Comparisons / Agno vs n8n AI
Agno vs n8n AI: Which Agent Framework to Use?
Agno (formerly Phidata) is a lightweight Python framework for building agents. n8n is a workflow automation platform that added AI agent capabilities with native LangChain integration. 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)
n8n AI
182.4k
56.5k
TypeScript
Sustainable Use License
2019-06-22
Jan Oberhauser
71.8k
n8n Cloud
Yes
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Agno | n8n AI |
|---|---|---|
| Agent | `Agent(model=OpenAIChat(), instructions=[...])` class with `run()` method | AI Agent node with model, tools, and memory connected via canvas wires |
| Tools | Function tools via `@tool` decorator or built-in toolkits (web search, SQL, etc.) | Tool nodes (HTTP Request, Code, database) wired into the agent node |
| Agent Loop | `Agent.run()` handles tool dispatch internally, configurable via `show_tool_calls` | Agent node internally loops: call LLM → detect tool use → run tool → repeat |
| 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 | — |
| Memory | — | Memory node (window buffer, vector store) connected to agent node |
| Integrations | — | 500+ pre-built nodes for Slack, Gmail, Notion, databases, APIs |
| Orchestration | — | Visual workflow canvas with triggers, conditionals, and parallel branches |
Agno vs n8n AI, head to head
Agno Agno (formerly Phidata) is a lightweight Python framework for building agents.
n8n AI n8n is a workflow automation platform that added AI agent capabilities with native LangChain integration.
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; n8n AI would force you to translate.
Pick n8n AI if
Pick n8n AI if n8n AI is the right choice when your team builds automations visually, needs 500+ integrations out of the box, and wants to self-host. But the AI agent logic inside each node is the same loop you would write in Python — the value is in the integration catalog and visual builder, not the agent pattern. n8n AI 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 n8n AI 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 n8n AI 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 →