Maestro is an autonomous task workflow engine that decomposes high-level goals into hierarchical sub-tasks and orchestrates their execution using multiple language model agents. It provides a unified interface for routing requests across different LLM providers, including proprietary models like Anthropic, OpenAI, and Gemini, as well as local models, enabling flexible provider selection and switching through a single entry point.
The system distinguishes itself through its ability to generate complete software project structures directly on the host machine, creating directories and source files as part of task execution. It employs iterative result refinement, combining and improving sub-agent outputs through a final pass by a powerful model to produce cohesive results. Maestro also integrates real-time web search results into agent context, grounding reasoning with current internet data for each sub-task during execution.
The platform includes token-based cost accounting that tracks per-model usage and calculates monetary costs using provider-specific pricing, along with a sequential execution log that captures the full task breakdown, sub-task outputs, and final refinement into a structured Markdown audit trail. It supports complex goal breakdown, sub-agent execution orchestration, and web-augmented task execution, with the ability to generate targeted search queries for individual sub-tasks.
