AI Coding With Python (2026)

Python is the most popular language for AI development - and for good reason. It has the best SDK support, the largest community, and the most tutorials. Here's how to get started.

Why Python for AI

First-class SDK support. Anthropic, OpenAI, Google - every major AI provider ships a Python SDK first.

Data ecosystem. NumPy, Pandas, scikit-learn, PyTorch. If you need to process data before or after AI calls, Python has the tools.

Community. The majority of AI tutorials, examples, and Stack Overflow answers are in Python.

Simplicity. Python's readable syntax makes AI code easy to write and maintain.

Getting Started

Install an AI SDK

The two most popular choices:

Anthropic (Claude): pip install anthropic - My recommended starting point. See Best AI APIs for why.

OpenAI: pip install openai - Largest ecosystem, most tutorials.

Your First AI Call

The pattern is identical across providers:

Import the client library
Initialize with your API key
Send a message with a system prompt and user input
Process the response

Key parameters to understand:

model - Which AI model to use
max_tokens - Maximum response length
temperature - Creativity level (0-1)
system - Behavioral instructions for the AI

Common Python AI Patterns

Streaming

For chat interfaces, stream responses token by token instead of waiting for the complete response. Use the SDK's streaming option and iterate over chunks.

Async Operations

For web applications, use async API calls (asyncio) so your server can handle other requests while waiting for AI responses. Both Anthropic and OpenAI SDKs support async.

Structured Output

Parse AI responses into Python objects using Pydantic models. This gives you type-safe, validated AI output that integrates cleanly with your application code.

Batch Processing

Process multiple AI requests efficiently:

Use asyncio.gather() for concurrent requests
Respect rate limits with semaphores
Track progress for long-running batch jobs

Building Real Features

Chat Application

Combine streaming + conversation history + system prompts. The classic AI application.

Data Analysis Pipeline

Feed data to AI for analysis: data in → structured analysis out → visualization. Python's data tools make pre/post-processing seamless.

Content Generation System

Template-based content creation: define templates, fill with AI-generated content, validate output. Great for product descriptions, reports, or emails.

RAG System

Retrieval-Augmented Generation in Python:

Embed documents with an embedding API
Store in a vector database (Pinecone, Chroma, pgvector)
Query with user questions
Feed relevant context to the AI

Python vs TypeScript for AI

| Factor | Python | TypeScript | |--------|--------|-----------| | AI SDK support | First-class | First-class | | Data processing | Superior (NumPy, Pandas) | Good (but fewer options) | | Web applications | Django/Flask/FastAPI | Next.js (stronger) | | ML/Training | Essential | Not practical | | Community size (AI) | Larger | Growing fast |

My approach: Python for data-heavy AI work and ML. TypeScript for web applications with AI features. Use Claude Code to build in either language.

Next Steps

Install the Anthropic SDK and make your first API call
Build a streaming chat to understand the fundamentals
Add structured outputs for reliable AI features
Build a RAG system using your own data

For more AI coding patterns, see Generative AI Code Examples. For the broader AI development picture, see How to Code AI.

AI Coding With Python: The Essential Starting Point