--- title: "databerry vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gmpetrov-databerry-vs-pguso-agents-from-scratch" tools: ["gmpetrov-databerry", "pguso-agents-from-scratch"] --- # databerry vs agents-from-scratch *GraphCanon updated Aug 15, 2026* ## Verdict Pick databerry if suitable for users looking to develop custom LLM agents without coding expertise; 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 or cloud dependencies. [databerry](https://chaindesk.ai) reports 3.0k GitHub stars, 420 forks, and 166 open issues, last pushed Jun 17, 2024. [agents-from-scratch](https://github.com/pguso/agents-from-scratch) has 954 stars, 240 forks, and 3 open issues, last pushed Jul 25, 2026. Figures are from public GitHub metadata via [databerry's repository](https://github.com/gmpetrov/databerry) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [databerry](/tools/gmpetrov-databerry.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | The no-code platform for building custom LLM Agents | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 2,965 | 954 | | Forks | 420 | 240 | | Open issues | 166 | 3 | | Language | - | Python | | Adopt for | Suitable for users looking to develop custom LLM agents without coding expertise. | 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 License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. | | Categories | AI Agents, Developer Tools | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [databerry](/tools/gmpetrov-databerry.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 788d | 18d | | Open issues (now) | 166 | 3 | | Stars delta | +4 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/gmpetrov-databerry/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Decision facts: databerry - **Adopt for:** Suitable for users looking to develop custom LLM agents without coding expertise. ## 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 databerry if… - Tags unique to databerry: ai, aichatbot, chatbot, no-code. - When you have non-technical team members who need to craft and deploy specific AI chatbot functionalities. - More GitHub stars (3.0k vs 954) - visibility, not fit. ### Choose agents-from-scratch if… - 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, local-llm, no-framework. - 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 databerry - If you are a seasoned developer looking for customizable control over agent functions beyond no-code capabilities. - In scenarios requiring integration with complex, non-standard APIs or systems that cannot be managed on a no-code platform. ## 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 databerry and agents-from-scratch? databerry: The no-code platform for building custom LLM Agents. 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 databerry over agents-from-scratch? Choose databerry over agents-from-scratch when Tags unique to databerry: ai, aichatbot, chatbot, no-code; When you have non-technical team members who need to craft and deploy specific AI chatbot functionalities; More GitHub stars (3.0k vs 954) - visibility, not fit. ### When should I choose agents-from-scratch over databerry? Choose agents-from-scratch over databerry when 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, local-llm, no-framework; 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 databerry? If you are a seasoned developer looking for customizable control over agent functions beyond no-code capabilities. In scenarios requiring integration with complex, non-standard APIs or systems that cannot be managed on a no-code platform. ### 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 databerry or agents-from-scratch more popular on GitHub? databerry has more GitHub stars (2,965 vs 954). Stars measure visibility, not whether either tool fits your constraints. ### Are databerry and agents-from-scratch open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to databerry or agents-from-scratch? GraphCanon lists graph-backed alternatives at [databerry alternatives](/tools/gmpetrov-databerry/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([databerry markdown twin](/tools/gmpetrov-databerry/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/gmpetrov-databerry-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, databerry or agents-from-scratch? databerry: Dormant. agents-from-scratch: Active. 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 databerry and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [databerry trust report](/tools/gmpetrov-databerry/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gmpetrov-databerry`](/api/graphcanon/graph?tool=gmpetrov-databerry) - 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/_