---
title: AI DevKit vs Spec Kit
description: A practical comparison of AI DevKit and GitHub Spec Kit for structured AI-assisted software development.
---

Both **AI DevKit** and **Spec Kit** solve the same core problem: AI coding agents are powerful, but they need structure. Without a clear process, results become inconsistent, context is lost between sessions, and important engineering steps get skipped.

These tools take different paths to fix that.

The main difference is what each tool focuses on:

- **AI DevKit** focuses on project scaffolding, reusable workflows, skills, and persistent knowledge memory.
- **Spec Kit** focuses on spec-driven execution using a strict command pipeline (`/speckit.constitution` -> `/speckit.specify` -> `/speckit.plan` -> `/speckit.tasks` -> `/speckit.implement`).

## Quick Comparison

| | AI DevKit | Spec Kit |
|---|---|---|
| **Type** | CLI toolkit with memory and skills | Spec-driven development toolkit |
| **Install** | `npm install -g ai-devkit` | `uv tool install specify-cli --from git+https://github.com/github/spec-kit.git` |
| **Approach** | Focuses on setup and team workflow: tools, memory, and project templates | Focuses on a spec workflow: constitution, spec, plan, tasks, and implementation flow |
| **Memory** | Built-in local memory service for storing/searching project knowledge | No dedicated built-in memory store; relies on specs/artifacts and repository history |
| **Skills** | Built-in workflow skills plus installable skills from registries | No central installable skill system; focuses on a command-driven spec workflow |
| **Commands** | Common flow: `/new-requirement` -> `/review-design` -> `/execute-plan` -> `/writing-test` -> `/code-review` | `/speckit.constitution`, `/speckit.specify`, `/speckit.plan`, `/speckit.tasks`, `/speckit.implement` |
| **Agents supported** | Broad support across many environments (for example Claude Code, Cursor, Codex, Copilot) | Broad support via `specify init --ai ...` and generated slash command packs |
| **Documentation** | Phase-based directory structure (`docs/ai/`) | Spec and plan files created by the Spec Kit workflow |
| **Execution model** | Single agent per feature with persistent memory | Step-by-step flow where each command creates input for the next step |
| **License** | MIT | MIT |
| **Best for** | Teams that want a durable workflow system plus memory and reusable skills across projects | Teams that want a spec-driven method with tighter process sequencing |

> Note: Supported agents and commands can change over time. Verify current support in each project's repository.

## Quick Decision Guide

- Choose **AI DevKit** if you want a reusable workflow system for AI work: project docs setup, step-by-step commands, memory, and skill management.
- Choose **Spec Kit** if you want to standardize on a spec-first pipeline and keep delivery anchored to one standard spec workflow.
- Use both if you want Spec Kit's strict spec workflow and AI DevKit's memory and team consistency.

## First 10 Minutes

### AI DevKit

```bash
npm install -g ai-devkit
ai-devkit init
# open your AI coding tool in this project folder
/new-requirement
```

### Spec Kit

Prerequisite: install `uv` first (https://docs.astral.sh/uv/getting-started/installation/).

```bash
# install CLI with uv
uv tool install specify-cli --from git+https://github.com/github/spec-kit.git

# initialize current project for your AI agent
specify init --here --ai <agent>

# in your AI assistant
/speckit.constitution
/speckit.specify
```

Use your tool name for `<agent>` (for example `claude`, `codex`, `cursor`).

## How They Work

### AI DevKit

AI DevKit is a **CLI toolkit** that you install globally and initialize per project. It creates a docs structure, provides slash commands for each development phase, includes built-in skills, and adds persistent local memory.

```bash
npm install -g ai-devkit
ai-devkit init
```

After initialization, your project gets a `docs/ai/` directory with subdirectories for requirements, design, planning, implementation, testing, deployment, and monitoring. Your AI agent uses commands like `/new-requirement`, `/execute-plan`, and `/code-review` to move through a structured workflow.

A key benefit is **saved project memory**. Teams can store and retrieve decisions, patterns, and conventions so agents can reuse prior context across sessions.

### Spec Kit

Spec Kit is a **spec-driven workflow toolkit** centered on a strict command sequence. You define principles (`/speckit.constitution`), specify what to build (`/speckit.specify`), create a technical plan (`/speckit.plan`), generate tasks (`/speckit.tasks`), then implement (`/speckit.implement`).

In Spec Kit, a constitution is a short set of project rules and principles.

The approach is intentionally opinionated: make requirements explicit before implementation and keep files consistent across phases. Each step creates files that the next step uses.

## Key Differences

### Memory and Context Persistence

**AI DevKit** includes a dedicated memory system. You can store and search knowledge using MCP tools or CLI commands (`ai-devkit memory store`, `ai-devkit memory search`). Memory is scoped and tagged for discovery across sessions.

**Spec Kit** does not include a dedicated memory database. Context persistence comes from the spec, plan, and task files generated by the workflow, plus repository history.

### Workflow Enforcement

**AI DevKit** provides structure but remains flexible. Teams can follow lifecycle guidance while adapting command order to project needs.

**Spec Kit** uses a stricter step order. The constitution/spec/plan/tasks/implement pipeline is designed so each phase is completed before the next.

### Planning and Specification Model

**AI DevKit** supports phase-based planning through project docs and workflow commands, with room for team-specific adaptations.

**Spec Kit** standardizes planning around clear spec files and command results, which increases consistency when teams want one standard process.

### Agent Support

**AI DevKit** supports many AI coding environments with environment-specific setup templates.

**Spec Kit** supports multiple agents through `specify init --ai ...` and generated command packs.

### Execution Model

**AI DevKit** usually runs one agent per feature, enhanced by persistent memory and lifecycle docs.

**Spec Kit** follows a phase-to-phase execution model where spec files are the main way work is passed to the next step.

## When to Use Which

### Choose AI DevKit if you want:

- A lightweight CLI toolkit that works with your existing workflow
- Persistent memory across sessions without repeating yourself
- Support for a wide range of AI coding environments
- Flexibility to adopt structured practices gradually
- Built-in and installable skills for team-specific practices
- Simple installation via npm

### Choose Spec Kit if you want:

- A clear spec-first delivery pipeline
- Constitution-driven project principles
- Strong guardrails from "what" to "how"
- A standardized `/speckit.*` command vocabulary
- Predictable handoffs through spec, plan, and task files

### Using Them Together

AI DevKit and Spec Kit are not mutually exclusive. AI DevKit can provide the **workflow foundation** (memory, skill management, lifecycle scaffolding), while Spec Kit can provide the **spec workflow** (constitution/spec/plan/task discipline). Teams that want both persistent memory and strict spec-first execution can combine them.

## Getting Started with AI DevKit

Ready to add structure to your AI-assisted development? Install AI DevKit and initialize your project:

```bash
npm install -g ai-devkit
ai-devkit init
```

Then use commands like `/new-requirement` to start building with a structured workflow, or explore the [documentation](https://ai-devkit.com/docs) to learn more.

## Sources

- AI DevKit repository: https://github.com/codeaholicguy/ai-devkit
- Spec Kit repository: https://github.com/github/spec-kit
