Agent Memory
Understanding how agents maintain state and context across interactions
Overview
Agent memory in Julep allows AI agents to maintain state and context across multiple interactions. This enables more coherent and contextually aware conversations and task executions.
Types of Memory
Julep provides several types of memory for agents:
1. Session Memory
Session memory persists throughout a single conversation session:
2. Long-term Memory
Long-term memory persists across sessions and is stored in the agent’s document store:
3. Working Memory
Working memory is available during task execution:
Memory Management
Context Window Management
Julep offers different strategies for managing context windows:
- Fixed: Maintains a fixed number of messages
- Adaptive: Dynamically adjusts based on token usage
- Summary: Periodically summarizes older context
Document Store
The document store serves as long-term memory:
Memory Access in Tasks
Tasks can access different types of memory:
Best Practices
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Memory Organization
- Use clear document titles and metadata
- Organize documents by type and purpose
- Regularly clean up outdated information
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Context Management
- Choose appropriate context overflow strategies
- Monitor token usage in sessions
- Use summaries for long conversations
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Memory Usage
- Store important information in long-term memory
- Use working memory for temporary data
- Leverage session memory for conversation context
Example: Complex Memory Usage
Here’s an example combining different types of memory: