memory-contract

To maintain agent context across multiple sessions, use a memory contract to retrieve prior decisions at task start and store conclusions after completion. This preserves long-term workspace memory and reasoning continuity between tasks.

Storing agent reasoning outcomes requires storing summaries at decision points using memory storage functions. This captures rationale and results, formatting memory structures for downstream graph extraction and future context retrieval.

Retrieve prior context in a multi-step agent workflow before choosing a path or making a decision. Retrieving prior constraints and decisions at these points ensures the agent maintains reasoning continuity and avoids repeating resolved work.

This memory management approach requires no external dependencies or components. It operates as a standalone memory contract that defines requirements for using retrieval and storage tools within Flowbaby-enabled agent environments.

Yes, you can use this to preserve long-term memory for autonomous agents. It manages memory by retrieving prior context and storing outcomes, applying at decision points and across sessions to maintain persistent workspace memory.