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Langgraph AI

Langgraph AI is a public repository of Jupyter notebooks, tutorials and implementation examples for building AI applications with LangGraph, including agentic RAG, workflow orchestration, human-in-the-loop systems and MCP clients and servers.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Langgraph AI — 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 to study or adapt a specific LangGraph implementation pattern, such as agentic RAG, workflow orchestration or MCP development.

Who it is for

  • Developers learning or implementing LangGraph-based AI applications
  • Practitioners exploring agentic RAG patterns such as adaptive routing and self-correction
  • Developers studying MCP client and server implementation examples

Documented uses

  • Study notebook examples for RAG fundamentals and advanced RAG agents.
  • Explore workflow patterns such as routing, parallelization and orchestrator-worker designs.
  • Review examples of human-in-the-loop systems and tool-calling patterns.
  • Use MCP examples as a starting point for studying client and server implementations.
Before you choose

Requirements & limits

The repository is public and declares an MIT license.

  • The repository is a collection of examples and tutorials rather than a framework package.
  • Each project may have its own dependencies and setup instructions.
  • The setup documentation lists Python 3.10 or higher, Git, and UV or pip as prerequisites.
  • The illustrated environment configuration uses an Anthropic API key; LangChain credentials are required only when tracing is enabled.

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

Prepare a local environment for a repository project

Set up a Python environment and Jupyter kernel for working with a selected project in the repository.

Before you start

  • Git
  • Python 3.10 or higher; the README notes requirements may depend on the project
  • UV or pip
  • The selected project's requirements file

Steps

  1. Clone the repository and navigate to the project directory you want to use.

  2. Create a Python 3.10 virtual environment with UV and activate it.

  3. Install that project's dependencies from its requirements file using UV or pip.

  4. For notebook use, install ipykernel in the environment and register it as a Jupyter kernel.

Expected outcome

A project-specific Python environment with its dependencies installed and, if needed, a selectable Jupyter kernel.

Guide limitations

  • The repository contains multiple independent projects with their own requirements and setup instructions.
  • The README's example project path should be checked against the selected project's own instructions.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Agent orchestration

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Langgraph AIStudy notebook examples for RAG fundamentals and advanced RAG agents.Cloud, ServerThe repository is public and declares an MIT license.
AgeniteDefine typed tools with JSON Schema validation and error handling.CLI
VeldraCreate an agent with a policy, tools, skills, knowledge base and reasoning method.Self-hostedThe documented deployment is self-hosted and uses a sign-in gate after a first-run administrator account is created.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
piyushagni5/langgraph-ai · Official homepage title · retained in its source language
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