openrig vs Spine Canvas: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of openrig and Spine Canvas — 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.
Spine Canvas
Spine AI
An unlimited visual workspace that lets teams explore, branch, and orchestrate ideas across hundreds of AI models in a single canvas.
Key features
- Infinite Canvas: A limitless visual workspace that lets users create and arrange multiple parallel threads, blocks, and branches to explore ideas without linear chat constraints.
- Multi-Model Orchestration: Run and compare outputs from 300+ AI models through a single subscription, enabling side-by-side evaluation and selection of best-performing models.
- Document Contexting: Upload proprietary documents and datasets to provide direct contextual grounding for model runs, improving relevance and accuracy of outputs.
- Branching & Versioning: Create branches of conversations and workflows to iterate on different approaches, preserving provenance and allowing return to prior states.
- Composable Blocks: Use modular blocks (e.g., Chat, Deep Research) that can be connected and reconfigured to build complex, repeatable AI workflows and experiments.
- Parallel Runs & Comparison: Execute many model runs in parallel on the canvas and maintain full visibility into differences, making model benchmarking and synthesis straightforward.
- Collaboration & Sharing: Share canvases with teammates, collaborate in a visual space, and communicate reasoning and decisions with clear context and outputs.
- Model Selection Controls: Manage model selection, prompts, and run parameters from the canvas to fine-tune experiments and orchestrated pipelines.
- Infinite visual canvas for organizing workflows and ideas
- Block-based elements (Chat, Deep Research, etc.) to compose pipelines
- Branching and parallel threads so multiple approaches run concurrently
- Orchestration of 300+ models via a single subscription (multi-model access)
- Upload proprietary documents to provide contextual grounding to models
- Shareable canvases and links for collaboration
- Web-based app accessible through app.getspine.ai / getspine.ai
Best for
- Research Synthesis: Upload internal reports and data to run contextualized research queries, synthesize findings, and branch alternative analyses on the canvas.
- Product Ideation & Roadmapping: Brainstorm features visually, run multiple proto-conversations or market-research models in parallel, and preserve branches for different directions.
- Model Benchmarking: Execute the same prompt across dozens or hundreds of models to compare outputs, measure quality, and select the best model for a task.
- Content & Narrative Development: Create branching storylines or marketing content variants, iterate on multiple drafts in parallel, and select or merge the best elements.
- Knowledge-Driven QA: Integrate proprietary documentation to enable grounded question-answering and decision support for internal teams or customer support.
- Collaborative Planning: Teams co-construct workflows and experiments on a shared canvas, keeping context, provenance, and discussion visible for stakeholders.
- Experimentation Pipelines: Compose modular blocks to create repeatable AI workflows (data ingest → model runs → synthesis) and iterate on parameters and models visually.
- Brainstorming and ideation with branching explorations
- Research workflows that combine multiple model runs and document context
- Orchestrating and comparing outputs from many model providers
- Collaborative planning and knowledge work with shared canvases
- Prototyping multi-step AI workflows visually without code