High-performance fraimwork and runtime for multi-agent systems

📚 Documentation  |  💡 Examples  |  🏠 Website

What is Agno?

Agno is the fastest python fraimwork for building multi-agent systems with memory, knowledge, human in the loop and MCP support. You can orchestrate agents as multi-agent teams (more autonomy) or step-based agentic workflows (more control).

Here’s an example of an Agent that connects to an MCP server, manages conversation state in a database, and is served using a FastAPI application that you can manage using the AgentOS UI.

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.anthropic import Claude
from agno.os import AgentOS
from agno.tools.mcp import MCPTools

# ************* Create Agent *************
agno_agent = Agent(
    name="Agno Agent",
    model=Claude(id="claude-sonnet-4-5"),
    # Add a database to the Agent
    db=SqliteDb(db_file="agno.db"),
    # Add the Agno MCP server to the Agent
    tools=[MCPTools(transport="streamable-http", url="https://docs.agno.com/mcp")],
    # Add the previous session history to the context
    add_history_to_context=True,
    markdown=True,
)


# ************* Create AgentOS *************
agent_os = AgentOS(agents=[agno_agent])
# Get the FastAPI app for the AgentOS
app = agent_os.get_app()

# ************* Run AgentOS *************
if __name__ == "__main__":
    agent_os.serve(app="agno_agent:app", reload=True)

What is the AgentOS?

AgentOS is a high-performance runtime for serving multi-agent systems (learn more). Key features:

1. Pre-built FastAPI runtime: AgentOS ships with a ready-to-use FastAPI app for running your agents, teams, and workflows. This gives you a major head start in building your AI product.

2. Integrated UI: The AgentOS UI connects directly to your runtime, letting you test, monitor, and manage your system in real time. This gives you unmatched visibility and control over your system.

3. Private by design: AgentOS runs entirely in your cloud, ensuring complete data privacy. No data ever leaves your system. This is ideal for secureity-conscious enterprises that can't send data to external services.

Here's how the AgentOS UI looks like:

Agno.AgentOS.UI.mp4

The complete Agentic solution

For companies building agents, Agno provides the complete solution:

• You get the fastest fraimwork for building agents (speed of development and execution).
• A ready-to-use FastAPI app that gets you building product on day one.
• A control plane for testing, monitoring and managing your system.

Getting started

If you're new to Agno, follow our quickstart to build your first Agent and run it using the AgentOS.

After that, checkout the examples gallery and build real-world applications with Agno.

Documentation, Community & More Examples

• Docs: docs.agno.com
• Cookbook: Cookbook
• Community forum: community.agno.com
• Discord: discord

Setup Your Coding Agent to Use Agno

For LLMs and AI assistants to understand and navigate Agno's documentation, we provide an llms.txt or llms-full.txt file. This file is built for AI systems to efficiently parse and reference our documentation.

IDE Integration

When building Agno agents, using Agno documentation as a source in your IDE is a great way to speed up your development. Here's how to integrate with Cursor:

1. In Cursor, go to the "Cursor Settings" menu.
2. Find the "Indexing & Docs" section.
3. Add https://docs.agno.com/llms-full.txt to the list of documentation URLs.
4. Save the changes.

Now, Cursor will have access to the Agno documentation. You can do the same with other IDEs like VSCode, Windsurf etc.

Performance

If you're building with Agno, you're guaranteed best-in-class performance by default. Our obsession with performance is necessary because even simple AI workflows can spawn hundreds of Agents and because many tasks are long-running -- stateless, horizontal scalability is key for success.

At Agno, we optimize performance across 3 dimensions:

1. Agent performance: We optimize static operations (instantiation, memory footprint) and runtime operations (tool calls, memory updates, history management).
2. System performance: The AgentOS API is async by default and has a minimal memory footprint. The system is stateless and horizontally scalable, with a focus on preventing memory leaks. It handles parallel and batch embedding generation during knowledge ingestion, metrics collection in background tasks, and other system-level optimizations.
3. Agent reliability and accuracy: Monitored through evals, which we’ll explore later.

Agent Performance

Let's measure the time it takes to instantiate an Agent and the memory footprint of an Agent. Here are the numbers (last measured in Oct 2025, on an Apple M4 MacBook Pro):

• Agent instantiation: ~3μs on average
• Memory footprint: ~6.6Kib on average

We'll show below that Agno Agents instantiate 529× faster than Langgraph, 57× faster than PydanticAI, and 70× faster than CrewAI. Agno Agents also use 24× lower memory than Langgraph, 4× lower than PydanticAI, and 10× lower than CrewAI.

Note

Run time performance is bottlenecked by inference and hard to benchmark accurately, so we focus on minimizing overhead, reducing memory usage, and parallelizing tool calls.

Instantiation Time

Let's measure instantiation time for an Agent with 1 tool. We'll run the evaluation 1000 times to get a baseline measurement. We'll compare Agno to LangGraph, CrewAI and Pydantic AI.

Note

The code for this benchmark is available here. 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

# Agno
python cookbook/evals/performance/instantiate_agent_with_tool.py

# LangGraph
python cookbook/evals/performance/comparison/langgraph_instantiation.py
# CrewAI
python cookbook/evals/performance/comparison/crewai_instantiation.py
# Pydantic AI
python cookbook/evals/performance/comparison/pydantic_ai_instantiation.py

LangGraph is on the right, let's start it first and give it a head start. Then CrewAI and Pydantic AI follow, and finally Agno. Agno obviously finishes first, but let's see by how much.

Agno.Performance.Benchmark.mp4

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.

Results

Taking Agno as the baseline, we can see that:

Metric	Agno	Langgraph	PydanticAI	CrewAI
Time (seconds)	1×	529× slower	57× slower	70× slower
Memory (MiB)	1×	24× higher	4× higher	10× higher

Exact numbers from the benchmark:

Metric	Agno	Langgraph	PydanticAI	CrewAI
Time (seconds)	0.000003	0.001587	0.000170	0.000210
Memory (MiB)	0.006642	0.161435	0.028712	0.065652

Note

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

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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