Summary

This repository provides a practical, code-driven masterclass on building AI agents, featuring implementations of over 17 state-of-the-art agentic architectures. Designed for developers, researchers, and AI enthusiasts, it bridges the gap between theoretical concepts and production-ready code using LangChain and LangGraph. The project serves as a living textbook, offering a structured learning path with runnable Jupyter notebooks that demonstrate foundational patterns, multi-agent collaboration, advanced reasoning, and safety-critical applications, ultimately enabling users to master the art of designing intelligent systems.

How It Works

The project leverages LangGraph as the core orchestration framework, enabling the development of complex, stateful, and cyclical agent workflows. Each of the 17+ architectures is implemented end-to-end in a runnable Jupyter notebook, allowing for progressive learning from basic agent enhancements to sophisticated multi-agent and self-aware systems. A key differentiator is the emphasis on quantitative evaluation through an LLM-as-a-Judge pattern, providing objective feedback on agent performance, a critical aspect for production deployment. Examples are grounded in real-world scenarios, making the learned concepts immediately applicable.

Quick Start & Requirements

1. Clone the Repository: git clone https://github.com/FareedKhan-dev/all-agentic-architectures.git
2. Set Up Virtual Environment: Use python3 -m venv venv and source venv/bin/activate (or Windows equivalent).
3. Install Dependencies: pip install -r requirements.txt. pygraphviz may be needed for graph visualization.
4. Configure Environment Variables: Create a .env file with API keys for Nebius AI, LangSmith, Tavily Search, and Neo4j credentials. A running Neo4j instance is required.
5. Run Notebooks: Launch Jupyter Notebook (jupyter notebook) and explore the notebooks in numerical order.

Highlighted Details

• Comprehensive implementation of 17+ distinct agentic architectures.
• "Living textbook" approach with runnable Jupyter notebooks for hands-on learning.
• Structured learning path progressing from foundational patterns to advanced multi-agent and self-aware systems.
• Integration of LLM-as-a-Judge for quantitative performance evaluation.
• Real-world application examples across finance, coding, and medical triage.
• Core framework utilizes LangGraph for modern, stateful agent design.

Maintenance & Community

No specific details regarding maintainers, community channels (like Discord/Slack), or a roadmap are provided in the README. Contributions are welcomed via pull requests and issues.

Licensing & Compatibility

This project is licensed under the MIT License, which generally permits broad use, modification, and distribution, including for commercial purposes, with minimal restrictions.

Limitations & Caveats

The setup requires obtaining and configuring multiple API keys for various services (LLMs, search, tracing) and necessitates a running Neo4j instance, which can be a significant barrier to entry. The project's "living textbook" nature suggests ongoing development, but specific details on stability or potential breaking changes are not elaborated upon.