AI DevKit

English | 中文

The control plane for AI coding agents.

AI DevKit gives Claude Code, Codex CLI, Gemini CLI, opencode, Pi, Cursor, GitHub Copilot, Devin, and other coding agents one shared operating layer: one config, one console, local memory retrieval, cross-agent communication, and composable engineering skills led by dev-lifecycle.

• One config for every agent — .ai-devkit.json reconciles setup across the coding tools your team uses
• One console for running sessions — agent console is a live TUI dashboard for supervising local agents across providers
• Cross-agent communication — agent send lets you route prompts, logs, and test output to running agents
• Memory retrieval without context bloat — @ai-devkit/memory stores decisions, conventions, and fixes in local SQLite so agents search when needed instead of carrying everything in every prompt
• Composable engineering skills — dev-lifecycle, verify, tdd, review, debugging, security, docs, and simplification skills combine into reliable workflows

The future is many AI coding agents. AI DevKit is the layer that makes them manageable.

Run npx ai-devkit@latest init and your project gets:

What you need	What AI DevKit installs
One setup source	.ai-devkit.json for the agents and workflow you choose
Running-agent visibility	agent list, agent detail, and agent console
Addressable agents	agent send, --stdin, --wait, and agent groups where supported
Retrieval-based memory	Local SQLite memory exposed through MCP and CLI, searched only when useful
Composable senior-engineer workflow	dev-lifecycle plus verification, TDD, debugging, review, security, docs, and simplification skills

Who this is for

Developers whose AI coding setup has grown from one assistant into a small, messy team of agents:

• multiple terminals with no shared control surface
• separate CLAUDE.md / .cursor/rules / AGENTS.md / MCP setup per tool
• no easy way to send context, logs, or follow-up work to a running agent
• the agent forgetting yesterday's conventions
• "I've successfully implemented the feature" with a red build
• the agent diving into code without a plan and producing the wrong thing

Before AI DevKit, your agents are powerful but scattered. After AI DevKit, they have shared setup, a control surface, searchable memory, communication paths, and reusable skills that travel with your repo without bloating every prompt.

Without AI DevKit	With AI DevKit
You manage agents as isolated terminal tabs	You supervise them from ai-devkit agent console
You hand-maintain every agent setup	One config reconciles agent files, skills, and MCP setup
You copy logs and context between sessions	agent send routes prompts and stdin to running agents
You repeat project rules in every chat	Agents retrieve relevant memory and docs only when useful
The agent jumps from prompt to code	dev-lifecycle guides requirements, design, planning, implementation, testing, and review
"Done" means the agent stopped editing	"Done" requires fresh verification output

Start in 30 seconds

npx ai-devkit@latest init

One wizard. Pick your agents, install the control-plane pieces you need, and give every tool the same operating model. It writes project-local files you can review and commit. Re-run it whenever your agent list or workflow changes.

Here's what lands in your repo:

your-project/
├── .ai-devkit.json              # single source of truth (re-run init anytime)
├── .claude/                     # or .cursor/, .codex/, etc. per agent you picked
│   ├── skills/                  # dev-lifecycle, verify, memory, tdd, ...
│   └── settings.json            # MCP servers wired up (incl. @ai-devkit/memory)
└── docs/ai/
    ├── requirements/            # phase 1 — what to build, why
    ├── design/                  # phase 2 — how it'll be built
    ├── planning/                # phase 3 — task-by-task plan
    ├── implementation/          # phase 4 — execution notes
    └── testing/                 # phase 5 — coverage strategy

Operate agents like infrastructure

AI DevKit ships a agent control plane for everyday multi-agent work:

# List running sessions across providers
ai-devkit agent list

# Open the live terminal UI
ai-devkit agent console

# Send a prompt to a running session and wait for the response
ai-devkit agent send "run the tests and report back" --id <agent-name> --wait

# Pipe multi-line output into a running session
npm test 2>&1 | ai-devkit agent send --id <agent-name> --stdin

# Send a prompt to a saved group of agents
ai-devkit agent send "review this branch for release risk" --group reviewers

# Pipe a session through Telegram — operate your agent from your phone
ai-devkit channel start telegram --agent <agent-name> --daemon

Use this when work spans long-running agents, multiple providers, scheduled checks, review loops, or remote control from another channel.

Add memory without bloating context

AI DevKit memory is local SQLite knowledge for project decisions, coding conventions, and reusable fixes. Agents retrieve it when a task needs context instead of carrying every fact in every prompt.

# Store a reusable project convention
ai-devkit memory store \
  --title "API handlers return DTOs" \
  --content "REST handlers should return response DTOs instead of domain entities." \
  --tags "api,backend" \
  --scope "repo:codeaholicguy/ai-devkit"

# Search before related work
ai-devkit memory search --query "API response convention"

Compose engineering workflows with skills

The control plane is useful on its own. For larger or riskier changes, AI DevKit also installs composable skills that make agents behave more like an engineering team.

dev-lifecycle is the anchor skill. It guides the agent through requirements, design, planning, implementation, testing, and review. Other skills plug into that flow:

• memory retrieves relevant project knowledge without stuffing all context into the session
• verify blocks completion claims without fresh test or build evidence
• tdd pushes test-first implementation when behavior changes
• structured-debug keeps debugging reproducible instead of guess-and-patch
• security-review, document-code, and simplify-implementation add focused review passes when the task needs them

Get the full engineering workflow stack

Save templates/senior-engineer.yaml locally and run:

ai-devkit init --template ./senior-engineer.yaml

Bundles the built-in skills with curated additions from Anthropic, Vercel, and others: TDD, frontend design, webapp testing, doc co-authoring, React best practices, security review, and more.

A feature, end-to-end

You:    Use the dev-lifecycle skill to start requirements for OAuth login with Google

Agent:  Searches memory for prior auth conventions. Asks clarifying
        questions about scope, users, success criteria. Drafts
        docs/ai/{requirements,design,planning}/feature-oauth-login.md
        in a feature worktree. Stops before coding.

You:    Ask for a design review of feature-oauth-login

Agent:  Audits the design doc against the requirements. Flags gaps,
        proposes fixes — before any code gets written.

You:    Ask it to execute the implementation plan

Agent:  Works the planning doc task-by-task. Updates progress after
        each task. The `verify` skill blocks a task from being
        marked done without fresh test/build output.

You:    Ask for a code review

Agent:  Audits the diff against the design doc — scope creep,
        missing tests, edge cases the requirements named —
        before you push.

What changes in agent behavior

The flow above is powered by eight built-in skills, each addressing a failure mode developers see in real AI coding sessions:

Failure mode	AI DevKit behavior
Agent starts coding too early	dev-lifecycle forces requirements, design, planning, implementation, tests, and review
Agent says "done" without proof	verify blocks completion claims without fresh test/build evidence
Agent forgets project decisions	memory gives it a local, searchable knowledge base across sessions and projects
New behavior ships without tests	tdd pushes test-first implementation
Debugging becomes guess-and-patch	structured-debug makes it reproduce, hypothesize, fix, and verify
Existing code is opaque	document-code maps entry points, dependencies, and behavior
Implementation gets bloated	simplify-implementation reduces complexity before code ships
Documentation is hard to follow	technical-writer audits docs for novice-user clarity

Need more? ai-devkit skill add <registry> <skill> pulls from 30+ publishers — Anthropic, Vercel, Supabase, Microsoft, Google.

Works across coding agents

One .ai-devkit.json configures all of them. Add a new agent to your team without rewriting your rules.

Agent	Setup	Remote control
Claude Code	yes	yes
Gemini CLI	yes	yes
Codex CLI	yes	yes
Junie	yes	—
Cline	yes	—
Devin	yes	—
opencode	yes	testing
Pi	yes	yes
Cursor	yes	—
GitHub Copilot	yes	—
Antigravity	yes	—
Amp	yes	—
Kilo Code	yes	—
Roo Code	testing	—

Setup — ai-devkit init writes the agent's config (rules, MCP servers, and skills) so it joins the same operating layer. Remote control — drive running sessions from ai-devkit agent send and route them through external channels.

How is this different from CLAUDE.md, .cursor/rules, or AGENTS.md?

Those files are static instructions the agent re-reads. AI DevKit gives agents a operating layer: generated setup, a control console, cross-agent messaging, local searchable memory, phase docs, skills loaded on demand, and verification gates. The rules still matter, but AI DevKit makes them operational across tools.

Static rules files	AI DevKit
Tell one agent what you prefer	Reconciles setup across supported agents
Do not show what is running	Lists, inspects, and controls live sessions
Cannot send work between sessions	Routes prompts, stdin, and channel messages to agents
Depend on the agent remembering every rule	Stores and searches reusable project knowledge
Cannot prove a task is complete	Requires fresh command output before completion claims

What this isn't

• Not a smarter LLM. Bad models stay bad — this raises the floor on process, not on raw capability.
• Not a replacement for Claude Code, Codex, Cursor, Gemini CLI, or opencode. AI DevKit configures, supervises, and coordinates the agents you already use.
• Not a magic "write the feature for me" button. You still review the requirements doc, accept the design, and read the diff. The workflow makes that review possible because you have artifacts to point at instead of only chat scrollback.
• Not a hosted service. MIT-licensed, runs locally, no telemetry. Memory is a SQLite file on your disk. The agent control plane talks to the agent SDKs you already use.

Documentation & community

• Full guides, workflow patterns, skill authoring → ai-devkit.com/docs
• Release notes → CHANGELOG.md
• Contributing → CONTRIBUTING.md

git clone https://github.com/Codeaholicguy/ai-devkit.git
cd ai-devkit && npm install && npm run build

License

MIT