Larkway

Turn your Claude Code or Codex subscription into a Feishu (Lark) agent your whole team can @-mention.

🌐 larkway.dev · 中文版

You @ the bot in a Feishu thread. It runs on your machine — reading your real codebase, executing commands, opening MRs — and posts the result back. You define what the agent knows and what it can do. Larkway just carries the messages.

Current release: v0.3.28

💬 Join the community (Feishu)

See Larkway in action and talk through how to set it up for your team — scan to join the Larkway 交流群 (QR never expires):

Why

The engineers on your team know how a feature is actually implemented. Everyone else has to ask them. That friction compounds: a data analyst wondering whether a metric counts deleted records, a PM asking which API endpoint powers a chart, a QA engineer trying to reproduce a bug without context.

A generic AI assistant cannot answer these questions reliably — it has no access to your code. A bot that runs Claude Code or Codex against your real repository can.

Larkway makes that bot trivially installable and persistent: one larkway start on an always-on machine, one @mention in any Feishu thread, and your team has a self-serve answer to "how is this actually implemented."

How it works

Feishu thread
  │  @bot "how does the retention metric handle deleted users?"
  │
  ▼  WebSocket long-connection (outbound only, no public endpoint needed)
larkway bridge
  │  spawns subprocess  ──►  claude --resume <session_id> -p "<prompt>"
  │                          (or: codex ...)
  │
  ▼  stream-json (NDJSON)
lark.card                ◄──  agent reads repo, runs bash, edits files,
  │  throttled PATCH           opens MR, starts dev preview
  │
  ▼
Feishu thread
     "retention.ts line 47: deleted_at IS NULL filter is applied before
      the aggregation window. Deleted users are excluded."

What Larkway does (and only this):

• Feishu long-connection subscriber (inbound events)
• Subprocess lifecycle: spawn, stream-json parse, session-ID persistence
• Feishu card: throttled real-time PATCH as the agent streams output
• Session KV: thread_id → session_id so follow-up messages resume context
• Idle GC: worktree housekeeping

What Larkway does NOT do — the agent decides everything else:

• No GitLab/GitHub API calls (the agent runs glab/gh itself)
• No worktree creation (the agent runs git worktree add)
• No dev server management (the agent starts it)
• No orchestration logic — the agent reads your AGENTS.md / CLAUDE.md / skills and plans its own steps

This boundary is intentional. Workflow changes go into your repository's agent guides, not into Larkway.

Quick start

Install globally with npm:

npm i -g larkway

# 1. Check your environment
larkway doctor          # lists missing dependencies; --fix auto-installs some

# 2. Register a Feishu app and configure your first bot
larkway init            # CLI wizard: scan QR code → name the bot → pick a backend

# 3. Start the bridge
larkway start           # long-running; runs in background, logs to ~/.larkway/logs/

# 4. Add the bot to a Feishu group
#    Group settings → Bots → Add bot → pick yours → @-mention it

Backends

Larkway never injects ANTHROPIC_API_KEY or any other API key. The subprocess inherits your local login state. If you switch to API-key mode, that is a deliberate opt-in in src/claude/runner.ts.

Defining a bot (three layers)

Secrets live only in ~/.larkway/.env (mode 0600). Config and memory contain no secrets.

Features

• Multiple bots on one bridge — a read-only Q&A bot and a write-capable engineering bot can share the same process, each with its own L1/L2/L3 definition
• Web UI — larkway ui opens a local management dashboard (127.0.0.1 + token); create bots, edit memory, watch live logs
• Session continuity — every Feishu thread maps to a persistent session_id; the agent remembers what it did in prior turns
• Agent Workspace — per-thread git worktrees; the agent can run multiple threads concurrently without git conflicts
• Codex runtime pre-checks — larkway doctor validates Codex state directory writability before start

Requirements

• Node.js 20+ LTS
• A Claude Code or Codex subscription with local CLI installed and logged in
• lark-cli — Feishu long-connection client and message utilities
• glab + git — for bots that open MRs (optional for read-only bots)
• An always-on host machine — the bridge must stay running to receive Feishu events; a laptop that sleeps will miss messages; a small server or desktop works well

Commands

Documentation

License

MIT