NVIDIA/NemoClaw
NemoClaw
NemoClaw is an LLM agent orchestrator and sandboxed execution environment designed to deploy and manage the lifecycles of large language model agents. It provides a secure runtime that isolates persistent agents from the underlying host system to ensure operational security.
The system includes a secure LLM inference gateway that acts as a managed routing layer, securing communication between AI agents and inference engines to prevent unauthorized access. It also integrates with NVIDIA OpenShell to run specialized agents within a secure shell environment.
Operational control is provided through an agent lifecycle management CLI used to configure and control the deployment state of agent instances. The platform further incorporates managed inference and request-response routing to optimize the communication between agents and language models.
Features
- Agent Runtime Sandboxing - Executes persistent AI agents within restricted, sandboxed environments to prevent unauthorized host system access.
- Agent Lifecycle Management - Manages the creation, configuration, and operational state of agent instances.
- Agent Orchestrators - Orchestrates the deployment and lifecycle of LLM agents within secure, persistent execution environments.
- AI Security Orchestrators - Manages isolated connections and enforces security boundaries between AI agents and execution environments.
- AI Execution Sandboxes - Provides a protected runtime that isolates persistent agents from the host system for operational security.
- AI Security Gateways - Ships a managed routing gateway that secures and monitors interactions between agents and language models.
- Model Routing Layers - Implements a centralized routing layer to control access and secure communication between agents and language models.
- CLI Agent Management - Provides a command-line interface to manage the deployment and operational state of AI agent instances.
- Agent Lifecycle Management - Provides tools for controlling the full deployment and operational lifecycle of persistent AI agents.
- Agent Execution Environments - Maintains long-running agent states within secure containers to support continuous operation without context loss.
- Model Request Routing - Routes agent inference requests through a managed layer to optimize communication with language models.
- Request Routing - Directs traffic between agent requests and backend inference engines to optimize resource allocation.
- Managed Inference Routing - Routes agent requests through a controlled layer to ensure secure and efficient communication with models.
- Agent Frameworks - Managed inference and execution for agentic workflows.
