AWS open-source SDK for building AI agents in Python with model-driven tool orchestration and built-in conversation memory.
AWS's open-source toolkit for building AI agents in Python — create agents that use tools and follow structured workflows.
Strands Agents is an open-source Python SDK developed by Amazon Web Services that provides a model-driven approach to building AI agents. Unlike rigid framework-based approaches, Strands lets the underlying language model dynamically decide which tools to use and in what order, making agent behavior more natural and adaptive. The SDK supports multiple LLM providers including Amazon Bedrock, Anthropic, LiteLLM, Ollama, and any OpenAI-compatible endpoint, giving developers flexibility in their model choice.
Strands provides built-in tools for file operations, shell commands, HTTP requests, code execution, RAG retrieval, and more. Developers can also create custom tools with a simple decorator pattern. The framework includes native conversation memory management, allowing agents to maintain context across multi-turn interactions without complex state management code.
One of the key strengths of Strands is its simplicity — a basic agent can be created in just three lines of code, while still supporting complex multi-agent orchestration patterns for production systems. The SDK integrates seamlessly with AWS services like Bedrock for model hosting and Lambda for serverless deployment, but is not locked into the AWS ecosystem. It also supports autonomous agent loops where the agent can plan, execute, and iterate on complex tasks with minimal human intervention. Strands has quickly gained traction in the agent development community as a lightweight but powerful alternative to heavier frameworks like LangChain and CrewAI.
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The LLM dynamically selects and sequences tools based on the task rather than following hardcoded workflows, enabling more natural and adaptive agent behavior.
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Works with Amazon Bedrock, Anthropic, OpenAI, Ollama, LiteLLM, and any OpenAI-compatible API, so you're never locked into a single model provider.
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Ships with ready-to-use tools for file I/O, shell commands, HTTP requests, code execution, RAG retrieval, and more — extensible via simple Python decorators.
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Manages multi-turn context automatically with built-in memory management, reducing boilerplate code for stateful agent interactions.
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Minimal boilerplate means you can have a working agent in just three lines of Python while still supporting complex multi-agent patterns when needed.
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Deep integration with Bedrock, Lambda, and other AWS services for production deployments, but fully functional outside the AWS ecosystem.
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View Pricing Options →Enterprise AWS-native agent deployment with security and compliance requirements
Multi-agent systems requiring sophisticated coordination and handoff patterns
Production agent applications needing comprehensive observability and monitoring
Financial services and regulated industries requiring enterprise-grade security
Large-scale agent deployments across AWS infrastructure and services
Organizations needing provider flexibility without vendor lock-in
We believe in transparent reviews. Here's what Strands Agents doesn't handle well:
Strands uses a model-driven approach where the LLM decides tool ordering, while CrewAI uses role-based agent orchestration with predefined workflows. Strands is more flexible for dynamic tasks; CrewAI is better for structured team-based workflows.
No. While Strands integrates deeply with AWS services, it works with any LLM provider including Ollama for fully local development.
Yes. Strands supports multi-agent patterns where agents can delegate tasks to other agents, enabling complex orchestration scenarios.
Currently Python only, with the SDK available via pip. TypeScript/JavaScript support may come in future releases.
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