advanced-augmentation.mdx

title	Advanced Augmentation
description	How Memori's Advanced Augmentation engine extracts structured facts, preferences, and knowledge from your AI conversations and agent trace — all managed in Memori Cloud.

Advanced Augmentation

Advanced Augmentation is the AI engine inside Memori Cloud that turns raw conversations and agent trace into structured, searchable memories. It runs asynchronously in the background to minimize impact on your response path.

What It Does

When your application has a conversation through a Memori-wrapped LLM client, the augmentation engine:

1. Reads the full conversation (user messages and AI responses)
2. Identifies facts, preferences, skills, and attributes
3. Extracts semantic triples (subject-predicate-object relationships)
4. Generates vector embeddings for semantic search
5. Stores everything in your managed memory space

No extra code required — just initialize Memori and set attribution.

How It Works

The augmentation flow is fully asynchronous and designed to avoid blocking your main request path.

1. Your app makes an LLM call through the wrapped client
2. Memori returns the response immediately
3. In the background, the conversation is queued for processing
4. The augmentation engine extracts structured memories
5. Memories are stored in Memori Cloud for future recall

from memori import Memori
from openai import OpenAI

client = OpenAI()
mem = Memori().llm.register(client)
mem.attribution(entity_id="user_123", process_id="my_agent")

# This returns immediately — no augmentation delay
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "I love hiking in the mountains."}
    ]
)
print(response.choices[0].message.content)

import OpenAI from 'openai';
import { Memori } from '@memorilabs/memori';

const client = new OpenAI();
const mem = new Memori().llm.register(client);
mem.attribution('user_123', 'my_agent');

// This returns immediately — no augmentation delay
const response = await client.chat.completions.create({
  model: 'gpt-4o-mini',
  messages: [
    { role: 'user', content: 'I love hiking in the mountains.' },
  ],
});
console.log(response.choices[0].message.content);

Extraction Types

Type	What it captures	Scope
Facts	Objective information with vector embeddings	Per entity — shared across processes
Preferences	User choices, opinions, and tastes	Per entity
Skills & Knowledge	Abilities and expertise levels	Per entity
Attributes	Process-level information about what your agent handles	Per process

Semantic Triples

Advanced Augmentation uses named-entity recognition to extract semantic triples (subject, predicate, object). These form the building blocks of the Knowledge Graph.

Example — from "My favorite database is PostgreSQL and I use it with FastAPI":

Subject	Predicate	Object
user	favorite_database	PostgreSQL
user	uses	FastAPI
user	uses_with	PostgreSQL + FastAPI

Memori automatically deduplicates triples — if the same fact is mentioned multiple times, it increments the mention count and updates the timestamp.

Context Recall

When a query is sent to an LLM through a wrapped client, Memori automatically:

1. Intercepts the outbound LLM call
2. Uses semantic search to find entity facts matching the query
3. Ranks facts by vector similarity
4. Injects the most relevant facts into the system prompt
5. Forwards the enriched request to the LLM provider

For Memori to provide all Advanced Augmentation capabilities, attribution must be set before making LLM calls. Without attribution, Memori cannot create or recall memories.