Comparisons / Agno vs AutoGen
Agno vs AutoGen: Which Agent Framework to Use?
Agno (formerly Phidata) is a lightweight Python framework for building agents. AutoGen by Microsoft models agents as ConversableAgents that chat with each other. 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)
AutoGen
56.7k
8.5k
Python
CC-BY-4.0
2023-08-18
Microsoft Research
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Agno | AutoGen |
|---|---|---|
| Agent | `Agent(model=OpenAIChat(), instructions=[...])` class with `run()` method | `ConversableAgent` with `system_message`, `llm_config` |
| Tools | Function tools via `@tool` decorator or built-in toolkits (web search, SQL, etc.) | `register_for_llm()` and `register_for_execution()` |
| 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 | — |
| Conversation | — | Two-agent chat with `initiate_chat()`, message history |
| Multi-Agent | — | `GroupChat` with `GroupChatManager`, speaker selection |
| Nested Chats | — | `register_nested_chats()` for sub-task handling |
| Termination | — | `is_termination_msg` callback, `max_consecutive_auto_reply` |
Agno vs AutoGen, head to head
Agno Agno (formerly Phidata) is a lightweight Python framework for building agents.
AutoGen AutoGen by Microsoft models agents as ConversableAgents that chat with each other.
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; AutoGen would force you to translate.
Pick AutoGen if
Pick AutoGen if autoGen excels at complex multi-agent workflows where agents need to debate or collaborate. For single-agent use cases or simple tool-calling agents, the plain Python version is significantly simpler. AutoGen 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 AutoGen 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 AutoGen 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 →