--- title: "memU vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nevamind-ai-memu-vs-pguso-agents-from-scratch" tools: ["nevamind-ai-memu", "pguso-agents-from-scratch"] --- # memU vs agents-from-scratch *GraphCanon updated Aug 26, 2026* ## Verdict Pick memU if memU offers fast memory retrieval and self-evolving skills for AI agents at lower operational costs; pick agents-from-scratch if agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. [memU](https://memu.pro) reports 14k GitHub stars, 1.1k forks, and 116 open issues, last pushed Aug 21, 2026. [agents-from-scratch](https://github.com/pguso/agents-from-scratch) has 954 stars, 240 forks, and 3 open issues, last pushed Jul 25, 2026. Figures are from public GitHub metadata via [memU's repository](https://github.com/NevaMind-AI/memU) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [memU](/tools/nevamind-ai-memu.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | Personal memory for agents with fast retrieval and self-evolving skills | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 14,347 | 954 | | Forks | 1,062 | 240 | | Open issues | 116 | 3 | | Language | Python | Python | | Adopt for | memU offers fast memory retrieval and self-evolving skills for AI agents at lower operational costs. | agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. | | Persona | - | - | | Runtime | - | - | | License | Other | MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. | | Categories | AI Agents | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [memU](/tools/nevamind-ai-memu.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 4d | 18d | | Open issues (now) | 116 | 3 | | Stars delta | +285 (30d) | Unknown | | Open issues delta | +22 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/nevamind-ai-memu/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Decision facts: memU - **Adopt for:** memU offers fast memory retrieval and self-evolving skills for AI agents at lower operational costs. ## Decision facts: agents-from-scratch - **Requirements:** Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs. - **Adopt for:** agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. - **License detail:** MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. ## Choose when ### Choose memU if… - License: memU is Other, agents-from-scratch is MIT. - Tags unique to memU: agent-memory, claude-skills, harness, loop-engineering. - Use memU when your project requires fast access to stored agent actions and memories which can adapt over time without needing human intervention. ### Choose agents-from-scratch if… - License: agents-from-scratch is MIT, memU is Other. - Requirements: Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs.. - Tags unique to agents-from-scratch: agent-architecture, ai-agents, llm, local-llm. - Also covers Developer Tools. - You plan to teach yourself or others about the fundamentals of creating AI agents from ground zero with complete transparency into each layer of architecture. ## When NOT to use memU - Avoid using memU if your application mandates proprietary memory systems that are tightly integrated with specific agent architectures not supported by memU. - memU is unsuitable for projects where the customizability and control over skill evolution are limited or require extensive manual adjustments, as this tool emphasizes self-evolving features. ## When NOT to use agents-from-scratch - You are working on an application that needs to be deployed quickly. The tool's approach from first principles can be time-consuming compared to using established frameworks. - If you need scalability or cloud capabilities such as easy scaling with demand, this tool will not provide these features since it strictly operates on local infrastructure. ## Common questions ### What is the difference between memU and agents-from-scratch? memU: Personal memory for agents with fast retrieval and self-evolving skills. agents-from-scratch: Build AI agents locally without relying on frameworks or cloud APIs.. See the comparison table for live GitHub stats and shared categories. ### When should I choose memU over agents-from-scratch? Choose memU over agents-from-scratch when License: memU is Other, agents-from-scratch is MIT; Tags unique to memU: agent-memory, claude-skills, harness, loop-engineering; Use memU when your project requires fast access to stored agent actions and memories which can adapt over time without needing human intervention. ### When should I choose agents-from-scratch over memU? Choose agents-from-scratch over memU when License: agents-from-scratch is MIT, memU is Other; Requirements: Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs.; Tags unique to agents-from-scratch: agent-architecture, ai-agents, llm, local-llm; Also covers Developer Tools; You plan to teach yourself or others about the fundamentals of creating AI agents from ground zero with complete transparency into each layer of architecture. ### When should I avoid memU? Avoid using memU if your application mandates proprietary memory systems that are tightly integrated with specific agent architectures not supported by memU. memU is unsuitable for projects where the customizability and control over skill evolution are limited or require extensive manual adjustments, as this tool emphasizes self-evolving features. ### When should I avoid agents-from-scratch? You are working on an application that needs to be deployed quickly. The tool's approach from first principles can be time-consuming compared to using established frameworks. If you need scalability or cloud capabilities such as easy scaling with demand, this tool will not provide these features since it strictly operates on local infrastructure. ### Is memU or agents-from-scratch more popular on GitHub? memU has more GitHub stars (14,347 vs 954). Stars measure visibility, not whether either tool fits your constraints. ### Are memU and agents-from-scratch open source? Yes - both are open-source projects on GitHub (memU: Other, agents-from-scratch: MIT). ### Where can I find alternatives to memU or agents-from-scratch? GraphCanon lists graph-backed alternatives at [memU alternatives](/tools/nevamind-ai-memu/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([memU markdown twin](/tools/nevamind-ai-memu/alternatives.md), [agents-from-scratch markdown twin](/tools/pguso-agents-from-scratch/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/nevamind-ai-memu-vs-pguso-agents-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, memU or agents-from-scratch? memU: Very active. agents-from-scratch: 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 memU and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [memU trust report](/tools/nevamind-ai-memu/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nevamind-ai-memu`](/api/graphcanon/graph?tool=nevamind-ai-memu) - 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/_