MemGPT/Letta 记忆与上下文管理深度解析

Stateful Agent = {
    system_prompt: "系统提示词",
    memory_blocks: [          # 记忆块
        {label: "human", value: "用户信息"},
        {label: "persona", value: "角色设定"},
        {label: "facts", value: "事实知识"}
    ],
    messages: [               # 消息历史
        # 包含用户消息、助手回复、工具调用
    ],
    tools: [                  # 可用工具
        "web_search",
        "memory_management",
        "file_operations"
    ]
}

# 简化的分页管理逻辑
class MemGPTManager:
    def __init__(self):
        self.warning_threshold = 0.7  # 70%警告
        self.flush_threshold = 1.0     # 100%强制换出
    
    def check_memory_pressure(self, context_usage):
        if context_usage > self.flush_threshold:
            self.evict_oldest()
        elif context_usage > self.warning_threshold:
            self.summarize_old_messages()
    
    def evict_oldest(self):
        # FIFO驱逐最旧消息
        old_messages = self.fifo_queue.dequeue()
        summary = self.summarize(old_messages)
        self.archival_memory.store(summary)

用户: 我叫张三
Agent: 你好张三！
--- 新会话 ---
用户: 我叫什么？
Agent: 我不知道

用户: 我叫张三
Agent: [调用 core_memory_append("human", "Name: 张三")]
Agent: 你好张三！
--- 新会话 ---
用户: 我叫什么？
Agent: [检索 core_memory]
Agent: 你叫张三！

from letta_client import Letta
import os

# 初始化客户端
client = Letta(api_key=os.getenv("LETTA_API_KEY"))

# 创建带记忆的 Agent
agent_state = client.agents.create(
    model="openai/gpt-4o",
    memory_blocks=[
        {
            "label": "human",
            "value": "Name: Robert. Occupation: Software Engineer"
        },
        {
            "label": "persona",
            "value": "I am a helpful AI assistant with memory capabilities."
        }
    ],
    tools=["web_search", "fetch_webpage", "memory_management"]
)

print(f"Agent created with ID: {agent_state.id}")

# 发送消息
response = client.agents.messages.create(
    agent_id=agent_state.id,
    input="What do you know about me?"
)

for message in response.messages:
    print(message)

import Letta from "@letta-ai/letta-client";

const client = new Letta({
  apiKey: process.env.LETTA_API_KEY
});

// 创建 Agent
const agentState = await client.agents.create({
  model: "openai/gpt-4o",
  memory_blocks: [
    {
      label: "human",
      value: "Name: Robert. Occupation: Software Engineer"
    },
    {
      label: "persona", 
      value: "I am a self-improving AI assistant."
    }
  ],
  tools: ["web_search", "fetch_webpage"]
});

// 发送消息
const response = await client.agents.messages.create(
  agentState.id,
  { input: "What do you know about me?" }
);

for (const message of response.messages) {
  console.log(message);
}