AgentOS

Welcome to your AgentOS: a robust, production-ready application for serving agents, multi-agent teams and agentic workflows. It includes:

• An AgentOS server for serving agents, multi-agent teams and agentic workflows.
• A PostgreSQL database for storing agent sessions, knowledge, and memories.
• A set of pre-built agents, teams and workflows to use as a starting point.

For more information, checkout Agno and give it a ⭐️

Quickstart

Follow these steps to get your AgentOS up and running:

Prerequisites: docker desktop should be installed and running.

Clone the repo

git clone https://github.com/agno-agi/mb-agent-os.git
cd mb-agent-os

Configure API keys

We'll use sonnet 4.5 as the default model, please export the ANTHROPIC_API_KEY environment variable to get started.

export ANTHROPIC_API_KEY="YOUR_API_KEY_HERE"

We also use OPENAI_API_KEY to generate embeddings for knowledge bases. So, please export the OPENAI_API_KEY environment variable to use agents with knowledge bases.

export OPENAI_API_KEY="YOUR_API_KEY_HERE"

Finally, we use Exa for the Research Agent. So, please export the EXA_API_KEY environment variable to use the Research Agent.

export EXA_API_KEY="YOUR_API_KEY_HERE"

Tip

You can use the example.env file as a template to create your own .env file.

Start the application

Run the application using docker compose:

docker compose up -d

This command starts:

• The AgentOS server, running on http://localhost:8000.
• The PostgreSQL database for storing agent sessions, knowledge, and memories, accessible on localhost:5432.

Once started, you can:

• View the AgentOS server documentation at http://localhost:8000/docs.

Connect the AgnoUI to the AgentOS server

• Open the Agno UI
• Login and add http://localhost:8000 as a new AgentOS. You can call it Local AgentOS (or any name you prefer).

Stop the application

When you're done, stop the application using:

docker compose down

Prebuilt Agents

The /agents folder contains pre-built agents that you can use as a starting point.

• SQL Agent: An Agent that can answer questions about the F1 database. Please run docker exec -it mb-agent-os-agent-os-1 python -m agents.sql.load_f1_data to load the F1 data into the database.

• Agno MCP Agent: An Agent that can help answer questions about Agno using Agno's MCP server. This is a great starting point for building Agents that need to MCP.

• Agno Knowledge Agent: An Agent that loads the Agno documentation in a knowledge base and answers questions about Agno. Please run docker exec -it mb-agent-os-agent-os-1 python -m agents.agno_knowledge_agent to load the Agno documentation into the knowledge base.

• Finance Agent: An agent that uses the YFinance API to get stock prices and financial data.

• Research Agent: An agent that can search the web/Exa for information.

• Memory Manager: An agent that can manage user memories.

• YouTube Agent: An agent that can search YouTube for videos and answer questions about them.

Prebuilt Teams

The /teams folder contains pre-built teams that you can use as a starting point.

• Finance Team: A team of agents that can work together to analyze financial data.

Prebuilt Workflows

The /workflows folder contains pre-built workflows that you can use as a starting point.

• Research Workflow: A workflow that can research information from multiple sources simultaneously.

Development Setup

To setup your local virtual environment:

Install uv

We use uv for python environment and package management. Install it by following the the uv documentation or use the command below for unix-like systems:

curl -LsSf https://astral.sh/uv/install.sh | sh

Create Virtual Environment & Install Dependencies

Run the dev_setup.sh script. This will create a virtual environment and install project dependencies:

./scripts/dev_setup.sh

Activate Virtual Environment

Activate the created virtual environment:

source .venv/bin/activate

(On Windows, the command might differ, e.g., .venv\Scripts\activate)

Managing Python Dependencies

If you need to add or update python dependencies:

Modify pyproject.toml

Add or update your desired Python package dependencies in the [dependencies] section of the pyproject.toml file.

Generate requirements.txt

The requirements.txt file is used to build the application image. After modifying pyproject.toml, regenerate requirements.txt using:

./scripts/generate_requirements.sh

To upgrade all existing dependencies to their latest compatible versions, run:

./scripts/generate_requirements.sh upgrade

Rebuild Docker Images

Rebuild your Docker images to include the updated dependencies:

docker compose up -d --build

Community & Support

Need help, have a question, or want to connect with the community?

• 📚 Read the Agno Docs for more in-depth information.
• 💬 Chat with us on Discord for live discussions.
• ❓ Ask a question on Discourse for community support.
• 🐛 Report an Issue on GitHub if you find a bug or have a feature request.

Running in Production

This repository includes a Dockerfile for building a production-ready container image of the application.

The general process to run in production is:

1. Update the scripts/build_image.sh file and set your IMAGE_NAME and IMAGE_TAG variables.
2. Build and push the image to your container registry:

./scripts/build_image.sh

3. Run in your cloud provider of choice.