> ## 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.
[Give us a star](https://github.com/AgentOps-AI/agentops) to bookmark on GitHub, save for later 🖇️)
[OpenAI Agents SDK](https://github.com/openai/agentsdk_prototype) is a lightweight yet powerful framework for building multi-agent workflows. The SDK provides a comprehensive set of tools for creating, managing, and monitoring agent-based applications.
## 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
```bash pip theme={null}
pip install agentops
```
```bash poetry theme={null}
poetry add agentops
```
```bash pip theme={null}
pip install openai-agents
```
```bash poetry theme={null}
poetry add openai-agents
```
This will be updated to a PyPI link when the package is officially released.
Make sure to call `agentops.init` before calling any `openai`, `cohere`, `crew`, etc models.
```python python theme={null}
import agentops
agentops.init()
```
Set your API key as an `.env` variable for easy access.
```python .env theme={null}
AGENTOPS_API_KEY=
OPENAI_API_KEY=
```
Read more about environment variables in [Advanced Configuration](/v1/usage/advanced-configuration)
Execute your program and visit [app.agentops.ai/drilldown](https://app.agentops.ai/drilldown) to observe your Agents! 🕵️
After your run, AgentOps prints a clickable url to console linking directly to your session in the Dashboard
## Hello World Example
```python theme={null}
from agents import Agent, Runner
import agentops
# Initialize AgentOps
agentops.init()
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)
# Output:
# Code within the code,
# Functions calling themselves,
# Infinite loop's dance.
```
## Handoffs Example
```python theme={null}
from agents import Agent, Runner
import asyncio
import agentops
# Initialize AgentOps
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)
# ¡Hola! Estoy bien, gracias por preguntar. ¿Y tú, cómo estás?
if __name__ == "__main__":
asyncio.run(main())
```
## Functions Example
```python theme={null}
import asyncio
from agents import Agent, Runner, function_tool
import agentops
# Initialize AgentOps
agentops.init()
@function_tool
def get_weather(city: str) -> str:
return f"The weather in {city} is sunny."
agent = Agent(
name="Hello world",
instructions="You are a helpful agent.",
tools=[get_weather],
)
async def main():
result = await Runner.run(agent, input="What's the weather in Tokyo?")
print(result.final_output)
# The weather in Tokyo is sunny.
if __name__ == "__main__":
asyncio.run(main())
```
## The Agent Loop
When you call `Runner.run()`, the SDK runs a loop until it gets a final output:
1. The LLM is called using the model and settings on the agent, along with the message history.
2. The LLM returns a response, which may include tool calls.
3. If the response has a final output, the loop ends and returns it.
4. If the response has a handoff, the agent is set to the new agent and the loop continues from step 1.
5. Tool calls are processed (if any) and tool response messages are appended. Then the loop continues from step 1.
You can use the `max_turns` parameter to limit the number of loop executions.
## Final Output
Final output is the last thing the agent produces in the loop:
* If you set an `output_type` on the agent, the final output is when the LLM returns something of that type using structured outputs.
* If there's no `output_type` (i.e., plain text responses), then the first LLM response without any tool calls or handoffs is considered the final output.