--- title: "langchain-tutorials vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gkamradt-langchain-tutorials-vs-pguso-agents-from-scratch" tools: ["gkamradt-langchain-tutorials", "pguso-agents-from-scratch"] --- # langchain-tutorials vs agents-from-scratch *GraphCanon updated Aug 15, 2026* ## Verdict Pick langchain-tutorials if langchain-tutorials offers educational material to aid in understanding and applying the LangChain library via Jupyter Notebooks; 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. [langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) reports 7.5k GitHub stars, 2.0k forks, and 15 open issues, last pushed Aug 5, 2024. [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 [langchain-tutorials's repository](https://github.com/gkamradt/langchain-tutorials) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [langchain-tutorials](/tools/gkamradt-langchain-tutorials.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | Overview and tutorial of the LangChain Library | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 7,480 | 954 | | Forks | 2,013 | 240 | | Open issues | 15 | 3 | | Language | Jupyter Notebook | Python | | Adopt for | langchain-tutorials offers educational material to aid in understanding and applying the LangChain library via Jupyter Notebooks. | 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 | The license details for this tool are unknown. | MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. | | Categories | Developer Tools, Model Training | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [langchain-tutorials](/tools/gkamradt-langchain-tutorials.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 740d | 18d | | Open issues (now) | 15 | 3 | | Stars delta | +10 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/gkamradt-langchain-tutorials/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Decision facts: langchain-tutorials - **Pricing:** freemium - The repository is freely accessible with no stated fees; however, specific services or advanced features (if any) may require payment and aren't detailed in the provided data. - **Adopt for:** langchain-tutorials offers educational material to aid in understanding and applying the LangChain library via Jupyter Notebooks. - **License detail:** The license details for this tool are unknown. ## 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 langchain-tutorials if… - langchain-tutorials is primarily Jupyter Notebook; agents-from-scratch is Python. - Pricing: The repository is freely accessible with no stated fees; however, specific services or advanced features (if any) may require payment and aren't detailed in the provided data.. - Tags unique to langchain-tutorials: jupyter-notebook, langchain, tutorials. - Also covers Model Training. - - When you're interested in hands-on learning through Jupyter Notebooks and want a structured approach to mastering LangChain with guided examples. ### Choose agents-from-scratch if… - agents-from-scratch is primarily Python; langchain-tutorials is Jupyter Notebook. - 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 AI Agents. - 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 langchain-tutorials - - When you prefer video tutorials or written articles over interactive notebooks; although the repository links to supplementary videos and online resources, its primary medium is Jupyter Notebooks. - - If your goal is immediate application without foundational knowledge, since langchain-tutorials emphasizes a learning path from basics up, which may add time before practical applications. ## 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 langchain-tutorials and agents-from-scratch? langchain-tutorials: Overview and tutorial of the LangChain Library. 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 langchain-tutorials over agents-from-scratch? Choose langchain-tutorials over agents-from-scratch when langchain-tutorials is primarily Jupyter Notebook; agents-from-scratch is Python; Pricing: The repository is freely accessible with no stated fees; however, specific services or advanced features (if any) may require payment and aren't detailed in the provided data.; Tags unique to langchain-tutorials: jupyter-notebook, langchain, tutorials; Also covers Model Training; - When you're interested in hands-on learning through Jupyter Notebooks and want a structured approach to mastering LangChain with guided examples. ### When should I choose agents-from-scratch over langchain-tutorials? Choose agents-from-scratch over langchain-tutorials when agents-from-scratch is primarily Python; langchain-tutorials is Jupyter Notebook; 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 AI Agents; 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 langchain-tutorials? - When you prefer video tutorials or written articles over interactive notebooks; although the repository links to supplementary videos and online resources, its primary medium is Jupyter Notebooks. - If your goal is immediate application without foundational knowledge, since langchain-tutorials emphasizes a learning path from basics up, which may add time before practical applications. ### 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 langchain-tutorials or agents-from-scratch more popular on GitHub? langchain-tutorials has more GitHub stars (7,480 vs 954). Stars measure visibility, not whether either tool fits your constraints. ### Are langchain-tutorials and agents-from-scratch open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to langchain-tutorials or agents-from-scratch? GraphCanon lists graph-backed alternatives at [langchain-tutorials alternatives](/tools/gkamradt-langchain-tutorials/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([langchain-tutorials markdown twin](/tools/gkamradt-langchain-tutorials/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/gkamradt-langchain-tutorials-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, langchain-tutorials or agents-from-scratch? langchain-tutorials: Dormant. 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 langchain-tutorials and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [langchain-tutorials trust report](/tools/gkamradt-langchain-tutorials/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gkamradt-langchain-tutorials`](/api/graphcanon/graph?tool=gkamradt-langchain-tutorials) - 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/_