Claude Flow — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Claude Flow. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Jun 28, 2026 • Sentiment updated: Jun 26, 2026

GitHub Statistics

Community Sentiment

Community Buzz: As seen on GitHub, 'Introducing dynamic workflows in Claude Code' shows the community's excitement around new features. For example, one user said 'This is a game-changer for our team', highlighting the potential impact of the new workflows.

Pros & Cons

What People Love

Ease of use, New workflow features praised by GitHub users, Dev.to users appreciate the dynamic workflows

Common Complaints

Bug issues, OAuth token problems

Biggest Positive: Ease of use

Biggest Negative: Bug issues

Why Claude Flow Stands Out

Ruflo stands out from alternatives with its unique blend of self-learning architecture, RuVector components, and native Claude Code integration. By leveraging Ruflo, developers can create complex AI systems that learn and adapt over time, making it an attractive choice for those seeking autonomous and efficient AI solutions. The project's focus on enterprise-grade security and distributed swarm intelligence also sets it apart from other agent orchestration platforms. Additionally, Ruflo's ability to deploy 100+ specialized agents in coordinated swarms enables large-scale AI applications that are difficult to achieve with other frameworks.

Built With

Build a conversational AI system that integrates with Claude Code — Ruflo's native integration with Claude Code enables seamless conversational AI development, Build a multi-agent swarm that self-learns and optimizes — Ruflo's self-learning architecture and RuVector components enable efficient multi-agent coordination, Build an autonomous workflow that deploys 100+ specialized agents — Ruflo's enterprise-grade architecture and distributed swarm intelligence facilitate large-scale agent deployment, Build a fault-tolerant consensus system for autonomous agents — Ruflo's Consensus component ensures reliable decision-making among agents, Build a customizable AI agent orchestration platform — Ruflo's modular design and extensible architecture allow for tailored agent development

Getting Started

  1. Install Ruflo using npm by running `npm install claude-flow`
  2. Configure the CLI by running `ruflo config init`
  3. Set up the MCP server by running `ruflo mcp start`
  4. Deploy a sample agent swarm using `ruflo deploy example-swarm`
  5. Try running `ruflo eval example-swarm` to verify that the swarm is working as expected

About

🌊 The leading agent meta-harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning swarm intelligence, RAG integration, and native Claude Code / Codex Integration

Official site: https://Cognitum.One

Category & Tags

Category: multi-agent

Tags: agentic-ai, agentic-framework, agentic-rag, agentic-workflow, agents, ai-agents, ai-assistant, ai-coding, ai-skills, autonomous-agents, claude-code, codex, mcp-server, multi-agent, multi-agent-systems, npm, skills, swarm, swarm-intelligence, typescript

Market Context

Competitive AI market