openrig vs SIMA 2: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of openrig and SIMA 2 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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openrig
mvschwarz
OpenRig is open-source software to define a team of Claude Code and Codex agents in YAML and run them as one persistent, managed system.
Key features
- YAML Rig Definitions: Describe an agent team with roles in YAML and boot it with a single command.
- Mixed Harnesses: Run Claude Code and Codex agents together in the same rig, managed as one system.
- Lead Agent Coordination: Talk to a lead agent that coordinates specialists across teams and reports back decisions needing attention.
- Persistent Teams: Keep team work and context at stable addresses instead of a pile of terminal sessions.
- Terminal UI: View the rig as a graph or a table of seats showing runtime, model, context and state, with per-seat detail.
- Guided First Use: A getting-started path installs, launches a two-agent team in your repository and produces one reviewed change.
- Permission Allowances: Optionally lets your agent add scoped native rules so OpenRig commands run without repeated prompts, with an undo.
- CLI Install: Distributed as @openrig/cli on npm, requiring Node.js 22 or 24 and tmux on macOS or Linux.
Best for
- Multi-Agent Coding Teams: Stand up a persistent team of coding agents with defined roles for a repository.
- Cross-Harness Workflows: Combine Claude Code and Codex strengths within one coordinated team.
- Outcome-Driven Delegation: Describe a desired outcome to a lead agent and let it split work among specialists.
- Agent Monitoring: Watch seats, models, context and state of many agents from one terminal UI.
- Reviewed Changes: Have agents produce a change in a repo and another review it before you merge.
- Agent Research Experiments: Prototype and study networks of cooperating agents on your own machine.
SIMA 2
A Gemini-powered multimodal agent that plays, reasons, and learns in rich 3D virtual worlds, following instructions and adapting to new games.
Key features
- Gemini Integration: Uses advanced Gemini models for higher-level reasoning, planning, and natural-language understanding to convert instructions into multi-step actions.
- Multimodal Perception and Control: Reads pixel and UI observations from 3D worlds and issues control inputs (e.g., mouse/keyboard) at interactive frame rates to operate within environments.
- Instruction Following and Dialogue: Accepts natural-language commands and holds conversational exchanges to clarify goals, report progress, and receive guidance from human users.
- Goal-Directed Planning: Explicitly represents and reasons about goals, formulates subgoals, and sequences actions to achieve complex, long-horizon tasks in virtual worlds.
- Skill Generalization: Transfers learned behaviors and strategies to novel games and environments, allowing zero- or few-shot adaptation to previously unseen tasks.
- Human-in-the-Loop Learning: Incorporates demonstrations and interactive feedback from humans to refine performance and learn new capabilities during play.
- Real-Time Interaction: Operates at interactive frame-rates (observed controlling inputs at ~30+ fps in demonstrations) enabling fluid gameplay and rapid reaction to changing environments.
- Integrates Gemini models for higher-level reasoning and decision-making
- Follows natural language instructions within 3D virtual worlds
- Goal-directed planning and reasoning about objectives
- Conversational interface for user interaction and guidance
- Real-time perception and control (reads screen and controls input at ~30+ fps)
- Self-improvement via learning from interaction and environment feedback
- Generalizes to previously unseen environments and tasks
- Trained and evaluated in complex simulated games/environments (e.g., Goat Simulator 3)
Best for
- Research on generalist embodied agents: studying how language, perception, and action combine to create adaptable agents in 3D simulated worlds.
- Game testing and playtesting: automating exploration and interaction with game mechanics to find bugs, balance issues, or emergent behaviors across complex titles.
- Human-in-the-loop training: enabling developers and researchers to teach and correct agent behavior interactively via natural language and demonstrations.
- Benchmarking multimodal reasoning: evaluating agent performance on tasks requiring planning, long-horizon goal management, and perceptual understanding.
- Simulated robotics and control research: using virtual 3D environments as safe, rich testbeds for developing transferable control and decision-making skills.
- Research on embodied agents and generalization in simulated 3D environments
- Human-agent collaborative play and instruction following in virtual worlds
- Automated playtesting and exploration of open-ended video games
- Prototyping and benchmarking reasoning-capable agents in simulation
- Developing interactive virtual assistants or tutors inside simulated environments