--- title: "axolotl vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/axolotl-ai-cloud-axolotl-vs-pguso-agents-from-scratch" tools: ["axolotl-ai-cloud-axolotl", "pguso-agents-from-scratch"] --- # axolotl vs agents-from-scratch *GraphCanon updated Aug 12, 2026* ## Verdict Pick axolotl if axolotl is a Python-based framework designed for fine-tuning LLMs, requiring NVIDIA or AMD GPUs, Python 3.11+, and PyTorch ≥2.11.0; 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. [axolotl](https://docs.axolotl.ai) reports 12k GitHub stars, 1.4k forks, and 277 open issues, last pushed Aug 1, 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 [axolotl's repository](https://github.com/axolotl-ai-cloud/axolotl) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [axolotl](/tools/axolotl-ai-cloud-axolotl.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | Go ahead and axolotl questions | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 12,297 | 954 | | Forks | 1,399 | 240 | | Open issues | 277 | 3 | | Language | Python | Python | | Adopt for | Axolotl is a Python-based framework designed for fine-tuning LLMs, requiring NVIDIA or AMD GPUs, Python 3.11+, and PyTorch ≥2.11.0. | 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 | LLM Frameworks, Model Training | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [axolotl](/tools/axolotl-ai-cloud-axolotl.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 18d | | Open issues (now) | 277 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/axolotl-ai-cloud-axolotl/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Shared compatibility - **Python**: [axolotl](/tools/axolotl-ai-cloud-axolotl.md) - Python runtime; [agents-from-scratch](/tools/pguso-agents-from-scratch.md) - Python runtime ## Decision facts: axolotl - **Requirements:** Hardware requirement includes either NVIDIA (Ampere series or newer) or AMD GPUs.; Software requirements dictate the use of Python >=3.11, with version 3.12 recommended and PyTorch ≥2.11.0. - **Adopt for:** Axolotl is a Python-based framework designed for fine-tuning LLMs, requiring NVIDIA or AMD GPUs, Python 3.11+, and PyTorch ≥2.11.0. ## 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 axolotl if… - License: axolotl is Apache-2.0, agents-from-scratch is MIT. - Requirements: Hardware requirement includes either NVIDIA (Ampere series or newer) or AMD GPUs.; Software requirements dictate the use of Python >=3.11, with version 3.12 recommended and PyTorch ≥2.11.0.. - Tags unique to axolotl: fine-tuning. - Also covers LLM Frameworks, Model Training. - When working with newer NVIDIA Ampere series GPUs or modern AMD GPUs to leverage `bf16` and Flash Attention. ### Choose agents-from-scratch if… - License: agents-from-scratch is MIT, axolotl 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, ai-agents, local-llm, no-framework. - Also covers AI Agents, 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 axolotl - If you are constrained by older GPU models that do not support the required features like `bf16` or if your system does not have an NVIDIA or AMD GPU. - In environments where Python version must be less than 3.11, as Axolotl requires at least Python 3.11 for operation. ## 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 axolotl and agents-from-scratch? axolotl: Go ahead and axolotl questions. 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 axolotl over agents-from-scratch? Choose axolotl over agents-from-scratch when License: axolotl is Apache-2.0, agents-from-scratch is MIT; Requirements: Hardware requirement includes either NVIDIA (Ampere series or newer) or AMD GPUs.; Software requirements dictate the use of Python >=3.11, with version 3.12 recommended and PyTorch ≥2.11.0.; Tags unique to axolotl: fine-tuning; Also covers LLM Frameworks, Model Training; When working with newer NVIDIA Ampere series GPUs or modern AMD GPUs to leverage `bf16` and Flash Attention. ### When should I choose agents-from-scratch over axolotl? Choose agents-from-scratch over axolotl when License: agents-from-scratch is MIT, axolotl 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, ai-agents, local-llm, no-framework; Also covers AI Agents, 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 axolotl? If you are constrained by older GPU models that do not support the required features like `bf16` or if your system does not have an NVIDIA or AMD GPU. In environments where Python version must be less than 3.11, as Axolotl requires at least Python 3.11 for operation. ### 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 axolotl or agents-from-scratch more popular on GitHub? axolotl has more GitHub stars (12,297 vs 954). Stars measure visibility, not whether either tool fits your constraints. ### Are axolotl and agents-from-scratch open source? Yes - both are open-source projects on GitHub (axolotl: Apache-2.0, agents-from-scratch: MIT). ### Where can I find alternatives to axolotl or agents-from-scratch? GraphCanon lists graph-backed alternatives at [axolotl alternatives](/tools/axolotl-ai-cloud-axolotl/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([axolotl markdown twin](/tools/axolotl-ai-cloud-axolotl/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/axolotl-ai-cloud-axolotl-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, axolotl or agents-from-scratch? axolotl: 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 axolotl and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [axolotl trust report](/tools/axolotl-ai-cloud-axolotl/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=axolotl-ai-cloud-axolotl`](/api/graphcanon/graph?tool=axolotl-ai-cloud-axolotl) - 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/_