--- title: "hivemind vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/activeloopai-hivemind-vs-pguso-agents-from-scratch" tools: ["activeloopai-hivemind", "pguso-agents-from-scratch"] --- # hivemind vs agents-from-scratch *GraphCanon updated Aug 22, 2026* ## Verdict Pick hivemind if hivemind stands out for its capabilities in converting traces into reusable skills across multiple AI agents using embeddings; 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. [hivemind](https://deeplake.ai/hivemind) reports 1.6k GitHub stars, 102 forks, and 46 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 [hivemind's repository](https://github.com/activeloopai/hivemind) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [hivemind](/tools/activeloopai-hivemind.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | Hivemind turns your traces into reusable skills across agents | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 1,573 | 954 | | Forks | 102 | 240 | | Open issues | 46 | 3 | | Language | TypeScript | Python | | Adopt for | Hivemind stands out for its capabilities in converting traces into reusable skills across multiple AI agents using embeddings. | 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 | Apache-2.0 | 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._ | | [hivemind](/tools/activeloopai-hivemind.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 18d | | Open issues (now) | 46 | 3 | | Stars delta | +74 (30d) | Unknown | | Open issues delta | +7 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/activeloopai-hivemind/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Decision facts: hivemind - **Adopt for:** Hivemind stands out for its capabilities in converting traces into reusable skills across multiple AI agents using embeddings. ## 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 hivemind if… - hivemind is primarily TypeScript; agents-from-scratch is Python. - License: hivemind is Apache-2.0, agents-from-scratch is MIT. - Tags unique to hivemind: ai-memory, embeddings, long-term-memory, memory-engine. - hivemind ships an MCP server manifest. - - When you are working on an environment where multiple AI agents need to share and use the same set of skills derived from historical interactions or traces. ### Choose agents-from-scratch if… - agents-from-scratch is primarily Python; hivemind is TypeScript. - License: agents-from-scratch is MIT, hivemind is Apache-2.0. - 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, llm, local-llm, no-framework. - 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 hivemind - - When your application does not require skill reuse across multiple AI agents, as Hivemind’s forte is in enabling such cross-agent knowledge and behavior sharing. - - If you are looking for a standalone solution without integrating traces into reusable skills; Hivemind leans towards managing skills via traces. ## 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 hivemind and agents-from-scratch? hivemind: Hivemind turns your traces into reusable skills across agents. 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 hivemind over agents-from-scratch? Choose hivemind over agents-from-scratch when hivemind is primarily TypeScript; agents-from-scratch is Python; License: hivemind is Apache-2.0, agents-from-scratch is MIT; Tags unique to hivemind: ai-memory, embeddings, long-term-memory, memory-engine; hivemind ships an MCP server manifest; - When you are working on an environment where multiple AI agents need to share and use the same set of skills derived from historical interactions or traces. ### When should I choose agents-from-scratch over hivemind? Choose agents-from-scratch over hivemind when agents-from-scratch is primarily Python; hivemind is TypeScript; License: agents-from-scratch is MIT, hivemind is Apache-2.0; 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, llm, local-llm, no-framework; 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 hivemind? - When your application does not require skill reuse across multiple AI agents, as Hivemind’s forte is in enabling such cross-agent knowledge and behavior sharing. - If you are looking for a standalone solution without integrating traces into reusable skills; Hivemind leans towards managing skills via traces. ### 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 hivemind or agents-from-scratch more popular on GitHub? hivemind has more GitHub stars (1,573 vs 954). Stars measure visibility, not whether either tool fits your constraints. ### Are hivemind and agents-from-scratch open source? Yes - both are open-source projects on GitHub (hivemind: Apache-2.0, agents-from-scratch: MIT). ### Where can I find alternatives to hivemind or agents-from-scratch? GraphCanon lists graph-backed alternatives at [hivemind alternatives](/tools/activeloopai-hivemind/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([hivemind markdown twin](/tools/activeloopai-hivemind/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/activeloopai-hivemind-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, hivemind or agents-from-scratch? hivemind: 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 hivemind and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hivemind trust report](/tools/activeloopai-hivemind/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=activeloopai-hivemind`](/api/graphcanon/graph?tool=activeloopai-hivemind) - 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/_