Suna AI is an open-source, all-in-one AI assistant designed for real-world productivity. It helps users tackle tasks like research, data analysis, and daily workflows through natural language conversation. Suna is powerful, intuitive, and focused on delivering the results users request.

Some key features of Suna AI:

• Completely free and open-source
• Runs locally on your computer
• Integrates with various tools and services (Superbase, Tavly, Firecrawl, etc.)
• Supports large language models from providers like Anthropic and OpenAI
• Includes a web browser agent for web scraping and data extraction
• Generates reports, dashboards, and other outputs based on user prompts

Suna AI is built with a backend API using Python FastAPI, a frontend with Next.js and React, and utilizes Superbase for database and authentication. It provides a fully functional, locally-run AI agent that can handle a wide range of tasks and workflows.

To set up Suna AI locally, follow these steps:

1. Ensure you have the following prerequisites:

   • Supabase project (set up for free)
   • Supabase CLI installed for your operating system
   • Python 3.11
   • Git installed
   • An IDE like VS Code

2. Clone the Suna AI repository:

   git clone https://github.com/suna-ai/suna.git
   cd suna
   

3. Configure the backend:

   • Create the .env file in the backend directory.
   • Fill in the required variables, such as Supabase credentials, language model provider, and optional integrations like Tavly and Firecrawl.

4. Set up the Supabase database:

   • Log in to the Supabase CLI.
   • Link your Supabase project.
   • Run the database migration command.

5. Configure the frontend:

   • Go to the frontend directory.
   • Create the .env.local file and populate it with the necessary variables, such as Supabase URL and anonymous key, and the backend URL.

6. Install dependencies:

   • You can either use Docker Compose or install the dependencies manually.
     • Docker Compose: docker-compose up
     • Manual installation:
       • In the frontend directory, run npm install.
       • In the backend directory, install the requirements.

7. Start the application:

   • Run npm run dev in the frontend directory.
   • Run the backend in a separate terminal.

8. Access Suna AI locally:

   • If using Docker Compose, access it at http://localhost:3000.
   • If using manual installation, access it at http://localhost:8000.

You can now interact with Suna AI locally and leverage its capabilities for your tasks, such as market analysis, data visualization, and more.

Suna AI is a powerful open-source AI assistant that can tackle a wide range of tasks, including market analysis and dashboard generation. In this example, the user prompts Suna to perform a market analysis on the Tesla stock and create a comprehensive dashboard showcasing the findings.

Suna's capabilities are demonstrated through its efficient execution of this task. It first creates a plan, outlining the data collection process using the Tavly API and web scraping techniques with Firecrawl and the built-in browser agent. Suna then gathers the necessary information and generates an insightful dashboard, displaying key insights, technical analysis, and a summary of the Tesla stock.

The resulting dashboard is visually appealing, with various Plotly visualizations and a well-structured layout. It provides a detailed overview of the stock's performance, including short-term, mid-term, and long-term outlooks. This showcases Suna's ability to understand the user's requirements, gather relevant data, and present the findings in a clear and concise manner.

The speed at which Suna completes this task, within a matter of minutes, further demonstrates its efficiency and productivity-focused design. This open-source AI assistant proves to be a capable alternative to commercial solutions, offering a free and locally-hosted option for users seeking a powerful AI tool for their workflows.

In summary, Sunna AI is a powerful open-source AI assistant that provides a free and locally-run alternative to commercial AI agents like Anthropic's Manticore. With its robust backend API, intuitive front-end, and seamless integration with various tools and services, Sunna AI offers a comprehensive solution for tackling a wide range of tasks, from research and data analysis to daily workflows.

The ability to run Sunna AI locally on your own machine, without relying on cloud-based services, is a significant advantage, ensuring your data and privacy remain secure. The platform's modular design allows for easy customization and integration with your preferred language models, databases, and other third-party tools, making it a highly versatile and adaptable AI assistant.

Through the detailed walkthrough provided, you should now have a clear understanding of how to set up and configure Sunna AI on your local machine, empowering you to harness its capabilities for your own productivity and research needs. As an open-source project, Sunna AI continues to evolve, with the community contributing to its ongoing development and improvement.

Whether you're a developer, researcher, or simply someone looking to enhance your daily workflows, Sunna AI is a compelling and accessible AI assistant worth exploring. Its combination of power, flexibility, and open-source ethos makes it a valuable addition to any AI enthusiast's toolkit.