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 local-first 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  --wait

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