The first modular infrastructure layer designed to build, deploy, and scale autonomous multi-agent systems. Powered by Amazon Bedrock.
Standardized LLM inference layer integrated with Amazon Bedrock for enterprise-grade reliability.
Native support for Amazon EKS and Step Functions to manage long-running agentic workflows.
Low-latency state synchronization powered by Amazon ElastiCache for agent persistence.
Everything you need to build, deploy, and scale AI agent systems.
Coordinate multiple agents with a powerful workflow engine that handles task delegation, communication, and conflict resolution.
Persistent agent state with real-time synchronization, allowing agents to maintain context across sessions and workflows.
Decentralized event system for agent communication, enabling reactive workflows and real-time collaboration.
Connect agents to external APIs and services with a standardized tool interface, supporting both synchronous and asynchronous operations.
Track agent performance, workflow efficiency, and system health with built-in dashboards and logging.
Enterprise-grade security features including role-based access control, data encryption, and audit logging.
A modular, scalable design built on Amazon Web Services.
Amazon Bedrock for foundation models and custom model deployment.
Amazon ElastiCache and DynamoDB for agent state management.
AWS Step Functions for agent orchestration and workflow management.
Isolated execution environment for agent logic.
Amazon EventBridge for agent communication.
Central repository for agent capabilities.
Amazon CloudWatch for monitoring and analytics.
Build with familiar tools and frameworks.
Intuitive Python library for building and deploying agents with minimal boilerplate.
Comprehensive API for integrating AgentStack with your existing systems.
Command-line utilities for agent management and workflow debugging.
Comprehensive guides, tutorials, and API references.
from agentstack 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()
Simple, transparent pricing based on usage.