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https://github.com/agno-agi/agno

Build multi-agent systems that learn and improve with every interaction.
https://github.com/agno-agi/agno

agents ai ai-agents developer-tools python

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Build multi-agent systems that learn and improve with every interaction.

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README 

          


  

    

      

      

      Agno

    

  






  Build, run, and manage agentic software at scale.






  Docs

   • 

  Cookbook

   • 

  Quickstart

   • 

  Discord




## What is Agno



Agno is the runtime for agentic software. Build agents, teams, and workflows. Run them as scalable services. Monitor and manage them in production.



| Layer | What it does |

|-------|--------------|

| **Framework** | Build agents, teams, and workflows with memory, knowledge, guardrails, and 100+ integrations. |

| **Runtime** | Serve your system in production with a stateless, session-scoped FastAPI backend. |

| **Control Plane** | Test, monitor, and manage your system using the [AgentOS UI](https://os.agno.com). |



## Quick Start



Build a stateful, tool-using agent and serve it as a production API in ~20 lines.



```python

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



agno_assist = Agent(

    name="Agno Assist",

    model=Claude(id="claude-sonnet-4-6"),

    db=SqliteDb(db_file="agno.db"),

    tools=[MCPTools(url="https://docs.agno.com/mcp")],

    add_history_to_context=True,

    num_history_runs=3,

    markdown=True,

)



agent_os = AgentOS(agents=[agno_assist], tracing=True)

app = agent_os.get_app()

```



Run it:



```bash

export ANTHROPIC_API_KEY="***"



uvx --python 3.12 \

  --with "agno[os]" \

  --with anthropic \

  --with mcp \

  fastapi dev agno_assist.py

```



In ~20 lines, you get:

- A stateful agent with streaming responses

- Per-user, per-session isolation

- A production API at http://localhost:8000

- Native tracing



Connect to the [AgentOS UI](https://os.agno.com) to monitor, manage, and test your agents.



1. Open [os.agno.com](https://os.agno.com) and sign in.

2. Click **"Add new OS"** in the top navigation.

3. Select **"Local"** to connect to a local AgentOS.

4. Enter your endpoint URL (default: `http://localhost:8000`).

5. Name it "Local AgentOS".

6. Click **"Connect"**.



https://github.com/user-attachments/assets/75258047-2471-4920-8874-30d68c492683



Open Chat, select your agent, and ask:



> What is Agno?



The agent retrieves context from the Agno MCP server and responds with grounded answers.



https://github.com/user-attachments/assets/24c28d28-1d17-492c-815d-810e992ea8d2



You can use this exact same architecture for running multi-agent systems in production.



## Why Agno?



Agentic software introduces three fundamental shifts.



### A new interaction model



Traditional software receives a request and returns a response. Agents stream reasoning, tool calls, and results in real time. They can pause mid-execution, wait for approval, and resume later.



Agno treats streaming and long-running execution as first-class behavior.



### A new governance model



Traditional systems execute predefined decision logic written in advance. Agents choose actions dynamically. Some actions are low risk. Some require user approval. Some require administrative authority.



Agno lets you define who decides what as part of the agent definition, with:



- Approval workflows

- Human-in-the-loop

- Audit logs

- Enforcement at runtime



### A new trust model



Traditional systems are designed to be predictable. Every execution path is defined in advance. Agents introduce probabilistic reasoning into the execution path.



Agno builds trust into the engine itself:



- Guardrails run as part of execution

- Evaluations integrate into the agent loop

- Traces and audit logs are first-class



## Built for Production



Agno runs in your infrastructure, not ours.



- Stateless, horizontally scalable runtime.

- 50+ APIs and background execution.

- Per-user and per-session isolation.

- Runtime approval enforcement.

- Native tracing and full auditability.

- Sessions, memory, knowledge, and traces stored in your database.



You own the system. You own the data. You define the rules.



## What You Can Build



Agno powers real agentic systems built from the same primitives above.



- [**Pal →**](https://github.com/agno-agi/pal) A personal agent that learns your preferences.

- [**Dash →**](https://github.com/agno-agi/dash) A self-learning data agent grounded in six layers of context.

- [**Scout →**](https://github.com/agno-agi/scout) A self-learning context agent that manages enterprise context knowledge.

- [**Gcode →**](https://github.com/agno-agi/gcode) A post-IDE coding agent that improves over time.

- [**Investment Team →**](https://github.com/agno-agi/investment-team) A multi-agent investment committee that debates and allocates capital.



Single agents. Coordinated teams. Structured workflows. All built on one architecture.



## Get Started



1. [Read the docs](https://docs.agno.com)

2. [Build your first agent](https://docs.agno.com/first-agent)

3. Explore the [cookbook](https://github.com/agno-agi/agno/tree/main/cookbook)



## IDE Integration



Add Agno docs as a source in your coding tools:



**Cursor:** Settings → Indexing & Docs → Add `https://docs.agno.com/llms-full.txt`



Also works with VSCode, Windsurf, and similar tools.



## Contributing



See the [contributing guide](https://github.com/agno-agi/agno/blob/main/CONTRIBUTING.md).



## Telemetry



Agno logs which model providers are used to prioritize updates. Disable with `AGNO_TELEMETRY=false`.



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