AutoGen AI智能体框架开发者指南

coding_agent = AssistantAgent(
    name="Python Developer",
    system_message="Expert Python developer with focus on code quality and optimization",
    llm_config={"temperature": 0.7}
)
 
reviewer_agent = AssistantAgent(
    name="Code Reviewer",
    system_message="Senior developer specialized in code review and best practices",
    llm_config={"temperature": 0.2}
)

user_proxy = UserProxyAgent(
    name="User_Proxy",
    system_message="A proxy for human user, providing project requirements and feedback.",
    human_input_mode="TERMINATE",
    code_execution_config={"work_dir": "coding_project"}
)
 
coding_agent = AssistantAgent(
    name="Coding_Assistant",
    system_message="You are a helpful AI assistant specialized in writing Python scripts with robust error handling.",
    llm_config={"config_list": [{"model": "gpt-4o"}]}
)
 
# Initiate the chat with the task
user_proxy.initiate_chat(
    recipient=coding_agent, 
    message="Develop a Python script for processing CSV files with error handling"
)

async def development_workflow():
    # Initial code development
    code_response = await coding_agent.generate_response(task_message)
    # Code review phase
    review_response = await reviewer_agent.review_code(code_response)
    # Iterative improvement based on review
    if review_response.has_feedback:
        improved_code = await coding_agent.update_code(review_response.feedback)

#  (Example code for ReAct would go here)

ReAct_prompt = """
Answer the following questions as best you can. You have access to tools provided.
 
Use the following format:
 
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take
Action Input: the input to the action
Observation: the result of the action
... (this process can repeat multiple times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
 
Begin!
Question: {input}
"""
 
# Define the ReAct prompt message. Assuming a "question" field is present in the context
 
 
def react_prompt_message(sender, recipient, context):
    return ReAct_prompt.format(input=context["question"])
 
 user_proxy.initiate_chat(
        assistant,
        message=react_prompt_message,
        question="What is the result of super bowl 2024?",
    )

memory = MemoryClient(api_key="mem0_key")  # Initialize Mem0 memory client
memory.add(messages=[{"role": "user", "content": "Query about TV issue"}], user_id="case_123")  # Store memory
memories = memory.search("TV issue", user_id="case_123")  # Retrieve relevant memories
agent.generate_reply(messages=[{"content": f"Context: {memories}", "role": "user"}])  # Use in agent response

def calculator(operation, x, y):
    if operation == 'add':
        return x + y
    if operation == 'subtract':
        return x - y
    if operation == 'multiply':
        return x * y

assistant = AssistantAgent(
    name="Math_Assistant",
    llm_config={
        "config_list": [{"model": "gpt-4"}],
        "tools": [
            {"function": calculator, "name": "math_operations"}
        ]
    }
)

coding_agent = AssistantAgent(
    name="Python_Developer",
    system_message="Expert in writing Python code with strong algorithmic skills",
    llm_config={"config_list": [{"model": "gpt-4"}]}
)
reviewer_agent = AssistantAgent(
    name="Code_Reviewer",
    system_message="Specialized in code quality assessment and optimization",
    llm_config={"config_list": [{"model": "gpt-4"}]}
)
group_chat = GroupChat(
    agents=[coding_agent, reviewer_agent],
    messages=[],
    max_round=5,
    speaker_selection_method="auto"
)
manager = GroupChatManager(groupchat=group_chat)