Modular AI agent platform with multi-provider LLM support.

📚 This is a demo project for learning and exploration purposes.

🇷🇺 Русская версия

Intelligent conversational agent with web capabilities

• Smart Search — Multi-source web search with result aggregation
• Page Reading — Extract and summarize content from any URL
• Context Awareness — Maintains conversation history and session state

Full-featured coding assistant that actually executes code

• File Operations — Create, read, list files in isolated workspace
• Code Execution — Run Python in sandboxed Docker environment
• Multi-File Projects — Import between files, build complete projects
• Native Tool Calling — Gemini 2.5 Pro uses native function calling
• Tool Support Indicator — ✅/⚠️ shows which models support native tools

Example:

User: Calculate fibonacci sequence up to 100

Agent: I'll create and run a Python script for you.
[Creates fibonacci.py → Executes → Returns result]

Output: [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]

Professional cryptocurrency analyzer with real-time Binance data

Real-time data from Binance API:

• 💰 Price & Candles — Current price, 24h change, multi-timeframe data
• 📈 12 Technical Indicators — RSI, MACD, EMA, Bollinger, StochRSI, ADX, ATR, VWAP, OBV, Ichimoku, SuperTrend, CMF
• 📊 Orderbook Analysis — Deltas at 7 levels (1.5%, 3%, 5%, 15%, 30%, 60%, 90%)
• 🔄 Trade Flow — Buy/sell pressure, whale activity, trade velocity, size distribution
• 📉 Futures Data — Funding Rate, Open Interest, Long/Short Ratio
• 🎯 Smart Money Concepts — FVG, Order Blocks, Market Structure (HH/HL/LL/LH), Liquidity Zones
• 🗓️ Multi-Timeframe — Short/Medium/Long-term analysis (3 horizons)
• 📊 Volume Analysis — Volume Delta, Relative Volume, Volume Profile (POC, VAH, VAL)
• 😨 Market Context — Fear & Greed Index, total market cap

Modular Queries:

"analyze BTC"      → Full analysis
"indicators ETH"  → Technical indicators only
"orderbook SOL"   → Orderbook analysis
"smc BTC"         → Smart Money Concepts
"sentiment ETH"   → Fear&Greed + Funding Rate
"volume SOL"      → Volume analysis
"mtf BTC"         → Multi-timeframe

Example Analysis:

📉 BTC/USDT Analysis | 2025-12-07

Price: $89,690.73 (-0.03% 24h)

MTF Signal: Bearish (Short: bullish, Medium: mixed, Long: bearish)

Order Flow: 82.6% buys — BUT whales selling (6 whale sells, 0 buys)

Smart Money: Downtrend structure (2 LH, 2 LL), 3 bearish FVG targets

Futures: L/S Ratio 2.17 (longs overloaded — squeeze risk)

Verdict: SELL — Retail buying into whale distribution

┌─────────────┐     ┌──────────────┐     ┌─────────────────┐
│   Frontend  │────▶│   FastAPI    │────▶│   LLM Provider  │
│   (React)   │     │   (routes)   │     │ Gemini/OpenRouter│
└─────────────┘     └──────────────┘     └─────────────────┘
                           │
                    ┌──────┴──────┐
                    ▼             ▼
              ┌─────────┐   ┌─────────┐
              │  Agent  │   │ Session │
              │ Dialog/ │   │ Manager │
              │ Coder/  │   └─────────┘
              │ Crypto  │
              └────┬────┘
                   ▼
              ┌─────────┐
              │  Tools  │
              │ search/ │
              │ files/  │
              │ code/   │
              │ crypto  │
              └─────────┘

This project implements multiple layers of security for code execution:

⚠️ Disclaimer: This is a demonstration project. For production use, consider additional hardening (gVisor, Firecracker, etc.)

git clone https://github.com/nssanta/Multi-Agent-Router.git
cd Multi-Agent-Router
cp .env.example .env
# Add your API keys to .env
docker compose up --build

Open: http://localhost:3000

# Required
GEMINI_API_KEY=your_gemini_key
OPENROUTER_API_KEY=your_openrouter_key

# Optional
LLM_PROVIDER=gemini          # or openrouter
LLM_MODEL=gemini-2.5-pro     # default model

# backend/agents/my_agent/agent.py
def create_my_agent(llm_provider, session_path):
    return Agent(
        name="my_agent",
        llm_provider=llm_provider,
        instruction="Your custom prompt",
        tool_definitions=[...],
    )

# backend/tools/my_tool.py
def my_tool(param: str) -> str:
    """Tool description for LLM"""
    return f"Result: {param}"

backend/
├── agents/           # Dialog, Coder, Crypto agents
│   ├── dialog/       # Conversational agent
│   ├── coder/        # Code execution agent
│   └── crypto/       # Cryptocurrency analyst
├── api/              # FastAPI routes
├── core/             # LLM providers, sessions, executor
└── tools/            # Agent tools
    ├── web/          # Search, page reader
    └── crypto/       # Binance API, indicators

frontend/
├── src/components/   # React components
└── src/services/     # API client

Full technical documentation: TECHNICAL_DOCS.md

MIT