head to head · open source
langchain vs GenerativeAIExamples
langchain has 148,892 GitHub stars, 25,031 forks, 492 open issues and last shipped yesterday. GenerativeAIExamples has 4,220 stars, 1,102 forks, 84 open issues and last shipped 2 months ago. langchain leads on adoption by 3,428% (148,892 vs 4,220 stars). langchain is written in Python under MIT; GenerativeAIExamples is written in Jupyter Notebook under Apache-2.0. langchain has attracted 17% as many forks as stars, GenerativeAIExamples 26%. langchain was the more recently maintained of the two, and both are self-hostable with no licence fee.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| langchain | GenerativeAIExamples | |
|---|---|---|
| GitHub stars | ★ 149K | ★ 4.2K |
| License | MIT | Apache-2.0 |
| Written in | Python | Jupyter Notebook |
| Last push | 2026-11-02 | 2026-09-09 |
| Forks | ⑂ 25K | ⑂ 1.1K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick langchain if
- You weight community size — 149K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick GenerativeAIExamples if
- You want the GenerativeAIExamples feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Jupyter Notebook
- You evaluated both and GenerativeAIExamples fits your workflow better
About langchain
The agent engineering platform. LangChain is a framework for building agents and LLM powered applications. It helps you chain together interoperable components and third party integrations to simplify AI application development — all while future proofing decisions as the underlying technology evolves. [!TIP] Just getting started? Check out Deep Agents — a higher level package built on LangChain for agents that have built in capabilities for common usage patterns such as planning, subagents, file system usage, and more. Quickstart If you're looking for more advanced customization or agent orchestration, check…
read the full langchain overview →
About GenerativeAIExamples
NVIDIA Generative AI Examples This repository is a starting point for developers looking to integrate with the NVIDIA software ecosystem to speed up their generative AI systems. Whether you are building RAG pipelines, agentic workflows, or fine tuning models, this repository will help you integrate NVIDIA, seamlessly and natively, with your development stack. Table of Contents What's New? Data Flywheel Safer Agentic AI Knowledge Graph RAG Agentic Workflows with Llama 3.1 RAG with Local NIM Deployment and LangChain Vision NIM Workflows Try it Now! Data Flywheel Tool Calling Notebooks RAG RAG Notebooks RAG…
read the full GenerativeAIExamples overview →
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Frequently asked questions
Is langchain or GenerativeAIExamples more popular?
langchain has 148,892 GitHub stars and GenerativeAIExamples has 4,220. langchain has the larger community by that measure.
Are langchain and GenerativeAIExamples free?
Both are open source. langchain is licensed under MIT and GenerativeAIExamples under Apache-2.0. Neither carries a licence fee.
What is the difference between langchain and GenerativeAIExamples?
langchain is written in Python and GenerativeAIExamples in Jupyter Notebook. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, langchain or GenerativeAIExamples?
Choose langchain if you want the larger community (148,892 stars) or its MIT licence terms. Choose GenerativeAIExamples if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.