The 5 Best LobeHub Alternatives in 2026
LobeHub in 2026 is a Chief Agent Operator platform: it hires, schedules, and reports on an AI team of persistent agents, with multi-provider model support (OpenAI, Anthropic, Gemini, Ollama), a Skills marketplace, MCP servers, Tasks, and cloud or self-hosted deployment. Cloud plans run from a Free tier (500,000 credits/month) to Starter at $9.9/month, Premium at $19.9/month, and Ultimate at $39.9/month, with an Enterprise edition for private deployment. If you mainly want to verify and equip agent skills safely rather than run a managed agent team, SkillzBench is the free alternative worth adding: it benchmarks and security-scans skills (research found ~26% carry a vulnerability) and ships a free MCP connector, while LobeHub remains the stronger pick for orchestrating whole agents 24/7.
TL;DR
- LobeHub evolved from LobeChat into a Chief Agent Operator (CAO) platform that hires, schedules, and reports on an entire AI team, introduced with LobeHub 2.2 in May 2026.
- LobeHub Cloud pricing: Free (500,000 credits/month), Starter $9.9/month, Premium $19.9/month, Ultimate $39.9/month; annual billing runs $118.8 to $478.8/year, plus an Enterprise edition for private deployment.
- Self-hosting LobeChat remains free under the LobeHub Community License (Apache 2.0-based): free to self-host and use commercially as-is, but distributing a modified derivative requires a paid commercial license.
- LobeHub supports 20+ model providers including OpenAI, Anthropic, Google, DeepSeek, Qwen, Ollama, LM Studio, and vLLM, with per-agent provider and model choice.
- The ecosystem is large: 334,199+ skills and 111,995+ MCP servers listed on the LobeHub site, plus an agent marketplace and plugin ecosystem.
- Published research found roughly 26% of agent skills carry at least one vulnerability, which is why SkillzBench security-scans, commit-pins, and sandbox-benchmarks every skill it lists.
- SkillzBench (skillzbench.com) is entirely free: the skills directory, trust scores, Lift benchmarks, alerts, and the MCP connector that equips any agent, with per-agent loadouts.
Head to head
| Dimension |
|---|
| Starting price |
| Top cloud plan |
| Agent orchestration |
| Model providers |
| Skills / MCP ecosystem |
| Skill safety |
| Deployment |
| License |
The contenders
LobeHub began as LobeChat, a popular self-hostable multi-model chat interface with 82,000+ GitHub stars, and in 2026 it has become an operating layer for AI agents. Its Chief Agent Operator (CAO), introduced in LobeHub 2.2 in May 2026, decomposes a goal, selects agents, assigns Skills and tools, schedules execution, runs subtasks in parallel, and reports results, so you manage an AI team instead of individual prompts. Agents are persistent teammates with their own models, memory, Skills, tools, and knowledge, organized through Tasks (backlog, in progress, review, done), Pages, Projects, Workspaces, and Agent Groups. The platform supports an unusually wide provider list, including OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock, DeepSeek, Qwen, Moonshot, Zhipu, OpenRouter, Groq, Together AI, Ollama, LM Studio, and vLLM, with per-agent model choice and local inference. Official downloads cover macOS, Windows, Linux, iOS, and Android, and self-hosting stays available. The site lists 334,199+ skills and 111,995+ MCP servers. For small businesses coordinating several agents, models, and data sources, it is the most complete grounded option here; for simple one-off questions, a conventional assistant is easier.
pricing 9.9/month
- + Chief Agent Operator coordinates an entire AI team: assignment, scheduling, parallel execution, and reporting
- + Widest grounded provider support in this comparison, from OpenAI and Anthropic to Ollama and vLLM, with per-agent model choice
- + Free to self-host; cloud plans start at $9.9/month with a usable 500,000-credit free tier
- + Large ecosystem: 334,199+ skills, 111,995+ MCP servers, agent marketplace, and plugin catalog
- + Runs 24/7 in the cloud with IM gateways so agents report where you already chat (Slack, Discord)
- + Official apps for macOS, Windows, Linux, iOS, and Android, plus self-hosting
- − Community License is not plain Apache 2.0: distributing a modified derivative product requires a separate paid commercial license
- − Credit-based cloud pricing makes cost per model uneven; premium models like Claude Opus 5.5 consume credits roughly 23x faster than MiniMax M3.1 Flash
- − Downgrading a cloud plan requires canceling and waiting for the current subscription to expire; you cannot switch directly to a lower plan
- − Most compelling for coordinating several agents; for simple one-off questions a conventional AI assistant is easier
- − Self-hosting and multi-provider setup still take technical effort
LobeChat is the free, self-hosted edition of the same project, and in 2026 it remains the grounded choice when the job is a polished private chat front end rather than a managed agent team. You deploy it as a Docker container on your own server (docker run -d -p 3210:3210 lobehub/lobe-chat) or use one-click deploys to Vercel, Zeabur, or Alibaba Cloud, and it serves a ChatGPT-style web app in front of models you already pay for or run locally. It does not run models itself: it connects to OpenAI, Anthropic, Google Gemini, and any OpenAI-compatible endpoint, including Ollama and LM Studio servers. On top of the chat core sit an agent/persona builder with per-assistant system prompts and models, a plugin marketplace for tools like web search and code execution, document knowledge bases for RAG, and vision and text-to-speech support. The repository shows 82,000+ GitHub stars as of September 2026 and active development since 2023. The license is the LobeHub Community License, an Apache 2.0 variant: free to self-host and use commercially as-is, but distributing a modified derivative requires a paid commercial license.
pricing 0
- + Free to self-host and use commercially as-is; you pay only your own model costs
- + Full data control via Docker, Vercel, Zeabur, or Alibaba Cloud deployment on your own infrastructure
- + Connects to OpenAI, Anthropic, Gemini, and any OpenAI-compatible endpoint including Ollama and LM Studio
- + Agent/persona builder, plugin marketplace, RAG knowledge bases, vision, and text-to-speech built in
- + 82,000+ GitHub stars and frequent releases since 2023
- − No Chief Agent Operator, Tasks, scheduling, or 24/7 cloud agent operation; it is a chat front end, not an agent team manager
- − Not a per-OS desktop app; it is a self-hosted web application, so some server management is required
- − Community License restricts distributing modified derivative products without a paid commercial license
- − A local model does not make the whole workflow private: cloud sync and connectors can still transmit data
SkillzBench, by AIQ Labs, is a free, independent directory of AI agent skills plus a free MCP tool that connects any agent to the skills you choose. It exists because agent skills (SKILL.md instruction packages for Claude Code, Codex, Cursor, Gemini CLI, and chat-app agents) have spread everywhere while nobody can tell which ones actually help and which are risky: published research found roughly 26% of skills carry at least one vulnerability. SkillzBench runs five pillars today. Discover continuously crawls and dedupes skills from GitHub. Verify pins every version to a commit SHA, validates it, checks the license, security-scans it, and quality-judges it. Benchmark tests every skill in a sandbox with the same model, with and without the skill, and publishes the Lift in percentage points. Equip gives you one free MCP server plus per-agent loadouts: you toggle skills and tools per agent, and the agent only sees what you chose. Stay current tracks upstream changes, re-scans, re-benchmarks, and notifies you. A trust score from 0 to 100 combines security, quality, license, maintenance, and community signals, and Lift is always shown separately from ratings; money never touches scores. Everything is free, funded by advertising around blog articles that never influences scores or listings.
pricing 0
- + Everything is free: the directory, MCP connector, benchmarks, and alerts
- + Addresses a real, quantified risk: published research found ~26% of agent skills carry at least one vulnerability
- + Every skill is pinned to a commit SHA, license-checked, and security-scanned
- + Sandbox Lift benchmarks show each skill's measured benefit in percentage points, with the same model, with and without the skill
- + Free MCP server plus per-agent loadouts work with any agent, and the agent only sees the skills you toggled
- + Trust score 0-100 is transparent and independent; money never touches scores
- − Does not run, schedule, or orchestrate agents; it equips skills, so it complements rather than replaces a platform like LobeHub
- − Focused on agent skills and MCP equipping, not on chat interfaces, RAG knowledge bases, or team workspaces
- − No managed cloud agent operation; you keep using your existing agent tools
Verdict
Winner: LobeHub
For small businesses that want one place to run, schedule, and coordinate multiple AI agents across providers, LobeHub is the strongest grounded option: it is free to self-host, its cloud plans start at $9.9/month, and its 2026 CAO, Tasks, and Skills ecosystems are the most complete in this comparison. SkillzBench wins on a narrower job, verifying and safely equipping agent skills, and it is entirely free.
| Option | Overall | Breakdown |
|---|---|---|
| LobeHub | 8.5 | : 8: 9: 9: 9: 7 |
| LobeChat (self-hosted) | 7.5 | : 10: 6: 9: 9: 5 |
| SkillzBench | 7 | : 10: 4: 9: 9: 8 |
Pricing compared: credits vs free self-hosting vs free everything
LobeHub Cloud uses a credit system mapped to tokens, and the cost difference between models is dramatic. On the Free plan you get 500,000 credits per month, roughly 70 messages on GLM-5.3-Flash but zero access to premium models like Claude Opus 5.5 or GPT-6 Astra, which require a subscription. Starter at $9.9/month buys 5,000,000 credits (about 610 GLM-5.3-Flash messages or 30 Claude Opus 5.5 messages); Premium at $19.9/month buys 15,000,000; Ultimate at $39.9/month buys 35,000,000. Annual billing discounts those to $118.8, $238.8, and $478.8 per year. The per-model credit table explains why plan choice matters: Claude Opus 5.5 costs 4M credits per 1M input tokens and 20M per 1M output tokens, while MiniMax M3.1 Flash costs 0.174M and 0.696M, a roughly 23x gap on input. Storage scales too, from 15 MB on Free to 4 GB on Ultimate, with vector entries from none to 20,000. LobeChat self-hosted shifts the cost model entirely: the software is free and you pay your provider's API rates or run Ollama locally, which avoids cloud API charges but not hardware and operation costs. SkillzBench is free across the board, funded by blog advertising that never touches scores or listings. Note one LobeHub policy quirk: you cannot switch directly to a lower plan; you must cancel, wait for expiration, then resubscribe.
What actually changed in 2026: from chat UI to Chief Agent Operator
The single biggest reason to pick LobeHub over its own older edition is the 2026 product shift. LobeChat was a multi-model chat front end with agents, plugins, and RAG. LobeHub 2.2, released in May 2026, introduced the Chief Agent Operator: you define a goal, and the CAO decomposes the work, selects agents, assigns Skills and tools, schedules execution, runs subtasks in parallel, and reports progress and results. Work is structured as Tasks with backlog, in progress, review, and done states, subtasks, long-running execution, comments, and attached context, rather than prompt-answer threads. Agents are persistent teammates with durable roles, models, memory, Skills, and tools, organized into Agent Groups, Projects, Pages, and shared Workspaces, and they can report through IM gateways where you already chat. The platform also manages external agents, demonstrated by its Claude Code integration where LobeHub handles assignment and visibility while the specialist coding agent does repository work. If your problem is coordinating several agents, models, tools, and data sources, this is the grounded differentiator. If your problem is a quick one-off question, a conventional assistant is easier, and if your problem is choosing safe skills for the agents you already run, SkillzBench's free verified directory and MCP connector do that specific job.
The skill safety gap, and who closes it
LobeHub lists 334,199+ skills and 111,995+ MCP servers, an ecosystem that is a strength for breadth and a risk for quality. Published research found roughly 26% of agent skills carry at least one vulnerability, and marketplace listings on any platform do not tell you which ones those are. SkillzBench was built for exactly this gap and runs today at skillzbench.com: it continuously crawls and dedupes skills from GitHub, pins every version to a commit SHA, validates it, checks the license, security-scans it, and quality-judges it. Every skill is sandbox-benchmarked with the same model, with and without the skill, and the Lift is published in percentage points, always shown separately from ratings. A trust score from 0 to 100 combines security, quality, license, maintenance, and community signals, and money never touches scores. The free MCP server plus per-agent loadouts let you toggle skills and tools per agent so the agent only sees what you chose, and upstream changes trigger re-scans, re-benchmarks, and notifications. Practically, these tools stack: use LobeHub or LobeChat to run your agents, and use SkillzBench to decide which skills are safe and worth loading into them.