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Vite-style CLI scaffolder for production-grade Agentic AI projects in Python

Project description

create-agent-app

Scaffold production-ready Agentic AI projects in Python with one command.

create-agent-app generates opinionated, extensible project templates built around LangGraph patterns, modern provider support, API-first structure, and practical defaults for real-world teams.

Quick Demo

create-agent-app demo

Why create-agent-app

  • Production-oriented templates (not toy examples)
  • Consistent project structure across agent architectures
  • Interactive setup flow for provider and feature selection
  • Optional FastAPI backend, streaming, tests, Docker, and observability hooks
  • Ready-to-customize prompts, graph logic, tools, and config files

Supported Templates

Template Best For Architecture
react_agent Tool-using assistants with iterative reasoning ReAct loop with LangGraph + tool execution
rag_agent Grounded answers from your documents Retrieval + grading + generation pipeline with optional guards/cache
multi_agent Coordinated specialist workflows Supervisor graph orchestrating worker agents
conversational Alias for conversational assistants Delegates to react_agent generation
hitl Human-in-the-loop orchestration baseline Delegates to multi_agent generation

Supported LLM Providers

  • Groq
  • Gemini
  • Azure OpenAI
  • Ollama

Provider credentials are configured via generated .env files.

Installation

pip install create-agent-app

For local development:

pip install -e .[dev]

Usage

create-agent-app my-agent-project

If your shell cannot find create-agent-app, use:

python -m create_agent_app my-agent-project

Optional output directory:

create-agent-app my-agent-project --output ./workspace

Alias command (equivalent):

create_agent_app my-agent-project

The CLI prompts you to configure:

  • Template type
  • LLM provider
  • API backend and streaming support
  • Pre-installed tools
  • RAG options (semantic cache and security guards)
  • Optional features (Docker, tests, observability, agent description)

What Gets Generated

Each project includes:

  • A runnable main.py entrypoint
  • Config-first setup (config.yaml, .env.example)
  • Template-specific modules (agent/, rag/, agents/, tools/, etc.)
  • Optional api/ routes and schemas
  • Optional tests/
  • Optional Docker artifacts

The generator also initializes git and creates a data/ directory scaffold.

Feature Flags (Template Rendering)

The generator conditionally includes files based on selected options:

  • include_api: includes or skips api/
  • include_tests: includes or skips tests/
  • include_docker: includes or skips Docker artifacts
  • include_guards (RAG): includes or skips security/
  • include_semantic_cache (RAG): includes or skips semantic cache template

Example Developer Workflow

create-agent-app customer-support-agent
cd customer-support-agent
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env
# Add provider credentials
python main.py

Project Quality Notes

  • Templates are generated from Jinja files under create_agent_app/templates/
  • Shared and template-specific rendering are centralized in create_agent_app/generator.py
  • Generation summary is displayed in rich tables for clarity
  • Templates are structured for straightforward extension, not lock-in

Development

pip install -e .[dev]
python -m build
python -m twine check dist/*

Troubleshooting (Windows)

If you see:

create-agent-app : The term 'create-agent-app' is not recognized ...

then the package is usually installed, but your Python Scripts directory is not on PATH.

Check install location:

py -m pip show create-agent-app
py -m site --user-base

Typical scripts path to add to PATH:

%APPDATA%\Python\Python3x\Scripts

After updating PATH, restart PowerShell and run:

create-agent-app --help

Fallback that always works when package is installed:

py -m create_agent_app --help

Publishing to PyPI

If a version already exists on PyPI, bump project.version in pyproject.toml before upload.

python -m build
twine upload dist/*

Optional:

twine upload --skip-existing dist/*

Contributing

  1. Fork the repository.
  2. Create a branch for your change.
  3. Validate generated templates locally.
  4. Submit a pull request with a clear summary and sample output.

License

MIT

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