes - both are open-source projects on GitHub (deep-chat: MIT, agents-from-scratch: MIT). ### Where can I find alternatives to deep-chat or agents-from-scratch? GraphCanon lists graph-backed alternatives at [deep-chat alternatives](/tools/ovidijusparsiunas-deep-chat/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([deep-chat markdown twin](/tools/ovidijusparsiunas-deep-chat/alternatives.md), [agents-from-scratch markdown twin](/tools/pguso-agents-from-scratch/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/ovidijusparsiunas-deep-chat-vs-pguso-agents-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deep-chat or agents-from-scratch? deep-chat: Very active. agents-from-scratch: Steady. 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-chat and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deep-chat trust report](/tools/ovidijusparsiunas-deep-chat/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ovidijusparsiunas-deep-chat`](/api/graphcanon/graph?tool=ovidijusparsiunas-deep-chat) - 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/_