Comparisons / OpenAI Agents SDK vs Rasa
OpenAI Agents SDK vs Rasa: Which Agent Framework to Use?
OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails. Rasa is an open-source framework for building conversational AI — chatbots and virtual assistants. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
OpenAI Agents SDK
20.6k
3.4k
Python
MIT
2025-03-11
OpenAI
Rasa
21.1k
4.9k
Python
Apache-2.0
2016-10-14
Rasa Technologies
Rasa Pro / Rasa Cloud
Yes
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | OpenAI Agents SDK | Rasa |
|---|---|---|
| Agent | `Agent(name, instructions, model, tools)` | Rasa agent with NLU pipeline, dialogue policies, and action server |
| Tools | Python functions with type hints, auto-converted to schemas | Custom actions running on a separate action server via HTTP |
| 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 | — |
| NLU | — | NLU pipeline: tokenizer, featurizer, intent classifier, entity extractor |
| Dialogue | — | Stories/Rules YAML + dialogue policies for conversation flow |
| Slots | — | Typed slots for tracking entities and state across turns |
| CALM | — | LLM for understanding + deterministic `Flows` for business logic |
OpenAI Agents SDK vs Rasa, head to head
OpenAI Agents SDK OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails.
Rasa Rasa is an open-source framework for building conversational AI — chatbots and virtual assistants.
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 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. OpenAI Agents SDK is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Rasa would force you to translate.
Pick Rasa if
Pick Rasa if rasa is purpose-built for production conversational AI with enterprise requirements — on-premise deployment, regulatory compliance, deterministic business logic. For general-purpose agents or simple chatbots, an LLM with a system prompt and a few tools is faster to build and more flexible. Rasa is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; OpenAI Agents SDK would force you to translate.
What both add
Both OpenAI Agents SDK and Rasa 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 OpenAI Agents SDK and Rasa 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 →