Building Location-Aware AI Agents with DeepAgents and Camino AI

AI agents are becoming increasingly sophisticated, but most lack real-world spatial awareness. Today, we'll show you how to build intelligent location-aware agents using DeepAgents and Camino AI that can understand, research, and reason about places and locations.

What We're Building

We'll create an AI research agent that combines:

• Location Intelligence via Camino AI
• Web Search Capabilities via Tavily
• Sophisticated Reasoning via DeepAgents

This agent can answer complex queries like:

• "What are the best coffee shops in Paris?"
• "Find family-friendly restaurants near Golden Gate Bridge"
• "Research the startup ecosystem around MIT"

Prerequisites

pip install deepagents camino-ai-sdk tavily-python python-dotenv

You'll need API keys for:

• Camino AI - For location intelligence
• Tavily - For web search
• Anthropic - For the language model (used by DeepAgents)

The Complete Implementation

import os
from typing import Literal
from tavily import TavilyClient
from camino_ai import CaminoAI, APIError
from deepagents import create_deep_agent
from dotenv import load_dotenv

load_dotenv()

# Initialize clients
tavily_client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])
camino_client = CaminoAI(api_key=os.environ.get("CAMINO_API_KEY"))

def internet_search(
    query: str,
    max_results: int = 5,
    topic: Literal["general", "news", "finance"] = "general",
    include_raw_content: bool = False,
):
    """
    Run a web search to find current information and context.
    
    Use this for general research, news, reviews, and background information
    that complements location data.
    """
    try:
        return tavily_client.search(
            query,
            max_results=max_results,
            include_raw_content=include_raw_content,
            topic=topic,
        )
    except Exception as e:
        return f"Web search error: {str(e)}"

# Define the agent's research methodology
research_instructions = """You are an expert location researcher and travel advisor. 

Your mission is to provide comprehensive, actionable insights about places, businesses, and locations.

## Your Tools

You have access to multiple Camino AI location intelligence tools:

**`query`**: General location search for finding places, businesses, restaurants, hotels, attractions, etc. This tool understands natural language queries like "coffee shops in downtown Seattle" or "family restaurants near Central Park."

**`search`**: Alternative search method with different ranking algorithms.

**`context`**: Get contextual information about a specific location or area.

**`journey`**: Plan routes and journeys between multiple locations.

**`relationship`**: Understand spatial relationships between locations.

**`internet_search`**: Use this for gathering additional context, reviews, recent news, opening hours, pricing, or any supplementary information about locations you've found.

## Research Methodology

1. **Location Discovery**: Start with Camino AI tools to find relevant places
2. **Context Gathering**: Use internet_search to get reviews, recent information, and context
3. **Synthesis**: Combine location data with web research for comprehensive insights
4. **Recommendations**: Provide specific, actionable recommendations with reasoning

## Output Format

Structure your responses as:
- **Summary**: Brief overview of findings
- **Top Recommendations**: 3-5 specific places with details
- **Insights**: Key patterns, trends, or notable findings
- **Practical Tips**: Hours, pricing, reservation info, etc.

Always cite your sources and be specific about locations, addresses, and practical details.
"""

# Create the location-aware research agent with all Camino AI tools
agent = create_deep_agent(
    tools=[
        camino_client.search, 
        camino_client.query, 
        camino_client.context, 
        camino_client.journey, 
        camino_client.relationship, 
        internet_search
    ],
    instructions=research_instructions,
)

# Example usage
if __name__ == "__main__":
    queries = [
        "What are the best coffee shops in Paris?"
    ]
    
    for query in queries:
        print(f"\n🔍 Query: {query}")
        print("=" * 50)
        
        result = agent.invoke({
            "messages": [{"role": "user", "content": query}]
        })
        
        print(result['messages'][-1])

Why This Combination Works

🎯 Camino AI Advantages

• Natural Language Understanding: Handles queries like "quiet coffee shops for work"
• AI-Powered Ranking: Results ranked by relevance and quality
• Spatial Reasoning: Understands proximity, directions, and relationships
• Structured Data: Clean, consistent location information

🌐 Tavily Integration

• Current Information: Reviews, hours, recent changes
• Context and Background: Neighborhood insights, trends
• Verification: Cross-reference location data with web sources

🤖 DeepAgents Framework

• Multi-Step Reasoning: Plans research methodology
• Tool Coordination: Intelligently combines location and web data
• Adaptive Responses: Adjusts approach based on query type

Advanced Use Cases

Travel Planning Agent

travel_query = """
Plan a perfect day in San Francisco for a tech entrepreneur visiting for the first time. 
Include must-see tech landmarks, great coffee shops for meetings, and dinner recommendations.
"""

result = agent.invoke({
    "messages": [{"role": "user", "content": travel_query}]
})

Market Research Agent

market_query = """
Analyze the restaurant scene in Brooklyn's DUMBO neighborhood. 
What types of cuisines are popular? Any gaps in the market?
"""

result = agent.invoke({
    "messages": [{"role": "user", "content": market_query}]
})

Real Estate Analysis

real_estate_query = """
Evaluate the amenities and walkability around 123 Main St, Boston. 
What restaurants, shops, and services are within walking distance?
"""

result = agent.invoke({
    "messages": [{"role": "user", "content": real_estate_query}]
})

Real-World Results

Here's what our agent produced for "best coffee shops in Paris" (view full trace on LangSmith):

Best Practices

1. Query Optimization

• Be specific about location and preferences
• Include context like "for families" or "budget-friendly"
• Mention proximity requirements when relevant

2. Tool Selection Strategy

• Use location_query first for discovery
• Follow with internet_search for validation and context
• Combine insights for comprehensive recommendations

3. Error Handling

def robust_location_query(query: str, max_results: int = 5):
    try:
        response = camino_client.query(query, limit=max_results)
        if not response.get('results'):
            return f"No locations found for: {query}"
        return response
    except APIError as e:
        return f"Location service temporarily unavailable: {str(e)}"

Cost Optimization

• Camino AI: $0.001 per query (1000x cheaper than Google Places)
• Tavily: Pay-per-search model
• Anthropic: Usage-based pricing

Combined cost typically under $0.01 per complex research query.

Deployment Options

Local Development

export CAMINO_API_KEY="your_camino_key"
export TAVILY_API_KEY="your_tavily_key"
export ANTHROPIC_API_KEY="your_anthropic_key"
python location_agent.py

Production Deployment

• Deploy on AWS Lambda, Google Cloud Functions, or similar
• Use environment variables for API key management
• Implement caching for frequently requested locations
• Add rate limiting and usage monitoring

Get Started Today

Ready to build location-intelligent agents?

1. Get your Camino AI API key: app.getcamino.ai
2. Install the packages: pip install deepagents camino-ai-sdk tavily-python python-dotenv
3. Copy the code above and start experimenting
4. Join our community: Share your builds and get help

About Camino AI: We provide affordable location intelligence APIs for AI agents, offering natural language processing, routing, and spatial reasoning at 17x less cost than traditional alternatives.