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.

Full Haystackcomparison →

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.

Full OpenAI Agents SDKcomparison →

What both add

Whichever you pick, you're inheriting a dependency tree and a vocabulary your team has to learn before they ship anything. Haystack has its own class hierarchy and tool registration conventions; OpenAI Agents SDK has its. Either way, when something misbehaves you'll be reading framework source before you reach the actual HTTP call.

If the real workload is one model and a handful of tools, both can feel like a workbench for driving a nail. The lesson below builds the same pattern in plain Python — useful as a comparison point even if you ultimately keep the framework.

By the numbers

By the numbers

Haystack

GitHub Stars

24.7k

Forks

2.7k

Language

Python

License

Apache-2.0

Created

2019-11-14

Created by

deepset

github.com/deepset-ai/haystack

OpenAI Agents SDK

GitHub Stars

20.6k

Forks

3.4k

Language

Python

License

MIT

Created

2025-03-11

Created by

OpenAI

github.com/openai/openai-agents-python

GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.

ConceptHaystackOpenAI 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 schemaPython 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
DeploymentPipeline 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
ContextTyped 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 →