Skip to main content

​
AI Router

​
Overview

AWS Strands is a framework for building AI agents with structured reasoning and tool use. By connecting AWS Strands to Orq.ai’s AI Router, you transform experimental agents into production-ready systems with enterprise-grade capabilities.

​
Key Benefits

Orq.ai’s AI Router enhances your AWS Strands Agents with:

Complete Observability

Track every agent step, tool use, and interaction with detailed traces and analytics

Built-in Reliability

Automatic fallbacks, retries, and load balancing for production resilience

Cost Optimization

Real-time cost tracking and spend management across all your AI operations

Multi-Provider Access

Access 300+ LLMs and 20+ providers through a single, unified integration

​
Prerequisites

Before integrating AWS Strands with Orq.ai, ensure you have:
  • An Orq.ai account and API Key
  • Python 3.8 or higher
  • AWS Strands SDK installed
To setup your API key, see API keys & Endpoints.

​
Installation

Install the Strands Agents SDK (requires Python 3.10+): 
# Install Strands Agents SDK
pip install strands-agents

# Optional: Install additional tools
pip install strands-agents-tools

​
Configuration

Configure Strands Agents to use Orq.ai’s AI Router by passing custom client arguments with the base URL: 
from strands import Agent
from strands.models.openai import OpenAIModel
import os

# Configure model with Orq.ai AI Router
model = OpenAIModel(
    model_id="gpt-4o",
    client_args={
        "api_key": os.getenv("ORQ_API_KEY"),
        "base_url": "https://api.orq.ai/v2/router"
    }
)

# Create agent with Orq.ai-powered model
agent = Agent(
    model=model,
    system_prompt="You are a helpful AI assistant."
)
base_url: https://api.orq.ai/v2/router

​
Basic Agent Example

Here’s a complete example of creating and running a Strands agent through Orq.ai: 
from strands import Agent
from strands.models.openai import OpenAIModel
import os

# Configure model with Orq.ai AI Router
model = OpenAIModel(
    model_id="gpt-4o",
    client_args={
        "api_key": os.getenv("ORQ_API_KEY"),
        "base_url": "https://api.orq.ai/v2/router"
    }
)

# Create a simple agent
agent = Agent(
    model=model,
    system_prompt="You are a research assistant that helps users find and summarize information."
)

# Run the agent
result = agent("Explain quantum computing in simple terms")
print(result)

​
Agent with Tools

Strands agents can use tools while routing through Orq.ai: 
from strands import Agent, tool
from strands.models.openai import OpenAIModel
import os

# Configure model
model = OpenAIModel(
    model_id="gpt-4o",
    client_args={
        "api_key": os.getenv("ORQ_API_KEY"),
        "base_url": "https://api.orq.ai/v2/router"
    }
)

# Define a custom tool using the @tool decorator
@tool
def search_database(query: str) -> str:
    """Search the knowledge database for relevant information."""
    # Your database search logic here
    return f"Search results for: {query}"

# Create agent with tools
agent = Agent(
    model=model,
    tools=[search_database],
    system_prompt="You are a knowledge assistant. Use the search_database tool to find information when needed."
)

# Run agent with tool access
result = agent("Find information about machine learning best practices")
print(result)

​
Fallback Configuration

Configure automatic fallbacks for reliability: 
from strands import Agent
from strands.models.openai import OpenAIModel
import os

# Configure model - Orq.ai handles fallbacks automatically
model = OpenAIModel(
    model_id="gpt-4o",
    client_args={
        "api_key": os.getenv("ORQ_API_KEY"),
        "base_url": "https://api.orq.ai/v2/router"
    }
)

agent = Agent(
    model=model,
    system_prompt="You are a helpful assistant."
)

​
Observability & Monitoring

All AWS Strands agent interactions routed through Orq.ai are automatically tracked and available in the AI Studio:
  • Agent Traces: View complete conversation flows and reasoning steps
  • Tool Usage: Monitor which tools are being called and their success rates
  • Performance Metrics: Track latency, token usage, and completion rates
  • Cost Analysis: Understand spending patterns across models and providers
Visit your AI Studio to view real-time analytics and traces.