Comparisons / LlamaIndex vs Mastra
LlamaIndex vs Mastra: Which Agent Framework to Use?
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. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
LlamaIndex
48.3k
7.2k
Python
MIT
2022-11-02
Jerry Liu
Mastra
22.7k
1.8k
TypeScript
MIT
2024-08-06
Mastra AI
244.0k
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | LlamaIndex | Mastra |
|---|---|---|
| Agent | `AgentRunner` with `AgentWorker`, or `ReActAgent` for tool-calling agents | `new Agent({ model, instructions, tools })` with automatic tool dispatch |
| Tools | `FunctionTool` for custom tools, `QueryEngineTool` to query an index as a tool | `createTool({ name, schema, execute })` with Zod validation |
| Agent Loop | `AgentRunner.chat()` manages step-by-step execution via `AgentWorker` tasks | — |
| RAG Integration | `VectorStoreIndex` + `QueryEngineTool` — the agent can query your data as a tool call | — |
| Memory | `ChatMemoryBuffer` with token limit, or custom memory modules | Short-term thread memory + long-term vector memory across sessions |
| Orchestration | `AgentRunner` step API for custom control flow, or multi-agent pipelines | — |
| Workflows | — | `Workflow` class with `.step()`, `.then()`, `.branch()` for orchestration |
| RAG | — | Built-in document syncing, chunking, embedding, and vector search |
| Studio | — | Mastra Studio: local GUI for testing agents, viewing traces, debugging |
LlamaIndex vs Mastra, head to head
LlamaIndex LlamaIndex started as a RAG framework — connect your data, query it with an LLM.
Mastra Mastra is a TypeScript-first framework for building AI agents, from the team behind Gatsby.
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 LlamaIndex if
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; Mastra would force you to translate.
Pick Mastra if
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. Mastra 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.
What both add
Both LlamaIndex and Mastra 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.
Or build your own in 60 lines
Both LlamaIndex and Mastra implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →