fastband-agent-control 1.2026.1.1

Fastband Agent Control - Universal platform for AI agent coordination, multi-agent orchestration, and autonomous development workflows

Fastband Agent Control

Universal platform for AI agent coordination - Multi-agent orchestration, autonomous development workflows, and adaptive tools with ticket management.

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Vision

"A toolbox that travels with you - always the right tools, never too many, ready to grow."

Fastband Agent Control transforms AI-assisted development from a project-specific setup into a universal platform that:

• Works with any AI provider (Claude, OpenAI, Gemini, local LLMs)
• Adapts to any project type (web, mobile, desktop, API)
• Scales from solo developers to enterprise teams
• Provides intelligent tool recommendations based on your project
• Includes built-in ticket management that adapts to your workflow
• Enables multi-agent coordination for parallel AI workflows

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Features

AI-Agnostic Design

# Switch between providers seamlessly
from fastband import get_provider

claude = get_provider("claude")
openai = get_provider("openai")
local = get_provider("ollama")

Tool Garage System

• Core tools always available
• AI-recommended tools based on your project
• Dynamic loading - add tools as your project grows
• Performance monitoring to prevent overload

Adaptive Ticket Manager

Project Type	Interface
Web App	CLI + Web Dashboard
Desktop App	Embedded Panel (Ctrl+Shift+T)
Mobile App	CLI + Companion Web
API/Library	CLI only

Multi-Agent Coordination

• Clearance system for parallel work
• Hold directives for critical operations
• Rebuild announcements to prevent conflicts

Automated Backups

• Change detection - only backup when needed
• Multiple database support - SQLite, PostgreSQL, MySQL
• Configurable retention policies

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Quick Start

Installation

# Standard installation (includes CLI + Hub dashboard)
pip install fastband-agent-control

# With Claude support
pip install fastband-agent-control[claude]

# With all AI providers
pip install fastband-agent-control[all-providers]

# Full installation (all features + dev tools)
pip install fastband-agent-control[full]

Initialize a Project

cd /your/project
fastband init

This launches the setup wizard that will:

1. Detect your project type
2. Recommend AI provider
3. Configure GitHub integration
4. Select appropriate tools
5. Set up ticket management
6. Configure backups

Basic Commands

# Check status
fastband status

# List available tools
fastband tools list

# Manage tickets
fastband tickets list
fastband tickets create "Add user authentication"
fastband tickets claim 1 --agent "MCP_Agent1"

# Backup management
fastband backup list
fastband backup create --name "before_refactor"

# Agent coordination
fastband agents status
fastband ops read --since 1h

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Configuration

Fastband stores configuration in .fastband/config.yaml:

fastband:
  version: "1.2025.12"

  ai:
    default_provider: "claude"
    providers:
      claude:
        model: "claude-sonnet-4-20250514"
      openai:
        model: "gpt-4-turbo"

  tools:
    max_active: 60
    auto_load_core: true

  tickets:
    enabled: true
    mode: "cli_web"
    web_port: 5050

  backup:
    enabled: true
    daily_retention: 7
    weekly_retention: 4

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Documentation

• Getting Started
• AI Providers Guide
• Tool Garage
• Ticket Manager
• Multi-Agent Coordination
• API Reference

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Roadmap

v1.2025.12.6 (Current - Stable)

• Core MCP server
• AI provider abstraction
• Tool garage system
• Setup wizard
• Full ticket manager with web UI
• Control Plane dashboard
• Plugin system
• Vision analysis tools

v1.2026.01.0 (Next)

• Enhanced multi-agent workflows
• E2E testing integration
• Visual regression testing

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Development Setup

git clone https://github.com/RemmyCH3CK/fastband-mcp
cd fastband-mcp
pip install -e ".[dev]"
pre-commit install
pytest

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License

MIT License - see LICENSE for details.

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Acknowledgments

Fastband Agent Control evolved from the MLB v4.0.1 development platform, incorporating lessons learned from building production AI-assisted development workflows.