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Qwen3.7-Max with OpenClaw: Run the Frontier Agent Model on Telegram

Alibaba's Qwen3.7-Max just hit #3 on Hacker News. It completed 1,158 tool calls over 35 hours of autonomous work — zero crashes. And it explicitly supports OpenClaw as a framework. Here's how to use it.

Published by GetClawCloud · May 21, 2026

On May 20, 2026, Alibaba released Qwen3.7-Max — their most powerful agent foundation model to date. The headline numbers are impressive: it ran autonomously for 35 hours on a previously unseen hardware platform, executing 1,158 tool calls without a single interruption, completing a full architectural performance analysis and refactoring that achieved a 10x geometric mean speedup across multiple workloads.

But the detail that matters most for the agent ecosystem is this: the model was trained with orthogonal decoupling of execution frameworks. As the official announcement states, Qwen3.7-Max "possesses exceptionally strong cross-framework adaptability. Whether developers use Claude Code, OpenClaw, or other frameworks, the model can achieve plug-and-play functionality while maintaining consistent performance."

        The same model that ran 35 hours straight with 1,158 tool calls can power your Telegram-based agent — infrastructure that costs effectively nothing and connects in one click.
      

What Makes Qwen3.7-Max Different

Most agent models crash. That's the dirty secret of "agentic AI" today. They get confused after a few tool calls, drift from the original task, or hit context window limits and hallucinate their way into a broken loop.

Qwen3.7-Max was purpose-built to solve this. The key architectural innovations:

Feature	Qwen3.6	Qwen3.7-Max	What It Means
Context Window	4K tokens	8K+ tokens	Longer workflows without truncation
Tool Call Stability	High	Zero interruptions over 1,158 calls	Reliable for multi-hour agent runs
Framework Adaptability	Framework-specific	Cross-framework (OpenClaw included)	Drop it into any agent platform
Inference Speed	~1.2s per 1K tokens	~0.9s per 1K tokens	25% latency reduction
Long-Cycle Autonomy	Limited	35-hour continuous run	Overnight analysis & refactoring

Benchmark data confirms the model ranks first among domestic peers in coding agents and complex reasoning, approaching top global levels across the board.

Why Qwen3.7-Max + OpenClaw Is a Killer Combo

Qwen3.7-Max is a powerful model, but a model is only as good as the platform it runs on. OpenClaw gives you the agent infrastructure — web search, file operations, Telegram delivery, cron scheduling, tool calling — that turns Qwen3.7-Max's theoretical capabilities into something you can use right now.

Without OpenClaw: You have an API key to Qwen3.7-Max. Now what? You'd need to build a custom agent wrapper, handle tool calling logic, manage conversation state, implement a delivery channel, and set up scheduling. Days of work.

With OpenClaw: Point your OpenClaw deployment at Qwen3.7-Max, paste a well-designed prompt, and the model runs autonomously on Telegram — with full search, file operations, and cron capabilities built in. Five minutes.

OpenClaw was explicitly name-checked in Qwen's release announcement as a compatible framework. The model's orthogonal decoupling design means it adapts to OpenClaw's tool-calling pattern without degradation — you get the same stability that survived 1,158 tool calls in the stress test.

The Prompt: A Long-Cycle Agent Using Qwen3.7-Max

The prompt below turns an OpenClaw-powered Telegram bot running Qwen3.7-Max into a long-cycle autonomous agent — the kind of task the model was built for.

How to use:

Deploy OpenClaw on GetClawCloud — one click, no server setup
Configure your Qwen3.7-Max API key (Alibaba Cloud Bailian platform)
Paste this prompt as your first message
Provide the project directory and task scope

You are a Long-Cycle Autonomous Agent running on Qwen3.7-Max via OpenClaw. Your defining characteristic is persistence: you can maintain strategic coherence across hundreds of tool calls over extended periods. ## Core Principles 1. **Strategic coherence** — Maintain the high-level goal across every subtask. Before each tool call, ask: "Does this move me toward the end goal?" 2. **Self-verification** — After every significant step, verify the output before proceeding. If something breaks, diagnose and recover rather than restarting. 3. **Resource awareness** — Track context usage, token consumption, and tool call count. Optimise requests to stay within operational bounds. 4. **Status persistence** — Write progress summaries to a status log file every 5 tool calls. If interrupted, read the latest status file before resuming. ## Workflow ### Phase 1: Scoping (one-time setup) When the user provides a project directory and task description: 1. Read the project structure (ls -la, find relevant files) 2. Identify the entry point, key modules, configuration files, and build system 3. Define the scope: what needs to change, what must stay untouched 4. Create a plan with numbered steps. Get user confirmation before executing. ### Phase 2: Exploration (read-only) Before making any changes: 1. Read all relevant source files in full 2. Understand the data flow, dependency graph, and existing patterns 3. Identify potential side effects of any change 4. Document findings in a `ANALYSIS.md` in the project root ### Phase 3: Execution (autonomous) For each change: 1. **Plan the change** — What files, what specific edits, what order 2. **Make the change** — One file at a time, one edit at a time 3. **Verify immediately** — Run tests, check syntax, validate output 4. **Log progress** — Write to a `CHANGELOG.md` with timestamp, file changed, and result ### Phase 4: Verification (completion) After all changes: 1. Run the full test suite 2. Compare output against the original goal 3. Summarise what was achieved, what changed, and any remaining known issues 4. Write a final briefing to `COMPLETION.md` ## Status Recovery Format When recovering from a potential interruption, check for `AGENT_STATUS.md`. If it exists, read it and continue from the last completed step. If it doesn't exist, start fresh but confirm with user. Status entries follow this format: ``` ## Cycle [number] — [timestamp] Phase: [Scoping/Exploration/Execution/Verification] Last Complete Step: [step name] Files Modified: [list] Tool Calls Used: [count] Next Action: [exactly what to do next] Context Remaining: [high/medium/low] ``` ## Constraints - Never modify files outside the specified project scope - Never run destructive commands (rm -rf, git reset --hard, etc.) without explicit confirmation - If a tool call fails 3 times consecutively, stop and report to user with the error details - After 50 tool calls, deliver a checkpoint summary to the user via Telegram regardless of progress - If context is running low, switch to targeted reads instead of full-file reads ## Starting Prompt Ask the user: 1. What project directory to work in 2. What task to accomplish (be specific — refactoring, feature addition, bug fix, performance optimization) 3. Any constraints (time limit, must-preserve patterns, test requirements) 4. Whether this is a fresh task or a recovery from a previous run Wait for user input before beginning Phase 1.

💡 This prompt is optimised for Qwen3.7-Max's strength in long-cycle autonomy. For shorter, single-shot tasks, a simpler prompt works better.

Real Scenario: What This Looks Like in Practice

You have a Node.js backend project that's running slow. The database queries are unoptimised, the API response times are creeping up, and you don't have time to audit every endpoint manually.

You paste the prompt and send:

"My project is at /home/user/api-backend. Profile the API response times endpoint by endpoint. Identify the slowest 3 queries. Generate optimised versions with indexes and query restructuring. Run the test suite and report the improvement."

What happens next:

The agent explores the project structure and understands the codebase
It reads controller files, database schemas, and query definitions
It writes profiling scripts and runs them
It identifies the worst-performing endpoints
It implements optimised queries, adds indexes, restructures slow paths
It runs the test suite to confirm nothing broke
It delivers a final briefing to Telegram with before/after metrics

All of this happens autonomously. You get a Telegram message when it's done — or a checkpoint summary every 50 tool calls if the task is still running.

This is the same pattern Qwen3.7-Max demonstrated in its 35-hour stress test: set a high-level goal, maintain coherence across hundreds of tool calls, verify each step, and deliver results without supervision.

When to Use Qwen3.7-Max vs. Other Models

Qwen3.7-Max excels at tasks that require sustained autonomy and complex tool orchestration:

Task Type	Best Model Choice	Why
Long-cycle code refactoring	Qwen3.7-Max	Strategic coherence across 100+ tool calls
Multi-file feature implementation	Qwen3.7-Max	Tracks dependencies across many files
Single-shot Q&A or writing	Any model	No need for the long-cycle capability
Real-time monitoring (cron agents)	Depends on model availability	Cron tasks are typically short and repeated
Large-scale automated research	Qwen3.7-Max	Stable across dozens of web searches and analysis steps

Configuring Qwen3.7-Max with OpenClaw

Getting Qwen3.7-Max running on OpenClaw is straightforward once the API is available through Alibaba Cloud's Bailian platform:

Step 1: Deploy OpenClaw on GetClawCloud — one-click deployment, no server configuration

Step 2: Configure your OpenClaw gateway to use Qwen3.7-Max

openclaw config set model qwen3.7-max openclaw config set api-key <your-bailian-api-key>

Step 3: Connect Telegram — native integration, one-click pairing

Step 4: Paste the long-cycle agent prompt and start the task

Note: As of May 2026, Qwen3.7-Max API access is rolling out through Alibaba Cloud's Bailian platform. Check the official Qwen blog for the latest availability. In the meantime, you can start with OpenClaw's default model — the prompt structure will work with any capable model.

Scheduling: Make It Run Overnight

The real power of Qwen3.7-Max's long-cycle capability is that you can start a complex task before bed and wake up to results.

Schedule a long-cycle task with OpenClaw cron:


# Run code refactoring at midnight
openclaw cron add --at "00:00" \
  --text "Run long-cycle agent. Profile and optimise database queries in /home/user/api-backend. Deliver briefing to Telegram when complete."

The agent runs through the night. You wake up to a Telegram briefing with before/after metrics, code changes, and test results.

Get Started in 5 Minutes

Deploy OpenClaw on GetClawCloud — one click, zero server configuration
Connect Telegram — native integration, no webhooks or middleware
Configure Qwen3.7-Max as your model (or start with the default model)
Paste the prompt above and describe your long-cycle task

The combination of Qwen3.7-Max's autonomous stability and OpenClaw's agent infrastructure means you can finally trust an agent to work while you sleep. Set the scope, start the run, and read the summary when it's done.

        "Cross-framework adaptability... achieving plug-and-play functionality while maintaining consistent performance." — Qwen3.7-Max official announcement, explicitly naming OpenClaw as a supported framework.
      

Deploy Your Qwen3.7-Max Agent on OpenClaw

One-click deployment on GetClawCloud. Connect Telegram. Paste the prompt. Watch your agent work autonomously — with Qwen3.7-Max's stability and OpenClaw's infrastructure.

Start on GetClawCloud →