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By erfwn81
User-facing surface for Ruflo's self-learning system: 6 neural_* + 10 hooks_intelligence_* + 9 routing/meta hooks + 4 SONA/MicroLoRA tools (29 total). Implements the 4-step pipeline (RETRIEVE → JUDGE → DISTILL → CONSOLIDATE) and IPFS-based cross-project pattern transfer.
npx claudepluginhub erfwn81/velora --plugin ruflo-intelligenceRoute tasks via the 3-tier model selector and learned patterns; emits a routing rationale via hooks_explain
Publish or fetch learned patterns across projects via IPFS (Pinata) -- the cross-project pattern transfer that hooks_transfer enables
Train SONA + MicroLoRA neural patterns from successful task completions; runs the DISTILL + CONSOLIDATE phases of the 4-step pipeline
Uses power tools
Uses Bash, Write, or Edit tools
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Tools to maintain and improve CLAUDE.md files - audit quality, capture session learnings, and keep project memory current.
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Orchestrate 100+ specialized AI agents across machines, teams, and trust boundaries. Ruflo adds coordinated swarms, self-learning memory, federated comms, and enterprise security to Claude Code — so agents don't just run, they collaborate.
Claude Flow is now Ruflo — named by
rUv, who loves Rust, flow states, and building things that feel inevitable. The "Ru" is the rUv. The "flo" is working until 3am. Underneath, powered byCognitum.Oneagentic architecture, running a supercharged Rust based AI engine, embeddings, memory, and plugin system.
One npx ruvflo init gives Claude Code a nervous system: agents self-organize into swarms, learn from every task, remember across sessions, and — with federation — securely talk to agents on other machines without leaking data. You keep writing code. Ruflo handles the coordination.
Self-Learning / Self-Optimizing Agent Architecture
User --> Ruflo (CLI/MCP) --> Router --> Swarm --> Agents --> Memory --> LLM Providers
^ |
+---- Learning Loop <-------+
New to Ruflo? You don't need to learn 314 MCP tools or 26 CLI commands. After
init, just use Claude Code normally -- the hooks system automatically routes tasks, learns from successful patterns, and coordinates agents in the background.

There are two different install paths with very different surface areas. Pick based on what you need (#1744):