--- title: "databerry vs beehive" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gmpetrov-databerry-vs-muesli-beehive" tools: ["gmpetrov-databerry", "muesli-beehive"] --- # databerry vs beehive *GraphCanon updated Sep 20, 2026* ## Verdict Pick databerry if suitable for users looking to develop custom LLM agents without coding expertise; pick beehive if beehive, an event-driven and agent-based automation framework in Go under AGPL-3.0 license, supports complex workflows via Docker and Ansible. [databerry](https://chaindesk.ai) reports 3.0k GitHub stars, 415 forks, and 166 open issues, last pushed Jun 17, 2024. [beehive](https://github.com/muesli/beehive) has 6.5k stars, 327 forks, and 119 open issues, last pushed Feb 25, 2023. Figures are from public GitHub metadata via [databerry's repository](https://github.com/gmpetrov/databerry) and [beehive's repository](https://github.com/muesli/beehive). | | [databerry](/tools/gmpetrov-databerry.md) | [beehive](/tools/muesli-beehive.md) | | --- | --- | --- | | Tagline | The no-code platform for building custom LLM Agents | A flexible event/agent & automation system with lots of bees | | Stars | 2,966 | 6,490 | | Forks | 415 | 327 | | Open issues | 166 | 119 | | Language | - | Go | | Adopt for | Suitable for users looking to develop custom LLM agents without coding expertise. | Beehive, an event-driven and agent-based automation framework in Go under AGPL-3.0 license, supports complex workflows via Docker and Ansible. | | Persona | - | - | | Runtime | - | - | | License | - | AGPL-3.0 | | 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) | [beehive](/tools/muesli-beehive.md) | | --- | --- | --- | | Days since push | 824d | 1298d | | Open issues (now) | 166 | 119 | | Stars delta | +1 (30d) | +5 (30d) | | Full report | [trust report](/tools/gmpetrov-databerry/trust.md) | [trust report](/tools/muesli-beehive/trust.md) | ## Decision facts: databerry - **Adopt for:** Suitable for users looking to develop custom LLM agents without coding expertise. ## Decision facts: beehive - **Adopt for:** Beehive, an event-driven and agent-based automation framework in Go under AGPL-3.0 license, supports complex workflows via Docker and Ansible. ## 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. - More recently updated (last pushed Jun 17, 2024). ### Choose beehive if… - Tags unique to beehive: automation, event-driven-architecture, workflow-management. - beehive ships Docker support for self-hosted deployment. - You prioritize Go for development and need a flexible tool supporting Docker and Ansible deployment. ## 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 beehive - If your project demands proprietary licensing, avoiding AGPL-3.0 is essential. - For projects requiring languages other than Go for automation frameworks. ## Common questions ### What is the difference between databerry and beehive? databerry: The no-code platform for building custom LLM Agents. beehive: A flexible event/agent & automation system with lots of bees. See the comparison table for live GitHub stats and shared categories. ### When should I choose databerry over beehive? Choose databerry over beehive 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; More recently updated (last pushed Jun 17, 2024). ### When should I choose beehive over databerry? Choose beehive over databerry when Tags unique to beehive: automation, event-driven-architecture, workflow-management; beehive ships Docker support for self-hosted deployment; You prioritize Go for development and need a flexible tool supporting Docker and Ansible deployment. ### 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 beehive? If your project demands proprietary licensing, avoiding AGPL-3.0 is essential. For projects requiring languages other than Go for automation frameworks. ### Is databerry or beehive more popular on GitHub? beehive has more GitHub stars (6,490 vs 2,966). Stars measure visibility, not whether either tool fits your constraints. ### Are databerry and beehive open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to databerry or beehive? GraphCanon lists graph-backed alternatives at [databerry alternatives](/tools/gmpetrov-databerry/alternatives) and [beehive alternatives](/tools/muesli-beehive/alternatives) ([databerry markdown twin](/tools/gmpetrov-databerry/alternatives.md), [beehive markdown twin](/tools/muesli-beehive/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-muesli-beehive.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, databerry or beehive? databerry: Dormant. beehive: 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 databerry and beehive? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [databerry trust report](/tools/gmpetrov-databerry/trust); [beehive trust report](/tools/muesli-beehive/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/_