quickagents 2.7.0

QuickAgents - AI Agent Enhancement Toolkit with Self-Evolution and Unified UTF-8 Encoding

QuickAgents

AI Agent Enhancement Toolkit with Self-Evolution | AI代理增强工具包，支持自我进化

中文文档 | English

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QuickAgents (中文)

📖 项目简介

QuickAgents是一个强大的AI代理增强工具包，通过本地处理最大化效率，最小化Token消耗。支持自我进化、记忆管理、知识图谱、TDD工作流等核心功能。

🎯 核心目标

• 最大化本地处理：减少API调用，节省Token消耗60-100%
• 自我进化系统：自动收集经验，持续优化Skills
• 统一数据管理：SQLite主存储 + Markdown辅助备份
• 跨会话记忆：三维记忆系统，支持项目上下文保持

✨ 核心功能

1. 统一数据库系统 (UnifiedDB V2)

V2 架构：分层设计，模块化，可测试

┌─────────────────────────────────────────────────────────────┐
│                    UnifiedDB V2 Architecture                 │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│   ┌─────────────────────────────────────────────────────┐   │
│   │              UnifiedDB (Facade)                      │   │
│   │  - set_memory() / get_memory() / search_memory()    │   │
│   │  - add_task() / update_task_status()                │   │
│   │  - init_progress() / update_progress()              │   │
│   │  - add_feedback() / get_feedbacks()                 │   │
│   └─────────────────────────────────────────────────────┘   │
│                          │                                  │
│   ┌──────────────────────┴──────────────────────────┐       │
│   │              Repository Layer                    │       │
│   ├─────────────────────────────────────────────────┤       │
│   │  MemoryRepo │ TaskRepo │ ProgressRepo │ FeedbackRepo │  │
│   └─────────────────────────────────────────────────┘       │
│                          │                                  │
│   ┌──────────────────────┴──────────────────────────┐       │
│   │              Core Components                     │       │
│   ├─────────────────────────────────────────────────┤       │
│   │  ConnectionManager │ TransactionManager │ MigrationManager │
│   └─────────────────────────────────────────────────┘       │
│                                                             │
└─────────────────────────────────────────────────────────────┘

V2 特性：

• ConnectionManager: 连接池、WAL模式、线程安全
• TransactionManager: ACID事务、嵌套SAVEPOINT
• MigrationManager: Schema版本管理、校验和验证
• Repository层: CRUD操作封装、类型安全

from quickagents import UnifiedDB, MemoryType, TaskStatus

db = UnifiedDB('.quickagents/unified.db')

# 设置记忆
db.set_memory('project.name', 'MyProject', MemoryType.FACTUAL)
db.set_memory('current.task', '实现认证', MemoryType.WORKING)

# 获取记忆
name = db.get_memory('project.name')

# 任务管理
db.add_task('T001', '实现认证', 'P0')
db.update_task_status('T001', TaskStatus.COMPLETED)

2. 自我进化系统 (SkillEvolution)

from quickagents import get_evolution

evolution = get_evolution()

# 任务完成时自动触发
evolution.on_task_complete({
    'task_id': 'T001',
    'task_name': '实现认证',
    'skills_used': ['tdd-workflow-skill'],
    'success': True
})

# Git提交时自动触发
evolution.on_git_commit()

# 检查定期优化
if evolution.check_periodic_trigger():
    evolution.run_periodic_optimization()

3. 知识图谱系统 (KnowledgeGraph)

from quickagents import KnowledgeGraph, NodeType, EdgeType

kg = KnowledgeGraph()

# 创建节点
node = kg.create_node(
    node_type=NodeType.REQUIREMENT,
    title='用户认证需求',
    content='实现JWT认证'
)

# 创建边
kg.create_edge(
    source_id=node.id,
    target_id='T001',
    edge_type=EdgeType.TRACES_TO
)

# 搜索
results = kg.search('认证')

# 需求追踪
trace = kg.trace_requirement(node.id)

4. 浏览器自动化 (Browser)

from quickagents import Browser

browser = Browser()
page = browser.new_page()

# 获取控制台日志
logs = page.get_console_logs()

# 获取网络请求
requests = page.get_network_requests()

# 执行JavaScript
result = page.evaluate('document.title')

browser.close()

🚀 安装

一句话安装（推荐）

直接告诉你的LLM代理工具（如OpenCode、Claude等）：

请按照 https://raw.githubusercontent.com/Coder-Beam/Quick-Agents-for-Z.AI-GLM/main/Docs/guide/installation.md 中的指引安装QuickAgents

代理会自动完成所有安装步骤。

手动安装

pip install quickagents

完整安装（包含Windows功能）

pip install quickagents[full]

开发模式

git clone https://github.com/Coder-Beam/Quick-Agents-for-Z.AI-GLM.git
cd Quick-Agents-for-Z.AI-GLM
pip install -e .

📋 CLI命令

# 统计信息
qa stats

# 同步到Markdown
qa sync

# 记忆操作
qa memory get <key>
qa memory set <key> <value>
qa memory search <query>

# 任务操作
qa tasks list
qa tasks add <id> <name> --priority P0
qa tasks status <id> <status>

# 进度查看
qa progress

# 进化系统
qa evolution status
qa evolution optimize

# Git钩子
qa hooks install
qa hooks status

# 知识图谱
qa kg create-node --type requirement --title "需求标题"
qa kg search <query>
qa kg trace <node-id>

# 模型配置（ZhipuAI GLM Coding Plan）
qa models show                    # 查看当前配置
qa models list                    # 列出可用模型
qa models check-updates           # 检查GLM更新
qa models upgrade --dry-run       # 预览升级
qa models upgrade --force         # 执行升级
qa models strategy coding-plan    # 切换到Coding Plan
qa models lock GLM-5              # 锁定单一模型
qa models unlock                  # 解除锁定

🏗️ 架构

.quickagents/
├── unified.db           # SQLite主存储
│   ├── memory           # 三维记忆
│   ├── progress         # 进度追踪
│   ├── feedback         # 经验收集
│   ├── tasks            # 任务管理
│   ├── decisions        # 决策日志
│   └── knowledge_*      # 知识图谱
│
Docs/                    # Markdown辅助备份
├── MEMORY.md           # 项目记忆
├── TASKS.md            # 任务管理
└── DECISIONS.md        # 决策日志

三维记忆系统

记忆类型	用途	示例
Factual	静态事实信息	项目名称、技术栈、架构决策
Experiential	动态经验信息	踩坑记录、最佳实践、用户反馈
Working	当前工作状态	当前任务、进度、阻塞点

📦 模块说明

模块	功能	Token节省
UnifiedDB	统一数据库管理	60%+
MarkdownSync	自动同步到Markdown	100%
FileManager	智能文件读写（哈希检测）	90%+
LoopDetector	循环检测	100%
Reminder	事件提醒	100%
SkillEvolution	Skills自我进化	0
KnowledgeGraph	知识图谱	80%+
Browser	浏览器自动化	50%+
Encoding	统一UTF-8编码（跨平台）	0

🧪 测试

# 运行所有测试
pytest tests/

# 运行特定测试
pytest tests/knowledge_graph/

# 覆盖率报告
pytest --cov=quickagents tests/

📚 文档

• API文档
• 架构设计
• 使用指南

🤝 贡献

欢迎贡献！请查看 CONTRIBUTING.md 了解详情。

📄 许可证

本项目采用 MIT 许可证 - 查看 LICENSE 文件了解详情。

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QuickAgents (English)

📖 Overview

QuickAgents is a powerful AI agent enhancement toolkit that maximizes efficiency through local processing and minimizes token consumption by 60-100%. It features self-evolution, memory management, knowledge graphs, TDD workflows, and more.

🎯 Core Goals

• Maximize Local Processing: Reduce API calls, save 60-100% tokens
• Self-Evolution System: Automatically collect experiences, continuously optimize skills
• Unified Data Management: SQLite primary storage + Markdown backup
• Cross-Session Memory: Three-dimensional memory system for project context preservation

✨ Core Features

1. Unified Database System (UnifiedDB)

from quickagents import UnifiedDB, MemoryType, TaskStatus

db = UnifiedDB('.quickagents/unified.db')

# Set memory
db.set_memory('project.name', 'MyProject', MemoryType.FACTUAL)
db.set_memory('current.task', 'Implement Auth', MemoryType.WORKING)

# Get memory
name = db.get_memory('project.name')

# Task management
db.add_task('T001', 'Implement Auth', 'P0')
db.update_task_status('T001', TaskStatus.COMPLETED)

2. Self-Evolution System (SkillEvolution)

from quickagents import get_evolution

evolution = get_evolution()

# Trigger on task completion
evolution.on_task_complete({
    'task_id': 'T001',
    'task_name': 'Implement Auth',
    'skills_used': ['tdd-workflow-skill'],
    'success': True
})

# Trigger on git commit
evolution.on_git_commit()

# Check periodic optimization
if evolution.check_periodic_trigger():
    evolution.run_periodic_optimization()

3. Knowledge Graph System (KnowledgeGraph)

from quickagents import KnowledgeGraph, NodeType, EdgeType

kg = KnowledgeGraph()

# Create node
node = kg.create_node(
    node_type=NodeType.REQUIREMENT,
    title='User Authentication',
    content='Implement JWT authentication'
)

# Create edge
kg.create_edge(
    source_id=node.id,
    target_id='T001',
    edge_type=EdgeType.TRACES_TO
)

# Search
results = kg.search('authentication')

# Trace requirement
trace = kg.trace_requirement(node.id)

4. Browser Automation (Browser)

from quickagents import Browser

browser = Browser()
page = browser.new_page()

# Get console logs
logs = page.get_console_logs()

# Get network requests
requests = page.get_network_requests()

# Execute JavaScript
result = page.evaluate('document.title')

browser.close()

🚀 Installation

One-Line Install (Recommended)

Tell your LLM agent tool (e.g., OpenCode, Claude):

Follow the instructions at https://raw.githubusercontent.com/Coder-Beam/Quick-Agents-for-Z.AI-GLM/main/Docs/guide/installation.md to install QuickAgents

The agent will automatically complete all installation steps.

Manual Installation

pip install quickagents

Full Installation (with Windows features)

pip install quickagents[full]

Development Mode

git clone https://github.com/Coder-Beam/Quick-Agents-for-Z.AI-GLM.git
cd Quick-Agents-for-Z.AI-GLM
pip install -e .

📋 CLI Commands

# Statistics
qa stats

# Sync to Markdown
qa sync

# Memory operations
qa memory get <key>
qa memory set <key> <value>
qa memory search <query>

# Task operations
qa tasks list
qa tasks add <id> <name> --priority P0
qa tasks status <id> <status>

# Progress view
qa progress

# Evolution system
qa evolution status
qa evolution optimize

# Git hooks
qa hooks install
qa hooks status

# Knowledge graph
qa kg create-node --type requirement --title "Requirement Title"
qa kg search <query>
qa kg trace <node-id>

# Model configuration (ZhipuAI GLM Coding Plan)
qa models show                    # View current config
qa models list                    # List available models
qa models check-updates           # Check GLM updates
qa models upgrade --dry-run       # Preview upgrade
qa models upgrade --force         # Execute upgrade
qa models strategy coding-plan    # Switch to Coding Plan
qa models lock GLM-5              # Lock single model
qa models unlock                  # Unlock

🏗️ Architecture

.quickagents/
├── unified.db           # SQLite primary storage
│   ├── memory           # Three-dimensional memory
│   ├── progress         # Progress tracking
│   ├── feedback         # Experience collection
│   ├── tasks            # Task management
│   ├── decisions        # Decision log
│   └── knowledge_*      # Knowledge graph
│
Docs/                    # Markdown backup
├── MEMORY.md           # Project memory
├── TASKS.md            # Task management
└── DECISIONS.md        # Decision log

Three-Dimensional Memory System

Memory Type	Purpose	Example
Factual	Static factual information	Project name, tech stack, architecture decisions
Experiential	Dynamic experiential information	Pitfalls, best practices, user feedback
Working	Current working state	Current task, progress, blockers

📦 Module Description

Module	Function	Token Savings
UnifiedDB	Unified database management	60%+
MarkdownSync	Auto-sync to Markdown	100%
FileManager	Smart file read/write (hash detection)	90%+
LoopDetector	Loop detection	100%
Reminder	Event reminders	100%
SkillEvolution	Skills self-evolution	0
KnowledgeGraph	Knowledge graph	80%+
Browser	Browser automation	50%+
Encoding	Unified UTF-8 encoding (cross-platform)	0

🧪 Testing

# Run all tests
pytest tests/

# Run specific tests
pytest tests/knowledge_graph/

# Coverage report
pytest --cov=quickagents tests/

📚 Documentation

• API Documentation
• Architecture Design
• User Guide

🤝 Contributing

Contributions are welcome! Please check CONTRIBUTING.md for details.

📄 License

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

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📊 Project Stats

• Version: 2.6.8
• Python Support: 3.8+
• Test Coverage: 254 tests passing
• License: MIT
• Author: Coder-Beam

🔗 Links

• GitHub: https://github.com/Coder-Beam/Quick-Agents-for-Z.AI-GLM
• PyPI: https://pypi.org/project/quickagents/
• Documentation: https://github.com/Coder-Beam/Quick-Agents-for-Z.AI-GLM/tree/main/Docs

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Made with ❤️ by Coder-Beam