A production-ready template to help you kickstart and organize your Generative AI projects with clarity and scalability in mind.
Designed to reduce chaos in early development and support long-term maintainability with proven structure and practices.

A production-ready template for building scalable Generative AI apps — structured, maintainable, and built on real-world best practices.

📁 config/ → YAML config for models, prompts, logging
📁 data/ → Prompts, embeddings, and other dynamic content
📁 examples/ → Minimal scripts to test key features
📁 notebooks/ → Quick experiments and prototyping
📁 tests/ → Unit, integration, and end-to-end tests

📁 src/ → The core engine — all logic lives here:
├── agents/ → Agent classes: planner, executor, base agent
├── memory/ → Short-term and long-term memory modules
├── pipelines/ → Chat flows, doc processing, and task routing
├── retrieval/ → Vector search and document lookup
├── skills/ → Extra abilities: web search, code execution
├── vision_audio/ → Multimodal processing: image and audio
├── prompt_engineering/→ Prompt chaining, templates, few-shot logic
├── llm/ → OpenAI, Anthropic, and custom LLM routing
├── fallback/ → Recovery logic when LLMs fail
├── guardrails/ → PII filters, output validation, safety checks
├── handlers/ → Input/output processing and error management
└── utils/ → Logging, caching, rate limiting, token counting

• Track prompt versions and results
• Separate configs using YAML files
• Structure code by clear module boundaries
• Cache responses to reduce latency and cost
• Handle errors with custom exceptions
• Use notebooks for rapid testing and iteration
• Monitor API usage and set rate limits
• Keep code and docs in sync

1. Clone the repo
2. Install via requirements.txt
3. Set up model configs
4. Check sample code
5. Begin in notebooks

• Use modular structure
• Test components early
• Track with version control
• Keep datasets fresh
• Monitor API usage

• requirements.txt – Package dependencies
• README.md – Project overview and usage
• Dockerfile – Container build instructions

This project is licensed under the Apache 2.0 License.
You are free to use, modify, and distribute with minimal restriction.