Comparisons / Agno vs Rasa
Agno vs Rasa: Which Agent Framework to Use?
Agno (formerly Phidata) is a lightweight Python framework for building agents. 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
Agno
39.2k
5.2k
Python
Apache-2.0
2022-05-04
Agno (formerly Phidata)
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 | Agno | Rasa |
|---|---|---|
| Agent | `Agent(model=OpenAIChat(), instructions=[...])` class with `run()` method | Rasa agent with NLU pipeline, dialogue policies, and action server |
| Tools | Function tools via `@tool` decorator or built-in toolkits (web search, SQL, etc.) | Custom actions running on a separate action server via HTTP |
| Agent Loop | `Agent.run()` handles tool dispatch internally, configurable via `show_tool_calls` | — |
| Memory / Knowledge | Knowledge bases (PDF, URL, vector DB) injected via `knowledge` param + built-in memory | — |
| Multi-Agent (Teams) | `Team` class with `agents` list, `mode` (sequential, parallel, coordinate), and shared memory | — |
| Storage | `SqlAgentStorage`, `PostgresAgentStorage` for persisting sessions and state | — |
| 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 |
Agno vs Rasa, head to head
Agno Agno (formerly Phidata) is a lightweight Python framework for building agents.
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 Agno if
Pick Agno if agno adds value when you want a batteries-included agent with minimal boilerplate — especially for multi-modal agents or team orchestration. But each of its abstractions maps to a small piece of plain Python. If your agent is straightforward, writing it directly gives you full control with zero framework overhead. Agno 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; Agno would force you to translate.
What both add
Both Agno 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 Agno 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 →