Comparisons / Agno vs AutoGen

Agno vs AutoGen: Which Agent Framework to Use?

Agno vs AutoGen, head to head

Agno and AutoGen both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.

Agno (formerly Phidata) is a lightweight Python framework for building agents.

AutoGen by Microsoft models agents as ConversableAgents that chat with each other.

Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.

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. The tradeoffs in its intro should match how your team already thinks about agents; AutoGen will feel like translation if they don't.

Full Agnocomparison →

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. The tradeoffs in its intro should match how your team already thinks about agents; Agno will feel like translation if they don't.

Full AutoGencomparison →

What both add

Whichever you pick, you're inheriting a dependency tree and a vocabulary your team has to learn before they ship anything. Agno has its own class hierarchy and tool registration conventions; AutoGen has its. Either way, when something misbehaves you'll be reading framework source before you reach the actual HTTP call.

If the real workload is one model and a handful of tools, both can feel like a workbench for driving a nail. The lesson below builds the same pattern in plain Python — useful as a comparison point even if you ultimately keep the framework.

By the numbers

By the numbers

Agno

GitHub Stars

39.2k

Forks

5.2k

Language

Python

License

Apache-2.0

Created

2022-05-04

Created by

Agno (formerly Phidata)

github.com/agno-agi/agno

AutoGen

GitHub Stars

56.7k

Forks

8.5k

Language

Python

License

CC-BY-4.0

Created

2023-08-18

Created by

Microsoft Research

github.com/microsoft/autogen

GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.

ConceptAgnoAutoGen
Agent`Agent(model=OpenAIChat(), instructions=[...])` class with `run()` method`ConversableAgent` with `system_message`, `llm_config`
ToolsFunction 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 / KnowledgeKnowledge 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
ConversationTwo-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`

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 →