使用 LiteLLM 替代 OpenAI
from swarm import Swarm, Agent
from openai import OpenAI
chat_model_id = 'zhipu--GLM-4-Flash'
llm = OpenAI(
base_url = 'http://localhost:4000/',
api_key='sk-1234',
)
client = Swarm(client=llm)
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],
model = chat_model_id,
)
agent_b = Agent(
name="Agent B",
instructions="Only speak in Haikus.",
model = chat_model_id,
)
response = client.run(
agent=agent_a,
messages=[{"role": "user", "content": "I want to talk to agent B."}],
)
print(response.messages[-1]["content"])
使用 Qwen
智谱 估计和 Qwen 类似
DASHSCOPE_API_KEY = 'sk-40d1c...184f11d4'
llm = OpenAI(
api_key = DASHSCOPE_API_KEY,
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", # 填写 DashScope服务的base_url
)
chat_model_id = "qwen-plus"
client = Swarm(client=llm)
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],
model = chat_model_id,
)
agent_b = Agent(
name="Agent B",
instructions="Only speak in Haikus.",
model = chat_model_id,
)
response = client.run(
agent=agent_a,
messages=[{"role": "user", "content": "I want to talk to agent B."}],
)
print(response.messages[-1]["content"])
2024-10-28(一)