--- title: "agno vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agno-agi-agno-vs-pguso-agents-from-scratch" tools: ["agno-agi-agno", "pguso-agents-from-scratch"] --- # agno vs agents-from-scratch *GraphCanon updated Aug 19, 2026* ## Verdict Pick agno if use agno for a high level of control over your AI agent platform, including 50+ production API endpoints and support for 100+ integrations. Ideal if you need to customize data ownership and permissions; 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. [agno](https://docs.agno.com) reports 42k GitHub stars, 5.8k forks, and 1.3k open issues, last pushed Aug 19, 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 [agno's repository](https://github.com/agno-agi/agno) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [agno](/tools/agno-agi-agno.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | Build, run, and manage your own agent platform. | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 41,785 | 954 | | Forks | 5,793 | 240 | | Open issues | 1,261 | 3 | | Language | Python | Python | | Adopt for | Use agno for a high level of control over your AI agent platform, including 50+ production API endpoints and support for 100+ integrations. Ideal if you need to customize data ownership and permissions. | 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, Developer Tools | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agno](/tools/agno-agi-agno.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 18d | | Open issues (now) | 1.3k | 3 | | Stars delta | +487 (30d) | Unknown | | Open issues delta | +264 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/agno-agi-agno/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Decision facts: agno - **Adopt for:** Use agno for a high level of control over your AI agent platform, including 50+ production API endpoints and support for 100+ integrations. Ideal if you need to customize data ownership and permissions. ## 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 agno if… - License: agno is Apache-2.0, agents-from-scratch is MIT. - Tags unique to agno: agents, developer-tools. - Use agno for a high level of control over your AI agent platform, including 50+ production API endpoints and support for 100+ integrations. Ideal if you need to customize data ownership and permissions. ### Choose agents-from-scratch if… - License: agents-from-scratch is MIT, agno 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. - 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 agno - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## 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 agno and agents-from-scratch? agno: Build, run, and manage your own agent platform.. 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 agno over agents-from-scratch? Choose agno over agents-from-scratch when License: agno is Apache-2.0, agents-from-scratch is MIT; Tags unique to agno: agents, developer-tools; Use agno for a high level of control over your AI agent platform, including 50+ production API endpoints and support for 100+ integrations. Ideal if you need to customize data ownership and permissions. ### When should I choose agents-from-scratch over agno? Choose agents-from-scratch over agno when License: agents-from-scratch is MIT, agno 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; 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 agno? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### 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 agno or agents-from-scratch more popular on GitHub? agno has more GitHub stars (41,785 vs 954). Stars measure visibility, not whether either tool fits your constraints. ### Are agno and agents-from-scratch open source? Yes - both are open-source projects on GitHub (agno: Apache-2.0, agents-from-scratch: MIT). ### Where can I find alternatives to agno or agents-from-scratch? GraphCanon lists graph-backed alternatives at [agno alternatives](/tools/agno-agi-agno/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([agno markdown twin](/tools/agno-agi-agno/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/agno-agi-agno-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, agno or agents-from-scratch? agno: 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 agno and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agno trust report](/tools/agno-agi-agno/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agno-agi-agno`](/api/graphcanon/graph?tool=agno-agi-agno) - 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/_