Training course · Replit Agent · Python Development

Replit Agent for Python Developers

Build, test and host apps in the browser: go from idea to a published application with a database using Replit Agent.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Replit Agent for Python Developers” is a hands-on course for roles such as Python back-end developers working with Django, Flask or FastAPI, Automation and scripting specialists in IT and business units and Data and ML engineers maintaining production Python code. Rather than learning Replit Agent in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Python Development.

Replit brings together a development environment, runtime, database and hosting in a single browser workspace. Replit Agent builds on top of that: you describe an application in plain language, and the agent drafts a plan, creates files, installs packages, sets up a database, starts the app and tests it on its own. In this one-day course you learn to steer that process deliberately, whether you are a professional developer, a product manager validating ideas, an educator, or a founder who needs a first product quickly.

You work on an application of your own, such as an internal tool, a small API, a data dashboard or an MVP with user accounts. Along the way you learn to state requirements clearly, use checkpoints for safe rollbacks, handle secrets and the database correctly and choose the right deployment type. For technically experienced participants we go deeper into code structure, Git integration and debugging in the shell. Finally, you take a realistic look at costs, security and limits, so that a quick prototype does not turn into an unmanageable production system.

Python powers web back ends, automation, data pipelines and AI applications – often inside the same organisation, which makes codebases notoriously diverse. AI coding assistants and agents have a particularly visible impact here: they write pytest suites around your existing fixtures, add type hints until mypy passes cleanly, migrate legacy scripts to modern packaging with pyproject.toml and walk you through an unfamiliar Django or FastAPI project in minutes rather than days. The key is steering these tools with the right context: project conventions, Ruff rules, test commands and architectural decisions belong in instruction files and prompts. Once you master that, you can delegate routine work with confidence and focus on design, review and business logic.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Python back-end developers working with Django, Flask or FastAPI
  • Automation and scripting specialists in IT and business units
  • Data and ML engineers maintaining production Python code
  • Tech leads responsible for code quality and review processes
  • Developers modernising legacy Python codebases

Typical challenges we address

  • Ageing codebases with no type hints and patchy test coverage
  • Dependency sprawl across requirements.txt, Poetry, uv and virtual environments
  • Time-consuming reviews and inconsistent style despite linters and formatters
  • Onboarding into unfamiliar projects that rely on undocumented tribal knowledge

What you will be able to do

  • You phrase requirements so that Replit Agent plans and builds a suitable application.
  • You extend an app step by step and use checkpoints to roll back unwanted changes safely.
  • You set up a database, user sign-in and secrets correctly.
  • You publish your application using the appropriate deployment type.
  • Have AI agents reliably deliver pytest suites, type hints and refactorings across your Python codebase.
  • Encode project conventions, tooling such as Ruff, mypy and uv, and test commands so the assistant follows them consistently.
  • Review generated Python code systematically for correctness, security and maintainability before it is merged.

Course outline

Module 1 The Replit workspace and Agent at a glance
  • Understanding the workspace: editor, preview, console, shell and files
  • How the agent works: plan, implementation, self-testing and clarifying questions
  • Agent versus Assistant for small, targeted changes
  • Recognising projects that suit Replit Agent and those that do not
Module 2 From idea to working app
  • Writing requirements as a short specification: users, features, data
  • Using plan mode to agree scope and approach up front
  • Extending the app in small steps and checking results in the preview
  • Using checkpoints and rollbacks when a change goes wrong
Module 3 Data, users and integrations
  • Creating the built-in PostgreSQL database, inspecting tables and editing data
  • Setting up sign-in and user management with Replit Auth
  • Storing API keys safely in Secrets rather than in code
  • Connecting external services such as payments, email or AI models
Module 4 Understanding code, debugging and collaboration
  • Reading the project structure: where frontend, backend and data logic live
  • Using console errors and logs and passing them back to the agent
  • Collaborating in real time and versioning projects with Git
  • Making targeted changes to Python and JavaScript projects without the agent
Module 5 Publishing, operations and limits
  • Choosing a deployment type: static, autoscale, always-on or scheduled
  • Setting up a custom domain, environment variables and monitoring
  • Estimating and capping costs for agent usage and hosting
  • Data protection, access rights and handover to a development team
Module 6 Practice lab: Python Development
  • Generating pytest tests
  • Type hints and mypy clean-up
  • Django migrations and ORM refactoring
  • FastAPI endpoints with Pydantic
  • Modernising legacy scripts
  • Understanding and documenting a codebase
Module 7 Data protection, quality & transfer to daily work
  • Define which source code may be sent to cloud-hosted models, keep secrets from .env files strictly out of context, and check generated code for licence carry-over and newly introduced PyPI dependencies (typosquatting, supply-chain risk). For products with digital elements, obligations under the Cyber Resilience Act are becoming increasingly relevant.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Generating pytest tests

The assistant inspects functions and existing fixtures, writes parametrised pytest tests covering edge cases and mocks for external services, then runs them iteratively until they pass and reach the coverage you are aiming for.

Type hints and mypy clean-up

Untyped code is annotated module by module with type hints, TypedDicts and Protocols. The agent runs mypy or Pyright, interprets the output and fixes issues step by step without changing runtime behaviour.

Django migrations and ORM refactoring

Model changes are planned together with data migrations, makemigrations conflicts are resolved and N+1 queries are fixed with select_related or prefetch_related. The AI flags risks to existing production data before you deploy.

FastAPI endpoints with Pydantic

From a functional description or an OpenAPI draft, the assistant produces Pydantic models, routers, dependency injection and validation logic, complete with error handling and matching tests using the TestClient.

Modernising legacy scripts

Old scripts become proper packages with pyproject.toml, global state is removed, argparse or Click interfaces are added and Ruff rules applied. The agent documents every change clearly in the commit history.

Understanding and documenting a codebase

The assistant explores an unfamiliar repository, produces architecture overviews, explains data flows between modules and fills in missing docstrings in Google or NumPy style so new team members become productive faster.

Possible hands-on projects

Log analysis CLI

A Typer-based command-line tool that reads application logs, clusters error patterns and outputs a concise summary as Markdown or JSON – shipped with tests and proper packaging.

FastAPI microservice with a database

A small REST service with SQLAlchemy, Alembic migrations, Pydantic validation and a Docker setup, planned, implemented and tested end to end by an AI agent.

Dependency audit script

A script that lists all project dependencies, checks for outdated or vulnerable packages against public advisory databases and produces a prioritised upgrade report.

Prerequisites

Confident computer use and curiosity about software development are enough to get started. Programming experience in Python or JavaScript helps but is not required. Solid Python skills plus hands-on experience with Git, virtual environments and at least one framework such as Django, Flask or FastAPI.

Technical requirements

A laptop with an up-to-date browser and a Replit account; a paid plan is required for working with the agent and for deployments.

Data protection & compliance

Define which source code may be sent to cloud-hosted models, keep secrets from .env files strictly out of context, and check generated code for licence carry-over and newly introduced PyPI dependencies (typosquatting, supply-chain risk). For products with digital elements, obligations under the Cyber Resilience Act are becoming increasingly relevant.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 Online Request
  • Thu, 10 December 2026 Salzburg Request
  • Tue, 15 December 2026 Online Request
  • Mon, 18 January 2027 Bremen Request
  • Tue, 26 January 2027 Online Request
  • Tue, 2 February 2027 Augsburg Request

Frequently asked questions

Can I attend without programming experience?

Yes. Replit Agent writes the code while you focus on requirements, testing and decisions. We explain the key concepts, such as database, API and deployment, in plain terms. Participants with programming experience get deeper dives into code structure and debugging, so both groups get real value from the day.

Who owns the code, and can I move away from Replit?

The generated code is a normal project in your workspace and can be pushed to your own repository via Git or downloaded. Moving to other infrastructure is possible but requires changes to the database, authentication and deployment setup. In the course we show you what to keep in mind from the start.

How do I keep costs under control?

Agent usage is billed by effort, and hosting is charged separately depending on deployment type and resources. Clear, incremental prompts, plan mode and using the Assistant for small tweaks reduce consumption considerably. We show where to monitor usage, how to set spending limits and which deployment type is cost-effective for your use case.

Does AI-assisted coding work in large Django monoliths?

Yes, provided the agent gets the right context. On the course you learn to create instruction files describing architecture rules, app structure and test commands, and to scope tasks so the assistant works on individual apps or modules. Changes stay manageable and reviewable even when the overall project is very large.

Will AI replace my code reviews?

No. AI assistants speed up writing code and can prepare reviews by pointing out missing tests or risky sections, but functional and security accountability stays with your team. We show how to combine review checklists, static analysis and tests so that generated code meets exactly the same quality bar as hand-written code.

How much does “Replit Agent for Python Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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