# Use OpenAI Agents SDK with LLaMA 3.3 and Regolo (Tool Calling)

## 👉[Try Build Agents with LLaMA 3.3 ThunderAI on Regolo for free](https://dashboard.regolo.ai)

The OpenAI Agents SDK is an incredibly powerful framework for building reasoned, multi-agent workflows. But usually, using it means **you're stuck paying for GPT-4 and sending your data to a closed ecosystem.**
Developers want the convenience of the Agents SDK—structured handoffs, built-in memory, easy tool use—**but with the privacy, cost-control, and flexibility of open models like LLaMA 3.3.**

**Connect the official OpenAI Agents SDK to LLaMA 3.3 (via [Regolo](https://regolo.ai/)) in under 10 minutes.** Build a currency bot with function calling that runs on open weights and European infrastructure.

## **Outcome**

- **Open Model Power:** LLaMA 3.3 is optimized for function calling, making it a drop-in replacement for GPT-4o in agentic workflows.
- **SDK Compatibility:** You don't need to rewrite your agent logic. Regolo's API is 100% OpenAI-compatible, so the SDK just works.
- **Privacy & Control:** Keep your agent's reasoning traces and data inputs within the EU (Regolo's green cloud), avoiding US data residency issues.

![](https://regolo.ai/wp-content/uploads/2025/07/Screenshot-From-2025-07-07-18-27-06-1024x662.png)## **Prerequisites (Fast)**

- **Regolo API Key**: From your [dashboard](https://regolo.ai/).
- **Python 3.10+**: pip install openai-agents openai streamlit.
- **LLaMA 3.3**: Supported natively on Regolo.

## **Step-by-Step (Code Blocks)**

### **1) Configure the Regolo Provider**

The Agents SDK allows custom model providers. We'll define one that points to [Regolo](https://regolo.ai/).

```
from openai import AsyncOpenAI
from agents import ModelProvider, OpenAIChatCompletionsModel, Model

class RegoloProvider(ModelProvider):
    def __init__(self):
        self.client = AsyncOpenAI(
            base_url="https://api.regolo.ai/v1",
            api_key="YOUR_REGOLO_KEY"
        )
    
    def get_model(self, model_name: str | None) -> Model:
        # Default to Llama-3.3 if no model specified
        return OpenAIChatCompletionsModel(
            model=model_name or "Llama-3.3-70B-Instruct",
            openai_client=self.client
        )

REGOLO_PROVIDER = RegoloProvider()Code language: Python (python)
```

## **2) Define a Real Tool**

LLaMA 3.3 excels at tool use. Let's create a live currency converter using httpx.

```
from agents import function_tool
import httpx

@function_tool
async def get_exchange_rate(base: str, target: str) -> str:
    """Get the current exchange rate between two currency codes (e.g., USD, EUR)."""
    url = f"https://api.frankfurter.dev/v1/latest?base={base.upper()}"
    async with httpx.AsyncClient() as client:
        resp = await client.get(url)
    
    if resp.status_code != 200:
        return "Error fetching rates."
    
    rate = resp.json().get("rates", {}).get(target.upper())
    return f"1 {base} = {rate} {target}" if rate else "Currency not found."Code language: Python (python)
```

## **3) Create the Agent**

Instantiate the agent with our tool and Regolo provider instructions.

```
from agents import Agent, Runner, RunConfig

agent = Agent(
    name="CurrencyBot",
    instructions="You are a helpful financial assistant. Use tools to check rates.",
    tools=[get_exchange_rate]
)

# Test run
async def main():
    result = await Runner.run(
        agent,
        "How many Euros can I get for 100 US Dollars?",
        run_config=RunConfig(model_provider=REGOLO_PROVIDER)
    )
    print(result.final_output)

# asyncio.run(main())Code language: Python (python)
```

Expected output: The agent calls the tool, calculates the total (100 \* rate), and answers naturally.

### **4) Build a Streamlit UI**

Wrap it all in a chat interface for a real demo experience.

```
import streamlit as st
import asyncio

st.title("🇪🇺 Regolo Currency Agent")
user_msg = st.chat_input("Ask about exchange rates...")

if user_msg:
    with st.spinner("Agent is reasoning..."):
        # Run the agent with our custom provider
        result = asyncio.run(Runner.run(
            agent, 
            user_msg, 
            run_config=RunConfig(model_provider=REGOLO_PROVIDER)
        ))
    st.write(result.final_output)Code language: Python (python)
```

## **Production-Ready: Agent Patterns**

The power of the Agents SDK is **Handoffs**. You can create a "Triage Agent" that routes queries to a "Finance Agent" (LLaMA 3.3) or a "Coding Agent" (Qwen 2.5-Coder), all running on Regolo.

```
finance_agent = Agent(name="Finance", tools=[get_exchange_rate])
triage_agent = Agent(
    name="Triage",
    handoffs=[finance_agent],
    instructions="Route financial questions to the Finance agent."
)Code language: Python (python)
```

This multi-agent pattern works seamlessly with Regolo's open models.

## **Benchmarks & Costs**

| **Feature** | **Regolo (LLaMA 3.3)** | **GPT-4o** |
|---|---|---|
| **Tool Calling** | **Reliable**. Zero-shot JSON schema adherence. | Excellent. |
| **Privacy** | **Zero Retention**. | Standard retention. |
| **Cost** | **Significantly Lower**. (~$0.70/1M tokens). | ~$2.50+/1M tokens. |
| **Ecosystem** | **Open**. Agents SDK works without lock-in. | Closed. |

## 👉[Try Build Agents with LLaMA 3.3 ThunderAI on Regolo for free](https://dashboard.regolo.ai)

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## **Resources & Community**

**Official Documentation:**

- [Regolo Platform](https://regolo.ai) - European LLM provider, Zero Data-Retention and 100% Green

**Related Guides:**

- [Boost Your Workflows with Regolo AI on n8n](https://regolo.ai/boost-your-workflows-with-regolo-ai-on-n8n/)
- [Build Multi-Agent Workflows with crewAI Teams](https://regolo.ai/build-multi-agent-workflows-with-crewai-teams/)

**Join the Community:**

- [Regolo Discord](https://discord.gg/ZzZvuR2y) - Share your automation builds
- [CheshireCat GitHub](https://github.com/cheshire-cat-ai) - Contribute plugins
- Follow Us on X[ @regolo\_ai](https://x.com/regolo_ai) - Show your integrations!
- Open discussion on our[ Subreddit Community](https://www.reddit.com/r/regolo_ai/)

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## [**Get Free Regolo Credits →**](https://dashboard.regolo.ai)

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