Comparisons / LlamaIndex vs Smolagents
LlamaIndex vs Smolagents: Which Agent Framework to Use?
LlamaIndex started as a RAG framework — connect your data, query it with an LLM. Smolagents is HuggingFace's minimalist agent library. 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
Smolagents
26.4k
2.4k
Python
Apache-2.0
2024-12-05
Hugging Face
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | LlamaIndex | Smolagents |
|---|---|---|
| Agent | `AgentRunner` with `AgentWorker`, or `ReActAgent` for tool-calling agents | `CodeAgent` or `ToolCallingAgent` with model and tools list |
| Tools | `FunctionTool` for custom tools, `QueryEngineTool` to query an index as a tool | `@tool` decorator or `Tool` class with name, description, and callable |
| Agent Loop | `AgentRunner.chat()` manages step-by-step execution via `AgentWorker` tasks | Internal loop: think (LLM reasons), act (code/tool call), observe (result) |
| 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 | — |
| Code Actions | — | `CodeAgent` writes Python code as its action, executed in sandbox |
| Sandbox | — | E2B, Docker, Modal, or Pyodide sandbox for safe code execution |
| Model Support | — | HuggingFace Hub models, OpenAI, Anthropic, local via LiteLLM |
LlamaIndex vs Smolagents, head to head
LlamaIndex LlamaIndex started as a RAG framework — connect your data, query it with an LLM.
Smolagents Smolagents is HuggingFace's minimalist agent library.
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; Smolagents would force you to translate.
Pick Smolagents if
Pick Smolagents if smolagents lives up to its name — it's genuinely minimal and the code-agent approach is a real innovation that reduces LLM calls by ~30%. If you want a lightweight agent library with HuggingFace ecosystem access, it's excellent. For understanding the fundamentals, the plain version is even simpler. Smolagents 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 Smolagents 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 Smolagents 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 →