Building a Multi-AI Development Team with CrewX

October 26, 2025 · 5 min read

Founder @ SowonLabs

When migrating projects between frameworks, we discovered something powerful: what if our AI tools could talk to each other? After spending 4 days shuttle-coding between two projects, acting as a human intermediary between Claude and Copilot, we realized we needed a better way. That's how CrewX was born.

The Problem: AI in Isolation

Most developers use AI assistants in silos. We open a Claude chat here, a GitHub Copilot session there, and manually transfer knowledge between them. It's inefficient, time-consuming, and loses valuable context.

The real problem? Our AI assistants can't collaborate. We need them to.

The Solution: Multi-AI Collaboration

We took our existing nestjs-mcp-adapter work and built CrewX in just 2 days. The result: AI agents with distinct personas that can communicate with each other through Slack and CLI, coordinating work across multiple AI models simultaneously.

# CrewX Configuration Example
agents:
  - id: "developer"
    name: "Developer Agent"
    provider: "cli/claude"
    inline:
      model: "sonnet"
      prompt: |
        You are a senior developer focused on implementation
        and architecture decisions.

  - id: "qa"
    name: "QA Agent"
    provider: "cli/copilot"
    inline:
      prompt: |
        You are a QA engineer focused on testing strategies
        and quality assurance.

Real-World Use Cases

1. Virtual Development Team

Assemble a full development team using your existing AI subscriptions:

Claude → Senior Developer
Copilot → Implementation Specialist
Gemini → Performance Optimizer
GLM → Documentation Specialist
Codex → Test Engineer

Each AI handles its strengths. No additional costs—just your existing subscriptions working together.

Result: Team bandwidth multiplied without hiring costs.

2. WBS-Driven Development Pipeline

Create a Work Breakdown Structure once, then automate the entire workflow:

crewx execute "@crew prepare release tests for v2.0"

In response:

Developer agent breaks down the requirements
QA agent designs test scenarios
Implementation agent writes test code
Coverage analyzer reviews completeness
All happen in parallel, automatically

Time saved: 2-4 hours per day of development work.

3. Strategic Decision Making

Set up agents in specific roles for high-level discussions:

CSO Agent (Chief Strategy Officer role)

Analyzes market positioning
Reviews competitive landscape

CLO Agent (Chief Legal Officer role)

Evaluates compliance implications
Reviews risk assessment

These agents discuss strategy in Slack threads, auto-generate reports, and sync findings to Obsidian for knowledge management.

4. Autonomous Bitcoin Simulator

Deploy agents for continuous financial analysis:

Runs every hour with fresh market data
Multiple AI models trade simultaneously
Tracks performance across different strategies
Self-improving: Agents learn from their own trades

One simple configuration, infinite analytical power running 24/7.

5. Remote Distributed Agents

Extend agents beyond your local machine:

providers:
  - id: remote_research_server
    type: remote
    location: "https://research-server.example.com"
    external_agent_id: "research_team"
    auth:
      type: bearer
      token: "${REMOTE_TOKEN}"

agents:
  - id: "remote-researcher"
    provider: "remote/remote_research_server"
    description: "Research team on dedicated server"

Access AI tools running on remote servers, cloud instances, or specialized hardware—seamlessly integrated into your local workflow.

The CrewX Advantage

BYOA: Bring Your Own AI

You already pay for Claude Pro, GitHub Copilot, or Gemini Advanced. Don't waste that investment.

CrewX multiplies the value of your existing subscriptions by:

Enabling agents to work in parallel
Letting them specialize by role
Automating coordination between them

No new AI costs. Just smarter usage.

Slack-Native Collaboration

Your team already lives in Slack. CrewX brings AI directly to where conversations happen:

@CrewX prepare a technical proposal for the streaming feature

Agents research, discuss, and deliver results right in your channel. Context stays intact. No context-switching needed.

Flexible Deployment

Use CrewX however suits your workflow:

CLI Mode: Local development and automation
Slack Bot: Team-wide AI collaboration
MCP Server: IDE integration (VS Code, JetBrains)

Getting Started

🚀 Quick Start (30 Seconds)

The fastest way to get started:

# One command to scaffold everything
npx crewx-quickstart

# Talk to your AI team immediately
crewx query "@quickstart hi"

This creates:

✅ crewx.yaml with a ready-to-use @quickstart agent
✅ .env.slack template for Slack integration
✅ start-slack.sh script to launch your bot
✅ Helpful documentation and examples

📦 Manual Setup (Full Control)

Prefer to configure everything yourself?

# 1. Install CrewX globally
npm install -g crewx

# 2. Initialize in your project
crewx init

# 3. Check your setup
crewx doctor

This creates a crewx.yaml file where you define your team:

# crewx.yaml
agents:
  - id: "analyst"
    name: "Data Analyst"
    provider: "cli/claude"
    inline:
      prompt: |
        You are a data analyst expert at finding insights.

  - id: "reporter"
    name: "Report Writer"
    provider: "cli/gemini"
    inline:
      prompt: |
        You are a technical writer skilled at clear communication.

4. Run Your First Multi-Agent Workflow

# Read-only analysis
crewx query "@analyst @reporter analyze our Q3 metrics"

# File creation/modification
crewx execute "@analyst @reporter analyze Q3 and draft a report"

What Makes CrewX Different

Feature	Traditional AI	CrewX
Model Usage	One at a time	Multiple in parallel
Agent Roles	Generic	Specialized personas
Collaboration	Manual handoff	Automatic coordination
Cost	Per-model subscription	Use what you already have
Integration	Isolated chat windows	Slack, CLI, IDE
Customization	Limited	Full YAML control

The Future of Development

CrewX represents a fundamental shift: from "I use AI tools" to "I lead an AI team."

Your AI agents don't need to be smart individually—they need to be coordinated collectively. That's where exponential productivity gains come from.

Whether you're:

Building complex features faster
Automating testing and QA
Making data-driven strategic decisions
Running continuous analysis workflows

...CrewX puts the power of a full development team at your fingertips.

Ready to Build Your AI Team?

# Get started in 30 seconds
npx crewx-quickstart
crewx query "@quickstart hi"

# Or install globally for more control
npm install -g crewx
crewx init

# Explore the docs
crewx query "@claude what can I do with CrewX?"

# Build something amazing
crewx execute "@your-team solve this problem"

Join the CrewX community:

📚 Full Documentation
🐙 GitHub Repository

What will you build with your AI team? Share your CrewX workflows and success stories with us!

The Problem: AI in Isolation​

The Solution: Multi-AI Collaboration​

Real-World Use Cases​

1. Virtual Development Team​

2. WBS-Driven Development Pipeline​

3. Strategic Decision Making​

4. Autonomous Bitcoin Simulator​

5. Remote Distributed Agents​

The CrewX Advantage​

BYOA: Bring Your Own AI​

Slack-Native Collaboration​

Flexible Deployment​

Getting Started​

🚀 Quick Start (30 Seconds)​

📦 Manual Setup (Full Control)​

4. Run Your First Multi-Agent Workflow​

What Makes CrewX Different​

The Future of Development​

Ready to Build Your AI Team?​