🧠 AI Brain Router (Working Name)

A modular, intelligent AI orchestration layer that dynamically selects the best model for every request — optimizing for cost, latency, or quality.

Built for SaaS platforms, indie hackers, and AI-first products that want a plug-and-play “AI brain” without reinventing the stack.

🚀 Overview

AI Brain Router is a unified abstraction layer over multiple LLM providers (via OpenRouter), enriched with custom logic, ML classifiers, and memory systems.

Instead of hardcoding a single model, this system:

• Understands the intent and context of each prompt
• Classifies the use case
• Routes the request to the most optimal model
• Manages memory, conversations, embeddings, and RAG

✨ Features

🧠 Intelligent Model Routing

• Classifies prompts (e.g., coding, chat, reasoning, summarization)

• Dynamically selects models based on:

  • Cost efficiency 💰
  • Performance ⚡
  • Output quality 🎯

🔌 OpenRouter Integration

• Unified access to multiple LLM providers
• Automatic fallback and failover handling
• Model benchmarking and scoring

🧩 Modular AI Pipeline

• Plug-and-play architecture for:

  • Classifiers
  • Routing strategies
  • Memory layers
  • Retrieval systems

💬 Conversation & Memory Management

• Persistent conversation tracking
• Short-term and long-term memory layers
• Context window optimization

📚 RAG (Retrieval-Augmented Generation)

• Built-in document ingestion
• Vector search support
• Context-aware retrieval pipelines

🔍 Embeddings Engine

• Supports multiple embedding providers
• Efficient semantic search
• Custom indexing strategies

⚡ Streaming Support

• Real-time token streaming
• Optimized for chat UIs and agents

🏗️ Architecture

                ┌────────────────────┐
                │     User Input      │
                └────────┬───────────┘
                         │
                         ▼
                ┌────────────────────┐
                │  Prompt Classifier  │
                └────────┬───────────┘
                         │
                         ▼
                ┌────────────────────┐
                │  Routing Engine     │
                │ (Cost / Speed / IQ) │
                └────────┬───────────┘
                         │
                         ▼
                ┌────────────────────┐
                │  Model चयन (LLMs)  │
                └────────┬───────────┘
                         │
                         ▼
      ┌────────────────────────────────────┐
      │ Memory │ RAG │ Embeddings │ Stream │
      └────────────────────────────────────┘
                         │
                         ▼
                ┌────────────────────┐
                │     Response        │
                └────────────────────┘

🛠️ Tech Stack (Suggested)

• Backend: Node.js / Python (FastAPI)
• LLM Gateway: OpenRouter
• Vector DB: Pinecone / Weaviate / Qdrant
• Cache: Redis
• Queue: Kafka / BullMQ
• Classifier Models: Lightweight local models / fine-tuned classifiers

📦 Installation

git clone https://github.com/yourusername/ai-brain-router.git
cd ai-brain-router
npm install

or

pip install -r requirements.txt

⚙️ Configuration

Create a .env file:

OPENROUTER_API_KEY=your_key_here
DEFAULT_ROUTING_STRATEGY=balanced
VECTOR_DB_URL=your_vector_db
REDIS_URL=your_redis_url

🧪 Example Usage

Basic Prompt Routing

const response = await aiBrain.ask({
  prompt: "Explain quantum computing in simple terms",
  priority: "quality"
});

Code Generation (Cost Optimized)

const response = await aiBrain.ask({
  prompt: "Write a REST API in Express",
  priority: "cost"
});

🧠 Routing Strategies

| Strategy | Description | | ---------- | --------------------------- | | cost | Uses cheapest viable model | | fast | Prioritizes low latency | | quality | Uses best-performing models | | balanced | Smart trade-off between all |

🔌 Extensibility

You can plug in your own:

• Custom classifiers
• Routing algorithms
• Model scoring logic
• Memory backends
• Retrieval pipelines

📈 Roadmap

• [ ] Auto-learning routing system (reinforcement feedback)
• [ ] Fine-tuned intent classifiers
• [ ] Multi-agent orchestration
• [ ] Visual dashboard for routing insights
• [ ] Cost analytics & optimization layer
• [ ] SDKs (JS, Python, Go)

🤝 Contributing

Contributions are welcome!

fork → clone → branch → commit → PR

📄 License

MIT License

💡 Vision

To become the default AI orchestration layer — so developers never have to think about which model to use again.

⚡ TL;DR

One API. All models. Zero guesswork.

📬 Contact

For ideas, collaborations, or feedback:

• Open an issue
• Start a discussion
• Reach out on Twitter / LinkedIn