Qwen3.x models fail silently in OpenClaw due to streaming output format mismatch
The Problem
When running Qwen3.x models locally with OpenClaw in streaming mode, the models output their responses to the reasoning field instead of the expected content field. OpenClaw interprets empty content as a failure and silently falls through to the next model in your fallback chain without throwing an error. This results in the wrong model answering queries.
The Solution
The fix involves implementing a small proxy that sits between OpenClaw and Ollama. This proxy performs two key functions:
- Translates the API format between the two systems
- Injects
think: falseinto the appropriate location
Once correctly configured, Qwen3.x models pass full tool-call evaluation with perfect scores (15/15), including execution, file reading, web search, Sheets integration, Slack integration, and memory operations.
Implementation Details
The source provides a comprehensive write-up covering:
- Proxy setup instructions
- Six specific configuration settings that must all be correct
- Monitoring approaches
- What doesn't work (common pitfalls to avoid)
The full technical details, including code snippets and configuration examples, are available in the linked gist.
📖 Read the full source: r/LocalLLaMA
👀 See Also
Qwen3.5-397B MoE Runs on 14GB RAM via Paged Expert Loading on M1 Ultra
Paged MoE engine keeps only 20 experts resident and lazy-loads the rest from SSD, running a 209GB 397B model on a 64GB Mac Studio with 1.59 tok/s and 14GB peak RAM. Includes smaller model benchmarks.
Three Essential OpenClaw Skills for a Stable Setup: Memory, Security, and Discovery
A Reddit post recommends installing three specific types of OpenClaw skills first: a memory fix skill to prevent context loss, a local security vetting skill to check for malicious code, and a curated discovery hub to find maintained tools.
How to safely run llama.cpp native tools (exec_shell_command) with multi-sandboxing on Linux
A practical guide to enabling llama.cpp native tools, especially exec_shell_command, and running them inside multiple sandboxes (Firejail + tiny Alpine VM) for safe web fetching and command execution via the llama-server web UI.
Four Common Setup Mistakes That Make People Quit OpenClaw
A Reddit user reports seeing over 50 people quit OpenClaw due to four specific setup issues: missing SOUL.md files, excessive API costs from using Opus model for everything, installing too many skills at once, and creating multiple agents before the first one works properly.