A lightweight multi-agent workflow building framework that supports inter-agent collaboration, tool invocation, and process orchestration.

OpenAI Agents Python Project Detailed Introduction

Project Overview

The OpenAI Agents SDK is a lightweight and powerful Python framework for building multi-agent workflows. This is OpenAI's official production-grade agent development tool, an upgrade from the previous experimental project Swarm. The framework is designed to be simple yet feature-complete, specifically for building complex multi-agent AI applications.

Core Features

1. Cross-Platform Compatibility

• Model Agnostic: Supports OpenAI's Responses and Chat Completions APIs.
• Broad Compatibility: Supports 100+ different large language models.
• API Flexibility: Easily switch between different AI service providers.

2. Core Components

Agents

• LLM instances configured with instructions, tools, security guardrails, and handoff mechanisms.
• Each agent has a clear responsibility and capability boundary.
• Supports custom instructions and behavior patterns.

Handoffs

• A dedicated tool invocation mechanism for transferring control between agents.
• Enables seamless collaboration between agents.
• Supports complex workflow orchestration.

Guardrails

• Configurable input/output validation security checks.
• Ensures the security and reliability of AI applications.
• Prevents malicious input and inappropriate output.

Tracing

• Built-in agent runtime tracing functionality.
• Supports viewing, debugging, and optimizing workflows.
• Extensible tracing system with support for various external integrations.

Technical Architecture

Installation and Configuration

# Create a Python virtual environment
python -m venv env
source env/bin/activate

# Install the basic version
pip install openai-agents

# Install the version with voice support
pip install 'openai-agents[voice]'

Basic Usage Examples

Simple Agent

from agents import Agent, Runner

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)

Multilingual Collaboration Agent

from agents import Agent, Runner
import asyncio

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)

if __name__ == "__main__":
    asyncio.run(main())

Tool Invocation Example

import asyncio
from agents import Agent, Runner, function_tool

@function_tool
def get_weather(city: str) -> str:
    return f"The weather in {city} is sunny."

agent = Agent(
    name="Weather Assistant",
    instructions="You are a helpful weather agent.",
    tools=[get_weather],
)

async def main():
    result = await Runner.run(agent, input="What's the weather in Tokyo?")
    print(result.final_output)

if __name__ == "__main__":
    asyncio.run(main())

Workflow Execution Mechanism

Running Loop Logic

1. LLM Invocation: Uses the agent's model and settings, combined with the message history, to make a call.
2. Response Handling: The LLM returns a response, which may contain tool invocations.
3. Output Check: If there is a final output, it is returned and the loop ends.
4. Handoff Handling: If there is a handoff request, switch to the new agent and restart.
5. Tool Execution: Process the tool invocation and add the response to the message, then restart the loop.

Final Output Mechanism

• Structured Output: If output_type is set, the loop ends when the LLM returns content matching the type.
• Text Output: If output_type is not set, the loop ends when the agent produces a message without tool invocations or handoffs.

Tool System

Three Types of Tool Support

1. Hosted Tools

• Run on the LLM server.
• OpenAI provides hosted tools such as retrieval, web search, and computer use.

2. Function Calling

• Supports using any Python function as a tool.
• Flexible custom tool development.

3. Agent as a Tool

• Allows agents to call other agents.
• Supports complex hierarchical collaboration structures.

Built-in Tools

• WebSearchTool: Web search functionality.
• Computer Use: Computer operation capabilities.
• Retrieval: Information retrieval functionality.

Tracing and Monitoring

Automatic Tracing Functionality

• Automatically tracks the agent's runtime process.
• Facilitates debugging and optimizing workflows.
• Extensible design supports custom spans.

External Integration Support

• Logfire: Log management.
• AgentOps: Agent operation monitoring.
• Braintrust: AI application development platform.
• Scorecard: Performance evaluation.
• Keywords AI: Keyword AI integration.

Application Scenarios

Applicable Scenarios

• Complex Business Process Automation: Multi-step business processing flows.
• Multilingual Customer Service Systems: Intelligent customer service supporting different languages.
• Professional Field Assistants: AI assistants in professional fields such as law, medicine, and education.
• Data Processing Pipelines: Multi-stage data analysis and processing.
• Creative Collaboration Systems: Multiple AI roles collaborate to complete creative tasks.

Workflow Patterns

• Deterministic Processes: Predefined workflow steps.
• Iterative Loops: Tasks that require multiple iterations for optimization.
• Conditional Branching: Selecting different processing paths based on conditions.
• Parallel Processing: Multiple agents process different tasks simultaneously.

Development Environment

Development Requirements

• Python 3.8+
• Requires setting the OPENAI_API_KEY environment variable.
• Supports both asynchronous and synchronous invocation methods.

Development Toolchain

# Install the uv package manager
uv --version

# Install dependencies
make sync

# Run tests
make tests

# Type checking
make mypy

# Code style check
make lint

Project Advantages

1. Simple Design

• Minimal abstraction layers.
• Core concepts are clear and well-defined.
• Easy to understand and use.

2. Production Ready

• Built based on OpenAI's practical experience.
• Stable and reliable API design.
• Comprehensive error handling mechanisms.

3. Highly Flexible

• Supports complex agent relationship modeling.
• Can express various workflow patterns.
• Powerful customization capabilities.

Summary

OpenAI Agents Python is a professional-grade multi-agent development framework that makes complex AI collaboration simple and easy to use. Through its core agent, handoff, guardrail, and tracing mechanisms, developers can quickly build powerful AI application systems. Whether it's a simple single-agent task or a complex multi-agent collaboration scenario, this framework provides elegant solutions.