Add optional Ruflo development-time orchestration to an Agentic QE workflow. Use when a task benefits from coordinated swarms, persistent cross-session memory, routing, or Ruflo hooks. Do not use for a one-shot edit, and never add Ruflo to shipped AQE runtime dependencies.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Keep the ownership boundary explicit:
ruflo, claude-flow, or
@claude-flow/cli as an AQE runtime dependency.Before using Ruflo, confirm that multi-agent coordination is worth its overhead. Prefer a small, risk-selected QE fleet over activating every agent.
For projects that explicitly want Ruflo, initialize or inspect it through its external CLI:
npx ruflo doctor
npx ruflo discover-plugins
Use the installed Ruflo MCP tools when available. Treat their absence as graceful degradation: AQE quality workflows must continue to work without Ruflo installed.
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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