Comparisons / Haystack vs OpenAI Agents SDK
Haystack vs OpenAI Agents SDK: Which Agent Framework to Use?
Haystack vs OpenAI Agents SDK, head to head
Haystack and OpenAI Agents SDK 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.
Haystack by deepset is a framework for building NLP and LLM pipelines.
OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails.
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 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. The tradeoffs in its intro should match how your team already thinks about agents; OpenAI Agents SDK will feel like translation if they don't.
Pick OpenAI Agents SDK if
Pick OpenAI Agents SDK if the Agents SDK is the thinnest framework on this list — it barely abstracts beyond what you'd write yourself. Use it when you want OpenAI's conventions and auto-schema generation. Skip it when you want full control or use non-OpenAI models. The tradeoffs in its intro should match how your team already thinks about agents; Haystack will feel like translation if they don't.
By the numbers
By the numbers
Haystack
24.7k
2.7k
Python
Apache-2.0
2019-11-14
deepset
OpenAI Agents SDK
20.6k
3.4k
Python
MIT
2025-03-11
OpenAI
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Haystack | OpenAI Agents SDK |
|---|---|---|
| Agent | `Agent` component with `ChatGenerator`, tool definitions, and message routing | `Agent(name, instructions, model, tools)` |
| Tools | `Tool` dataclass with function reference, name, description, parameters schema | Python functions with type hints, auto-converted to schemas |
| 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 | — |
| Agent Loop | — | `Runner.run()` handles the loop internally |
| Handoffs | — | `Handoff` between `Agent` objects for multi-agent routing |
| Guardrails | — | `InputGuardrail` and `OutputGuardrail` with tripwire pattern |
| Context | — | Typed context object passed through the agent lifecycle |
Or build your own in 60 lines
Both Haystack and OpenAI Agents 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 →