Suna

Suna is an orchestration platform designed for the deployment, management, and governance of autonomous AI agents. It provides a centralized system for defining agent behaviors and tool integrations, enabling the automation of complex business processes through a unified interface.

The platform distinguishes itself by applying infrastructure-as-code principles to AI, utilizing version-controlled repositories to manage agent configurations, skills, and guardrails. It ensures secure and predictable operations by spawning ephemeral, isolated virtual machines for every individual task, preventing state collisions and process interference. To maintain organizational oversight, the system integrates formal code review workflows, role-based access controls, and audit trails for all agent modifications.

The infrastructure supports flexible deployment models, including self-hosted, multi-tenant, and air-gapped environments, allowing for full data sovereignty. It also features a unified gateway that routes requests across multiple model providers while tracking performance metrics and operational costs. The platform is managed via a command-line interface that streamlines the deployment of agent configurations and communication channels directly into production environments.

Features

AI Agent Orchestrators - Provides a centralized orchestration platform for deploying, managing, and securing autonomous AI agents.

Autonomous Agent Orchestration - Provides a platform for building and deploying autonomous agents that automate complex business processes.

GitOps Deployment Management - Applies infrastructure-as-code principles to manage agent configurations, skills, and guardrails through version-controlled repositories.

AI Execution Sandboxes - Spawns ephemeral, isolated virtual machines for every individual task to ensure secure and predictable execution.

Model Gateways - Provides a centralized interface for routing requests across multiple AI model providers while tracking costs and performance.

AI Compliance Governance - Implements formal review processes, access controls, and security guardrails to maintain oversight for automated AI activities.

Agent Configuration Management - Manages agent behaviors and operational parameters using version-controlled files for repeatable deployments.

AI Request Routing - Routes requests through a centralized gateway that manages multiple providers while tracking performance metrics and operational costs.

Agent Governance - Implements review processes and guardrails to maintain oversight and compliance for automated agent activities.

AI Workflow Orchestration - Executes AI-driven tasks by deploying agents that interact with internal tools and external APIs to automate business processes.

Declarative Agent Schemas - Defines agent behaviors and tool integrations through versioned, declarative configuration files.

LLM Gateways - Features a unified gateway that routes requests across multiple model providers while tracking performance and costs.

Self-Hosted AI Infrastructure - Supports flexible deployment models including self-hosted and air-gapped environments to maintain full data sovereignty.

Model Provider Configurations - Manages API keys and subscriptions to connect preferred model providers for agent intelligence.

Agent Frameworks - Generalist agent for open-source environments.

Task Sandboxing - Spawns ephemeral, isolated virtual machines for every task to prevent state collisions and process interference.

Git-Based Repositories - Maintains agent parameters and operational logic within version-controlled repositories for auditability and consistent deployment.

Self-Hosted Infrastructure - Enables self-hosted deployment on-premises or in private networks to maintain full data sovereignty and control.

Agent Security Runtimes - Runs AI agent tasks within secure, ephemeral, and sandboxed environments to prevent unauthorized access and state collisions.

Enterprise AI Security - Integrates formal code review workflows, role-based access controls, and audit trails for organizational oversight of AI workflows.

Sandboxed Execution Environments - Ensures secure execution by spawning isolated, ephemeral virtual machines for every agent task.

Deployment Command Line Interfaces - Streamlines deployment workflows by pushing agent configurations and triggers to production via a command-line interface.

Containerized Deployments - Packages the management stack into portable container images for secure execution in private or air-gapped environments.

Infrastructure as Code - Uses version-controlled repositories to define and deploy agent configurations as code.

Multi-Tenant Hosting - Provisions scalable, multi-tenant platforms that support concurrent users and agents across various interfaces.

Private Network Security - Supports deployment within private cloud or air-gapped networks to meet strict security and data residency requirements.

Role-Based Access Control Systems - Enforces security and team collaboration by integrating single sign-on protocols to manage user permissions.

External Service Integrations - Connects agents to third-party applications and services while securely managing encrypted secrets and environment variables.

Role-Based Access Control - Enforces role-based access control to manage user permissions and ensure secure team collaboration.

Team Collaboration Tools - Facilitates team collaboration by requiring formal code reviews for all agent modifications before deployment.

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Features

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