做长期记忆的智能体开发者可以看看，SodaMem用图结构加证据链，在LongMemEval-S上准确率92.8%，成本还低，比普通RAG日记靠谱。
SodaMem是一种为长期对话LLM智能体设计的记忆框架，通过提取带来源证据的FactEvents，并记录提及时间、发生时间和有效性，构建时间图。在LongMemEval-S基准上，SodaMem达到92.8%的准确率（464/500），每次查询平均成本约0.00161美元，使用deepseek-v4-flash模型。相比扁平RAG日记和Markdown日志，SodaMem更擅长处理时效性、来源和有序时间推理。其代码已在GitHub开源。
SodaMem: Evidence-Grounded Temporal Graph Memory for LLM Agents
Large language model (LLM) agents that assist users over weeks of conversation must remember what is currently true, not merely what was once said. Flat RAG diaries and Markdown logs optimize needle retrieval but under-serve currency, provenance, and ordered temporal reasoning (Maharana et al. 2024; Wu et al. 2024; Packer et al. 2023; Chhikara et al. 2025). We present SodaMem, an evidence-grounded temporal graph memory that (i) extracts typed FactEvents with mandatory provenance spans, (ii) persists mention time, occurrence time, and validity with SUPERSEDES/CONTRADICTS/UPDATES edges under hybrid lexical-dense indexing, and (iii) answers via a planner-reader loop that gathers citable evidence before composing a final response. On LongMemEval-S, our store-of-record configuration reaches 92.8% accuracy (464/500; best of N=3) at mean $0.00161/question (approximately 18.3k tokens; median $0.00111 / approximately 14.6k) with deepseek-v4-flash. We compile public systems with estimable API cost into a cost table and cost-accuracy map; under these estimates SodaMem sits near the accuracy frontier at Flash-tier spend and strictly dominates several higher-cost, lower-accuracy points. Accuracy uses the same Flash model as reader and judge (self-grading); costs exclude ingest/judge and cross-system comparisons are compiled estimates rather than a single-harness bake-off.Our code is available at https://github.com/SodaMem/SodaMem