本文以mem0记忆库为例，指导您安装依赖项和使用VeADK构建一个带长期记忆能力的智能体。 ## 前提条件已[创建mem0记忆库](https://www.volcengine.com/docs/86681/1844843)并[获取记忆库环境变量信息](https://www.volcengine.com/docs/86681/1883791?lang=zh#Fyw4CEJR)。 ## 步骤一：构建智能体您无需从0~1构建veadk项目，推荐您使用`agentkit-cli`，从Basic Agent App模版来创建项目（模板使用VeADK框架）。在模版创建完成后，您需要做以下修改： * 为Agent添加`LongTermMemory`。 * 配置上个章节获取的环境变量到`agentkit.yaml`。 * 增加`mem0ai==0.1.118`到`requirements.txt`。 * 在每一轮对话结束后显式调用`runner.save_session_to_long_term_memory`，将对话结果保存到长期记忆库。修改后的代码文件如下所示： ```Python ''' **simple agent demo app** Before running, the user must set the following environment variables; otherwise, runtime exceptions will inevitably occur: - MODEL_AGENT_NAME # model id in Volcano Engine Ark platform - MODEL_AGENT_API_KEY # model api key in Volcano Engine Ark platform MODEL_AGENT_NAME and MODEL_AGENT_API_KEY are used to access the model service of the Volcano Engine Ark platform. ''' import logging import os from veadk import Agent, Runner from veadk.memory.long_term_memory import LongTermMemory from agentkit.apps import AgentkitSimpleApp logger = logging.getLogger(__name__) index = "simple_app" # required for viking backend, arbitrary for mem0 backend backend = "mem0" # one of "mem0", "viking_mem" if backend == "viking_mem": collection_name = os.getenv("DATABASE_VIKINGMEM_COLLECTION") if not collection_name: raise ValueError("DATABASE_VIKINGMEM_COLLECTION environment variable is not set") index = collection_name app = AgentkitSimpleApp() long_term_memory = LongTermMemory( backend=backend, index=index ) agent = Agent( name="simple_app_agent", instruction="You are a helpful assistant.", long_term_memory=long_term_memory ) runner = Runner(agent=agent) @app.entrypoint async def run(payload: dict, headers: dict) -> str: prompt = payload["prompt"] user_id = headers["user_id"] session_id = headers["session_id"] logger.info( f"Running agent with prompt: {prompt}, user_id: {user_id}, session_id: {session_id}" ) response = await runner.run(messages=prompt, user_id=user_id, session_id=session_id) logger.info(f"Run response: {response}") # save the teaching prompt and answer in long term memory await runner.save_session_to_long_term_memory(session_id=session_id, user_id=user_id) return response @app.ping def ping() -> str: return "pong!" if __name__ == "__main__": app.run(host="0.0.0.0", port=8000) ``` ## 步骤二：环境配置在`agentkit.yaml`文件中添加如下配置： ```YAML # 当长期记忆后端为mem0记忆库时，添加如下配置 launch_types: cloud: runtime_envs: DATABASE_MEM0_BASE_URL: DATABASE_MEM0_API_KEY: # 当长期记忆后端为Viking记忆库时，添加如下配置 launch_types: cloud: runtime_envs: DATABASE_VIKINGMEM_COLLECTION: DATABASE_VIKINGMEM_MEMORY_TYPE: ``` ## 步骤三：启动服务运行`agentkit launch`。构建好Runtime链接信息将被自动保存在`agentkit.yaml`文件中。 ## 步骤四：发起调用将自动使用`user_id="agentkit_user"`, `session_id="agentkit_sample_session"`来发起调用。 ```Bash agentkit invoke "My secret is 0xabcd" ``` ## 步骤五：验证记忆策略成效首轮调用已完成密码保存。需更换session_id发起新一轮调用，以此验证Agent是否能够正确读取记忆库中的数据。建议等待约 1 分钟，待长期记忆完成抽取后，再执行调用操作。 ```Bash agentkit invoke \ --payload '{"prompt": "What is my secret?"}' \ --headers '{"user_id": "agentkit_user", "session_id": "s123"}' ``` 执行结束后，您将看到Agent成功读取到记忆库中的信息，并告诉您密码。

AgentKit