devsquad 3.6.6

Production-Ready Multi-Role AI Task Orchestrator

DevSquad — Multi-Role AI Task Orchestrator

🎯 把「单个 AI 助手」升级成「7 人 AI 专业团队」
One task → Multi-role AI collaboration → One conclusion | V3.6.6 Enterprise Ready

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📖 太长不看？先看这个（30 秒）

DevSquad 是什么？

DevSquad 是一个多角色 AI 任务编排器。当你提交一个任务时，它不再是单个 AI 回答，而是让 7 个专业角色（架构师、安全专家、测试员、开发者等）并行协作，最后给出经过多方审核的结论。

传统 AI:  你 ──→ ChatGPT ──→ 一个回答（可能不全面）
DevSquad:  你 ──→ DevSquad ──→ [架构师+安全+测试+开发...] ──→ 多维度共识结论

核心优势（对比单 AI）

痛点	传统单 AI	DevSquad
视角单一	只有通用视角	7 个专业角色并行审视 ✅
质量不可控	可能遗漏安全问题	多维度交叉验证 + 共识机制 ✅
无审计追踪	不知道回答依据什么	完整审计链 + SHA256 完整性校验 ✅
复杂任务崩溃	长任务容易丢失上下文	Checkpoint 断点续传 + 工作流引擎 ✅

最快上手（5 分钟）

# 安装
pip install devsquad

# 运行 - 让 AI 团队帮你设计认证系统
devsquad run "设计一个安全的用户认证系统" --roles architect,security,tester,coder

# 输出结构化报告：
# ✅ 架构师建议：采用 JWT + Refresh Token 方案...
# ✅ 安全专家审查：需防范 CSRF、XSS、SQL 注入...
# ✅ 测试策略：单元测试覆盖率达 90%+...
# ✅ 开发实现：提供完整代码框架...
# 📊 共识结论：方案可行，风险可控...

什么时候用 DevSquad？

你的需求	推荐方案
简单问答（"Python 怎么写 for 循环？"）	直接用 ChatGPT/Claude ✅
代码片段审查	DevSquad 单角色模式 ✅
复杂系统设计（需要多视角）	DevSquad 多角色协作 🎯
生产环境自动化流程	DevSquad + REST API + Dashboard 🎯

📚 想深入了解？ → 完整快速入门指南 | 70+ 模块详细参考

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🔍 点击展开：完整功能介绍与架构详解

🚀 V3.6.6: Enterprise Edition Release

DevSquad V3.6.6 adds enterprise-grade features and comprehensive E2E testing: RBAC Engine with 15+ permissions and 5 roles, Audit Logger with SHA256 integrity chain, Multi-Tenancy Manager with 3 isolation levels, Sensitive Data Masker for PII protection, AsyncIO transformation for 2x throughput improvement, Redis Cache Integration with L1→L2→L3 three-level cache achieving 95%+ hit rate, Prometheus Monitoring with 12 core metrics, E2E Test Suite with 27 test cases covering 5 scenarios (CLI/API/Collaboration/Enterprise/Error Recovery) with 100% pass rate in 9 seconds, Code Quality improvements including print() → logging migration, TODO/FIXME cleanup, pre-commit hooks, and .editorconfig — making DevSquad truly enterprise-ready with 13,000+ lines new code, 1731 tests passing, and 97% maturity score.

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⚡ Quick Start (4 Ways to Use DevSquad)

方式 1：命令行（推荐新手）

# Interactive setup wizard (1-2 minutes)
python scripts/cli.py init

# Then start collaborating!
devsquad dispatch -t "your task description"

方式 2：Web Dashboard（推荐团队）

# Start Streamlit dashboard with authentication
streamlit run scripts/dashboard.py

# Open http://localhost:8501
# Login with default account, then change password immediately.
# Username: admin   Password: <your-secure-password>

方式 3：REST API（推荐集成）

# Install dependencies
pip install fastapi uvicorn

# Start API server
uvicorn scripts.api_server:app --host 0.0.0.0 --port 8000 --reload

# Access Swagger UI: http://localhost:8000/docs
# Access ReDoc:      http://localhost:8000/redoc

方式 4：Python API（推荐开发者）

from scripts.collaboration.dispatcher import MultiAgentDispatcher

dispatcher = MultiAgentDispatcher()
result = dispatcher.dispatch(
    task="Optimize database query performance",
    roles=["architect", "security", "tester"],
)
print(result.report)
print(result.consensus)

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👥 7 Core Roles

Role	CLI ID	Aliases	Weight	Best For
🏗️ Architect	arch	architect	1.5	System design, tech stack, performance/security architecture
📋 Product Manager	pm	product-manager	1.2	Requirements, user stories, acceptance criteria
🛡️ Security Expert	sec	security	1.1	Threat modeling, vulnerability audit, compliance
🧪 Tester	test	tester, qa	1.0	Test strategy, quality assurance, edge cases
💻 Coder	coder	solo-coder, dev	1.0	Implementation, code review, performance optimization
🔧 DevOps	infra	devops	1.0	CI/CD, containerization, monitoring, infrastructure
🎨 UI Designer	ui	ui-designer	0.9	UX flow, interaction design, accessibility

Auto-match: If no roles specified, the dispatcher automatically matches based on task keywords.

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🏗️ Five Capability Domains (Architecture Overview)

DevSquad's 70+ modules are organized into 5 capability domains, each solving a specific problem:

🎯 Domain 1: Task Orchestration Engine (Core)

让 7 个角色高效协作的「指挥中心」

Module	Purpose	When to Use
MultiAgentDispatcher	Unified dispatch entry point	All tasks automatically
Coordinator	Task decomposition + role assignment	Complex tasks needing breakdown
Scratchpad	Shared blackboard for real-time info exchange	Inter-role collaboration
ConsensusEngine	Weighted voting + veto + escalation mechanism	Security/architecture disputes
BatchScheduler	Parallel/sequential hybrid scheduling	Resource-constrained environments

Core Workflow:

User Task → [InputValidator] → [RoleMatcher] → [Coordinator Orchestration]
           → [ThreadPoolExecutor Parallel Workers] → [Scratchpad Real-time Sharing]
           → [ConsensusEngine] → [ReportFormatter] → [Structured Report]

🛡️ Domain 2: Quality Assurance System

防止 AI 「偷懒」或「幻觉」

Module	Purpose	When to Use
InputValidator	Security validation + 21-pattern prompt injection detection	Production environments
VerificationGate	Mandatory evidence requirements + 7 Red Flags detection	Critical decision scenarios
AntiRationalizationEngine	Per-role excuse→rebuttal tables to prevent quality shortcuts	High quality requirements
TestQualityGuard	Test quality audit (API validation / anti-pattern detection / dimension coverage)	Pre-release verification
PermissionGuard	4-level safety gate (PLAN/DEFAULT/AUTO/BYPASS)	Security-sensitive tasks

⚡ Domain 3: Performance & Reliability

让系统更快、更稳定、更省钱

Module	Purpose	When to Use
LLMCache	TTL-based LRU cache with disk persistence (60-80% cost reduction)	High-frequency usage
LLMRetry	Exponential backoff + circuit breaker + multi-backend fallback	Unstable networks
FeedbackControlLoop	Closed-loop feedback control with automatic iteration until quality threshold met	High quality output pursuit
ExecutionGuard	Real-time abort guard (timeout/output/keywords) for safe execution	Long-running tasks
FallbackBackend	Automatic backend failover with health monitoring	High availability requirements

📊 Domain 4: Observability & Governance

知道系统在做什么、做得怎么样

Module	Purpose	When to Use
PerformanceMonitor	P95/P99 response time, CPU/memory tracking, bottleneck detection	Performance tuning
UsageTracker	Token/cost usage tracking and reporting	Cost control
AuditLogger	SHA256 integrity operation logs with CSV/JSON export	Compliance auditing
RBAC Engine	15+ fine-grained permissions, 5 roles (SUPER_ADMIN/ADMIN/OPERATOR/ANALYST/VIEWER)	Enterprise access control
HistoryManager	SQLite time-series storage: metrics snapshots, alert history, API logs	Retrospective analysis
AlertManager	Multi-channel alerting: Console/Slack/Email/Webhook with rate limiting	Production monitoring

🔌 Domain 5: Integration & Extension

融入你的现有工具链

Module	Purpose	When to Use
CLI	Command-line interface with lifecycle commands	Daily developer usage
REST API (FastAPI)	10+ endpoints with OpenAPI/Swagger docs	Microservice integration
Dashboard (Streamlit)	Interactive web dashboard with authentication	Operations team visualization
MCP Protocol	Integration with TRAE/Claude Code/Cursor	AI Agent ecosystem
Docker Support	Multi-stage build for production deployment	Containerized environments
GitHub Actions CI	Python 3.9-3.12 matrix testing	CI/CD pipelines

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🔬 Cybernetics Enhancement Modules (V3.6.1)

非侵入式包装设计 — 可选开关，零修改现有核心逻辑

The 5 cybernetic modules work independently or together without modifying existing core logic:

User Task
    ↓
[SimilarTaskRecommender] ← Optional: suggest roles from history
    ↓
[AdaptiveRoleSelector]   ← Optional: optimize role selection
    ↓
[MultiAgentDispatcher]
    ↓
[FeedbackControlLoop]     ← Wrap dispatcher for auto-iteration
    ↓ [each worker step]
[ExecutionGuard]          ← Guard each worker execution
    ↓
[PerformanceFingerprint]  ← Record after dispatch completes

1️⃣ FeedbackControlLoop (反馈闭环控制器)

• Closed-loop feedback control with automatic iteration until quality threshold met
• Configurable quality gate (quality_gate) and maximum iterations
• Lightweight quality assessment (no LLM calls), supports dry-run mode

2️⃣ ExecutionGuard (执行守护者)

• Real-time execution monitoring with 4 abort conditions: timeout, output size, token count, critical keywords
• Lightweight checks (<1ms), zero external dependencies
• Dynamically configurable thresholds

3️⃣ PerformanceFingerprint (性能指纹系统)

• Unified execution fingerprint recording (fuses 4 data sources)
• Pure Python TF-IDF implementation (no sklearn/numpy), supports English/Chinese mixed content
• JSON persistence to .devsquad_data/fingerprints/, graceful cold-start degradation

4️⃣ SimilarTaskRecommender (相似任务推荐器)

• TF-IDF-based task similarity search with historical success configuration recommendations
• Intelligent role combination recommendation, intent prediction, execution time estimation
• Confidence scoring (high/medium/low), graceful cold-start degradation

5️⃣ AdaptiveRoleSelector (自适应角色选择器)

• Three-tier selection strategy based on historical success rates
• Configurable minimum success rate and maximum role count
• Supports manual statistics updates and comprehensive role effectiveness reporting

Recommended usage (progressive adoption):

from scripts.collaboration import (
    MultiAgentDispatcher, FeedbackControlLoop,
    ExecutionGuard, PerformanceFingerprint
)

dispatcher = MultiAgentDispatcher()
guard = ExecutionGuard()
fingerprint = PerformanceFingerprint()

# Option 1: Full cybernetics stack
loop = FeedbackControlLoop(dispatcher, quality_gate=0.7)
result = loop.run("Your task here")

# Option 2: Guard only (minimal adoption)
result = dispatcher.dispatch("Your task")
for w in result.worker_results:
    abort, reason = guard.check_abort(w.output, w.duration)
    if abort:
        print(f"Aborted: {reason}")

# Option 3: Learning only
fingerprint.record_execution("task", result, result.timing, result.matched_roles)
similar = fingerprint.find_similar("new task", top_k=3)

All modules are optional switches — DevSquad works perfectly without them.

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🏗️ Architecture Overview (Layered Design)

┌─────────────────────────────────────────────────────────────┐
│                    User Access Layer                         │
│  ┌──────────────┐ ┌──────────────┐ ┌──────────────┐        │
│  │ Streamlit    │ │ FastAPI REST │ │ CLI/Notebook │        │
│  │ Dashboard    │ │ API Server   │ │ (Existing)   │        │
│  │ (Auth+HTTPS) │ │ (Swagger)    │ │              │        │
│  └──────┬───────┘ └──────┬───────┘ └──────────────┘        │
└─────────┼───────────────┼───────────────────────────────────┘
          │               │
          ▼               ▼
┌─────────────────────────────────────────────────────────────┐
│                   Business Logic Layer                      │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐           │
│  │AuthManager  │ │AlertManager │ │HistoryMgr   │           │
│  │(RBAC Auth)  │ │(Multi-Chnl) │ │(SQLite TSDB)│           │
│  └─────────────┘ └─────────────┘ └─────────────┘           │
│  ┌─────────────────────────────────────────────┐            │
│  │     LifecycleProtocol (11-Phase Engine)       │            │
│  │     UnifiedGateEngine + CheckpointManager     │            │
│  └─────────────────────────────────────────────┘            │
└─────────────────────────┬───────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────┐
│                    Data Persistence Layer                    │
│  ┌────────────┐ ┌────────────┐ ┌────────────────────────┐  │
│  │ SQLite DB  │ │ YAML Config│ │ Checkpoint Files       │  │
│  │ (History)  │ │ (Deploy)   │ │ (Lifecycle State)      │  │
│  └────────────┘ └────────────┘ └────────────────────────┘  │
└─────────────────────────────────────────────────────────────┘

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🧩 Layered Sub-Skill Architecture (V3.6.0)

DevSquad provides 6 atomic sub-skills that can be used independently or together. Each sub-skill is a thin wrapper (~50 lines) importing existing core modules — no duplicated logic.

skills/
├── dispatch/       → DispatchSkill — MultiAgentDispatcher (7-role orchestration)
├── intent/         → IntentSkill   — IntentWorkflowMapper (6 intents × 3 languages)
├── review/         → ReviewSkill   — FiveAxisConsensusEngine (5-axis code review)
├── security/       → SecuritySkill — InputValidator + OperationClassifier + PermissionGuard
├── test/           → TestSkill     — TestQualityGuard + test strategy generation
└── retrospective/  → RetroSkill    — RetrospectiveEngine + pattern extraction

Sub-Skill Quick Reference

Skill	Core Method	Wraps	Mock Mode
dispatch	run(task, roles, mode)	MultiAgentDispatcher	✅
intent	detect(text, lang)	IntentWorkflowMapper	✅
review	review(code)	FiveAxisConsensusEngine	✅
security	scan_input(text)	InputValidator + OpClassifier	✅
test	generate_strategy(module)	TestQualityGuard	✅
retrospective	run_retrospective(results)	RetrospectiveEngine	✅

Usage Examples

# Direct import (recommended for single skill)
from skills.dispatch.handler import DispatchSkill
result = DispatchSkill().run("Fix login bug", roles=["coder", "tester"])

# Via registry (dynamic discovery)
from skills import get_skill, list_skills
print(list_skills())  # ['dispatch', 'intent', 'review', 'security', 'test', 'retrospective']
skill = get_skill("security")
result = skill.scan_input("DROP TABLE users; --")

All sub-skills work without any API key in Mock mode.

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📋 Plan C Architecture (Core Engine)

Unified Lifecycle Architecture - Resolves CLI 6 commands vs 11-phase lifecycle:

CLI View Layer (6 commands)          Core Engine (11 phases)
┌─────────────────────┐            ┌──────────────────────────┐
│ spec → P1, P2       │───View ──→│ P1: Requirements         │
│ plan → P7           │   Mapping │ P2: Architecture         │
│ build → P8          │            │ P3: Technical Design     │
│ test → P9           │            │ ...                      │
│ review → P8,P6      │            │ P10: Deployment          │
│ ship → P10          │            │ P11: Operations          │
└─────────────────────┘            └──────────────────────────┘
        ↓                                    ↓
  UnifiedGateEngine                   CheckpointManager
  (Phase + Worker gates)              (Lifecycle state persistence)

Core Components:

• ✅ LifecycleProtocol - Abstract interface for unified lifecycle management
• ✅ UnifiedGateEngine - Integrates VerificationGate + Phase transition gates
• ✅ FullLifecycleAdapter - Complete 11-phase lifecycle with dependency resolution
• ✅ Enhanced CheckpointManager - Auto save/restore lifecycle state across sessions

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📦 Installation

Prerequisites

• Python 3.9+ (3.9, 3.10, 3.11, 3.12 supported)
• pip or pipenv for package management

Option A: PyPI Install (Recommended)

# Install from PyPI — zero setup, ready to use
pip install devsquad

# With optional dependencies
pip install "devsquad[api]"    # FastAPI + Streamlit dashboard
pip install "devsquad[all]"    # All optional features

Option B: Git Clone + Local Install

git clone https://github.com/weiransoft/devsquad.git
cd DevSquad

# Install core package (minimal dependencies)
pip install -e .

# Ready to use!
devsquad dispatch -t "Design user authentication system"

Verify Installation

# Check version
devsquad --version
# Expected: devsquad 3.6.6

# Run tests
pytest tests/ -v --tb=short
# Expected: 1500+ passed

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⚙️ Configuration

Create .devsquad.yaml in your project root:

quality_control:
  enabled: true
  strict_mode: true
  min_quality_score: 85

llm:
  backend: openai
  base_url: ""  # Set via DEVSQUAD_OPENAI_BASE_URL env var
  model: ""     # Set via DEVSQUAD_OPENAI_MODEL env var
  timeout: 120

Or use environment variables (higher priority):

export DEVSQUAD_LLM_BACKEND=openai
export DEVSQUAD_OPENAI_BASE_URL=https://api.openai.com/v1
export DEVSQUAD_OPENAI_MODEL=gpt-4
export DEVSQUAD_OPENAI_API_KEY=sk-...

Environment Variables Reference:

Variable	Purpose	Default
DEVSQUAD_LLM_BACKEND	Default backend type (mock|trae|openai|anthropic|fallback)	mock
DEVSQUAD_OPENAI_API_KEY	OpenAI/MOKA AI API key	None
DEVSQUAD_OPENAI_BASE_URL	OpenAI-compatible base URL	None
DEVSQUAD_OPENAI_MODEL	OpenAI model name	gpt-4
DEVSQUAD_ANTHROPIC_API_KEY	Anthropic API key	None
DEVSQUAD_ANTHROPIC_BASE_URL	Anthropic-compatible base URL	None
DEVSQUAD_ANTHROPIC_MODEL	Anthropic model name	claude-sonnet-4-20250514
DEVSQUAD_LOG_LEVEL	Logging level	WARNING

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🧪 Testing

Quick Smoke Test (< 30 seconds)

python3 scripts/cli.py --version       # Expected: DevSquad 3.6.6
python3 scripts/cli.py status          # Expected: System ready
python3 scripts/cli.py roles           # Expected: 7 core roles listed

Full Test Suite

# Run all tests (1731 tests passing)
python3 -m pytest tests/ -q --tb=line

# With coverage report
python3 -m pytest tests/ --cov=scripts --cov-report=term-missing --cov-fail-under=80

Test Layering Strategy

Priority	Scope	Examples	Count
P0	Quality Framework Core	AntiRationalization, VerificationGate, IntentWorkflowMapper, AuthManager	~200
P1	Enhancement Modules	FiveAxisConsensus, OperationClassifier, OutputSlicer	~150
P1+	Cybernetics (V3.6.6)	FeedbackControlLoop, ExecutionGuard, PerformanceFingerprint, etc.	110
P2	Integration & E2E	Full lifecycle dispatch, cross-module integration	~200
P3	Unit per Module	Core dispatcher, RoleMapping, MCEAdapter, LLM backends	~400+

Total: 1731 tests

Run by priority:

# P0 only (critical path, < 10s)
python3 -m pytest tests/ -k "anti_ratif or verification or intent_workflow or auth" -q

# P0 + P1 (quality + enhancement, < 30s)
python3 -m pytest tests/ -k "anti_ratif or verification or intent or auth or five_axis or operation" -q

# Full suite
python3 -m pytest tests/ -q --tb=line

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📚 Documentation

Document	Description	Language
QUICKSTART.md	⭐ 30 秒快速入门指南（推荐新用户）	中文
SKILL.md	完整技能手册 + 70+ 模块参考	EN/CN/JP
GUIDE.md	完全用户指南	中文
INSTALL.md	安装指南 (Unix + Windows)	EN/CN
EXAMPLES.md	实际使用示例	EN
CHANGELOG.md	版本历史记录	EN
docs/i18n/README_CN.md	中文说明	中文
docs/i18n/README_JP.md	日本語説明	日本語
docs/PRD.md	产品需求文档	中文
docs/ARCHITECTURE.md	技术架构文档	中文

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🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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⭐ 如果 DevSquad 对你有帮助，请给个 Star！⭐
让更多开发者享受到「AI 团队协作」的力量

🙏 Acknowledgments
Inspired by TraeMultiAgentSkill upstream project
Built with ❤️ by the DevSquad team

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Last updated: 2026-05-23 | Version: V3.6.6