--- title: "databerry vs agent-framework" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gmpetrov-databerry-vs-microsoft-agent-framework" tools: ["gmpetrov-databerry", "microsoft-agent-framework"] --- # databerry vs agent-framework *GraphCanon updated Aug 15, 2026* ## Verdict Pick databerry if suitable for users looking to develop custom LLM agents without coding expertise; pick agent-framework if the agent-framework provides tools for developers to build and deploy AI agents and multi-agent workflows in Python and .NET environments. [databerry](https://chaindesk.ai) reports 3.0k GitHub stars, 420 forks, and 166 open issues, last pushed Jun 17, 2024. [agent-framework](https://aka.ms/agent-framework) has 13k stars, 2.1k forks, and 685 open issues, last pushed Aug 10, 2026. Figures are from public GitHub metadata via [databerry's repository](https://github.com/gmpetrov/databerry) and [agent-framework's repository](https://github.com/microsoft/agent-framework). | | [databerry](/tools/gmpetrov-databerry.md) | [agent-framework](/tools/microsoft-agent-framework.md) | | --- | --- | --- | | Tagline | The no-code platform for building custom LLM Agents | Framework for building and deploying AI agents and multi-agent workflows | | Stars | 2,965 | 12,718 | | Forks | 420 | 2,143 | | Open issues | 166 | 685 | | Language | - | Python | | Adopt for | Suitable for users looking to develop custom LLM agents without coding expertise. | The agent-framework provides tools for developers to build and deploy AI agents and multi-agent workflows in Python and .NET environments. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | 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) | [agent-framework](/tools/microsoft-agent-framework.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 788d | 0d | | Open issues (now) | 166 | 685 | | Stars delta | +4 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/gmpetrov-databerry/trust.md) | [trust report](/tools/microsoft-agent-framework/trust.md) | ## Decision facts: databerry - **Adopt for:** Suitable for users looking to develop custom LLM agents without coding expertise. ## Decision facts: agent-framework - **Requirements:** Python version 3.6 or newer is required for Python installations.; The .NET Core SDK must be installed for utilizing the .NET packages. - **Adopt for:** The agent-framework provides tools for developers to build and deploy AI agents and multi-agent workflows in Python and .NET environments. ## Choose when ### Choose databerry if… - Tags unique to databerry: ai, aichatbot, chatbot, llm. - When you have non-technical team members who need to craft and deploy specific AI chatbot functionalities. - Leaner open-issue backlog (166). ### Choose agent-framework if… - Requirements: Python version 3.6 or newer is required for Python installations.; The .NET Core SDK must be installed for utilizing the .NET packages.. - Tags unique to agent-framework: agent-framework, agentic-ai, agents, multi-agent. - Choose agent-framework if your project requires support for both Python and .NET, allowing you to develop across different ecosystems. ## 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 agent-framework - Avoid using the agent-framework if your team does not have proficiency in either Python or.NET, as this may cause difficulties in leveraging its features effectively. - Do not opt for agent-framework if you only need lightweight support for AI agents without a comprehensive orchestration and deployment framework. ## Common questions ### What is the difference between databerry and agent-framework? databerry: The no-code platform for building custom LLM Agents. agent-framework: Framework for building and deploying AI agents and multi-agent workflows. See the comparison table for live GitHub stats and shared categories. ### When should I choose databerry over agent-framework? Choose databerry over agent-framework when Tags unique to databerry: ai, aichatbot, chatbot, llm; When you have non-technical team members who need to craft and deploy specific AI chatbot functionalities; Leaner open-issue backlog (166). ### When should I choose agent-framework over databerry? Choose agent-framework over databerry when Requirements: Python version 3.6 or newer is required for Python installations.; The .NET Core SDK must be installed for utilizing the .NET packages.; Tags unique to agent-framework: agent-framework, agentic-ai, agents, multi-agent; Choose agent-framework if your project requires support for both Python and .NET, allowing you to develop across different ecosystems. ### 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 agent-framework? Avoid using the agent-framework if your team does not have proficiency in either Python or.NET, as this may cause difficulties in leveraging its features effectively. Do not opt for agent-framework if you only need lightweight support for AI agents without a comprehensive orchestration and deployment framework. ### Is databerry or agent-framework more popular on GitHub? agent-framework has more GitHub stars (12,718 vs 2,965). Stars measure visibility, not whether either tool fits your constraints. ### Are databerry and agent-framework open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to databerry or agent-framework? GraphCanon lists graph-backed alternatives at [databerry alternatives](/tools/gmpetrov-databerry/alternatives) and [agent-framework alternatives](/tools/microsoft-agent-framework/alternatives) ([databerry markdown twin](/tools/gmpetrov-databerry/alternatives.md), [agent-framework markdown twin](/tools/microsoft-agent-framework/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-microsoft-agent-framework.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, databerry or agent-framework? databerry: Dormant. agent-framework: Very 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 agent-framework? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [databerry trust report](/tools/gmpetrov-databerry/trust); [agent-framework trust report](/tools/microsoft-agent-framework/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/_