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Building a Coding Agent: Python/Django Edition

August 14, 2025, 02:00 Europe/Berlin
Alexey Grigorev
ai-agents coding-agents django python llm-engineering

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Open the canonical pages, recording, materials, and code repo.

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For a newer, combined take, see Coding Agent with Skills.

We build a small project bootstrapper for Django, a coding agent that takes a plain-language app request and copies a working Django template. From there it reads and writes files through tools, iterating until the generated app runs. The first version uses the OpenAI Responses API through ToyAIKit. Then we try the same idea with OpenAI Agents SDK, PydanticAI, Anthropic, and Z.AI.

Links

The main resources:

The app you will build

You chat with the coding agent in a notebook. It's backed by an LLM and a small set of filesystem tools. You give it a request like to-do list, and the agent edits a copied Django template and leaves you with a project you can run.

flowchart LR USER["You short app request"] CHAT["Jupyter chat UI ToyAIKit"] RUNNER["Agent runner Responses API or framework"] TOOLS["AgentTools read, write, tree, grep, bash"] DJANGO["Copied Django template project folder"] LLM["LLM provider OpenAI, Anthropic, Z.AI"] USER -->|type request| CHAT CHAT --> RUNNER RUNNER -->|tool calls| TOOLS TOOLS -->|modify files| DJANGO RUNNER -->|messages and tools| LLM DJANGO -->|make run| USER

Two screenshots show what the finished workshop output looks like. The first one shows the notebook chat after the agent plans and starts calling file tools:

Coding agent chat

The second one shows one of the generated Django todo apps:

Generated todo app

Result

The simplest version is intentionally small. It runs in Jupyter, uses local filesystem tools, and edits one copied Django project folder. That's enough to understand how larger coding agents work under the hood.

The same four steps scale to any size of agent:

  • Prepare a template.
  • Expose the right tools.
  • Give the model precise instructions.
  • Iterate on the generated code.

Hosted by

Alexey Grigorev

Alexey Grigorev

Chief Agent Officer at AI Shipping Labs

Software engineer and machine learning practitioner with 15+ years of experience building production ML systems. I focus on practical, production-grade ML and AI systems, from early prototypes to reliable systems in production.

I'm the founder of DataTalks.Club, a free community that connects tens of thousands of practitioners worldwide, and the creator of the Zoomcamp series, free, code-first programs that have reached 100,000+ learners globally.

At AI Shipping Labs, I'm building the kind of environment that would have accelerated my own career growth. After years of teaching at scale, I wanted something more focused: a space for action-oriented builders who want to turn AI ideas into real projects. The community gives members the structure, accountability, and peer support to ship practical AI products consistently, even alongside their main jobs.

alexey@aishippinglabs.com

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