Best AI Agent Skills for Open WebUI Integrations in 2026

Open-source tools, connectors, and functions for Open WebUI — ChatGPT-style self-hosted UI for local LLMs (Ollama, LM Studio, vLLM). Ranked by stars and community adoption.

🔍 Browse 25 open webui integrations ⭐ 194.3k total stars 🔄 Refreshed every 8h
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Quick Pick — If you only pick one, go with SuperPowersWUI ★ 63 — Open WebUI tool port of Superpowers by Jesse Vincent — brainstorm → spec → plan

The Complete Guide to Open WebUI Integrations Tools (2026)

What Are Open WebUI Integrations Tools?

Open WebUI Integrations tools are AI-powered software designed to help developers and teams tackle open webui integrations-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 25 quality-scored open webui integrations tools across languages including Python, Shell, JavaScript.

Why Use Open WebUI Integrations Tools?

In 2026, the AI agent ecosystem is maturing rapidly. Open WebUI Integrations tools can significantly boost development efficiency by automating repetitive tasks, reducing human error, and providing intelligent suggestions. The top 3 tools — SuperPowersWUI, selfhost-ai, openwebui-extensions — have earned an average of 7,774 GitHub stars, reflecting strong community validation. 23 of the listed tools come with clear open-source licenses, ensuring freedom to use and modify.

How to Choose the Best Open WebUI Integrations Tool?

When choosing a open webui integrations 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 Python; 4) Quality score — Agent Skills Hub's composite score evaluates code quality, documentation completeness, and maintenance activity. Our recommendation: start with SuperPowersWUI — it ranks highest in both star count and quality score.

Top 25 Open WebUI Integrations

1 SuperPowersWUI by tkalevra
★ 63 Python Agent Tool

Open WebUI tool port of Superpowers by Jesse Vincent — brainstorm → spec → plan → execute agentic dev workflow for local LLMs

View Details → GitHub →
2 selfhost-ai by kossakovsky
★ 938 Shell MCP Server

One-command installer for a self-hosted AI stack on your own server: n8n, Ollama, Open WebUI, OpenClaw, Dify, Flowise, Supabase, ComfyUI, Qdrant & 30+ tools. Docker Compose, automatic HTTPS via Caddy, built-in monitoring. Free Zapier/Make alternative.

View Details → GitHub →
3 openwebui-extensions by Fu-Jie
★ 303 Python MCP Server

A collection of enhancements, plugins, and prompts for Open WebUI, developed and curated for personal use to extend functionality and improve experience.

View Details → GitHub →
★ 60 Python LLM Plugin

Native Word (.docx) generator tool for Open WebUI — Markdown/JSON in, cover pages, styled tables, callouts, TOC out. MIT.

View Details → GitHub →
★ 58 Python Agent Tool

Native PowerPoint (.pptx) generator tool for Open WebUI — JSON spec in, native charts/icons/layouts out. MIT.

View Details → GitHub →
6 open-webui-tools by Haervwe
★ 801 Python Agent Tool

Open‑WebUI Tools is a modular toolkit designed to extend and enrich your Open WebUI instance, turning it into a powerful AI workstation. With a suite of over 15 specialized tools, function pipelines, and filters, this project supports academic research, agentic autonomy, multimodal creativity, workflows, and more

View Details → GitHub →
7 OpenWebui-Tools by iChristGit
★ 135 Python LLM Plugin

Custom tools to enhance your Open-Webui Experience! 🚀

View Details → GitHub →
8 open-webui by open-webui
★ 153.8k Python MCP Server

User-friendly AI Interface (Supports Ollama, OpenAI API, ...)

View Details → GitHub →
9 open-webui-plugins by Classic298
★ 568 Python MCP Server

A curated collection of Open WebUI plugins - tools, skills, filters, pipes, actions and events that extend your AI chat experience.

View Details → GitHub →
10 opencode-llm-proxy by KochC
★ 67 JavaScript MCP Server

Local OpenCode-backed LLM gateway for OpenAI, Anthropic, Gemini, and Responses API-compatible tools, with streaming and tool/function calling.

View Details → GitHub →
11 houtini-lm by houtini-ai
★ 119 TypeScript MCP Server

MCP server that saves Claude Code tokens by delegating bounded tasks to local or cloud LLMs. Works with LM Studio, Ollama, vLLM, DeepSeek, Groq, Cerebras.

View Details → GitHub →
12 AirClaw by nickzsche21
★ 116 Python Codex Skill

Run OpenClaw on a local model. Zero API cost. One OpenAI-compatible endpoint over Ollama, llama.cpp, vLLM or LM Studio — plus AirLLM for models bigger than your GPU.

View Details → GitHub →
13 lilbee by tobocop2
★ 61 Python MCP Server

The whole local AI stack in one executable: it runs and manages local AI models across every GPU, and it's a search engine you can talk to, with cited answers from your files, code, and the web. MCP server for coding agents, web crawler, TUI, CLI, REST API, Python library. No Ollama or LM Studio needed, works with both.

View Details → GitHub →
14 open-tabletop-gm by Bobby-Gray
★ 56 Python Agent Tool

LLM-agnostic tabletop RPG Game Master framework. Runs on OpenCode, LM Studio, or any LLM service. D&D 5e included as reference system — add any TTRPG via a system module.

View Details → GitHub →
15 local-deep-research by LearningCircuit
★ 9.1k Python Agent Tool

~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.

View Details → GitHub →
16 open-multi-agent by open-multi-agent
★ 7.0k TypeScript MCP Server

Self-hosted TypeScript agent runtime with durable approvals and verifiable run records. Own it, approve it, audit it.

View Details → GitHub →
17 open-multi-agent by JackChen-me
★ 6.1k TypeScript MCP Server

From a goal to a task DAG, automatically. TypeScript-native multi-agent orchestration with MCP and live tracing. Three runtime dependencies.

View Details → GitHub →
18 open-terminal by open-webui
★ 3.2k Python Agent Tool

A computer you can curl ⚡

View Details → GitHub →
19 mcpo by open-webui
★ 4.4k Python MCP Server

A simple, secure MCP-to-OpenAPI proxy server

View Details → GitHub →
20 little-coder by itayinbarr
★ 2.6k TypeScript Agent Tool

A harness optimized to smaller LLMs

View Details → GitHub →
21 metamcp by metatool-ai
★ 2.7k TypeScript MCP Server

MCP Aggregator, Orchestrator, Middleware, Gateway in one docker

View Details → GitHub →
22 mcp-client-for-ollama by jonigl
★ 827 Python MCP Server

Harness the power of local LLMs with this TUI MCP Client for Ollama. Featuring all core MCP primitives (tools, prompts, resources), agent mode, multi-server, model switching, streaming responses, human-in-the-loop, thinking mode, model params config, system prompts, and saved preferences.

View Details → GitHub →
23 ProxmoxMCP-Plus by RekklesNA
★ 566 Python MCP Server

Use MCP and OpenAPI to safely control Proxmox VE VMs, LXCs, backups, and snapshots from LLMs and AI agents.

View Details → GitHub →
24 slotstream by carloslfu
★ 412 Swift Codex Skill

Run a 105 GB AI model on a Mac that can't hold it. Slotstream streams Qwen3.8-Flash-Next (125B mixture of experts) from your SSD and caches the busiest experts in memory, so it runs on Macs with 16 to 64 GB. One native Swift binary on MLX and Metal, no Python, offline. Works with Claude Code, Codex and Ollama or OpenAI clients.

View Details → GitHub →
25 home-generative-agent by goruck
★ 312 Python Agent Tool

AI agent for Home Assistant — talk to your home, create automations in plain language, analyze cameras with face recognition, and get proactive anomaly alerts. Cloud LLMs or fully local via Ollama.

View Details → GitHub →

Comparison

Tool Stars Language License Score
SuperPowersWUI ★ 63 Python MIT 65
selfhost-ai ★ 938 Shell Apache-2.0 72
openwebui-extensions ★ 303 Python MIT 63
openwebui-generate-documents ★ 60 Python MIT 63
openwebui-generate-slides ★ 58 Python MIT 64
open-webui-tools ★ 801 Python MIT 69
OpenWebui-Tools ★ 135 Python MIT 64
open-webui ★ 153.8k Python — 87
open-webui-plugins ★ 568 Python BSD-3-Clause 69
opencode-llm-proxy ★ 67 JavaScript MIT 69
houtini-lm ★ 119 TypeScript Apache-2.0 72
AirClaw ★ 116 Python MIT 65
lilbee ★ 61 Python MIT 59
open-tabletop-gm ★ 56 Python — 60
local-deep-research ★ 9.1k Python MIT 72
open-multi-agent ★ 7.0k TypeScript MIT 73
open-multi-agent ★ 6.1k TypeScript MIT 71
open-terminal ★ 3.2k Python MIT 84
mcpo ★ 4.4k Python MIT 67
little-coder ★ 2.6k TypeScript Apache-2.0 72
metamcp ★ 2.7k TypeScript MIT 65
mcp-client-for-ollama ★ 827 Python MIT 70
ProxmoxMCP-Plus ★ 566 Python MIT 74
slotstream ★ 412 Swift MIT 67
home-generative-agent ★ 312 Python MIT 68

Related Categories

Frequently Asked Questions

What are the best open webui integrations in 2026?

The top open webui integrations in 2026 are SuperPowersWUI, selfhost-ai, openwebui-extensions. Agent Skills Hub ranks 25 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.

How do I choose between SuperPowersWUI and selfhost-ai?

SuperPowersWUI (63 stars) is the most adopted choice for general open webui integrations workflows, written in Python. selfhost-ai (938 stars) is a strong alternative and uses Shell instead. Pick by your existing stack: match the language and runtime your team already uses to minimize integration cost. If unsure, start with SuperPowersWUI — it has the deepest community and the most examples online.

When should I NOT use open webui integrations?

Avoid pre-built open webui integrations 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.

What's the difference between open webui integrations and free mcp servers?

Open WebUI Integrations focuses specifically on open-source tools, connectors, and functions for open webui — chatgpt-style self-hosted ui for local llms (ollama, lm studio, vllm). ranked by stars and community adoption. Free MCP Servers is a related but distinct category — see https://agentskillshub.top/best/free-mcp-servers/ for those tools. The two often appear in the same agent pipeline but solve different problems: choose open webui integrations when your primary goal is the specific task, and free mcp servers when the workflow is broader.

Is SuperPowersWUI better than building it yourself?

For most teams, yes. SuperPowersWUI has 63 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.

Are these open webui integrations free to use?

Most open webui integrations 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.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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