✨ feat(agent): 增加智能体存储和记忆功能

- 引入SqliteAgentStorage和SqliteMemoryDb来实现智能体的持久化存储
- 优化智能体的记忆管理，以支持用户记忆和会话摘要功能

数据库优化工程师.py

@@ -15,7 +15,7 @@ from typing import Optional
 from dataclasses import dataclass
 from dotenv import load_dotenv
 
-from agno.agent import Agent
+from agno.agent import Agent, AgentMemory
 from agno.models.deepseek import DeepSeek
 from agno.knowledge.pdf import PDFKnowledgeBase
 from agno.vectordb.lancedb import LanceDb, SearchType
@@ -25,8 +25,9 @@ from agno.tools.mcp import MCPTools
 from mcp.client.stdio import stdio_client
 from mcp import ClientSession, StdioServerParameters
 from agno.knowledge.website import WebsiteKnowledgeBase
+from agno.storage.agent.sqlite import SqliteAgentStorage
+from agno.memory.db.sqlite import SqliteMemoryDb
 
- 
 
 async def main():
     load_dotenv()
@@ -94,13 +95,31 @@ async def main():
     ):
         agent = Agent(
             model=DeepSeek(id="deepseek-chat"),
+            storage=SqliteAgentStorage(
+                table_name="agent_sessions", db_file="tmp/agent_storage.db"
+            ),
+            memory=AgentMemory(
+                db=SqliteMemoryDb(
+                    table_name="agent_memory", db_file="tmp/agent_storage.db"
+                ),
+                create_user_memories=True,
+                create_session_summary=True,
+            ),
+            # Set add_history_to_messages=true to add the previous chat history to the messages sent to the Model.
+            #add_history_to_messages=True,
+            # Number of historical responses to add to the messages.
+            #num_history_responses=3,
             markdown=True,
             knowledge=knowledge_base,
             search_knowledge=True,
             show_tool_calls=True,
             tools=[postgres_tools, searxng_tools],
+            session_id="1",
+            # Enable the agent to read the chat history
+            read_chat_history=True,
         )
-        await agent.aprint_response("帮我分析一下aq.public数据库,并给出优化建议", stream=True)
+        await agent.aprint_response("我现在想要记录用户对智能体和课程的使用权限及使用情况，需要如何设计表结构", stream=True)
+        # await agent.aprint_response("帮我分析一下aq.public数据库,并给出优化建议", stream=True)
         # await agent.aprint_response("阅读下 https://www.lucidchart.com/blog/database-design-best-practices 这篇文章", stream=True)