--- title: "Agent_Memory_Techniques vs automem" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nirdiamant-agent-memory-techniques-vs-verygoodplugins-automem" tools: ["nirdiamant-agent-memory-techniques", "verygoodplugins-automem"] --- # Agent_Memory_Techniques vs automem *GraphCanon updated Aug 22, 2026* ## Verdict Pick Agent_Memory_Techniques if agent_Memory_Techniques provides thirty Jupyter Notebooks that detail advanced memory techniques for LLMs; pick automem if autoMem leverages both graph and vector database technologies to provide AI assistants with durable relational memory. [Agent_Memory_Techniques](https://diamantai.substack.com/) reports 924 GitHub stars, 120 forks, and 0 open issues, last pushed Aug 15, 2026. [automem](https://automem.ai/) has 802 stars, 102 forks, and 15 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [Agent_Memory_Techniques's repository](https://github.com/NirDiamant/Agent_Memory_Techniques) and [automem's repository](https://github.com/verygoodplugins/automem). | | [Agent_Memory_Techniques](/tools/nirdiamant-agent-memory-techniques.md) | [automem](/tools/verygoodplugins-automem.md) | | --- | --- | --- | | Tagline | Agent memory for LLMs: runnable Jupyter notebooks on various memory and knowledge techniques. | Graph-vector memory service for durable, relational AI assistant memory | | Stars | 924 | 802 | | Forks | 120 | 102 | | Open issues | 0 | 15 | | Language | Jupyter Notebook | Python | | Adopt for | Agent_Memory_Techniques provides thirty Jupyter Notebooks that detail advanced memory techniques for LLMs. | AutoMem leverages both graph and vector database technologies to provide AI assistants with durable relational memory. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | AutoMem is licensed under the MIT License, which means it is free to use, modify, and distribute as long as license terms are met. | | Categories | AI Agents, Evaluation & Observability, Model Training, Vector Databases | AI Agents, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Agent_Memory_Techniques](/tools/nirdiamant-agent-memory-techniques.md) | [automem](/tools/verygoodplugins-automem.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 6d | 7d | | Open issues (now) | 0 | 15 | | Stars delta | +119 (30d) | +9 (30d) | | Open issues delta | -1 (30d) | +4 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/nirdiamant-agent-memory-techniques/trust.md) | [trust report](/tools/verygoodplugins-automem/trust.md) | ## Decision facts: Agent_Memory_Techniques - **Adopt for:** Agent_Memory_Techniques provides thirty Jupyter Notebooks that detail advanced memory techniques for LLMs. ## 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. ## Choose when ### Choose Agent_Memory_Techniques if… - Agent_Memory_Techniques is primarily Jupyter Notebook; automem is Python. - License: Agent_Memory_Techniques is Apache-2.0, automem is MIT. - Tags unique to Agent_Memory_Techniques: agent-memory, ai-agents, episodic-memory, generative-ai. - Also covers Evaluation & Observability, Model Training. - Need to integrate multiple types of memory systems such as episodic, semantic, or vector stores ### Choose automem if… - automem is primarily Python; Agent_Memory_Techniques is Jupyter Notebook. - License: automem is MIT, Agent_Memory_Techniques is Apache-2.0. - Pricing: Free for open-source use, with no explicit commercial licensing information provided.. - Tags unique to automem: ai-memory, falkordb, graph-database, llm. - automem ships Docker support for self-hosted deployment. - Use AutoMem when you need an AI assistant capable of maintaining rich, relational memories over time. ## When NOT to use Agent_Memory_Techniques - Looking for a lightweight solution with minimal setup; this has extensive notebooks and dependencies - Require real-time memory management without heavy computational overhead, as some techniques are more geared toward detailed offline analysis ## 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. ## Common questions ### What is the difference between Agent_Memory_Techniques and automem? Agent_Memory_Techniques: Agent memory for LLMs: runnable Jupyter notebooks on various memory and knowledge techniques.. automem: Graph-vector memory service for durable, relational AI assistant memory. See the comparison table for live GitHub stats and shared categories. ### When should I choose Agent_Memory_Techniques over automem? Choose Agent_Memory_Techniques over automem when Agent_Memory_Techniques is primarily Jupyter Notebook; automem is Python; License: Agent_Memory_Techniques is Apache-2.0, automem is MIT; Tags unique to Agent_Memory_Techniques: agent-memory, ai-agents, episodic-memory, generative-ai; Also covers Evaluation & Observability, Model Training; Need to integrate multiple types of memory systems such as episodic, semantic, or vector stores. ### When should I choose automem over Agent_Memory_Techniques? Choose automem over Agent_Memory_Techniques when automem is primarily Python; Agent_Memory_Techniques is Jupyter Notebook; License: automem is MIT, Agent_Memory_Techniques is Apache-2.0; Pricing: Free for open-source use, with no explicit commercial licensing information provided.; Tags unique to automem: ai-memory, falkordb, graph-database, llm; automem ships Docker support for self-hosted deployment; Use AutoMem when you need an AI assistant capable of maintaining rich, relational memories over time. ### When should I avoid Agent_Memory_Techniques? Looking for a lightweight solution with minimal setup; this has extensive notebooks and dependencies Require real-time memory management without heavy computational overhead, as some techniques are more geared toward detailed offline analysis ### 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. ### Is Agent_Memory_Techniques or automem more popular on GitHub? Agent_Memory_Techniques has more GitHub stars (924 vs 802). Stars measure visibility, not whether either tool fits your constraints. ### Are Agent_Memory_Techniques and automem open source? Yes - both are open-source projects on GitHub (Agent_Memory_Techniques: Apache-2.0, automem: MIT). ### Where can I find alternatives to Agent_Memory_Techniques or automem? GraphCanon lists graph-backed alternatives at [Agent_Memory_Techniques alternatives](/tools/nirdiamant-agent-memory-techniques/alternatives) and [automem alternatives](/tools/verygoodplugins-automem/alternatives) ([Agent_Memory_Techniques markdown twin](/tools/nirdiamant-agent-memory-techniques/alternatives.md), [automem markdown twin](/tools/verygoodplugins-automem/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/nirdiamant-agent-memory-techniques-vs-verygoodplugins-automem.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Agent_Memory_Techniques or automem? Agent_Memory_Techniques: Very active. automem: 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 Agent_Memory_Techniques and automem? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Agent_Memory_Techniques trust report](/tools/nirdiamant-agent-memory-techniques/trust); [automem trust report](/tools/verygoodplugins-automem/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nirdiamant-agent-memory-techniques`](/api/graphcanon/graph?tool=nirdiamant-agent-memory-techniques) - 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/_