Craig
Craig is a local terminal UI that manages multiple AI coding agent tasks across repositories without requiring a hosted account or external control plane. It creates isolated git worktrees and branches for each agent task, allowing parallel work without merge conflicts, and syncs pull request and CI check status in real time within the terminal workspace. Developers switch between tasks using keyboard shortcuts, inspect file diffs and PR state without opening new windows, and can drive the workspace programmatically via CLI commands. Craig integrates with Claude Code, Codex, Cursor, and GitHub CLI, letting agents run in their native terminal sessions while Craig provides task navigation and review context around them.
Craig is a local terminal UI, integrating with Claude Code, Codex, Cursor and Pi. InnovaAI rates it 4 of 10 for agency adoption, best for Developer, Tech Lead and Project Manager roles.
Agency Audit
Craig is a local terminal UI that orchestrates multiple AI coding agents (Claude Code, Codex, Cursor) across repositories by isolating each task in its own git worktree and keeping pull requests, diffs, and CI checks visible in one workspace. Development agencies and engineering teams managing parallel coding workflows benefit most, as Craig eliminates context-switching between agent CLIs and repository states. The tool runs locally without requiring a hosted account, integrates with GitHub CLI, and lets agents drive the workspace programmatically. Adopt Craig if your development team runs 3+ concurrent AI-assisted coding tasks per week and currently loses time navigating between agent terminals and PR review contexts.
5recommended
60/mo
No paid plan published
Moderate
Illustrative scenario. Not a guarantee. Net capacity needs a verified paid base plan, and none is published for this service, so it is not modeled. Hours saved come from the service estimate; implementation, taxes, and unprovided usage charges are excluded.
- Developer handling parallel AI agent task orchestration
- Tech Lead handling multi-repository branch coordination
- Project Manager handling pull request and CI status monitoring
- Your development team runs fewer than 2 concurrent AI coding tasks per week or assigns one agent per repository, making the parallel-task orchestration feature unnecessary.
- Your team does not use Claude Code, Codex, Cursor, or other CLI-based AI coding agents, as Craig is an orchestrator for existing agent tools, not a replacement for them.
- Your developers are unfamiliar with terminal UIs or git worktrees and lack the bandwidth to learn a new keyboard-driven workflow, as Craig's value depends on terminal fluency.
Internal Adoption Path
No paid plan published
60 hr/mo
5 seats × 12 hr each
$4,500/mo
modeled at $75/hr labor rate
No paid plan published
Illustrative scenario. Not a guarantee. No verified paid base plan is published for this service, so subscription cost and net capacity are not modeled. Implementation, taxes, and unprovided usage charges are excluded.
Platform Features
Core capabilities of Craig
Parallel agent task isolation
Craig creates a separate git worktree and branch for each AI agent task, allowing multiple agents to work simultaneously on the same repository without blocking each other. Development teams save 1-2 hours per week by eliminating manual branch coordination and merge-conflict resolution.
Unified terminal workspace
Switch between agent tasks, inspect file diffs, and view PR status from a single terminal session without opening multiple windows or tabs. Developers reduce context-switching overhead by 40-60% when managing 3+ concurrent agent workflows.
Real-time PR and CI status sync
Craig displays pull request state and GitHub CI check results within the terminal workspace, letting developers monitor agent progress without leaving the keyboard. Project Managers and Tech Leads gain visibility into agent task completion without polling multiple GitHub tabs.
Agent-facing CLI for programmatic control
Agents and scripts can inspect task context, create new tasks, and operate the workspace via CLI commands, enabling fully automated multi-agent workflows. Development teams automate repetitive task-creation and status-reporting steps that currently consume 2-3 hours per week.
Multi-repository task navigation
Manage frontend, API, and other repositories from one workspace and switch between agent tasks across repos using keyboard shortcuts. Teams working on monorepos or multi-service architectures eliminate the friction of managing separate terminal sessions per repository.
Local-first, no hosted account required
Craig runs entirely on the developer's machine using existing git and agent CLI authentication, with no external control plane or SaaS account. Agencies avoid vendor lock-in and maintain full control over agent task data and repository state.
What Makes Craig Different
Unique advantages vs similar tools in this niche
Per-task git worktree isolation for parallel agents
vs Running multiple agents in one shared working treeCraig creates a branch and worktree for each task so agents work side by side without collisions.
Agnostic orchestration across Claude Code, Codex, and Cursor
vs Being locked into a single vendor's agent CLIYour agents run in real terminal sessions with their own prompts, tools, and authentication while Craig adds navigation and review context.
Local-first with no hosted control plane
vs Cloud-hosted agent orchestration platformsCraig runs locally in your existing terminal with no Craig account required.
Value Equation
Outcome-likelihood-time-effort assessment for Craig
Value math requires real pricing
The Value Equation (dream outcome × likelihood ÷ time × effort) feeds directly into ROI math. Craig has no published pricing, so we hold this section until real numbers are available.
Contact CraigPricing
Pricing data not yet available for Craig.
Reality Check
Craig requires Node.js 22+, Git, and pre-authenticated agent CLIs (Claude Code, Codex, or Cursor) to function, creating a setup barrier for teams without existing agent tooling. The terminal-first interface demands keyboard fluency and may slow adoption among developers accustomed to GUI-based git workflows.
Moderate effort: standard configuration with some customization needed
How This Accelerates White-Label Services
Who It's For
- ✓development-agencies-building-software-for-clients
- ✓engineering-teams-managing-multiple-repositories
- ✓agencies-running-parallel-ai-coding-agent-workflows
Acceleration Steps
- 1Create your account and complete setup wizard
- 2Configure run multiple ai coding agents in parallel across repositories
- 3Connect Claude Code
- 4Launch your first client project
Academy for Craig
Work through it in order: the course for this service first, then the modules behind it.
Course for this service
Craig Agency Implementation, Scaling AI Coding Agent Delivery
Learn how to structure productized coding services around Craig's parallel agent task isolation and terminal workspace, enabling agencies to deliver multi-agent projects without manual branch coordination. This course covers workspace setup, agent task orchestration, client delivery workflows, and pricing models for AI-assisted development retainers.
Open the courseNo Academy modules are published for this service yet. Browse the full Academy
Why this category matters
The commercial case before the tooling.
Core concepts
The mental model you need to price and scope the work.
- Wiring Over WidgetsConcept
The AI agent itself is a commodity, but the value for agencies lies in the integration layer: connecting a pre-built agent to a client's CRM, calendar, and review cycle. This framework shifts focus from selecting the 'best' agent to mastering the wiring process. For example, an agency using Vendasta's white-label AI receptionist for a local business must configure it to match the client's booking rules and follow-up cadence, turning a generic tool into a tailored service. As agentic AI adoption grows (77% of decision-makers now run agents in production), clients expect this customization. Agencies that treat agents as components and invest in repeatable wiring processes can charge retainers for ongoing optimization, rather than one-off setup fees.
- Wiring Over WidgetsConcept
The AI agent market sells finished workers, but the strategic value for agencies lies not in the agent itself, which is increasingly a commodity, but in the wiring that connects it to a specific client's CRM, calendar, and review cycle. This framework, 'Wiring Over Widgets,' argues that agencies that treat agents as components rather than products win. The agent is the widget; the wiring is the integration, customization, and ongoing optimization that turns a generic tool into a tailored solution. For example, a white-label platform like Vendasta provides AI employees, but the agency's role is to configure them for each local business's unique lead flow and follow-up process. This wiring is where retainer pricing originates, as it requires ongoing maintenance and adjustment. Recent research shows that 88% of B2B marketers face foundational gaps, meaning clients need help not just deploying agents, but ensuring their operations can support them. Agencies that master the wiring can charge a premium for the irreducible value they add.
- Wiring Premium Over Agent CommodityConcept
Pre-built agents are converging on the same underlying model capability, so the agent itself prices toward zero. What holds value is the wiring: the mapping of a specific client's CRM fields, calendar rules, escalation paths, and review cadence into the agent's loop. Agencies that sell the agent as the product compete on seat price against every reseller of the same worker. Agencies that sell the wiring charge for discovery, field mapping, exception handling, and monthly tuning, which is retainer work. Vendasta's white-label AI workforce and Relevance AI's pre-built sales agents both arrive configured out of the box, which means the configuration is not the moat; the client-specific plumbing is. A practical test: if a competitor could swap your agent vendor next quarter without the client noticing, you sold a commodity. If the swap would break their CRM hygiene, approval chain, or reporting, you sold wiring.
Decision and risk
How to judge the fit, and the ways it goes wrong.
- AI Agents Rule: Wire the Agent, Not the ProductEvaluation Rule
Treat the AI agent as a commodity component and focus your value on the integration into the client's specific workflows, systems, and review processes.
- AI Agents Rule: Wire the Agent, Not the ProductEvaluation Rule
Treat the AI agent as a commodity component and charge for the integration into the client's specific systems and workflows.
- The Productized Agent Trap: Why AI Agent Services Stall Without Client-Specific WiringFailure Pattern
- The Demo-to-Retainer Gap: Why AI Agents Stall After the Pilot CallFailure Pattern
8 modules selected for Craig
Frequently Asked Questions
Answers about pricing, setup
Craig is a terminal UI that orchestrates multiple AI coding agents (Claude Code, Codex, Cursor) across one or more repositories. It creates a separate git worktree and branch for each agent task, syncs pull request and CI check status in real time, and lets developers switch between tasks and inspect diffs without leaving the terminal. Agents can also drive Craig programmatically via CLI commands, enabling automated multi-agent workflows.
Craig is open source under the MIT license and free to install via npm. There is no per-seat pricing, subscription, or hosted account requirement.
Development teams and Tech Leads benefit most by gaining unified visibility into parallel AI agent workflows and eliminating context-switching between terminals. Project Managers and Founders overseeing software delivery for clients gain real-time progress tracking without manual status checks. Account Executives managing client expectations on feature delivery timelines can rely on accurate, automated agent task status rather than developer estimates.
A development team running 3+ concurrent AI agent tasks across multiple repositories typically saves 3-5 hours per week by eliminating manual branch coordination, context-switching between terminals, and manual PR status polling. Savings scale with task parallelism; teams running 5+ concurrent tasks may save 6-8 hours per week. Actual savings depend on current workflow friction and terminal fluency.
Craig requires Node.js 22 or later, Git, and at least one authenticated AI coding agent CLI (Claude Code, Codex, Cursor, Pi, or similar). GitHub CLI is required to use PR creation and merge features. Craig runs on macOS, Linux, and Windows with WSL.
Craig is agent-agnostic and works with any CLI-based AI coding agent that can operate in a terminal session, including Cursor, Pi, and custom agent scripts. The agent runs in its native CLI within Craig's worktree, so it retains its own prompts, tools, and authentication.