Memory System

The memory system allows the AI agent to remember context, learn from interactions, and provide personalized assistance over time.

Personal Memory

Your memories are private to you. The agent builds a personal knowledge graph from your interactions, activity, and notes. Other team members cannot see your memories, ensuring your work patterns and preferences remain confidential.

Overview

Memory in Raven Docs works like a knowledge graph:

Memory Types

Conversation Memory

Captured from agent chat interactions:

• Questions asked
• Decisions made
• Preferences expressed
• Feedback given

Activity Memory

Automatically tracked from your usage:

• Pages viewed and edited
• Tasks completed
• Search queries
• Time spent on content

Ingested Memory

Explicitly stored context:

• Meeting notes
• Project context
• External information
• Manual notes

How Memory Works

Ingestion

Memory is captured through multiple channels:

// Automatic - from activity
User views page → Memory stored:
  "User viewed 'API Documentation' for 15 minutes"

// Automatic - from chat
User asks question → Memory stored:
  "User asked about OAuth implementation"

// Manual - explicit storage
await agent.memory.ingest({
  content: "Prefers TypeScript over JavaScript",
  source: "user_preference",
  tags: ["coding", "preferences"],
});

Retrieval

The agent queries memory for relevant context:

// When you ask about OAuth
Agent retrieves:
  - Previous OAuth discussions
  - Related documentation views
  - Connected tasks
  - Team decisions on auth

Memory Graph

Memories form a connected graph:

Configuration

Memory Settings

Configure memory behavior in workspace settings:

Setting	Description	Default
Retention Period	How long memories are kept	90 days
Auto-capture	Track activity automatically	Enabled
Memory Limit	Max memories per workspace	10,000
Entity Linking	Auto-link to tasks/pages	Enabled

Per-Space Settings

Override settings for specific spaces:

Space Memory Config {
  enabled: true
  retention: "365d"
  autoCapture: true
  sensitiveMode: false // extra privacy
}

Memory Features

Daily Summaries

Get a digest of memories from any day:

You: What did I work on yesterday?

Agent: Yesterday's summary:
- Edited 3 pages in API documentation
- Completed task "Update auth endpoints"
- Had discussion about rate limiting
- Viewed deployment guide twice

Memory Search

Search through stored memories:

You: What did we decide about caching?

Agent: On January 10th, you noted:
"Use Redis for session caching, 15-minute TTL"
This was linked to the Architecture space.

User Profile & Behavioral Insights

The agent builds a comprehensive behavioral profile from your activity:

• Seven Core Traits - Focus, Execution, Creativity, Communication, Leadership, Learning, Resilience
• Trait Scores - Each trait scored 0-10 based on activity patterns
• Behavioral Patterns - Completion rate, consistency, diversity, collaboration
• Trend Analysis - Track improvement or decline over time
• AI-generated Insights - Personalized strengths, challenges, and recommendations

The profile system goes beyond simple activity tracking to provide meaningful behavioral insights that help you understand your work patterns and areas for growth.

Learn more about User Profiles

Entity Relationships

See how memories connect:

Memory Graph Visualization

Viewing the Graph

Access the memory graph:

1. Open Agent Settings
2. Click Memory Graph
3. Explore connections

Graph Elements

• Nodes - Individual memories
• Edges - Relationships between memories
• Clusters - Related memory groups
• Highlights - Recently accessed memories

Filtering

Filter the graph by:

• Date range
• Tags
• Source type
• Entity connections

Privacy & Control

Viewing Memories

See what's stored:

1. Open Agent Settings
2. Click Memory
3. Browse or search memories

Deleting Memories

Remove specific memories:

// Delete a single memory
await agent.memory.delete(memoryId);

// Delete by criteria
await agent.memory.deleteMany({
  olderThan: "30d",
  tags: ["temporary"],
});

// Clear all memories
await agent.memory.clear();

Sensitive Content

Mark spaces as sensitive to limit memory:

• Reduced auto-capture
• Shorter retention
• No cross-space linking

API Access

Ingesting Memory

await client.agent.memory.ingest({
  workspaceId: 'ws_123',
  content: 'Important project decision',
  source: 'meeting_notes',
  tags: ['project-x', 'decisions'],
  entityLinks: [
    { type: 'task', id: 'task_456' },
    { type: 'page', id: 'page_789' },
  ],
});

Querying Memory

const memories = await client.agent.memory.query({
  workspaceId: 'ws_123',
  query: 'caching decisions',
  tags: ['architecture'],
  dateRange: {
    start: '2024-01-01',
    end: '2024-01-31',
  },
  limit: 10,
});

Daily Summary

const summary = await client.agent.memory.dailySummary({
  workspaceId: 'ws_123',
  date: '2024-01-15',
});

Best Practices

1. Tag consistently - Use standard tags for easy retrieval
2. Review periodically - Check stored memories for accuracy
3. Clean up - Delete outdated or incorrect memories
4. Use sources - Label memory sources for context
5. Link entities - Connect memories to tasks and pages

Troubleshooting

Memory Not Found

If the agent can't recall something:

• Check if auto-capture was enabled
• Verify the memory wasn't deleted
• Try different search terms
• Check retention period

Too Much Memory

If responses are slow:

• Reduce retention period
• Delete old memories
• Disable auto-capture for some spaces
• Archive completed projects

Related

• User Profiles - Behavioral insights system
• AI Agent - Agent overview
• MCP Memory Tools - API access
• Privacy - Access control