clawhive
Lightweight, Rust-native AI Agent platform. Security sandbox from day one.
- Origine
- github
- Linguaggio
- Rust
Repository A2A misurati nel catalogo pubblico KoPass.
Lightweight, Rust-native AI Agent platform. Security sandbox from day one.
External, world memory for AI agents. It is deterministic, shared, content-addressed computation state built on the principles of satellites. Enables agents survive handoffs, context compaction, referential drifts and makes the workflow AI vendor neutral.
.NET implementation of the Agent2Agent (A2A) protocol to enable secure, interoperable communication between autonomous agents across frameworks and vendors.
Build LLM systems you actually control. A free, open engineering book + course — from tokenization to serving your own models. Mechanisms, trade-offs, and numbers, not prompt tips
layer that lets you connect any agent, any tool, any api together.
🤖 AI-Mocks is a Kotlin-based toolkit for mocking and testing LLM APIs with real streaming and SSE support. Simulate OpenAI, Anthropic, Gemini, Ollama, and A2A services locally for reliable integration testing.
A full-stack AI chat application powered by Amazon Bedrock and Strands Agents
The Proxmox MCP you can hand the keys. All four products: PVE, PBS, PMG, PDM.
A curated list of awesome resources, tools, libraries, tutorials, and references for Google Application Development Kit (ADK).
Open-source infrastructure for AI agents — each gets an email address, folder, inbox, and API key. Move files through named inboxes instead of shared cloud credentials; signed receipts and MCP built in. Human email verification can unlock 5 GB transfers, durable storage, outbound email, and an HTTPS app subdomain. One Go binary, MIT.
Archived — superseded by built-in Hermes A2A (https://hermes-agent.nousresearch.com/docs/user-guide/messaging/a2a)
Build your AI team with Crewform. Orchestrate specialized, autonomous agents to collaborate on complex tasks and connect outputs to your stack. — AI Orchestration for Everyone
Production-grade multi-agent orchestration engine. Compose agentic workflows from a pluggable catalog of Agents, LLMs, tools, and retrievers. Execute locally with LangGraph or distributed with Temporal. Built-in RAG pipeline for enterprise knowledge retrieval. A2A and MCP protocol support. Visual drag-and-drop blueprint builder.
AI-powered flood intelligence system featuring h2oGPTe Agent + NVIDIA NIM integration (A2A), NAT pipeline with Nemotron 49B, and real-time flood risk assessment. Part of NVIDIA–H2O.ai AI for Good Blueprint for disaster response and monitoring.
Discord control plane for project-bound ACP agents (Kiro CLI/OMP) with MCP policy, cron/audit, and A2A over NATS/JetStream.
AgentStack is a production-grade multi-agent framework built on Mastra, delivering 50+ enterprise tools, 25+ specialized agents, and A2A/MCP orchestration for scalable AI systems. Focuses on financial intelligence, RAG pipelines, observability, and secure governance. ACP Openclaw, Gemini CLI, Opencode
Open-source AI agent with MCP UI, app builder, A2A protocol, skills marketplace, and multi-provider chat. Built with Mastra. Alternative to OpenClaw.
Agent runtime with full system prompt control. Define agents in markdown, run them in a terminal TUI or multi-user web UI. Powered by the Claude Agent SDK.
ScaledMCP is a horizontally scalabled MCP and A2A Server. You know, for AI.
Agent Interface Compiler — One command. Every agent speaks your product. Transform OpenAPI specs into MCP Servers, Skills, CLAUDE.md, AGENTS.md, and more.
Contract lab for agent protocol envelopes - versioned MCP/A2A message contracts, envelope validation, and compatibility diffs (compatible/forward/breaking). Python 3 stdlib + optional .NET 9 runtime.
Enterprise-grade distributed AI agent framework | Develop → Deploy → Observe | K8s-native | Dynamic DI | Auto-failover | Multi-LLM | Python + Java + TypeScript | MCP + A2A + REST
Public adapters and discovery catalog for Triptych OS (Agent OS): agent frameworks, MCP/A2A/x402 protocols, workflows, wallets, SDKs, and examples for execute-first routing, governed handoffs, and receipt-aware agent commerce.
Pre-inference tool and agent routing for MCP, A2A and tool-rich LLM systems. Reduce candidate tools before model inference with policy-aware routing and recovery.
AI agents that survive production. Self-hosted TypeScript runtime: multi-provider LLMs, tools with approvals, memory & RAG, durable workflows, multi-agent swarms, sandboxed execution, MCP and A2A.
Skills & plugins for agentic commerce : UCP, ACP, AP2, A2A, WebMCP, Magento 2, BigCommerce, WooCommerce
Self-hosted, non-custodial gateway between the API you already run and the protocols AI agents use to discover, call and pay for it
A sample agent demonstrating A2A + ADK + MCP working together.
A2A protocol wrappers that turn AI backends into interoperable agents. Drop a JSON config, get a spec-compliant A2A server. Supports GitHub Copilot SDK, OpenCode, Claude, Codex and more.
Build, deploy, and connect AI agents to SAP Joule via the A2A protocol on BTP Cloud Foundry — directly from Claude Code. Supports TypeScript (Express or CAP) and Python agents with LangGraph and SAP GenAI Hub.
The Private Agent OS — search files, run AI agents, connect to 10,000+ tools via the complete protocol stack (MCP, AG-UI, A2UI, A2A). Zero cloud. Zero telemetry. On-device inference.
Cross-vendor agent-to-agent protocol — Claude, Codex, and Gemini communicate via file-based P2P messaging.
.NET Agents technologies (Microsoft Agent Framework, MCP, A2A, AG-UI)
Microsoft Agent Framework - learn how to adress agentic enterprise scenarios
The networked cross-session for coding agents — @mention another team's AI so yours and theirs integrate directly across sessions, machines, and orgs, from the terminal.
🤖💬🤖 Agent-to-Agent (A2A Protocol) built on top of LangGraph
A cookiecutter template for Python agent projects that use uv for dependency management
AI agent security harness, adversarial testing: 640 executable tests in the v4.26.1 release, across MCP, A2A, x402/L402, decision governance, benchmark integrity, human-in-the-loop, skill supply chain. Four target shapes: a verdict must be able to be wrong AND to be right. OWASP Agentic v1.1 T1-T17, AIUC-1 19/20 (2026-Q1/Q2 set), NIST AI 800-2.
Distributed AI multi-agent autonomous system — digital employees for long-horizon tasks, running 24×7 — Meta-bootstrapping · Real-autonomous · Omni-evolution
Expose OpenCode through A2A with an inbound server surface and embedded outbound client.
Awesome A2A Agents is a curated list of tools, frameworks, and projects built on the Agent-to-Agent (A2A) communication protocol. It aims to showcase interoperable agent systems, share best practices, and help developers explore the emerging A2A ecosystem.
HYBRO Hub: local & remote AI agents — private, powerful, unified.
6 specialized AI agents collaborating via A2A protocol to power e-commerce — product discovery, orders, pricing, reviews, inventory & support. Dual-stack (Python + .NET/C#) with generative UI. Built with Microsoft Agent Framework, FastAPI, Next.js, and PostgreSQL + pgvector.
AI Governance Infrastructure — local evaluation. The governance layer for AI agents: a single compiled Rust binary that authenticates, authorises, rate-limits, logs, and costs every AI interaction. Self-hosted, air-gap capable, provider-agnostic.
Agents you can inspect, replay and trust. An agent framework for TypeScript and Python, built on an append-only event log.
Scalable agent registry for AI agents using A2A protocol AgentCard with semantic search. AWS serverless (Lambda, S3 Vectors, Bedrock). Python SDK & React Web UI.
Agent Network System (ANS): A Foundational Hybrid Architecture for Secure AI Agent Ecosystems
Instead of dumping 1000+ tools into a model’s prompt and expecting it to choose wisely, the Unified MCP Tool Graph equips your LLM with structure, clarity, and relevance. It fixes tool confusion, prevents infinite loops, and enables modular, intelligent agent workflows.
This repository demonstrates an end-to-end scenario where multiple organizations collaborate through domain-specific agents (discovered via ORD and communicating via A2A) to resolve a customer dispute.
Creating realistic RL environments requires collaboration between researchers, engineers, and domain experts across many dimensions: artifacts, environments tools, dynamism of the environment, reproducibility, and more. There is no open source framework for building these environments effectively. Until now.
Master Agentic AI with Google ADK: 100+ runnable Python agents for the Agent Development Kit - tools, MCP, A2A, multi-agent workflows, sessions and memory, RAG, Live API voice, adk eval, tracing and Cloud Run deployment