Wenlan is a knowledge base for the AI-native age. Your AI agents capture what they learn, Wenlan keeps it current and distills it into source-cited wiki pages you can trust
> /plugin marketplace add 7xuanlu/wenlan> /plugin install wenlan@7xuanlu-wenlan
What's inside
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.
https://github.com/user-attachments/assets/d8b2ad4a-f97a-4a15-97a8-9105478de18a
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 from the Releases page:
.dmg and drag Wenlan to Applications. The app is signed and notarized, so there is no warning on first launch. From the terminal instead: /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/7xuanlu/wenlan/main/scripts/install-macos-app.sh)" (downloads, checks the SHA-256, moves it to Applications).-setup.exe. It is not signed yet, so when SmartScreen says "Windows protected your PC", choose "More info", then "Run anyway".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. To upgrade, drag the new app over the old one and open it (Wenlan 0.17.0 and older must be quit by hand first).
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?
npx -y wenlan setup
npx requires Node.js; without it, run curl -fsSL https://raw.githubusercontent.com/7xuanlu/wenlan/main/install.sh | bash then wenlan setup --basic.
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 automated shell setup path; Windows x64 uses the matching archive from Releases. macOS Intel currently has no 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.
One knowledge system, three roles:
The LLM-wiki foundation, extended:
For the complete workflow, see the LLM-wiki implementation guide.
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.
The entity-relation graph is one part of Wenlan's wider connected wiki. Knowledge Pages hold maintained synthesis, Entities anchor reusable people, projects, and concepts, Source Pages make imported or synchronized material inspectable, and atomic Memories preserve 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 ->
uses, part_of, contradicts, and replaced_by; unknown types fall back to related_to and become reviewable vocabulary proposals.During retrieval, dense entity matching finds query-relevant entities. When eligible graph links exist, the default graph-memory stream boosts linked Memories as a third RRF signal. The path is data- and scope-dependent, and Space boundaries (Spaces are defined under Capabilities) still apply. How the graph path works ->
Wenlan's core search is a local hybrid pipeline, not a single vector lookup. Each stage has a different job:
Qdrant/bge-base-en-v1.5-onnx-Q: a quantized English model creates 768-dimensional embeddings; libSQL cosine DiskANN indexes them for approximate nearest-neighbor retrieval.k = 60): lexical and semantic rank lists are fused without pretending their raw scores share a scale; cosine similarity also weights the vector contribution.jinaai/jina-reranker-v1-turbo-en or BAAI/bge-reranker-base reads each query-candidate pair and reorders the smaller pool; reranking is off by default.Page, episodic, and fact channels are opt-in and degrade to the remaining search signals if unavailable. Space still limits the read scope. Methods, defaults, and limitations ->
Showing a partial view of a very large repo.
View @7xuanlu’s profileUltrapowers is a dynamic build workflow for Claude Code. It takes a goal or a task list and builds it for you, unattended: it plans, builds each task test-first, has a stronger model review every task, loops a critic until the goal is met, and hands back one
@7xuanluFAQ
wenlan is a Claude Code plugin with 15 hand-picked skills for documentation work, indexed on Flowy. Install it with the command on its page. It includes prove, run-wenlan-app, run-wenlan. Its skills do not fire on their own yet. Request auto-invocation to have Flowy route them as you prompt. Free and open source.
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