Building advanced AI agents no longer requires months of effort or a team of specialists. With Strands Agents, AWS's open source SDK, developers of all skill levels can create, deploy, and scale AI-powered agents in minutes. This tool puts the capabilities of large language models (LLMs) at your disposal, making AI innovation more accessible than ever.
Key Features That Set Strands Agents Apart
Strands Agents redefines the agent development process by taking a model-driven approach. Traditional frameworks often bog developers down with complicated workflows and orchestration layers. Strands Agents eliminates that complexity. Simply define what you want your agent to do, pick the tools it needs, and let the LLM handle the heavy lifting, from planning to tool usage and reflection.
- Simplicity: Create agents with just a prompt and a tool list - no need for intricate workflows.
- Flexibility: Compatible with any reasoning-capable model, integrating seamlessly with providers like Amazon Bedrock, Anthropic, Meta, and more via LiteLLM.
- Customization: Easily adapt tool selection, session state, and agent behaviors to fit unique needs.
- Scalability: Effortlessly transition from local prototypes to production-ready deployments
Strands Agents powers production AI systems at AWS, such as Amazon Q Developer and AWS Glue, and enjoys community support from leaders including Accenture and Meta.
Agility Over Complexity: The Strands Philosophy
Originally, building AI agents meant wrestling with complex frameworks and endless prompt engineering. But as LLMs advanced, AWS recognized that much of this setup was unnecessary.
By trusting the reasoning abilities of LLMs, their teams cut development times dramatically, turning months-long projects into tasks completed in weeks or days.
How Strands Agents Work: Core Concepts
Every Strands agent is defined by three core components:
- Model: The intelligence behind the agent, supporting a variety of providers and custom options.
- Tools: The agent’s capabilities, such as file access, API calls, or document searches using semantic technology.
- Prompt: Instructions that guide the agent’s actions, easily tailored for specific tasks or behaviors.
Agents use an agentic loop; the model reasons, plans, selects tools, and iterates until the job is done. This enables sophisticated actions, from multi-agent teamwork to dynamic problem-solving using “thinking tools.” For example, agents can retrieve the right tool for any situation or coordinate sub-agents for complex processes.
Rapid Prototyping: From Idea to Agent in Minutes
One of Strands Agents’ biggest strengths is its speed. Developers can quickly build prototypes, like an AI assistant that suggests project names and checks for domain or repository availability, with minimal code. Thanks to integration with MCP servers and ready-made tools, deployment is as easy as a few commands in Python.
Robust Production Architecture and Observability
Strands Agents isn’t just for prototyping. Deploy AI agents locally, as APIs, or as distributed services using AWS Lambda, Fargate, or EC2. You can customize tool execution for performance and security, and built-in OpenTelemetry support ensures you can trace, monitor, and evaluate every agent’s performance across your infrastructure.
Join the Open Source Community
Released under the Apache 2.0 license, Strands Agents welcomes contributions for new models, features, and documentation. Its active GitHub community and industry support mean the platform is evolving quickly. Whether you’re building chatbots, automation tools, or multi-agent systems, Strands Agents provides a flexible, scalable foundation for your AI projects.
AWS Strands Agents Simplifies AI Agent Development for Everyone