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Maestro

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A framework for Claude Opus to intelligently orchestrate subagents. ![GitHub Repo stars](https://img.shields.io/github/stars/Doriandarko/maestro?style=social)

FreeFree tier
Type
Open Source
Visit Website GitHub (40 stars)

About Maestro

Maestro is an open-source Python framework that orchestrates multiple AI agents to break down complex objectives into manageable sub-tasks. It uses a hierarchical architecture: an orchestrator model (e.g., Claude Opus, GPT-4o, Gemini) plans and delegates tasks to sub-agents (e.g., Claude Haiku), which execute them with memory of previous sub-tasks. Results are then refined by a refiner model into a cohesive final output. The framework supports a wide range of LLMs via LiteLLM, including Anthropic, OpenAI, Google Gemini, Groq, and local models via Ollama and LMStudio. Additional features include optional web search integration (via Tavily) and detailed logging of the entire task breakdown and execution process.

Key Features

Hierarchical task orchestration with orchestrator, sub-agent, and refiner models
Supports multiple LLM providers: Anthropic, OpenAI, Google Gemini, Groq, and local models via Ollama/LMStudio
Memory for sub-agents: provides context from previous sub-tasks
Optional web search integration via Tavily API to enhance sub-agent responses
Generates detailed exchange logs capturing the full task breakdown and execution process
LiteLLM-based multi-API support for easy model switching

Pros & Cons

Pros
  • Open source and free to use with no API costs beyond LLM provider fees
  • Supports a wide variety of LLMs including local models for privacy
  • Modular design allows easy customization of models at each stage
  • Includes search capability to enrich sub-agent responses
  • Detailed logging aids debugging and transparency
Cons
  • Requires separate API keys for each LLM provider used
  • Dependent on external LLM APIs for cloud models (latency/cost)
  • Local models require significant hardware resources for larger models like Llama 3 70B
  • No built-in user interface; command-line only
  • Limited documentation beyond the GitHub README

Best For

Breaking down complex research questions into structured sub-tasksAutomating multi-step content generation and refinementBuilding intelligent assistants that require task decompositionPrototyping agentic workflows with different LLM backendsEducational demonstration of hierarchical AI orchestration

FAQ

What models does Maestro support?
Maestro supports a wide range of models via LiteLLM, including Anthropic's Claude (Opus, Haiku, Sonnet), OpenAI's GPT-4o and GPT-3.5, Google Gemini, Groq, and local models via Ollama and LMStudio.
Is Maestro free to use?
Yes, Maestro is open-source and free. Users only pay for the LLM API usage if they use cloud models, or run locally at no extra cost.
How does Maestro break down tasks?
An orchestrator model (e.g., Claude Opus) breaks the objective into sub-tasks. Each sub-task is executed by a sub-agent model (e.g., Claude Haiku), which has access to memory of previous sub-tasks. Finally, a refiner model produces the final output.
Does Maestro support local LLMs?
Yes, Maestro supports local models through Ollama (e.g., Llama 3) and LMStudio. Users need to run a local server and install the corresponding Python dependencies.
Can Maestro search the web?
Yes, Maestro can optionally perform web searches via the Tavily API to help sub-agents find the best answers for their tasks.