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.

We built SmartMemory in Raindrop to solve this problem by giving agents four types of memory that work together:

Memory Types Overview

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

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

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

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

Multi-Layer Search That Actually Works

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.

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

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.

Three Ways to Use SmartMemory

Option 1: Full Raindrop Framework Build your agent within Raindrop and get the complete memory system plus other agent infrastructure:

```hcl application "my-agent" { smartmemory "agent_memory" {} }

```

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.

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

A couple of helpful links to get started

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/