Comparisons / Haystack vs Rasa
Haystack vs Rasa: Which Agent Framework to Use?
Haystack by deepset is a framework for building NLP and LLM pipelines. 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
Haystack
24.7k
2.7k
Python
Apache-2.0
2019-11-14
deepset
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 | Haystack | Rasa |
|---|---|---|
| Agent | `Agent` component with `ChatGenerator`, tool definitions, and message routing | Rasa agent with NLU pipeline, dialogue policies, and action server |
| Tools | `Tool` dataclass with function reference, name, description, parameters schema | Custom actions running on a separate action server via HTTP |
| 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 | — |
| 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 |
Haystack vs Rasa, head to head
Haystack Haystack by deepset is a framework for building NLP and LLM pipelines.
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 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; 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; Haystack would force you to translate.
What both add
Both Haystack 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 Haystack 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 →