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LLM wiki and AI knowledge base guides that stay current.
Wenlan turns trusted sources and durable work facts into a local, source-backed AI knowledge base with maintained LLM-wiki pages for agents and people.
Quick answer
An AI knowledge base stores and retrieves trusted sources. A maintained LLM wiki adds reusable pages, citations, review, and refresh state. Wenlan combines those layers locally for AI agents.
Learn topics
AI knowledge base architecture
Source-backed LLM wiki
Claude Code and MCP
Knowledge-base maintenance
Tool comparisons
Local-first trust
Start here
Choose the job first: build an AI knowledge base, inspect the LLM-wiki lifecycle, connect a client, or compare adjacent tools.
Popular search paths
Search demand
Choose an AI knowledge base tool
Compare document upload, RAG, note access, and maintained agent knowledge with eight repeatable tests.
Build a local AI knowledge base
Turn Markdown, text files, text PDFs, or an Obsidian vault into a source-backed knowledge workflow.
AI knowledge base for agents
Build maintained pages with sources, review, refresh state, and a verifiable local workflow.
Claude Code memory
See what native CLAUDE.md and auto memory cover, then decide when shared local context helps.
Basic Memory comparison
Compare Wenlan and Basic Memory on provenance, local retrieval, shared clients, and wiki pages.
MCP memory server
Connect Claude Code, Codex, Cursor, ChatGPT, and other MCP clients to Wenlan.
Karpathy LLM wiki for AI agents
Turn trusted sources and durable work context into a maintained AI knowledge base with inspectable support.
Cursor memory MCP
Wire Cursor to Wenlan's local MCP memory server and verify recall.
AI work memory vs knowledge base
Separate reusable decisions and handoffs from the source-backed pages they maintain.
Obsidian AI memory
Compare an Obsidian vault with a local memory layer shared by Claude Code and MCP clients.
Local AI memory
Keep project context local, inspectable, and under your control.
How Wenlan works
See how the daemon, MCP connector, local index, and source-backed pages fit together.
Concepts
16 articles
Concept
What Is AI Work Memory?
AI work memory carries sessions, decisions, lessons, project context, and wiki pages across tools and time.
Protocol
MCP Knowledge Base Server for AI Agents
Connect Claude Code, Codex, ChatGPT, Cursor, and other AI clients to a local, source-backed knowledge base through MCP.
Privacy
Local-First AI Work Memory: Keep Context on Your Machine
Local-first AI work memory keeps sensitive project knowledge, decisions, and preferences under your control while still making them useful to assistants.
Concept
Karpathy LLM Wiki: Build a Source-Backed AI Knowledge Base
The Karpathy LLM Wiki pattern turns trusted sources into maintained AI knowledge-base pages that agents can load on demand.
Architecture
Why Wenlan Uses Readable Artifacts plus a Local Store
Wenlan keeps raw captures in a daemon-owned local store and projects readable artifacts, so AI memory stays inspectable and useful.
Architecture
AI Agent Memory Types: Working, Episodic, Semantic, and Procedural
Learn what the four AI agent memory types do, where each should live, and why facts, events, current context, and procedures need different lifecycles.
Decision
AI Agent Memory: Local vs Cloud
Choose between local-first memory and hosted memory based on privacy, portability, collaboration, and operational needs.
Problem
Why AI Coding Agents Lose Context Between Sessions
Diagnose context loss after a fresh session, compaction, or tool switch, then choose resume, project instructions, handoffs, or durable memory.
Concept
Persistent Project Context for AI Agents
Keep project decisions, constraints, and handoffs available across Claude Code, Cursor, Codex, and other AI tools.
Setup
MCP Memory Server on localhost:7878: What to Check
Understand the local daemon boundary behind Wenlan's MCP memory tools and how to troubleshoot port 7878.
Capture
What to Capture in AI Work Memory
Use a simple test for deciding what belongs in Wenlan and what should stay out of memory.
Trust
Build a Source-Backed AI Knowledge Base for Agents
Build a source-backed AI knowledge base by connecting trusted sources, atomic knowledge, maintained LLM-wiki pages, citations, review, and refresh state.
Trust
AI Memory Provenance: Why Source IDs Matter
Understand why AI memory needs source trails, supersession, and review before old context steers new work.
Trust
Local Git History for AI Memory Artifacts
Why Wenlan versions readable memory artifacts in a local git repository under ~/.wenlan/.git.
Trust
How to Fix Stale AI Agent Memory
A practical diagnostic for stale, contradictory, or wrong AI agent memory: inspect the source, preserve corrections, and delete only when necessary.
Trust
Project Scope for AI Memory in Wenlan
Understand how Wenlan scopes local AI work memory with spaces, project context, and deliberate product boundaries.
Comparisons
9 articles
Comparison
AI Work Memory vs Knowledge Base: What’s the Difference?
A knowledge base maintains what is currently known. AI work memory preserves the decisions, lessons, corrections, and handoffs that agents need while working.
Comparison
Wenlan vs Basic Memory: Source-Backed AI Work vs Shared Markdown Knowledge
Compare Wenlan and Basic Memory across Markdown, MCP, local-first control, workflow fit, and how each product helps AI tools use durable context.
Comparison
Wenlan vs claude-mem: Which AI Memory Tool Should You Use?
claude-mem automatically captures and compresses your agent sessions; Wenlan keeps explicit, source-backed memory you review and maintain as readable pages. Compare capture, retrieval, cross-agent support, and control.
Comparison
Wenlan vs SuperLocalMemory v3.8.3: Which Local AI Memory Fits Your Work?
SuperLocalMemory is a local memory control plane for teams; Wenlan is a source-backed LLM wiki for deliberate knowledge work. Compare retrieval, team controls, audit, and artifacts.
Comparison
Wenlan vs mcp-memory-service: Local AI Work Memory or Agent Pipeline Backend?
Compare Wenlan with mcp-memory-service across user workflow, transports, storage control, and agent-pipeline scope.
Comparison
Wenlan vs ChatGPT Memory: Built-In Personalization or Local AI Work Memory?
Compare built-in assistant memory with Wenlan's local, inspectable, cross-tool work-memory layer.
Comparison
Obsidian + Claude Code: Vault Access, MCP, and a Durable AI Knowledge Base
Use Obsidian with Claude Code through direct vault files, live editor context, or MCP—and add a source-backed knowledge lifecycle only when access is not enough.
Comparison
Wenlan vs Notion AI: Local AI Work Memory or Team Workspace AI?
Compare Wenlan's local AI work memory with Notion AI's workspace, agents, meetings, and enterprise search features.
Comparison
Wenlan vs Mem0: Personal AI Work Memory or App Memory Infrastructure?
Compare Wenlan's local AI work-memory loop with Mem0's memory infrastructure for AI agents and applications.
Workflows
39 articles
Developer workflow
Claude Code Memory: CLAUDE.md, /memory, and MCP Context
Understand CLAUDE.md, Claude Code auto memory, /memory, and when to add Wenlan's local MCP memory for shared project context.
Workflow
Wenlan for Claude Code Memory: The Daily /brief and /handoff Loop
Use Wenlan inside Claude Code with /setup, /brief, /capture, /recall, /handoff, and /distill so coding context carries across sessions.
Workflow
The AI Agent Handoff Loop: How Work Carries Across Sessions
A practical model for carrying decisions, lessons, gotchas, and next steps from one AI work session into the next.
Setup
Where Wenlan Stores Claude Code Memory
Find the local files Wenlan writes when Claude Code captures memories, handoffs, and distilled pages.
Setup
How to Add Memory to Claude Code
Install Wenlan's Claude Code plugin, run /setup, and verify a local memory round trip.
Claude Code
Claude Code /memory vs Wenlan: Native Memory or Shared Local Context?
Use Claude Code /memory for native project memory inspection, and use Wenlan when context needs provenance, handoff, and cross-tool MCP access.
Setup
How to Give Codex Persistent Memory
Connect Codex to Wenlan through MCP so sessions can recall local project context instead of starting from scratch.
Setup
Cursor Memory MCP: How to Add Local AI Work Memory
Wire Cursor to Wenlan's local MCP memory server so coding sessions can capture and recall project context.
Setup
Claude Desktop MCP Memory Setup with Wenlan
Connect Claude Desktop to Wenlan's local memory daemon through MCP and verify the first memory loop.
Workflow
Wenlan Workflow for Codex
Use Wenlan with Codex for session context, durable captures, recall, and cross-tool handoff.
Coding agent knowledge
How to Give Codex a Source-Backed Project Knowledge Base
Keep repository rules, source-of-truth documents, and maintained project knowledge separate so Codex can retrieve evidence without loading everything.
Workflow
Wenlan Workflow for Cursor
Use Wenlan from Cursor to keep project memory available across edits, branches, and future AI sessions.
Workflow
Wenlan Workflow for Claude Desktop
Use Claude Desktop with Wenlan MCP memory for planning, research, and handoff context that later coding agents can reuse.
Workflow
Wenlan Workflow for Gemini CLI
Connect Gemini CLI as an MCP client and use Wenlan for local capture, recall, and handoff-style notes.
Workflow
Wenlan Workflow for VS Code MCP Clients
Use Wenlan as a local memory server from VS Code surfaces that support MCP.
Workflow
Claude Code Session Handoff with Wenlan
Close Claude Code sessions with enough context for the next agent to resume without replaying the chat.
Workflow
How to Share Memory Between Cursor and Claude Code
Connect Cursor and Claude Code to one local Wenlan daemon so both tools can recall the same source-backed decisions and handoffs.
Workflow
How to Share Memory Between Codex and Claude Code
Wenlan keeps one local, source-backed memory store for both tools: Codex connects over MCP, Claude Code through the plugin, and captures from one are recallable from the other.
Workflow
A Multi-Agent Memory Workflow That Stays Local
Coordinate multiple AI clients through one local, source-backed Wenlan system without turning project context into a cloud black box.
Workflow
AI Agent Project Status Handoff
Keep project status usable for the next AI session without bloating memory with transient todos.
Workflow
How to Build a Local AI Knowledge Base from Markdown, PDFs, and Obsidian
Use supported document sources, repeatable sync, source-backed pages, and verification to build a local AI knowledge base for coding agents.
Research workflow
Build a Research Knowledge Base from Papers and PDFs
Turn a bounded paper set into inspectable research notes that preserve claims, methods, limitations, contradictions, citations, and source updates.
Selection guide
How to Choose an AI Knowledge Base Tool: 8 Tests That Matter
Choose an AI knowledge base by testing sources, freshness, review, ownership, privacy, agent access, input limits, and real answer quality.
Migration guide
Migrate an Obsidian Vault to a Source-Backed LLM Wiki
Register an Obsidian vault as a read-only Wenlan source, resync it on demand, and distill the most-reused notes into citation-backed wiki pages that AI agents can query.
Setup guide
Set Up a Knowledge Base Your Coding Agents Can Query
Install the Wenlan runtime, connect Claude Code, Codex, and Cursor, add one project source set, and prove the capture, distill, cite, and refresh loop before expanding.
Consulting workflow
How to Build a Client Project Knowledge Base for Consulting
Keep one consulting engagement's sources, research, decisions, deliverables, and handoff context in a traceable client-project knowledge base.
Investment research workflow
How to Build an Investment Research Knowledge Base
Turn annual reports, filings, earnings-call notes, and research questions into a source-backed company dossier that stays reviewable as evidence changes.
SRE knowledge workflow
How to Build an SRE Incident Knowledge Base
Turn runbooks, postmortems, and approved incident notes into current, source-backed operational knowledge for on-call engineers.
Competitive intelligence workflow
How to Build a Source-Backed Competitive Intelligence Knowledge Base
Keep competitor research current with a bounded source register, dated evidence, explicit uncertainty, and reviewable knowledge-base Pages.
Supplier review workflow
How to Build an ICT Supplier Due Diligence Evidence Pack
Organize approved software-supplier documents into a source-backed evidence pack with provenance, scope, gaps, owners, and review dates.
Support answer workflow
How to Build a Customer Support Answer Knowledge Base
Keep customer-support answers reviewable after product or policy changes with approved sources, explicit scope, prohibited promises, owners, and review dates.
Course wiki workflow
How to Build a Course Wiki from Lecture Notes and PDFs
Turn one course's readable lecture slides, notes, and PDFs into a maintained wiki with exact sources, prerequisite links, revisions, and review state.
Data dictionary workflow
How to Build a Business Metric Definition Knowledge Base
Turn approved KPI specifications into a source-backed data dictionary with formula text, grain, exclusions, owners, revisions, and review state.
Shared knowledge maintenance
How to Prevent Multi-Agent Knowledge Conflicts and Stale Conclusions
Prevent AI agents from overwriting shared knowledge, promoting unsupported claims, or reusing conclusions after their sources change.
Ingestion troubleshooting
PDF Failed to Ingest into Your AI Knowledge Base? Diagnose It First
Diagnose empty, skipped, malformed, oversized, or image-only PDFs before trusting an AI knowledge base or RAG answer.
Retrieval policy
When Should an AI Agent Query a Knowledge Base?
Use a query-or-skip policy so AI agents retrieve authoritative knowledge when needed without repeatedly loading irrelevant documents into context.
Trust check
How to Verify AI Knowledge Base Citations and Unsupported Claims
Audit a RAG or AI knowledge-base answer claim by claim to find wrong pages, mismatched chunks, stale sources, and unsupported conclusions.
Retrieval regression
How to Regression-Test AI Knowledge Base Retrieval After Changes
Use a versioned golden query set to find RAG retrieval regressions after corpus, embedding, chunking, hybrid search, or reranker changes.
Product research workflow
Build a Product Research Knowledge Base Before Writing a PRD
Turn approved research notes, support and sales signals, and prior decisions into a source-backed evidence base for a defensible PRD.
Ready to try the local memory loop?
Make AI work carry forward.
Install Wenlan, connect your AI tools, and verify the first memory loop locally.