devclaw-gitea/README2.md

DevClaw

Turn any group chat into a dev team that ships.

DevClaw is a plugin for OpenClaw that turns your orchestrator agent into a development manager. It hires developers, assigns tasks, reviews code, and keeps the pipeline moving — across as many projects as you have group chats.

---

What it looks like

Add your OpenClaw agent to a Telegram group. Register a project. That's it — you now have a dev team:

You:    "Check the queue"
Agent:  "3 issues in To Do. DEV is idle. QA is idle."

You:    "Pick up #42 for DEV"
Agent:  "⚡ Sending DEV (medior) for #42: Add login page"
        (a Sonnet session opens, reads the repo, starts coding)

        ... 10 minutes later ...

Agent:  "✅ DEV DONE #42 — Login page with OAuth. Moved to QA."
        (a reviewer session opens automatically, starts reviewing)

        ... 5 minutes later ...

Agent:  "🎉 QA PASS #42. Issue closed."

No configuration between those steps. No manual handoff. The developer finished, QA started automatically, the issue closed itself. You watched it happen in your group chat.

Add another group → another project. Same agent, fully isolated teams.

---

The problem DevClaw solves

OpenClaw is a great multi-agent runtime. It handles sessions, tools, channels, gateway RPC — everything you need to run AI agents. But it's a general-purpose platform. It has no opinion about how software gets built.

Without DevClaw, your orchestrator agent has to figure out on its own how to:

• Pick the right model for the task complexity
• Create or reuse the right worker session
• Transition issue labels in the right order
• Track which worker is doing what across projects
• Chain DEV completion into QA review
• Detect crashed workers and recover
• Log everything for auditability

That's a lot of reasoning per task. LLMs do it imperfectly — they forget steps, corrupt state, pick the wrong model, lose session references. You end up babysitting the thing you built to avoid babysitting.

DevClaw moves all of that into deterministic plugin code. The agent says "pick up issue #42." The plugin handles the other 10 steps atomically. Every time, the same way, zero reasoning tokens spent on orchestration.

---

Meet your team

DevClaw doesn't think in model IDs. It thinks in people.

When a task comes in, you don't configure anthropic/claude-sonnet-4-5 — you assign a medior developer. The orchestrator evaluates task complexity and picks the right person for the job:

Developers

Who	What they do	Under the hood
Junior	Typos, CSS fixes, renames, single-file changes	Haiku
Medior	Features, bug fixes, multi-file changes	Sonnet
Senior	Architecture, migrations, system-wide refactoring	Opus

QA

Who	What they do	Under the hood
Reviewer	Code review, test validation, PR inspection	Sonnet
Tester	Manual testing, smoke tests	Haiku

A CSS typo gets the intern. A database migration gets the architect. You're not burning Opus tokens on a color change, and you're not sending Haiku to redesign your auth system.

Every mapping is configurable — swap in any model you want per level.

---

How a task moves through the pipeline

Every issue follows the same path, no exceptions. DevClaw enforces it:

Planning → To Do → Doing → To Test → Testing → Done

stateDiagram-v2
    [*] --> Planning
    Planning --> ToDo: Ready for development

    ToDo --> Doing: DEV picks up
    Doing --> ToTest: DEV done

    ToTest --> Testing: QA picks up (or auto-chains)
    Testing --> Done: QA pass (issue closed)
    Testing --> ToImprove: QA fail (back to DEV)
    Testing --> Refining: QA needs human input

    ToImprove --> Doing: DEV fixes (or auto-chains)
    Refining --> ToDo: Human decides

    Done --> [*]

These labels live on your actual GitHub/GitLab issues. Not in some internal database — in the tool you already use. Filter by Doing in GitHub to see what's in progress. Set up a webhook on Done to trigger deploys. The issue tracker is the source of truth.

What "atomic" means here

When you say "pick up #42 for DEV", the plugin does all of this in one operation:

1. Verifies the issue is in the right state
2. Picks the developer level (or uses what you specified)
3. Transitions the label (To Do → Doing)
4. Creates or reuses the right worker session
5. Dispatches the task with project-specific instructions
6. Updates internal state
7. Logs an audit entry

If step 4 fails, step 3 is rolled back. No half-states, no orphaned labels, no "the issue says Doing but nobody's working on it."

---

What happens behind the scenes

Workers report back themselves

When a developer finishes, they call work_finish directly — no orchestrator involved:

• DEV "done" → label moves to To Test, QA starts automatically
• DEV "blocked" → label moves back to To Do, task returns to queue
• QA "pass" → label moves to Done, issue closes
• QA "fail" → label moves to To Improve, DEV gets re-dispatched

The orchestrator doesn't need to poll, check, or coordinate. Workers are self-reporting.

Sessions accumulate context

Each developer level gets its own persistent session per project. Your medior dev that's done 5 features on my-app already knows the codebase — it doesn't re-read 50K tokens of source code every time it picks up a new task.

That's a ~40-60% token saving per task from session reuse alone.

Combined with tier selection (not using Opus when Haiku will do) and the token-free heartbeat (more on that next), DevClaw significantly reduces your token bill versus running everything through one large model.

The heartbeat runs for free

Every 60 seconds, a background service:

• Checks if any workers have been stuck for >2 hours (reverts them)
• Scans the queue for available tasks
• Dispatches workers to fill empty slots

All of this is deterministic code — CLI calls and JSON reads. Zero LLM tokens. Workers only consume tokens when they're actually writing code or reviewing PRs.

Everything is logged

Every tool call writes an NDJSON line to audit.log:

cat audit.log | jq 'select(.event=="work_start")'

Full trace of every task, every level selection, every label transition, every health fix. No manual logging needed.

---

Your issues, your tracker

DevClaw doesn't replace your issue tracker — it uses it. All task state lives in GitHub Issues or GitLab Issues (auto-detected from your git remote). The eight pipeline labels are created on your repo when you register a project.

The abstraction layer (IssueProvider) is pluggable. GitHub and GitLab work today. Jira, Linear, or anything else just needs to implement the same interface.

This means:

• Your project manager sees task progress in GitHub/GitLab without knowing DevClaw exists
• Your CI/CD can trigger on label changes
• Your existing dashboards and filters keep working
• If you stop using DevClaw, your issues and labels stay exactly where they are

---

Custom instructions per project

Each project gets its own instruction files that workers receive with every task:

workspace/projects/roles/
├── my-webapp/
│   ├── dev.md     "Run npm test before committing. Deploy URL: staging.example.com"
│   └── qa.md      "Check OAuth flow. Verify mobile responsiveness."
├── my-api/
│   ├── dev.md     "Run cargo test. Follow REST conventions in CONTRIBUTING.md"
│   └── qa.md      "Verify all endpoints return correct status codes."
└── default/
    ├── dev.md     (fallback for projects without custom instructions)
    └── qa.md

Deployment steps, test commands, coding standards, acceptance criteria — all injected automatically at dispatch time.

---

Getting started

Prerequisites

• OpenClaw installed (openclaw --version)
• Node.js >= 20
• gh CLI (GitHub) or glab CLI (GitLab), authenticated

Install

cp -r devclaw ~/.openclaw/extensions/

Set up through conversation

The easiest way to configure DevClaw is to just talk to your agent:

You:   "Help me set up DevClaw"
Agent: "I'll walk you through it. Should I use this agent as the
        orchestrator, or create a new one?"
You:   "Use this one"

Agent: "Want to bind a messaging channel?"
You:   "Telegram"

Agent: "Here are the default developer assignments:
        Junior → Haiku, Medior → Sonnet, Senior → Opus
        Reviewer → Sonnet, Tester → Haiku
        Keep these or customize?"
You:   "Keep them"

Agent: "Done. Want to register a project?"
You:   "Yes — my-app at ~/git/my-app, main branch"

Agent: "Project registered. 8 labels created on your repo.
        Role instructions scaffolded. Try: 'check the queue'"

You can also use the CLI wizard or non-interactive setup for scripted environments.

---

The toolbox

DevClaw gives the orchestrator 11 tools. These aren't just convenience wrappers — they're guardrails. Each tool encodes a complex multi-step operation into a single atomic call. The agent provides intent, the plugin handles mechanics. The agent physically cannot skip a label transition, forget to update state, or dispatch to the wrong session — those decisions are made by deterministic code, not LLM reasoning.

Tool	What it does
work_start	Pick up a task — resolves level, transitions label, dispatches session, logs audit
work_finish	Complete a task — transitions label, updates state, auto-chains next step, ticks queue
task_create	Create a new issue (used by workers to file bugs they discover)
task_update	Manually change an issue's state label
task_comment	Add a comment to an issue (with role attribution)
status	Dashboard: queue counts + who's working on what
health	Detect zombie workers, stale sessions, state inconsistencies
work_heartbeat	Manually trigger a health check + queue dispatch cycle
project_register	One-time project setup: creates labels, scaffolds instructions, initializes state
setup	Agent + workspace initialization
onboard	Conversational setup guide

Full parameters and usage in the Tools Reference.

---

Parallel everything

Each project is fully isolated — its own queue, workers, sessions, and state. No cross-project contamination.

Two execution modes:

• Project-level — DEV and QA work simultaneously on different tasks (default) or take turns
• Plugin-level — all projects run in parallel (default) or one at a time

One agent, many groups, many projects, all at once.

---

Documentation

Architecture	System design, session model, data flow, end-to-end diagrams
Tools Reference	Complete reference for all 11 tools
Configuration	openclaw.json, projects.json, heartbeat, notifications
Onboarding Guide	Full step-by-step setup
QA Workflow	QA process and review templates
Context Awareness	How tools adapt to group vs. DM vs. agent context
Testing	Test suite, fixtures, CI/CD
Management Theory	The delegation model behind the design
Roadmap	What's coming next

---

License

MIT