使用 AutoGen 追踪

from langsmith.integrations.otel import configure
from openinference.instrumentation.autogen import AutogenInstrumentor
from openinference.instrumentation.openai import OpenAIInstrumentor

# Configure LangSmith tracing
configure(project_name="autogen-demo")

# Instrument AutoGen and OpenAI calls
AutogenInstrumentor().instrument()
OpenAIInstrumentor().instrument()

import autogen
from openinference.instrumentation.autogen import AutogenInstrumentor
from openinference.instrumentation.openai import OpenAIInstrumentor
from langsmith.integrations.otel import configure
import os
import dotenv

# Load environment variables
dotenv.load_dotenv(".env.local")

# Configure LangSmith tracing
configure(project_name="autogen-code-review")

# Instrument AutoGen and OpenAI
AutogenInstrumentor().instrument()
OpenAIInstrumentor().instrument()

# Configure your agents
config_list = [
    {
        "model": "gpt-4",
        "api_key": os.getenv("OPENAI_API_KEY"),
    }
]

# Create a code reviewer agent
code_reviewer = autogen.AssistantAgent(
    name="code_reviewer",
    llm_config={"config_list": config_list},
    system_message="""You are an expert code reviewer. Your role is to:
    1. Review code for bugs, security issues, and best practices
    2. Suggest improvements and optimizations
    3. Provide constructive feedback
    Always be thorough but constructive in your reviews.""",
)

# Create a developer agent
developer = autogen.AssistantAgent(
    name="developer",
    llm_config={"config_list": config_list},
    system_message="""You are a senior software developer. Your role is to:
    1. Write clean, efficient code
    2. Address feedback from code reviews
    3. Explain your implementation decisions
    4. Implement requested features and fixes""",
)

# Create a user proxy agent
user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=8,
    is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
    code_execution_config={"work_dir": "workspace"},
    llm_config={"config_list": config_list},
)

def run_code_review_session(task_description: str):
    """Run a multi-agent code review session."""

    # Create a group chat with the agents
    groupchat = autogen.GroupChat(
        agents=[user_proxy, developer, code_reviewer],
        messages=[],
        max_round=10
    )

    # Create a group chat manager
    manager = autogen.GroupChatManager(
        groupchat=groupchat,
        llm_config={"config_list": config_list}
    )

    # Start the conversation
    user_proxy.initiate_chat(
        manager,
        message=f"""
        Task: {task_description}

        Developer: Please implement the requested feature.
        Code Reviewer: Please review the implementation and provide feedback.

        Work together to create a high-quality solution.
        """
    )

    return "Code review session completed"

# Example usage
if __name__ == "__main__":
    task = """
    Create a Python function that implements a binary search algorithm.
    The function should:
    - Take a sorted list and a target value as parameters
    - Return the index of the target if found, or -1 if not found
    - Include proper error handling and documentation
    """

    result = run_code_review_session(task)
    print(f"Result: {result}")

from opentelemetry import trace

# Get the current tracer
tracer = trace.get_tracer(__name__)

def run_code_review_session(task_description: str):
    with tracer.start_as_current_span("autogen_code_review") as span:
        # Add custom metadata
        span.set_attribute("langsmith.metadata.session_type", "code_review")
        span.set_attribute("langsmith.metadata.agent_count", "3")
        span.set_attribute("langsmith.metadata.task_complexity", "medium")
        span.set_attribute("langsmith.span.tags", "autogen,code-review,multi-agent")

        # Your AutoGen code here
        groupchat = autogen.GroupChat(
            agents=[user_proxy, developer, code_reviewer],
            messages=[],
            max_round=10
        )

        manager = autogen.GroupChatManager(
            groupchat=groupchat,
            llm_config={"config_list": config_list}
        )

        user_proxy.initiate_chat(manager, message=task_description)
        return "Session completed"

from langsmith.integrations.otel import configure
from openinference.instrumentation.autogen import AutogenInstrumentor
from openinference.instrumentation.openai import OpenAIInstrumentor
from openinference.instrumentation.dspy import DSPyInstrumentor

# Configure LangSmith tracing
configure(project_name="multi-framework-app")

# Initialize multiple instrumentors
AutogenInstrumentor().instrument()
OpenAIInstrumentor().instrument()
DSPyInstrumentor().instrument()

# Your application code using multiple frameworks