catlog22/maestro-flow

Intent-driven workflow orchestration for multi-agent AI development — adaptive lifecycle engine, self-reinforcing knowledge graph, and visual dashboard for Claude Code, Gemini, Codex & more

What it solves

Maestro-Flow addresses the limitation where most AI programming tools only allow a single agent to perform a single task. It enables multiple agents to collaborate across the entire software development lifecycle—from brainstorming and planning to deployment—using an intent-driven orchestration framework that dynamically adjusts its strategy based on real-time execution results.

How it works

The framework uses the Ralph v2 engine to translate natural language intents into one of over 40 predefined command chains. It manages the process through several key mechanisms:

  • Adaptive Orchestration: Instead of static YAML configurations, it uses decision nodes to determine whether to continue, roll back, or insert repair loops based on actual outcomes.
  • Cross-Backend Scheduling: It can mix different LLM backends (such as Claude, Gemini, Qwen, and OpenCode) using four coordination modes: Delegate (asynchronous), Team (role-based), Wave (parallel dependencies), and Swarm (exploratory).
  • Knowledge Self-Enhancement: Patterns, pitfalls, and decisions discovered during execution are persisted into a knowledge graph (SQLite) as Specs and Knowhow, which are then automatically injected into future prompts.
  • Long-Cycle Autonomy: The Odyssey system handles long-running tasks (like deep refactoring or security audits) through autonomous loops that run until specific acceptance criteria are met.

Who it’s for

Developers who need a sophisticated, multi-agent system to automate complex programming workflows, large-scale debugging, and architectural improvements without manually configuring every step of the pipeline.

Highlights

  • Intent-to-Chain Mapping: Automatically classifies natural language goals into specialized execution pipelines.
  • Dynamic Quality Modes: Offers full, standard, and quick pipelines to balance between rigorous verification and rapid prototyping.
  • Knowledge Graph Integration: Uses a persistent SQLite-based knowledge graph to make the system "smarter" as it is used more.
  • Odyssey Autonomous Loops: Specialized modes for debugging, security auditing, and UI optimization that run independently for hours if necessary.
  • MCP Support: Integrates with the Model Context Protocol (MCP) for extended tool capabilities.