--- title: "Made-With-ML vs langchain" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-langchain-ai-langchain" tools: ["gokumohandas-made-with-ml", "langchain-ai-langchain"] --- # Made-With-ML vs langchain *GraphCanon updated Jul 15, 2026* ## Verdict Pick Made-With-ML when made-With-ML is primarily Jupyter Notebook; langchain is Python; pick langchain when langchain is primarily Python; Made-With-ML is Jupyter Notebook. [Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 27 open issues, last pushed Mar 4, 2026. [langchain](https://docs.langchain.com/langchain/) has 142k stars, 24k forks, and 419 open issues, last pushed Jul 14, 2026. Figures are from public GitHub metadata via [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [langchain's repository](https://github.com/langchain-ai/langchain). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [langchain](/tools/langchain-ai-langchain.md) | | --- | --- | --- | | Tagline | Learn how to develop, deploy and iterate on production-grade ML applications. | The agent engineering platform. | | Stars | 48,703 | 141,713 | | Forks | 7,661 | 23,545 | | Open issues | 27 | 419 | | Language | Jupyter Notebook | Python | | Adopt for | - | LangChain is an open-source platform designed specifically for building agents and applications that leverage large language models (LLMs). It provides a standard framework to develop interoperable components and connect | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License, allowing free use for both personal and commercial purposes under its stipulated terms. | | Categories | AI Agents, LLM Frameworks, Model Training | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [langchain](/tools/langchain-ai-langchain.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 132d | 0d | | Open issues (now) | 27 | 419 | | Owner type | User | Organization | | Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/langchain-ai-langchain/trust.md) | ## Shared compatibility - **Python**: [Made-With-ML](/tools/gokumohandas-made-with-ml.md) - Python runtime; [langchain](/tools/langchain-ai-langchain.md) - Python runtime ## Decision facts: langchain - **Pricing:** freemium - LangChain itself is open-source and free to use. However, it might rely on paid services or premium models from external platforms like OpenAI. - **Adopt for:** LangChain is an open-source platform designed specifically for building agents and applications that leverage large language models (LLMs). It provides a standard framework to develop interoperable components and connect - **License detail:** MIT License, allowing free use for both personal and commercial purposes under its stipulated terms. ## Choose when ### Choose Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; langchain is Python. - Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning. - Also covers Model Training. ### Choose langchain if… - langchain is primarily Python; Made-With-ML is Jupyter Notebook. - Pricing: LangChain itself is open-source and free to use. However, it might rely on paid services or premium models from external platforms like OpenAI.. - Tags unique to langchain: agents, ai-agents, anthropic, chatgpt. - * When aiming to build complex AI-powered agents or applications requiring high-level capabilities like planning, subagent interaction, and file system operations. ## When NOT to use Made-With-ML - Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use langchain - * When working on smaller, less complex projects where full-scale integration with sophisticated components is not necessary as LangChain's extensive features might introduce unnecessary complexity. - * If you are primarily focused on JavaScript or TypeScript development as the primary focus of LangChain is Python. Although there is a JS/TS equivalent (LangChain.js), it may not offer the same depth - * For projects requiring heavy customization at lower levels, where a more granular control over individual components is required rather than working with an integrated framework. ## Common questions ### What is the difference between Made-With-ML and langchain? Made-With-ML: Learn how to develop, deploy and iterate on production-grade ML applications.. langchain: The agent engineering platform.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over langchain? Choose Made-With-ML over langchain when Made-With-ML is primarily Jupyter Notebook; langchain is Python; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; Also covers Model Training. ### When should I choose langchain over Made-With-ML? Choose langchain over Made-With-ML when langchain is primarily Python; Made-With-ML is Jupyter Notebook; Pricing: LangChain itself is open-source and free to use. However, it might rely on paid services or premium models from external platforms like OpenAI.; Tags unique to langchain: agents, ai-agents, anthropic, chatgpt; * When aiming to build complex AI-powered agents or applications requiring high-level capabilities like planning, subagent interaction, and file system operations. ### When should I avoid Made-With-ML? Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid langchain? * When working on smaller, less complex projects where full-scale integration with sophisticated components is not necessary as LangChain's extensive features might introduce unnecessary complexity. * If you are primarily focused on JavaScript or TypeScript development as the primary focus of LangChain is Python. Although there is a JS/TS equivalent (LangChain.js), it may not offer the same depth * For projects requiring heavy customization at lower levels, where a more granular control over individual components is required rather than working with an integrated framework. ### Is Made-With-ML or langchain more popular on GitHub? langchain has more GitHub stars (141,713 vs 48,703). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and langchain open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, langchain: MIT). ### Where can I find alternatives to Made-With-ML or langchain? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [langchain alternatives](/tools/langchain-ai-langchain/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/alternatives.md), [langchain markdown twin](/tools/langchain-ai-langchain/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/gokumohandas-made-with-ml-vs-langchain-ai-langchain.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Made-With-ML or langchain? Made-With-ML: Slowing. langchain: Very 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 Made-With-ML and langchain? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [langchain trust report](/tools/langchain-ai-langchain/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gokumohandas-made-with-ml`](/api/graphcanon/graph?tool=gokumohandas-made-with-ml) - 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/_