--- title: "deep-searcher vs memsearch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/zilliztech-deep-searcher-vs-zilliztech-memsearch" tools: ["zilliztech-deep-searcher", "zilliztech-memsearch"] --- # deep-searcher vs memsearch *GraphCanon updated Jul 12, 2026* ## Verdict Pick deep-searcher if 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; pick memsearch if memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage. [deep-searcher](https://zilliztech.github.io/deep-searcher/) reports 7.9k GitHub stars, 768 forks, and 53 open issues, last pushed Nov 19, 2025. [memsearch](https://zilliztech.github.io/memsearch/) has 2.2k stars, 194 forks, and 224 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [deep-searcher's repository](https://github.com/zilliztech/deep-searcher) and [memsearch's repository](https://github.com/zilliztech/memsearch). | | [deep-searcher](/tools/zilliztech-deep-searcher.md) | [memsearch](/tools/zilliztech-memsearch.md) | | --- | --- | --- | | Tagline | Open Source Deep Research Alternative to Reason and Search on Private Data. | A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus. | | Stars | 7,941 | 2,228 | | Forks | 768 | 194 | | Open issues | 53 | 224 | | Language | Python | 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. | memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | AI Agents, LLM Frameworks, Vector Databases | AI Agents, Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deep-searcher](/tools/zilliztech-deep-searcher.md) | [memsearch](/tools/zilliztech-memsearch.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 234d | 1d | | Open issues (now) | 53 | 224 | | Full report | [trust report](/tools/zilliztech-deep-searcher/trust.md) | [trust report](/tools/zilliztech-memsearch/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. ## Decision facts: memsearch - **Adopt for:** memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage. ## Choose when ### Choose deep-searcher if… - License: deep-searcher is Apache-2.0, memsearch is MIT. - Tags unique to deep-searcher: agent, agentic-rag, deep-research, llm. - Also covers LLM Frameworks. - 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. ### Choose memsearch if… - License: memsearch is MIT, deep-searcher is Apache-2.0. - Tags unique to memsearch: agent-memory, long-term-memory, milvus, semantic-search. - Also covers Data & Retrieval. - When you need robust integration with AI agents like Claude Code or Codex ## 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. ## When NOT to use memsearch - If your application doesn't require integration with specific AI agents like Claude Code - In cases where only simple text data storage without semantic search is needed ## Common questions ### What is the difference between deep-searcher and memsearch? deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data.. memsearch: A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus.. See the comparison table for live GitHub stats and shared categories. ### When should I choose deep-searcher over memsearch? Choose deep-searcher over memsearch when License: deep-searcher is Apache-2.0, memsearch is MIT; Tags unique to deep-searcher: agent, agentic-rag, deep-research, llm; Also covers LLM Frameworks; 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 choose memsearch over deep-searcher? Choose memsearch over deep-searcher when License: memsearch is MIT, deep-searcher is Apache-2.0; Tags unique to memsearch: agent-memory, long-term-memory, milvus, semantic-search; Also covers Data & Retrieval; When you need robust integration with AI agents like Claude Code or Codex. ### 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. ### When should I avoid memsearch? If your application doesn't require integration with specific AI agents like Claude Code In cases where only simple text data storage without semantic search is needed ### Is deep-searcher or memsearch more popular on GitHub? deep-searcher has more GitHub stars (7,941 vs 2,228). Stars measure visibility, not whether either tool fits your constraints. ### Are deep-searcher and memsearch open source? Yes - both are open-source projects on GitHub (deep-searcher: Apache-2.0, memsearch: MIT). ### Where can I find alternatives to deep-searcher or memsearch? GraphCanon lists graph-backed alternatives at [deep-searcher alternatives](/tools/zilliztech-deep-searcher/alternatives) and [memsearch alternatives](/tools/zilliztech-memsearch/alternatives) ([deep-searcher markdown twin](/tools/zilliztech-deep-searcher/alternatives.md), [memsearch markdown twin](/tools/zilliztech-memsearch/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/zilliztech-deep-searcher-vs-zilliztech-memsearch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deep-searcher or memsearch? deep-searcher: Slowing. memsearch: 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 deep-searcher and memsearch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deep-searcher trust report](/tools/zilliztech-deep-searcher/trust); [memsearch trust report](/tools/zilliztech-memsearch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=zilliztech-deep-searcher`](/api/graphcanon/graph?tool=zilliztech-deep-searcher) - 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/_