Comparing agentic AI frameworks

When selecting an agentic AI framework for autonomous agent development, consider how each option aligns with your specific requirements. Consider not only its technical capabilities but also its organizational fit, including team expertise, existing infrastructure, and long-term maintenance requirements. Many organizations might benefit from a hybrid approach, leveraging multiple frameworks for different components of their autonomous AI ecosystem.

The following table compares the maturity levels (strongest, strong, adequate, or weak) of each framework across key technical dimensions. For each framework, the table also includes information about production deployment options and learning curve complexity.

Framework	AWS integration	Autonomous multi-agent support	Autonomous workflow complexity	Multimodal capabilities	Foundation model selection	LLM API integration	Production deployment	Learning curve
Amazon BedrockAgents	Strongest	Adequate	Adequate	Strong	Strong	Strong	Fully managed	Low
AutoGen	Weak	Strong	Strong	Adequate	Adequate	Strong	Do it yourself (DIY)	Steep
CrewAI	Weak	Strong	Adequate	Weak	Adequate	Adequate	DIY	Moderate
LangChain/LangGraph	Adequate	Strong	Strongest	Strongest	Strongest	Strongest	Platform or DIY	Steep
Strands Agents	Strongest	Strong	Strongest	Strong	Strong	Strongest	DIY	Moderate

Considerations in choosing an agentic AI framework

When developing autonomous agents, consider the following key factors:

• AWS infrastructure integration – Organizations heavily invested in AWS will benefit most from the native integrations of Strands Agents with AWS services for autonomous workflows. For more information, see AWS Weekly Roundup (AWS Blog).

• Foundation model selection – Consider which framework provides the best support for your preferred foundation models (for example, Amazon Nova models on Amazon Bedrock or Anthropic Claude), based on your autonomous agent's reasoning requirements. For more information, see Building Effective Agents on the Anthropic website.

• LLM API integration – Evaluate frameworks based on their integration with your preferred large language model (LLM) service interfaces (for example, Amazon Bedrock or OpenAI) for production deployment. For more information, see Model Interfaces in the Strands Agents documentation.

• Multimodal requirements – For autonomous agents that need to process text, images, and speech, consider the multimodal capabilities of each framework. For more information, see Multimodality in the LangChain documentation.

• Autonomous workflow complexity – More complex autonomous workflows with sophisticated state management might favor the advanced state machine capabilities. of LangGraph.

• Autonomous team collaboration – Projects that require explicit role-based autonomous collaboration between specialized agents can benefit from the team-oriented architecture of CrewAI.

• Autonomous development paradigm – Teams that prefer conversational, asynchronous patterns for autonomous agents might prefer the event-driven architecture of AutoGen.

• Managed or code-based approach – Organizations that want a fully managed experience with minimal coding should consider Amazon Bedrock Agents. Organizations that require deeper customization might prefer Strands Agents or other frameworks with specialized capabilities that better align with specific autonomous agent requirements.

• Production readiness for autonomous systems – Consider deployment options, monitoring capabilities, and enterprise features for production autonomous agents.