LLM Wiki Monorepo

Agent-native knowledge compiler.
AI agents turn raw documents into persistent, cross-linked Markdown wikis.
No database. No API lock-in. One git clone.

What • Features • Quick Start • Architecture • Packages • Templates • Docs • Credits • License

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pip install baissarienterprises-llm-wiki

What's New in v0.3.0

Modular architecture. The Python package has been reorganized from 28 flat modules into 10 domain-organized packages — core/, quality/, ingest/, providers/, graph/, search/, ops/, wiki/, research/, contracts/. Every module now answers: what domain owns this? What category within that domain? What specific function?

MCP server modularized. The 1,287-line index.ts is gone. The MCP server is now 20 focused files: per-tool handlers under tools/, adapters under adapters/, project scanning under projects/, path safety under security/. All 14 tool names, schemas, and responses preserved byte-for-byte.

Shared TypeScript types. Canonical GraphNode/GraphEdge types extracted into packages/shared-types/ — a single source of truth for 3 packages that previously defined near-identical interfaces independently.

Documentation taxonomy. Root-level docs reorganized into docs/architecture/, docs/getting-started/, docs/reference/, docs/legal/, docs/release/. README.md and AGENTS.md kept at root for PyPI packaging and AI agent auto-discovery.

7 pre-existing test failures eliminated. OpenCode imports fixed. MCP integration tests now match the sidecar response schema. Link suggest optimization added. 472 tests pass (up from 445).

What is this?

LLM Wiki is a production-grade knowledge engine that turns raw documents into a living, cross-linked Markdown wiki. Instead of re-retrieving documents on every query (RAG), the system incrementally builds and maintains a persistent knowledge base. Sources are compiled once, kept current, and compound over time.

It's everything needed to run a self-building wiki in one monorepo: a Python CLI package, an MCP server, a knowledge graph engine with community detection, a Chrome web clipper, a web viewer, an Obsidian plugin, and 20 domain templates — wired through an agent skill that works with any LLM.

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Features

• Two-Step Ingest — LLM analyzes sources first, then generates structured wiki pages. SHA256 caching skips unchanged files. Streaming progress, multi-provider support (OpenAI, Anthropic, DeepSeek), and agent-native mode that needs zero API keys.

• Agent-Native Provider — route ingest through Hermes, Claude Code, or Codex directly. No external API keys required when running inside an AI agent session.