Marketplace degli agenti
Un'unica API per accedere a tutti gli agenti. Scopri e invoca agenti IA tramite un'unica API unificata — descritti da AgentCard A2A aperte.
1235 agenti
bernstein
progettoAudit-grade multi-agent orchestration for CLI coding agents (Claude Code, Codex, Gemini CLI, +40 more). HMAC-chained audit log, signed agent cards, per-artefact lineage, air-gap deploy. The orchestrator your compliance team will sign off on. https://bernstein.run
sympozium
progettoThe Coordination Layer for Multi-Agent AI
Cambium
progettoGovernance standard and reference toolset for LLM-maintained knowledge corpora
llama-cpp-agent
progettoThe llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured output. Works also with models not fine-tuned to JSON output and function calls.
AgentLab
progettoAgentLab: An open-source framework for developing, testing, and benchmarking web agents on diverse tasks, designed for scalability and reproducibility.
Open-AgentRL
progettoRLAnything (ICML 2026) & AutoTool (ICML 2026), DemyAgent: Open-Source RL for LLMs and Agentic Scenarios
agentic
progettoAn agentic workflow tool that provides context engineering support for opencode
AgentTorch
progettolarge population models
python-utcp
progettoOfficial python implementation of UTCP. UTCP is an open standard that lets AI agents call any API directly, without extra middleware.
arbigent
progettoAI Agent for testing Android, iOS, and Web apps. Get Started in 5 Minutes. Arbigent's intuitive UI and powerful code interface make it accessible to everyone, while its scenario breakdown feature ensures scalability for even the most complex tasks.
Agent
progettoAgent! now supports macOS 14.6 or later, Apple Silicon and Intel, latest release. The Best AI agent for Mac: drives any app via Accessibility, codes/builds in Xcode, automates AppleScript, JXA, Swift and SMAppService shell (user/root). 23 LLM providers, local/cloud, plus on-device Apple Intelligence and Jev guard. Auto-Pilot, MCP, Avatar.
hermes-agent-control-room
progettoControl Room-first template for managing Hermes agents from one VPS agent to specialist teams and orchestrated workflows
LLMTornado
progettoThe .NET library to build AI agents with 30+ built-in connectors.
Gamma-World
progettoImplementation of Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players
burner-agents
progettoA swarm of disposable agents for unattributable web interaction. The user never acts on the web directly, delegating instead to agents that are destroyed on completion, leaving nothing that leads back.
burner-agents
progettoA swarm of disposable agents for unattributable web interaction. The user never acts on the web directly, delegating instead to agents that are destroyed on completion, leaving nothing that leads back.
Agently-Daily-News-Collector
progettoAn open-source LLM based automatically daily news collecting workflow showcase powered by Agently AI application development framework.
mateclaw
progetto🤖 MateClaw — Your second brain with Multi-Agent Orchestration, MCP Protocol, Skills & Memory, Dream, and Multi-Channel Support. Built on Spring AI Alibaba.
hyperfide
progettoThe ai agent workflow tool that scales from small, simple projects to large, complex codebases.
project-golem
progettoOS-level autonomous AI agent with long-term memory, multi-agent coordination, Titan Chronos scheduler & Moltbot Social Core
sandbase-harness
progettoLocal-first AI agent runtime with sandboxed sessions, MCP tools, memory, credentials, audit/replay, and a built-in console. Run OpenAI, Anthropic, MiniMax, DeepSeek V4, and OpenAI-compatible models on your infrastructure.
BenchMARL
progettoBenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.
ma-gym
progettoA collection of multi agent environments based on OpenAI gym.
agent_learning
progettoA systematic AI Agent development tutorial covering LLM agents, RAG, tool use, memory systems, multi-agent systems, LangChain, LangGraph, MCP, and agentic RL.|从零开始学 AI Agent 开发 | 系统、全面、实战导向的 Agent 开发教程 | 每日自动追踪 arXiv 最新论文 | Learn AI Agent Development from Scratch