[V2] I built a Markdown memory layer so coding agents stop rediscovering the same project context

Most useful answers in a community disappear into the feed.

Someone solves a hard problem today. A month later, the same question returns because the answer was never turned into something durable. The community keeps paying the cost of rediscovery.

That is why I built LLM Wiki: a simple shared memory for AI agents.

Instead of saving every conversation, it keeps the knowledge worth reusing:

1. Save a useful article, transcript, note, or exported community thread.

2. Let the agent compile it into linked Markdown pages.

3. Ask a question later and retrieve the right section with evidence.

4. File useful conclusions back so the next answer starts from what the community already learned.

This is useful for recurring questions, research libraries, customer conversations, project decisions, and any topic a group explores over time.

LLM Wiki v2 improves how that memory is searched:

- exact section search finds names, flags, and phrases

- optional semantic search finds paraphrased ideas

- reciprocal rank fusion combines both result lists

- citations and source metadata show where an answer came from

Cerebras recently published a detailed article about its internal knowledge base. Employees ask it more than 15,000 questions a day, according to the team. Their system is enterprise-scale, but the need is universal: information is scattered, and good answers should not have to be rebuilt every time.

No embedding service is required. The default is local, Markdown-first, and dependency-free. The clever parts are optional; the knowledge stays readable.

Open-source project:

https://github.com/praneybehl/llm-wiki-plugin

Cerebras article:

https://www.cerebras.ai/blog/how-we-built-our-knowledge-base

What valuable answer in this community keeps getting rediscovered?

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[V2] I built a Markdown memory layer so coding agents stop rediscovering the same project context