Auto Memory

Automatic memory extraction and storage from conversations

The --auto-memory flag enables automatic extraction and storage of important information from conversations.

Quick Start

praisonai "My name is John and I prefer Python" --auto-memory

Usage

Basic Auto Memory

praisonai "I work at Acme Corp as a software engineer" --auto-memory

Expected Output:

🧠 Auto Memory enabled

╭─ Agent Info ─────────────────────────────────────────────────────────────────╮
│  👤 Agent: DirectAgent                                                       │
│  Role: Assistant                                                             │
│  🧠 Auto Memory: Enabled                                                    │
╰──────────────────────────────────────────────────────────────────────────────╯

╭────────────────────────────────── Response ──────────────────────────────────╮
│ Nice to meet you! It's great to know you're a software engineer at Acme     │
│ Corp. How can I help you today?                                              │
╰──────────────────────────────────────────────────────────────────────────────╯

💾 Memories Extracted:
┌─────────────────────┬────────────────────────────┐
│ Type                │ Content                    │
├─────────────────────┼────────────────────────────┤
│ Entity (Person)     │ User works at Acme Corp    │
│ Entity (Role)       │ Software Engineer          │
│ Long-term           │ User's workplace: Acme Corp│
└─────────────────────┴────────────────────────────┘

With User Isolation

praisonai "Remember my preferences" --auto-memory --user-id user123

Expected Output:

🧠 Auto Memory enabled (user: user123)

╭────────────────────────────────── Response ──────────────────────────────────╮
│ I'll remember your preferences. What would you like me to remember?          │
╰──────────────────────────────────────────────────────────────────────────────╯

💾 Memory stored for user: user123

Combine with Other Features

# Auto memory with session
praisonai "Learn about my project" --auto-memory --session my-project

# Auto memory with planning
praisonai "Plan my learning path" --auto-memory --planning

# Auto memory with metrics
praisonai "Remember this" --auto-memory --metrics

How It Works

1. Conversation Analysis: The system analyzes the conversation
2. Information Extraction: Important information is identified
3. Categorization: Information is categorized (entities, facts, preferences)
4. Storage: Memories are stored for future retrieval
5. Retrieval: Memories are automatically injected into future conversations

Memory Types Extracted

Type	Description	Example
Entities	People, places, organizations	”User works at Google”
Facts	Factual information	”Project deadline is Dec 31”
Preferences	User preferences	”Prefers Python over JavaScript”
Context	Contextual information	”Working on ML project”

Use Cases

Personal Assistant

# First conversation
praisonai "I'm learning Rust and prefer hands-on examples" --auto-memory

# Later conversation (memories recalled)
praisonai "Give me a coding exercise" --auto-memory

Expected Output (second conversation):

🧠 Auto Memory enabled

📚 Recalled Memories:
  • User is learning Rust
  • User prefers hands-on examples

╭────────────────────────────────── Response ──────────────────────────────────╮
│ Since you're learning Rust and prefer hands-on examples, here's a practical │
│ exercise:                                                                    │
│                                                                              │
│ **Exercise: Build a Simple CLI Calculator**                                 │
│                                                                              │
│ ```rust                                                                      │
│ use std::io;                                                                 │
│                                                                              │
│ fn main() {                                                                  │
│     // Your code here                                                        │
│ }                                                                            │
│ ```                                                                          │
╰──────────────────────────────────────────────────────────────────────────────╯

Project Context

# Share project details
praisonai "I'm building an e-commerce platform using Django and React" --auto-memory

# Ask for help (context remembered)
praisonai "How should I structure my API?" --auto-memory

Team Preferences

# Different team members
praisonai "I prefer detailed explanations" --auto-memory --user-id alice
praisonai "I prefer concise answers" --auto-memory --user-id bob

Viewing Stored Memories

Use the memory command to view what’s been stored:

# Show memory statistics
praisonai memory show

# Search memories
praisonai memory search "Python"

Expected Output:

              Memory Statistics               
┏━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Property         ┃ Value                   ┃
┡━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ user_id          │ default                 │
│ short_term_count │ 0                       │
│ long_term_count  │ 4                       │
│ entity_count     │ 2                       │
│ episodic_days    │ 0                       │
│ summary_count    │ 0                       │
│ storage_path     │ .praison/memory/default │
└──────────────────┴─────────────────────────┘

Recent Short-term Memories:
  No short-term memories

Long-term Memories:
  1. [0.7] Python for backend
  2. [0.6] User's location: Acme Corp

Entities:
  • John (person)
  • Acme Corp (organization)

Memory Persistence

Memories are stored locally and persist across sessions:

.praison/
└── memory/
    ├── default/
    │   ├── short_term.json
    │   ├── long_term.json
    │   └── entities.json
    └── user123/
        ├── short_term.json
        ├── long_term.json
        └── entities.json

Best Practices

Use --user-id to keep memories separate for different users or projects.

Auto memory increases token usage as memories are injected into prompts. Monitor with --metrics.

User Isolation

Use --user-id for multi-user scenarios

Regular Cleanup

Periodically clear old memories with praisonai memory clear

Combine with Sessions

Use with --session for project-specific memory

Monitor Usage

Use --metrics to track memory-related token costs

Privacy Considerations

Memories are stored locally on your machine. No data is sent to external servers for memory storage.

• Memories are stored in .praison/memory/
• Use praisonai memory clear all to delete all memories
• Each user ID has isolated memory storage

Related

• Memory Concept
• Memory CLI Commands
• Session CLI