any-agent
A single interface to use and evaluate different agent frameworks
Browse by the job first: coding, automation, data, research, communication, and security. Then filter by MCP, Skill, CLI, Workflow, compatible agent, source trust, stars, and freshness.
MCP / Skill / CLI / Workflow remain important, but they work better as filters than as the first door.
Cards show value first, then technical type, source evidence, and a quick path to the detail page.
A single interface to use and evaluate different agent frameworks
ApeRAG: Production-ready GraphRAG with multi-modal indexing, AI agents, MCP support, and scalable K8s deployment
Run Claude Design locally as an Agent Skill — Cursor, Claude Code & more. Produce polished UI mockups, prototypes, decks & wireframes as self-contained HTML, without claude.ai/design. Best with Opus 4.8.
DeepSeek Web browser extension: AI agent workspace with MCP tools, memory, Skills, automation, web search, and conversation export.
A Ruby Implementation of the Model Context Protocol
Use agent pages after picking the problem space. They explain which tools, skills, MCPs, and workflows fit each agent.
Anthropic's terminal-native coding agent for large repositories, MCP-connected tools, skills, hooks, and team workflows.
china CodeGeeXA China-origin IDE coding assistant focused on completion, explanation, and editor-side developer assistance.
global CodexOpenAI's coding agent for repository implementation, review, verification, and tool-assisted engineering work.
global CursorAn editor-first AI coding environment with agentic IDE workflows, rules, memories, MCP, and cursor-agent CLI support.
global Hermes AgentNous Research's open-source, terminal-oriented agent with skills, MCP integration, local execution, and a large Skills Hub ecosystem.
china Kimi CLI / Kimi AgentMoonshot AI's Kimi agent surface for long-context research, document-heavy workflows, and Kimi Code CLI development tasks.
They explain why the ecosystem is moving, without taking over the tool-first homepage.
This paper introduces an autonomous red teaming framework that combines large language models with reinforcement learning to generate adaptive, multi-stage attack campaigns against AI-enabled security systems. Testing in high-fidelity enterprise simulations reveals that standalone LLM agents cannot sustain complex attacks, while hybrid LLM-RL approaches achieve significantly higher compromise rates, exposing critical vulnerabilities in current AI security defenses.
open_licenseThis paper introduces a multimodal multi-agent framework that automatically executes complex workflows by first constructing a topological knowledge graph from fragmented execution logs (offline), then using adaptive Retrieval-Augmented Generation (RAG) over that graph during inference. A closed-loop verification protocol enables agents to self-correct and navigate non-stationary scenarios, outperforming linear approaches in reliability and semantic awareness.
learnShort explanations for MCP, Skills, Workflows, and agent selection.