AI Agent Workshop is an interactive Python codebase and guide for creating autonomous AI Agents. It covers agent architectures, tool integrations, memory management, and prompt engineering using the OpenAI API. Participants implement task automation agents, conversational assistants, and retrieval-based QA systems, enabling hands-on understanding of agent behavior, error handling, and performance optimization through practical exercises.
AI Agent Workshop is an interactive Python codebase and guide for creating autonomous AI Agents. It covers agent architectures, tool integrations, memory management, and prompt engineering using the OpenAI API. Participants implement task automation agents, conversational assistants, and retrieval-based QA systems, enabling hands-on understanding of agent behavior, error handling, and performance optimization through practical exercises.
AI Agent Workshop is a comprehensive repository offering practical examples and templates for developing AI Agents with Python. The workshop includes Jupyter notebooks demonstrating agent frameworks, tool integrations (e.g., web search, file operations, database queries), memory mechanisms, and multi-step reasoning. Users learn to configure custom agent planners, define tool schemas, and implement loop-based conversational workflows. Each module presents exercises on handling failures, optimizing prompts, and evaluating agent outputs. The codebase supports OpenAI’s function calling and LangChain connectors, allowing seamless extension for domain-specific tasks. Ideal for developers seeking to prototype autonomous assistants, task automation bots, or question-answering agents, it provides a step-by-step path from basic agents to advanced workflows.
Who will use AI Agent Workshop?
AI developers
Machine learning researchers
Data scientists
Software engineers
Students in AI and NLP
How to use the AI Agent Workshop?
Step1: Clone the repository from GitHub.
Step2: Install dependencies via pip install -r requirements.txt.
Step3: Set your OpenAI API key in environment variables.
Step4: Explore provided Jupyter notebooks in the tutorials folder.
Step5: Run example notebooks to observe agent behaviors.
Step6: Modify tool configurations or prompts to create custom agents.
Step7: Extend workflows by adding new tools and memory modules.
Platform
mac
windows
linux
AI Agent Workshop's Core Features & Benefits
The Core Features
Modular agent framework in Python
Jupyter notebook tutorials
Tool integration modules (web search, file ops, DB queries)
Memory management examples
Multi-step reasoning and planner setup
Error handling and evaluation scripts
OpenAI function calling demos
LangChain connector support
The Benefits
Accelerates AI agent prototyping
Hands-on guided learning
Extensible templates and modules
Reusable code for production
Practical exercises with real tools
Enhances understanding of agent workflows
AI Agent Workshop's Main Use Cases & Applications
Building conversational customer support assistants
Automating data analysis and report generation
Creating retrieval-based question-answering systems
Orchestrating web-scraping and file-processing agents
Prototyping custom task automation workflows
FAQs of AI Agent Workshop
What is AI Agent Workshop?
Which programming languages does the workshop use?