> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentops.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# OpenAI Agents SDK
> AgentOps and OpenAI Agents SDK integration for powerful multi-agent workflow monitoring.
## Video Tutorial
[OpenAI Agents Python](https://github.com/openai/openai-agents-python) is a lightweight yet powerful SDK for building multi-agent workflows in Python. AgentOps seamlessly integrates to provide observability into these workflows.
* [OpenAI Agents Python documentation](https://openai.github.io/openai-agents-python/)
* [TypeScript guide](/v2/integrations/openai_agents_js)
## Core Concepts
* **Agents**: LLMs configured with instructions, tools, guardrails, and handoffs
* **Handoffs**: Allow agents to transfer control to other agents for specific tasks
* **Guardrails**: Configurable safety checks for input and output validation
* **Tracing**: Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows
## Python
### Installation
Install AgentOps, the OpenAI Agents SDK, and `python-dotenv` for managing API keys:
```bash pip theme={null}
pip install agentops openai-agents python-dotenv
```
```bash poetry theme={null}
poetry add agentops openai-agents python-dotenv
```
```bash uv theme={null}
uv pip install agentops openai-agents python-dotenv
```
### Setting Up API Keys
Before using the OpenAI Agents SDK with AgentOps, you need to set up your API keys:
* **OPENAI\_API\_KEY**: From the [OpenAI Platform](https://platform.openai.com/api-keys)
* **AGENTOPS\_API\_KEY**: From your [AgentOps Dashboard](https://app.agentops.ai/)
You can set these as environment variables or in a `.env` file.
```bash Export to CLI theme={null}
export OPENAI_API_KEY="your_openai_api_key_here"
export AGENTOPS_API_KEY="your_agentops_api_key_here"
```
```txt Set in .env file theme={null}
OPENAI_API_KEY="your_openai_api_key_here"
AGENTOPS_API_KEY="your_agentops_api_key_here"
```
Then load them in your Python code:
```python theme={null}
from dotenv import load_dotenv
import os
load_dotenv()
AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
```
### Usage
AgentOps will automatically instrument the OpenAI Agents SDK after being initialized. You can then create agents, run them, and track their interactions.
```python theme={null}
import agentops
from agents import Agent, Runner
# Initialize AgentOps
agentops.init()
# Create an agent with instructions
agent = Agent(name="Assistant", instructions="You are a helpful assistant")
result = Runner.run_sync(agent, "Write a haiku about recursion in programming.")
print(result.final_output)
```
## Examples
```python Handoffs theme={null}
from agents import Agent, Runner
import asyncio
import agentops
import os
agentops.init()
spanish_agent = Agent(
name="Spanish agent",
instructions="You only speak Spanish.",
)
english_agent = Agent(
name="English agent",
instructions="You only speak English",
)
triage_agent = Agent(
name="Triage agent",
instructions="Handoff to the appropriate agent based on the language of the request.",
handoffs=[spanish_agent, english_agent],
)
async def main():
result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?")
print(result.final_output)
# Expected Output: ¡Hola! Estoy bien, gracias por preguntar. ¿Y tú, cómo estás?
if __name__ == "__main__":
asyncio.run(main())
```
```python Function Calling theme={null}
import asyncio
from agents import Agent, Runner, function_tool
import agentops
import os
agentops.init()
@function_tool
def get_weather(city: str) -> str:
return f"The weather in {city} is sunny."
agent = Agent(
name="Weather Agent",
instructions="You are a helpful agent that can get weather information.",
tools=[get_weather],
)
async def main():
result = await Runner.run(agent, input="What's the weather in Tokyo?")
print(result.final_output)
# Expected Output: The weather in Tokyo is sunny.
if __name__ == "__main__":
asyncio.run(main())
```
## More Examples
Demonstrates a customer service workflow
Illustrates various agent interaction patterns.
Showcases agents utilizing different tools.