---
title: "Agent Memory"
description: "Understanding how agents maintain context and learn"
icon: "brain"
---

# Agent Memory

Memory enables AI agents to remember past interactions, maintain context, and provide more coherent responses over time. Without memory, agents would treat each interaction as if it was their first.

## Why Memory Matters

<CardGroup cols={2}>
  <Card title="Context Awareness" icon="comments">
    Remembers previous parts of a conversation
  </Card>
  <Card title="Personalization" icon="user-gear">
    Recalls user preferences and history
  </Card>
  <Card title="Continuous Learning" icon="graduation-cap">
    Builds on past experiences
  </Card>
  <Card title="Task Persistence" icon="list-check">
    Maintains progress on long-running tasks
  </Card>
</CardGroup>

## Types of Agent Memory

### 1. Short-Term Memory (Conversation Memory)

This type of memory stores recent interactions within a session.

```mermaid
graph LR
    A[User Input 1] --> B[Agent Response 1]
    B --> C[User Input 2]
    C --> D[Agent Response 2]
    D --> E[User Input 3]
```

The agent can refer back to earlier messages in the same conversation.

### 2. Long-Term Memory

This allows agents to remember information across different sessions.

<CardGroup cols={1}>
  <Card title="Use Cases" icon="clock">
    - Remembering user preferences
    - Recalling previous tasks completed
    - Storing learned information
    - Maintaining relationship history
  </Card>
</CardGroup>

### 3. Working Memory

This is where agents process current information and combine it with retrieved memories.

```mermaid
graph TD
    A[Current Input] --> D[Working Memory]
    B[Short-Term Memory] --> D
    C[Long-Term Memory] --> D
    D --> E[Agent Response]
```

## How Memory Works in PraisonAI

In the PraisonAI framework, memory is handled automatically for basic cases and can be customized for more advanced needs:

```python
from praisonaiagents import Agent

# Basic agent with default memory handling
agent = Agent(
    instructions="You are a helpful assistant that remembers our conversations"
)

# The conversation history is maintained automatically
agent.start("My name is Alex")
agent.continue("What's my name?")# Note: TODO: This Feature yet to be developed  # Agent will remember "Alex"
```

## Memory Limitations

<Tip>
All AI memories have limitations. They aren't perfect and may occasionally forget or misremember information.
</Tip>

Common limitations include:

<CardGroup cols={2}>
  <Card title="Memory Capacity" icon="database">
    Limit to how much information can be stored
  </Card>
  <Card title="Context Window" icon="window">
    Only a portion of memory fits in the active context
  </Card>
  <Card title="Memory Decay" icon="hourglass-half">
    Older memories may become less accessible
  </Card>
  <Card title="Memory Retrieval" icon="magnifying-glass">
    Finding the right memory at the right time
  </Card>
</CardGroup>

## Implementing Persistent Memory

For more advanced applications, you can implement custom memory systems:

```python
# Note: TODO: This Feature yet to be tested
from praisonaiagents import Agent, Memory

# Create a simple memory store
memory = Memory()

# Add information to memory
memory.add("user_name", "Alex")
memory.add("favorite_color", "blue")

# Create agent with this memory
agent = Agent(
    instructions="You are a personal assistant",
    memory=memory
)

# The agent can now access these memories
agent.start("What's my favorite color?")  # Agent should respond "blue"
```

## Memory Best Practices

<CardGroup cols={2}>
  <Card title="Prioritize Important Info" icon="star">
    Not everything needs to be remembered
  </Card>
  <Card title="Verify When Uncertain" icon="question">
    Have agents confirm uncertain memories
  </Card>
  <Card title="Structured Storage" icon="folder-tree">
    Organize memories by categories
  </Card>
  <Card title="Regular Updates" icon="arrows-rotate">
    Update memories when information changes
  </Card>
</CardGroup>

## Memory in Multi-Agent Systems

In systems with multiple agents, memory can be:

1. **Private**: Each agent has its own memories
2. **Shared**: Agents have access to a common memory store
3. **Hybrid**: Some memories are private, others are shared

```mermaid
graph TD
    A[Agent 1] --> B[Private Memory 1]
    C[Agent 2] --> D[Private Memory 2]
    A -.-> E[Shared Memory]
    C -.-> E
```

In the next lesson, we'll learn about how to set up multiple agents to work together effectively.