CrewAI and Strands Agents are both Python frameworks for building AI agent systems, but they have meaningfully different architectures and AWS integration stories. This comparison helps teams choose the right framework for their use case.

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.

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 agent with tools. Multi-agent patterns emerge from tools that call other agents, rather than from a framework-level crew concept. The execution model is implicit: the model decides what to do next; you do not define task sequences.

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. It deploys to Bedrock AgentCore without code changes, uses Bedrock models through IAM role authentication, and integrates with MCP servers that expose AWS services as tools. It is the framework AWS recommends for production agent deployments on AWS infrastructure.

CrewAI is cloud-agnostic and supports Bedrock models via LiteLLM or the Bedrock provider. Integration works but requires more configuration than Strands. CrewAI does not have a native deployment target on AWS - you deploy it to Lambda (with size/timeout constraints) or ECS.

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 platform (cloud offering with crew hosting 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.