CrewAI and Strands Agents are both open-source frameworks for building AI agent systems, but they have meaningfully different architectures and AWS integration stories. CrewAI (Python, MIT licensed, created by Joao Moura) is independent of LangChain and organizes agents into role-based crews. Strands Agents is maintained by AWS and ships both Python and TypeScript SDKs. This comparison helps teams choose the right framework for their use case. (Verified June 2026 against the latest releases: CrewAI 1.14.7 and Strands Agents 1.43.0.)

Architecture

CrewAI is built around the concept of a “crew” - a team of agents working toward a shared goal. Each agent has a defined role (researcher, writer, analyst), a backstory, assigned tools, and a goal. A Crew object coordinates the agents, routing tasks between them according to a process (sequential or hierarchical). The framework is explicit: you define who does what, in what order, and with what handoffs.

python
from crewai import Agent, Task, Crew

researcher = Agent(
    role="Research Analyst",
    goal="Find accurate information about {topic}",
    tools=[search_tool, web_scraper]
)

writer = Agent(
    role="Content Writer",
    goal="Write clear summaries of research findings"
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process=Process.sequential
)

Strands Agents is built around a single model-driven agent with tools. Its core execution model is implicit: the model decides what to do next, and you do not have to define task sequences. The simplest multi-agent pattern emerges from tools that call other agents (agents-as-tools), shown below. Since Strands Agents 1.0 (July 2025), the SDK also ships explicit multi-agent primitives so you are not limited to the implicit loop:

  • Agents-as-tools - hierarchical delegation, where specialized agents are exposed as callable tools.
  • Swarms - autonomous teams of agents that coordinate through shared memory.
  • Graphs - deterministic workflows with explicit routing and decision points.
  • Handoffs - explicit transfer of control, including handing off to a human, while preserving conversation context.

Strands also supports the Agent-to-Agent (A2A) protocol for interoperability across platforms, and the Model Context Protocol (MCP) for tools.

python
from strands import Agent, tool

@tool
def research_topic(topic: str) -> str:
    """Research a topic and return findings."""
    research_agent = Agent(model="...", tools=[search_tool])
    return research_agent(f"Research: {topic}")

main_agent = Agent(
    model="us.anthropic.claude-sonnet-4-6",
    tools=[research_topic, write_summary]
)

AWS Integration

Strands is AWS-native. By default it uses Amazon Bedrock as the model backend (Claude Sonnet 4.6 in recent releases) with IAM role authentication, and it deploys to Amazon Bedrock AgentCore without code changes. The same @tool functions and agent code that run locally deploy to the AgentCore Runtime, which provides managed identity, memory, observability, and support for long-running tasks. Strands is used in production inside AWS by teams behind Kiro, Amazon Q, and AWS Glue, and it integrates with MCP servers that expose AWS services as tools. AWS positions it as a first-class option for production agents on AWS infrastructure. Deployment targets also include Lambda, Fargate, EKS, and Docker.

CrewAI is cloud-agnostic and supports Bedrock models via LiteLLM or the Bedrock provider. Integration works but requires more configuration than Strands. CrewAI has no built-in serverless deployment target on AWS the way Strands has AgentCore, so you self-host on Lambda (with size and timeout constraints) or ECS. CrewAI’s own managed offering, the CrewAI Agent Management Platform (AMP, launched January 2026), can run on managed infrastructure or be deployed into a private VPC in AWS, Azure, or GCP, or on-premises.

When to Use CrewAI

  • You need explicit multi-agent coordination with defined roles and handoffs
  • The workflow has deterministic task sequences that benefit from being codified
  • Your team wants a framework with a large community and many pre-built tools
  • Cloud portability matters (same codebase targeting AWS, Azure, and GCP)
  • You want the CrewAI Agent Management Platform (AMP), the managed offering with crew and flow deployment, execution traces, and monitoring

When to Use Strands

  • Your infrastructure is AWS-native and you want first-class AgentCore deployment
  • You prefer implicit model-driven task selection over explicit task routing
  • MCP integration is important (Strands has native MCP support)
  • You want AWS-supported open-source with Bedrock as the default model backend
  • Simpler single-agent patterns are the primary use case

Performance and Cost

Both frameworks invoke foundation models for agent reasoning. Cost depends on the model chosen and the number of reasoning steps, not the framework. Strands’ implicit loop can sometimes require more model calls to complete a task (the model explores before converging) compared to CrewAI’s explicit sequential steps.

For predictable cost at scale, CrewAI’s sequential process with defined tasks is easier to cost-model.

Sources