---
title: "Building a Memory-Driven Agent with NVIDIA NemoClaw"
description: "Enterprise agents need more than conversation history to connect decisions, obligations, people, and projects that change over time. Using NVIDIA NemoClaw, the example builds a memory-driven Chief of Staff around a human-readable self model in structured Markdown, a SQLite ledger for obligations and judgments, scheduled maintenance, bounded retrieval, and user correction with append-only auditing. Its architecture separates evidence, knowledge, and governed execution, while NVIDIA OpenShell sandboxes runtime access and keeps managed inference and MCP credentials outside the sandbox; memory can inform an action but cannot authorize it. In the supplied evaluation, the self model reached 90.9% overall accuracy versus 82.8% for an agentic RAG baseline, with larger gains on hard questions, changing facts, point-in-time reasoning, entity disambiguation, and multisource synthesis, but lower scores on corpus-faithful answers and single-hop lookup."
---

# Building a Memory-Driven Agent with NVIDIA NemoClaw

[NVIDIA](https://yomu.fyi/company/nvidia-developer-blog) · Tanya Lenz · Sep 4, 2026

**Type:** Tutorial

## Summary

Enterprise agents need more than conversation history to connect decisions, obligations, people, and projects that change over time. Using NVIDIA NemoClaw, the example builds a memory-driven Chief of Staff around a human-readable self model in structured Markdown, a SQLite ledger for obligations and judgments, scheduled maintenance, bounded retrieval, and user correction with append-only auditing. Its architecture separates evidence, knowledge, and governed execution, while NVIDIA OpenShell sandboxes runtime access and keeps managed inference and MCP credentials outside the sandbox; memory can inform an action but cannot authorize it. In the supplied evaluation, the self model reached 90.9% overall accuracy versus 82.8% for an agentic RAG baseline, with larger gains on hard questions, changing facts, point-in-time reasoning, entity disambiguation, and multisource synthesis, but lower scores on corpus-faithful answers and single-hop lookup.

## Context

Enterprise work involves messages, decisions, projects, and obligations that change over time, while conversation history mixes current priorities with past decisions and temporary requests. The agent must connect information across time without treating derived memory as source evidence or allowing memory to act as authorization.

## Approach / What changed

The design uses a structured Markdown self model for people, projects, priorities, goals, concepts, and work patterns; a SQLite ledger for obligations, rankings, corrections, and audit events; bounded retrieval; scheduled maintenance; user correction; and an append-only audit trail. NVIDIA NemoClaw integrates the example with NVIDIA OpenShell, which provides sandboxing and runtime policy enforcement for file system, process, and network access.

## Takeaways

- The self model stores derived knowledge separately from source evidence, making it possible to distinguish errors in evidence, memory maintenance, retrieval, and final model decisions.
- NVIDIA OpenShell enforces runtime boundaries for file system, process, and network access; retrieved context is treated as model input rather than trusted security policy.
- The self model scored 90.9% overall accuracy versus 82.8% for the agentic RAG baseline, while the baseline scored higher on corpus-faithful answers and single-hop lookup.

**Tags:** [AI Agents](https://yomu.fyi/topic/ai-agents), [Sandboxing](https://yomu.fyi/topic/sandboxing), [SQLite](https://yomu.fyi/topic/sqlite)

- Source: [NVIDIA](https://developer.nvidia.com/blog/building-a-memory-driven-agent-with-nvidia-nemoclaw)
- Source URL: https://developer.nvidia.com/blog/building-a-memory-driven-agent-with-nvidia-nemoclaw
- Ingested by Yomu: 2026-09-04T20:00:53.320Z

[Read original post](https://developer.nvidia.com/blog/building-a-memory-driven-agent-with-nvidia-nemoclaw)