volcengine/OpenViking：README 来源编辑指南

volcengine/OpenViking 的 README 将项目描述为"Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills."。本文只整理仓库能直接核验的内容，不把星标、Fork 或宣传语当成质量证明。README 在"What is OpenViking"下的说明是：OpenViking is an open-source context database for AI agents. It stores memories, resources, and skills as one virtual filesystem under the viking:// protocol, so an agent browses its own context with ls, tree, and find instead of querying。这给出的首先是项目边界，而不是已经完成的生产验证。

从 README 的"Why OpenViking"和相关条目看，读者可以先判断它是否解决自己的具体问题：Tiered loading cuts token spend. Every entry is processed into L0 (abstract), L1 (overview), and L2 (details) on write, then loaded only as deep as the task requires. → Context layers。如果你的目标与这段说明不一致，就不应仅凭项目热度采用它。这里保留原项目名、命令和组件名，方便回到一手来源核对。 README 还列出了另一条可核对的信息：One filesystem for all context. Memories, resources, and skills each get a viking:// URI. Agents locate and manipulate context deterministically, like a developer working with files. → Viking URI。这类原文条目可以帮助读者设计试运行步骤，但不能代替自己的环境测试。

README 把工作方式分散写在"Why OpenViking"等段落中。可确认的线索包括：Each directory carries its own L0/L1 layers, so relevance can be judged before any full file is read:。这篇整理没有把未写出的架构、性能或安全边界补成结论；真正的运行链仍应结合仓库目录、配置文件和版本标签检查。

第一次安装应从 README 给出的入口开始。当前可复核的命令是： pip install openviking --upgrade openviking-server init # interactive wizard: providers, models, ov.conf openviking-server doctor # validate setup openviking-server # start (background: nohup openviking-server > openviking.log 2>&1 &) 如果仓库没有提供命令，本文不会替它编造安装步骤，而是建议先打开 README 的"What is OpenViking"部分，确认系统依赖、默认端口和首次初始化动作。

日常使用的细节取决于项目实际文档。README 的"Proof it works"段落提到：OpenViking 0.3.22 has been evaluated on long-conversation user memory (LoCoMo) and multi-turn agent tasks (tau2-bench). Full results and setup details, including knowledge-base QA, are in the benchmark report.。对于配置文件、环境变量、权限和数据目录，当前稿只记录来源明确的部分；未写明的默认值必须在测试环境中验证，并保留可回滚的配置副本。 同一部分还提到：Directory recursive retrieval. Vector search first locates the highest-scoring directory, then drills down layer by layer, so results arrive with their surrounding context intact. → Retrieval。