In-context learning (ICL) adapts LLM behavior by placing task-relevant context in the prompt. While powerful, this mechanism can be inefficient: the context must be reprocessed for every query, and when it is substantially longer than the query itself, it dominates inference cost in both latency and token overhead. This paper contends that the behavioral shift induced by an in-context signal can be compiled directly into the model weights, enabling subsequent inference without including the original context in the prompt. To this end, we propose In-Context Compilation (ICC), a gradient free method that encodes a context-induced behavioral shift as a sparse closed-form edit to attention output-projection weights. ICC is calibrated solely from the activation differences between no-context and with-context forward passes on a small set of behaviorally informative examples, requiring neither backpropagation nor trainable parameters. We evaluate ICC in the few-shot setting, where labeled contexts provide controlled behavioral targets. Across diverse LLM architectures and classification and reasoning benchmarks, the ICC-adapted LLM closely approximates the performance of the same LLM prompted with the full context, while omitting that context at inference time. In some cases, it even surpasses the with-context model. Moreover, on most benchmarks, ICC is competitive with and often outperforms gradient-based PEFT methods (LoRA, DoRA, IA3, Prompt/Prefix Tuning, LoReFT) and training-free activation-injection baselines (Function Vectors, ICV), while completing compilation in 10–18s with only ∼64–120 MB of overhead.