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Agno is a lightweight library for building Multimodal Agents. It exposes LLMs as a unified API and gives them superpowers like memory, knowledge, tools and reasoning.

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Agno is a lightweight library for building multi-modal Agents. It is designed with core principles of simplicity, uncompromising performance, and agnosticism, allowing users to create blazing fast agents with minimal memory footprint. Agno supports any model, any provider, and any modality, making it a versatile container for AGI. Users can build agents with lightning-fast agent creation, model agnostic capabilities, native support for text, image, audio, and video inputs and outputs, memory management, knowledge stores, structured outputs, and real-time monitoring. The library enables users to create autonomous programs that use language models to solve problems, improve responses, and achieve tasks with varying levels of agency and autonomy.

README:

📚 Documentation | 💡 Examples | 🌟 Star Us

Introduction

Agno is a lightweight library for building Multimodal Agents. It exposes LLMs as a unified API and gives them superpowers like memory, knowledge, tools and reasoning.

Build lightning-fast Agents that can generate text, image, audio and video.
Add memory, knowledge, tools and reasoning as needed.
Run anywhere, Agno is open-source.

Here's an Agent that can search the web:

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()],
    markdown=True
)
agent.print_response("What's happening in New York?", stream=True)

Key features

Agno is simple, fast and model agnostic. Here are some key features:

Lightning Fast: Agent creation is 10,000x faster than LangGraph (see performance).
Model Agnostic: Use any model, any provider, no lock-in.
Multi Modal: Native support for text, image, audio and video.
Multi Agent: Build teams of specialized agents.
Memory Management: Store agent sessions and state in a database.
Knowledge Stores: Use vector databases for RAG or dynamic few-shot learning.
Structured Outputs: Make Agents respond in a structured format.
Monitoring: Track agent sessions and performance in real-time on agno.com.

Getting Started

Start by building your first Agent
Check out the examples
Read the documentation

Installation

pip install -U agno

What are Agents?

Agents are intelligent programs that solve problems autonomously.

Agents have memory, domain knowledge and the ability to use tools (like searching the web, querying a database, making API calls). Unlike traditional programs that follow a predefined execution path, Agents dynamically adapt their approach based on the context and tool results.

Instead of a rigid binary definition, let's think of Agents in terms of agency and autonomy.

Level 0: Agents with no tools (basic inference tasks).
Level 1: Agents with tools for autonomous task execution.
Level 2: Agents with knowledge, combining memory and reasoning.
Level 3: Teams of specialized agents collaborating on complex workflows.

Example - Basic Agent

The simplest Agent is just an inference task, no tools, no memory, no knowledge.

from agno.agent import Agent
from agno.models.openai import OpenAIChat

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You are an enthusiastic news reporter with a flair for storytelling!",
    markdown=True
)
agent.print_response("Tell me about a breaking news story from New York.", stream=True)

To run the agent, install dependencies and export your OPENAI_API_KEY.

pip install agno openai

export OPENAI_API_KEY=sk-xxxx

python basic_agent.py

View this example in the cookbook

Example - Agent with tools

This basic agent will obviously make up a story, lets give it a tool to search the web.

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

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You are an enthusiastic news reporter with a flair for storytelling!",
    tools=[DuckDuckGoTools()],
    show_tool_calls=True,
    markdown=True
)
agent.print_response("Tell me about a breaking news story from New York.", stream=True)

Install dependencies and run the Agent:

pip install duckduckgo-search

python agent_with_tools.py

Now you should see a much more relevant result.

View this example in the cookbook

Example - Agent with knowledge

Agents can store knowledge in a vector database and use it for RAG or dynamic few-shot learning.

Agno agents use Agentic RAG by default, which means they will search their knowledge base for the specific information they need to achieve their task.

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.embedder.openai import OpenAIEmbedder
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb, SearchType

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You are a Thai cuisine expert!",
    instructions=[
        "Search your knowledge base for Thai recipes.",
        "If the question is better suited for the web, search the web to fill in gaps.",
        "Prefer the information in your knowledge base over the web results."
    ],
    knowledge=PDFUrlKnowledgeBase(
        urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
        vector_db=LanceDb(
            uri="tmp/lancedb",
            table_name="recipes",
            search_type=SearchType.hybrid,
            embedder=OpenAIEmbedder(id="text-embedding-3-small"),
        ),
    ),
    tools=[DuckDuckGoTools()],
    show_tool_calls=True,
    markdown=True
)

# Comment out after the knowledge base is loaded
if agent.knowledge is not None:
    agent.knowledge.load()

agent.print_response("How do I make chicken and galangal in coconut milk soup", stream=True)
agent.print_response("What is the history of Thai curry?", stream=True)

Install dependencies and run the Agent:

pip install lancedb tantivy pypdf duckduckgo-search

python agent_with_knowledge.py

View this example in the cookbook

Example - Multi Agent Teams

Agents work best when they have a singular purpose, a narrow scope and a small number of tools. When the number of tools grows beyond what the language model can handle or the tools belong to different categories, use a team of agents to spread the load.

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools
from agno.team import Team

web_agent = Agent(
    name="Web Agent",
    role="Search the web for information",
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGoTools()],
    instructions="Always include sources",
    show_tool_calls=True,
    markdown=True,
)

finance_agent = Agent(
    name="Finance Agent",
    role="Get financial data",
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)],
    instructions="Use tables to display data",
    show_tool_calls=True,
    markdown=True,
)

agent_team = Agent(
    mode="coordinate",
    members=[web_agent, finance_agent],
    model=OpenAIChat(id="gpt-4o"),
    success_criteria="A comprehensive financial news report with clear sections and data-driven insights.",
    instructions=["Always include sources", "Use tables to display data"],
    show_tool_calls=True,
    markdown=True,
)

agent_team.print_response("What's the market outlook and financial performance of AI semiconductor companies?", stream=True)

Install dependencies and run the Agent team:

pip install duckduckgo-search yfinance

python agent_team.py

View this example in the cookbook

Performance

At Agno, we're obsessed with performance. Why? because even simple AI workflows can spawn thousands of Agents to achieve their goals. Scale that to a modest number of users and performance becomes a bottleneck. Agno is designed to power high performance agentic systems:

Agent instantiation: ~2μs on average (~10,000x faster than LangGraph).
Memory footprint: ~3.75Kib on average (~50x less memory than LangGraph).

Tested on an Apple M4 Mackbook Pro.

While an Agent's run-time is bottlenecked by inference, we must do everything possible to minimize execution time, reduce memory usage, and parallelize tool calls. These numbers may seem trivial at first, but our experience shows that they add up even at a reasonably small scale.

Instantiation time

Let's measure the time it takes for an Agent with 1 tool to start up. We'll run the evaluation 1000 times to get a baseline measurement.

You should run the evaluation yourself on your own machine, please, do not take these results at face value.

# Setup virtual environment
./scripts/perf_setup.sh
source .venvs/perfenv/bin/activate
# OR Install dependencies manually
# pip install openai agno langgraph langchain_openai

# Agno
python evals/performance/instantiation_with_tool.py

# LangGraph
python evals/performance/other/langgraph_instantiation.py

The following evaluation is run on an Apple M4 Mackbook Pro. It also runs as a Github action on this repo.

LangGraph is on the right, let's start it first and give it a head start.

Agno is on the left, notice how it finishes before LangGraph gets 1/2 way through the runtime measurement, and hasn't even started the memory measurement. That's how fast Agno is.

https://github.com/user-attachments/assets/ba466d45-75dd-45ac-917b-0a56c5742e23

Dividing the average time of a Langgraph Agent by the average time of an Agno Agent:

0.020526s / 0.000002s ~ 10,263

In this particular run, Agno Agents startup is roughly 10,000 times faster than Langgraph Agents. The numbers continue to favor Agno as the number of tools grow, and we add memory and knowledge stores.

Memory usage

To measure memory usage, we use the tracemalloc library. We first calculate a baseline memory usage by running an empty function, then run the Agent 1000x times and calculate the difference. This gives a (reasonably) isolated measurement of the memory usage of the Agent.

We recommend running the evaluation yourself on your own machine, and digging into the code to see how it works. If we've made a mistake, please let us know.

Dividing the average memory usage of a Langgraph Agent by the average memory usage of an Agno Agent:

0.137273/0.002528 ~ 54.3

Langgraph Agents use ~50x more memory than Agno Agents. In our opinion, memory usage is a much more important metric than instantiation time. As we start running thousands of Agents in production, these numbers directly start affecting the cost of running the Agents.

Conclusion

Agno agents are designed for performance and while we do share some benchmarks against other frameworks, we should be mindful that accuracy and reliability are more important than speed.

We'll be publishing accuracy and reliability benchmarks running on Github actions in the coming weeks. Given that each framework is different and we won't be able to tune their performance like we do with Agno, for future benchmarks we'll only be comparing against ourselves.

Cursor Setup

When building Agno agents, using Agno documentation as a source in Cursor is a great way to speed up your development.

In Cursor, go to the settings or preferences section.
Find the section to manage documentation sources.
Add https://docs.agno.com to the list of documentation URLs.
Save the changes.

Now, Cursor will have access to the Agno documentation.

Documentation, Community & More examples

Docs: docs.agno.com
Getting Started Examples: Getting Started Cookbook
All Examples: Cookbook
Community forum: community.agno.com
Chat: discord

Contributions

We welcome contributions, read our contributing guide to get started.

Telemetry

Agno logs which model an agent used so we can prioritize updates to the most popular providers. You can disable this by setting AGNO_TELEMETRY=false in your environment.

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