TL;DR:

  • Agno (the rebranded PhiData) is a lightweight Python framework for building single and multi-agent systems with built-in memory, storage, and tool support.
  • Its “Agent Teams” concept makes it easy to coordinate specialised agents without complex orchestration boilerplate.
  • It’s faster to get started than LangChain or LangGraph, but trades off some of the fine-grained control those frameworks offer.

If you’ve been tracking the Python AI agent framework space, you’ll have noticed that PhiData rebranded to Agno in late 2024. It wasn’t just a name change. The team used the rebrand to clean up the API, improve performance, and reposition the library around a cleaner mental model: agents are first-class objects, tools are just Python functions, and teams are how you coordinate multiple agents.

The result is a framework that’s genuinely fast to prototype with, and reasonably production-ready if your use case fits its model.

What Agno Actually Is

Agno is a pure-Python library for building AI agents. An agent in Agno is a Python object that wraps an LLM, a set of tools, memory, and optional storage. You define agents declaratively, compose them into teams, and run them with a single call.

The library sits at a different abstraction level from LangChain (which is more primitives-oriented) and LangGraph (which is explicitly about stateful graph execution). Agno is closer to CrewAI in spirit — high-level and opinionated — but it tends to be faster to write and easier to debug because there’s less magic happening behind the scenes.

Getting Started

Installation is a single pip command:

pip install agno

A basic agent looks like this:

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGoTools()],
    description="You are a research assistant.",
    markdown=True,
)

agent.print_response("What are the latest developments in fusion energy?")

That’s it. The agent handles tool calling, result parsing, and follow-up reasoning automatically. You can swap the model for Claude, Gemini, Mistral, or a local Ollama model by changing one import.

Agent Teams

The feature that separates Agno from simpler wrapper libraries is Agent Teams. Instead of writing custom orchestration code to coordinate multiple agents, you declare a team:

from agno.agent import Agent
from agno.team import Team

researcher = Agent(
    name="Researcher",
    role="Research and summarise information on a given topic",
    model=OpenAIChat(id="gpt-4o-mini"),
    tools=[DuckDuckGoTools()],
)

writer = Agent(
    name="Writer",
    role="Write clear, engaging blog posts based on research",
    model=OpenAIChat(id="gpt-4o"),
)

team = Team(
    name="Content Team",
    mode="coordinate",  # or "route" or "collaborate"
    members=[researcher, writer],
)

team.print_response("Write a post about WebAssembly at the edge")

The mode parameter controls how the team operates. coordinate gives a lead agent responsibility for delegating to members. route sends the task directly to the most appropriate member. collaborate allows members to communicate directly. Each mode suits different workflows.

Built-in Memory and Storage

Agno ships with memory and storage backends that work out of the box:

  • Agent Memory: stores conversation history within a session
  • Agent Storage: persists state across sessions using SQLite, PostgreSQL, or MongoDB
  • Agent Knowledge: connects to a knowledge base (PDFs, websites, databases) via built-in RAG

Adding persistent memory to an agent is three lines:

from agno.storage.sqlite import SqliteStorage

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    storage=SqliteStorage(table_name="agent_sessions", db_file="sessions.db"),
    add_history_to_messages=True,
)

This is notably easier to configure than the equivalent setup in LangChain or LangGraph, where you’d wire together a memory class, a checkpointer, and a persistence layer separately.

Tool Integration

Tools in Agno are Python functions decorated with @agent.tool or pulled from the built-in tool library. The built-in toolkit is substantial: DuckDuckGo search, Wikipedia, Arxiv, GitHub, file I/O, SQL databases, web scraping via Firecrawl, email, calendar, and more.

For custom tools, you just write a function with a docstring:

def get_stock_price(ticker: str) -> str:
    """Get the current stock price for a given ticker symbol."""
    # your implementation here
    return f"${price}"

agent = Agent(tools=[get_stock_price], ...)

Agno handles the JSON schema generation for tool calling automatically based on Python type hints and the docstring.

Multi-Modal Support

Agno agents can process images, audio, video, and documents natively — not as a bolt-on, but as first-class input types. An agent that analyses an uploaded image is the same code pattern as a text agent, with the input type changed.

This matters if you’re building agents for document processing, visual QA, or multimodal workflows. You don’t need to wire up separate models or preprocessing pipelines.

When to Use Agno

Agno is a good fit when:

  • You want to get a working multi-agent prototype running in under an hour
  • Your team knows Python and doesn’t want to learn a new DSL or graph abstraction
  • You need built-in RAG, memory, and storage without configuring each piece separately
  • You’re building agent teams with clear role separation

Consider alternatives when:

  • You need fine-grained control over the execution graph (LangGraph is better here)
  • You’re building complex conditional workflows with parallel branches (Temporal or LangGraph)
  • You’re in a non-Python environment (Agno is Python-only)
  • You need enterprise-grade audit trails and compliance features (look at vendor-managed solutions like AWS Bedrock Agents)

Agno vs CrewAI

The most common comparison is Agno vs CrewAI, since both are high-level Python frameworks for multi-agent coordination. The key differences:

  • Performance: Agno is measurably faster at agent initialisation — the team has benchmarked this and it shows in practice for high-throughput use cases
  • Flexibility: Agno’s team modes are more configurable than CrewAI’s crew concept
  • Ecosystem: CrewAI has more third-party integrations and a larger community at time of writing
  • Storage: Agno’s built-in storage and memory options are more polished

Neither is objectively better. If you’re already using CrewAI and it’s working, there’s no compelling reason to switch. If you’re starting fresh, Agno is worth the evaluation.

The Playground

Agno ships with a local playground — a web UI that lets you interact with your agents without writing a frontend. Run agno playground in a project directory and you get a chat interface, tool call visibility, and session management. It’s useful for debugging and for demonstrating agents to stakeholders who don’t want to run Python.

Getting Production-Ready

Agno agents run as standard Python — you deploy them however you deploy Python code. The library doesn’t impose a server architecture. If you need observability, it integrates with Langfuse and other tracing tools. For containerised deployments, a standard Dockerfile works without modification.

The storage backends (SQLite for local, PostgreSQL for production) mean you can start simple and scale the persistence layer without changing application code.

Agno occupies a sweet spot in the Python agent framework landscape: more opinionated than LangChain, more flexible than vendor-managed solutions, and faster to prototype with than anything that requires a graph abstraction. If you’ve been meaning to evaluate it since the PhiData days, the current version is a significant improvement and worth a few hours of your time.