Comparisons / LlamaIndex vs Vercel AI SDK
LlamaIndex vs Vercel AI SDK: Which Agent Framework to Use?
LlamaIndex started as a RAG framework — connect your data, query it with an LLM. The Vercel AI SDK is a TypeScript-first toolkit for building LLM apps. 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
Vercel AI SDK
16.8k
2.7k
TypeScript
Apache-2.0
2023-06-13
Vercel
Vercel (public)
2.4M
Works on any host; tightly integrated with Vercel deploy + AI Gateway
Yes
Used by: v0.dev, Cursor, Sourcegraph
github.com/vercel/ai→GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | LlamaIndex | Vercel AI SDK |
|---|---|---|
| Agent | `AgentRunner` with `AgentWorker`, or `ReActAgent` for tool-calling agents | `generateText({ model, tools, maxSteps })` runs the loop and returns final text |
| Tools | `FunctionTool` for custom tools, `QueryEngineTool` to query an index as a tool | `tool({ description, parameters: z.object(...), execute })` |
| 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 | — |
| Orchestration | `AgentRunner` step API for custom control flow, or multi-agent pipelines | — |
| Streaming | — | `streamText` returns a `ReadableStream` of deltas with built-in parsing |
| Structured output | — | `generateObject({ schema })` returns parsed/validated objects |
| UI hook | — | `useChat()` returns `{ messages, input, handleSubmit, isLoading }` |
| Provider swap | — | Change one import: `openai('gpt-4o')` → `anthropic('claude-3-5-sonnet')` |
LlamaIndex vs Vercel AI SDK, head to head
LlamaIndex LlamaIndex started as a RAG framework — connect your data, query it with an LLM.
Vercel AI SDK The Vercel AI SDK is a TypeScript-first toolkit for building LLM apps.
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; Vercel AI SDK would force you to translate.
Pick Vercel AI SDK if
Pick Vercel AI SDK if vercel AI SDK is the right pick for TypeScript apps where the LLM is one piece of a bigger React app — you get streaming primitives, provider-portable tool calling, and useChat hooks all in one package. For a server-side agent or a learning exercise, the plain fetch version is simpler and shows you what's happening on the wire. Vercel AI SDK 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 Vercel AI SDK 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 Vercel AI SDK 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 →