b8e9a80d93
* wait I forgot to comit locally * cp the entire core directory and then rm the .git subdir
52 lines
2.4 KiB
Plaintext
52 lines
2.4 KiB
Plaintext
---
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title: Agent Memory
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subtitle: What is agent memory, and how does it work?
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slug: guides/agents/memory
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---
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Agent memory is what enables AI agents to maintain persistent state, learn from interactions, and develop long-term relationships with users. Unlike traditional chatbots that treat each conversation as isolated, agents with sophisticated memory systems can build understanding over time.
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## The MemGPT Approach to Memory
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Letta is built by the creators of [MemGPT](https://arxiv.org/abs/2310.08560), a research paper that introduced the concept of an "LLM Operating System" for memory management. The base agent design in Letta is a MemGPT-style agent, which means it inherits the core principles of:
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- **Self-editing memory**: Agents can modify their own memory using tools
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- **Memory hierarchy**: Different types of memory for different purposes
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- **Context window management**: Intelligent loading and unloading of information
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## Types of Memory in Letta
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Letta agents have access to multiple memory systems:
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### Core Memory (In-Context)
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Fast, always-accessible memory that stays in the agent's context window. This includes:
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- **Persona**: The agent's personality and role
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- **Human**: Information about the user
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- **Custom memory blocks**: Additional structured information
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### External Memory (Out-of-Context)
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Long-term storage for large amounts of information:
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- Conversation history beyond context limits (e.g. "recall memory")
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- Vector databases for semantic search (e.g. "archival memory")
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- Uploaded documents and files
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## Why Agent Memory Matters
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Effective memory management enables:
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- **Personalization**: Agents remember user preferences and history
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- **Learning**: Agents improve performance through accumulated experience
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- **Context preservation**: Important information persists across conversations
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- **Scalability**: Handle unlimited conversation length and data volume
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## Memory Management in Practice
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Letta provides multiple ways to work with agent memory:
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- **Automatic management**: Agents intelligently decide what to remember
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- **Manual control**: Developers can directly view and modify memory blocks
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- **Shared memory**: Multiple agents can access common memory blocks
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- **External data sources**: Connect agents to files, databases, and APIs
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Memory blocks are the fundamental units of Letta's memory system - they can be modified by the agent itself, other agents, or developers through the API.
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