--- title: "awesome-ai-sdks vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/e2b-dev-awesome-ai-sdks-vs-pguso-agents-from-scratch" tools: ["e2b-dev-awesome-ai-sdks", "pguso-agents-from-scratch"] --- # awesome-ai-sdks vs agents-from-scratch *GraphCanon updated Aug 21, 2026* ## Verdict Pick awesome-ai-sdks if awesome-ai-sdks offers an extensive directory of SDKs for AI agents, emphasizing its role in managing tools across different languages and ecosystems; 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. [awesome-ai-sdks](https://github.com/e2b-dev/awesome-ai-sdks) reports 1.2k GitHub stars, 361 forks, and 241 open issues, last pushed Jul 9, 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 [awesome-ai-sdks's repository](https://github.com/e2b-dev/awesome-ai-sdks) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [awesome-ai-sdks](/tools/e2b-dev-awesome-ai-sdks.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | A database of SDKs for AI agents creation and management | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 1,213 | 954 | | Forks | 361 | 240 | | Open issues | 241 | 3 | | Language | - | Python | | Adopt for | awesome-ai-sdks offers an extensive directory of SDKs for AI agents, emphasizing its role in managing tools across different languages and ecosystems. | 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 | - | 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._ | | [awesome-ai-sdks](/tools/e2b-dev-awesome-ai-sdks.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 42d | 18d | | Open issues (now) | 241 | 3 | | Stars delta | +6 (30d) | Unknown | | Open issues delta | +29 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/e2b-dev-awesome-ai-sdks/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Shared compatibility - **Python**: [awesome-ai-sdks](/tools/e2b-dev-awesome-ai-sdks.md) - Python runtime; [agents-from-scratch](/tools/pguso-agents-from-scratch.md) - Python runtime ## Decision facts: awesome-ai-sdks - **Adopt for:** awesome-ai-sdks offers an extensive directory of SDKs for AI agents, emphasizing its role in managing tools across different languages and ecosystems. ## 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 awesome-ai-sdks if… - Tags unique to awesome-ai-sdks: agent, framework, langchain, llmops. - When you are looking to compile and access various SDKs and libraries for AI agent development from one centralized resource. - More GitHub stars (1.2k vs 954) - visibility, not fit. ### Choose agents-from-scratch if… - 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 awesome-ai-sdks - For projects requiring a real-time or regularly updated list since the repository acknowledges it's based on their best knowledge and might not be comprehensive. - If you specifically need production-ready tools. The repository contains links to alpha-stage projects like Chidori, which may not be suitable for immediate deployment. ## 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 awesome-ai-sdks and agents-from-scratch? awesome-ai-sdks: A database of SDKs for AI agents creation and management. 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 awesome-ai-sdks over agents-from-scratch? Choose awesome-ai-sdks over agents-from-scratch when Tags unique to awesome-ai-sdks: agent, framework, langchain, llmops; When you are looking to compile and access various SDKs and libraries for AI agent development from one centralized resource; More GitHub stars (1.2k vs 954) - visibility, not fit. ### When should I choose agents-from-scratch over awesome-ai-sdks? Choose agents-from-scratch over awesome-ai-sdks when 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 awesome-ai-sdks? For projects requiring a real-time or regularly updated list since the repository acknowledges it's based on their best knowledge and might not be comprehensive. If you specifically need production-ready tools. The repository contains links to alpha-stage projects like Chidori, which may not be suitable for immediate deployment. ### 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 awesome-ai-sdks or agents-from-scratch more popular on GitHub? awesome-ai-sdks has more GitHub stars (1,213 vs 954). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-ai-sdks and agents-from-scratch open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-ai-sdks or agents-from-scratch? GraphCanon lists graph-backed alternatives at [awesome-ai-sdks alternatives](/tools/e2b-dev-awesome-ai-sdks/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([awesome-ai-sdks markdown twin](/tools/e2b-dev-awesome-ai-sdks/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/e2b-dev-awesome-ai-sdks-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, awesome-ai-sdks or agents-from-scratch? awesome-ai-sdks: Steady. 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 awesome-ai-sdks and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-ai-sdks trust report](/tools/e2b-dev-awesome-ai-sdks/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=e2b-dev-awesome-ai-sdks`](/api/graphcanon/graph?tool=e2b-dev-awesome-ai-sdks) - 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/_