Comparisons / Agno vs Google ADK
Agno vs Google ADK: Which Agent Framework to Use?
Agno (formerly Phidata) is a lightweight Python framework for building agents. Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems. 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)
Google ADK
18.7k
3.2k
Python
Apache-2.0
2025-04-01
Google/Alphabet
Vertex AI
Yes
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Agno | Google ADK |
|---|---|---|
| Agent | `Agent(model=OpenAIChat(), instructions=[...])` class with `run()` method | `LlmAgent` class with model, instructions, and `sub_agents` list |
| Tools | Function tools via `@tool` decorator or built-in toolkits (web search, SQL, etc.) | `FunctionTool`, built-in tools (Search, Code Exec), third-party integrations |
| Agent Loop | `Agent.run()` handles tool dispatch internally, configurable via `show_tool_calls` | `Runner.run()` with automatic tool dispatch and sub-agent delegation |
| 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 | — |
| Multi-Agent | — | Hierarchical agent tree with root agent delegating to specialized sub-agents |
| Workflows | — | `SequentialAgent`, `ParallelAgent`, `LoopAgent` workflow primitives |
| Session | — | Session and State service with typed channels and persistence |
Agno vs Google ADK, head to head
Agno Agno (formerly Phidata) is a lightweight Python framework for building agents.
Google ADK Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems.
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; Google ADK would force you to translate.
Pick Google ADK if
Pick Google ADK if aDK earns its complexity when you need multi-agent orchestration on Google Cloud with Vertex AI deployment. If you're using Gemini and need production-grade agent infrastructure, it's well-designed. For single-agent use cases or non-Google stacks, plain Python keeps things simpler. Google ADK 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 Google ADK 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 Google ADK 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 →