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

Practical Multi-Agent System Architecture Advice from Experience
A developer shares five specific patterns for building multi-agent AI systems based on experience running a 7-agent daily system: start with one agent, use an orchestrator pattern, implement shared memory with JSON files, route models by task, and add confirmation loops.

Using AI to Write Better Code More Slowly: A Bug-Finding Workflow
Nolan Lawson describes a workflow using multiple AI agents (Claude, Codex, Cursor Bugbot) to find and prioritize bugs in PRs, improving code quality over raw velocity.

Running OpenClaw Locally with Ollama to Avoid API Costs
A Reddit user shares their experience switching from API-based OpenClaw to running it locally with Ollama, eliminating API costs while maintaining workflows. They created a step-by-step installation video guide.

Local Translation Model Recommendations for 32GB VRAM GPUs
A developer shares tested recommendations for local translation models on a 32GB VRAM setup, highlighting Unsloth Gemma3 27b Instruct UD Q6_K_XL for general languages and Bartowski Utter Project EuroLLM 22B Instruct 2512 Q8_0 for European languages plus Korean.