> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.
# What is Agno?
> Agno is a python framework for building multi-agent systems with shared memory, knowledge and reasoning.
Engineers and researchers use Agno to build:
* **Level 1:** Agents with tools and instructions ([example](/introduction/agents#level-1%3A-agents-with-tools-and-instructions)).
* **Level 2:** Agents with knowledge and storage ([example](/introduction/agents#level-2%3A-agents-with-knowledge-and-storage)).
* **Level 3:** Agents with memory and reasoning ([example](/introduction/agents#level-3%3A-agents-with-memory-and-reasoning)).
* **Level 4:** Agent Teams that can reason and collaborate ([example](/introduction/multi-agent-systems#level-4%3A-agent-teams-that-can-reason-and-collaborate)).
* **Level 5:** Agentic Workflows with state and determinism ([example](/introduction/multi-agent-systems#level-5%3A-agentic-workflows-with-state-and-determinism)).
**Example:** Level 1 Reasoning Agent that uses DuckDuckGo to answer questions:
```python Reasoning Finance Agent theme={null}
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.reasoning import ReasoningTools
from agno.tools.duckduckgo import DuckDuckGoTools
reasoning_agent = Agent(
model=Claude(id="claude-sonnet-4-20250514"),
tools=[
ReasoningTools(add_instructions=True),
DuckDuckGoTools(search=True, news=True),
],
instructions="Use tables to display data.",
markdown=True,
)
```
# Getting Started
If you're new to Agno, learn how to build your [first Agent](/introduction/agents), chat with it on the [playground](/introduction/playground) and [monitor](/introduction/monitoring) it on [app.agno.com](https://app.agno.com).
Learn how to build Agents with Agno
Chat with your Agents using a beautiful Agent UI
Monitor your Agents on [agno.com](https://app.agno.com)
After that, dive deeper into the [concepts below](/introduction#dive-deeper) or explore the [examples gallery](/examples) to build real-world applications with Agno.
# Why Agno?
Agno will help you build best-in-class, highly-performant agentic systems, saving you hours of research and boilerplate. Here are some key features that set Agno apart:
* **Model Agnostic**: Agno provides a unified interface to 23+ model providers, no lock-in.
* **Highly performant**: Agents instantiate in **\~3μs** and use **\~6.5Kib** memory on average.
* **Reasoning is a first class citizen**: Reasoning improves reliability and is a must-have for complex autonomous agents. Agno supports 3 approaches to reasoning: Reasoning Models, `ReasoningTools` or our custom `chain-of-thought` approach.
* **Natively Multi-Modal**: Agno Agents are natively multi-modal, they accept text, image, audio and video as input and generate text, image, audio and video as output.
* **Advanced Multi-Agent Architecture**: Agno provides an industry leading multi-agent architecture (**Agent Teams**) with reasoning, memory, and shared context.
* **Built-in Agentic Search**: Agents can search for information at runtime using 20+ vector databases. Agno provides state-of-the-art Agentic RAG, **fully async and highly performant.**
* **Built-in Memory & Session Storage**: Agents come with built-in `Storage` & `Memory` drivers that give your Agents long-term memory and session storage.
* **Structured Outputs**: Agno Agents can return fully-typed responses using model provided structured outputs or `json_mode`.
* **Pre-built FastAPI Routes**: After building your Agents, serve them using pre-built FastAPI routes. 0 to production in minutes.
* **Monitoring**: Monitor agent sessions and performance in real-time on [agno.com](https://app.agno.com).
# Dive deeper
Agno is a battle-tested framework with a state of the art reasoning and multi-agent architecture, read the following guides to learn more:
Learn how to build lightning fast Agents.
Build autonomous multi-agent teams.
Use any model, any provider, no lock-in.
100s of tools to extend your Agents.
Make Agents "think" and "analyze".
Give Agents domain-specific knowledge.
Store and search your knowledge base.
Persist Agent session and state in a database.
Remember user details and session summaries.
Generate embeddings for your knowledge base.
Deterministic, stateful, multi-agent workflows.
Evaluate, monitor and improve your Agents.