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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.

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Why Memory Matters

Context Awareness

Remembers previous parts of a conversation

Personalization

Recalls user preferences and history

Continuous Learning

Builds on past experiences

Task Persistence

Maintains progress on long-running tasks

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Types of Agent Memory

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1. Short-Term Memory (Conversation Memory)

This type of memory stores recent interactions within a session.

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

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2. Long-Term Memory

This allows agents to remember information across different sessions.

Use Cases

  • Remembering user preferences
  • Recalling previous tasks completed
  • Storing learned information
  • Maintaining relationship history

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3. Working Memory

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

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How Memory Works in PraisonAI

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

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"

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Memory Limitations

All AI memories have limitations. They aren’t perfect and may occasionally forget or misremember information.

Common limitations include:

Memory Capacity

Limit to how much information can be stored

Context Window

Only a portion of memory fits in the active context

Memory Decay

Older memories may become less accessible

Memory Retrieval

Finding the right memory at the right time

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Implementing Persistent Memory

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

# 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"

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Memory Best Practices

Prioritize Important Info

Not everything needs to be remembered

Verify When Uncertain

Have agents confirm uncertain memories

Structured Storage

Organize memories by categories

Regular Updates

Update memories when information changes

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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

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