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
Selfware: Rust-based local AI agent framework with PDVR architecture
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Agent Skill Harbor: GitHub-native skill management for AI agent teams
Agent Skill Harbor is an open-source platform for teams to share, track, and govern AI agent skills using GitHub-native workflows. It collects skills from GitHub repos, tracks provenance, supports safety checks, and publishes a static catalog site with GitHub Actions and Pages.
Be My Butler: Multi-Agent Pipeline for AI Code Verification
Be My Butler is an open-source multi-agent pipeline where different AI models review each other's code through blind verification. The system addresses the problem of AI agents incorrectly reporting their own code as functional.
Agent Memory Protocol (AMP): Open Spec for Interoperable AI Agent Memory on Top of MCP
AMP defines a standard interface for persistent memory in MCP-compatible agents with six core verbs: encode, recall, forget, consolidate, pin, and stats. Includes compliance test suite and reference implementation.