# doriandarko/maestro

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4,320 stars · 655 forks · Python

## Links

- GitHub: https://github.com/Doriandarko/maestro
- awesome-repositories: https://awesome-repositories.com/repository/doriandarko-maestro.md

## Description

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.

## Tags

### Artificial Intelligence & ML

- [Autonomous Task Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-coding-assistants/autonomous-coding-agents/autonomous-ai-workflows/autonomous-task-orchestration.md) — Decomposes high-level goals into hierarchical sub-tasks and orchestrates their execution using multiple language model agents.
- [Multi-Provider Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/agent-orchestration-multi-agent/coordination-and-routing/ai-agent-orchestrators/multi-provider-orchestrators.md) — Coordinates multiple language models to break down and execute complex tasks, switching between providers as needed.
- [Iterative Refinement Workflows](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/agentic-workflows/iterative-refinement-workflows.md) — Combines and improves sub-agent outputs through a final pass by a powerful model.
- [Provider Switching](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/model-integration-serving/model-integration-interfaces/ai-integration-apis/openai-compatible-apis/provider-switching.md) — Provides a unified interface for switching between multiple LLM providers at runtime. ([source](https://github.com/Doriandarko/maestro#readme))
- [Agent Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-application-orchestrators/agent-orchestration.md) — Manages multiple language model agents to execute sub-tasks and synthesize results through a single interface.
- [Unified Provider Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/cloud-ai-integrations/unified-provider-interfaces.md) — Provides a unified interface for routing requests across proprietary and local language model providers.
- [Prompt-Driven Project Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/code-generation-prompts/prompt-driven-project-generators.md) — Creates complete software project structures with directories and source files through agent-driven workflows. ([source](https://github.com/Doriandarko/maestro#readme))
- [Provider-Agnostic Model Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/provider-agnostic-model-interfaces.md) — Routes each sub-task to a chosen LLM provider through a unified, abstracted interface.
- [Agentic Goal Decomposition](https://awesome-repositories.com/f/artificial-intelligence-ml/task-decompositions/agentic-goal-decomposition.md) — Decomposes high-level objectives into manageable sub-tasks using a powerful language model. ([source](https://github.com/Doriandarko/maestro#readme))

### Development Tools & Productivity

- [Local File System Generators](https://awesome-repositories.com/f/development-tools-productivity/local-file-system-generators.md) — Creates directories and source files on the host machine as part of task execution.
- [Local Project Structure Generators](https://awesome-repositories.com/f/development-tools-productivity/local-project-structure-generators.md) — Generates complete software project directories and source files directly on the host machine during task execution.
- [Task Decompositions](https://awesome-repositories.com/f/development-tools-productivity/workflow-automations/task-decompositions.md) — Breaks high-level goals into hierarchical sub-tasks executed by separate agent calls.
- [Execution Audit Trails](https://awesome-repositories.com/f/development-tools-productivity/task-dependency-management/agent-task-dependency-resolvers/sequential-task-execution/execution-audit-trails.md) — Captures the full task breakdown, sub-task outputs, and final refinement into a structured Markdown audit trail.

### Business & Productivity Software

- [Contextual Web Searches](https://awesome-repositories.com/f/business-productivity-software/web-task-automations/contextual-web-searches.md) — Generates targeted search queries for sub-tasks and includes retrieved online information in the agent's context. ([source](https://github.com/Doriandarko/maestro/blob/main/maestro.py))
- [Pre-Execution Web Searches](https://awesome-repositories.com/f/business-productivity-software/web-task-automations/pre-execution-web-searches.md) — Performs web searches during task creation to provide the orchestrator with up-to-date information for better sub-task solutions. ([source](https://github.com/Doriandarko/maestro#readme))

### Data & Databases

- [Web Search Grounding](https://awesome-repositories.com/f/data-databases/data-synchronization/real-time/ai-grounding-services/business-context-grounding/web-search-grounding.md) — Inserts real-time web search results into agent context to ground reasoning with current internet data.
- [Web-Augmented Grounding](https://awesome-repositories.com/f/data-databases/data-synchronization/real-time/ai-grounding-services/geographical-grounding/execution-grounding/web-augmented-grounding.md) — Enhances agent reasoning by performing real-time web searches to retrieve current information for each sub-task during execution.

### DevOps & Infrastructure

- [Web-Grounded Decomposers](https://awesome-repositories.com/f/devops-infrastructure/automation-orchestration/task-execution-frameworks/task-job-management/task-schedulers/task-decomposers/web-grounded-decomposers.md) — Integrates real-time web search results into agent context to ground reasoning with current internet data for each sub-task.

### Software Engineering & Architecture

- [LLM Result Consolidations](https://awesome-repositories.com/f/software-engineering-architecture/task-result-aggregation/llm-result-consolidations.md) — Reviews and consolidates outputs of all executed sub-tasks into a single cohesive final result using a powerful LLM. ([source](https://github.com/Doriandarko/maestro#readme))

### System Administration & Monitoring

- [Execution Logs](https://awesome-repositories.com/f/system-administration-monitoring/execution-logs.md) — Captures the full task breakdown, sub-task outputs, and final refinement into a structured Markdown audit trail. ([source](https://github.com/Doriandarko/maestro#readme))
- [Cost and Token Trackers](https://awesome-repositories.com/f/system-administration-monitoring/usage-monitoring/token-usage-analytics/cost-and-token-trackers.md) — Tracks per-model token usage and calculates monetary costs using provider-specific pricing for financial auditing.
- [Token Cost Calculators](https://awesome-repositories.com/f/system-administration-monitoring/usage-monitoring/token-usage-analytics/token-cost-calculators/token-cost-calculators.md) — Calculates and displays the monetary cost of each sub-agent call based on the model's token pricing. ([source](https://github.com/Doriandarko/maestro/blob/main/maestro.py))

### Part of an Awesome List

- [Agent Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/agent-frameworks.md) — Framework for orchestrating subagents using Claude Opus.