openai-agents-python vs haystack
Two of the top projects in Agent frameworks and SDKs, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
openai-agents-python
Python SDK for multi-agent workflows with handoffs, guardrails and tracing
haystack
Python framework for building RAG pipelines and agents from modular components
Verdict
openai-agents-python ranks higher, 92 to 89. Of the 4 score parts we measure for both, openai-agents-python leads on 2, haystack on none and 2 are even.
- Adoptionopenai-agents-python leads by 4 points, 86 to 82: close to even.
- FreshnessEven at 100.
- Maintenanceopenai-agents-python leads by 4 points, 97 to 93: close to even.
- Agent-readyEven at 70.
Pick openai-agents-python if you are
Python developers building tool-using or multi-agent LLM apps
- Small API: Agent and Runner cover a text agent in a few lines
- Tools can be Python functions, MCP servers or hosted tools
Pick haystack if you are
Python developers building production RAG systems and agents
- Pipelines support loops, branches and conditional routing, so context flow is explicit and traceable
- Agent hooks (before_llm, before_tool, on_exit) plus step_count and token_usage tracking
| What we compare | openai-agents-python | haystack |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 86, widely used | 82, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 97, healthy | 93, healthy |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 30.6k | 27k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Oct 2026 | Nov 2026 |
| Language | Python | Python |
| Docker | No | Yes |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
openai-agents-python
Python library from OpenAI for building agents with instructions, tools, guardrails and handoffs between agents. It supports the OpenAI Responses and Chat Completions APIs and, per the README, 100+ other LLMs. It also includes sandbox agents, realtime and voice agents, sessions for conversation history, human-in-the-loop controls and built-in tracing.
Who it is for: Python developers building tool-using or multi-agent LLM apps
Strengths
- Small API: Agent and Runner cover a text agent in a few lines
- Tools can be Python functions, MCP servers or hosted tools
- Built-in tracing, guardrails, sessions and human-in-the-loop support
- Includes sandbox, realtime and voice agents; MIT license, Python 3.10+
Weaknesses
- Examples assume OPENAI_API_KEY; non-OpenAI models go through optional LiteLLM or any-llm
- Pull requests limited to repository collaborators
- Python only; JS/TS version is a separate repository
- Realtime agents use gpt-realtime-2.1, an OpenAI-hosted model
- no GPU
- Needs OpenAI API key, Python 3.10+, optional: Redis, optional: SQLAlchemy, optional: websockets, optional: LiteLLM or any-llm, optional: Docker
- Models: OpenAI Responses API, OpenAI Chat Completions API, 100+ other LLMs via LiteLLM or any-llm
haystack
Haystack is a Python framework for composing LLM applications as pipelines of components covering retrieval, routing, memory, tool calling and generation. One Pipeline runs sync or async and streams tokens, and the Agent class supports lifecycle hooks and concurrent tool calls. It integrates with OpenAI, Anthropic, Mistral, Cohere, Hugging Face, Google, Azure OpenAI, AWS Bedrock and local models.
Who it is for: Python developers building production RAG systems and agents
Strengths
- Pipelines support loops, branches and conditional routing, so context flow is explicit and traceable
- Agent hooks (before_llm, before_tool, on_exit) plus step_count and token_usage tracking
- Model-agnostic: many hosted providers and local models, swappable without rewriting pipelines
- Installs with pip install haystack-ai; Apache-2.0 licensed
Weaknesses
- Collects anonymous component-usage telemetry by default; opt-out is documented separately
- Serving as REST or MCP needs the separate Hayhooks project
- Governance, access controls and managed observability are in the paid Enterprise Platform
- Many integrations live in a separate haystack-core-integrations repo
- GPU optional
- Docker
- Needs Python, LLM provider or local model
- Models: OpenAI, Anthropic, Mistral, Cohere, Hugging Face
More in Agent frameworks and SDKs
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.