https://github.com/temporalio/edu-ai-workshop-foundations
https://github.com/temporalio/edu-ai-workshop-foundations
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/temporalio/edu-ai-workshop-foundations
- Owner: temporalio
- License: mit
- Created: 2025-08-08T14:41:41.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-11-20T19:07:00.000Z (7 months ago)
- Last Synced: 2025-11-20T21:06:06.054Z (7 months ago)
- Language: Jupyter Notebook
- Size: 9.88 MB
- Stars: 11
- Watchers: 0
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
# Foundations of Durable AI with Temporal
This repository contains a hands-on workshop demonstrating how to build AI agents with Temporal Workflows.
The workshop teaches the progression from fixed flow AI applications to production-ready, durable systems that handle failures gracefully and support human-in-the-loop interactions.
## How to Use this Repository
1. To try this workshop, use in Codespace so that you don't need to download any software on their machines. To do so, refer to [this document](./codespaces.md).
2. This repository contains the Jupyter notebooks under [the notebooks directory](./notebooks). The notebooks are used as an educational tool for to get practice with being hands-on with Temporal.
3. The slides that accompany the Jupyter notebooks are [here](/assets/slides).
4. When there is a a little icon of a person at a keyboard on the bottom right of the slides, this lets you know that it's time to get hands-on and move to the notebooks.
- If you start running out of time, in the `solution` directories, there are `solution` files (e.g. `notebooks/Solutions_03_Human_in_the_Loop/03_Human_in_the_Loop_Solution.ipynb`) with filled out versions of the notebooks
5. There will also be time for you to practice working independently with the material in the [exercises directory](./exercises/). The Jupyter notebooks will reference when it's time to do work on an exercise directory.
6. For the self-serve version of this workshop (no need for slides), refer to the `self-serve-version` branch on this repository.
## Workshop Overview
This workshop demonstrates four key concepts:
1. **Fixed flow AI application** - A simple research agent that calls an LLM and generates a PDF report
2. **Durable Execution** - The same agent built with Temporal workflows for fault tolerance and automatic retries
3. **Human-in-the-Loop** - Adding Temporal Signals to enable human decision-making within AI workflows
4. **AI Agents** - An introduction into the Agentic Loop
## Repository Structure
```
├── notebooks/ # Interactive Jupyter notebooks for the workshop
│ ├── Solution # Solutions for the code-alongs during the workshop
│ ├── Content # Jupyter notebooks to run during workshop
├── exercises/ # Hands-on exercises for the workshop
│ ├── Practice # Every chapter will have a Practice dir where students do their work in
│ ├── Solution # Every chapter will have a Solution dir where students can refer
├── demos/ # Directory for demos for the instructor
│ ├── module_one_01_foundations_ai/ # Simple fixed flow
│ ├── module_one_02_adding_durability/ # Temporal-based durable workflow
│ └── module_one_03_human_in_the_loop/ # Adding human in the loop into our application
| └── module_one_04_agentic_loop/ # Agent that can make its own decisions
| └── README.md # Instructions on how to run demos
```
## Prerequisites
- [OpenAI API key](https://platform.openai.com/api-keys)
## Running the Workshop: Codespaces
You can run this workshop on Codespaces as an Exercise Environment.
You can launch an exercise environment for this course using GitHub Codespaces by following [this](codespaces.md) walkthrough.
## Key Learning Outcomes
By completing this workshop, you'll learn:
1. **Why AI Agents are Distributed Systems** - Understanding the complexity that emerges when AI agents call external APIs and other services
2. **Durability and Fault Tolerance** - How Temporal workflows provide automatic recovery from failures without losing progress
3. **Human-in-the-Loop Patterns** - Using Temporal Signals to incorporate human decision-making into AI workflows
## Workshop Structure
### Part 1: Building Your First AI Fixed Flow
- Creating basic LLM interactions
- Calling Actions from your LLM interactions
- Identifying distributed systems challenges
### Part 2: Adding Durability
- Introduction to Temporal
- Converting agents to durable Workflows
- Implementing automatic retries
- Monitoring Workflow Execution
### Part 3: Human Integration
- Temporal Signals for real-time communication
- Workflow pause/resume patterns
- Sending Queries to your Workflows
### Part 4: AI Agents
- Introduction to Agentic loop
- Determining when the goal is complete
## Contributing
This workshop is designed for educational purposes. Feel free to:
- Submit issues for bugs or unclear instructions
- Propose improvements to the examples
- Share your own AI agent implementations