AWS Bedrock AgentCore vs LlamaIndex: Which Agent Framework to Use?

AWS Bedrock AgentCore Bedrock AgentCore is AWS's managed runtime for production agents, launched in July 2025.

LlamaIndex LlamaIndex started as a RAG framework — connect your data, query it with an LLM.

Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.

Pick AWS Bedrock AgentCore if agentCore is for production AWS deployments where you want to skip the runtime, memory, identity, and observability work and pay AWS to do it instead. It is framework-agnostic — bring Strands, LangGraph, CrewAI, or your own. For non-AWS teams, prototypes, or anything where you want to see what the agent is doing, plain Python on Lambda or a container is simpler. AWS Bedrock AgentCore is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; LlamaIndex would force you to translate.

Pick LlamaIndex if llamaIndex adds genuine value when your agent needs to query structured or unstructured data as part of its reasoning — that's the index-as-tool pattern, and it's well-executed. But if you're building a general-purpose agent that doesn't need RAG, the agent framework is overhead. The plain Python version of the agent loop is the same 60 lines either way. LlamaIndex is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; AWS Bedrock AgentCore would force you to translate.

Both AWS Bedrock AgentCore and LlamaIndex pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.