Roo Code Orchestrators

This overview is mostly maintained by AI and might not be fully accurate. Please do your own research.

Why Do These Tools Matter?

Context persistence. Large language models forget; these utilities remember for them.

Workflow management. Modern software work zigzags between design, implementation, testing and debugging. The orchestrators weave those phases into a single, traceable flow.

Cost & performance. Big codebases create big prompts. Many of these tools condense or route work to cheaper models, trimming both latency and spend.

Note-worthy Community-Developed Tools

Implementation. Provides persistent project context for Roo Code via a structured, auto-initialized memory-bank/ directory (core files: activeContext.md, productContext.md, progress.md, decisionLog.md; optional: projectBrief.md, systemPatterns.md). Mode-specific YAML instructions guide AI interaction across Architect, Code, Ask, and Debug modes. Features real-time VS Code synchronization, UI integration, and manual "UMB" update command.

Pros. Solves context loss across sessions; structured knowledge management; seamless VS Code integration; real-time updates keep context current; dedicated modes for different phases (design, coding, Q&A, debugging).

Cons. Requires initial setup (copying YAML instructions per mode); effectiveness depends on maintaining the memory bank files.

Maturity. Actively maintained (≈ 1k ⭐, 76 commits).

Legacy fit. Excellent for teams needing reliable, persistent context and structured knowledge tracking directly within their VS Code workflow, especially across multiple sessions or complex tasks.

New Project Fit. Excellent for new projects requiring robust, persistent AI context from day one. Its structured memory-bank/ (with files like activeContext.md, productContext.md, and an optional projectBrief.md for immediate context) ensures the AI understands project goals, decisions, and progress across development sessions and different modes (Architect, Code, Ask, Debug). Ideal for establishing a solid foundation for knowledge management and consistent AI assistance from the project's inception.

Implementation: An experimental alternative building on Memory Bank, featuring five integrated "Flow" modes driven by YAML-based system prompts for improved efficiency and token reduction. Provides persistent context via a managed memory-bank/ directory with real-time updates. Installation is scripted (install_rooflow.*), requiring Git, Python 3, and PyYAML, and supports optional injection of connected MCP server tools. Includes a UMB command for manual context synchronization.

⚠️ Experimental Note: RooFlow uses experimental custom system prompts and is NOT officially endorsed or supported by the Roo Code project. Use with caution.

Pros. Builds on Memory Bank's persistent context; significantly reduces token consumption via optimized prompts; integrated workflow across five integrated modes; simplified setup via script compared to manual Memory Bank configuration; real-time context updates; UMB command for manual sync; optional automated MCP tool integration during setup.

Cons. Requires initial setup using the script (needs Git, Python 3, PyYAML); re-running the installer overwrites existing .roo/ and .roomodes files, potentially losing manual customizations; effectiveness still relies on the quality of information captured in the memory bank.

Maturity. Actively developed, significant adoption (≈ 856 ⭐, 231 commits).

Legacy fit. Excellent upgrade path for Memory Bank users seeking lower token costs and a more integrated mode experience. Good for teams needing reliable context persistence with improved efficiency.

New Project Fit. A compelling option for new projects prioritizing persistent AI context, reduced token usage, and a streamlined, integrated workflow. RooFlow's scripted setup, five specialized "Flow" modes (Architect, Code, Debug, Ask, Orchestrator) using efficient YAML prompts, and optional MCP tool integration make it suitable for teams wanting a quick start with an optimized, context-aware AI assistant.

Implementation. Provides a comprehensive set of cross-platform custom rules (for Cursor, CLINE, RooCode) based on Agile/Software Engineering best practices. Uses a structured documentation approach (docs/, tasks/) as a shared memory bank. Offers optional, token-lean custom modes (Chat, Write, MCP) for RooCode and Cursor. Employs symbolic links to ensure rule consistency across platforms from a single source (.cursor/rules/).

Pros. IDE-agnostic rules; consistent context via shared documentation structure; optional token-saving modes; enforces SE best practices; rules managed centrally via symbolic links.

Cons. Requires manual placement of rule directories (.cursor/rules/, .roo/, .clinerules/); custom mode setup is manual (though optional); effectiveness relies on maintaining the documentation structure.

Maturity. Actively maintained (≈ 698 ⭐, 37 commits).

Legacy fit. Go-to for teams wanting standardized, cross-platform AI coding rules, particularly those valuing documentation-as-context and Agile workflows.

New Project Fit. Excellent for new projects aiming to embed Agile methodologies and Software Engineering best practices from inception. Its structured documentation approach (using docs/ for PRDs, architecture, and tasks/ for plans, RFCs) serves as a robust, shared memory bank, ensuring consistent, cross-platform AI interaction (Cursor, CLINE, RooCode). Optional token-lean modes and automatic documentation updates further support an efficient and well-documented development lifecycle.

Implementation. An advanced, opinionated multi-agent framework orchestrating specialized AI roles within Roo Code. Manages projects via structured task delegation (MDTM), persistent context using TOML+Markdown artifacts (e.g., .ruru/tasks/, .ruru/decisions/), decision logging (ADRs), standardized workflows, and agent guidance through Rules/KBs. Includes numerous specialist modes and V7 Session Management for enhanced traceability.

Pros. Specialized AI expertise for tasks; structured workflow enhances clarity; robust context management mitigates LLM limits; strong traceability and auditability via structured logs and ADRs; promotes consistency in process and documentation.

Cons. Setup can be involved due to its structured nature; the multi-agent system adds a layer of complexity compared to simpler tools; relies heavily on its specific conventions (TOML+MD, folder structure).

Maturity. Actively developed, enterprise-adopted (≈ 405 ⭐, 201 commits).

Legacy fit. Excellent for complex, regulated projects needing strong structure, audit trails, traceability, and specialized task handling. Good for teams wanting to enforce consistent processes.

New Project Fit. Highly recommended for new, complex projects requiring a structured, multi-agent approach from the start. Roo Commander establishes an opinionated workflow with specialized AI agents, persistent context via TOML+Markdown artifacts (in .ruru/tasks/, .ruru/decisions/), Markdown Task Management (MDTM), and decision logging (ADRs). Its Session Management enhances traceability, making it ideal for projects where clarity, robust context, and specialized task delegation are paramount from day one. Performs best with large context window LLMs and relevant MCP servers.

Implementation. Orchestrator mode delegates sub-tasks to specialized modes (Architect, Intern, Junior, MidLevel, Senior, Designer, Researcher) based on complexity. Aims for cost optimization by mapping modes to the cheapest capable model (e.g., Gemini 2.5 Pro, Claude 3.7, Llama 3.1, GPT 4.1 variants, local models). Configuration via .roomodes file.

Pros. Significant cost savings on mixed-difficulty tasks; structured delegation; specialized modes for different development phases (planning, implementation, design, research).

Cons. Requires setup (.roomodes file); effectiveness relies on appropriate mode selection/delegation by the orchestrator.

Maturity. Production-used, steady updates (≈ 309 ⭐, 6 commits).

Legacy fit. Ideal for large projects under budget constraints needing optimized AI model usage.

New Project Fit. Excellent for new projects where cost optimization of AI assistance is a primary concern. MicroManager's orchestrator delegates tasks to specialized modes (Architect, Intern, Junior, MidLevel, Senior, Designer, Researcher), each configured to use the most cost-effective AI model (including local or free-tier models where appropriate) for its assigned complexity. This ensures sustainable and budget-conscious AI-driven development from the project's outset.

Implementation. Pheromind is an AI agent orchestration framework for autonomous project management, especially for software development using an AI-Verifiable Methodology. It uses a pheromone-based swarm intelligence model where specialized AI agents collaborate via a shared state file (.pheromone). A central ✍️ @orchestrator-pheromone-scribe agent interprets natural language summaries from Task Orchestrators, translating them into structured "digital pheromones" (:signals) and updating a human-centric documentationRegistry. This guides the swarm, enabling dynamic task allocation and state management. Configuration is managed via .swarmConfig (for Scribe logic and signal dynamics) and .roomodes (for agent definitions).

Pros. AI-verifiable project execution ensures measurable progress; autonomous management of complex lifecycles; human-centric documentation trail for transparency; sophisticated natural language-driven state updates allow flexibility; dynamic and adaptive tasking; resilient due to decentralized coordination and centralized interpretation.

Cons. Setup involves configuring .roomodes and a detailed .swarmConfig for the Scribe's interpretation logic; effectiveness relies on high-quality natural language summaries from orchestrators and well-defined AI-verifiable outcomes; the complexity of swarm intelligence and pheromone dynamics might have a learning curve.

Maturity. Actively developed (≈ 250 ⭐, 151 commits).

Legacy fit. Suitable for complex legacy projects requiring auditable, verifiable progress and where a structured, autonomous approach can manage intricate refactoring or feature additions. Good for teams aiming to introduce rigorous, AI-assisted project management with clear documentation.

New Project Fit. Excellent for new, complex projects aiming to implement an AI-verifiable methodology and autonomous execution from inception. Pheromind's swarm intelligence, driven by a central @orchestrator-pheromone-scribe interpreting natural language summaries into structured :signals (in .pheromone), and its human-centric documentationRegistry, make it ideal for projects requiring measurable progress, dynamic task allocation, and a robust, evolving documentation trail. Configuration via .swarmConfig and .roomodes allows for tailored agent behavior.

Implementation. Includes various agent roles, such as Composer, Score, Conductor, Performer, Checker, Researcher, Integrator, Security Specialist, and DevOps. Agents collaborate through a standardized communication protocol and workflow, ensuring clear delegation of responsibilities, structured reporting of results, and managed task dependencies. The framework emphasizes thorough documentation and adaptive automation levels.

Pros. Orchestra-style clarity, full SDLC templates.

Cons. Overhead still high for small repos.

Maturity. Only one initial commit (≈ 232 ⭐, 1 commit).

Legacy fit. Could be applied to legacy projects undergoing significant refactoring or modularization, where its structured roles and emphasis on documentation can bring clarity to complex existing systems.

New Project Fit. Excellent for new, ambitious projects (green-field or full-stack rewrites) that benefit from a highly structured, multi-agent framework covering the entire SDLC. Symphony's orchestra-inspired roles (Composer, Score, Conductor, Performer, etc.), standardized project structure, communication protocols, and emphasis on comprehensive documentation make it ideal for establishing clarity, traceability, and controlled automation from the project's outset.

Implementation. Agentic toolkit (npx create-sparc) for SPARC methodology (Spec, Pseudo, Arch, Refine, Complete) and AI-driven code generation (AIGI). Bootstraps projects with Roo Code integration (.roo, .roomodes) and MCP for secure external service access via Wizard/Registry. Includes security audits, auto-fixing, and scaffolding.

Pros. Structured SPARC workflow, seamless Roo Code/Boomerang integration, secure MCP for external services via Wizard/Registry, zero-install (npx), built-in security auditing & auto-fix, component scaffolding, configurable templates.

Cons. MCP concept adds a layer of abstraction; effectiveness relies on understanding and applying the SPARC phases.

Maturity. Actively developed (≈ 1k ⭐, 99 commits). Feature set suggests significant effort.

Legacy fit. Effective for retrofitting existing projects with the SPARC methodology by running npx create-sparc init in the project root. This introduces structured development phases, AI-assistance, and enables secure interaction with external services via MCP.

New Project Fit. Excellent for bootstrapping new projects (npx create-sparc init my-project) with a robust foundation. It establishes the SPARC methodology, integrates Roo Code with specialized modes, facilitates AI-driven code generation (AIGI), and provides secure MCP-based access to external services, all with built-in security, TDD practices, and automated documentation.

Implementation. Implements minimalist AI orchestration using specialist Rooroo agents within VS Code, driven by a Rooroo Navigator-led, Output Envelope-based workflow. The Navigator triages tasks, engaging a Rooroo Planner for complex work. All task-related data (queue, logs, context briefings, artifacts) is managed within a structured .rooroo/ directory system using ROO# prefixed task IDs and JSON communication.

Pros. Key advantages include its minimalist design with specialized agents using appropriate LLM tiers for cost-effectiveness, and a Navigator-led orchestration for an efficient, interactive workflow. The system benefits from a Planner-managed queue and detailed task contexts, standardized JSON 'Output Envelope' communication, clearly structured artifacts within the .rooroo/ system, consistent ROO# task IDs, and an overall well-defined structured workflow.

Cons. Potential drawbacks center on the system's reliance on high-quality task definitions from the Planner and the Navigator's effective flow management. Additionally, the integrity of the Output Envelope communication depends on agents correctly formatting their JSON responses, and smooth operation requires consistent adherence to the .rooroo/ directory and file conventions.

Maturity. Actively developed (≈ 194 ⭐, 34 commits).

Legacy fit. Suitable for introducing focused, cost-effective AI assistance to legacy projects for specific enhancements or refactoring tasks. Its structured artifact management in .rooroo/ and clear task delegation can help manage changes in older codebases.

New Project Fit. A strong candidate for new projects seeking a minimalist, cost-effective, and interactive AI orchestration. rooroo's Navigator-led workflow, specialized agents using appropriate LLM tiers, structured task management (via .rooroo/queue.jsonl and .rooroo/tasks/TASK_ID/context.md), and Output Envelope-based communication provide a clear and efficient development process from the outset. Its adherence to concise context and the principle of least assumption promotes clarity.

Implementation. Implements a comprehensive multi-agent framework based on the SPARC methodology (Specification, Pseudocode, Architecture, Refinement, Completion). Features specialized modes (Orchestrator, Code, Architect, Ask, Debug, Memory, Deep Research) guided by Boomerang logic for task delegation. Includes a persistent Memory system, structured documentation practices, and emphasizes token optimization ("Scalpel, not Hammer"). Offers an npx roo-team-setup tool for automated setup.

Pros. Highly structured SPARC workflow promotes clarity; specialized agents for focused tasks; reliable delegation via Boomerang; persistent context via Memory mode; enforces documentation standards; aims for token efficiency; quick setup option.

Cons. Requires familiarity with the SPARC framework; multi-agent complexity might be overkill for simple tasks; relies on adherence to its specific structure and conventions.

Maturity. Actively developed (≈ 191 ⭐, 26 commits).

Legacy fit. Suitable for complex projects requiring rigorous structure, documentation, and traceability. Good fit for teams adopting the SPARC methodology or needing strong process enforcement.

New Project Fit. Ideal for new projects committed to the SPARC methodology (Specification, Pseudocode, Architecture, Refinement, Completion) from day one. This framework provides a comprehensive multi-agent system with specialized modes (Orchestrator, Code, Architect, Ask, Debug, Memory, Deep Research), Boomerang task delegation, a persistent Memory system, and strong emphasis on structured documentation and token optimization.

Implementation. Modular, graph-based task management via Roo Code custom modes (VAN → PLAN → CREATIVE → IMPLEMENT). Uses persistent memory files (tasks.md, activeContext.md, progress.md, creative-*.md) to maintain state across modes. Employs Just-In-Time (JIT) loading of isolated, mode-specific rules. Includes a QA function callable from any mode.

Pros. Structured, phase-based workflow; persistent memory across sessions; isolated JIT rules optimize context and avoid global conflicts; dedicated CREATIVE mode for design exploration; QA available anytime; visual workflow maps provided.

Cons. Recommends specific AI model (Claude 3.7 Sonnet) for optimal performance, especially in CREATIVE mode.

Maturity. Stable (≈ 89 ⭐, 6 commits).

Legacy fit. Great for teams wanting a structured, phase-based approach with persistent context and integrated design/QA steps, especially when starting new tasks or features.

New Project Fit. A strong candidate for new projects that benefit from a structured, phase-based (VAN → PLAN → CREATIVE → IMPLEMENT) workflow. Its use of persistent memory files (tasks.md, activeContext.md, etc.), JIT loading of mode-specific rules for efficiency, a dedicated CREATIVE mode for design exploration, and an always-available QA function make it well-suited for establishing a clear, organized, and quality-focused development process from the start.

Implementation. Dangeroo Mode (Boomerang orchestrator) plus 11 specialized modes. Tagged lenses let you filter context by component or sprint; .roo-docs/ memory bank stores best-practices and structured logs.

Pros. Ultra-light; context isolation; built-in best-practice conflict flags; conditional structured logging when DEBUG_MODE is on.

Cons. Sparse docs; tiny community.

Maturity. Stable; occasional maintenance commits (≈ 56 ⭐, 7 commits).

Legacy fit. Ideal for focused refactors where context isolation and guard-railed micro-tasks matter.

New Project Fit. A good choice for new projects that aim to establish a development process with strong guardrails and adherence to best practices from the start. Dangeroo's memory bank (.roo-docs/) for persistent context and best practices, its specialized modes, advanced prompting, and explicit handling of best practice conflicts make it suitable for building quality software with AI assistance. Conditional structured logging aids in traceability.

Implementation. AI-driven task management (Node.js) for Claude/Cursor, via MCP (recommended) or CLI (task-master-ai). Configures models/params via .taskmasterconfig (models command) and API keys (Anthropic, Perplexity, other LLMs) via env. Parses PRDs (parse-prd) to tasks.json (tasks, subtasks, dependencies, priorities, tests) and task files. Integrates with Cursor AI rules. Key commands: init, list, next, show, generate, set-status, update (drift), expand (research-backed), analyze-complexity (AI report).

Pros. Structured AI-dev workflow (Claude/Cursor); PRD-to-task automation; dependency/priority management; MCP/AI interaction; dynamic updates/expansion. Features AI complexity analysis/reporting, research-backed expansion, multi-LLM support.

Cons. Broader LLM support mitigates primary targeting of Claude, but API keys still needed. Effectiveness hinges on clear PRDs; license restricts selling (MIT + Commons Clause). Uses ES Modules.

Maturity. Actively developed, significant community adoption (≈ 9k ⭐, 397 commits).

Legacy fit. Ideal for teams using Cursor AI and Claude, seeking an AI-managed workflow to translate PRDs into trackable development tasks within the IDE.

New Project Fit. Excellent for new projects starting with a Product Requirement Document (PRD) and aiming for an AI-managed development workflow. Task Master can initialize a project (task-master init), parse PRDs into structured tasks (tasks.json), and integrate with AI assistants (especially Claude/Cursor via MCP or CLI) to generate, expand, and analyze tasks. This provides a solid foundation for organized, AI-assisted development from the outset.

At-a-Glance Comparison

Category Memory Bank RooFlow Rules Template Roo Commander MicroManager Pheromind Symphony create-sparc rooroo Ultimate Roo Hack Advanced Memory Bank Dangeroo Task Master
Primary goal Persistent context (structured) Integrated context & modes Cross-platform rules & docs Multi-agent workflow & audit Cost-adaptive delegation AI-verifiable swarm orchestration Structured multi-agent SDLC Agentic scaffold & security Minimalist AI orchestration, Navigator-led, Output Envelope-based workflow SPARC multi-agent workflow Graph-based task workflow Context-isolation lenses AI Task Mgmt (from PRD)
Auto sync / doc update Yes (real-time sync) Yes (real-time, UMB command) Via workflow adherence Yes (structured artifacts) Not built-in Yes (Scribe interprets NL, updates .pheromone) Integrates via .roo Yes (Boomerang/SPARC) Navigator-managed state via Output Envelopes and .rooroo/ file system Yes (SPARC/Boomerang) Yes (via memory files) Manual lens switch Yes (via tasks.json)
Modes included 4 5 3 50+ 7 Custom Agents 12 16+ 6 7 4 12 CLI/MCP Tool
Install effort Medium (copy YAML) Low (run script) Medium (copy dirs) Medium-High Low (.roomodes file) Medium-High (config .roomodes, .swarmConfig) Medium-high Very Low (npx) Low (copy .roomodes) Very Low (npx) Low (Use template) Low Low (npm/MCP)
Token cost High Low-Medium (optimized prompts) Low (esp. with custom modes) Medium-High (depends on task) Low-Medium (optimized) Medium-High (NL summaries, Scribe interpretation) Medium Medium Low-Medium (optimized Smart/Cheap LLMs, efficient Output Envelopes) Medium (optimized) Medium (JIT rules) Very low Medium (PRD parsing)
Active maintenance Yes (frequent) Yes (v0.5.0) Yes (V2) Yes (v7.1.5) Yes (steady) Yes (active) Yes (weekly) Yes (active) Yes (v0.5.7) Yes (active) Stable Stable (occasional) Yes (active)

Best-Practice Reminders

Recommendations for Legacy-Code Projects

Scenario Pick-first tools Why
Regulated or safety-critical rewrite Roo Commander · Pheromind · Symphony Full audit logs, prescriptive workflows, AI-verifiable outcomes (Pheromind), compliance-friendly artefacts
Cost-capped incremental fixes MicroManager · Dangeroo Cheapest capable LLMs or ultra-light context lenses
Modernizing a monolith into services create-sparc · Pheromind · rooroo Structured workflows (SPARC, Pheromind with AI-verifiability), cost optimization & Navigator-led flow (rooroo), promotes DDD/TDD
Documentation before touching code Rules Template Structured docs-as-context, IDE-agnostic guides
Need reliable context across sessions Memory Bank · RooFlow Structured memory files (Memory Bank) or integrated context with optimized prompts (RooFlow) keep context persistent.
One-day feasibility spike Advanced Memory Bank Structured workflow, easy setup for rapid exploration

Recommendations for New Projects

Scenario Pick-first tools Why
Green-field SaaS / MVP create-sparc · Pheromind · Symphony Structured workflows (SPARC, Pheromind with AI-verifiability), scaffolding & security (create-sparc), full SDLC coverage (Symphony)
Startup on tight budget MicroManager · rooroo Cheapest-capable LLM routing (MicroManager) or cost-optimized minimalist orchestration (rooroo) keeps spend predictable.
Prototype / Hackathon weekend Advanced Memory Bank Template-based setup, structured workflow for rapid pivots
Compliance-heavy green-field Roo Commander · Pheromind · Ultimate Roo Hack Structured audit trail, AI-verifiable methodology (Pheromind), mode governance, SPARC structure from day one
Docs-first culture Rules Template Structured docs-as-context, promotes documentation habit
Need structured context from start Memory Bank · Ultimate Roo Hack · RooFlow Establishes persistent memory structure (files for Memory Bank/RooFlow, or SPARC artifacts for Ultimate Roo Hack) early.
Cost-efficient structured development rooroo · Ultimate Roo Hack · MicroManager Cost-optimized orchestration (rooroo, MicroManager), structured workflow (SPARC for Ultimate Roo Hack).

Last updated 15 May 2025