How to Use OpenAI Agents SDK with FXMacroData for FX Trading

OpenAI Agents SDK is a strong fit when an FX trading workflow needs a model to plan, call tools, trace its work, and pause before risky steps. FXMacroData supplies the market evidence those agents need: confirmed release calendars, announcement history, EUR/USD and USD/JPY context, COT positioning, commodities, and FX sessions.

The boundary matters. OpenAI should reason over the question, choose tools, and write the brief. FXMacroData should answer factual questions about what printed, what was expected, what is scheduled, and which source path produced each row. Risk controls should remain deterministic application code, especially around US CPI, Non-Farm Payrolls, Federal Reserve policy-rate decisions, and release-driven FX setups.

Why OpenAI Fits FX Trading Agents

OpenAI's current agent stack gives developers two useful routes. The Responses API is the lower-level interface for model responses, built-in tools, function calling, state, and external data. The Agents SDK is the code-first layer for recurring agent loops, specialist ownership, handoffs, sessions, tracing, guardrails, and approval pauses.

That is relevant for FX trading because the hard part is not defining CPI, NFP, policy rates, or COT. The hard part is making sure the agent uses current rows, knows which calendar events are confirmed, names data gaps, and keeps interpretation separate from execution. An OpenAI model can draft the briefing, but the market facts should come from FXMacroData before any conclusion is written.

Workflow Shape

Responses, Agents SDK, REST, or MCP?

Prerequisites

• An OpenAI API key for the Responses API or Agents SDK.
• An FXMacroData API key stored in server-side configuration.
• Python 3.10 or later.
• A first workflow that is read-only: calendar, announcements, FX, COT, commodities, or sessions.
• A clear rule that no model output places orders, changes risk, or sends customer-visible decisions without deterministic approval.

pip install -U openai openai-agents requests
export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
export FXMD_API_KEY="YOUR_FXMACRODATA_API_KEY"

For direct REST checks, use production FXMacroData endpoints and query-parameter authentication:

curl "https://api.fxmacrodata.com/v1/calendar/usd?api_key=YOUR_API_KEY"
curl "https://api.fxmacrodata.com/v1/announcements/usd/inflation?api_key=YOUR_API_KEY"
curl "https://api.fxmacrodata.com/v1/forex/eur/usd?api_key=YOUR_API_KEY"
curl "https://api.fxmacrodata.com/v1/cot/usd?api_key=YOUR_API_KEY"

Step 1: Wrap FXMacroData REST

Start with a small server-side helper. The model should receive endpoint paths, returned rows, and timestamps. It should not receive raw credentials.

import os
import requests

API_ROOT = "https://api.fxmacrodata.com/v1"

def fxmd_get(path: str, **params) -> dict:
    params["api_key"] = os.environ["FXMD_API_KEY"]
    response = requests.get(f"{API_ROOT}{path}", params=params, timeout=20)
    response.raise_for_status()
    return {
        "source": f"/v1{path}",
        "payload": response.json(),
    }

Expose only the tool surface the workflow needs. For a USD release-risk agent, start with calendar and announcement tools. Add FX, COT, commodities, and sessions after the output contract is stable.

Step 2: Use Responses API Function Tools

OpenAI function calling lets a model request data from tools your application defines with JSON schema. In the Responses API path, your application receives the function call, executes FXMacroData, sends the result back, and then asks the model for the final answer.

from openai import OpenAI

client = OpenAI()
tools = [{
    "type": "function",
    "name": "fxmacrodata_calendar",
    "description": "Return scheduled macro releases for one currency.",
    "parameters": {
        "type": "object",
        "properties": {"currency": {"type": "string", "enum": ["usd", "eur", "gbp", "jpy"]}},
        "required": ["currency"],
        "additionalProperties": False,
    },
    "strict": True,
}]

response = client.responses.create(
    model="gpt-5.6",
    tools=tools,
    input="Brief the next USD macro release risk for EUR/USD."
)

The application side should then inspect output items, execute the requested tool, append a function_call_output item, and call client.responses.create(...) again. Keep that loop capped, logged, and limited to read-only market data.

import json

input_items = [{"role": "user", "content": "Brief next USD release risk."}]
response = client.responses.create(model="gpt-5.6", tools=tools, input=input_items)

for item in response.output:
    if item.type == "function_call" and item.name == "fxmacrodata_calendar":
        args = json.loads(item.arguments)
        evidence = fxmd_get(f"/calendar/{args['currency']}")
        input_items.append(item)
        input_items.append({
            "type": "function_call_output",
            "call_id": item.call_id,
            "output": json.dumps(evidence),
        })

Step 3: Move the Loop into Agents SDK

Move to the Agents SDK when the same workflow will run repeatedly, when you want the SDK runner to manage tool calls, or when the workflow needs handoffs, tracing, guardrails, or resumable approval states.

from agents import Agent, Runner, function_tool

@function_tool
def fxmacrodata_calendar(currency: str) -> dict:
    """Return scheduled macro releases for one currency."""
    return fxmd_get(f"/calendar/{currency.lower()}")

agent = Agent(
    name="FX release risk analyst",
    instructions=(
        "Use FXMacroData tools for current facts. "
        "Separate facts, interpretation, source paths, and data gaps."
    ),
    tools=[fxmacrodata_calendar],
)

result = Runner.run_sync(agent, "Brief next USD release risk for EUR/USD.")
print(result.final_output)

Do not start with a broad agent that can browse the web, call arbitrary URLs, and invoke market execution at the same time. A better first production shape is one read-only research agent, one output validator, and one human approval checkpoint before any downstream action.

Step 4: Add FXMacroData Remote MCP

Use remote MCP when you want OpenAI's tool layer to discover the hosted FXMacroData tool surface. The canonical FXMacroData MCP endpoint is:

https://mcp.fxmacrodata.com

The Responses API supports remote MCP servers through the built-in mcp tool type. Keep approvals on while developing, and restrict tools to the workflow you are testing.

response = client.responses.create(
    model="gpt-5.6",
    tools=[{
        "type": "mcp",
        "server_label": "fxmacrodata",
        "server_url": "https://mcp.fxmacrodata.com",
        "allowed_tools": ["release_calendar", "indicator_query", "forex"],
        "require_approval": "always",
    }],
    input="Check the next USD release and current EUR/USD context."
)

REST and MCP can coexist. REST is usually better for application-owned workflows that need stable schemas, cache keys, retries, and audit logs. MCP is usually better when the host should discover a shared tool catalog and let the model choose between release calendars, indicator rows, FX rates, COT, commodity, or session tools.

Step 5: Add Finance Risk Controls

For FX trading work, the model should not be the final risk authority. Use the agent to assemble and explain evidence. Use deterministic code to validate the output, check data freshness, and decide whether a human must approve the next step.

A reviewable output contract keeps the desk workflow clear:

{
  "facts": [
    {"source": "/v1/calendar/usd", "summary": "Next confirmed USD event..."}
  ],
  "interpretation": "The event risk is concentrated before New York open.",
  "missing_data": [],
  "risk_state": "human_review_required",
  "not_trade_advice": true
}

Common Questions

Can OpenAI Agents SDK use FXMacroData?

Yes. The Agents SDK can use FXMacroData through application-defined function tools that call https://api.fxmacrodata.com. OpenAI's remote MCP support can also connect to the hosted FXMacroData MCP server at https://mcp.fxmacrodata.com.

Should I use the Responses API or the Agents SDK?

Use the Responses API when your application should own every tool call and continuation step. Use the Agents SDK when you want a reusable agent loop with tools, sessions, tracing, guardrails, handoffs, or approval pauses.

Should OpenAI use FXMacroData REST or MCP?

Use REST when your server owns credentials, schemas, retries, caching, and audit logs. Use MCP when an OpenAI-compatible host should discover a shared FXMacroData tool catalog and call selected tools with approvals.

Can this place FX trades automatically?

This pattern is for read-only research and reviewable decision support. Do not let a model place trades, change risk, or trigger execution without deterministic checks and explicit human or policy approval.

Related FXMacroData Guides

• FXMacroData API documentation
• FXMacroData MCP server documentation
• How to Use OpenAI Codex with FXMacroData for FX Trading
• How to Build a ChatGPT Custom GPT with FXMacroData
• Build LangGraph FX Macro Agents with FXMacroData

Sources and References

• OpenAI Agents SDK guide
• OpenAI Responses API reference
• OpenAI function calling guide
• OpenAI MCP and connectors guide
• FXMacroData API documentation
• FXMacroData MCP documentation
• FXMacroData production OpenAPI schema