Team-Tasks：多智能体协作工作流的Python编排工具

Team-Tasks is an open-source Python CLI tool designed to solve multi-agent collaboration challenges in AI development. It uses shared JSON task files as the collaboration medium and supports three core workflow modes: linear, DAG, and debate. Inspired by CI/CD pipeline ideas, it helps manage task dependencies, track execution states, and enable efficient multi-agent interaction.

In AI development, single agents often struggle with complex business scenarios. Coordinating multiple agents—managing their interactions, task dependencies, and execution states—has become an urgent engineering problem. Team-Tasks addresses this gap by providing a lightweight, practical solution for multi-agent workflow orchestration.

Linear Mode

A fixed-order workflow where agents execute tasks sequentially (e.g., document processing: text extraction → summary generation → quality check). Pros: Simple, easy to debug; Cons: No parallel processing.

DAG Mode

Supports parallel execution paths with branch/merge logic (e.g., data analysis: parallel data cleaning, feature engineering, model selection). Improves efficiency by handling complex dependencies.

Debate Mode

Simulates brainstorming: multiple agents discuss a problem from different perspectives (e.g., code review with performance, readability, security roles) to reach a consensus. Ideal for multi-dimensional decision-making.

JSON Task File Protocol

Shared JSON files contain task definitions, input/output parameters, execution states. Agents collaborate via file system (松耦合, no direct communication), enhancing fault tolerance.

Python CLI Toolchain

Offers subcommands for project creation, agent management, task definition, execution monitoring, and result export—easy to integrate into automation scripts.

State Tracking & Visualization

Real-time progress tracking,耗时统计, and log output help debug complex workflows by quickly locating issues.

1. AI-Assisted Software Development: Orchestrate agents for需求分析, architecture design, code generation, testing, and code review.
2. Multi-Dimensional Data Analysis: Use DAG mode for parallel statistical analysis, anomaly detection, trend prediction, and report summarization.
3. Creative Content Generation: Debate mode enables AI 'editorial teams' (editors, fact-checkers, style advisors) to improve content quality.

特性	Team-Tasks	AutoGen	CrewAI
架构复杂度	低（文件驱动）	中（对话驱动）	中（角色驱动）
学习曲线	平缓	较陡	中等
协作模式	三种明确模式	灵活对话	角色扮演
适用场景	结构化工作流	开放式对话	团队协作模拟
技术依赖	Python标准库	较重	中等

Team-Tasks is ideal for projects needing clear collaboration patterns and quick implementation; AutoGen/CrewAI suit flexible, complex interactions.

Current Limitations:

• File system-based collaboration may bottleneck in high concurrency.
• No built-in agent communication protocol (complex logic requires user implementation).
• Basic visualization for large workflows.

Future Plans:

• Introduce message queues as optional backends.
• Add richer agent communication primitives.
• Develop a web interface for workflow design and monitoring.

Team-Tasks represents a practical, engineering-focused approach to multi-agent collaboration. It proves effective orchestration is possible without complex infrastructure, making it an ideal starting point for developers exploring multi-agent applications. As AI agents evolve, tools like Team-Tasks will accumulate valuable engineering practices for this emerging field.