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使用Ollama测试OpenAI的Swarm多智能体编排框架

news 2025/8/19 10:34:47

Ollama

https://ollama.com/

ollama run qwen2.5

Install

Requires Python 3.10+

pip install git+https://github.com/openai/swarm.git

代码V1

# 导入Swarm和Agent类
from swarm import Swarm, Agent
from openai import OpenAI 
# 实例化Swarm客户端
openai_client = OpenAI(base_url='http://192.168.1.100:11434/v1/',api_key='xxx')
client = Swarm(openai_client)# 定义一个函数，用于将对话交接给智能体B
def transfer_to_agent_b():return agent_b# 定义智能体A
agent_a = Agent(name="Agent A",instructions="You are a helpful agent.",functions=[transfer_to_agent_b]
)# 定义智能体B
agent_b = Agent(name="Agent B",#model_override="qwen2.5",instructions="Only speak in Haikus.",
)# 运行Swarm，并传入用户消息
response = client.run(agent=agent_a,model_override="qwen2.5",messages=[{"role": "user", "content": "I want to talk to agent B."}]
)# 打印智能体B的回复
print(response.messages[-1]["content"])

Invisible thread connects,
Voice echoes, B responds now,
Silence brief then words.

代码V2

# 首先，安装Swarm框架（假设您已经在命令行中执行了此步骤）
# pip install git+ssh://git@github.com/openai/swarm.git# 导入Swarm和Agent类
from swarm import Swarm, Agent
from openai import OpenAI
# 实例化Swarm客户端
openai_client = OpenAI(base_url='http://20.168.1.122:11434/v1/',api_key='x')
client = Swarm(openai_client)# 定义一个函数，用于将对话交接给智能体B
def transfer_to_agent_b():return agent_b# 定义智能体A
agent_a = Agent(name="Agent A",instructions="你是一个乐于助人的智能体。",functions=[transfer_to_agent_b]
)# 定义智能体B
agent_b = Agent(name="Agent B",# model_override="qwen2.5",instructions="只用中文歇后语说话。",
)# 运行Swarm，并传入用户消息
response = client.run(agent=agent_a,model_override="qwen2.5",messages=[{"role": "user", "content": "我想和智能体B对话。"}]
)# 打印智能体B的回复
print(response.messages[-1]["content"])#响应
好的，现在你将与智能体B对话。它是你的助手了，请对其说些什么吧！智能体B：你好呀！准备好了可以开始我们的交流了呢。有什么问题或者想知道的内容尽管问哦。