Comparisons / Haystack vs Smolagents
Haystack vs Smolagents: Which Agent Framework to Use?
Haystack by deepset is a framework for building NLP and LLM pipelines. 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
Haystack
24.7k
2.7k
Python
Apache-2.0
2019-11-14
deepset
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 | Haystack | Smolagents |
|---|---|---|
| Agent | `Agent` component with `ChatGenerator`, tool definitions, and message routing | `CodeAgent` or `ToolCallingAgent` with model and tools list |
| Tools | `Tool` dataclass with function reference, name, description, parameters schema | `@tool` decorator or `Tool` class with name, description, and callable |
| Pipeline Architecture | `Pipeline()` with `add_component()` and `connect()` — a directed graph of typed components | — |
| RAG / Retrieval | `DocumentStore` + `Retriever` + `PromptBuilder` + `Generator` wired in a `Pipeline` | — |
| Memory | `ChatMessageStore` with `ConversationMemory` component in pipeline | — |
| Deployment | Pipeline YAML serialization, `Hayhooks` REST server | — |
| Code Actions | — | `CodeAgent` writes Python code as its action, executed in sandbox |
| Sandbox | — | E2B, Docker, Modal, or Pyodide sandbox for safe code execution |
| Agent Loop | — | Internal loop: think (LLM reasons), act (code/tool call), observe (result) |
| Model Support | — | HuggingFace Hub models, OpenAI, Anthropic, local via LiteLLM |
Haystack vs Smolagents, head to head
Haystack Haystack by deepset is a framework for building NLP and LLM pipelines.
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 Haystack if
Pick Haystack if haystack earns its complexity when you're building RAG pipelines with multiple retrieval stages, document processing, and production deployment needs. But for straightforward agents with a few tools, the plain Python version is simpler to write and debug. Haystack 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; Haystack would force you to translate.
What both add
Both Haystack 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 Haystack 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 →