Overview
Why Atulya?
AI agents forget everything between sessions. Every conversation starts from zero—no context about who you are, what you've discussed, or what the assistant has learned. This isn't just an implementation detail; it fundamentally limits what AI Agents can do.
The problem is harder than it looks:
- Simple vector search isn't enough — "What did Alice do last spring?" requires temporal reasoning, not just semantic similarity
- Facts get disconnected — Knowing "Alice works at Google" and "Google is in Mountain View" should let you answer "Where does Alice work?" even if you never stored that directly
- AI Agents needs to form opinions — A coding assistant that remembers "the user prefers functional programming" should weigh that when making recommendations
- Context matters — The same information means different things to different memory banks with different personalities
Atulya solves these problems with a memory system designed specifically for AI agents.
What Atulya Does
Your AI agent stores information via retain(), searches with recall(), and reasons with reflect() — all interactions with its dedicated memory bank
Key Components
Three Memory Types
Atulya separates memories by type for epistemic clarity:
| Type | What it stores | Example |
|---|---|---|
| World | Objective facts received | "Alice works at Google" |
| Bank | Bank's own actions | "I recommended Python to Bob" |
| Opinion | Formed beliefs + confidence | "Python is best for ML" (0.85) |
Multi-Strategy Retrieval (TEMPR)
Four search strategies run in parallel:
| Strategy | Best for |
|---|---|
| Semantic | Conceptual similarity, paraphrasing |
| Keyword (BM25) | Names, technical terms, exact matches |
| Graph | Related entities, indirect connections |
| Temporal | "last spring", "in June", time ranges |
Disposition Traits
Memory banks have disposition traits that influence how opinions are formed during Reflect:
| Trait | Scale | Low (1) | High (5) |
|---|---|---|---|
| Skepticism | 1-5 | Trusting | Skeptical |
| Literalism | 1-5 | Flexible interpretation | Literal interpretation |
| Empathy | 1-5 | Detached | Empathetic |
These traits only affect the reflect operation, not recall.
Next Steps
Getting Started
- Quick Start — Install and get up and running in 60 seconds
- RAG vs Atulya — See how Atulya differs from traditional RAG with real examples
Core Concepts
- Retain — How memories are stored with multi-dimensional facts
- Recall — How TEMPR's 4-way search retrieves memories
- Reflect — How disposition influences reasoning and opinion formation
API Methods
- Retain — Store information in memory banks
- Recall — Search and retrieve memories
- Reflect — Reason with disposition
- Memory Banks — Configure disposition and background
- Documents — Manage document sources
- Operations — Monitor async tasks
Deployment
- Server Setup — Deploy with Docker Compose, Helm, or pip