cmdbox

cmdbox is a command foundation for AI system development that lets you launch with minimal implementation. A single feature implementation can be exposed through CLI / REST API / MCP SV / Web UI / Edge UI / remote execution.

cmdbox operation image

Why cmdbox for AI System Developers

  • No repeated reimplementation
    • Implement a Feature command once, then use the same capability via CLI, Web UI, and REST API.
    • It is easy to promote a PoC CLI workflow into operational Web/API interfaces without rewriting business logic.
  • AI-ready components out of the box
    • Modes such as agent, llm, mcpsv, and a2asv let you manage agent execution, model operations, MCP exposure, and A2A integration on one platform.
    • You can separate tool execution, inference, knowledge retrieval, and external integration while keeping a consistent operational model.
  • Rich Web UI for daily operations
    • Beyond APIs and CLI, cmdbox provides operational screens for commandlets, agents, filer, limiter, audit, and user management.
    • Teams can run, monitor, and control workflows from a browser without building a separate admin console.
  • Designed for remote execution
    • Server-side commands can run through Redis, which fits node separation and scale-out architectures.
    • Heavy workloads can be offloaded to servers while keeping client components lightweight.
  • Fast, configuration-driven extensibility
    • features.yml defines command discovery, aliases, argument rules, and agent execution rules, separating implementation from operations policy.
    • You can adjust exposed commands and execution policies without code changes, which is useful for environment-specific control.
  • Built-in governance capabilities
    • You can combine audit logging, authentication/authorization (signin / oauth2 / saml / rule settings), and limiter controls.
    • It is easier to trace who executed which command under which conditions, enabling enterprise-grade operational control.

Problems It Solves Well

  • You need to launch internal AI tools quickly
    • Start with CLI features, then expose the same capabilities internally via Web and REST API.
  • Multiple teams/environments need one shared foundation
    • Run the same codebase across dev/stg/prod while switching execution policies through configuration.
  • You need safe integration between agents and existing systems
    • Combine MCP/A2A integration with authorization rules to control the scope of agent actions.
  • You need usage control and cost governance for AI operations
    • Use Limiter settings to enforce quotas/rate rules, prevent abuse, and keep usage and billing behavior predictable.
    • Define reusable plans and billing policies (period-based or metered) to align access control with commercial and internal chargeback models.

What You Can Build

  • Custom command development
    • Support complex option definitions, client/server execution, and Web execution.
    • Organize functionality by mode / cmd and standardize execution through a shared option model.
  • LLM integration
    • Chat, embedding, and configuration save/load/list workflows.
    • Manage model configurations and treat inference calls as operational commands.
  • Agent integration
    • Save/load agent settings, integrate MCP servers, and connect A2A servers.
    • Expand agent capabilities in phases while keeping clear boundaries on exposed tools.
  • Web UI operations
    • Use built-in screens for commandlets, agents, filer, limiter, audit, and user management.
    • Manage day-to-day operations from the browser while keeping the same backend command model.
  • Operational controls
    • Audit logs, authentication, command execution policy, and REST API controls.
    • Publish safely with permissions segmented by organization or user group and full traceability.
    • Configure plan and billing operations to support subscription-style access windows and metered charging models.

Typical Use Cases

  • Internal knowledge search combining document processing + LLM
  • Converting routine operations into commands and running them from Web UI with no-code interaction
  • Letting agents call internal tools safely within controlled boundaries
  • Running operational administration (filer, limiter, audit, and user control) from the built-in Web UI

Installation

pip install cmdbox[app]
cmdbox -v

Docker Environment Setup

  • Redis
cmdbox -m cmdbox -c redis_install
cmdbox -m cmdbox -c up -C redis
  • PostgreSQL (used by some commands)
cmdbox -m cmdbox -c pgsql_install
cmdbox -m cmdbox -c up -C pgsql
  • Full cmdbox suite
cmdbox -m cmdbox -c server_install
cmdbox -m cmdbox -c up -C cmdbox

Quick Start

cmdbox -m server -c start
cmdbox -m web -c start --signin_file .cmdbox/user_list.yml
cmdbox -m mcpsv -c start --signin_file .cmdbox/user_list.yml
cmdbox -m a2asv -c start --signin_file .cmdbox/user_list.yml

Tutorial

The tutorial content has moved to the following documentation:

License

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

Project links

Key dates

PyPI data
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  • Released:
Newer release available (0.8.4.6)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for hamacom2004jp from gravatar.com hamacom2004jp

Credits

PyPI data
Data sourced directly from PyPI's database.

Author: hamacom2004jp

License

MIT License (MIT)

Requires

Python >=3.10

Provides Extra

agent-mcp agent-adk agent-mem rag omni saml dev app

Tags

audit a2a agent cli cmdbox excel fastapi mcp redis restapi web

Classifiers

Report project as malware

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