编辑:Panda
毫无疑问,多智能体肯定是 OpenAI 未来重要的研究方向之一,前些天 OpenAI 著名研究科学家 Noam Brown 还在 X 上为 OpenAI 正在组建的一个新的多智能体研究团队招募机器学习工程师。
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项目地址:https://github.com/openai/swarm
pip install git+ssh://git@github.com/openai/swarm.git
from swarm import Swarm, Agent
client = Swarm()
def transfer_to_agent_b():
return agent_b
agent_a = Agent(
name="Agent A",
instructions="You are a helpful agent.",
functions=[transfer_to_agent_b],
)
agent_b = Agent(
name="Agent B",
instructions="Only speak in Haikus.",
)
response = client.run(
agent=agent_a,
messages=[{"role": "user", "content": "I want to talk to agent B."}],
)
print(response.messages[-1]["content"])
Hope glimmers brightly,
New paths converge gracefully,
What can I assist?
from swarm import Swarm
client = Swarm()
-
先让当前智能体完成一个结果 -
执行工具调用并附加结果 -
如有必要,切换智能体 -
如有必要,更新上下文变量 -
如果没有新的函数调用,则返回
agent = Agent(
instructions="You are a helpful agent."
)
def instructions(context_variables):
user_name = context_variables["user_name"]
return f"Help the user, {user_name}, do whatever they want."
agent = Agent(
instructions=instructions
)
response = client.run(
agent=agent,
messages=[{"role":"user", "content": "Hi!"}],
context_variables={"user_name":"John"}
)
print(response.messages[-1]["content"])
Hi John, how can I assist you today?
-
Swarm Agent 可以直接调用 Python 函数。 -
函数通常应返回一个字符串(数值会被转换为字符串)。 -
如果一个函数返回了一个 Agent,则执行过程将转交给该 Agent。 -
如果函数定义了 context_variables 参数,则它将由传递到 client.run() 的 context_variables 填充。
def greet(context_variables, language):
user_name = context_variables["user_name"]
greeting = "Hola" if language.lower() == "spanish" else "Hello"
print(f"{greeting}, {user_name}!")
return "Done"
agent = Agent(
functions=[print_hello]
)
client.run(
agent=agent,
messages=[{"role": "user", "content": "Usa greet() por favor."}],
context_variables={"user_name": "John"}
)
Hola, John!
sales_agent = Agent(name="Sales Agent")
def transfer_to_sales():
return sales_agent
agent = Agent(functions=[transfer_to_sales])
response = client.run(agent, [{"role":"user", "content":"Transfer me to sales."}])
print(response.agent.name)
Sales Agent
sales_agent = Agent(name="Sales Agent")
def talk_to_sales():
print("Hello, World!")
return Result(
value="Done",
agent=sales_agent,
context_variables={"department": "sales"}
)
agent = Agent(functions=[talk_to_sales])
response = client.run(
agent=agent,
messages=[{"role": "user", "content": "Transfer me to sales"}],
context_variables={"user_name": "John"}
)
print(response.agent.name)
print(response.context_variables)
Sales Agent
{'department': 'sales', 'user_name': 'John'}
-
文档字符串会转换为函数 description。 -
没有默认值的参数会设置为 required。 -
类型提示会映射到参数的 type(默认为 string)。 -
不明确支持对每个参数进行描述,但如果只是在文档字符串中添加,应该能以相似的方式工作。
def greet(name, age: int, location: str = "New York"):
"""Greets the user. Make sure to get their name and age before calling.
Args:
name: Name of the user.
age: Age of the user.
location: Best place on earth.
"""
print(f"Hello {name}, glad you are {age} in {location}!")
{
"type": "function",
"function": {
"name": "greet",
"description": "Greets the user. Make sure to get their name and age before calling.nnArgs:n name: Name of the user.n age: Age of the user.n location: Best place on earth.",
"parameters": {
"type": "object",
"properties": {
"name": {"type": "string"},
"age": {"type": "integer"},
"location": {"type": "string"}
},
"required": ["name", "age"]
}
}
}
stream = client.run(agent, messages, stream=True)
for chunk in stream:
print(chunk)
-
{“delim”:”start”} 和 {“delim”:”start”},用于在 Agent 每次处理单个消息(响应或函数调用)时发出信号。这有助于识别 Agent 之间的切换。 -
为方便起见,{“response”: Response} 将在流的末尾返回带有已聚合的(完整)响应的 Response 对象。
-
Ilan Bigio – ibigio -
James Hills – jhills20 -
Shyamal Anadkat – shyamal-anadkat -
Charu Jaiswal – charuj -
Colin Jarvis – colin-openai
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