Claw-Written Plugin Adds Qwen 3.8 27B Thinking Level Support to OpenClaw
Running Qwen 3.8 27B on llama.cpp with OpenClaw 2026.6.9 but missing native thinking-level control? One OpenClaw user hit the same wall and let their AI agent write a plugin to fix it — fully generated, untested by human eyes.
Why You Need This Plugin
Qwen 3.8 controls thinking levels via the chat template rather than a dedicated parameter. OpenClaw's llama.cpp backend at version 2026.6.9 doesn't map this out of the box, so thinking levels get ignored or misapplied. The plugin bridges that gap.
What It Does
- Per-API-call thinking levels — correctly injects the
enable_thinkingvalue into the chat template for each request. - Separate level for heartbeats — set a different thinking level for background heartbeat calls (the author uses
xhigh). - Instruct mode (thinking off) — disables thinking and applies sampling parameters recommended by unsloth for optimal output.
- Optional timeout guard — force compaction to instruct mode to avoid timeouts during long reasoning chains.
How to Get It
The plugin is published on GitLab:
git clone https://gitlab.com/moltwithhat/llamacpp-qwen-thinkingAuthor's note: “I didn't even look at the code” — the entire thing was written by the AI agent itself. It's a proof-of-concept that agents can produce useful, shareable tools without human review.
Who It's For
Anyone running Qwen 3.8 (or similar models) on llama.cpp through OpenClaw who wants fine-grained control over thinking levels per request. If you rely on heartbeats or need instruct mode as a fallback, this saves you the plumbing work.
📖 Read the full source: r/openclaw
👀 See Also
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Detrix is a free, open-source MCP server that enables MCP-compatible agents to observe live variables in running code without restarts or code changes. It supports Python, Go, and Rust applications running locally or in Docker.
NarrateAI MCP Server Demo Shows Claude Adding Voiceover to Videos
A live demo shows Claude using the NarrateAI MCP server to automatically narrate videos from a URL, handling async polling and generating narration by analyzing silent screen recordings.
Steerling-8B: An Interpretable Language Model with Token-Level Attribution
Guide Labs released Steerling-8B, an 8-billion-parameter language model trained on 1.35 trillion tokens that can trace any generated token to input context, human-understandable concepts, and training data sources. The model achieves competitive performance with models trained on 2-7× more data.