Master Strands Agents with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make Strands Agents powerful for ai agent builders workflows.
The LLM dynamically selects and sequences tools based on the task rather than following hardcoded workflows, enabling more natural and adaptive agent behavior that adjusts its approach based on intermediate results.
A customer support agent dynamically decides whether to search a knowledge base, query a database, or escalate to a human based on the conversation context, without rigid if/then workflow rules.
Works with Amazon Bedrock, Anthropic, OpenAI, Ollama, LiteLLM, and any OpenAI-compatible API. Switch providers by changing a single configuration without modifying agent logic or tool definitions.
A company develops agents on local Ollama models during development, deploys to Bedrock for production, and can switch to Anthropic if pricing or performance changes with zero code modifications.
Ships with 20+ ready-to-use tools for file I/O, shell commands, HTTP requests, code execution, RAG retrieval, and AWS service interactions. Extend with custom tools using a simple @tool Python decorator.
A data pipeline agent uses built-in file and HTTP tools to fetch data, a custom @tool-decorated function to transform it, and the built-in code executor to validate results, all in one agent.
Supports hierarchical delegation, parallel execution, swarm coordination, and graph-based workflows. The Agent-as-Tool pattern lets agents delegate subtasks to specialized sub-agents with A2A communication.
A research agent delegates web scraping to a browser agent, data analysis to a Python agent, and report writing to a content agent, coordinating all three in parallel and merging results.
Deep integration with Bedrock AgentCore for managed hosting, Lambda for serverless execution, EKS for containers, and EC2 for VMs. Includes Bedrock Guardrails for content safety and IAM for access control.
Deploy a customer-facing agent to Bedrock AgentCore with auto-scaling, content guardrails to prevent inappropriate responses, and IAM policies restricting which AWS resources the agent can access.
Built-in Model Context Protocol client support for connecting to thousands of external tool servers. Native OpenTelemetry integration provides tracing, logging, and metrics for debugging agent behavior in production.
Connect an agent to an MCP-compatible database tool server while monitoring every tool call, LLM invocation, and error through CloudWatch dashboards with full request tracing.
Strands uses a model-driven approach where the LLM decides tool ordering dynamically, while LangChain provides lower-level chain composition and CrewAI uses role-based agent orchestration with predefined workflows. Strands is simpler to start with (3-line agents) and more adaptive for dynamic tasks, but offers less granular control than LangChain for deterministic pipelines.
No. Strands is open-source and works with any supported LLM provider including Ollama for fully local, offline development. AWS services are optional. They provide a managed production deployment path but are not required.
Yes. The SDK is available for both Python (via pip) and TypeScript (via npm), covering both major AI development ecosystems.
Agent-as-Tool lets you wrap an entire agent as a tool that another agent can call. This enables hierarchical architectures where a coordinator agent delegates specialized subtasks to child agents, for example a manager agent delegating research to one sub-agent and code generation to another.
When deployed on AWS, Strands integrates with Bedrock Guardrails for content safety filtering, AWS IAM for access control, and Amazon Cognito for user authentication. OpenTelemetry integration provides audit trails and observability for compliance requirements.
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Tutorial updated March 2026