LlamaIndex vs Mastra: Which Agent Framework to Use?

LlamaIndex and Mastra both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.

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

Mastra is a TypeScript-first framework for building AI agents, from the team behind Gatsby.

Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.

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. The tradeoffs in its intro should match how your team already thinks about agents; Mastra will feel like translation if they don't.

Pick Mastra if mastra is the best option for TypeScript teams that want a batteries-included agent framework without leaving the Node.js ecosystem. The workflow engine and Studio are genuinely productive. For simple agents or Python teams, the plain approach avoids an unnecessary dependency. The tradeoffs in its intro should match how your team already thinks about agents; LlamaIndex will feel like translation if they don't.

Whichever you pick, you're inheriting a dependency tree and a vocabulary your team has to learn before they ship anything. LlamaIndex has its own class hierarchy and tool registration conventions; Mastra has its. Either way, when something misbehaves you'll be reading framework source before you reach the actual HTTP call.

If the real workload is one model and a handful of tools, both can feel like a workbench for driving a nail. The lesson below builds the same pattern in plain Python — useful as a comparison point even if you ultimately keep the framework.