Comparisons / Agno
Agno (Phidata) vs Building from Scratch
Agno (formerly Phidata) is a lightweight Python framework for building agents. It gives you an Agent class with model, tools, instructions, knowledge, and storage — plus team-based multi-agent orchestration. It's closer to plain Python than LangChain, but still wraps patterns you can write yourself. Here's what each piece actually does.
| Concept | Agno | Plain Python |
|---|---|---|
| Agent | Agent(model=OpenAIChat(), instructions=[...]) class with run() method | A function that POSTs to /chat/completions and returns the response |
| Tools | Function tools via @tool decorator or built-in toolkits (web search, SQL, etc.) | A dict of callables: tools = {"search": search_web, "sql": run_query} |
| Agent Loop | Agent.run() handles tool dispatch internally, configurable via show_tool_calls | A while loop: call LLM, check for tool_calls, execute, repeat |
| Memory / Knowledge | Knowledge bases (PDF, URL, vector DB) injected via knowledge param + built-in memory | A list of relevant chunks injected into the system prompt via a retrieval function |
| Multi-Agent (Teams) | Team class with agents list, mode (sequential, parallel, coordinate), and shared memory | A function that calls agent functions in sequence or parallel, passing results between them |
| Storage | SqlAgentStorage, PostgresAgentStorage for persisting sessions and state | json.dump() / json.load() to a file, or a simple DB insert |
The verdict
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.
What Agno does
Agno gives you a single Agent class that wires together an LLM, tools, instructions, knowledge bases, and storage. You configure an agent declaratively — pass in a model, a list of tools, and optional knowledge sources — and call agent.run(). It handles the tool-calling loop, injects knowledge into context, and persists conversation state. Agno also supports multi-modal agents (vision, audio) and team-based orchestration where multiple agents coordinate on tasks. The framework ships with built-in toolkits for common tasks: web search, SQL queries, file operations. Compared to LangChain, it's lighter — fewer abstractions, less indirection. The tradeoff is a smaller ecosystem and fewer third-party integrations.
The plain Python equivalent
Every Agno abstraction maps to plain Python. The Agent class is a function that POSTs to the LLM API, checks for tool_calls, dispatches them from a dict, and loops. Knowledge bases are a retrieval function that fetches relevant chunks and injects them into the system prompt. Memory is a messages list. Storage is json.dump(). Teams are a function that calls multiple agent functions and combines their outputs. The entire agent — with tools, knowledge retrieval, memory, and multi-agent coordination — fits in about 60 lines. No base classes, no decorators. When something breaks, you debug your function, not a framework's internals.
When to use Agno
Agno earns its place when you want a lightweight agent framework that stays close to Python without the weight of LangChain. Its multi-modal support (vision, audio) is genuinely useful if you're building agents that process images or audio. Team-based orchestration saves time when you need multiple agents coordinating — sequential pipelines, parallel fan-out, or coordinator patterns. The built-in toolkits (web search, SQL, file I/O) eliminate boilerplate for common tasks. If you want structure without heavy abstraction, Agno is a reasonable middle ground between raw Python and a full framework.
When plain Python is enough
If your agent calls one LLM, uses a few custom tools, and doesn't need multi-modal input or team orchestration — plain Python is simpler. You don't need an Agent class to write a function. You don't need SqlAgentStorage to call json.dump(). You don't need a Team to call two functions in sequence. Agno is lightweight, but it's still a dependency that sits between you and the API. Start with the plain version. If you find yourself reimplementing Agno's patterns (especially multi-agent coordination or knowledge base management), then consider adopting it. Don't add a framework to avoid writing a while loop.
Frequently asked questions
What is Agno and how does it relate to Phidata?
Agno is the new name for Phidata, a lightweight Python agent framework. It provides an Agent class with built-in support for tools, knowledge bases, memory, storage, and multi-agent teams. The rename happened in early 2025 — the core API stayed the same.
How does Agno compare to LangChain?
Agno is significantly lighter than LangChain — fewer abstractions, less indirection, closer to plain Python. LangChain has a larger ecosystem of integrations and LangSmith for observability. Agno is better for straightforward agents; LangChain is better when you need many third-party integrations working together.
Is Agno better than Phidata?
Agno is Phidata — it's the same project under a new name. If you're already using Phidata, upgrading to Agno gets you the latest features (improved multi-modal support, better team orchestration) with the same API patterns you already know.