Agent卡成PPT？6 大核弹级提速方案+一线大厂代码级拆解

# 实战代码片段 - 延迟热力图生成
import logging
import langsmith

# 配置日志记录
langsmith_logger = logging.getLogger("langsmith")
langsmith_logger.setLevel(level=logging.DEBUG)
# 性能指标获取 • 可以获取延迟相关指标  
# • 包括:latency_p50、latency_p99、first_token_p50、first_token_p99

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# 从 LangSmith 导出数据
data = client.list_runs(...)
df = pd.DataFrame(data)

# 生成热力图
plt.figure(figsize=(10, 6))
sns.heatmap(df[['latency', 'metadata']], cmap='YlOrRd')
plt.title('LangSmith 延迟热力图')
plt.show()

// 前端实现伪代码 - 多层级状态流
function renderAgentThinking(step) {
  const stages = ["🔍 检索知识库", "🧠 构建推理链", "⚙️ 调用支付API"];
  stages.slice(0, step+1).forEach((text, index) => {
    createFakeProgressBar(index, text);
  });
}

from langgraph.graph import StateGraph, START, END
from typing import TypedDict

# 定义状态类型
class State(TypedDict):
    topic: str
    joke: str
    story: str
    combined_output: str

# 定义并行的 LLM 调用函数
def call_llm_1(state: State):
    """第一个 LLM 调用生成笑话"""
    msg = llm.invoke(f"Write a joke about {state['topic']}")
    return {"joke": msg.content}

def call_llm_2(state: State):
    """第二个 LLM 调用生成故事"""
    msg = llm.invoke(f"Write a story about {state['topic']}")
    return {"story": msg.content}

def aggregator(state: State):
    """聚合所有生成的内容"""
    combined = f"Here's a story, joke, and poem about {state['topic']}!\n\n"
    combined += f"STORY:\n{state['story']}\n\n"
    combined += f"JOKE:\n{state['joke']}\n\n"
    return {"combined_output": combined}

# 构建工作流
parallel_builder = StateGraph(State)

# 添加节点
parallel_builder.add_node("call_llm_1", call_llm_1)
parallel_builder.add_node("call_llm_2", call_llm_2)
parallel_builder.add_node("aggregator", aggregator)

# 添加边连接节点
parallel_builder.add_edge(START, "call_llm_1")
parallel_builder.add_edge(START, "call_llm_2")
parallel_builder.add_edge("call_llm_1", "aggregator")
parallel_builder.add_edge("call_llm_2", "aggregator")
parallel_builder.add_edge("aggregator", END)

# 编译工作流
parallel_workflow = parallel_builder.compile()

# 调用工作流
state = parallel_workflow.invoke({"topic": "cats"})
print(state["combined_output"])

# 预加载代码示例
prefetch_tools(["sentiment_analysis", "geoip_lookup"])