Agentic Ai Engineering

Agentic AI Engineering

[](https://www.python.org/) [](https://langchain.com/) [](https://langchain-ai.github.io/langgraph/) [](LICENSE)

Agentic AI Engineering is a production-grade engineering resource for building modern agentic AI systems with LangChain, LangGraph, RAG, MCP, local models, and deployable Python services.

The repository leads toward the architecture implemented in Chapter 5: a multi-node LangGraph assistant connected to a standalone MCP server, with intent routing, tool execution, response summarization, email tooling, math tools, automated tests, and GitHub Actions CI. Earlier chapters build the required layers underneath it: provider abstraction, LCEL orchestration, vector retrieval, memory, ReAct agents, router graphs, sequential workflows, multi-agent collaboration, and human-in-the-loop control.

This is not a beginner chatbot walkthrough. It is a structured engineering path for developers building systems that need state, tools, routing, retrieval, observability, modularity, and model-provider flexibility.

What You'll Build

• Multi-node LangGraph assistant with router, execution, summarization, and conversation nodes.
• MCP tool server exposing isolated math and email tools over a decoupled server boundary.
• Tool-using ReAct workflows that call external capabilities through typed tool contracts.
• RAG pipelines using vector stores, embeddings, retrieval chains, and local document context.
• Provider-flexible LLM interfaces across OpenAI, Gemini, and Ollama.
• Agent routing systems for sequential, router-based, ReAct, and multi-agent workflows.
• Human-in-the-loop execution paths for safer agent behavior in production-style flows.
• Tested agent components with pytest coverage for Chapter 5 node behavior and tool contracts.
• CI-backed agent validation through GitHub Actions for repeatable checks on graph and MCP behavior.

Engineering Roadmap

| Chapter | Engineering Milestone | Core Systems | Status | | :-- | :-- | :-- | :-- | | [Chapter 1](./Chapter1/Chapter1.md) | LLM provider foundation | OpenAI, Gemini, Ollama, streaming, system prompts | Complete | | [Chapter 2](./Chapter2/Chapter2.md) | LangChain orchestration layer | LCEL, chains, tool binding, router and sequential composition | Complete | | [Chapter 3](./Chapter3/Chapter3.md) | Retrieval and memory infrastructure | Conversation memory, entity tracking, ChromaDB, FAISS, Sentence Transformers | Complete | | [Chapter 4](./Chapter4/Chapter4.md) | Agent graph patterns | LangGraph StateGraph, ReAct, routers, sequential agents, multi-agent collaboration, HITL | Complete | | [Chapter 5](./Chapter5/SimpleChatAgent/README.md) | Production-style agent runtime | Multi-node LangGraph assistant, MCP server integration, tool isolation, tests, CI | Complete |

System Architecture Focus

Chapter 5 is the reference implementation for the repository's production architecture:

• LangGraph StateGraph coordinates routing, execution, summarization, and conversation paths.
• Router node classifies user intent before allocating work to the right execution path.
• Execution node binds LangChain tool-calling agents to MCP-exposed capabilities.
• Summarization node converts raw tool responses into user-facing output.
• Conversation node handles non-tool interactions without invoking the heavier tool path.
• FastMCP server isolates operational tools from the agent runtime.
• Pytest suite and GitHub Actions CI validate tool behavior, routing assumptions, and graph execution contracts.

Start with the full implementation here: [Chapter 5: Multi-Node LangGraph Agent with MCP Tools](./Chapter5/SimpleChatAgent/README.md).

Tech Stack

• Agent frameworks: LangChain, LangGraph
• Protocols: Model Context Protocol (MCP), FastMCP
• LLM providers: OpenAI, Google Gemini, Ollama
• Retrieval: ChromaDB, FAISS, Sentence Transformers
• Backend tooling: Python, FastAPI, pytest, GitHub Actions
• ML ecosystem: PyTorch, TensorFlow, Hugging Face

Repository Structure

.
├── Chapter1/                         # LLM providers, streaming, prompt foundations
├── Chapter2/                         # LangChain LCEL, chains, tools, routing
├── Chapter3/                         # Memory, retrieval, vector stores, RAG
├── Chapter4/                         # LangGraph agent patterns and HITL workflows
├── Chapter5/
│   └── SimpleChatAgent/
│       ├── AganticAssistant/         # LangGraph assistant runtime
│       ├── McpServer/                # MCP tool server
│       ├── tests/                    # pytest coverage for tools and nodes
│       └── demo/                     # screenshots and demo recordings
├── requirements.txt
└── README.md

Quick Start

1. Clone the Repository

git clone git@github.com:zkzkGamal/agentic-ai-engineering.git
cd agentic-ai-engineering

Or with HTTPS:

git clone https://github.com/zkzkGamal/agentic-ai-engineering.git
cd agentic-ai-engineering

2. Create an Environment

python3 -m venv venv
source venv/bin/activate

For Windows:

venv\Scripts\activate

3. Install Dependencies

Install the full repository stack:

pip install -r requirements.txt

Or install only the chapter you are running:

pip install -r Chapter1/requirements.txt
pip install -r Chapter2/requirements.txt
pip install -r Chapter3/requirements.txt
pip install -r Chapter4/requirements.txt
pip install -r Chapter5/SimpleChatAgent/AganticAssistant/requirements.txt
pip install -r Chapter5/SimpleChatAgent/McpServer/requirements.txt

4. Configure Environment Variables

Each chapter that needs credentials includes its own .env.example.

cp Chapter1/.env.example Chapter1/.env
cp Chapter2/.env.example Chapter2/.env
cp Chapter3/.env.example Chapter3/.env
cp Chapter4/.env.example Chapter4/.env

Chapter 5 has separate runtime boundaries for the assistant and MCP server:

cp Chapter5/SimpleChatAgent/AganticAssistant/.env.example Chapter5/SimpleChatAgent/AganticAssistant/.env
cp Chapter5/SimpleChatAgent/McpServer/.env.example Chapter5/SimpleChatAgent/McpServer/.env

Running the Chapter 5 Agent System

Start the MCP server:

python3 Chapter5/SimpleChatAgent/McpServer/main.py

In another terminal, run the LangGraph assistant:

python3 Chapter5/SimpleChatAgent/AganticAssistant/main.py

Run the Chapter 5 test suite:

cd Chapter5/SimpleChatAgent
pytest tests

Chapter Details

[Chapter 1: LLM Provider Foundation](./Chapter1/Chapter1.md)

Direct integration with OpenAI, Gemini, and Ollama. Covers provider calls, streaming responses, prompt structure, and model-facing interfaces used later by orchestration and agent layers.

[Chapter 2: LangChain Orchestration Layer](./Chapter2/Chapter2.md)

Builds LCEL pipelines, sequential chains, router chains, prompt templates, output handling, and tool binding. This chapter establishes the composition patterns used by larger agent systems.

[Chapter 3: Retrieval and Memory Infrastructure](./Chapter3/Chapter3.md)

Implements conversation memory, entity tracking, embeddings, local vector stores, and RAG flows with ChromaDB, FAISS, and Sentence Transformers.

[Chapter 4: Agent Graph Patterns](./Chapter4/Chapter4.md)

Moves from chains into stateful agent workflows with LangGraph StateGraph. Covers ReAct, router agents, sequential agents, multi-agent collaboration, self-refine loops, and human-in-the-loop control.

[Chapter 5: Multi-Node LangGraph and MCP Runtime](./Chapter5/SimpleChatAgent/README.md)

Implements a decoupled agent runtime with a LangGraph assistant and MCP server. The assistant routes requests across specialized nodes, invokes MCP tools, summarizes tool outputs, and validates behavior with automated tests and CI.

Production Repositories

| Repository | Focus | | :-- | :-- | | zkzkAgent | Production agent platform work and applied agent engineering patterns. | | concurrent-llm-serving | Concurrent LLM serving patterns for higher-throughput inference systems. |

Author

Zkaria Gamal - AI Engineer

• GitHub: @zkzkGamal
• LinkedIn: Zkaria Gamal

License

This repository is available under the [MIT License](LICENSE).

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: zkzkGamal
• Source: zkzkGamal/agentic-ai-engineering
• License: MIT
• Homepage: https://zkaria-gamal.vercel.app/post/35

Install and usage instructions live in the source repository linked above.