Technical
AIMatrix for Developers

AIMatrix for Developers

AIMatrix Developer Platform

Build the future of business automation with AI agents that understand context, execute actions, and learn from interactions.

Quick Start 

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# Install AIMatrix CLI
npm install -g @aimatrix/cli

# Initialize your first agent
aimatrix init my-agent

# Run locally
aimatrix dev

Core Concepts

🌉 MCP (Model Context Protocol) Servers

Transform REST APIs into AI-understandable interfaces

  • Protocol translation layer
  • Stateful context management
  • Real-time synchronization
  • Security and authentication

🤖 Agent Development

Build agents that can see, understand, and act

  • Desktop Agents: Screen capture, UI automation, system integration
  • Mobile Agents: Camera vision, voice processing, native apps
  • Web Agents: Browser automation, DOM manipulation, API calls
  • Conversational Agents: NLU/NLG, context awareness, multi-turn dialogue

🧠 Knowledge & Context

Give your agents perfect memory and understanding

  • Vector embeddings and semantic search
  • Knowledge graphs and relationships
  • RAG (Retrieval Augmented Generation)
  • GraphRAG for complex queries
  • Fine-tuning and model adaptation

Architecture Overview 

┌─────────────────────────────────────────┐
│           Client Applications           │
│   (Desktop, Mobile, Web, IoT)          │
└─────────────┬───────────────────────────┘
              │
┌─────────────▼───────────────────────────┐
│         AIMatrix Agent Layer            │
│   (Processing, Routing, Context)        │
└─────────────┬───────────────────────────┘
              │
┌─────────────▼───────────────────────────┐
│          MCP Bridge Layer               │
│   (Protocol Translation, Security)      │
└─────────────┬───────────────────────────┘
              │
┌─────────────▼───────────────────────────┐
│      Business Systems (APIs)            │
│   (BigLedger, ERP, CRM, Custom)        │
└─────────────────────────────────────────┘

Building Your First Agent

1. Define the Agent Interface 

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from aimatrix import Agent, Context, Tool

class ExpenseAgent(Agent):
    """Process expense receipts automatically"""
    
    @Tool(description="Extract data from receipt image")
    async def process_receipt(self, image_path: str, context: Context):
        # Use vision model to extract receipt data
        data = await self.vision.extract(image_path)
        
        # Classify expense category using context
        category = await self.classify_expense(data, context)
        
        # Create expense entry in BigLedger
        result = await self.bigledger.create_expense(
            amount=data.total,
            category=category,
            vendor=data.vendor,
            context=context
        )
        
        return result

2. Connect to Business Systems 

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// mcp-server.js
import { MCPServer } from '@aimatrix/mcp';

const server = new MCPServer({
  name: 'bigledger-bridge',
  version: '1.0.0'
});

// Map REST endpoints to AI-understandable functions
server.function('create_invoice', async (params) => {
  const response = await fetch('https://api.bigledger.com/invoices', {
    method: 'POST',
    body: JSON.stringify(params)
  });
  return response.json();
});

server.start();

3. Deploy and Scale 

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# aimatrix.yaml
agents:
  - name: expense-processor
    runtime: python3.11
    memory: 512M
    scaling:
      min: 1
      max: 10
    triggers:
      - type: webhook
      - type: email
      - type: schedule

Platform Capabilities

Multi-Model Support

  • OpenAI: GPT-4, GPT-4V, Whisper
  • Anthropic: Claude 3, Claude Vision
  • Google: Gemini, PaLM
  • Open Source: Llama, Mistral, Custom
  • Specialized: OCR, Voice, Translation

Integration Frameworks

  • AutoGen: Multi-agent conversations
  • Semantic Kernel: Orchestration and planning
  • LangChain: Chain complex operations
  • Claude CLI: Direct model interaction
  • Custom: Your own frameworks

Data & Storage

  • Vector DBs: Pinecone, Weaviate, Qdrant
  • Graph DBs: Neo4j, Amazon Neptune
  • Traditional: PostgreSQL, MongoDB
  • Cache: Redis, Memcached
  • Files: S3, Google Drive, Local

Developer Tools

AIMatrix CLI 

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# Create new agent
aimatrix create agent --template=customer-service

# Test locally
aimatrix test --input="Process this receipt" --image=receipt.jpg

# Deploy to production
aimatrix deploy --env=production

# Monitor performance
aimatrix logs --follow

SDKs & Libraries

Python SDK 

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pip install aimatrix

JavaScript/TypeScript 

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npm install @aimatrix/sdk

Go SDK 

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import "github.com/aimatrix/go-sdk"

API Reference

REST API 

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POST /api/v1/agents/invoke
Authorization: Bearer YOUR_API_KEY
Content-Type: application/json

{
  "agent": "expense-processor",
  "action": "process_receipt",
  "params": {
    "image_url": "https://..."
  }
}

WebSocket (Real-time) 

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const ws = new WebSocket('wss://api.aimatrix.com/v1/stream');
ws.send(JSON.stringify({
  type: 'subscribe',
  agent: 'sales-assistant',
  events: ['message', 'action']
}));

Advanced Topics

Fine-tuning Models

Adapt models to your specific business domain

  • Custom training datasets
  • Domain-specific vocabularies
  • Performance optimization
  • Cost reduction strategies

Building MCP Servers

Bridge any system to the AI world

  • Protocol design patterns
  • State management
  • Error handling
  • Security best practices

Multi-Agent Systems

Orchestrate teams of specialized agents

  • Agent communication protocols
  • Task distribution
  • Consensus mechanisms
  • Fault tolerance

Production Deployment

Scale from prototype to enterprise

  • Container orchestration
  • Load balancing
  • Monitoring and observability
  • Security and compliance

Example Projects

📸 Visual Invoice Processor

Complete example of receipt/invoice processing

🎤 Voice Order System

Natural language order taking system

🤖 Customer Service Bot

Multi-channel support automation

Community & Support

Resources

Contributing

Getting Help

Start Building Today

Get Started → View Examples