AI agents are a new class of software applications that use generative AI models to reason, plan, act, learn, and adapt in pursuit of goals set by users. By following this steps in a loop, they can act with limited human oversight.
When I first started building AI agents, I faced a common challenge: how do I move from a working prototype to a production-ready system that can scale, maintain context across conversations, and integrate seamlessly with enterprise infrastructure? That's the reason why Amazon Bedrock AgentCore was built: to provide a comprehensive set of enterprise-grade services that help developers quickly and securely deploy and operate AI agents at scale, regardless of which framework or model I choose to build with.
In this blog series, I'm sharing my implementation journey across multiple AI agent frameworks, all unified through AgentCore's powerful infrastructure. I'll demonstrate how different frameworks can leverage the same production-grade deployment and memory management capabilities that AgentCore provides, giving you the flexibility to choose the right tool for each use case while maintaining consistent operational practices.
All the code examples and complete implementations for this series are available on GitHub at agentcore-multi-framework-examples.
Why Multiple Frameworks with One Infrastructure?
Each AI agent framework brings its own strengths to the table. Some excel at multi-agent collaboration, others at type safety, and some at document processing or complex workflows. Rather than being locked into a single approach, I wanted the flexibility to choose the right tool for each use case while maintaining consistent deployment, memory management, and operational practices.
This is where AgentCore shines. It's both framework-agnostic and model-agnostic, providing infrastructure and tools that works with any agent implementation. Whether I'm building with Strands Agents' clean, hook-based architecture or LangGraph's sophisticated graph workflows, AgentCore handles the heavy lifting of session isolation, memory persistence, and secure scaling.
The current approach focuses two key AgentCore services:
AgentCore Runtime: A secure, serverless runtime that provides complete session isolation, ensuring each user's data remains private and protected. It supports any agent framework and handles both real-time interactions and long-running tasks up to 8 hours.
AgentCore Memory: A fully managed memory service that enables agents to maintain both short-term conversation context and long-term knowledge across sessions. It provides built-in strategies for extracting user preferences, semantic facts, and session summaries.
I am planning to provide future updates to show in a simlar way how to use other AgentCore services, such as Identity and Gateways, with multiple frameworks.
The Frameworks in This Series
Throughout this series, I'll demonstrate how to build production-ready agents with five popular frameworks, each bringing unique capabilities to the table:
1. Strands Agents
Strands Agents is an SDK for building AI agents with a focus on simplicity and production readiness. It provides a clean, intuitive API for creating agents with built-in support for tools and various LLM providers. Strands Agents emphasizes developer experience with minimal boilerplate code, making it easy to build and deploy sophisticated AI agents that can handle complex tasks while maintaining clean, maintainable code.
For more info:
2. CrewAI
CrewAI is a multi-agent AI framework that enables the creation of collaborative AI agents that work together to solve complex tasks. It provides a structured approach to building AI crews where specialized agents can be assigned specific roles, collaborate through shared memory, and execute tasks in parallel or sequentially. CrewAI simplifies the orchestration of multiple AI agents, making it easy to build sophisticated AI systems that can handle complex workflows and decision-making processes.
For more info:
3. Pydantic AI
Pydantic AI is a Python framework for building AI agents that leverages Pydantic type validation and serialization capabilities. It provides a structured approach to creating AI agents with strong typing, automatic validation, and seamless integration with various LLM providers. PydanticAI enables developers to build reliable, type-safe AI applications with clear data contracts and robust error handling, making it ideal for production-ready AI agent development.
For more info:
4. LlamaIndex
LlamaIndex is a data framework for LLM applications that provides tools for ingesting, structuring, and accessing private or domain-specific data. It enables developers to build RAG (Retrieval-Augmented Generation) applications by creating indexes from various data sources and providing query interfaces for LLMs. LlamaIndex simplifies the process of connecting LLMs to external data, making it easy to build intelligent applications that can reason over private documents, databases, and APIs.
5. LangGraph
LangGraph is a library for building stateful, multi-actor applications with LLMs form the same team as LangChain. It extends LangChain's core concepts by providing a graph-based approach to agent workflows, where nodes represent functions or agents and edges define the flow of execution. LangGraph enables the creation of complex, stateful AI applications with cycles, conditional logic, and human-in-the-loop interactions, making it ideal for building sophisticated agent systems that can handle multi-step reasoning and dynamic decision-making.
Unified Architecture, Diverse Capabilities
What makes this approach powerful is that all five frameworks share the same underlying infrastructure and memory management. I've designed a common memory module that's identical across all implementations, ensuring:
- Consistency: All agents interact with memory in the same way
- Portability: Memory created by one framework can be used by another
- Maintainability: Bug fixes and improvements benefit all implementations
- Simplicity: New frameworks can be added without reimplementing core logic
This means you can build a document processing pipeline with LlamaIndex, a multi-agent customer service system with CrewAI, and a type-safe API integration with Pydantic AI—all while using the same deployment infrastructure, memory management, and operational tools.
What You'll Learn
In each article of this series, I'll walk through:
- Framework Integration: How to integrate each framework with AgentCore Runtime for secure, scalable deployment
- Memory Management: Implementing both short-term conversation context and long-term memory extraction
- Local Development: Testing and iterating on your agent locally before deployment
- Production Deployment: Using the AgentCore Starter Toolkit to deploy to AWS with a single command
- Best Practices: Framework-specific patterns and optimizations for production use
Get Started
The complete code for all implementations is available on GitHub. Each project includes:
- Complete working code with the framework integrated with AgentCore
- Detailed README with step-by-step deployment instructions
- Shared memory management module used across all frameworks
- Configuration scripts for setting up AgentCore Memory
Whether you're already using one of these frameworks or exploring options for your next AI agent project, this series will show you how to leverage Amazon Bedrock AgentCore to move from prototype to production with confidence.
Coming Up Next
In the next article, I'll dive deep into our baseline implementation using Strands Agents. You'll learn how to build a production-ready agent with persistent memory, integrate it with AgentCore Runtime, and deploy it to AWS—all while maintaining clean, maintainable code.
Stay tuned as we explore the exciting world of production AI agents across multiple frameworks!
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