CRDT-Based Memory for Distributed Agents with Provable Convergence and GDPR-Compliant Deletion

Multi-agent systems require shared memory that tolerates partitions and concurrent updates without coordination while meeting regulatory obligations such as GDPR data subject rights. We present a CRDT-based memory for production AI systems that achieves strong eventual consistency with merge-safe semantics and supports GDPR-compliant deletion via tombstones.

Building on LWW-element-set and delta-state techniques, we formalize operations, give convergence proofs (associative/commutative/idempotent joins), and integrate causal metadata (vector clocks) for happens-before reasoning. We show how tombstones preserve merge laws while implementing right-to-erasure at the query/export interfaces, and how compaction strategies bound storage growth without violating correctness.

The design is realized as a typed-effects capability and composes with a tamper-evident audit ledger, enabling deterministic replay and automated compliance evidence. We outline evaluation of convergence, overhead, and GC trade-offs, demonstrating that CRDT memory provides reliable, compliant state for distributed agents.