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I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet.It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you.The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard.I wanted to go back to the basics and see how far markdown + git could go before I added anything heavier.What it does:
-> Each agent gets a private notebook at agents/{slug}/notebook/.md, plus access to a shared team wiki at team/.-> Draft-to-wiki promotion flow. Notebook entries are reviewed (agent or human) and promoted to the canonical wiki with a back-link. A small state machine drives expiry and auto-archive.-> Per-entity fact log: append-only JSONL at team/entities/{kind}-{slug}.facts.jsonl. A synthesis worker rebuilds the entity brief every N facts. Commits land under a distinct "Pam the Archivist" git identity so provenance is visible in git log.-> [[Wikilinks]] with broken-link detection rendered in red.-> Daily lint cron for contradictions, stale entries, and broken wikilinks.-> /lookup slash command plus an MCP tool for cited retrieval. A heuristic classifier routes short lookups to BM25 and narrative queries to a cited-answer loop.Substrate choices:
Markdown for durability. The wiki outlives the runtime, and a user can walk away with every byte. Bleve for BM25. SQLite for structured metadata (facts, entities, edges, redirects, and supersedes). No vectors yet. The current benchmark (500 artifacts, 50 queries) clears 85% recall@20 on BM25 alone, which is the internal ship gate. sqlite-vec is the pre-committed fallback if a query class drops below that.Canonical IDs are first-class. Fact IDs are deterministic and include sentence offset. Canonical slugs are assigned once, merged via redirect stubs, and never renamed. A rebuild is logically identical, not byte-identical.Known limits:
-> Recall tuning is ongoing. 85% on the benchmark is not a universal guarantee.-> Synthesis quality is bounded by agent observation quality. Garbage facts in, garbage briefs out. The lint pass helps. It is not a judgment engine.-> Single-office scope today. No cross-office federation.Demo. 5-minute terminal walkthrough that records five facts, fires synthesis, shells out to the user's LLM CLI, and commits the result under Pam's identity: https://asciinema.org/a/vUvjJsB5vtUQQ4EbScript lives at ./scripts/demo-entity-synthesis.sh.Context. The wiki ships as part of WUPHF, an open source collaborative office for AI agents like Claude Code, Codex, OpenClaw, and local LLMs via OpenCode. MIT, self-hosted, bring-your-own keys. You do not have to use the full office to use the wiki layer. If you already have an agent setup, point WUPHF at it and the wiki attaches.Source: https://github.com/nex-crm/wuphfInstall: npx wuphf@latestHappy to go deep on the substrate tradeoffs, the promotion-flow state machine, the BM25-first retrieval bet, or the canonical-ID stability rules. Also happy to take "why not an Obsidian vault with a plugin" as a fair question.
Show HN: A Karpathy-style LLM wiki your agents maintain (Markdown and Git)
A durable, self-hosted, LLM-native knowledge substrate for AI agents, emphasizing simplicity (Markdown/Git), local control, and persistent context across sessions, as an alternative to complex vector/graph database solutions.
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AI Executive Synthesis
A durable, self-hosted, LLM-native knowledge substrate for AI agents, emphasizing simplicity (Markdown/Git), local control, and persistent context across sessions, as an alternative to complex vector/graph database solutions.
This LLM-native wiki addresses a critical challenge in agentic systems: persistent, shareable, and verifiable knowledge management. By leveraging Markdown and Git, it offers a durable, human-readable, and version-controlled knowledge base, directly countering the opacity and vendor lock-in associated with proprietary vector or graph databases. The emphasis on local execution, self-hosting, and explicit provenance via Git commits enhances trust and control for enterprises. Features like draft-to-wiki promotion, entity fact logs, and automated linting ensure knowledge quality and consistency. This approach provides a pragmatic, auditable foundation for agent memory and RAG, appealing to organizations prioritizing data sovereignty and transparent AI operations over complex, black-box solutions.
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The original author or development team describes the product as follows: "I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet.It runs locally in ~/.wuphf/wiki/ and you ..."
Community Voice & Feedback
Someone should build a StackOverflow revival as the solution to this, a distributed knowledge graph curated by humans but driven by collective LLMs trying to problem solve their way out of things and stopping to ask questions in an old fashioned way.I would be fine with my agent saying “hey, we hit a wall here, here’s the question posted on SO, I flagged to come back to it later once we have an answer”
Have you seen this one ? https://www.reddit.com/r/ClaudeAI/comments/1sv7fvc/im_a_nurs...
The "garbage facts in, garbage briefs out" caveat is the part I'd want stress tested. In my own LLM features the context that decays fastest is what agents wrote without a human glance. Six months in, you have entries that are confidently wrong and the lint pass can't tell which. Does the promotion flow require human review or can agents self promote?
Reviewed: https://zby.github.io/commonplace/agent-memory-systems/revie...It is a third llm wiki on front page in 24 hours!
Obviously it is a hot topic. I have my own horse in that race - so I might not be objective - but I've compiled a wishlist for these system: https://zby.github.io/commonplace/notes/designing-agent-memo...I wish there was a chance for collaboration - everybody coding their own system seems like a lot of effort duplication.
Obviously it is a hot topic. I have my own horse in that race - so I might not be objective - but I've compiled a wishlist for these system: https://zby.github.io/commonplace/notes/designing-agent-memo...I wish there was a chance for collaboration - everybody coding their own system seems like a lot of effort duplication.
For everyone working with markdown, I’d like to share leaf with you, a terminal markdown previewer :https://github.com/RivoLink/leaf
The space of self building artefacts is interesting and is booming now because recent LLM versions are becoming good at it fast (in particular if they are of the "coding" kind).I've also experimented recently with such a project [0] with minimal dependencies and with some emphasis on staying local and in control of the agent.It's building and organising its own sqlite database to fulfil a long running task given in a prompt while having access to a local wikipedia copy for source data.A very minimal set of harness and tools to experiment with agent drift.Adding image processing tool in this framework is also easy (by encoding them as base64 (details can be vibecoded by local LLMs) and passing them to llama.cpp ).It's a useful versatile tool to have.For example, I used to have some scripts which processed invoices and receipts in some folders, extracting amount date and vendor from them using amazon textract, then I have a ui to manually check the numbers and put the result in some csv for the accountant every year. Now I can replace the amazon textract requests by a llama.cpp model call with the appropriate prompt while still my existing invoices tools, but now with a prompt I can do a lot more creative accounting.I have also experimented with some vibecoded variation of this code to drive a physical robot from a sequence of camera images and while it does move and reach the target in the simple cases (even though the LLM I use was never explicitly train to drive a robot), it is too slow (10s to choose the next action) for practical use. (The current no deep-learning controller I use for this robot does the vision processing loop at 20hz).[0]https://github.com/GistNoesis/Shoggoth.db/
LLM models and the agents that use them are probabilistic, not deterministic. They accomplish something a percentage of the time, never every time.That means the longer an agent runs on a task, the more likely it will fail the task. Running agents like this will always fail and burn a ton of token cash in the process.One thing that LLM agents are good at is writing their own instructions. The trick is to limit the time and thinking steps in a thinking model then evaluate, update, and run again. A good metaphor is that agents trip. Don't let them run long enough to trip. It is better to let them run twice for 5 minutes than once for 10 minutes.Give it a few weeks and self-referencing agents are going to be at the top of everybody's twitter feed.
Put AI in your product name, make billion dollars. Put Karpathy in your blog article, get hired by Anthropic as Principal engineer. Milk money as long as fad last. No one is thinking about customer needs, everyone is trying to wash hands in the wave as it last.
I don't understand the point of automating note taking. It never worked for me to copy paste text into my notes and now you can 100x that?The whole point of taking notes for me is to read a source critically, fit it in my mental model, and then document that. Then sometimes I look it up for the details. But for me the shaping of the mental model is what counts
Karpathy's original post for context:https://x.com/karpathy/status/2039805659525644595https://xcancel.com/karpathy/status/2039805659525644595
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