--- title: "automem vs deep-searcher" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/verygoodplugins-automem-vs-zilliztech-deep-searcher" tools: ["verygoodplugins-automem", "zilliztech-deep-searcher"] --- # automem vs deep-searcher *GraphCanon updated Aug 21, 2026* ## Verdict Pick automem if autoMem leverages both graph and vector database technologies to provide AI assistants with durable relational memory; 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. [automem](https://automem.ai/) reports 802 GitHub stars, 102 forks, and 15 open issues, last pushed Aug 14, 2026. [deep-searcher](https://zilliztech.github.io/deep-searcher/) has 8.1k stars, 775 forks, and 53 open issues, last pushed Nov 19, 2025. Figures are from public GitHub metadata via [automem's repository](https://github.com/verygoodplugins/automem) and [deep-searcher's repository](https://github.com/zilliztech/deep-searcher). | | [automem](/tools/verygoodplugins-automem.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Tagline | Graph-vector memory service for durable, relational AI assistant memory | Open Source Deep Research Alternative to Reason and Search on Private Data. | | Stars | 802 | 8,060 | | Forks | 102 | 775 | | Open issues | 15 | 53 | | Language | Python | Python | | Adopt for | AutoMem leverages both graph and vector database technologies to provide AI assistants with durable relational memory. | 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 | AutoMem is licensed under the MIT License, which means it is free to use, modify, and distribute as long as license terms are met. | Apache-2.0 | | Categories | AI Agents, Vector Databases | AI Agents, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [automem](/tools/verygoodplugins-automem.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 7d | 272d | | Open issues (now) | 15 | 53 | | Stars delta | +9 (30d) | +59 (30d) | | Open issues delta | +4 (30d) | 0 (30d) | | Full report | [trust report](/tools/verygoodplugins-automem/trust.md) | [trust report](/tools/zilliztech-deep-searcher/trust.md) | ## Decision facts: automem - **Pricing:** freemium - Free for open-source use, with no explicit commercial licensing information provided. - **Adopt for:** AutoMem leverages both graph and vector database technologies to provide AI assistants with durable relational memory. - **License detail:** AutoMem is licensed under the MIT License, which means it is free to use, modify, and distribute as long as license terms are met. ## 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 automem if… - License: automem is MIT, deep-searcher is Apache-2.0. - Pricing: Free for open-source use, with no explicit commercial licensing information provided.. - Tags unique to automem: ai-memory, anthropic, falkordb, graph-database. - Use AutoMem when you need an AI assistant capable of maintaining rich, relational memories over time. ### Choose deep-searcher if… - License: deep-searcher is Apache-2.0, automem is MIT. - Tags unique to deep-searcher: agent, agentic-rag, deep-research. - Also covers LLM Frameworks. - 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 automem - Avoid using AutoMem if your application does not benefit from persistent memory or relational context, as it might add unnecessary overhead. - If you require a simpler key-value storage system for less complex or non-relational data, AutoMem's graph and vector capabilities may be overkill. ## 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 automem and deep-searcher? automem: Graph-vector memory service for durable, relational AI assistant memory. 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 automem over deep-searcher? Choose automem over deep-searcher when License: automem is MIT, deep-searcher is Apache-2.0; Pricing: Free for open-source use, with no explicit commercial licensing information provided.; Tags unique to automem: ai-memory, anthropic, falkordb, graph-database; Use AutoMem when you need an AI assistant capable of maintaining rich, relational memories over time. ### When should I choose deep-searcher over automem? Choose deep-searcher over automem when License: deep-searcher is Apache-2.0, automem is MIT; Tags unique to deep-searcher: agent, agentic-rag, deep-research; Also covers LLM Frameworks; 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 automem? Avoid using AutoMem if your application does not benefit from persistent memory or relational context, as it might add unnecessary overhead. If you require a simpler key-value storage system for less complex or non-relational data, AutoMem's graph and vector capabilities may be overkill. ### 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 automem or deep-searcher more popular on GitHub? deep-searcher has more GitHub stars (8,060 vs 802). Stars measure visibility, not whether either tool fits your constraints. ### Are automem and deep-searcher open source? Yes - both are open-source projects on GitHub (automem: MIT, deep-searcher: Apache-2.0). ### Where can I find alternatives to automem or deep-searcher? GraphCanon lists graph-backed alternatives at [automem alternatives](/tools/verygoodplugins-automem/alternatives) and [deep-searcher alternatives](/tools/zilliztech-deep-searcher/alternatives) ([automem markdown twin](/tools/verygoodplugins-automem/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/verygoodplugins-automem-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, automem or deep-searcher? automem: Active. 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 automem and deep-searcher? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [automem trust report](/tools/verygoodplugins-automem/trust); [deep-searcher trust report](/tools/zilliztech-deep-searcher/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=verygoodplugins-automem`](/api/graphcanon/graph?tool=verygoodplugins-automem) - 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/_