Build, evaluate and train General Multi-Agent Assistance with ease
Production-Ready MCP Server Framework • Build, deploy & scale secure AI agent infrastructure • Includes Auth, Observability, Debugger, Telemetry & Runtime • Run real-world MCPs powering AI Agents
Agents daemon that hides nothing
Open-source sandboxes for code execution, browser use, and AI agents.
🛡️Decision infrastructure for AI agents. Intercept actions, enforce guard policies, require approvals, and produce audit-ready decision trails.
ClawLess — A serverless browser-based runtime for Claw AI Agents powered by WebContainers
MCPcat is an analytics platform for MCP server owners 🐱.
Open-source sandboxes where coding agents build and deploy. Spin up isolated environments where Claude Code, Cursor, and other agents code and deploy software.
Personal User-Centric Immutable OS Based on Fedora BootC and Pi.Dev AI Agent, where the agent is a first class citizen of the OS, and also dictates the UX experience
Open-source AI agent runtime built in Rust. Define once, deploy isolated instances per tenant with built-in memory, encrypted vault, self-extending skills, cron, and multi-channel support.
MCPcat is an analytics platform for MCP server owners 🐱.
AxonFlow: Runtime control layer for production AI
The agentic proxy for modern agent runtimes. Smart, safe, agent-controlled routing across LLMs, tools, and agents.
The control layer between human intent and AI execution. Organize and run an AI team locally.
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.
OpenVitamin is a local-first AI execution platform that unifies Agents, Workflows, and multi-model inference into a single programmable system — designed for building real, production-grade AI applications.
Production-ready personal AI agent runtime in Go with WebUI, multi-agent orchestration, memory, audit, and distributed node support.
Orpheus is an Agent Runtime that solves the 'Silent Failure' of CPU autoscaling. It scales on queue depth, persists memory, and recovers from crashes automatically.
Multi-model AI agent runtime. Define agents in YAML, connect 6 LLM providers, orchestrate with ReAct/Plan&Execute/Fan-Out/Pipeline/Supervisor/Swarm patterns, and deploy as REST/WebSocket API with RAG, memory, MCP tools, guardrails, and OpenTelemetry observability.
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