https://github.com/readytensor/rt-agentic-ai-cert-week5
Hands-on code and examples for Week 5 of the Ready Tensor Agentic AI Developer Certification Program – building agentic systems with LangGraph, custom tools, and multi-agent workflows.
https://github.com/readytensor/rt-agentic-ai-cert-week5
agentic-ai ai-agents certification langchain langgraph langsmith llms ready-tensor
Last synced: about 1 month ago
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Hands-on code and examples for Week 5 of the Ready Tensor Agentic AI Developer Certification Program – building agentic systems with LangGraph, custom tools, and multi-agent workflows.
- Host: GitHub
- URL: https://github.com/readytensor/rt-agentic-ai-cert-week5
- Owner: readytensor
- License: other
- Created: 2025-06-15T22:35:50.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-08-23T06:17:14.000Z (10 months ago)
- Last Synced: 2025-08-24T09:28:07.107Z (10 months ago)
- Topics: agentic-ai, ai-agents, certification, langchain, langgraph, langsmith, llms, ready-tensor
- Language: Python
- Homepage: https://app.readytensor.ai/publications/HrJ0xWtLzLNt
- Size: 4.32 MB
- Stars: 3
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Ready Tensor Agentic AI Certification – Unit 5
This repository contains lesson materials, code examples, reference scripts for **Unit 5** of the [Agentic AI Developer Certification Program](https://app.readytensor.ai/publications/HrJ0xWtLzLNt) by Ready Tensor. This week marks the transition from traditional prompt pipelines to building **agent-based AI systems** using tools like **LangGraph**, **LangChain**, and **LangSmith**.
---
## What You'll Learn
- When to use workflows vs. agents
- How to build and run LangGraph projects
- How to trace and debug agentic flows with LangSmith
- How to integrate tools — both built-in and custom — into agent loops
- How to manage shared state and node-level behavior in LangGraph
---
## Lessons in This Repository
### 1. From Workflows to Agents: When Predictable Paths Aren’t Enough
Learn when and why to move beyond static LLM workflows toward more flexible, adaptive agentic systems.
### 2a. Building Agentic Systems at Scale: An Introduction to LangGraph
Get introduced to LangGraph — a framework for designing agentic flows as graphs of nodes, edges, and state — built for scalability and control.
### 2b. Your First LangGraph Project: Building a Joke Bot
Build a simple, non-LLM LangGraph joke bot to learn about graph structure, state objects, and routing logic.
### 2c. Agentic AI With LangGraph: Building a Writer–Critic Loop
Enhance your joke bot with LLMs and a writer–critic architecture. The agent generates, evaluates, and refines its output before sharing it with the user.
### 3. Inside the Mind of an Agent: Observability with LangSmith
Use LangSmith to trace every step of your agent's process — from LLM calls to tool usage — with full visibility into the graph’s state transitions.
### 4a. Beyond Conversation: Giving Your AI Agent the Power of Tools
Integrate built-in tools into your LangGraph workflow and design a **Think–Act–Think** loop. Let your agent retrieve real data and make decisions dynamically.
### 4b. Custom Tools, Custom Powers: Extending Your Agent’s Capabilities
Build your own tools from scratch and register them with your agent. Extend functionality in ways that suit your own domain or product use case.
---
## Repository Structure
```txt
rt-agentic-ai-cert-unit5/
├── code/
│ ├── custom_tools.py # Custom tool implementations for Lesson 3b
│ ├── llm.py # LLM utility wrapper
│ ├── paths.py # Standardized file path management
│ ├── prompt_builder.py # Modular prompt construction functions
│ ├── run_wk5_l2b_pyjokes_joke_bot.py # Lesson 2b: Run joke-bot using pyjokes
│ ├── run_wk5_l2c_llm_joke_bot.py # Lesson 2c: Run joke-bot using ai agents
│ ├── run_wk5_l4b_custom_tools.py # Lesson 4b: Run agent with custom tools
│ └── utils.py # Common utilities
├── config/
│ ├── config.yaml # Configuration file for tool registration or agent setup
│ └── prompt_config.yaml # Prompt configurations for agents
├── lessons/ # Lesson content and images
├── outputs/
│ └── graph.png # Example LangGraph visualization
├── .env.example # Sample environment variable file (e.g., Groq API key)
├── .gitignore
├── LICENSE
├── README.md # You are here
└── requirements.txt # Required Python dependencies
```
---
## Installation & Setup
1. **Clone the repository:**
```bash
git clone https://github.com/readytensor/rt-agentic-ai-cert-week5.git
cd rt-agentic-ai-cert-week5
```
2. **Install dependencies:**
```bash
pip install -r requirements.txt
```
3. **Set up your environment variables:**
Copy the `.env.example` to `.env` and update the values (e.g., Groq API key):
```bash
cp .env.example .env
```
You can get your API key from [Groq](https://console.groq.com/).
---
## License
This project is licensed under the CC BY-NC-SA 4.0 License – see the [LICENSE](LICENSE) file for details.
---
## Contact
**Ready Tensor, Inc.**
- Email: contact at readytensor dot com
- Issues & Contributions: Open an issue or PR on this repo
- Website: [https://readytensor.ai](https://readytensor.ai)