Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.aegismemory.com/llms.txt

Use this file to discover all available pages before exploring further.

​
Smart Memory Guide

Smart Memory is Aegis’s intelligent extraction layer that automatically determines what’s worth remembering from conversations. Instead of storing everything (noise) or requiring manual decisions (burden), Smart Memory uses a two-stage process to extract and store only valuable information.

​
Quick Start

from aegis_memory import SmartMemory

# Initialize with your API keys
memory = SmartMemory(
    aegis_api_key="your-aegis-key",
    llm_api_key="your-openai-key"
)

# After each conversation turn, process it
memory.process_turn(
    user_input="I'm John, a Python developer from Chennai. I prefer dark mode.",
    ai_response="Nice to meet you, John! I'll remember your preferences.",
    user_id="user_123"
)

# Later, get relevant context for a new query
context = memory.get_context(
    query="What color theme should I use?",
    user_id="user_123"
)

print(context.context_string)
# Output:
# - User's name is John
# - User is a Python developer
# - User is based in Chennai
# - User prefers dark mode for applications

​
How It Works

Smart Memory uses a two-stage process to avoid expensive LLM calls while maintaining quality: 
┌─────────────────────────────────────────────────────────────────┐
│  STAGE 1: FAST FILTER (Rule-based, ~0.1ms)                      │
│                                                                  │
│  Checks for memory signals:                                      │
│  ✓ "I'm" → Personal fact signal                                 │
│  ✓ "developer" → Professional fact signal                       │
│  ✓ "from Chennai" → Location signal                             │
│                                                                  │
│  Decision: WORTH EXTRACTING                                      │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│  STAGE 2: LLM EXTRACTION (~200ms, only if Stage 1 passes)       │
│                                                                  │
│  Extracts atomic facts:                                          │
│  1. "User's name is John" (confidence: 0.95)                    │
│  2. "User is a developer" (confidence: 0.90)                    │
│  3. "User is based in Chennai" (confidence: 0.92)               │
└─────────────────────────────────────────────────────────────────┘

​
Cost Comparison

ApproachLLM CallsCostQuality
Store everything0LowPoor (noisy)
LLM for everything100%HighGood
Two-stage (Smart)~30%LowGood
The filter catches obvious non-memories (greetings, confirmations) without LLM calls, saving ~70% of extraction costs.

​
Use Cases

memory = SmartMemory(use_case="conversational", ...)
Extracts: Preferences, personal facts, relationshipsIgnores: Greetings, one-time questions, temporary states

​
Configuration

​
Sensitivity Levels

# High sensitivity - extract more, risk some noise
memory = SmartMemory(sensitivity="high", ...)

# Balanced (default) - good balance
memory = SmartMemory(sensitivity="balanced", ...)

# Low sensitivity - extract less, only high-confidence
memory = SmartMemory(sensitivity="low", ...)

​
LLM Providers

# OpenAI (default)
memory = SmartMemory(
    llm_provider="openai",
    llm_api_key="sk-...",
    llm_model="gpt-4o-mini"
)

# Anthropic
memory = SmartMemory(
    llm_provider="anthropic",
    llm_api_key="sk-ant-...",
    llm_model="claude-3-haiku-20240307"
)

​
SmartAgent (Full Auto)

For the simplest experience, use SmartAgent which handles everything: 
from aegis_memory import SmartAgent

agent = SmartAgent(
    aegis_api_key="your-aegis-key",
    llm_api_key="your-openai-key",
    system_prompt="You are a helpful coding assistant."
)

# Memory is completely automatic
response = agent.chat("I'm John, I prefer Python over JavaScript", user_id="user_123")
response = agent.chat("What language should I use?", user_id="user_123")
# Agent automatically knows user prefers Python!

​
What Gets Stored

​
Categories

CategoryDescriptionExample
preferenceLikes, dislikes, style”User prefers dark mode”
factPersonal information”User is a developer in Chennai”
decisionChoices made”User decided to use React”
constraintLimits and requirements”Budget is $5000”
goalWhat user wants”User wants to build a chatbot”
strategyWhat worked”Using async improved performance”
mistakeWhat didn’t work”Don’t use range() for large pagination”

​
Best Practices

1

Choose the Right Use Case

Match use case to your domain. Don’t use “conversational” for coding tasks.
2

Use Appropriate Sensitivity

High sensitivity for personal assistants. Low sensitivity for task agents.
3

Monitor Extraction Stats

stats = memory.get_stats()
print(f"Filter rate: {stats['filter_rate']:.1%}")
# If filter_rate is too high, increase sensitivity
4

Combine with Explicit Storage

Use Smart Memory for conversations, explicit storage for known-important info.

​
Troubleshooting

  1. Check sensitivity: memory = SmartMemory(sensitivity="high", ...)
  2. Use force_extract=True to bypass filter
  3. Check stats: print(memory.get_stats())
  1. Lower sensitivity: sensitivity="low"
  2. Use a more specific use case
  3. Create custom filter patterns
  1. Use cheaper models: gpt-4o-mini or claude-3-haiku
  2. Lower sensitivity to reduce LLM calls
  3. Use auto_store=False for custom storage logic