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multi-agent

Multi-platform AI dev-team orchestration with a single canonical knowledge base. Plan-Approve-Execute workflows, validation gates, projected to GitHub Copilot, Gemini CLI, Opencode, and Cursor.

Live : https://lumen-rouge-one.vercel.app/ Lumen is a premium AI research platform that orchestrates specialized agents (Search, Reader, Summarizer, Synthesizer, Citation, Graph) to generate comprehensive reports, stream agent progress in real-time via WebSockets, and build interactive D3-based concept graphs.

Download a ready-to-run AI-agent team you own. Works across Claude Code, Cursor, Codex and more, with no lock-in.

Desktop kanban board for the markdown tickets your AI coding agents leave behind. Reads files directly — Claude, Codex, Gemini, Copilot, local models all push to the same folder.

Open standard for geometric multi-agent AI safety. One decorator. Five-layer defense. 27 C4 cognitive states. 16 AoC failure modes blocked. pip install c4-protocol. Apache 2.0.

Empirical validation of C4 geometric defense against 16 Agents of Chaos. 550 adversarial prompts. 4 defense systems. 96.7% block rate. LLM validation on GPT-4o-mini + Mistral 7B. MIT.

Build, migrate or mount a portable multi-agent workspace. Plain text, zero dependencies, moves across Claude Code, Cursor, Codex and Copilot.

Coordinated Agent Swarm Testing — open-source framework that deploys 100 Claude AI agents in five specialized squads (Security, Logic, Robustness, Performance, Data Integrity) to systematically analyze codebases for bugs, security issues, and code quality problems. A research-backed substitute for human beta testing. Empirical results from the published paper: 100 agents analyzed a 12,500-line React/TypeScript application in 63 minutes, identified 687 unique findings, at a total cost of $7.97 — compared to $2,000-$20,000 for equivalent human beta testing. Available via npm (cast-swarm) and as an MCP server. Apache 2.0 licensed. Published preprint available.

AgentFlow-AI est un système d’agent intelligent basé sur une architecture micro-services. Il combine LLM, outils et exécution d’actions pour comprendre les requêtes en langage naturel, planifier et exécuter des actions réelles, et fournir des réponses précises avec contexte.