# Frameworks
[Foundations of agentic AI on AWS](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-foundations/) examines the core patterns and workflows that enable autonomous, goal-directed behavior. At the heart of implementing these patterns lies the choice of framework. A *framework* is the software foundation of prewritten code that provides a structured environment and common functionality for building and managing,tools, and orchestration capabilities needed to build production-ready autonomous AI agents.
Effective agentic AI frameworks provide several essential capabilities that transform raw large language model (LLM) interactions into coordinated, intelligent systems capable of reasoning, collaboration, and action:
+ **Agent orchestration** coordinates the flow of information and decision-making across single or multiple agents to achieve complex goals without human intervention.
+ **Tool integration** enables agents to interact with external systems, APIs, and data sources to extend their capabilities beyond language processing. For more information, see [Tools Overview](https://strandsagents.com/0.1.x/user-guide/concepts/tools/tools_overview/) in the Strands Agents documentation.
+ **Memory management** provides persistent or session-based state to maintain context across interactions, essential for long-running or adaptive tasks. More advanced frameworks incorporate long-term memory to store summaries and user preferences, enabling personalized and contextually aware agentic experiences. For more information, see [How to think about agent frameworks](https://blog.langchain.com/how-to-think-about-agent-frameworks/) on the LangChain Blog.
+ **Workflow definition** supports structured patterns like chains, routing, parallelization, and reflection loops that enable sophisticated autonomous reasoning.
+ **Deployment and monitoring** facilitate the transition from development to production with observability for autonomous systems. For more information, see the [Amazon Bedrock AgentCore general availability](https://aws.amazon.com/blogs/machine-learning/amazon-bedrock-agentcore-is-now-generally-available/) announcement.
These capabilities are implemented with varying approaches and emphases across the framework landscape, each offering distinct advantages for different autonomous agent use cases and organizational contexts.
This section profiles and compares the leading frameworks for building agentic AI solutions, with a focus on their strengths, limitations, and ideal use cases for autonomous operation:
+ [Strands Agents](strands-agents.md)
+ [LangChain and LangGraph](langchain-langgraph.md)
+ [CrewAI](crewai.md)
+ [AutoGen](autogen.md)
+ [LlamaIndex](llamaindex.md)
+ [Comparing agentic AI frameworks](comparing-agentic-ai-frameworks.md)
**Note**
This section covers the frameworks that specifically support agency of the AI and doesn't cover frontend interfaces or generative AI without agency.
# Strands Agents
Strands Agents is an open-source SDK that was initially released by AWS, as described in the [AWS Open Source Blog](https://aws.amazon.com/blogs/opensource/introducing-strands-agents-an-open-source-ai-agents-sdk/). Strands Agents is designed for building autonomous AI agents with a model-first approach It provides a flexible, extensible framework designed to work seamlessly with AWS services while remaining open to integration with third-party components. Strands Agents is ideal for building fully autonomous solutions.
## Key features of Strands Agents
Strands Agents includes the following key features:
+ **Model-first design** – Built around the concept that the foundation model is the core of agent intelligence, enabling sophisticated autonomous reasoning. For more information, see [Agent Loop](https://strandsagents.com/latest/user-guide/concepts/agents/agent-loop/) in the Strands Agents documentation.
+ **Multi-agent collaboration patterns** – Built-in coordination models such as Swarm, Graph, and Workflow patterns that enable scalable collaboration and governance across distributed agent networks. For more information, see [Multi-agent Patterns](https://strandsagents.com/docs/user-guide/concepts/multi-agent/multi-agent-patterns/) in the Strands Agents documentation.
+ **MCP integration** – Native support for the [Model Context Protocol](https://modelcontextprotocol.io/) (MCP), enabling standardized context provision to LLMs for consistent autonomous operation.
+ **AWS service integration** – Seamless connection to Amazon Bedrock, AWS Lambda, AWS Step Functions, and other AWS services for comprehensive autonomous workflows. For more information, see [AWS Weekly Roundup](https://aws.amazon.com/blogs/aws/aws-weekly-roundup-strands-agents-aws-transform-amazon-bedrock-guardrails-aws-codebuild-and-more-may-19-2025/) (AWS Blog).
+ **Foundation model selection** – Supports various foundation models including Anthropic Claude, Amazon Nova (Premier, Pro, Lite, and Micro) on Amazon Bedrock, and others to optimize for different autonomous reasoning capabilities. For more information, see [Amazon Bedrock](https://strandsagents.com/latest/user-guide/concepts/model-providers/amazon-bedrock/) in the Strands Agents documentation.
+ **LLM API integration** – Flexible integration with different LLM service interfaces including Amazon Bedrock, OpenAI, and others for production deployment. For more information, see [Amazon Bedrock Basic Usage](https://strandsagents.com/latest/user-guide/concepts/model-providers/amazon-bedrock/#basic-usage) in the Strands Agents documentation.
+ **Multimodal capabilities** – Support for multiple modalities including text, speech, and image processing for comprehensive autonomous agent interactions. For more information, see [Amazon Bedrock Multimodal Support](https://strandsagents.com/latest/user-guide/concepts/model-providers/amazon-bedrock/#multimodal-support) in the Strands Agents documentation.
+ **Tool ecosystem** – Rich set of tools for AWS service interaction, with extensibility for custom tools that expand autonomous capabilities. For more information, see [Tools Overview](https://strandsagents.com/0.1.x/user-guide/concepts/tools/tools_overview/) in the Strands Agents documentation.
## When to use Strands Agents
Strands Agents is particularly well-suited for autonomous agent scenarios including:
+ Organizations that build on AWS infrastructure who want native integration with AWS services for autonomous workflows
+ Teams that require enterprise-grade security, scalability, and compliance features for production autonomous systems
+ Projects that need flexibility in model selection across different providers for specialized autonomous tasks
+ Use cases that require tight integration with existing AWS workflows and resources for end-to- end autonomous processes
## Implementation approach for Strands Agents
Strands Agents provides a straightforward implementation approach for business stakeholders, as outlined in its [Quickstart Guide](https://strandsagents.com/0.1.x/user-guide/quickstart/). The framework allows organizations to:
+ Select foundation models like Amazon Nova (Premier, Pro, Lite, or Micro) on Amazon Bedrock based on specific business requirements.
+ Define custom tools that connect to enterprise systems and data sources.
+ Process multiple modalities including text, images, and speech.
+ Deploy agents that can autonomously respond to business queries and perform tasks.
This implementation approach enables business teams to rapidly develop and deploy autonomous agents without deep technical expertise in AI model development.
## Real-world example of Strands Agents
AWS Transform for .NET uses Strands Agents to power its application modernization capabilities, as described in [AWS Transform for .NET, the first agentic AI service for modernizing .NET applications at scale](https://aws.amazon.com/blogs/aws/aws-transform-for-net-the-first-agentic-ai-service-for-modernizing-net-applications-at-scale/) (AWS Blog). This production service employs multiple specialized autonomous agents. The agents work together to analyze legacy .NET applications, plan modernization strategies, and execute code transformations to cloud-native architectures without human intervention. [AWS Transform for .NET](https://aws.amazon.com/transform/net/) demonstrates the production readiness of Strands Agents for enterprise autonomous systems.
# LangChain and LangGraph
LangChain is one of the most established frameworks in the agentic AI ecosystem. LangGraph extends its capabilities to support complex, stateful agent workflows as described in the [LangChain Blog](https://blog.langchain.dev/how-to-think-about-agent-frameworks/). Together, they provide a comprehensive solution for building sophisticated autonomous AI agents with rich orchestration capabilities for independent operation.
## Key features of LangChain and LangGraph
LangChain and LangGraph include the following key features:
+ **Component ecosystem** – Extensive library of pre-built components for various autonomous agent capabilities, enabling rapid development of specialized agents. For more information, see [Quickstart](https://docs.langchain.com/oss/python/langchain/quickstart) in the LangChain documentation.
+ **Foundation model selection** – Support for diverse foundation models including Anthropic Claude, Amazon Nova models (Premier, Pro, Lite, and Micro) on Amazon Bedrock, and others for different reasoning capabilities. For more information, see [Inputs and outputs](https://python.langchain.com/docs/concepts/chat_models/#inputs-and-outputs) in the LangChain documentation.
+ **LLM API integration** – Standardized interfaces for multiple large language model (LLM) service providers including Amazon Bedrock, OpenAI, and others for flexible deployment. For more information, see [LLMs](https://python.langchain.com/docs/integrations/llms/) in the LangChain documentation.
+ **Multimodal processing** – Built-in support for text, image, and audio processing to enable rich multimodal autonomous agent interactions. For more information, see [Multimodality](https://python.langchain.com/docs/concepts/chat_models/#multimodality) in the LangChain documentation.
+ **Graph-based workflows** – LangGraph enables defining complex autonomous agent behaviors as state machines, supporting sophisticated decision logic. For more information, see the [LangGraph Platform GA](https://blog.langchain.dev/langgraph-platform-ga/) announcement.
+ **Memory abstractions** – Multiple options for short and long-term memory management, which is essential for autonomous agents that maintain context over time. For more information, see [How to add memory to chatbots](https://python.langchain.com/docs/how_to/chatbots_memory/) in the LangChain documentation.
+ **Tool integration** – Rich ecosystem of tool integrations across various services and APIs, extending autonomous agent capabilities. For more information, see [Tools](https://python.langchain.com/docs/how_to/#tools) in the LangChain documentation.
+ **LangGraph platform** – Managed deployment and monitoring solution for production environments, supporting long-running autonomous agents. For more information, see the [LangGraph Platform GA](https://blog.langchain.dev/langgraph-platform-ga/) announcement.
## When to use LangChain and LangGraph
LangChain and LangGraph are particularly well-suited for autonomous agent scenarios including:
+ Complex multi-step reasoning workflows that require sophisticated orchestration for autonomous decision-making
+ Projects that need access to a large ecosystem of prebuilt components and integrations for diverse autonomous capabilities
+ Teams with existing Python-based machine learning (ML) infrastructure and expertise that want to build autonomous systems
+ Use cases that require complex state management across long-running autonomous agent sessions
## Implementation approach for LangChain and LangGraph
LangChain and LangGraph provide a structured implementation approach for business stakeholders, as detailed in the [LangGraph documentation](https://python.langchain.com/docs/langgraph). The framework enables organizations to:
+ Define sophisticated workflow graphs that represent business processes.
+ Create multi-step reasoning patterns with decision points and conditional logic.
+ Integrate multimodal processing capabilities for handling diverse data types.
+ Implement quality control through built-in review and validation mechanisms.
This graph-based approach allows business teams to model complex decision processes as autonomous workflows. Teams have clear visibility into each step of the reasoning process and the ability to audit decision paths.
## Real-world example of LangChain and LangGraph
Vodafone has implemented autonomous agents using LangChain (and LangGraph) to enhance its data engineering and operations workflows, as detailed in their [LangChain Enterprise case study](https://blog.langchain.com/customers-vodafone/). They built internal AI assistants that autonomously monitor performance metrics, retrieve information from documentation systems, and present actionable insights—all through natural language interactions.
The Vodafone implementation uses LangChain modular document loaders, vector integration, and support for multiple LLMs (OpenAI, LLaMA 3, and Gemini) to rapidly prototype and benchmark these pipelines. They then used LangGraph to structure the multi-agent orchestration by deploying modular sub agents. These agents perform collection, processing, summarization, and reasoning tasks. LangGraph integrated these agents through APIs into their cloud systems.
# CrewAI
CrewAI is an open-source framework focused specifically on autonomous multi-agent orchestration, available on [GitHub](https://github.com/crewAIInc/crewAI). It provides a structured approach to creating teams of specialized autonomous agents that collaborate to solve complex tasks without human intervention. CrewAI emphasizes role-based coordination and task delegation.
## Key features of CrewAI
CrewAI provides the following key features:
+ **Role-based agent design** – Autonomous agents are defined with specific roles, goals, and back stories to enable specialized expertise. For more information, see [Crafting Effective Agents](https://docs.crewai.com/en/guides/agents/crafting-effective-agents) in the CrewAI documentation.
+ **Task delegation** – Built-in mechanisms for autonomously assigning tasks to appropriate agents based on their capabilities. For more information, see [Tasks](https://docs.crewai.com/en/concepts/tasks) in the CrewAI documentation.
+ **Agent collaboration** – Framework for autonomous inter-agent communication and knowledge sharing without human mediation. For more information, see [Collaboration](https://docs.crewai.com/en/concepts/collaboration) in the CrewAI documentation.
+ **Process management** – Structured workflows for sequential and parallel autonomous task execution. For more information, see [Processes](https://docs.crewai.com/en/concepts/processes) in the CrewAI documentation.
+ **Foundation model selection** – Support for various foundation models including Anthropic Claude, Amazon Nova models (Premier, Pro, Lite, and Micro) on Amazon Bedrock, and others to optimize for different autonomous reasoning tasks. For more information, see [LLMs](https://docs.crewai.com/en/concepts/llms) in the CrewAI documentation.
+ **LLM API integration** – Flexible integration with multiple LLM service interfaces including Amazon Bedrock, OpenAI, and local model deployments. For more information, see [Provider Configuration Examples ](https://docs.crewai.com/en/concepts/llms#provider-configuration-examples)in the CrewAI documentation.
+ **Multimodal support** – Emerging capabilities for handling text, image, and other modalities for comprehensive autonomous agent interactions. For more information, see [Using Multimodal Agents](https://docs.crewai.com/en/learn/multimodal-agents) in the CrewAI documentation.
## When to use CrewAI
CrewAI is particularly well-suited for autonomous agent scenarios including:
+ Complex problems that benefit from specialized, role-based expertise working autonomously
+ Projects that require explicit collaboration between multiple autonomous agents
+ Use cases where team-based problem decomposition improves autonomous problem-solving
+ Scenarios that require clear separation of concerns between different autonomous agent roles
## Implementation approach for CrewAI
CrewAI provides a role-based implementation of teams of AI agents approach for business stakeholders, as detailed in [Getting Started](https://github.com/crewAIInc/crewAI?tab=readme-ov-file#getting-started) in the CrewAI documentation. The framework enables organizations to:
+ Define specialized autonomous agents with specific roles, goals, and expertise areas.
+ Assign tasks to agents based on their specialized capabilities.
+ Establish clear dependencies between tasks to create structured workflows.
+ Orchestrate collaboration between multiple agents to solve complex problems.
This role-based approach mirrors human team structures, making it intuitive for business leaders to understand and implement. Organizations can create autonomous teams with specialized expertise areas that collaborate to achieve business objectives, similar to how human teams operate. However, the autonomous team can work continuously without human intervention.
## Real-world example of CrewAI
AWS has implemented autonomous multi-agent systems using CrewAI integrated with Amazon Bedrock, as detailed in the [CrewAI published case study](https://www.crewai.com/case-studies/aws-powers-bedrock-agents-with-crewai). AWS and CrewAI developed a secure, vendor‑neutral framework. The CrewAI open‑source "flows‑and‑crews" architecture seamlessly integrates with Amazon Bedrock foundation models, memory systems, and compliance guardrails.
Key elements of the implementation include:
+ **Blueprints and open sourcing** – AWS and CrewAI [released reference designs](https://aws.amazon.com/blogs/machine-learning/build-agentic-systems-with-crewai-and-amazon-bedrock/) that map CrewAI agents to Amazon Bedrock models and observability tools. They also released exemplar systems such as a multi‑agent AWS security audit crew, code modernization flows, and consumer packaged goods (CPG) back‑office automation.
+ **Observability stack integration** – The solution embeds monitoring with Amazon CloudWatch, AgentOps, and LangFuse, enabling traceability and debugging from proof‑of‑concept to production.
+ **Demonstrated return on investment (ROI)** – Early pilots showcase major improvements—70 percent faster execution for a large code modernization project and about 90 percent reduction in processing time for a CPG back‑office flow.
# AutoGen
[https://www.microsoft.com/en-us/research/project/autogen/](https://www.microsoft.com/en-us/research/project/autogen/) is an open-source framework that was released initially by Microsoft. AutoGen focuses on enabling conversational and collaborative autonomous AI agents. It provides a flexible architecture for building multi-agent systems with an emphasis on asynchronous, event-driven interactions between agents for complex autonomous workflows.
## Key features of AutoGen
AutoGen provides the following key features:
+ **Conversational agents** – Built around natural language conversations between autonomous agents, enabling sophisticated reasoning through dialogue. For more information, see [Multi-agent Conversation Framework](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat) in the AutoGen documentation.
+ **Asynchronous architecture** – Event-driven design for non-blocking autonomous agent interactions, supporting complex parallel workflows. For more information, see [Solving Multiple Tasks in a Sequence of Async Chats](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_multi_task_async_chats/) in the AutoGen documentation.
+ **Human-in-the-loop** – Strong support for optional human participation in otherwise autonomous agent workflows when needed. For more information, see [Allowing Human Feedback in Agents](https://microsoft.github.io/autogen/0.2/docs/tutorial/human-in-the-loop/) in the AutoGen documentation.
+ **Code generation and execution** – Specialized capabilities for code-focused autonomous agents that can write and run code. For more information, see [Code Execution](https://microsoft.github.io/autogen/stable/user-guide/core-user-guide/design-patterns/code-execution-groupchat.html) in the AutoGen documentation.
+ **Customizable behaviors** – Flexible autonomous agent configuration and conversation control for diverse use cases. For more information, see [agentchat.conversable\$1agent](https://microsoft.github.io/autogen/docs/reference/agentchat/conversable_agent) in the AutoGen documentation.
+ **Foundation model selection** – Support for various foundation models including Anthropic Claude, Amazon Nova models (Premier, Pro, Lite, and Micro) on Amazon Bedrock, and others for different autonomous reasoning capabilities. For more information, see [LLM Configuration](https://microsoft.github.io/autogen/docs/topics/llm_configuration) in the AutoGen documentation.
+ **LLM API integration** – Standardized configuration for multiple LLM service interfaces including Amazon Bedrock, OpenAI, and Azure OpenAI. For more information, see [oai.openai\$1utils](https://microsoft.github.io/autogen/docs/reference/oai/openai_utils) in the AutoGen API Reference.
+ **Multimodal processing** – Support for text and image processing to enable rich multimodal autonomous agent interactions. For more information, see [Engaging with Multimodal Models: GPT-4V in AutoGen](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_lmm_gpt-4v/) in the AutoGen documentation.
## When to use AutoGen
AutoGen is particularly well-suited for autonomous agent scenarios including:
+ Applications that require natural conversational flows between autonomous agents for complex reasoning
+ Projects that need both fully autonomous operation and optional human oversight capabilities
+ Use cases that involve autonomous code generation, execution, and debugging without human intervention
+ Scenarios that require flexible, asynchronous autonomous agent communication patterns
## Implementation approach for AutoGen
AutoGen provides a conversational implementation approach for business stakeholders, as detailed in [Getting Started](https://microsoft.github.io/autogen/docs/Getting-Started) in the AutoGen documentation. The framework enables organizations to:
+ Create autonomous agents that communicate through natural language conversations.
+ Implement asynchronous, event-driven interactions between multiple agents.
+ Combine fully autonomous operation with optional human oversight when needed.
+ Develop specialized agents for different business functions that collaborate through dialogue.
This conversational approach makes the autonomous system's reasoning transparent and accessible to business users. Decision-makers can observe the dialogue between agents to understand how conclusions are reached and optionally participate in the conversation when human judgment is required.
## Real-world example of AutoGen
Magentic-One is an open‑source, generalist multi‑agent system designed to autonomously solve complex, multi‑step tasks across diverse environments, as described in the [Microsoft AI Frontiers blog](https://www.microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks/). At its core is the Orchestrator agent, which decomposes high‑level goals and tracks progress by using structured ledgers. This agent delegates subtasks to specialized agents (such as WebSurfer, FileSurfer, Coder, and ComputerTerminal) and adapts dynamically by re‑planning when necessary.
The system is built on the AutoGen framework and is model‑agnostic, defaulting to GPT‑4o. It achieves state‑of‑the‑art performance across benchmarks like GAIA, AssistantBench, and WebArena—all without task‑specific tuning. Additionally, it supports modular extensibility and rigorous evaluation through AutoGenBench suggestions.
# LlamaIndex
[https://www.llamaindex.ai/](https://www.llamaindex.ai/) is a data framework designed specifically for connecting large language models (LLMs) with external data sources to enable sophisticated Retrieval Augmented Generation (RAG) and agentic AI applications. The framework provides abstractions and accelerated development workflows for agentic systems, custom orchestration patterns, and system integrations that reduce time-to-production for knowledge-driven AI solutions.
## Key features of LlamaIndex
LlamaIndex provides a comprehensive set of capabilities that makes it particularly well-suited for enterprise agentic AI applications:
+ **Data-centric architecture** – Excels at ingesting, indexing, and retrieving information from over 100 data formats including PDFs, Microsoft Word documents, spreadsheets, and more. The framework transforms enterprise data into queryable knowledge bases that are optimized for AI agents. For more information, see the [LlamaIndex documentation](https://developers.llamaindex.ai/).
+ **Production-ready deployment** – LlamaIndex offers both open-source frameworks and managed services through LlamaCloud, providing enterprise-grade features including security controls, scalability, observability integrations, and deployment flexibility. For more information, see the [LlamaIndex framework documentation](https://developers.llamaindex.ai/python/framework/).
+ **Advanced document processing** – LlamaCloud provides document parsing, extraction, indexing, and retrieval capabilities that handle complex layouts, nested tables, multi-modal content, and even handwritten notes. This sophisticated parsing enables agents to work effectively with real-world enterprise documents that contain charts, diagrams, and complex formatting. For more information, see the [LlamaCloud documentation](https://developers.llamaindex.ai/python/cloud/).
+ **Workflows orchestration** – LlamaAgents provides an event-driven, async-first orchestration engine for building multi-step agentic systems. Workflows support complex patterns including loops, parallel execution, conditional branching, and stateful resumption, making them ideal for sophisticated agent interactions. For more information, see the [LlamaIndex workflows documentation](https://developers.llamaindex.ai/python/framework/understanding/workflows/).
+ **Agentic retrieval capabilities** – Advanced retrieval modes including hybrid search, semantic search, and auto-routing that intelligently determine the best retrieval strategy for each query. The framework supports composite retrieval across multiple knowledge bases with reranking for enhanced accuracy. For more information, see the [LlamaIndex RAG documentation](https://developers.llamaindex.ai/python/framework/understanding/rag/).
+ **Observability and evaluation** – LlamaIndex integrates with a variety of observability and evaluation tools. This integration capability helps you to trace and debug your applications, evaluate their performance, and monitor costs. For more information, see the [Tracing and Debugging](https://developers.llamaindex.ai/python/framework/understanding/tracing_and_debugging/tracing_and_debugging/) and [Evaluating](https://developers.llamaindex.ai/python/framework/module_guides/evaluating) LlamaIndex documentation.
## When to use LlamaIndex
LlamaIndex is particularly well-suited for agentic AI scenarios that emphasize data-intensive workflows and knowledge management:
+ Document-heavy applications that require agents to process, analyze, and extract insights from large volumes of enterprise documents such as contracts, reports, manuals, and regulatory filings
+ Rapid prototyping to production scenarios where organizations want to quickly build and deploy document-centric agents without extensive infrastructure management overhead
+ RAG-first architectures that prioritize retrieval accuracy and context relevance, especially when working with complex, multi-modal documents containing tables, images, and structured data
+ Multi-agent document workflows that require specialized agents for different aspects of document processing, such as parsing, analysis, summarization, and compliance checking
## Implementation approach for LlamaIndex
LlamaIndex provides both low-level building blocks and high-level abstractions that accommodate different implementation approaches:
+ Rapid development of functional RAG applications in just a few lines of code by using LlamaIndex high-level APIs. This approach makes LlamaIndex accessible for business teams and developers who are new to agentic AI.
+ Enterprise integration through LlamaHub for popular enterprise systems including SharePoint, Amazon Simple Storage Service (Amazon S3), databases, and APIs. This approach enables seamless integration with existing data infrastructure.
+ Flexible deployment options between open-source self-hosted deployments for maximum control, or LlamaCloud managed services for reduced operational overhead and enterprise features.
+ Applications can start with simple query engines and progressively add agentic capabilities, multi-agent orchestration, and complex workflows as requirements evolve.
## Real-world example of LlamaIndex
This example focuses on a subsidiary of an aerospace company that specializes in aviation navigation and operations solutions. They need to address a growing challenge which involves piloting uncoordinated AI chatbot trials. The trials resulted in repeated work, long development cycles, compliance roadblocks, and isolated implementations across the organization.
They developed a unified agent framework, a reusable, template-based solution built on the LlamaIndex open-source framework that makes agent creation far more efficient. They compared several competing frameworks, both chain-oriented and graph-based. Ultimately, they selectedLlamaIndex for three critical advantages: its flexible design, modular components, and production-ready orchestration controls.
The platform reduces agent development and deployment time by 87% from 512 to 64 hours. This reduction was achieved by enabling teams to build agents with approximately 50 lines of code and a JSON configuration file. The teams leveraged a unified framework with built-in security, compliance, and privileged system access. For more details, see [LlamaIndexcustomer case studies](https://www.llamaindex.ai/customers).
# 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** |
| --- |--- |--- |--- |--- |--- |--- |--- |--- |
| 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 |
| LlamaIndex | Adequate | Adequate | Strong | Adequate | Strong | Strong | Platform or DIY | Moderate |
| 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](https://aws.amazon.com/blogs/aws/aws-weekly-roundup-strands-agents-aws-transform-amazon-bedrock-guardrails-aws-codebuild-and-more-may-19-2025/) (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](https://www.anthropic.com/engineering/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](https://strandsagents.com/latest/user-guide/concepts/model-providers/amazon-bedrock/#basic-usage) 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](https://python.langchain.com/docs/concepts/chat_models/#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.