Agent 集成

from langgraph.graph import StateGraph
from agkit.storage.langgraph import TDAICheckpointSaver

# 创建 TDAI checkpoint saver
checkpoint_saver = TDAICheckpointSaver(
    api_key=os.getenv('TDAI_API_KEY'),
    base_url=os.getenv('TDAI_BASE_URL'),
    checkpoint_type='checkpoints',
    checkpoint_writes_type='checkpoint_writes'
)

# 创建带有 checkpoint saver 的 graph
graph = StateGraph({
    'messages': []
})
graph.add_node('agent', agent_node)
graph.add_edge('__start__', 'agent')

compiled_graph = graph.compile(
    checkpointer=checkpoint_saver
)

# 运行并持久化线程
result = await compiled_graph.ainvoke(
    {'messages': [{'role': 'user', 'content': 'Hello'}]},
    config={'configurable': {'thread_id': 'user-123'}}
)

// 生产环境配置
const memory = MySQLMemory.create({
  host: process.env.DB_HOST,
  database: process.env.DB_NAME,
  sessionId: userId,

  // 上下文工程
  maxTokens: 8000,
  enableContextManagement: true
});

const agent = new Agent({
  name: 'production-assistant',
  model: provider,
  memory: memory,

  // 内存感知配置
  maxContextTokens: 8000,
  memoryRetrievalLimit: 10
});

# ❌ 错误：内存实例未共享
agent1 = Agent(memory=InMemoryMemory())
agent2 = Agent(memory=InMemoryMemory())

# ✅ 正确：共享内存实例
shared_memory = InMemoryMemory()
agent1 = Agent(memory=shared_memory)
agent2 = Agent(memory=shared_memory)

# ❌ 错误：不同用户使用相同会话
agent = Agent(memory=memory, session_id='default')

# ✅ 正确：每个用户使用唯一会话
agent = Agent(
    memory=memory,
    session_id=f'user-{user_id}'
)