tkalevra/faultline

断层线 为你的人工智能提供验证、私密、可共享的记忆。

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FaultLine 为你的本地 AI 添加了持久且经过验证的内存。事实存储在结构化的知识图谱中——不是被总结成文本块，也不是从文档中猜测出来的。当你纠正了某件事，它会更新。当你事后询问时，答案是正确的。

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为什么选择断层线

1.私营设计

一切都运行在你的机器上。你的对话、记忆、数据——都不会离开。没有云同步，没有账户，没有订阅。

2.在任何人工智能之间共享内存

FaultLine 暴露了一个 MCP 服务器。这意味着 OpenWebUI、Claude Desktop 或其他支持MCP的终端可以使用相同的内存存储。在一次对话中更改服务器 IP，所有型号都能看到更新。

3.写入验证的事实——而非RAG的猜测

RAG 检索文档并请求大型语言模型（LLM）来解释。答案取决于措辞、上下文和运气。FaultLine 在存储前验证事实，并在门口拒绝幻觉。你的 AI 知道“AlphaNode 的 IP 是 10.44.5.20”——而不是“文档提到服务器的内容”。

4.可纠正的关系型数据

FaultLine Validated, private, shareable memory for your AI.

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FaultLine adds persistent, validated memory to your local AI. Facts are stored in a structured knowledge graph — not summarised into text blobs, not guessed from documents. When you correct something, it updates. When you ask about it later, the answer is right.

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Why FaultLine

1. Private by design

Everything runs on your machine. Your conversations, your memories, your data — none of it leaves. No cloud sync, no accounts, no subscriptions.

2. Share memory across any AI

FaultLine exposes an MCP server. That means the same memory store is available to OpenWebUI, Claude Desktop, or any other MCP-capable endpoint. Change your server IP in one conversation, every model sees the update.

3. Write-validated facts — not RAG guesswork

RAG retrieves documents and asks the LLM to interpret them. The answer depends on wording, context, and luck. FaultLine validates facts before storing them and rejects hallucinations at the gate. Your AI knows “AlphaNode’s IP is 10.44.5.20” — not “a document mentioned something about a server.”

4. Correctable, relational data

Facts are stored as typed relationships — person, age, occupation, network device, IP address, MAC, hostname. Corrections update the record cleanly; the old value is archived, not lost. Memory strengthens over time: things mentioned once are held lightly, confirmed facts become authoritative.

5. Teach it any domain — on your terms

/expand networking teaches FaultLine how networking works before you mention your first device. /expand kubernetes online https://kubernetes.io/docs/concepts/ reads the actual docs and builds the ontology from them. You point it at the source, it learns the structure, and every fact you mention in that domain from then on is stored and retrieved in context — not as an isolated string. And everything it learns is still correctable by you.

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What it looks like in practice

You:  "I'm a sysadmin. My main workstation is called DevBox, IP 10.0.1.5."

Next session:
You:  "What do you know about DevBox?"
AI:   "DevBox is your workstation, IP address 10.0.1.5."

You:  "Actually DevBox moved to 10.0.1.10 after the network change."

Next session:
You:  "What's DevBox's IP?"
AI:   "DevBox has IP address 10.0.1.10."

You:  /expand networking

Later, after mentioning your firewall: