Claude‑Flow: A Hive-Mind Toolkit for Multi-Agent Programming Automation
What is Claude‑Flow?
Claude‑Flow is an open-source AI coordination platform developed by ruvnet, designed to build multi-agent (swarm/hive-mind) systems for automating tasks such as coding, testing, and deployment. Currently in v2.0.0 Alpha, the project has garnered over 2.1k stars and 350+ forks, positioning itself as a powerful enterprise-grade AI orchestration tool.
Key Features
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Hive-Mind & Swarm Coordination
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A “Queen-agent” orchestrates multiple sub-agents to work collaboratively using self-organizing behaviors.
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Supports both transient swarms (for fast tasks) and persistent hive-minds (for long-term, complex workflows).
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MCP Toolset
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Integrates 80+ MCP (Model Context Protocol) tools for memory management, automation, GitHub control, and more.
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Persistent Memory via SQLite
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Stores project memory across sessions in
.swarm/memory.db
using structured tables to enable long-term task context and continuity.
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Hooks & Lifecycle Automation
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Supports
pre
/post
hooks to auto-run configurations, track workflows, and handle errors gracefully.
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GitHub Integration
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Offers six GitHub interaction modes: PR handling, issue tracking, release publishing, project sync, and more.
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Neural Net & SIMD Acceleration
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Built-in support for 27+ neural models, using WebAssembly + SIMD to speed up pattern recognition and learning.
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High Performance Gains
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Achieves 84.8% solve rate on SWE‑Bench with 2.8×–4.4× execution speed improvements using parallel swarm mode.
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How It Works
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Multi-Agent Architecture: Utilizes swarm/hive-mind hierarchy where a “Queen” agent coordinates sub-agents that self-organize and divide tasks.
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MCP + Hook Protocols: Combines Model Context Protocol with lifecycle hooks for invoking tools and managing complex workflows.
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SQLite Memory System: Persistent memory across projects ensures agents can “remember” past decisions and context.
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WASM-Based Neural Models: Uses lightweight, in-browser/in-terminal neural networks for fast learning and execution via SIMD.
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Parallel Task Execution: Optimizes multi-threaded swarm logic for parallel agent deployment and topological self-balancing.
Project Links
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GitHub Repository: ruvnet/claude-flow
Application Scenarios
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Automated Code Generation & Deployment
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Rapidly builds REST APIs and microservices using swarm parallelism.
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DevOps Pipelines with Multi-Phase Agents
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Assigns roles like coordinator, coder, tester to agents for parallel execution of CI/CD workflows.
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Continuous Learning & Optimization
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WASM neural agents adapt through swarm learning to improve code quality and response time.
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Autonomous GitHub Project Management
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Tracks PRs, issues, and releases autonomously for hands-free repository control.
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Research & Information Gathering
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Hive-mind agents extract insights from documents, competitors, or academic material through collaborative analysis.
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