解决代理记忆的魔法棒
我与数百位人工智能代理开发者进行了交流，针对“如果你有一根魔法棒可以解决一个问题，那会是什么？”这个问题，大家的答案都是代理记忆。
我们在Raindrop中构建了SmartMemory，以解决这个问题，为代理提供了四种协同工作的记忆类型：
### 记忆类型概述
**工作记忆**
- 在会话中保持活跃的对话上下文
- 将思维组织成不同的时间线（主题）
- 代理可以搜索您讨论过的内容，并在之前的基础上进行扩展
- 类似于正在进行的对话的短期记忆
**情节记忆**
- 将完成的对话会话存储为可搜索的历史记录
- 记住您几周或几个月前讨论的内容
- 可以恢复之前的对话，从您离开的地方继续
- 您代理的长期对话档案
**语义记忆**
- 存储事实、文档和参考材料
- 在所有对话中保持知识的持续性
- 积累关于您项目和偏好的信息
- 您代理的知识库，随着时间的推移而增长
**程序记忆**
- 保存工作流程、工具交互模式和程序
- 学习如何一致地处理不同情况
- 存储决策树和响应模式
- 您代理的学习技能和操作程序
### 实际有效的多层搜索
**工作记忆**使用嵌入和向量搜索。当您搜索“身份验证问题”时，它可以找到关于“登录问题”或“安全漏洞”的记忆，即使确切的词语不匹配。
**情节记忆、语义记忆和程序记忆**采用三层搜索方法：
- 基于语义意义的向量搜索
- 基于提取的实体和关系的图搜索
- 精确查询的关键词和主题匹配
这种多层方法意味着无论您是通过概念、特定的思想关系还是确切的术语进行搜索，您的代理都能找到相关信息。
### 使用SmartMemory的三种方式
**选项1：完整的Raindrop框架**
在Raindrop中构建您的代理，并获得完整的记忆系统以及其他代理基础设施：
```hcl
application "my-agent" {
smartmemory "agent_memory" {}
}
```
**选项2：MCP集成**
已经有代理了吗？将我们的MCP（模型上下文协议）服务器连接到您现有的设置。启动一个SmartMemory实例，您的代理可以通过MCP调用访问所有记忆功能——无需重建任何东西。
**选项3：API/SDK**
如果您已经有代理但不熟悉MCP，我们也提供了简单的API和SDK（Python、TypeScript、Java和Go），供您使用。
### 一些有用的链接以便开始
- 注册请访问：[https://liquidmetal.ai/](https://liquidmetal.ai/)
- 概念文档请访问：[https://docs.liquidmetal.ai/concepts/smartmemory/](https://docs.liquidmetal.ai/concepts/smartmemory/)
- 实施文档请访问：[https://docs.liquidmetal.ai/reference/resources/smartmemory/](https://docs.liquidmetal.ai/reference/resources/smartmemory/)
- 快速入门请访问：[https://docs.liquidmetal.ai/tutorials/smartmemory-app-deployment/](https://docs.liquidmetal.ai/tutorials/smartmemory-app-deployment/)
查看原文
I spoke to hundreds of AI agent developers and the answer to the question - "if you had one magic wand to solve one thing, what would it be?" - was agent memory.<p>We built SmartMemory in Raindrop to solve this problem by giving agents four types of memory that work together:<p>We built SmartMemory in Raindrop to solve this problem by giving agents four types of memory that work together:<p>Memory Types Overview<p>Working Memory
• Holds active conversation context within sessions
• Organizes thoughts into different timelines (topics)
• Agents can search what you've discussed and build on previous points
• Like short-term memory for ongoing conversations<p>Episodic Memory
• Stores completed conversation sessions as searchable history
• Remembers what you discussed weeks or months ago
• Can restore previous conversations to continue where you left off
• Your agent's long-term conversation archive<p>Semantic Memory
• Stores facts, documents, and reference materials
• Persists knowledge across all conversations
• Builds up information about your projects and preferences
• Your agent's knowledge base that grows over time<p>Procedural Memory
• Saves workflows, tool interaction patterns, and procedures
• Learns how to handle different situations consistently
• Stores decision trees and response patterns
• Your agent's learned skills and operational procedures<p>Multi-Layer Search That Actually Works<p>*Working Memory* uses embeddings and vector search. When you search for "authentication issues," it finds memories about "login problems" or "security bugs" even though the exact words don't match.<p>*Episodic, Semantic, and Procedural Memory* use a three-layer search approach:
• Vector search for semantic meaning
• Graph search based on extracted entities and relationships
• Keyword and topic matching for precise queries<p>This multi-layer approach means your agent can find relevant information whether you're searching by concept, by specific relationships between ideas, or by exact terms.<p>Three Ways to Use SmartMemory<p>Option 1: Full Raindrop Framework
Build your agent within Raindrop and get the complete memory system plus other agent infrastructure:<p>```hcl
application "my-agent" {
smartmemory "agent_memory" {}
}<p>```<p>Option 2: MCP Integration
Already have an agent? Connect our MCP (Model Context Protocol) server to your existing setup. Spin up a SmartMemory instance and your agent can access all memory functions through MCP calls - no need to rebuild anything.<p>Option 3: API/SDK If you already have an agent but are not familar with MCP we also have a simple API and SDK (pytyon, TypeScript, Java and Go) you can use<p>A couple of helpful links to get started<p>For signup check: https://liquidmetal.ai/
For concepts documentation check: https://docs.liquidmetal.ai/concepts/smartmemory/
For implementation documentation check: https://docs.liquidmetal.ai/reference/resources/smartmemory/
For quick start check https://docs.liquidmetal.ai/tutorials/smartmemory-app-deployment/