Long-term assistants must answer questions over conversations beyond the context window while grounding responses in verifiable dialogue evidence. Existing memory systems retrieve compact representations, but these may omit critical details, while similarity search may favor related records over answer-bearing source turns. We introduce GRACE-Mem, a long-term memory architecture that separates candidate discovery, candidate reranking, and final evidence construction. It uses graph records for discovery, compressed records for reranking, and provenance-linked raw dialogue spans as final evidence. After hybrid graph retrieval, multi-hop expansion, and two-stage reranking, the top-K records seed an Evidence Curation Agent. The agent searches the raw corpus, removes irrelevant candidates, recovers missing evidence, and returns provenance identifiers to reconstruct the final context from raw dialogue. GRACE-Mem achieves 85.3% on LoCoMo and 73.2% on LongMemEval, surpassing the previously reported state of the art, AMA, by 7.9 and 3.4 percentage points, respectively.