Deploy AI agents and Skills to production — gstack, Fly.io, Vercel, Cloudflare Workers, Modal. From local dev to scalable inference, including CI/CD pipelines.
Agent Deployment tools are AI-powered software designed to help developers and teams tackle agent deployment-related tasks more efficiently. These tools are typically published as open-source projects on GitHub and can be integrated into existing workflows via MCP (Model Context Protocol), Claude Skills, or standalone agent frameworks. On Agent Skills Hub, we index 30 quality-scored agent deployment tools across languages including TypeScript, Python, Rust.
In 2026, the AI agent ecosystem is maturing rapidly. Agent Deployment tools can significantly boost development efficiency by automating repetitive tasks, reducing human error, and providing intelligent suggestions. The top 3 tools — gstack, OpenPawlet, agent-browser — have earned an average of 9,252 GitHub stars, reflecting strong community validation. 26 of the listed tools come with clear open-source licenses, ensuring freedom to use and modify.
When choosing a agent deployment tool, consider these factors: 1) Community activity — GitHub stars and recent commit frequency indicate reliability; 2) Integration method — check if it supports MCP, Claude, or your preferred agent framework; 3) Language compatibility — the most common language in this list is TypeScript; 4) Quality score — Agent Skills Hub's composite score evaluates code quality, documentation completeness, and maintenance activity. Our recommendation: start with gstack — it ranks highest in both star count and quality score.
Use Garry Tan's exact Claude Code setup: 23 opinionated tools that serve as CEO, Designer, Eng Manager, Release Manager, Doc Engineer, and QA
OpenPawlet (PyPI package name open-pawlet) is a single-process web console for the OpenPawlet ecosystem. It exposes an HTTP API, a browser UI, an OpenAI-compatible /v1/* surface and the embedded agent runtime
Claude Agent SDK with a web browsing tool
```bash
$ npx skills add browserbase/skills
```Deploy Your Frontend in a Single Command. Claude Code Skills supported.
A production-ready runtime framework for agent apps with secure tool sandboxing, Agent-as-a-Service APIs, scalable deployment, full-stack observability, and broad framework compatibility.
Devopness: AI DevOps on your cloud. Deploy apps, infra and CI/CD. Any cloud and any stack, one MCP. Deterministic API, opinionated and fully configurable. No cloud credentials in AI chats. Free plan.
The open-source Render alternative — AI-native. Git push → build → deploy on your own infrastructure; agents are first-class users.
A Runtime Framework for Agent Deployment and Tool Sandbox. AgentScope Runtime Java Implementation.
This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.
Let your AI coding agent self-host any open-source app for you. 2,200+ verified recipes — provisioning, DNS, TLS, hardening. Works with Claude Code, Codex, Cursor, Aider, OpenClaw, Hermes.
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
ApeRAG: Production-ready GraphRAG with multi-modal indexing, AI agents, MCP support, and scalable K8s deployment
A curated list of skills, plugins, tools, integrations, and resources for Hermes Agent by Nous Research
Published in CNCF Landscape: A MCP server for Kubernetes.
MCP server for Coolify — 42 optimized tools for managing self-hosted PaaS through AI assistants
OpenSail is the open-source alternative to Codex App, Claude Desktop, Cursor, and Cowork for agentic software work.
Platform dedicated to building an open foundation for applied Artificial Intelligence, designed for people seeking production-ready AI systems they can truly control, extend and deploy anywhere.
MCP SSH Server: 37 tools for remote SSH management | Claude Code & OpenAI Codex | DevOps automation, backups, database operations, health monitoring
Openclaw多智能体协同系统 | Multi-Agent OS for Decision Makers — 基于 OpenClaw (Clawbot) + Slack，让 AI 团队各司其职、自主稳定迭代。
🚀 Clone your OpenClaw AI Agent to a new device in ~25 minutes — configs, memory, skills, everything.
AI agent skills for Sealos Cloud — deploy any project, provision databases, object storage & more with one command. Works with Claude Code, Gemini CLI, Codex.
The full-stack TypeScript framework to build, test, and deploy production-ready MCP servers and AI-native apps.
Fastest way to build and deploy reliable AI agents, MCP tools and agent-to-agent. Deploy in a production ready serverless environment.
OpsinTech Platform — An enterprise-grade AI agent platform built on LangGraph, featuring multi-tenant architecture, role-based access control, sandboxed tool execution, and an admin UI for managing models, MCP servers, skills, and tools. Designed for production deployment with Docker, PostgreSQL support, and comprehensive governance capabilities.
🌊 The leading agent meta-harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
A comprehensive toolkit for deploying production-ready Generative AI infrastructure on Amazon EKS. Includes pre-configured components for: 🚀 AI Gateway (LiteLLM) 🤖 LLM Serving (vLLM, SGLang, Ollama) 📊 Vector Databases, 🔍 Embedding Models (TEI) 📈 Observability (Langfuse, Phoenix) etc. Fast-track your GenAI deployment with Kubernetes
The Operating System for Scalable Enterprise AI Agents - Run, orchestrate, and deploy Compliant Enterprise AI Agents at scale across frameworks, without lock-in, rewrites or fragile glue code. Native support for MCP, A2A. Interface with all mainstream communication channels seamlessly out of the box, production ready from day one.
| Tool | Stars | Language | License | Score |
|---|---|---|---|---|
| gstack | ★ 120.4k | TypeScript | MIT | 82 |
| OpenPawlet | ★ 104 | Python | MIT | 55 |
| agent-browser | ★ 38.1k | Rust | Apache-2.0 | 78 |
| skills | ★ 2.8k | JavaScript | — | 68 |
| pinme | ★ 3.7k | TypeScript | MIT | 75 |
| agentscope-runtime | ★ 811 | Python | Apache-2.0 | 57 |
| devopness | ★ 434 | Python | Apache-2.0 | 65 |
| bex | ★ 408 | TypeScript | Apache-2.0 | 67 |
| clawhost | ★ 351 | TypeScript | MIT | 69 |
| clawhost | ★ 349 | TypeScript | MIT | 67 |
| agentscope-runtime-java | ★ 142 | Java | Apache-2.0 | 63 |
| End-to-End-Agentic-Ai-Automation-Lab | ★ 80 | Jupyter Notebook | MIT | 62 |
| open-forge | ★ 67 | Shell | MIT | 51 |
| golf | ★ 823 | Python | Apache-2.0 | 74 |
| ApeRAG | ★ 1.2k | Python | Apache-2.0 | 56 |
| awesome-hermes-agent | ★ 1.8k | — | MIT | 71 |
| kubectl-mcp-server | ★ 868 | Python | MIT | 75 |
| coolify-mcp | ★ 476 | TypeScript | MIT | 66 |
| OpenSail | ★ 557 | Python | Apache-2.0 | 54 |
| minds-platform | ★ 39.2k | Python | — | 66 |
| mcp-ssh-manager | ★ 332 | JavaScript | MIT | 67 |
| opencrew | ★ 414 | Shell | MIT | 55 |
| agent-pack-n-go | ★ 90 | Shell | — | 60 |
| sealos-skills | ★ 53 | Python | — | 63 |
| nitrostack | ★ 155 | TypeScript | Apache-2.0 | 65 |
| agentor | ★ 162 | Python | Apache-2.0 | 54 |
| opsintech-platform | ★ 89 | Python | MIT | 67 |
| ruflo | ★ 63.5k | TypeScript | MIT | 79 |
| sample-genai-on-eks-starter-kit | ★ 76 | JavaScript | MIT-0 | 57 |
| agent-kernel | ★ 62 | Python | Apache-2.0 | 60 |
The top agent deployment tools in 2026 are gstack, OpenPawlet, agent-browser. Agent Skills Hub ranks 30 options by GitHub stars, quality score (6 dimensions including completeness, examples, and agent readiness), and recent activity. The list is rebuilt every 8 hours from live GitHub data.
gstack (120.4k stars) is the most adopted choice for general agent deployment workflows, written in TypeScript. OpenPawlet (104 stars) is a strong alternative and uses Python instead. Pick by your existing stack: match the language and runtime your team already uses to minimize integration cost. If unsure, start with gstack — it has the deepest community and the most examples online.
Avoid pre-built agent deployment tools when (1) your use case requires deep customization that the tool's plugin system doesn't support, (2) you have strict compliance requirements that ban third-party dependencies, (3) the tool's maintenance is inactive (last commit >6 months ago), or (4) your data volume is small enough that a 50-line custom script is cheaper than learning the tool. For most production workflows above 100 requests/day, the time savings from a maintained tool outweigh the customization loss.
Agent Deployment focuses specifically on deploy ai agents and skills to production — gstack, fly.io, vercel, cloudflare workers, modal. from local dev to scalable inference, including ci/cd pipelines. CI/CD & DevOps is a related but distinct category — see https://agentskillshub.top/best/ci-cd/ for those tools. The two often appear in the same agent pipeline but solve different problems: choose agent deployment when your primary goal is the specific task, and ci/cd & devops when the workflow is broader.
For most teams, yes. gstack has 120.4k stars worth of community testing, handles edge cases you haven't thought of, and ships with documentation. Build your own only when (1) your requirements are deeply non-standard, (2) you have a security/compliance reason to avoid OSS dependencies, or (3) the maintenance burden is small enough (<200 lines of code) that you'll save time long-term. The break-even point is usually around 2-3 weeks of dev time saved.
Most agent deployment tools listed are open source under permissive licenses (MIT, Apache 2.0). A handful offer paid managed/cloud versions on top of free self-hosted core. Always check the LICENSE file on each tool's GitHub repository before commercial use — some use AGPL or non-commercial restrictions that may not fit your deployment model.