Wenlan
About
Local-first AI knowledge base and LLM wiki with source-cited pages and MCP access for coding agents.
Details
- Author
- 7xuanlu
- Categories
- AI, Knowledge Base, Developer Tools, Other
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Setup
Install Wenlan in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/7xuanlu/wenlan
Follow the installation instructions in the repository README, then restart your MCP client.
Useful work with AI shouldn't disappear when a conversation ends. Wenlan builds the right pages and keeps them current as sources change, asking only when judgment is needed.
Get started·What is this?·Capabilities·Daily workflow·Evaluation·Learn more
A maintained Page in the desktop app: open any citation to inspect the Source or Memory behind the claim.
Wenlan runs as one local daemon. The desktop app carries that daemon inside it; the headless install gives you the same daemon without a window. Your AI clients reach the same knowledge base either way.
Download Wenlan for macOS(Apple Silicon), open the.dmg, and drag the app to Applications.
Nothing else to install. The app bundles the daemon, CLI, and MCP connector, starts the daemon on launch, and offers to connect the AI clients it detects: the plugin for Claude Code and Codex, an MCP entry for the rest. From there you read Pages, inspect the Source behind any citation, and curate the knowledge system.
This preview is not notarized yet, so macOS blocks the first launch. Allow it once under System Settings, Privacy & Security, "Open Anyway". One command skips that step: it verifies the download against GitHub's published SHA-256, clears quarantine for this app alone, and changes no macOS security settings.
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/7xuanlu/wenlan/main/scripts/install-macos-app.sh)"
On Windows x64, run the-setup.exefrom the sameReleasespage. It installs the daemon, CLI, and MCP connector along with the runtime libraries they load, so there is nothing else to install. Linux has no desktop build yet; use the headless runtime below.
Paste this into Claude Code, Codex, or another tool that can follow a setup guide:
Set up Wenlan for this AI client by following: https://raw.githubusercontent.com/7xuanlu/wenlan/main/docs/setup-with-ai.md Install only what this client needs. Then verify the local runtime, its Wenlan connection, and a capture/recall round trip.
The guide detects which client you are using and keeps client-specific commands out of this README. It does not configure every AI tool unless you ask it to.
Need only the headless runtime on macOS Apple Silicon?
This downloads the prebuilt CLI, daemon, and MCP connector, starts the local runtime, and verifies it. No Rust toolchain or Cargo is required. Linux x64/ARM64 with glibc has an automatedshell setup path; Windows x64 uses the matching archive fromReleases. macOS Intel currently hasno supported complete-runtime install.
Manual and client-specific instructions:AI-assisted setup·Claude Code plugin·Codex plugin·CLI and MCP.
Wenlan turns documents, notes, and past AI conversations into a source-backed knowledge base that stays current as your work evolves. Sources remain traceable; decisions, lessons, and corrections become durable memories; both can support the same maintained Pages.
Built for work that continues.Wenlan is for researchers, writers, consultants, product teams, and software teams whose knowledge is scattered across documents, notes, and AI conversations. It turns that material into inspectable Pages that can improve across projects and weeks, not another chat history or isolated memory store. It is not a life-management system or a memory SDK embedded inside another product.
- Sources keep the material Wenlan reads traceable.Imported conversations remain as captured records; registered files sync their current contents as they change.
- Memories preserve what work teaches you.Agents capture atomic decisions, lessons, corrections, and supersession with provenance.
- Pages compile current knowledge.Wenlan turns relevant Sources and Memories into source-cited Markdown you can reuse, refresh, and review.
- LLM-wiki v1:Karpathy defined immutable Sources, an AI-maintained Markdown Wiki, and a co-evolving Schema of rules for structuring and maintaining it. Wenlan implements that foundation withtyped Memory fieldsand built-in rules for Page structure, provenance, citations, refresh, ownership, and review.
- LLM-wiki v2:Rohitg00 added a memory lifecycle. Wenlan makes that direction concrete with traceable Sources, agent-captured Zettelkasten-style atomic Memories (one complete idea each), and maintained Pages built from both.
Wenlan's distinctive move:Sources and atomic Memories independently support maintained Pages. Memory history preserves how knowledge changed; Page history shows which current evidence supports the synthesis. Machine-maintained Pages can rebuild from current support, while changes to human writing wait as reviewable revisions.
A knowledge graph that gets more useful over time
The entity-relation graph is one part of Wenlan's wider connected wiki.Knowledge Pageshold maintained synthesis,Entitiesanchor reusable people, projects, and concepts,Source Pagesmake imported or synchronized material inspectable, and atomicMemoriespreserve decisions and changes. They work through separate, explicit links: Page-to-Page wikilinks, Page evidence, Memory-to-Entity links, and directed Entity relations.
Within the entity graph, a configured enrichment model extracts typed Entities, observations, and directed relations from Memories. Entity linking and resolution reuse existing nodes instead of treating every mention as new; each Memory keeps its Source and can link to multiple Entities.How the connected model is stored ->
- Meaning and direction:Relations use a seeded vocabulary such asuses,part_of,contradicts, andreplaced_by; unknown types fall back torelated_toand become reviewable vocabulary proposals.
- Strength and provenance:A relation can store confidence, an explanation, and its source Memory, so stronger and weaker claims remain distinguishable and inspectable.
- Communities that compound:Label propagation groups Entities by relation density, weighted by the relation count between each pair. These groups can organize optional corpus summaries while Entity links add retrieval context.
- Correction without erasure:Related claims, corrections, and explicit supersession stay inspectable together while original Sources and Memory history remain.
During retrieval, dense entity matching finds query-relevant entities. When eligible graph links exist, the default graph-memory stream boosts linked Memories as a thirdRRFsignal. The path is data- and scope-dependent, and Space boundaries still apply.[How the graph path works ->
Retrieval across words, meaning, and connections
Wenlan's core search is a local hybrid pipeline, not a single vector lookup. Each stage has a different job:
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