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This guide shows you how to connect your OpenAI Agents SDK agent to Caylex using Streamable HTTP transport.

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Prerequisites

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Installation

pip install openai-agents

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Full example

main.py
import asyncio
import os

from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp


async def main() -> None:
    # Load credentials from environment variables
    api_key = os.environ["CAYLEX_API_KEY"]
    user_email = os.environ["CAYLEX_USER_EMAIL"]

    # Connect to Caylex MCP via Streamable HTTP
    async with MCPServerStreamableHttp(
        name="Caylex",
        params={
            "url": "https://proxy.caylex.ai/mcp",
            "headers": {
                "x-api-key": api_key,
                "x-user-email": user_email,
            },
        },
    ) as server:
        # Create an agent with Caylex tools
        agent = Agent(
            name="Assistant",
            model="gpt-5.2",
            instructions="You are a helpful assistant that can access external systems using Caylex.",
            mcp_servers=[server],
        )

        # Run the agent with a user query
        result = await Runner.run(
            agent,
            "Read through my recent emails, get the latest updates on my customer accounts, and update the CRM with any important changes.",
        )
        print(result.final_output)


asyncio.run(main())

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Step-by-step

1

Set environment variables

export CAYLEX_API_KEY="ck_abc123.your-secret-key"
export CAYLEX_USER_EMAIL="user@example.com"
2

Create the MCP server connection

Use MCPServerStreamableHttp to connect to the Caylex Navigator. The params dict must include the url and headers with your API key and user email.
async with MCPServerStreamableHttp(
    name="Caylex",
    params={
        "url": "https://proxy.caylex.ai/mcp",
        "headers": {
            "x-api-key": api_key,
            "x-user-email": user_email,
        },
    },
) as server:
    # server is now connected
3

Create an agent with Caylex tools

Pass the Caylex server to your agent’s mcp_servers list. The agent automatically discovers all available tools through MCP.
agent = Agent(
    name="Assistant",
    model="gpt-5.2",
    instructions="Your agent instructions here.",
    mcp_servers=[server],
)
4

Run the agent

Use Runner.run() to execute the agent with a user query. The agent uses Caylex tools to interact with external systems.
result = await Runner.run(agent, "Your query here.")
print(result.final_output)

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Dynamic user email

In a production application, the user email typically comes from your application’s authentication context rather than an environment variable: 
async def handle_user_request(user_email: str, query: str) -> str:
    api_key = os.environ["CAYLEX_API_KEY"]

    async with MCPServerStreamableHttp(
        name="Caylex",
        params={
            "url": "https://proxy.caylex.ai/mcp",
            "headers": {
                "x-api-key": api_key,
                "x-user-email": user_email,  # Dynamic per user
            },
        },
    ) as server:
        agent = Agent(
            name="Assistant",
            model="gpt-5.2",
            instructions="You are a helpful assistant.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, query)
        return result.final_output
The x-api-key stays the same for all users (it identifies your navigator instance), but the x-user-email changes per user (it determines which credentials the Navigator uses).

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Further reading