跟踪 AG2🤖

AG2 是一个用于构建和编排 AI 代理交互的开源框架。

MLflow 跟踪为 AG2（一个开源多智能体框架）提供了自动跟踪功能。通过调用 mlflow.ag2.autolog() 函数启用 AG2 的自动跟踪，MLflow 将捕获嵌套跟踪并在智能体执行时将它们记录到活动的 MLflow 实验中。请注意，由于 AG2 是基于 AutoGen 0.2 构建的，因此当您使用 AutoGen 0.2 时可以使用此集成。

python

import mlflow

mlflow.ag2.autolog()

MLflow 捕获有关多智能体执行的以下信息

• 在不同轮次中调用了哪个智能体
• 智能体之间传递的消息
• 每个智能体发起的 LLM 和工具调用，按智能体和轮次组织
• 延迟
• 如果抛出任何异常

基本示例

python

import os
from typing import Annotated, Literal

from autogen import ConversableAgent

import mlflow

# Turn on auto tracing for AG2
mlflow.ag2.autolog()

# Optional: Set a tracking URI and an experiment
mlflow.set_tracking_uri("https://:5000")
mlflow.set_experiment("AG2")


# Define a simple multi-agent workflow using AG2
config_list = [
    {
        "model": "gpt-4o-mini",
        # Please set your OpenAI API Key to the OPENAI_API_KEY env var before running this example
        "api_key": os.environ.get("OPENAI_API_KEY"),
    }
]

Operator = Literal["+", "-", "*", "/"]


def calculator(a: int, b: int, operator: Annotated[Operator, "operator"]) -> int:
    if operator == "+":
        return a + b
    elif operator == "-":
        return a - b
    elif operator == "*":
        return a * b
    elif operator == "/":
        return int(a / b)
    else:
        raise ValueError("Invalid operator")


# First define the assistant agent that suggests tool calls.
assistant = ConversableAgent(
    name="Assistant",
    system_message="You are a helpful AI assistant. "
    "You can help with simple calculations. "
    "Return 'TERMINATE' when the task is done.",
    llm_config={"config_list": config_list},
)

# The user proxy agent is used for interacting with the assistant agent
# and executes tool calls.
user_proxy = ConversableAgent(
    name="Tool Agent",
    llm_config=False,
    is_termination_msg=lambda msg: msg.get("content") is not None
    and "TERMINATE" in msg["content"],
    human_input_mode="NEVER",
)

# Register the tool signature with the assistant agent.
assistant.register_for_llm(name="calculator", description="A simple calculator")(
    calculator
)
user_proxy.register_for_execution(name="calculator")(calculator)
response = user_proxy.initiate_chat(
    assistant, message="What is (44231 + 13312 / (230 - 20)) * 4?"
)

Token 用量

MLflow >= 3.2.0 支持 AG2 的 token 使用量跟踪。每次 LLM 调用的 token 使用量将记录在 mlflow.chat.tokenUsage 属性中。整个跟踪过程的总 token 使用量将可在跟踪信息对象的 token_usage 字段中找到。

python

import json
import mlflow

mlflow.ag2.autolog()

# Register and run the tool signature with the assistant agent which is defined in above section.
assistant.register_for_llm(name="calculator", description="A simple calculator")(
    calculator
)
user_proxy.register_for_execution(name="calculator")(calculator)
response = user_proxy.initiate_chat(
    assistant, message="What is (44231 + 13312 / (230 - 20)) * 4?"
)

# Get the trace object just created
last_trace_id = mlflow.get_last_active_trace_id()
trace = mlflow.get_trace(trace_id=last_trace_id)

# Print the token usage
total_usage = trace.info.token_usage
print("== Total token usage: ==")
print(f"  Input tokens: {total_usage['input_tokens']}")
print(f"  Output tokens: {total_usage['output_tokens']}")
print(f"  Total tokens: {total_usage['total_tokens']}")

# Print the token usage for each LLM call
print("\n== Detailed usage for each LLM call: ==")
for span in trace.data.spans:
    if usage := span.get_attribute("mlflow.chat.tokenUsage"):
        print(f"{span.name}:")
        print(f"  Input tokens: {usage['input_tokens']}")
        print(f"  Output tokens: {usage['output_tokens']}")
        print(f"  Total tokens: {usage['total_tokens']}")

bash

== Total token usage: ==
  Input tokens: 1569
  Output tokens: 229
  Total tokens: 1798

== Detailed usage for each LLM call: ==
chat_completion_1:
  Input tokens: 110
  Output tokens: 61
  Total tokens: 171
chat_completion_2:
  Input tokens: 191
  Output tokens: 61
  Total tokens: 252
chat_completion_3:
  Input tokens: 269
  Output tokens: 24
  Total tokens: 293
chat_completion_4:
  Input tokens: 302
  Output tokens: 23
  Total tokens: 325
chat_completion_5:
  Input tokens: 333
  Output tokens: 22
  Total tokens: 355
chat_completion_6:
  Input tokens: 364
  Output tokens: 38
  Total tokens: 402

禁用自动跟踪

可以通过调用 mlflow.ag2.autolog(disable=True) 或 mlflow.autolog(disable=True) 在全局范围内禁用 AG2 的自动跟踪。