--- title: "langchain vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/langchain-ai-langchain-vs-windmaple-awesome-automl" tools: ["langchain-ai-langchain", "windmaple-awesome-automl"] --- # langchain vs awesome-AutoML *GraphCanon updated Jul 11, 2026* ## Verdict Pick langchain when license: langchain is MIT, awesome-AutoML is GPL-3.0; pick awesome-AutoML when license: awesome-AutoML is GPL-3.0, langchain is MIT. [langchain](https://docs.langchain.com/langchain/) reports 142k GitHub stars, 24k forks, and 419 open issues, last pushed Jul 11, 2026. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 940 stars, 155 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [langchain's repository](https://github.com/langchain-ai/langchain) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [langchain](/tools/langchain-ai-langchain.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | The agent engineering platform. | Curating a list of AutoML-related research, tools, projects and other resources | | Stars | 141,504 | 940 | | Forks | 23,516 | 155 | | Open issues | 419 | 1 | | Language | 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 License, allowing free use for both personal and commercial purposes under its stipulated terms. | GPL-3.0 | | Categories | LLM Frameworks, AI Agents | LLM Frameworks, Model Training, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [langchain](/tools/langchain-ai-langchain.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 109d | | Open issues (now) | 419 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/langchain-ai-langchain/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## 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 langchain if… - License: langchain is MIT, awesome-AutoML is GPL-3.0. - 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, gemini, deepagents, generative-ai. - * When aiming to build complex AI-powered agents or applications requiring high-level capabilities like planning, subagent interaction, and file system operations. ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, langchain is MIT. - Also covers Model Training. - Leaner open-issue backlog (1). ## 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. ## When NOT to use awesome-AutoML - Last GitHub push was 110 days ago (slowing maintenance, Mar 24, 2026). Validate activity before betting a new project on awesome-AutoML. - 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. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ## Common questions ### What is the difference between langchain and awesome-AutoML? langchain: The agent engineering platform.. awesome-AutoML: Curating a list of AutoML-related research, tools, projects and other resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose langchain over awesome-AutoML? Choose langchain over awesome-AutoML when License: langchain is MIT, awesome-AutoML is GPL-3.0; 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, gemini, deepagents, generative-ai; * 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 choose awesome-AutoML over langchain? Choose awesome-AutoML over langchain when License: awesome-AutoML is GPL-3.0, langchain is MIT; Also covers Model Training; Leaner open-issue backlog (1). ### 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. ### When should I avoid awesome-AutoML? Last GitHub push was 110 days ago (slowing maintenance, Mar 24, 2026). Validate activity before betting a new project on awesome-AutoML. 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. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ### Is langchain or awesome-AutoML more popular on GitHub? langchain has more GitHub stars (141,504 vs 940). Stars measure visibility, not whether either tool fits your constraints. ### Are langchain and awesome-AutoML open source? Yes - both are open-source projects on GitHub (langchain: MIT, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to langchain or awesome-AutoML? GraphCanon lists graph-backed alternatives at [langchain alternatives](/tools/langchain-ai-langchain/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([langchain markdown twin](/tools/langchain-ai-langchain/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/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/langchain-ai-langchain-vs-windmaple-awesome-automl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, langchain or awesome-AutoML? langchain: Very active. awesome-AutoML: Slowing. 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 and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [langchain trust report](/tools/langchain-ai-langchain/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=langchain-ai-langchain`](/api/graphcanon/graph?tool=langchain-ai-langchain) - 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/_