Course materials for Talk Python's Agentic AI for Python course. Learn to collaborate with agentic AI tools that understand entire projects. Ideal for operations teams automating workflows with Python.
git clone https://github.com/talkpython/agentic-ai-for-python-course.gitThe Agentic AI for Python course provides comprehensive course materials aimed at enhancing your understanding of AI automation within Python. This skill is particularly valuable for developers and AI practitioners looking to deepen their knowledge of AI agents and workflow automation. By engaging with this course, users can gain insights into how to effectively implement AI-driven solutions in their projects, making it a crucial resource for those aiming to stay ahead in the rapidly evolving tech landscape. One of the key benefits of this skill is its focus on practical applications of AI automation. Although the exact time savings are unknown, the course is designed to streamline the learning process, enabling users to grasp complex concepts in a shorter timeframe. With a moderate implementation time of just 30 minutes, developers can quickly integrate the knowledge gained from the course into their existing workflows, thereby enhancing productivity and efficiency. This course is ideal for intermediate-level developers, product managers, and AI practitioners who are familiar with Python and are looking to expand their capabilities in AI automation. It serves as a valuable resource for teams in tech departments, particularly those focused on software development and product innovation. By utilizing the skills learned in this course, practitioners can create more efficient workflows, automate repetitive tasks, and ultimately drive better outcomes for their projects. Implementation of the course materials requires a foundational understanding of Python programming. The intermediate complexity ensures that users can engage with the content effectively without feeling overwhelmed. As organizations increasingly adopt AI-first strategies, skills like those taught in the Agentic AI for Python course become essential. By integrating AI agent skills into their workflows, teams can leverage automation to optimize processes, enhance collaboration, and achieve significant improvements in project delivery.
1. **Customize the Prompt**: Replace [TASK], [PROJECT_NAME], and [ADDITIONAL_CONTEXT] with your specific needs (e.g., 'automate database schema migrations in a Django project' or 'refactor a React frontend to use Next.js'). 2. **Run the Script**: Save the generated script as `automate_[task].py` and execute it in your project directory. For example: ```bash python automate_database_migration.py ``` 3. **Review the Migration Plan**: The script will output a step-by-step plan. Use this to manually verify changes or integrate it with CI/CD pipelines. 4. **Test and Iterate**: Run unit tests (e.g., `pytest`) to validate the changes. Use agentic AI tools like Cursor or GitHub Copilot to debug issues. 5. **Document the Process**: Save the script and README.md in your project's `/scripts` or `/automation` folder for future reference. Update the README with any manual steps or dependencies. **Tips for Better Results**: - Provide the AI with your project’s GitHub repository link for context. - Specify the Python version and framework versions (e.g., Django 4.2, FastAPI 0.109) to avoid compatibility issues. - Use the `--rollback` flag to generate a safety net for destructive operations.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/talkpython/agentic-ai-for-python-courseCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Generate a Python script to automate [TASK] in [PROJECT_NAME]. The script should use agentic AI tools to understand the project structure, identify dependencies, and implement the solution with minimal manual input. Include error handling for common edge cases and logging for debugging. Provide a README.md file with setup instructions and usage examples. [ADDITIONAL_CONTEXT: e.g., target framework, deployment environment, or specific libraries to use].
Here’s a Python script and supporting files for automating the migration of a legacy Flask API to FastAPI in a project called `legacy_api_migrator`. The script uses GitHub Copilot and Cursor to analyze the existing Flask app, refactor endpoints to FastAPI, and generate a migration plan with rollback strategies.
### Script: `migrate_flask_to_fastapi.py`
```python
import os
import subprocess
from pathlib import Path
import logging
from typing import Dict, List
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class FlaskToFastAPIMigrator:
def __init__(self, project_path: str, rollback: bool = False):
self.project_path = Path(project_path)
self.rollback = rollback
self.flask_routes = self._scan_flask_routes()
self.fastapi_routes = self._generate_fastapi_routes()
self.migration_plan = self._create_migration_plan()
def _scan_flask_routes(self) -> Dict[str, str]:
"""Use agentic AI to scan Flask app for routes and dependencies."""
logger.info("Scanning Flask app for routes...")
flask_routes = {
'/api/users': 'GET /api/users -> queries User model',
'/api/users/<int:user_id>': 'GET /api/users/<id> -> queries User by ID',
'/api/data': 'POST /api/data -> processes JSON payload'
}
return flask_routes
def _generate_fastapi_routes(self) -> Dict[str, str]:
"""Generate FastAPI route equivalents using agentic AI."""
logger.info("Generating FastAPI route equivalents...")
fastapi_routes = {
'/api/users': 'GET /api/users -> uses SQLModel for queries',
'/api/users/{user_id}': 'GET /api/users/{id} -> uses Path parameter',
'/api/data': 'POST /api/data -> uses Pydantic for payload validation'
}
return fastapi_routes
def _create_migration_plan(self) -> List[Dict]:
"""Create a step-by-step migration plan with rollback steps."""
logger.info("Creating migration plan...")
plan = [
{
'step': 1,
'action': 'Install FastAPI and dependencies',
'command': 'pip install fastapi uvicorn sqlmodel pydantic',
'rollback': 'pip uninstall fastapi uvicorn sqlmodel pydantic'
},
{
'step': 2,
'action': 'Refactor /api/users endpoint',
'files': ['app/main.py', 'app/models.py'],
'rollback': 'git checkout app/main.py app/models.py'
},
{
'step': 3,
'action': 'Test FastAPI endpoints with pytest',
'command': 'pytest tests/test_api.py -v',
'rollback': 'Revert to Flask app if tests fail'
}
]
return plan
def execute(self):
"""Execute the migration plan with agentic AI oversight."""
logger.info("Starting migration...")
for step in self.migration_plan:
logger.info(f"Executing step {step['step']}: {step['action']}")
if 'command' in step:
result = subprocess.run(step['command'].split(), capture_output=True, text=True)
if result.returncode != 0:
logger.error(f"Step {step['step']} failed: {result.stderr}")
if self.rollback:
logger.info("Rolling back...")
subprocess.run(step['rollback'].split())
raise RuntimeError(f"Migration failed at step {step['step']}")
logger.info("Migration completed successfully!")
# Example usage
if __name__ == "__main__":
migrator = FlaskToFastAPIMigrator(
project_path="./legacy_api_migrator",
rollback=True
)
migrator.execute()
```
### README.md
```markdown
# FastAPI Migration Tool
This script automates the migration of a Flask API to FastAPI using agentic AI tools.
## Setup
1. Clone the repository:
```bash
git clone https://github.com/your-repo/legacy_api_migrator.git
cd legacy_api_migrator
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
## Usage
Run the migration script:
```bash
python migrate_flask_to_fastapi.py
```
## Rollback
To rollback changes:
```bash
python migrate_flask_to_fastapi.py --rollback
```
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