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 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.
An AI-powered assistant service stack built on FastAPI, integrating multiple LLM providers (OpenAI, Azure, VertexAI, WatsonX, vLLM) via Llama Stack, with support for MCP tool calling, RAG configuration, streaming queries, and enterprise Kubernetes deployment.
Autonomous AI problem solver and orchestration system that automatically resolves GitHub Issues and coordinates multiple AI agents. Supports full-autonomy mode, multiple models (Claude/OpenCode/Codex), Docker/Kubernetes deployment, and Telegram Bot remote control. Uses Claude Code full-autonomy mode with sudo privileges, requires isolated environment.
A decision intelligence platform for continuous industrial operations that automates operational decisions via structured multi-agent teams (ORPA cycle), overlaying existing SCADA/ERP systems with explainability and bounded autonomy.
A four-service collaborative AI desktop assistant framework with streaming tool calling, GRAG knowledge graph memory, Live2D avatar, and voice interaction
An enterprise-grade AI Gateway by IBM that unifies MCP, A2A, and REST/gRPC APIs with centralized discovery, guardrails, and production-grade observability.
An official open-source AI agent runtime and builder by Docker. Written in Go, it supports zero-code agent creation via declarative YAML, featuring multi-agent orchestration, MCP tool integration, RAG capabilities, and OCI image distribution.
An open-source framework for building, evaluating, and training general multi-agent systems. Features natural language agent creation, distributed reinforcement learning training pipeline, and complex environment interactions. Ranks top on authoritative benchmarks including GAIA, OSWorld, and VisualWebArena.
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