--- title: "databerry vs agent-starter-pack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gmpetrov-databerry-vs-googlecloudplatform-agent-starter-pack" tools: ["gmpetrov-databerry", "googlecloudplatform-agent-starter-pack"] --- # databerry vs agent-starter-pack *GraphCanon updated Jul 12, 2026* ## Verdict Pick databerry when tags unique to databerry: ai, aichatbot, chatbot, chatbots; pick agent-starter-pack when requirements: Requires additional software installation: Google Cloud SDK, Terraform for deployment, Make for development tasks.. [databerry](https://chaindesk.ai) reports 3.0k GitHub stars, 422 forks, and 166 open issues, last pushed Jun 17, 2024. [agent-starter-pack](http://goo.gle/agents-cli) has 6.5k stars, 1.5k forks, and 48 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [databerry's repository](https://github.com/gmpetrov/databerry) and [agent-starter-pack's repository](https://github.com/GoogleCloudPlatform/agent-starter-pack). | | [databerry](/tools/gmpetrov-databerry.md) | [agent-starter-pack](/tools/googlecloudplatform-agent-starter-pack.md) | | --- | --- | --- | | Tagline | The no-code platform for building custom LLM Agents | Ship AI Agents to Google Cloud in minutes, not months. Production-ready templates with built-in CI/CD, evaluation, and observability. | | Stars | 2,960 | 6,514 | | Forks | 422 | 1,496 | | Open issues | 166 | 48 | | Language | - | Python | | Adopt for | - | agent-starter-pack is a specialized toolset for deploying AI agents on the Google Cloud Platform with built-in CI/CD, evaluation tools, and observability features. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | AI Agents, LLM Frameworks | AI Agents, Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [databerry](/tools/gmpetrov-databerry.md) | [agent-starter-pack](/tools/googlecloudplatform-agent-starter-pack.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 753d | 0d | | Open issues (now) | 166 | 48 | | Owner type | User | Organization | | Full report | [trust report](/tools/gmpetrov-databerry/trust.md) | [trust report](/tools/googlecloudplatform-agent-starter-pack/trust.md) | ## Decision facts: agent-starter-pack - **Requirements:** Requires additional software installation: Google Cloud SDK, Terraform for deployment, Make for development tasks. - **Adopt for:** agent-starter-pack is a specialized toolset for deploying AI agents on the Google Cloud Platform with built-in CI/CD, evaluation tools, and observability features. ## Choose when ### Choose databerry if… - Tags unique to databerry: ai, aichatbot, chatbot, chatbots. ### Choose agent-starter-pack if… - Requirements: Requires additional software installation: Google Cloud SDK, Terraform for deployment, Make for development tasks.. - Tags unique to agent-starter-pack: agents, gcp, gemini, genai-agents. - Also covers Inference & Serving. - When you require production-ready templates specifically adapted for deployment to Google Cloud. ## When NOT to use databerry - Last GitHub push was 755 days ago (dormant maintenance, Jun 17, 2024). Validate activity before betting a new project on databerry. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use agent-starter-pack - If you are using another cloud provider (e.g., AWS, Azure) and do not plan on moving your operations to Google Cloud. - When your team lacks familiarity with Python 3.10+ or does not wish to install and manage dependencies such as the Google Cloud SDK locally. ## Common questions ### What is the difference between databerry and agent-starter-pack? databerry: The no-code platform for building custom LLM Agents. agent-starter-pack: Ship AI Agents to Google Cloud in minutes, not months. Production-ready templates with built-in CI/CD, evaluation, and observability.. See the comparison table for live GitHub stats and shared categories. ### When should I choose databerry over agent-starter-pack? Choose databerry over agent-starter-pack when Tags unique to databerry: ai, aichatbot, chatbot, chatbots. ### When should I choose agent-starter-pack over databerry? Choose agent-starter-pack over databerry when Requirements: Requires additional software installation: Google Cloud SDK, Terraform for deployment, Make for development tasks.; Tags unique to agent-starter-pack: agents, gcp, gemini, genai-agents; Also covers Inference & Serving; When you require production-ready templates specifically adapted for deployment to Google Cloud. ### When should I avoid databerry? Last GitHub push was 755 days ago (dormant maintenance, Jun 17, 2024). Validate activity before betting a new project on databerry. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid agent-starter-pack? If you are using another cloud provider (e.g., AWS, Azure) and do not plan on moving your operations to Google Cloud. When your team lacks familiarity with Python 3.10+ or does not wish to install and manage dependencies such as the Google Cloud SDK locally. ### Is databerry or agent-starter-pack more popular on GitHub? agent-starter-pack has more GitHub stars (6,514 vs 2,960). Stars measure visibility, not whether either tool fits your constraints. ### Are databerry and agent-starter-pack open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to databerry or agent-starter-pack? GraphCanon lists graph-backed alternatives at [databerry alternatives](/tools/gmpetrov-databerry/alternatives) and [agent-starter-pack alternatives](/tools/googlecloudplatform-agent-starter-pack/alternatives) ([databerry markdown twin](/tools/gmpetrov-databerry/alternatives.md), [agent-starter-pack markdown twin](/tools/googlecloudplatform-agent-starter-pack/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-googlecloudplatform-agent-starter-pack.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, databerry or agent-starter-pack? databerry: Dormant. agent-starter-pack: 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-starter-pack? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [databerry trust report](/tools/gmpetrov-databerry/trust); [agent-starter-pack trust report](/tools/googlecloudplatform-agent-starter-pack/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/_