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MagiCrew vs openrig: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of MagiCrew and openrig — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

MagiCrew logo

MagiCrew

Guangdong Lighthouse Engine Technology

Freemium

An Apache-2.0 open-source enterprise AI agent platform that turns internal systems and expertise into reusable digital workers every employee can deploy.

Key features

  • Digital Worker Marketplace: ERP, CRM, database and business knowledge are encapsulated into reusable agents built once and deployed company-wide, with ready-made finance, legal, support, sales, analytics and project-manager roles.
  • Multi-Agent Orchestration: An orchestrator agent dispatches specialist agents that work in parallel with a clear division of labour rather than running one task at a time.
  • Deliverable-Ready Output: A rendering framework converts agent results directly into PowerPoint decks, data dashboards, meeting notes, professional reports, Excel files and infinite canvases ready for business use.
  • Human Approval Loop: Agents complete safe operations autonomously, but high-risk actions such as permanently deleting records or sending email are queued for explicit human confirmation.
  • Three-Tier Budget Control: Daily budgets are set and tracked per department, per user and per agent, with live cost attribution making AI spending predictable.
  • Sandbox and VPC Isolation: Each agent runs in its own container in a separate VPC connected by private endpoints, with multi-tenant resource isolation, a per-user sidecar network proxy and security review of plugins before listing.
  • Skills Ecosystem Compatibility: Anthropic Skills and OpenClaw Skills work directly with zero migration cost, and Skill Creator defines new custom skills through conversation.
  • Team Collaboration: Multiple people share one project with modules progressing in parallel and results syncing live, with integrations for Enterprise WeChat, DingTalk and Feishu.

Best for

  • Small Team Output Scaling: A three-person marketing team runs competitor research, industry reports, social copy and event planning with agents collaborating throughout, covering work that would otherwise need a much larger department.
  • Contract Risk Review: Upload a contract and a legal expert agent analyses risk clauses, identifies unequal obligations, flags hidden traps and proposes revisions.
  • Automated Reporting: Data extraction, comparative analysis, chart generation and layout export run end to end so a weekly report that took four hours is produced in minutes on a schedule.
  • Cross-Border Trade Operations: A trade assistant drafts emails that match local business customs across ten languages and orchestrates regulatory research, compliance content, marketplace integration and order tracking for a small overseas team.
  • Institutional Knowledge Retention: Capture a retiring engineer's after-sales expertise, from symptom to diagnostic path to solution to parts dispatch, into an agent that gives new staff senior-level guidance.
  • New-Hire Onboarding: Connect a new starter to project-management expert agents, knowledge bases and case libraries on day one, compressing ramp-up from months to weeks.
  • Governed Enterprise AI Rollout: Replace scattered personal use of third-party AI tools with one platform that enforces departmental budgets, sandbox isolation and approval gates.
View MagiCrew details
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openrig

mvschwarz

Free

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.
View openrig details