--- title: "databerry vs deep-searcher" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gmpetrov-databerry-vs-zilliztech-deep-searcher" tools: ["gmpetrov-databerry", "zilliztech-deep-searcher"] --- # databerry vs deep-searcher *GraphCanon updated Jul 12, 2026* ## Verdict Pick databerry when tags unique to databerry: ai, aichatbot, chatbot, chatbots; pick deep-searcher when tags unique to deep-searcher: agent, agentic-rag, deep-research, vector-database. [databerry](https://chaindesk.ai) reports 3.0k GitHub stars, 422 forks, and 166 open issues, last pushed Jun 17, 2024. [deep-searcher](https://zilliztech.github.io/deep-searcher/) has 7.9k stars, 768 forks, and 53 open issues, last pushed Nov 19, 2025. Figures are from public GitHub metadata via [databerry's repository](https://github.com/gmpetrov/databerry) and [deep-searcher's repository](https://github.com/zilliztech/deep-searcher). | | [databerry](/tools/gmpetrov-databerry.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Tagline | The no-code platform for building custom LLM Agents | Open Source Deep Research Alternative to Reason and Search on Private Data. | | Stars | 2,960 | 7,941 | | Forks | 422 | 768 | | Open issues | 166 | 53 | | Language | - | Python | | Adopt for | - | DeepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | AI Agents, LLM Frameworks | AI Agents, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [databerry](/tools/gmpetrov-databerry.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 753d | 234d | | Open issues (now) | 166 | 53 | | Owner type | User | Organization | | Full report | [trust report](/tools/gmpetrov-databerry/trust.md) | [trust report](/tools/zilliztech-deep-searcher/trust.md) | ## Decision facts: deep-searcher - **Adopt for:** DeepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license. ## Choose when ### Choose databerry if… - Tags unique to databerry: ai, aichatbot, chatbot, chatbots. ### Choose deep-searcher if… - Tags unique to deep-searcher: agent, agentic-rag, deep-research, vector-database. - Also covers Vector Databases. - deep-searcher ships Docker support for self-hosted deployment. - When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3. ## 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 deep-searcher - Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems. - Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily. ## Common questions ### What is the difference between databerry and deep-searcher? databerry: The no-code platform for building custom LLM Agents. deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data.. See the comparison table for live GitHub stats and shared categories. ### When should I choose databerry over deep-searcher? Choose databerry over deep-searcher when Tags unique to databerry: ai, aichatbot, chatbot, chatbots. ### When should I choose deep-searcher over databerry? Choose deep-searcher over databerry when Tags unique to deep-searcher: agent, agentic-rag, deep-research, vector-database; Also covers Vector Databases; deep-searcher ships Docker support for self-hosted deployment; When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3. ### 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 deep-searcher? Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems. Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily. ### Is databerry or deep-searcher more popular on GitHub? deep-searcher has more GitHub stars (7,941 vs 2,960). Stars measure visibility, not whether either tool fits your constraints. ### Are databerry and deep-searcher open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to databerry or deep-searcher? GraphCanon lists graph-backed alternatives at [databerry alternatives](/tools/gmpetrov-databerry/alternatives) and [deep-searcher alternatives](/tools/zilliztech-deep-searcher/alternatives) ([databerry markdown twin](/tools/gmpetrov-databerry/alternatives.md), [deep-searcher markdown twin](/tools/zilliztech-deep-searcher/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-zilliztech-deep-searcher.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, databerry or deep-searcher? databerry: Dormant. deep-searcher: Slowing. 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 deep-searcher? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [databerry trust report](/tools/gmpetrov-databerry/trust); [deep-searcher trust report](/tools/zilliztech-deep-searcher/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/_