sambanova/agents

sambanova/agents

Language: Python

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Forks: 13

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Created: 2025-01-17T15:30:53Z

Pushed: 2026-05-05T21:48:57Z

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README: !Samba Agents Logo

Agents

The Agents application is an advanced multi-agent AI system that intelligently routes requests to specialized agents and subgraphs for comprehensive assistance. The system features a compound agent architecture with XML-based routing, code execution capabilities, and multi-step research workflows. The Agents application helps users by:

• Providing intelligent assistance through a unified compound agent system.
• Executing code in secure Daytona sandbox environments.
• Performing comprehensive data science workflows with multi-agent collaboration.
• Generating detailed research reports and educational content.
• Conducting advanced financial analysis with real-time data.
• Automatically routing queries to appropriate specialized subgraphs.
• Supporting voice input for natural interaction.

The basic process of the Agents application is described below.

1. Enhanced agent processing

• User submits a query via text or voice input.
• The compound agent system uses XML-based routing to determine the best approach.
• Queries are processed through the main agent or routed to specialized subgraphs.

1. Intelligent subgraph routing

• The system automatically determines if queries require specialized subgraph processing.
• Available subgraphs include: Financial Analysis, Deep Research, Data Science, and Code Execution.
• Multi-agent collaboration within subgraphs for complex workflows.

1. Tool and data integration

• Dynamic tool loading based on user context and permissions.
• Integration with external APIs, databases, and knowledge sources.
• Secure code execution and file generation in Daytona sandbox.

1. Real-time response generation

• WebSocket-based streaming for real-time updates and agent reasoning.
• Structured responses with metadata for appropriate UI rendering.
• File artifacts (PDF, HTML, images, CSV) automatically processed and displayed.

1. Adaptive user interaction

• Frontend intelligence automatically detects agent behaviors and adapts UI.
• Agent reasoning panel shows real-time thought processes.
• Continuous learning from interactions to improve future responses.

• API Access
• The application provides a REST API for programmatic access to its features such as deep research and data science workflows.

> Note: View the Agent Reasoning panel on the right side of the application to see the real-time thought output. The Daytona Sidebar automatically opens when code execution is detected.

Prerequisites

Ensure to install the prerequisites.

• Python 3.11 (exact version required)
• Node.js 18.17.0 or later
• Yarn
• Redis (via Docker or Homebrew)

# Run Redis with Docker
docker run -p 6379:6379 redis/redis-stack:latest

# Install Redis with Homebrew on macOS
brew install redis-stack
brew services start redis-stack # or redis-stack-server

Get the following API keys to setup the Agents application.

• SambaNova API key (required)
• Serper API key for web search (required)
• Exa API key for company data (required)
• Tavily API key for deep research capabilities (required)
• Daytona API key for secure code execution sandbox (required)
• Clerk for authentication (you'll need both publishable and secret keys)
• Hume API key and Secret key for voice interaction capabilities (required for voice features)
• LangSmith API key for optional usage tracking and monitoring (optional)

>Note: The system supports multiple LLM providers including SambaNova's DeepSeek V3, Llama 3.3 70B, Llama Maverick, and DeepSeek R1 models.

Setup and run the application

You can setup and run the application in two ways: Cloud hosted version or locally hosted version.

Cloud hosted version

This version is hosted on SambaNova Cloud. No need to install dependencies locally.

1. Go to the Agents application login page. 1. Sign in using Clerk authentication (you will receive an email with login instructions). 1. Once you login, go to settings and add the API keys. 1. Start using the application to enhance workflows, conduct research, execute code, and gain actionable insights.

Locally hosted version

Frontend setup

Follow the steps below to install the frontend for the Agents application.

> Note: For the following commands, go to /frontend/sales-agent-crew/ directory.

1. Install Vue.js dependencies.

yarn install

1. Run a local development environment.

yarn dev

1. Create a production build.

yarn build

Backend setup

Follow the steps below to install the backend for the Agents application.

> Note: For the following commands, go to /backend/ directory.

1. Install Python dependencies: Create and activate a virtual environment (for example with venv) and install the project dependencies inside it. Make sure to use Python 3.11.

# Install uv first
pip install uv

cd backend
uv sync
source .venv/bin/activate

2. Run the application.

If you are running it on mac export the following variables

export DYLD_LIBRARY_PATH="/opt/homebrew/lib:$DYLD_LIBRARY_PATH"
export PKG_CONFIG_PATH="/opt/homebrew/lib/pkgconfig:$PKG_CONFIG_PATH"

Run the Backend server with the following command

uvicorn agents.api.main:app --reload --host 127.0.0.1 --port 8000 --no-access-log

Environment variables setup

Frontend environment variables

> Note: For the frontend environment variables, go to /frontend/sales-agent-crew/.

1. Create a .env file with the following variables.

VITE_API_URL=/api
VITE_WEBSOCKET_URL=ws://localhost:8000
VITE_AUTH0_DOMAIN=your_auth0_domain
VITE_AUTH0_CLIENT_ID=your_auth0_client_id
VITE_AUTH0_AUDIENCE=your_auth0_audience

Backend environment variables

> Note: For the backend environment…