MemorySync
Official SDK

NVIDIA NeMo Agent Toolkit + MemorySync

A pip-installable MemoryEditor plugin: one YAML block gives any toolkit workflow long-term memory — explicit memory tools or fully automatic via auto_memory_agent.

Overview

Install nat-memorysync and `_type: memorysync_memory` becomes available in every NeMo Agent Toolkit workflow YAML — a real entry-point plugin, not example code to vendor (the toolkit’s Mem0 and Zep editors live in its repo as examples). It works with both wiring modes: NVIDIA’s built-in add_memory/get_memory tools, and the automatic auto_memory_agent wrapper that stores every turn and enriches every prompt with no tools and no prompt changes. The editor is engineered against the in-repo editors’ sharp edges: search without user_id raises a ValueError naming the kwarg (the Mem0 editor throws a bare KeyError), your metadata dict is never mutated (the Mem0 editor pops keys out of it), search returns one MemoryItem per fact with similarity_score populated (the Zep editor returns a single joined blob; the Mem0 editor discards scores), and remove_items with no kwargs raises instead of silently doing nothing. Deletes are session-scoped by default — wiping a user’s entire memory requires the explicit scope="user" opt-in. Rows are always keyed by each item’s user_id, so an unset conversation id can never mix users (the in-repo Zep editor routes everyone to one shared "default_zep_thread"). Recall runs under a hard 1.2s budget and fails open, so automatic memory can never stall a turn; deterministic idempotency seeds make RetryMixin retries converge on one stored row.

Setup status and requirements

Supported version
nat-memorysync 1.0.0 (PyPI)
Last setup review
2026-08-24
Permissions
A MemorySync API key with read and write scopes.
Limits
Requires nvidia-nat-core 1.5+ (Python 3.11+, the toolkit’s own floor).
Open setup documentation →

Capabilities

Quick Start

memory:
saas_memory:
_type: memorysync_memory # key from MEMORYSYNC_API_KEY
functions:
add_memory:
_type: add_memory
memory: saas_memory
description: Save any user preference or fact.
get_memory:
_type: get_memory
memory: saas_memory
description: Recall saved user preferences and facts.
workflow:
_type: react_agent
tool_names: [add_memory, get_memory]
llm_name: my_llm

Use Cases

NVIDIA-stack agents (NIM, Nemotron) that remember users across sessions

Zero-code memory for existing workflows via auto_memory_agent

Replacing the in-repo Mem0/Zep example editors with a maintained plugin

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