O'Reilly AI Agents

v1.0.0Coding Agentsstable

An introduction to the world of AI Agents

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What is O'Reilly AI Agents?

O'Reilly AI Agents is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to introduction to the world of ai agents

An introduction to the world of AI Agents

This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.

Features

  • An introduction to the world of AI Agents

Use Cases

Study AI agent frameworks
Compare AutoGen, CrewAI approaches
Learn agentic AI concepts
sinanuozdemir

Maintainer

LicenseMIT
Languagejupyter notebook
Versionv1.0.0
UpdatedMay 14, 2026
Statushealthy
Maintenanceactive

Works with

ClaudeOpenAIwindowsmacoslinux

Installation

Manual Installation

npx oreilly-ai-agents

Configuration

Configuration Details

Config File

claude_desktop_config.json

Performance

Response Metrics

Response Time< 200ms
ThroughputMedium

Resource Usage

Memory UsageLow
CPU UsageLow

How to Set Up and Use O'Reilly AI Agents

The O'Reilly AI Agents repository is a comprehensive educational resource accompanying the O'Reilly live training course on building AI agents, covering frameworks including LangGraph, CrewAI, AutoGen, SmolAgents, Google ADK, and the OpenAI Agents SDK. It contains a large collection of Jupyter notebooks demonstrating multi-agent systems, RAG workflows, Plan-and-Execute patterns, Reflection agents, computer-use automation, and MCP integration examples. Developers learning to build production-ready agentic AI systems will find it a practical, code-first reference spanning both prototyping and deployment patterns.

Prerequisites

  • Python 3.11 installed (virtual environment recommended)
  • API keys for the LLM providers you plan to use (OpenAI, Anthropic, Google, or others covered in notebooks)
  • Jupyter or JupyterLab installed to run the notebooks
  • Git to clone the repository
1

Clone the repository

Clone the course repository from GitHub to get all notebooks and supporting files.

git clone https://github.com/sinanuozdemir/oreilly-ai-agents.git
cd oreilly-ai-agents
2

Create and activate a virtual environment

Isolate the course dependencies in a Python virtual environment to avoid conflicts with other projects.

python3.11 -m venv venv
source venv/bin/activate  # macOS/Linux
# On Windows: venv\Scripts\activate
3

Install Python dependencies

Install all required packages from the requirements file. This includes LangGraph, CrewAI, AutoGen, and other frameworks used across the notebooks.

pip install -r requirements.txt
4

Set your API keys

Export the API keys for the LLM providers used in the notebooks. Most notebooks require at least one of these.

export OPENAI_API_KEY="sk-your-openai-key"
export ANTHROPIC_API_KEY="sk-ant-your-key"
export GOOGLE_API_KEY="your-google-key"
5

Launch Jupyter and open a notebook

Start JupyterLab or the classic notebook server and navigate to the notebook you want to study.

jupyter lab

O'Reilly AI Agents Examples

Client configuration

This repository is a Jupyter notebook course, not a standalone MCP server. The minimal environment setup to run the notebooks is shown here.

{
  "setup": "python virtual environment",
  "python_version": "3.11",
  "install": "pip install -r requirements.txt",
  "required_env": [
    "OPENAI_API_KEY",
    "ANTHROPIC_API_KEY"
  ]
}

Topics and notebooks to explore

Key areas covered in the course notebooks that you can work through to learn agentic AI patterns.

- Multi-agent systems with LangGraph (parallel execution, supervisor patterns)
- Building ReAct agents with tool use and reasoning loops
- CrewAI: role-based agent crews for collaborative tasks
- Microsoft AutoGen: conversational multi-agent programming
- Plan-and-Execute agents: separating planning from action
- MCP integration: connecting agents to external tools via Model Context Protocol
- Agent evaluation: rubrics, alignment scoring, and tool-selection bias analysis
- Computer use: vision-model automation with reasoning LLMs

Troubleshooting O'Reilly AI Agents

pip install -r requirements.txt fails with dependency conflicts

Ensure you are using Python 3.11 specifically, as some agent frameworks have strict version requirements. Create a fresh virtual environment and retry the install.

Notebooks fail with API authentication errors

Each notebook uses different providers. Check which framework the notebook imports (openai, anthropic, google.generativeai) and ensure you have exported the corresponding API key in your session before launching Jupyter.

LangGraph or CrewAI imports fail even after pip install

Confirm your virtual environment is activated (you should see the venv prefix in your prompt) and that you ran pip install inside that environment, not at the system Python level.

Frequently Asked Questions about O'Reilly AI Agents

What is O'Reilly AI Agents?

O'Reilly AI Agents is a Model Context Protocol (MCP) server that introduction to the world of ai agents It connects AI assistants to external tools and data sources through a standardized interface.

How do I install O'Reilly AI Agents?

Follow the installation instructions on the O'Reilly AI Agents GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.

Which AI clients work with O'Reilly AI Agents?

O'Reilly AI Agents works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.

Is O'Reilly AI Agents free to use?

Yes, O'Reilly AI Agents is open source and available under the MIT license. You can use it freely in both personal and commercial projects.

Browse More Coding Agents MCP Servers

Explore all coding agents servers available in the MCPgee directory. Each server includes setup guides for Claude, Cursor, and VS Code.

Quick Config Preview

{ "mcpServers": { "oreilly-ai-agents": { "command": "npx", "args": ["-y", "oreilly-ai-agents"] } } }

Add this to your claude_desktop_config.json or .cursor/mcp.json

Read the full setup guide →

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