MultiMind SDK: Unified AI Development Toolkit

Build, Fine-Tune, and Deploy Advanced AI Applications with Ease

🚧 Project Status: In Active Development 🚧

Join the future of AI development! We're actively building MultiMind SDK and looking for contributors. Check our TODO list to see what's implemented and what's coming next. Connect with our growing community on Discord to discuss ideas, get help, and contribute to the project.

Why MultiMind SDK? • Key Features • Quick Start • Documentation • Examples • Contributing

Why MultiMind SDK?

🚀 Why MultiMind SDK?

🧠 MultiMind SDK is the only open-source toolkit that unifies Fine-Tuning, RAG, and Agent Orchestration — all in one modular, extensible Python framework. Forget silos. While others focus on chaining, agents, or retrieval alone, MultiMind integrates them into one coherent developer-first experience, with:

• 🪄 Declarative YAML + CLI + SDK interfaces
• 📚 RAG with hybrid (vector + knowledge graph) retrieval
• 🤖 Role-based agents with memory, tools, and task flow
• 🔁 Self-improving agents with cognitive loop support
• 🔐 Enterprise-ready: logging, compliance, GDPR, cost tracking
• 🌍 Cloud + Edge deploy (Jetson, RPi, Offline mode)

📑 Check out our Strategic Roadmap to see where we're headed!

Key Benefits

• 🚀 Unified Interface: Streamline your AI development with one consistent API
• 💡 Production-Ready: Enterprise-grade deployment, monitoring, and scaling
• 🛠️ Framework Agnostic: Seamless integration with LangChain, CrewAI, and more
• 🔌 Extensible: Customizable architecture for your specific needs
• 📊 Enterprise Features: Comprehensive logging, monitoring, and cost tracking

✨ Key Features

1. Advanced Fine-Tuning

• Parameter-Efficient Methods: LoRA, Adapters, Prefix Tuning, and more

• Meta-Learning: MAML, Reptile, and prototype-based few-shot learning

• Transfer Learning: Layer transfer and multi-task optimization

• Resource-Aware Training: Automatic device selection and optimization

2. RAG System

• Document Processing: Smart chunking and metadata management

• Vector Storage: Support for FAISS and ChromaDB

• Embedding Models: Integration with OpenAI, HuggingFace, and custom models

• Query Optimization: Efficient similarity search and context management

3. Agent Development

• Tool Integration: Built-in support for common tools and custom extensions
• Memory Management: Short and long-term memory systems
• Task Orchestration: Complex workflow management and prompt chaining
• Model Composition: Protocol for combining multiple models and tools

4. Framework Integrations

• LangChain: Seamless integration with LangChain components
• CrewAI: Support for multi-agent systems
• LiteLLM: Unified model interface
• SuperAGI: Advanced agent capabilities

🚀 Quick Start

Installation

# Basic installation
pip install multimind-sdk

# With development dependencies
pip install multimind-sdk[dev]

# With specific framework support
pip install multimind-sdk[langchain,lite-llm,superagi]

Environment Setup

Copy the example environment file and add your API keys and configuration values:

cp examples/multi-model-wrapper/.env.example examples/multi-model-wrapper/.env

Note: Never commit your .env file to version control. Only .env.example should be tracked in git.

Build Your First RAG Application

from multimind.client.rag_client import RAGClient, Document

# Initialize the client
client = RAGClient()

# Add documents
docs = [
    Document(
        text="MultiMind SDK is a powerful AI development toolkit.",
        metadata={"type": "introduction"}
    )
]
await client.add_documents(docs)

# Query the system
results = await client.query("What is MultiMind SDK?")
print(results)

Fine-Tuning a Model

from multimind.fine_tuning import UniPELTPlusTuner

# Initialize the tuner
tuner = UniPELTPlusTuner(
    base_model_name="bert-base-uncased",
    output_dir="./output",
    available_methods=["lora", "adapter"]
)

# Train the model
tuner.train(
    train_dataset=your_dataset,
    eval_dataset=your_eval_dataset
)

📚 Documentation

• API Reference - Complete API documentation
• Examples - Production-ready code examples
• Architecture - Detailed system design
• Contributing Guide - Join our development team
• Code of Conduct - Community guidelines
• Issue Tracker - Report bugs or request features

Local Documentation

# Run documentation locally
cd multimind-docs
npm install
npm start

🎓 Examples

Explore our examples directory for:

• Basic RAG Usage - Simple RAG implementation
• Fine-Tuning - Model adaptation examples
• Agent Development - Building AI agents
• Framework Integration - Using with popular frameworks

🤝 Contributing

We love your input! We want to make contributing to MultiMind SDK as easy and transparent as possible.

• Contributing Guide - How to contribute
• Code of Conduct - Community guidelines
• Issue Tracker - Report bugs or request features

Development Setup

# Clone the repository
git clone https://github.com/multimind-dev/multimind-sdk.git
cd multimind-sdk

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Start documentation
cd multimind-docs
npm install
npm start

💖 Support MultiMind SDK

If you find MultiMind SDK helpful, please consider supporting us to sustain development and grow the community.

Your support will help fund:

• ⚙️ Feature development and maintenance
• 📖 Better documentation and onboarding
• 🌍 Community outreach and support
• 🧪 Infrastructure, testing, and CI/CD

👉 Contribute here

📝 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

For more information about the Apache License 2.0, visit apache.org/licenses/LICENSE-2.0.

🌟 Support

• Discord Community - Join our active developer community
• GitHub Issues - Get help and report issues
• Documentation - Comprehensive guides

📣 About

MultiMind SDK is developed and maintained by the AI2Innovate team, dedicated to simplifying AI development for everyone. Visit multimind.dev to learn more about our mission to democratize AI development.

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Made with ❤️ by the AI2Innovate Team | License