---
title: "ZeroMemory Overview"
description: "ZeroMemory session and semantic memory for AI agents — working, episodic, and long-term memory tiers with automatic consolidation, decay scoring, and GraphRAG hybrid retrieval."
canonical: "https://docs.ainative.studio/docs/zeromemory/overview"
last-updated: "2026-10-03T21:47:10.764Z"
---

# ZeroMemory Overview

Source: https://docs.ainative.studio/docs/zeromemory/overview

> ZeroMemory session and semantic memory for AI agents — working, episodic, and long-term memory tiers with automatic consolidation, decay scoring, and GraphRAG hybrid retrieval.

# ZeroMemory

ZeroMemory gives AI agents persistent cognitive memory. Three memory tiers — working, episodic, and semantic — with automatic consolidation, decay scoring, and GraphRAG hybrid retrieval.

## Why ZeroMemory

| Problem | Solution |
|---------|----------|
| Agents forget between sessions | Persistent memory across sessions |
| Flat vector search misses connections | GraphRAG combines vectors + knowledge graph |
| No way to prioritize memories | Importance-weighted decay scoring |
| Manual memory management | Automatic consolidation between tiers |
| Entity relationships lost | Auto-extraction into knowledge graph |

## Memory Tiers

| Tier | Purpose | Lifespan |
|------|---------|----------|
| **Working** | Active task context | Hours |
| **Episodic** | Past interactions and events | Days-weeks |
| **Semantic** | Long-term knowledge and facts | Permanent |

Memories automatically consolidate from working → episodic → semantic based on access patterns and importance scores.

## Quick Start

### 1. Store a memory

```bash
curl -X POST https://api.ainative.studio/api/v1/public/memory/v2/remember \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "User prefers dark mode and uses Python for backend work",
    "metadata": {"user_id": "u_123", "source": "onboarding"}
  }'
```

### 2. Recall by meaning

```bash
curl -X POST https://api.ainative.studio/api/v1/public/memory/v2/recall \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What does the user prefer?",
    "user_id": "u_123",
    "limit": 5
  }'
```

### 3. Forget

```bash
curl -X POST https://api.ainative.studio/api/v1/public/memory/v2/forget \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "u_123"
  }'
```

## Core Endpoints

| Method | Path | Description |
|--------|------|-------------|
| POST | `/memory/v2/remember` | Store a memory |
| POST | `/memory/v2/recall` | Semantic search across memories |
| POST | `/memory/v2/forget` | Delete memories |
| POST | `/memory/v2/reflect` | Agent self-reflection |
| GET | `/memory/v2/profile` | Build user profile from memories |
| POST | `/memory/v2/relate` | Find entity relationships |
| POST | `/memory/v2/process` | Batch process memories |

## Scoring Algorithm

Memories are ranked using blended scoring:

```
final_score = (similarity × 0.5) + (importance × 0.3) + (recency × 0.2)
```

- **Similarity** — Cosine distance between query and memory embeddings
- **Importance** — Assigned at storage time, increases with access
- **Recency** — Decays over time, refreshes on access

## Benchmarks

ZeroMemory achieved **100% Recall@1** and **94% QA accuracy** on the [LongMemEval benchmark](https://arxiv.org/abs/2410.10813) (ICLR 2025):

| System | Recall@1 | QA Accuracy |
|--------|----------|-------------|
| **ZeroMemory** | **100%** | **94%** |
| GPT-4o (full context) | ~70% | ~70% |
| Letta/MemGPT | 40% | — |
| Mem0 | 25% | — |

## Next Steps

- [GraphRAG](/docs/guides/graphrag) — Hybrid vector + knowledge graph search
- [Context Graph API](/docs/api/context-graph) — Entity management and traversal
- [MCP Memory Server](/docs/mcp/overview) — 6-tool MCP integration