create-agent-app 0.1.2

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

create-agent-app

Scaffold production-ready Agentic AI Python projects in seconds.

Quick Demo

Install

pip install create-agent-app

For local development:

pip install -e .

Usage

create-agent-app my-agent-project

The CLI prompts for:

• template (single_agent, multi_agent, rag_agent)
• LLM provider (Groq, Gemini, Azure OpenAI, Ollama)
• model name (provider-specific)

Then it generates a complete project folder and prints next steps.

Template Comparison

Template	Best For	Generated Architecture
single_agent	One assistant with tool-calling	LangGraph single-node loop + ToolNode
multi_agent	Staged workflows (research + writing)	Supervisor + worker agents (researcher, writer)
rag_agent	Document-grounded answers	ChromaDB + local embeddings + retriever tool + agent

LLM Provider Setup

Set values in the generated .env file:

Provider	Required Keys
Groq	GROQ_API_KEY, LLM_PROVIDER=groq, MODEL_NAME=...
Gemini	GEMINI_API_KEY, LLM_PROVIDER=gemini, MODEL_NAME=...
Azure OpenAI	AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, optional AZURE_OPENAI_DEPLOYMENT, LLM_PROVIDER=azure, MODEL_NAME=...
Ollama	OLLAMA_BASE_URL (default http://localhost:11434), LLM_PROVIDER=ollama, MODEL_NAME=...

Development

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

Contributing

1. Fork the repository.
2. Create a feature branch.
3. Run local checks and verify generated templates.
4. Open a pull request with a clear summary and sample scaffold output.