Web Search with Exa

This guide shows you how to build an agent that can search the web using Exa and synthesize the results using OpenAI. This is perfect for building research agents or bots that need real-time information.

Prerequisites

cycls installed
OpenAI API key
Exa API key (get one here)

pip install cycls

Step 1: Define the Search Tool

First, we’ll define a Python function that uses the exa-py client to search the web.

def search_web(query):
    """Search the web for a given query."""
    from exa_py import Exa
    import os

    exa = Exa(api_key=os.getenv("EXA_API_KEY"))

    # Search and get the top 3 results with text content
    response = exa.search_and_contents(
        query,
        num_results=3,
        text=True
    )

    # Format results for the LLM
    results = []
    for r in response.results:
        results.append(f"Title: {r.title}\nURL: {r.url}\nContent: {r.text[:1000]}...")

    return "\n\n".join(results)

Step 2: Define the Tool Schema

Tell OpenAI about our search tool so it knows when to use it.

tools = [{
    "type": "function",
    "name": "search_web",
    "description": "Search the web for real-time information.",
    "parameters": {
        "type": "object",
        "properties": {
            "query": {"type": "string", "description": "The search query"}
        },
        "required": ["query"]
    }
}]

Step 3: Create the Agent

import cycls
import json
import os

@cycls.app(pip=["openai", "exa-py"], copy=[".env"])
async def app(context):
    from openai import OpenAI
    from exa_py import Exa

    client = OpenAI()

    # Define Search Tool
    def search_web(query):
        exa = Exa(api_key=os.getenv("EXA_API_KEY"))
        response = exa.search_and_contents(query, num_results=3, text=True)
        results = []
        for r in response.results:
            results.append(f"Title: {r.title}\nURL: {r.url}\nContent: {r.text[:1000]}...")
        return "\n\n".join(results)

    # Tool Schema
    tools = [{
        "type": "function",
        "name": "search_web",
        "description": "Search the web for real-time information.",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "The search query"}
            },
            "required": ["query"]
        }
    }]

    # First Call to LLM
    response = client.responses.create(
        model="gpt-4o",
        input=context.messages,
        tools=tools
    )

    # Update history
    context.messages.extend(response.output)

    # Check for Function Calls
    tool_called = False
    for item in response.output:
        if item.type == "function_call":
            tool_called = True
            if item.name == "search_web":
                args = json.loads(item.arguments)
                result = search_web(args["query"])
                context.messages.append({
                    "type": "function_call_output",
                    "call_id": item.call_id,
                    "output": result
                })

    # Final Response
    if tool_called:
        final = client.responses.create(
            model="gpt-4o",
            input=context.messages
        )
        yield final.output_text
    else:
        yield response.output_text

app.local()

Full Code

Create an app.py file with the following content:

import cycls
import json
import os

@cycls.app(pip=["openai", "exa-py"], copy=[".env"])
async def app(context):
    from openai import OpenAI
    from exa_py import Exa

    client = OpenAI()

    # Define Search Tool
    def search_web(query):
        exa = Exa(api_key=os.getenv("EXA_API_KEY"))
        response = exa.search_and_contents(query, num_results=3, text=True)
        results = []
        for r in response.results:
            results.append(f"Title: {r.title}\nURL: {r.url}\nContent: {r.text[:1000]}...")
        return "\n\n".join(results)

    # Tool Schema
    tools = [{
        "type": "function",
        "name": "search_web",
        "description": "Search the web for real-time information.",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "The search query"}
            },
            "required": ["query"]
        }
    }]

    # First Call to LLM
    response = client.responses.create(
        model="gpt-4o",
        input=context.messages,
        tools=tools
    )

    # Update history
    context.messages.extend(response.output)

    # Check for Function Calls
    tool_called = False
    for item in response.output:
        if item.type == "function_call":
            tool_called = True
            if item.name == "search_web":
                args = json.loads(item.arguments)
                result = search_web(args["query"])
                context.messages.append({
                    "type": "function_call_output",
                    "call_id": item.call_id,
                    "output": result
                })

    # Final Response
    if tool_called:
        final = client.responses.create(
            model="gpt-4o",
            input=context.messages
        )
        yield final.output_text
    else:
        yield response.output_text

app.local()

Configuration

Make sure your .env file contains both API keys:

OPENAI_API_KEY=sk-...
EXA_API_KEY=...

​Prerequisites

​Step 1: Define the Search Tool

​Step 2: Define the Tool Schema

​Step 3: Create the Agent

​Full Code

​Configuration