Opus 4.6 Harness: A Python Toolkit for Adaptive Thinking and Compaction
opus-4-6-harness is a lightweight Python toolkit for experimenting with two of Claude Opus 4.6's most interesting capabilities: Adaptive Thinking and the Compaction API. It exposes an OpusModel class for generating responses with optional multi-step reasoning traces, and a CompactionManager for intelligently compressing prompt data to fit within context windows. If you have been looking for a clean way to prototype around these features without wiring up a full application, this is a solid starting point.
Why It's Useful
Context window management is one of the least glamorous but most important problems in agentic workflows. Once your conversation history grows beyond a few thousand tokens, you either truncate blindly or build your own summarization layer. The CompactionManager in this harness lets you specify a target compression ratio and handles the reduction for you, which is exactly the kind of utility that saves hours of boilerplate. On the other side, Adaptive Thinking gives you visibility into the model's reasoning steps before the final answer — useful for debugging agent chains or understanding why a model chose a particular path.
Technical Takeaway
The project is structured as a standard pip-installable package with no heavy dependencies, which makes it easy to drop into an existing pipeline. The key design decision is separating the model interface (OpusModel) from the context management layer (CompactionManager) — this means you can use compaction independently, for example to pre-process prompts before sending them to any model, not just Opus 4.6. That kind of composability is what turns a demo into a real tool.