# Round Table Workspace: A Local-First Multi-Agent Decision Workflow Framework

> A roundtable discussion workflow designed for local CLI agents like Codex and Claude Code. It uses two modes—/room (exploratory discussion) and /debate (formal review)—to manage the full process from fuzzy problem exploration to high-stakes decision-making.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-29T01:14:31.000Z
- 最近活动: 2026-04-29T02:25:53.251Z
- 热度: 160.8
- 关键词: Codex, Claude Code, 多智能体, 本地优先, 决策工作流, 圆桌讨论, AI 协作, 证据驱动, CLI 代理
- 页面链接: https://www.zingnex.cn/en/forum/thread/round-table-workspace
- Canonical: https://www.zingnex.cn/forum/thread/round-table-workspace
- Markdown 来源: floors_fallback

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## Round Table Workspace: Local-first Multi-agent Decision Workflow Framework (Main Post)

Round Table Workspace is a local-first multi-agent decision workflow framework designed for Codex, Claude Code, and other local CLI agents. It features two core modes—/room (exploratory discussion) and /debate (formal decision review)—to manage the full process from fuzzy problem exploration to high-stakes decision-making. Adhering to local-first philosophy and evidence-driven principles, it ensures data control, reproducible discussions, and reliable decision outputs.

## Project Vision & Background

In the age of AI programming assistants, a key problem arises: how to ensure orderly, deep, and reproducible collaboration among multiple AI agents. Round Table Workspace addresses this with a local-first approach—discussion data stays local, agents run locally, and users retain full control, contrasting with mainstream cloud-native AI services.

## Core Workflow Modes: /room & /debate

Two core commands power the workflow:
1. **/room**: For exploratory discussions on fuzzy/complex issues. Mechanisms include expert panel selection, structured rounds, deep questioning, and focus control. Example scenario: exploring an AI learning product for students with product strategists, architects, etc.
2. **/debate**: For formal high-stakes decisions (product direction, tech architecture). Features adversarial argumentation, clear decisions (allow/reject/follow-up), and traceable records. Advanced usage: `/debate --with Jobs,Taleb` to simulate specific experts.

## Local-first Architecture & Technical Details

Local-first architecture details:
- **Data Sovereignty**: All records are stored locally, no cloud dependency, Git version control supported.
- **Modular Runtime**: Folder structure includes prompts, agent skills, reports, etc.
- **Multi-platform Adaptation**: Native Codex support, Claude Code via skill discovery, and adapters for other CLI agents. Each support claim requires validation evidence.

## Evidence-driven Development Culture

The project emphasizes evidence over claims:
- **Release Check**: Generate verification reports (e.g., `live_lane_evidence_report.py`).
- **Regression Test**: Local Codex regression tests (e.g., `local_codex_regression.py`).
- **Release Candidate Review**: Strict checks (fixture runs, Git cleanliness) via `release_candidate_report.py`.

## Workflow Transition & Quick Start Guide

Workflow transition: Use `/upgrade-to-debate` to move from /room to formal review (e.g., from exploring an AI product to debating its viability). Quick start steps:
1. Clone repo: `git clone https://github.com/MarkDonish/round-table-workspace.git`
2. Health check: `./rtw doctor`
3. Start /room: `./rtw room "I want to discuss a college student-oriented AI learning product, covering direction, entry points, and risks step by step"`
4. Upgrade to /debate: `./rtw debate "Is this startup direction worth pursuing?"`

## Applicable Scenarios & Current Limitations

**Best Scenarios**: Startup direction exploration, tech architecture selection, product priority ranking, risk assessment.
**Limitations**: Requires local CLI agents (Codex/Claude Code), some features are stubbed, cloud integration needs extra configuration.

## Conclusion & Comparison with Cursor Agent Workflow

Round Table Workspace represents AI tools evolving from answering questions to organizing discussions. It helps teams make complex decisions via local-first, evidence-driven collaboration. Compared to Cursor Agent Workflow (focused on code workflow automation), this framework focuses on decision-making processes—both reflect AI workflow evolution towards better human-AI collaboration.