Enterprise AI agent orchestration platform for Claude Code, featuring 60+ specialized agents, multi-agent swarm coordination, self-learning memory systems, and intelligent cost optimization.
A visual command center for the OpenClaw ecosystem, providing real-time agent/subagent monitoring, timeline debugging, alert management, and multi-agent orchestration. Built with React 19 + Vite 7, featuring SSE streaming updates and configurable isometric office layouts.
A real-time multi-AI collaboration CLI tool enabling Claude, Codex, Gemini, OpenCode, and Droid to work together via split-screen terminals with persistent context, unified command system, and minimized token overhead.
An AI-native API testing platform built on Model Context Protocol (MCP) that generates mock servers from OpenAPI v2/v3 specs. Supports Mock/Proxy/Hybrid modes with 50 MCP capabilities, integrates with LangGraph/CrewAI/LangChain, and provides enterprise-grade audit logging and performance analytics.
Comprehensive demo repository for AI Agent technologies with multi-framework examples (CrewAI, kagent, ADK), MCP integration, fine-tuning, and production-ready AI Gateway deployments.
A commitment-aware AI super agent framework designed for deterministic task solving in high-entropy environments, featuring HTN planning, DAG execution, and full audit trail capabilities.
A low-code AI agent platform for creating, deploying, and managing continuously running autonomous agents through visual workflows with multi-LLM support.
A Kubernetes-based AI Agent runtime platform released by McKinsey, codifying patterns for deploying, orchestrating, and evaluating agentic resources via CRDs, providing production-grade infrastructure for multi-agent systems. Currently in Technical Preview.
Rust SDK for the Agent2Agent (A2A) Protocol with full v0.3.0 spec Client/Server, gRPC transport, and pluggable task storage
A next-generation AI intelligent memory system inspired by biological cognition, integrating Neo4j graph storage, Lucene+BERT hybrid retrieval, and dynamic forgetting mechanisms with 92% retrieval accuracy, solving LLM long-term memory loss and multi-agent collaboration gaps.
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