AI Agents for Beginners vs LangChain
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A free course of 18 lessons teaching beginners how to build AI agents, with Python code samples.
Open source · MIT
A Python framework for building AI agents and apps powered by language models.
Our verdict
AI Agents for Beginners is a free, MIT-licensed course of 18 lessons that teaches you how agents are built, with Python examples built on Microsoft tools. LangChain is a Python framework you install and code against to build LLM-powered apps that can connect to many model providers and data sources. One is for learning the ideas and practicing with guided code; the other is a library you use to build the actual application.
Pick AI Agents for Beginners if you are new to AI agents and want guided lessons with example code before building anything of your own.
Pick LangChain if you already code in Python and want a library to build an LLM-powered app that can switch between model providers.
| Key difference | AI Agents for Beginners | LangChain |
|---|---|---|
| What it is | A learning course with text, video, and code samples | A Python framework installed as a library for coding |
| Setup difficulty | Beginner | Intermediate |
| Cost and access | Free; main examples require Microsoft Foundry and an Azure account | Free and open source; quickstart needs access to a model provider's API |
| Best at | Understanding how agent systems are structured, lesson by lesson | Connecting models, tools, and data sources in a Python application |
| Not ideal for | Anyone wanting a ready-made agent product without coding | Non-coders, JavaScript projects, or low-level agent orchestration |
Written with AI from both products' catalogue entries · updated 2026-10-29
Who each one is for
Choose AI Agents for Beginners if you need…
- Developers new to AI agents who want guided lessons with code
- Students learning how agentic AI systems are structured
- People who already know basic generative AI and want to move to agents
- Readers who prefer working through example code in Jupyter-style notebooks
Choose LangChain if you need…
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Side-by-side
| AI Agents for Beginners | LangChain | |
|---|---|---|
| Type | Learning resource | Framework |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 78.7k | 149k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | Jupyter Notebook | Python |
| Last code update | 4 days ago | 4 days ago |
What each one is
AI Agents for Beginners
This is a course, not a software product, that teaches the basics of building AI agents. Each lesson has a written guide, a short video, and Python code samples built on Microsoft Agent Framework and Microsoft Foundry Agent Service. You can start with any lesson, since each covers its own topic.
LangChain
LangChain is a Python framework for building applications powered by large language models (LLMs) and agents. It provides a standard interface for models, embeddings, and vector stores, plus a library of integrations with model providers and tools. Developers use it to connect LLMs to their own data and systems and to swap models without rewriting their application.
Best for
AI Agents for Beginners
- Developers new to AI agents who want guided lessons with code
- Students learning how agentic AI systems are structured
- People who already know basic generative AI and want to move to agents
- Readers who prefer working through example code in Jupyter-style notebooks
LangChain
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Strengths
AI Agents for Beginners
- Free, MIT-licensed, and organised as 18 lessons with text, video, and code
- Lessons can be read in any order, so you can jump to the topic you need
- Includes translations into many languages, according to the README
LangChain
- Standard interface lets you swap model providers with limited code changes
- Large library of integrations with model providers, tools, and vector stores
- Open source under the MIT license with a documentation site and free courses
Not ideal for
AI Agents for Beginners
- People who want a ready-made agent product to use without coding, since this teaches building agents
- Those without an Azure account, because the README says the main path requires Microsoft Foundry (Azure Account Required)
- Readers looking for a framework-neutral comparison, since the examples centre on Microsoft tools
LangChain
- People who want a finished end-user app rather than a library to code against
- Developers who need low-level control over agent orchestration; the README points to LangGraph for that
- JavaScript or TypeScript projects, which the README directs to the separate LangChain.js library
Trade-offs
AI Agents for Beginners
- The examples depend on Microsoft Foundry and an Azure account, which may cost money
- The README does not describe a fixed schedule or prerequisites beyond the setup course, so the time commitment is unclear
- Some lessons may depend on specific Microsoft framework versions that change over time
LangChain
- The README does not detail the API surface, so you rely on external docs for depth
- Several related products (LangGraph, Deep Agents, LangSmith) are needed for advanced or production work, which adds to the learning curve
- The README's quickstart targets a specific model string, so you must have access to that provider's API
Getting started
AI Agents for Beginners
Clone the repository with sparse checkout (skips translations, faster download)
git clone --filter=blob:none --sparse https://github.com/microsoft/ai-agents-for-beginners.gitEnter the project folder and set sparse checkout (Bash / macOS / Linux)
cd ai-agents-for-beginners && git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'Frequently asked questions
Should I use AI Agents for Beginners or LangChain?
AI Agents for Beginners is a free, MIT-licensed course of 18 lessons that teaches you how agents are built, with Python examples built on Microsoft tools. LangChain is a Python framework you install and code against to build LLM-powered apps that can connect to many model providers and data sources. One is for learning the ideas and practicing with guided code; the other is a library you use to build the actual application. Pick AI Agents for Beginners if you are new to AI agents and want guided lessons with example code before building anything of your own. Pick LangChain if you already code in Python and want a library to build an LLM-powered app that can switch between model providers.
What is the difference between AI Agents for Beginners and LangChain?
AI Agents for Beginners: A free course of 18 lessons teaching beginners how to build AI agents, with Python code samples. LangChain: A Python framework for building AI agents and apps powered by language models. AI Agents for Beginners suits developers new to ai agents who want guided lessons with code, while LangChain suits python developers building llm-powered applications.
Are AI Agents for Beginners and LangChain free?
AI Agents for Beginners: Free and open source. LangChain: Free and open source.
Which is easier to set up, AI Agents for Beginners or LangChain?
AI Agents for Beginners is easier to get started with (rated beginner); LangChain is rated intermediate and usually needs more setup.
Is AI Agents for Beginners or LangChain more popular?
LangChain has more GitHub stars (149k) than AI Agents for Beginners (78.7k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to AI Agents for Beginners and LangChain?
Other options people compare them with include Generative AI for Beginners, Semantic Kernel, LlamaIndex, CrewAI, OpenUI.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.