StackAgent: GPU-Accelerated Infrastructure for Autonomous AI Agent Swarms

Orchestrate Your AI Agent Swarms with One Stack.

The first GPU-accelerated modular infrastructure layer designed to build, deploy, and scale autonomous multi-agent systems. Powered by NVIDIA and Amazon Bedrock.

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Foundation Models

Standardized LLM inference layer integrated with Amazon Bedrock for enterprise-grade reliability.

Scalable Orchestration

Native support for Amazon EKS and Step Functions to manage long-running agentic workflows.

Real-time Memory

Low-latency state synchronization powered by Amazon ElastiCache for agent persistence.

Platform Features

Everything you need to build, deploy, and scale AI agent systems.

Agent Orchestration

Coordinate multiple agents with a powerful workflow engine that handles task delegation, communication, and conflict resolution.

State Management

Persistent agent state with real-time synchronization, allowing agents to maintain context across sessions and workflows.

Event Bus

Decentralized event system for agent communication, enabling reactive workflows and real-time collaboration.

Tool Integration

Connect agents to external APIs and services with a standardized tool interface, supporting both synchronous and asynchronous operations.

Monitoring & Analytics

Track agent performance, workflow efficiency, and system health with built-in dashboards and logging.

Security & Compliance

Enterprise-grade security features including role-based access control, data encryption, and audit logging.

Architecture

A modular, scalable design built on Amazon Web Services and accelerated by NVIDIA GPUs.

Inference Layer

Amazon Bedrock with NVIDIA G5/P4 instances for high-concurrency model inference.

State Layer

Amazon ElastiCache and DynamoDB for agent state management.

Workflow Layer

AWS Step Functions for agent orchestration and workflow management.

NVIDIA GPU Powered
StackAgent Architecture Diagram

Key Components

Agent Runtime

Optimized for NVIDIA CUDA cores to achieve sub-100ms response times in agent reasoning.

Message Bus

Amazon EventBridge for agent communication.

Tool Registry

Central repository for agent capabilities.

Metrics Engine

Amazon CloudWatch for monitoring and analytics.

For Developers

Build with familiar tools and frameworks.

Python SDK

Intuitive Python library for building and deploying agents with minimal boilerplate.

REST API

Comprehensive API for integrating StackAgent with your existing systems.

CLI Tools

Command-line utilities for agent management and workflow debugging.

Documentation

Comprehensive guides, tutorials, and API references.

Python
from stackagent import Agent, Workflow

# Create a workflow
workflow = Workflow("customer_support")

# Define agents
support_agent = Agent(
    name="support_agent",
    model="anthropic.claude-3-sonnet-20240229-v1:0",
    instructions="Handle customer inquiries"
)

research_agent = Agent(
    name="research_agent",
    model="anthropic.claude-3-opus-20240229-v1:0",
    instructions="Research product information"
)

# Add agents to workflow
workflow.add_agent(support_agent)
workflow.add_agent(research_agent)

# Deploy workflow
workflow.deploy()

Pricing

Simple, transparent pricing based on usage.

Starter

$0
per month
  • 100 agent executions per month
  • 1 workflow
  • Basic monitoring
  • Community support
Get Started
Most Popular

Professional

$299
per month
  • 10,000 agent executions per month
  • 10 workflows
  • Advanced monitoring and analytics
  • Email support
  • API access
Get Started

Enterprise

Custom
per month
  • Unlimited agent executions
  • Unlimited workflows
  • Custom monitoring and analytics
  • 24/7 priority support
  • Dedicated account manager
  • Custom integrations
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Start Building Now

Get started with StackAgent in just a few steps.

1

Set Up AWS Account

Create an AWS account or use an existing one. StackAgent requires access to Amazon Bedrock, EKS, and other AWS services.

2

Install StackAgent CLI

Use pip to install the StackAgent CLI tool, which provides commands for managing your agent workflows.

3

Create Your First Workflow

Define agents, tasks, and workflows using the Python SDK or YAML configuration files.

4

Deploy and Test

Deploy your workflow to AWS and test it with sample inputs. Monitor performance and iterate as needed.

Terminal
# Install StackAgent CLI
pip install stackagent-cli

# Configure AWS credentials
stackagent configure

# Create a new project
stackagent init my-agent-project

# Deploy workflow
stackagent deploy

Documentation

Comprehensive guides and API references to help you build with StackAgent.

Getting Started

  • Quickstart Guide
  • Installation Instructions
  • AWS Setup Requirements
  • Project Structure

Core Concepts

  • Agents
  • Workflows
  • Tasks
  • State Management
  • Event System

API Reference

  • Python SDK
  • REST API
  • CLI Commands
  • Configuration Options
  • Error Codes

The NVIDIA & StackAgent Synergy

Accelerating Agentic Intelligence with NVIDIA

GPU-Accelerated Inference Layer

StackAgent utilizes NVIDIA Tensor Core GPUs (via Amazon EC2 G5/P4 instances) to power our modular inference engine. By leveraging NVIDIA NIM, we reduce the time-to-first-token for complex multi-agent reasoning tasks.

CUDA-Optimized Orchestration

Our agent runtime is being optimized for CUDA-based parallel processing. This allows for the simultaneous coordination of hundreds of autonomous agents with sub-100ms state synchronization, essential for real-time enterprise workflows.

High-Speed Vector Operations

We integrate GPU-accelerated vector databases to manage agent long-term memory, ensuring high-throughput similarity searches across massive agentic datasets.

Future Roadmap

We plan to implement NVIDIA NeMo for fine-tuning domain-specific "Small Language Models" (SLMs) that will run as specialized workers within the StackAgent ecosystem.

The NVIDIA Connection

How StackAgent leverages NVIDIA's cutting-edge technology to power AI agent swarms.

Inference Optimization

StackAgent plans to utilize NVIDIA NIM (Microservices) to accelerate the local model inference layer, enabling faster and more efficient agent decision-making.

Parallel Computing

AI Agent swarms require massive parallel computing power for multi-role coordination. NVIDIA GPUs provide the perfect foundation for this parallelism, enabling seamless agent collaboration.

Future Roadmap

StackAgent plans to support GPU-accelerated vector indexing for large-scale agent memory banks, enabling agents to access and process vast amounts of information with unprecedented speed.

NVIDIA Partnership

StackAgent is committed to leveraging NVIDIA's latest GPU technologies to push the boundaries of what's possible with AI agent swarms, creating more intelligent, responsive, and capable autonomous systems.