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Maestro

Maestro is a TypeScript-based desktop orchestration tool for coordinating multiple AI coding agents (Claude Code, OpenAI Codex, OpenCode, etc.) in parallel. It provides a keyboard-first interface for batch task execution, git worktrees, multi-agent coordination, and automated playbooks—designed for developers juggling multiple AI-assisted projects.

Source: GitHub — github.com/RunMaestro/Maestro
3.1k
GitHub stars
326
Forks
TypeScript
Primary language
AGPL-3.0
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
RepositoryRunMaestro/Maestro
OwnerRunMaestro
Primary languageTypeScript
LicenseAGPL-3.0 — OSI-approved
Stars3.1k
Forks326
Open issues103
Latest releasev0.17.3 (2026-07-04)
Last updated2026-07-07
Sourcehttps://github.com/RunMaestro/Maestro

What Maestro is

A pass-through orchestration layer built in TypeScript that bridges multiple AI agent CLIs (Claude Code, Codex, OpenCode, Factory Droid, Copilot-CLI) into a unified desktop environment. Includes features for parallel session management, file-system-based playbook execution, git worktree automation, group chat via AI moderator, and analytics dashboards. Runs on Electron with local web server for remote control.

Quickstart

Get the Maestro source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/RunMaestro/Maestro.gitcd Maestro# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Parallel AI-Assisted Development on Multiple Branches

Use git worktrees to spin up isolated AI sub-agents per branch while maintaining interactive work on main. Solves context fragmentation when one developer needs multiple coding agents working in parallel on different features without interference.

Batch Task Automation via Markdown Playbooks

Convert repeatable specification documents into Auto Run playbooks that execute sequentially or in loops, with each task getting a fresh AI session. Ideal for scaffolding projects, code generation, testing, or long-running unattended jobs (documented up to 24 hours runtime).

Cross-Project Coordination with Group Chat

Orchestrate conversations between multiple AI agents with a moderator AI routing questions and synthesizing answers. Valuable for architecture reviews, refactoring discussions, or dependency analysis across loosely coupled services.

Implementation considerations

  • Desktop installation required; Electron-based, so plan for cross-platform build/distribution if team deployment is needed. CLI alternative exists for headless use.
  • Each agent session inherits MCP tools and permissions from the underlying provider (e.g., Claude Code's installed tools). Validate provider sandbox model meets your security boundary requirements.
  • Auto Run playbooks use filesystem (markdown checklists) as task definitions. Version control playbooks in git; no built-in workflow engine persistence or audit log system stated.
  • Remote access via Cloudflare tunnel introduces third-party network dependency for off-LAN control. Evaluate security posture for sensitive agent outputs.
  • Cost tracking is per-session token-based; requires valid provider account with transparent pricing. Multi-tenant cost allocation or chargeback not explicitly documented.

When to avoid it — and what to weigh

  • Proprietary AI Agent Ecosystem Lock-in Required — Maestro only supports Claude Code, OpenAI Codex, OpenCode, Factory Droid, and Copilot-CLI. If your workflow depends on an unsupported agent, integration is not possible without source modification.
  • Strict Commercial Use with Enterprise SLA Guarantees — AGPL-3.0 license requires derivative work source disclosure. Cannot be used in proprietary products without careful legal review. Commercial use is not clearly supported; requires vendor clarification before deployment in commercial settings.
  • Headless Cloud Deployment as Primary Use Case — Maestro is primarily a desktop app (Electron). While CLI support and remote access exist, cloud-native or fully headless production orchestration may require significant customization. Remote control is limited to local network or Cloudflare tunnel.
  • Zero Dependency on External AI Provider Authentication — Maestro is a pass-through to external AI providers. You must maintain separate authentication (API keys, session tokens) with Claude, OpenAI, GitHub, etc. No provider abstraction layer for token management.

License & commercial use

AGPL-3.0 (GNU Affero General Public License v3.0). This is a copyleft license requiring source code disclosure for derivative works and any network-based use. It is not a permissive OSI license in the sense of commercial-friendly redistribution.

AGPL-3.0 is not a standard commercial or proprietary license. Use of Maestro in a commercial product or as a hosted service may trigger source disclosure obligations. Commercial deployment requires explicit legal review with counsel familiar with AGPL-3.0 compliance. No commercial support or license waiver mentioned in provided data.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationStrong
License clarityNeeds review
Deployment complexityModerate
DEV.co fitGood
Assessment confidenceHigh
Security considerations

Maestro is a pass-through to external AI providers; security posture depends entirely on underlying agent (Claude Code, Codex, OpenCode) sandbox model and authentication. Desktop app (Electron) has typical electron security considerations (code update, local file access). Remote access via Cloudflare tunnel introduces network trust boundary; sensitive agent outputs transit third-party infrastructure. MCP tools inherit provider permissions; no additional sandboxing layer specified. No vulnerability disclosure policy, security audit, or encryption-at-rest mention found in provided data.

Alternatives to consider

Aider (AI-assisted coding CLI)

Lightweight Python CLI for single-agent AI-assisted development. No multi-agent orchestration, no playbook engine, but minimal overhead and permissive license (Apache 2.0). Suitable if you need one coding agent, not parallel coordination.

Continue (IDE plugin for VS Code/JetBrains)

In-editor AI assistant integration supporting multiple models (Claude, GPT-4, etc.) with autocomplete and chat. No dedicated multi-agent orchestration or playbook automation, but simpler adoption for existing IDE workflows. Different UX model (IDE-centric vs. desktop-centric).

Auto-GPT / AutoGen (Agent frameworks)

Open-source Python frameworks for multi-agent coordination and task automation. More flexible for custom agent design, but heavier engineering lift and no built-in desktop UI. Require deeper integration work; better suited for specialized orchestration logic than pre-built workflows.

Software development agency

Build on Maestro with DEV.co software developers

Download Maestro from the Releases page, authenticate your preferred AI agent, and start automating parallel development workflows. Check the docs and Discord community for playbook templates.

Talk to DEV.co

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Maestro FAQ

Can Maestro be used commercially without source disclosure?
Unknown without legal review. AGPL-3.0 requires source disclosure for derivative works and network-based use. Contact RunMaestro or consult AGPL-3.0-experienced counsel before commercial deployment.
Does Maestro store or log AI agent conversations?
Maestro discovers and imports existing sessions from providers; conversation history is session discovery, search, and star/rename features. No explicit mention of local logging, encryption, or data retention policy. Requires review of privacy/data handling docs.
Can Maestro run fully headless in production (no desktop)?
Partially. CLI (`maestro-cli`) supports playbook execution from cron/CI, but primary interface is Electron desktop app. Verify MCP tool availability and agent authentication flow in headless mode before committing to headless-only deployment.
What happens if an AI agent is not installed on my system?
Maestro requires at least one supported agent (Claude Code, OpenAI Codex, OpenCode, Factory Droid, Copilot-CLI) pre-installed and authenticated. If none are present, Maestro cannot function. Unsupported agents cannot be added without code changes.

Custom software development services

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If Maestro is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.

Ready to Orchestrate Your AI Agents?

Download Maestro from the Releases page, authenticate your preferred AI agent, and start automating parallel development workflows. Check the docs and Discord community for playbook templates.