MaxClaw: A Practical Guide to “Out-of-the-Box” AI Agents on MiniMax
MaxClaw is a cloud-hosted AI agent platform released by MiniMax on February 26, 2026. It is built on the open-source framework OpenClaw and runs on the MiniMax M2.5 large language model.
The value proposition is straightforward:
- no server to rent
- no Docker setup
- no API key wrangling
- no manual skill installation
You click a button and, within about 20 seconds, you get an agent with end-to-end capabilities like web search, image generation, code execution, and file handling. MaxClaw also supports integrations across Feishu, DingTalk, Telegram, Discord, Slack, and more. On top of that, MiniMax ships an “Expert 2.0” community: 16,000+ ready-made workflows spanning development, finance, writing, and office automation.
If you’ve been curious about AI agents but bounced off OpenClaw’s setup complexity, MaxClaw is positioned as a lower-friction entry point.
1. Why This Exists: OpenClaw Is Popular—and Hard to Use
To understand MaxClaw, you need the context of its predecessor: OpenClaw.
What OpenClaw is
OpenClaw (previously named Clawdbot and Moltbot) is an open-source personal AI agent platform created by Austrian developer Peter Steinberger. It gained traction quickly in January 2026, at one point reaching 68,000+ GitHub stars. It’s often described as “an AI assistant that actually does work.”
The key distinction is intent:
- A chatbot explains.
- An agent executes.
OpenClaw’s core capabilities include:
- using messaging platforms (WhatsApp, Telegram, Discord, etc.) as primary interfaces
- running shell commands, controlling a browser, managing local files
- operating calendars and email; scheduling meetings
- a heartbeat mechanism that monitors tasks and proactively pushes reminders
- persistent memory across sessions (preferences and history)
A simple mental model from the original text: if ChatGPT is “a consultant that talks,” OpenClaw is “an assistant that acts.”
The project’s naming and stewardship changes
OpenClaw started in November 2025 as Clawdbot. Due to trademark disputes, it was renamed twice: first to Moltbot (implying “metamorphosis”), and finally to OpenClaw in late January 2026, emphasizing open-source and community-driven development.
On February 14, 2026, Peter Steinberger announced he joined OpenAI, and OpenClaw was transferred to an open-source foundation for continued maintenance. The arc—personal prototype → rapid adoption → naming friction → foundation stewardship—reflects how fast the open-source AI agent space is evolving.
The technical stack and the “skill explosion” problem
OpenClaw lives in the JavaScript/TypeScript ecosystem and depends heavily on Node.js (v22+). It uses Express and Hono for routing and API handling.
OpenClaw’s official skill marketplace, ClawHub, reportedly has 9,000+ skills covering scraping, content generation, customer support, scheduling, and more.
The upside is obvious: lots of capabilities.
The downside is equally real: each capability adds configuration surface area. Users commonly report spending hours installing skills, configuring API credentials, and debugging compatibility issues.
Security concerns: powerful agents expand your risk surface
Because OpenClaw needs access to email, calendars, chat platforms, and other sensitive services, misconfiguration or public exposure can create security and privacy risks.
The original text cites a case where Cisco’s AI security research team tested a third-party OpenClaw skill and found it executed data exfiltration and prompt-injection behavior without the user’s awareness—suggesting the skill ecosystem still needs stronger security review mechanisms.
The practical takeaway: self-hosting is not only “hard,” it can also be “risky” if you don’t know what you’re doing.
What OpenClaw setup looks like in practice
A complete OpenClaw deployment typically involves:
Provision an environment
You need a machine (local or cloud) and Node.js 22+. For many non-technical users, “Node.js” is already a blocker.Install OpenClaw
You run command-line installs, configure firewall ports (commonly 18789), set npm mirrors, and so on.Configure an LLM provider
OpenClaw doesn’t ship with a built-in model. You must obtain an API key (e.g., from Anthropic, OpenAI, or Alibaba Bailian), then edit a JSON config. A typical configuration from the original text:
{
"models": {
"providers": {
"bailian": {
"baseUrl": "https://dashscope.aliyuncs.com/compatible-mode/v1",
"apiKey": "你的API_KEY",
"api": "openai-completions",
"models": [
{
"id": "qwen3-max-2026-01-23",
"name": "qwen3-max-2026-01-23",
"reasoning": false,
"contextWindow": 262144,
"maxTokens": 65536
}
]
}
}
}
}
Connect a messaging channel
If you want Feishu or Telegram control, you create a bot app, obtain tokens/App IDs, then bind them via CLI.Install skills
Search, image generation, etc., are not “just there.” You install them from ClawHub and configure each one.
Even for developers, this tends to take 30–60 minutes end-to-end. For non-technical users, it’s often a dead end. One OpenClaw maintainer (“Shadow” in the original text) summed it up bluntly: if you don’t know the command line, the project may be too risky for you.
This gap—between “heard about it” and “actually using it”—is the problem MaxClaw is trying to solve.
2. What MaxClaw Is: A Hosted OpenClaw With MiniMax’s Stack
One-sentence definition
MaxClaw is MiniMax’s cloud-hosted OpenClaw-based agent service, integrated into the MiniMax Agent web product, packaged as click-to-deploy.
What used to be the user’s burden—servers, containers, API keys, skills, operations—is bundled into a managed service.
Architecture, broken into three layers
Layer 1: MiniMax M2.5 (the “brain”)
MaxClaw runs on MiniMax M2.5, described here as a Mixture-of-Experts (MoE) model with about 229B total parameters while activating around 10B per inference.
Claims in the original text include:
- fast inference, supporting 100 TPS (tokens per second)
-
benchmark results:
- SWE-Bench Verified: 80.2%
- Multi-SWE-Bench: 51.3%
- BrowseComp: 76.3%
- GDPval-MM (office tasks): 59.0% average win rate
trained using MiniMax’s Forge framework and CISPO algorithm with large-scale reinforcement learning optimized for agent scenarios
Process Reward mechanisms to monitor multi-step execution quality, improving completion speed by 37% vs M2.1 and reducing search iterations by about 20%
Layer 2: OpenClaw (the “skeleton”)
OpenClaw provides a modular agent framework that standardizes how the model, channels, and tools are orchestrated. Core components described in the original text:
-
Gateway
- coordinates tool execution
- manages client connections (often via WebSocket for real-time interaction)
- enforces security policies
-
Skills
- plugin-based capability expansion
- follows standard OpenAPI conventions
-
Memory
- persistent cross-session storage for context, preferences, history
-
Channels
- standardized message interfaces to connect IM platforms
In MaxClaw, MiniMax hosts and manages these components on its infrastructure.
Layer 3: MiniMax Agent UI + Expert 2.0 ecosystem (the “skin”)
Users interact via the MiniMax Agent web interface at:
On top sits Expert 2.0, a community-driven workflow ecosystem intended to expand MaxClaw with reusable “expert agents.”
3. MaxClaw vs. Self-Hosted OpenClaw
Here is the same comparison from the original article, reconstructed in a developer-friendly table.
| Dimension | OpenClaw (Self-hosted) | MaxClaw (Managed cloud) |
|---|---|---|
| Deployment | Bring your own machine; install Node.js; set up environment | Click a button; deployed in ~20 seconds |
| Model setup | Obtain API keys; edit JSON config | Built-in MiniMax M2.5; no model config |
| Skills | Install from ClawHub; configure each API | Built-in core skills (image/video/search/web deploy, etc.) |
| Channels | Manually create bots/tokens; bind via CLI | Guided via natural-language setup; supports Feishu/DingTalk/etc. |
| Ops | You handle updates, dependencies, process supervision | Fully managed by MiniMax |
| Cloud storage | No default cloud storage | Includes 50GB dedicated storage |
| Long-term memory | You configure persistence yourself | Native long-term memory across sessions |
| Best fit | Developers / tinkerers / strict data control | Broad users; minimal setup; “no infra” |
4. Built-in Tooling: What You Get on Day One
A big difference is that MaxClaw comes with a pre-integrated toolchain rather than requiring you to install each skill.
Information retrieval tools
- web search for up-to-date information
- image search for finding visual references online
- web extraction for pulling and structuring content from a URL
Together, these let the agent behave like a research assistant: gather sources, extract key points, structure results.
Content creation tools
- text-to-image generation
- video generation (short-form creation)
In the OpenClaw world, you typically wire these up yourself via third-party APIs. MaxClaw positions them as built-in.
Office/document tools
- Word formatting
- PowerPoint editing
- Excel data processing
The original text attributes this to M2.5 being reinforced specifically for office workflows.
Developer tools
- code execution across multiple languages
- web deployment (publish generated web content online)
The combination is framed as enabling even non-coders to produce simple pages or tools via natural language.
Understanding/analysis tools
- image understanding (analyze uploaded images)
- video understanding (extract and analyze video content)
The goal is a full loop: not only generate content, but also interpret it.
All of these tools are hosted and maintained by MiniMax, so users don’t manage API versions or low-level integrations.
5. Getting Started: Creating Your First MaxClaw (End-to-End)
The setup is intentionally minimal.
Step 1: Open the MiniMax Agent site
Go to:
If you don’t have an account, register (phone/email verification).
Step 2: Find the MaxClaw entry
After logging in, look for MaxClaw in the left navigation and click into it.
Step 3: One-click creation
Click “Start” / “Create MaxClaw.” The platform deploys a full OpenClaw instance in the cloud, typically in 10–20 seconds, then drops you into a chat-like interface for your agent.
At no point do you need to:
- rent a server
- install dependencies
- edit config files
- apply for third-party API keys
- write code
The article’s argument here is simple: because MiniMax is the model vendor, “model access” is a first-class part of the product, not something you bolt on.
Step 4: Confirm the baseline capabilities
Your agent is created with:
- web search
- image understanding + generation
- video understanding + generation
- web extraction
- code execution
- file handling (Word/Excel/PPT)
- image search
- web deployment
In OpenClaw, you’d often install/configure these one by one. In MaxClaw, they are presented as out-of-the-box, without extra API charges.
6. Deep Integration Example: Using Feishu for Cross-Platform Work
MaxClaw emphasizes messaging-platform integration, especially for mainstream Chinese workplace tools.
Why connect Feishu?
Once connected, you can message the agent directly inside Feishu to assign tasks, without opening the web UI. Deliverables and results can still be viewed in the web interface, enabling cross-device collaboration.
Step-by-step Feishu integration
Step 1: Request setup guidance inside MaxClaw
In the MaxClaw chat, type:
I want to integrate with Lark. Please guide me through the configuration process.
MaxClaw recognizes the intent and guides you through the configuration steps.
Step 2: Create an app on Feishu Open Platform
Following the guidance, go to open.feishu.cn and:
- log in
- create an app
- choose “enterprise self-built app”
- fill in name/description (anything is fine)
- enable the “bot” capability
- configure required permissions under “events & callbacks”
Step 3: Provide App ID and App Secret to MaxClaw
Once created, Feishu gives you an App ID and App Secret. Send them to MaxClaw; it completes the remaining configuration.
No forms. No config files. The workflow is conversational: you say what you want; it tells you what to do.
Step 4: Verify
Find the bot in Feishu and send a test message.
The article notes MaxClaw supports similar flows for DingTalk, Telegram, WhatsApp, Discord, Slack, etc.
7. “Expert Modes”: More Than Chat, More Like Configured Tools
MaxClaw includes multiple “expert configuration modes,” each mapping to a professional working style. Switching modes is intended to load a different set of capabilities and workflows quickly.
Switching modes
In the MaxClaw UI, go to Settings → Current Configuration and select a mode.
Image creation mode
In “Image Creation,” MaxClaw acts like a design assistant. Example prompt from the article:
Please help me create a tech-style poster with the theme "AI Redefining Efficiency".
The color scheme should be mainly dark blue and silver-white, and it needs to incorporate futuristic geometric elements.
The size should be in portrait mode for mobile phones, with space at the bottom for text.
MaxClaw generates an image and can iterate via natural-language feedback.
The contrast with OpenClaw is operational: on OpenClaw, you’d typically install an image-generation skill and wire up an API first.
MAX mode (default)
“MAX” is the general-purpose mode and is framed as automatically choosing the right Office skills based on task type—especially for Word/PPT/Excel workloads.
Custom experts
Beyond presets, you can define custom experts via natural language. That leads to the larger concept: Expert 2.0.
8. Expert 2.0: A Community Workflow Library
What Expert 2.0 is
Expert 2.0 is MiniMax Agent’s ecosystem for reusable “expert agents.” Each “expert” is a pre-configured workflow: domain knowledge + tools + execution logic.
As of February 2026, the article claims there are 16,000+ expert agents created and used across areas like development, creative writing, office productivity, and finance.
What it changes, operationally
Before Expert 2.0, building a serious agent often meant manually configuring:
- skills
- sub-agents
- MCP (Model Context Protocol)
- prompt structures and orchestration logic
Expert 2.0 reframes this as: describe the goal in natural language, and the system derives SOP, tool orchestration, and capability configuration.
Example from the article (financial modeling expert):
You need to create an expert for me, skilled in using Excel's native capabilities to build professional financial models (DCF, sensitivity analysis), and deliver a complete, error-free .xlsx file.
You need to break down the necessary knowledge, skills, and process configurations required for this expert role.
The system is described as automatically injecting domain knowledge (DCF, sensitivity analysis, Excel function conventions), configuring tools/sub-agents, generating example scenarios, and enforcing output rigor.
Using existing experts
If you don’t want to build your own, browse the community, click “Use,” and then provide minimal input.
The finance example in the original text: you specify a company, and the expert agent runs a pipeline:
- map company → ticker
- pull financial data
- retrieve recent news and industry context
- run DCF analysis
- generate a complete report (business model, financial health, team, competition, valuation conclusion)
The comparison is again about time-to-value: OpenClaw can do it, but you assemble the pipeline yourself; Expert 2.0 is positioned as click + one sentence.
Creating your own expert
If no existing expert matches, you define one via natural language.
Example: an e-commerce competitor monitoring expert, with responsibilities, data dimensions, output requirements, and triggers (like weekly Monday reports).
The article notes MiniMax provides 15 free rounds of creation/debugging per user to refine an expert.
Why a community matters
The piece frames Expert 2.0 as a knowledge-sharing mechanism: professional experience can be “packaged” into executable workflows.
It also mentions future plans for creator pricing/revenue sharing and team-level expert sharing—turning individual expertise into reusable team infrastructure.
9. Advanced Workflows: Prompt Templates You Can Reuse
This section is intentionally hands-on: complete prompt templates you can copy.
Scenario 1: Scheduled news collection + topic selection
For creators, marketers, researchers.
The article highlights transparency: you can see which sites it visits and what it reads, making it easier to trust it’s not fabricating.
Scenario 2: GitHub project parsing + outline generation
For technical bloggers, PMs, or readers who struggle with long English READMEs.
Scenario 3: Business trip planning automation
For frequent travelers, assistants, admins.
Scenario 4: Multilingual translation + localization workflow
Not “sentence translation,” but a professional-style pipeline: analyze → terminology → translate → QA.
Scenario 5: Automated code review workflow
For teams, tech leads, indie developers.
The article adds an important limitation: even with strong coding benchmarks (SWE-Bench Verified 80.2%), AI review should be treated as guidance. For critical production logic, experienced engineers should make the final call.
10. M2.5, Explained: Why MaxClaw Behaves Like an Agent (Not Just a Chatbot)
MaxClaw’s behavior is attributed to M2.5’s agent-oriented design.
MoE: strong capability without always paying full cost
M2.5 uses Mixture-of-Experts: ~229B parameters total, ~10B activated per inference.
The article’s analogy: a large hospital with many specialist departments—patients don’t require every doctor at once; triage routes them to the relevant specialists. That’s the idea behind sparse activation.
Forge + CISPO: reinforcement learning for agents
MiniMax trains M2.5 using its own Forge RL framework and a CISPO algorithm designed to keep large-scale training stable. The text describes CISPO as clipping importance-sampling weights to constrain training while still allowing exploration.
Interleaved Thinking: “think → act → observe → reflect → act”
M2.5 includes “Interleaved Thinking,” enabling dynamic reasoning at multiple points during execution rather than “think once, answer once.” This matters for agents that search, browse, and adapt mid-run (e.g., revising search queries if results are poor).
Native agent optimization and “spec-first” behavior
The article claims M2.5 was reinforced across 10+ programming languages and hundreds of thousands of real environments, supporting full lifecycle work: system design, environment setup, iteration, testing.
It also highlights “native spec behavior”: before coding, the model tends to decompose requirements, plan system structure, and even outline UI layouts—more like an architect than a code autocomplete engine.
Long context
M2.5 supports up to 262,144 tokens of context (the article notes this is roughly 200k Chinese characters), useful for long documents and complex multi-turn tasks.
Benchmarks summarized
From the original text:
- SWE-Bench Verified: 80.2%
- Multi-SWE-Bench: 51.3%
- BrowseComp: 76.3%
- GDPval-MM: 59.0% average win rate (office tasks)
- RISE: “leading level” (real-world expert search tasks)
Open-source weights
The article notes that M2.5 weights are fully open-sourced on HuggingFace. The implication: MiniMax differentiates via the hosted product experience (MaxClaw), not only by keeping the model closed.
11. Security and Privacy: What You Should Consider Before Using It
Agents are powerful because they touch real systems. That comes with responsibility.
Data security
MaxClaw is cloud-hosted, meaning interaction data goes through MiniMax servers. If you handle highly sensitive business data or personal privacy data, you should evaluate whether cloud usage fits your security posture.
If you need maximum data control, self-hosting OpenClaw can keep data on your own infrastructure.
Credentials (App ID / App Secret) handling
When integrating Feishu/DingTalk, you provide credentials equivalent to keys. Configure in a trusted environment and treat them as sensitive.
Permission boundaries
Follow least privilege: grant only what’s necessary. Avoid broad, persistent permissions when a narrower scope works.
Prompt injection risk
Like all browsing agents, MaxClaw can be exposed to malicious instructions embedded in web pages or external content (prompt injection). The article says MaxClaw includes some mitigations, but users should still verify outputs—especially for important decisions.
12. Competitive Landscape: Where MaxClaw Fits
MaxClaw vs self-hosted OpenClaw
The core conclusion is consistent:
- OpenClaw: best for technical users and those with strict data control requirements
- MaxClaw: best for people who want “fast onboarding” and don’t want to manage infrastructure
MaxClaw vs Alibaba CoPaw
The article describes CoPaw as a domestic OpenClaw alternative with broad IM integration (DingTalk/Feishu/QQ) and both local + cloud deployment options.
The difference, as framed here:
- CoPaw aligns with Alibaba Cloud’s ecosystem and enterprise use cases
- MaxClaw aligns with MiniMax’s ecosystem and emphasizes agent-optimized model behavior plus the Expert 2.0 workflow community
MaxClaw vs lightweight variants (ZeroClaw, NanoClaw)
ZeroClaw and NanoClaw are lightweight OpenClaw implementations (thousands of lines or even hundreds). They’re great for teaching and understanding core agent mechanics, but they don’t offer the managed hosting, built-in toolchain, or expert ecosystem described for MaxClaw.
MaxClaw vs developer frameworks (LangChain, AutoGen)
This is a category difference:
- LangChain / AutoGen: building blocks and orchestration frameworks; developers assemble, host, and maintain agents themselves
- MaxClaw: a packaged, ready-to-use agent product
If you want deep customization and you’re writing code, frameworks fit better. If you want an agent that works immediately, MaxClaw is the closer match.
Broader China agent ecosystem context
The article notes the domestic agent landscape in early 2026 is active: Alibaba (CoPaw, Bailian), ByteDance (Coze), Baidu (Qianfan AppBuilder), Tencent (Yuanqi), and others.
MiniMax’s differentiation is summarized as:
- M2.5 is optimized for agent use (tools + multi-step reasoning)
- Expert 2.0 provides UGC workflow depth
- Deep integration with OpenClaw inherits ecosystem resources and community experience
13. Practical Usage Advice and Common Questions
A suggested onboarding plan (first 5 days)
- Day 1: try basic tasks (search, generate an image) to feel how it differs from a standard chatbot
- Day 2: build one simple automation (e.g., “when I send a URL, summarize it”)
- Day 3: use one existing Expert 2.0 workflow
- Day 4: connect your primary chat tool (Feishu/DingTalk)
- Day 5+: create a custom expert for your real job workflow
Prompting tips from the original article
- specify role (“senior market analyst”, “technical documentation specialist”)
- describe requirements structurally (steps + expected output)
- define output format (Markdown/table/JSON)
- provide positive/negative examples if quality matters
FAQ (as stated in the original text)
Is MaxClaw free?
It requires a MiniMax Agent basic subscription. Pricing should follow the latest information on agent.minimaxi.com.MaxClaw vs MiniMax Agent—what’s the difference?
MiniMax Agent is the general AI chat platform; MaxClaw is a specific module focused on automated agent execution—an “agent mode” within the platform.Will my workflows and experts be lost?
MaxClaw includes 50GB dedicated cloud storage, and configurations/data persist in the cloud. The article still recommends backing up important configurations for safety.What languages are supported?
M2.5 supports Chinese and English, among others. You can interact in Chinese while processing English content (e.g., reading English docs).
14. The Trend View: Why MaxClaw Matters (According to This Article)
The original text frames MaxClaw as part of a broader shift in the agent space:
From capability competition to experience competition
By early 2026, the differentiator is less “who has the biggest benchmark score” and more “shortest path from idea to working automation.”From tool to assistant
Agents move beyond input/output into proactive behaviors: schedules, triggers, cross-platform execution.From individual capability to ecosystem capability
Expert 2.0 turns individual expertise into reusable workflows, scaling “collective intelligence” through UGC.
Conclusion: Who MaxClaw Is For (and Who It Isn’t)
This article’s conclusion can be distilled into three points:
It removes deployment friction.
Hosted infrastructure and one-click provisioning collapse a complex setup into a simple action.It ships a full toolchain by default.
Search, image/video generation, code execution, and document handling are available without manual API wiring.It leans on an expert workflow ecosystem.
Expert 2.0 is positioned as “solutions, not just tools,” enabling reuse and knowledge sharing through workflows.
Practical guidance from the original author:
- If you wanted to try OpenClaw but got blocked by setup, MaxClaw is a low-friction entry point.
- If you’re a developer, it can be a fast way to validate ideas without rebuilding an environment each time.
- If you’re a creator or operator, Expert 2.0’s ready-made workflows can bootstrap an automation pipeline quickly.
- If you have strict security requirements, you can learn agent usage on MaxClaw first, then consider self-hosting OpenClaw once you’re confident.
The agent era is still early. MaxClaw and Expert 2.0 are presented here as a step toward making “everyone has their own AI assistant” feel less like a slogan and more like something you can actually use.
Official access: https://agent.minimaxi.com/
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