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Introduction

beginner

Engramma is a cognitive memory engine for AI agents. Not a vector database — a thinking memory that reasons, consolidates, and explains.

What is Engramma?

Engramma Memory Cloud is a cognitive memory engine designed for AI agents. Unlike vector databases that simply store and retrieve embeddings, Engramma processes memories through a 10-phase cognitive cycle that mirrors how biological memory works.

The result: your AI agents don't just search — they remember, reason, and learn.

Why not a vector database?

Vector databases solve similarity search. Engramma solves memory — a fundamentally different problem:

CapabilityVector DBEngramma
Store & retrieveYesYes
Causal reasoningNoYes
Memory consolidationNoYes
ExplainabilityNoYes
Adaptive retrievalNoYes
Memory decay & strengtheningNoYes
Regime detectionNoYes

Think of it this way: a vector database is a filing cabinet. Engramma is the brain that decides what to keep, what matters, and why something is relevant.

Who is it for?

  • AI agent developers who need persistent, reasoning memory
  • Chatbot builders who want conversations that truly learn
  • Research teams building knowledge systems with causal links
  • Enterprise teams who need explainable AI decisions

How it works (30-second version)

  1. You store text or embeddings via the API
  2. Engramma routes them through 3 retrieval pathways (Exact, Energy, Attention)
  3. When you retrieve, the Confidence Router scores across all pathways
  4. Over time, memories consolidate — strengthening important patterns, pruning noise
  5. You can ask why any result was returned (explainability)
from engramma_cloud import EngrammaClient
import os

client = EngrammaClient(api_key=os.environ["ENGRAMMA_API_KEY"])

# Store a memory
result = client.store("The user prefers dark mode and concise answers")
print(result.id)          # "pat_8f3a..."
print(result.importance)  # 0.72

# Retrieve with reasoning
results = client.retrieve("What are the user's preferences?")
print(results[0].text)       # "The user prefers dark mode..."
print(results[0].confidence) # 0.94
print(results[0].pathway)    # "exact"

# Understand why
explanation = client.explain(query="What are the user's preferences?")
print(explanation.summary)   # "High confidence via exact match..."

Key concepts

ConceptWhat it means
Memory TypesThree retrieval pathways — Exact, Energy, Attention — each suited for different queries
Causal ReasoningAsk "what if?" and "why?" — your memories form causal relationships
ConsolidationMemories strengthen over time through sleep cycles, just like biological memory
ExplainabilityEvery result comes with a reason — understand the confidence scores
RegimesThe engine adapts its behavior when it detects novelty or anomalies

Next steps

Tip

Already familiar with vector databases? Check the Migration Guide for a direct comparison and step-by-step migration.