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LangChain- Develop AI Agents with LangChain & LangGraph

A project-based Python course repository that teaches developers to build generative AI applications and agents with LangChain and LangGraph. Its examples include search agents, retrieval-augmented generation, reflection and code interpretation, with some exercises in repository branches and others in linked project repositories.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

LangChain- Develop AI Agents with LangChain & LangGraph — OpenViST project profile in Agent Frameworks.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a code-led learning path for LangChain and LangGraph projects and can work in Python with Git and an LLM provider.

Who it is for

  • Developers with basic software-engineering and Python familiarity who want practical experience building AI-agent applications.
  • Learners who can work with Git, environment variables, testing and debugging, and have access to an LLM.

Documented uses

  • Build a search agent using tools and structured outputs.
  • Create a retrieval-augmented generation application using a document collection or vector database.
  • Study reflection and self-correction patterns in agent workflows.
  • Explore tool calling and code-interpreter applications through project examples.
Before you choose

Requirements & limits

The course material is in a public GitHub repository identified as Apache-2.0 licensed. The README links to a Udemy course; the repository's setup instructions are separate from that course link.

  • The README describes the material as not aimed at beginners and expects basic software-engineering familiarity.
  • The listed learning projects include both branches in this repository and external repositories with their own setup steps.
  • The README highlights LangChain and LangGraph and also names Pinecone, FAISS and Streamlit among its technology stack.
  • The README's broader learning objectives include production deployment, while its concrete setup procedure demonstrates local branch-based project execution.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and follow a branch-based project

Prepare the repository and follow a documented branch-based exercise workflow.

Before you start

  • Git and Python 3.10+
  • Basic familiarity with Python, environment variables, classes, testing and debugging
  • uv, Poetry or Pipenv; the README excludes Conda
  • Access to an LLM, such as Ollama or a cloud provider

Steps

  1. Clone the repository and change into its directory.

  2. Check out a branch-based project such as project/hello-world or project/code-interpreter.

  3. For the listed uv workflow, run uv sync and then uv run python main.py.

  4. Review the commit history with git log --oneline to follow the learning progression.

Expected outcome

A local project environment and a commit-by-commit path through the selected example.

Guide limitations

  • The README's execution commands specifically use uv; external project repositories have separate setup instructions.
  • LLM access is a prerequisite, but project-specific credentials and provider configuration are not detailed in these general steps.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
8 October 2026
Original website title
emarco177/langchain-course · Official homepage title · retained in its source language
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