From Memory to Action: Executable and Self-Evolving Contract Graphs for Tool Agents

Tool-using agents can improve future decisions by extracting reusable knowledge from past interactions and retrieving it when needed. Existing approaches often distill such interactions into textual experience that is retrieved as guidance for future tasks. However, this form of experience reuse leaves unclear how retrieved experience should be executed, composed with other experience, and updated from feedback. We introduce ContractFlow, a framework for transforming accumulated experience into executable, self-evolving knowledge. ContractFlow first compiles experience into executable contracts specifying goals, prerequisites, stable knowledge, runtime variables, tool actions, and completion criteria. It then organizes these contracts into a dependency graph that coordinates their execution according to the current task state. Within each episode, the graph guides observation gathering, binds variables to current evidence, and permits actions only when their prerequisites and execution conditions are satisfied, while tracking completion. Across episodes, successful executions, failures, and local repairs drive updates to contract nodes and relations, allowing subsequent tasks to reuse a refined execution structure. This process preserves reusable action rules across episodes, while keeping runtime-specific variables and completion states local to each episode, making the same graph both an execution structure and an object of continual improvement. Extensive evaluations across diverse tool-use benchmarks demonstrate that ContractFlow delivers consistent and substantial improvements over strong baselines. These results support a shift from treating experience as textual advice to representing it as executable knowledge that is organized, tested, and refined through action.