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Memory Overview

Memory Interface Memory enables agents to learn from past experiences and improve over time. Instead of treating each task as isolated, agents can remember what worked, what didn’t, and apply those lessons to future tasks.

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What is Memory?

Memory stores execution results, insights, and experiences from tasks:
  • Task patterns - What approaches worked for similar tasks
  • User preferences - Communication style, format preferences
  • Execution insights - Successes, failures, lessons learned
  • Best practices - Effective tool combinations and strategies

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Why Memory Matters

Continuous Learning

Agents improve with each task execution

Pattern Recognition

Identify what works across similar tasks

Context Enhancement

Enrich new tasks with relevant past experiences

Efficiency

Avoid repeating mistakes and redundant steps

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How Memory Works

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Memory Storage

After completing a task, Xagent automatically:
  1. Analyzes results - What succeeded, what failed
  2. Extracts insights - Key learnings and patterns
  3. Stores memory - Saves structured notes to memory
  4. Indexes content - Makes memories searchable

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Memory Retrieval

When starting a new task, Xagent:
  1. Searches memory - Finds relevant past experiences
  2. Selects matches - Filters by similarity and category
  3. Enhances goal - Uses memory to improve task understanding
  4. Applies learning - Avoids past mistakes, reuses successes

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Memory Categories

Plan Execution Memory
  • Task planning strategies
  • Step decomposition approaches
  • DAG generation patterns
Execution Memory
  • Execution results and outcomes
  • User feedback and corrections
  • Success/failure patterns
  • Performance insights
ReAct Memory
  • ReAct loop execution records
  • Tool usage effectiveness
  • Reasoning patterns
General Memory
  • User preferences
  • Behavioral patterns
  • Project-specific context
  • Domain knowledge

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Memory Storage Types

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In-Memory Storage

For: Development, testing, short-term tasks Characteristics:
  • Fast performance
  • No persistence (lost on restart)
  • In-memory dictionary
  • Text-based search
Use when:
  • Testing and development
  • Temporary tasks
  • Low-latency requirements

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Vector Database Storage

For: Production, long-term learning, semantic search Characteristics:
  • Persistent storage (LanceDB)
  • Semantic similarity search
  • Embedding-based retrieval
  • Scales to large memory
Use when:
  • Production environments
  • Long-running agents
  • Large task volumes
  • Need semantic search

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Memory in Action

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Learning from Success

Task 1: "Create a data analysis report"
Execution: Successful
Stored Memory: "For data analysis tasks, use Python executor
with pandas. Create visualizations with matplotlib."

Task 2 (similar): "Analyze sales data and create charts"
Agent retrieves memory and uses Python executor + matplotlib
Result: Faster execution, better quality

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Avoiding Mistakes

Task 1: "Scrape data from website X"
Execution: Failed - site blocks automated scraping
Stored Memory: "Website X blocks automation, try manual
download or official API instead."

Task 2: "Get recent data from website X"
Agent retrieves memory and avoids scraping
Result: Suggests API approach instead

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User Preferences

Session 1:
User: "I prefer detailed technical explanations"
Stored Memory: "User prefers detailed technical explanations
over high-level summaries"

Session 2:
User: "How does this work?"
Agent retrieves memory and provides detailed explanation
Result: Personalized response style

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Memory Lifecycle

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Storage Trigger

Memory is automatically stored when:
  • Task completes (successfully or with learnings from failures)
  • Agent identifies useful insights
  • User provides explicit preferences
  • Patterns emerge from execution

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Retrieval Trigger

Memory is retrieved when:
  • Starting a new task
  • Similar patterns detected
  • User asks about past experiences
  • Agent needs context for decisions

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Memory Maintenance

  • Automatic deduplication - Removes duplicate memories
  • Relevance scoring - Tracks memory usefulness
  • Periodic cleanup - Removes outdated or unused memories
  • Search optimization - Updates indices for performance

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Memory vs Knowledge Base

FeatureMemoryKnowledge Base
SourceGenerated from task executionUploaded documents
ContentInsights, patterns, learningsFacts, documentation, data
Structure - Unstructured notesStructured documents
Search - Semantic similarityVector similarity + keyword
PurposeLearn and improveReference information
Together they enable:
  • Knowledge base provides domain information
  • Memory provides experiential learning
  • Agents use both for optimal performance

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Benefits

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For Users

  • Consistent experience - Agents remember your preferences
  • Better results - Learnings improve over time
  • Fewer corrections - Avoids repeating mistakes
  • Faster execution - Reuses proven approaches

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For Agent Builders

  • Continuous improvement - Agents get better automatically
  • Reduced tuning - Less manual configuration needed
  • Better user experience - More personalized interactions
  • Performance insights - See what works and what doesn’t

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Privacy and Security

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Data Isolation

  • Per-user memory - Each user has separate memory space
  • Per-agent isolation - Agents don’t share memory by default
  • Encrypted storage - Memory data encrypted at rest

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User Control

While memory is automatically managed by Xagent, you can:
  • View memories - See what Xagent has learned from your tasks
  • Delete memories - Remove specific memories or clear all
  • Export data - Download your memory data
Memory storage and retrieval is fully automatic. The LLM decides what to store and when to use it based on task execution patterns.

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Next Steps