ekkOS_docs
Core Concepts

How ekkOS Works

Understanding the memory system that makes AI agents smarter over time.

The Problem

AI assistants like Claude and GPT are powerful, but they have a fundamental limitation: they don't remember. Every conversation starts fresh.

  • • You explain your project setup — again
  • • You correct the same mistakes — again
  • • You establish the same preferences — again
  • • Knowledge from yesterday is gone today

The Solution

ekkOS gives AI agents persistent, learning memory. It captures what works, learns from outcomes, and retrieves relevant context automatically.

5.94x
Faster on recurring problems
81%
Pattern success rate
18ms
Retrieval latency

The Process

1. Capture

Every conversation with your AI is automatically captured and stored in working memory. This includes your questions, the AI's responses, code changes, and outcomes.

2. Learn

Background workers analyze conversations to extract meaningful patterns. When you solve a problem, that solution becomes a reusable pattern with tracked success metrics.

3. Retrieve

When you ask a new question, ekkOS searches across all memory layers using semantic similarity. Relevant patterns, context, and past solutions are found in ~18ms.

4. Inject

Retrieved context is formatted and injected into the AI's prompt. The AI sees relevant patterns, past decisions, and established preferences without you having to repeat them.

5. Measure

Outcomes are tracked. Did the pattern help? Success increases its confidence score. Failure triggers learning. Over time, the system gets smarter at knowing what works.

What Makes ekkOS Different

vs. Simple RAG

RAG systems just retrieve documents. ekkOS learns from outcomes, tracks success rates, and evolves its knowledge over time. It's not just retrieval — it's learning.

vs. Chat History

Chat history fills context windows and gets truncated. ekkOS compresses knowledge into semantic layers with different retention policies — working memory expires, patterns persist forever.

vs. Fine-tuning

Fine-tuning is expensive, slow, and can't be easily updated. ekkOS learns in real-time, and new patterns are immediately available. No training required.

vs. Vector Databases

Vector DBs store embeddings. ekkOS is a complete cognitive architecture with 10 specialized layers, outcome tracking, pattern discovery, and the Golden Loop learning cycle.

Deep Dive