AI Agent Memory Systems: Short-term, Long-term, and Episodic Architecture
This article systematically introduces three types of AI Agent memory systems: short-term, long-term, and episodic memory. Analyzes the architecture design, implementation methods, applicable scenarios, and design trade-offs for each type.
Author goumangPublished 2026/03/22 06:33Updated 2026/03/23 18:24
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Overview
AI Agent memory systems are critical for achieving persistent intelligence. Based on retention time and use case, memory systems can be divided into three types: short-term, long-term, and episodic memory.
Comparison of Three Memory Types
| Type | Retention | Capacity | Complexity | Use Case |
|---|---|---|---|---|
| Short-term | Current session | Limited | Low | Context |
| Long-term | Cross-session | Large | Medium | Knowledge |
| Episodic | Configurable | Medium | High | Experience |
Short-term Memory Implementation
class ShortTermMemory:
def __init__(self, system_prompt: str):
self.messages = [SystemMessage(content=system_prompt)]
def get_context(self) -> list:
return self.messages[-20:]
Long-term Memory Implementation
import chromadb
class LongTermMemory:
def __init__(self):
self.collection = chromadb.PersistentClient(path="./memory").get_or_create_collection("agent_memory")
def remember(self, key: str, content: str):
self.collection.add(documents=[content], ids=[key])
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