Overview

OpenRig transforms your AI coding agents from scattered terminal sessions into a cohesive, persistent team. This open-source system allows you to define, boot, and manage networks of agents with distinct roles, shared context, and collaborative workflows. Orchestrate sophisticated AI development processes by assigning tasks to a lead agent, who can then delegate to specialists, ensuring efficient task completion and informed decision-making.

Key Features

• Agent Orchestration: Define and manage teams of AI agents (e.g., Claude Code, Codex) within a single, unified system.
• YAML-Defined Topologies (RigSpec): Declaratively specify agent roles, team structures (pods), communication channels (edges), and continuity policies.
• Persistent Teams with Roles: Agents maintain stable identities and addresses (Seat), allowing for consistent communication and task ownership.
• Shared Context & Collaboration: Agents can share context and work together, with the lead agent coordinating specialists.
• Effortless Bootstrapping: Launch your entire agent network, including tmux sessions and startup configurations, with a single command (rig up).
• Interactive TUI: Visualize and manage your agent network through a rich terminal UI, offering graph and table views of rigs, pods, and seats.
• Discover and Adopt: Seamlessly integrate existing AI coding sessions into your managed rigs.
• Snapshot and Restore: Save the state of your agent topology for easy restoration and recovery.
• Agent-Managed Software: Package and run software alongside your agents, exemplified by the secrets-manager rig.

Typical Use Cases

• Automated Development Workflows: Build and test code, generate reviews, and manage pull requests with a team of specialized AI agents.
• AI Civilization Experiments: Create and manage complex AI ecosystems for research and exploration.
• Product Development Squads: Assemble teams comprising orchestrators, implementers, QA specialists, and reviewers for end-to-end product development.
• Task-Specific Agent Networks: Deploy tailored agent configurations for specific domains like research, security analysis, or complex problem-solving.
• Streamlined AI Coding Environments: Consolidate and manage multiple AI coding assistants into a single, productive workspace.