Best AI Agents for Python Developers: Complete 2025 Guide

Python developers have unique needs - from web frameworks to data science, automation to AI/ML. Here are the best AI agents tailored for Python workflows.

Why Python Needs Custom Agents

Python is versatile, which means generic AI often gets it wrong:

• Suggests Django when you need FastAPI
• Uses pandas when NumPy is better
• Ignores PEP 8 and type hints
• Misses Python 3.12+ features

Custom agents solve this.

Best Agents by Use Case

1. Web Development

Django Expert

Enforces Django best practices, ORM optimization, and security.

What it does:

• Uses class-based views correctly
• Implements proper middleware
• Follows Django project structure
• Adds security hardening (CSRF, XSS protection)

Install from: AgentDepot Django collection

FastAPI Pro

Modern async Python API development.

What it does:

• Uses proper type hints and Pydantic models
• Implements async/await correctly
• Adds proper error handling
• Includes OpenAPI documentation

2. Data Science & ML

Pandas Performance Optimizer

Writes efficient pandas code that doesn't kill your RAM.

What it does:

• Uses vectorized operations instead of loops
• Suggests appropriate dtypes
• Implements chunking for large datasets
• Avoids common anti-patterns

NumPy Expert

Scientific computing with proper NumPy usage.

What it does:

• Uses broadcasting correctly
• Suggests efficient array operations
• Implements proper indexing
• Avoids copying when slicing

Scikit-learn Guide

Machine learning with best practices.

What it does:

• Proper train/test splits
• Pipeline usage
• Hyperparameter tuning patterns
• Model evaluation metrics

3. Code Quality

PEP 8 Enforcer

Ensures your Python follows official style guidelines.

What it does:

• Enforces proper naming conventions
• Manages imports correctly
• Sets appropriate line lengths
• Uses f-strings over .format()

Example rule:

Always follow PEP 8:
- snake_case for functions/variables
- PascalCase for classes
- UPPER_CASE for constants
- 4-space indentation
- Max 88 characters per line (Black standard)

Type Hint Master

Adds proper type hints (Python 3.10+ syntax).

What it does:

• Uses modern type syntax (list[str] not List[str])
• Adds return type hints
• Uses TypedDict for structured dicts
• Implements proper Optional/Union usage

# What this agent generates:
def process_users(
    users: list[dict[str, str | int]],
    active_only: bool = False
) -> list[str]:
    """Process user data and return names."""
    return [u["name"] for u in users if not active_only or u.get("active")]

4. Testing

Pytest Pro

Write comprehensive tests automatically.

What it does:

• Creates fixtures properly
• Uses parametrize for multiple test cases
• Implements proper mocking
• Adds docstrings to tests

# AI-generated test with this agent:
import pytest
from myapp.services import UserService

@pytest.fixture
def user_service():
    """Fixture for UserService with mocked database."""
    return UserService(db=MockDatabase())

@pytest.mark.parametrize("username,expected", [
    ("john", True),
    ("", False),
    ("x" * 100, False),
])
def test_validate_username(user_service, username, expected):
    """Test username validation with various inputs."""
    assert user_service.validate(username) == expected

5. Async Python

Asyncio Expert

Proper async/await patterns.

What it does:

• Uses asyncio correctly
• Avoids blocking the event loop
• Implements proper error handling in async code
• Uses async context managers

Example pattern:

async def fetch_data(session: aiohttp.ClientSession, url: str) -> dict:
    """Fetch data from URL with proper error handling."""
    try:
        async with session.get(url) as response:
            response.raise_for_status()
            return await response.json()
    except aiohttp.ClientError as e:
        logger.error(f"Failed to fetch {url}: {e}")
        raise

6. DevOps & Automation

Python CLI Builder

Create professional CLI tools with argparse/Click/Typer.

What it does:

• Proper argument parsing
• Help text generation
• Error handling
• Progress bars for long operations

Docker Python Expert

Containerize Python apps correctly.

What it does:

• Multi-stage builds
• Proper base images (slim, alpine)
• Security best practices
• Caching optimization

# AI-generated Dockerfile with this agent:
FROM python:3.12-slim as builder
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

FROM python:3.12-slim
WORKDIR /app
COPY --from=builder /usr/local/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages
COPY . .
RUN useradd -m appuser && chown -R appuser:appuser /app
USER appuser
CMD ["python", "main.py"]

Installing Python Agents

For Cursor

1. Browse AgentDepot's Python collection
2. Copy the rule you want
3. Create .cursorrules in your project root
4. Paste the rule
5. Start coding!

For Claude Code

# Install MCP server for Python tools
npx @anthropic-ai/mcp install python-tools

# Configure in settings

Combining Multiple Agents

Example .cursorrules for a Django project:

You are an expert Python developer specializing in Django.

Language & Style:
- Python 3.12+
- Type hints everywhere
- Follow PEP 8 (Black formatting, 88 char lines)
- Use f-strings, not .format()

Django Specifics:
- Use class-based views (not function views)
- Implement proper permissions and authentication
- Follow Django project structure
- Use Django ORM efficiently (select_related, prefetch_related)
- Add security middleware

Testing:
- Write tests with pytest-django
- Use fixtures for test data
- Test happy path and edge cases
- Aim for 80%+ coverage

Never:
- Use `any` type
- Skip type hints
- Write code without tests
- Ignore security (CSRF, SQL injection, XSS)

Python-Specific Tips

Tip 1: Specify Python Version

Use Python 3.12 features including:
- PEP 695 type parameter syntax
- PEP 701 f-string improvements
- Enhanced error messages

Tip 2: Mention Your Stack

Tech stack:
- Django 5.0
- PostgreSQL
- Redis for caching
- Celery for async tasks
- pytest for testing

Tip 3: Define Performance Goals

Optimize for:
- Database queries (minimize N+1)
- Memory usage (use generators for large datasets)
- Response time (< 200ms for API endpoints)

Common Python Pitfalls (That Agents Fix)

❌ Mutable Default Arguments

# Bad (AI without agents might do this)
def append_to(element, list=[]):
    list.append(element)
    return list

# Good (Python agent fixes this)
def append_to(element, list=None):
    if list is None:
        list = []
    list.append(element)
    return list

❌ Late Binding Closures

# Bad
functions = [lambda: i for i in range(3)]

# Good (agent knows this pattern)
functions = [lambda i=i: i for i in range(3)]

❌ Inefficient String Concatenation

# Bad
result = ""
for item in items:
    result += str(item)

# Good
result = "".join(str(item) for item in items)

Conclusion

Python AI agents transform how you code:

• ✅ Enforce best practices automatically
• ✅ Avoid common pitfalls
• ✅ Write tests alongside code
• ✅ Maintain consistent style
• ✅ Ship production-ready code faster

Start with 2-3 agents that match your current project, then explore more.

Browse Python agents on AgentDepot →