---
title: "Example: Agent Memory"
description: "Build an agent with persistent memory that remembers across sessions using ZeroMemory"
canonical: "https://docs.ainative.studio/docs/examples/agent-memory"
last-updated: "2026-10-03T21:47:10.764Z"
---

# Example: Agent Memory

Source: https://docs.ainative.studio/docs/examples/agent-memory

> Build an agent with persistent memory that remembers across sessions using ZeroMemory

## Agent with Persistent Memory

Build an agent that stores and recalls information across sessions using ZeroMemory.

## Prerequisites

- AINative API key ([get one free](/docs/getting-started/quick-start))

## Store Memories

```python
import requests

TOKEN = "your-api-key"
BASE = "https://api.ainative.studio/api/v1/public/memory/v2"
HEADERS = {"Authorization": f"Bearer {TOKEN}", "Content-Type": "application/json"}

## Store user preferences
requests.post(f"{BASE}/remember", headers=HEADERS, json={
    "content": "User prefers dark mode and uses Python for backend",
    "entity_id": "user_123",
    "memory_type": "semantic",
    "importance": 0.9,
    "tags": ["preferences"],
})

## Store a conversation fact
requests.post(f"{BASE}/remember", headers=HEADERS, json={
    "content": "User is building a RAG chatbot for their company wiki",
    "entity_id": "user_123",
    "memory_type": "episodic",
    "importance": 0.7,
    "tags": ["project"],
})

## Store a relationship
requests.post(f"{BASE}/relate", headers=HEADERS, json={
    "subject": "user_123",
    "predicate": "works_on",
    "object": "RAG Chatbot Project",
    "confidence": 0.9,
})
```

## Recall in a New Session

```python
## Later, in a completely new session...
response = requests.post(f"{BASE}/recall", headers=HEADERS, json={
    "query": "What is the user working on?",
    "entity_id": "user_123",
    "limit": 5,
})

for memory in response.json()["results"]:
    print(f"[{memory['score']:.2f}] {memory['content']}")
```

**Output:**
```
[0.94] User is building a RAG chatbot for their company wiki
[0.72] User prefers dark mode and uses Python for backend
```

## Build a Profile

```python
profile = requests.get(
    f"{BASE}/profile/user_123",
    headers=HEADERS,
).json()

print(profile)
```

## What to Try Next

- Use [GraphRAG](/docs/examples/graphrag) for relationship-aware retrieval
- Add [reflection](/docs/zeromemory/api-reference#post-reflectentity_id) for AI-generated insights
- Connect via [MCP](/docs/mcp/memory-server) for agent tool access