TASL: Adapter Library for Evolving Tools

Tool-augmented language agents extend LLM capabilities by calling external tools, but API evolution can invalidate previously successful calls. Tool calls therefore need to be adapted to match the updated interfaces. Existing methods that rely on model generation to adapt calls still require repeated model inference for new calls affected by the same API change. To reduce repeated adaptation, we introduce TASL (Tool-Adapter Synthesis Library), a training-free framework that synthesizes persistent executable adapters for evolving APIs. TASL turns each API change into a reusable adapter, validating it via execution and round-trip checks so that subsequent calls apply the stored program directly, with no adaptation-related model inference. Model parameters remain fixed, and no task-answer supervision is used. Experiments across 7 domains, 11 tools, and 2 evolved API sets in ToolQA-D show that TASL achieves higher accuracy than all evaluated training-free baselines while using fewer adaptation-related LLM calls. Specifically, it improves mean exact-match accuracy by 13.2 and 7.2 percentage points over the strongest baseline with 4B and 14B backbones, respectively. Code will be released after acceptance.