GitHubGitHub RepoNeeds Review

nashsu/llm_wiki

LLM Wiki compiles source files into a persistent, traceable wiki that agents can query through local APIs, MCP, and skills, reducing repeated retrieval and re-analysis.

GitHub · Trending today

Open Source Open Markdown Open JSON

Source Summary

LLM Wiki turns imported material into a persistent three-layer system: **raw sources → generated wiki → schema**. Its two-call ingest separates analysis from page generation, records source links, skips unchanged files with a SHA-256 cache, and recovers a persistent queue after restarts.

Practical Implication

For agent context, treat the maintained wiki as a reusable compiled layer rather than rebuilding answers from raw documents every turn. The local API, **MCP server**, and agent skill expose hybrid search, file reads, graph traversal, and rescans to Claude Code or Codex.

Agent-Ready Context

LLM Wiki turns imported material into a persistent three-layer system: **raw sources → generated wiki → schema**. Its two-call ingest separates analysis from page generation, records source links, skips unchanged files with a SHA-256 cache, and recovers a persistent queue after restarts.

For agent context, treat the maintained wiki as a reusable compiled layer rather than rebuilding answers from raw documents every turn. The local API, **MCP server**, and agent skill expose hybrid search, file reads, graph traversal, and rescans to Claude Code or Codex.

Vector retrieval is optional and reportedly raises recall from **58.2% to 71.4%**, but the material does not describe the benchmark dataset or evaluation method. Generated pages can still distort sources, so source-only answering, traceability, linting, and human review remain important.

Connected Context · Feed7 Judgment

This makes external agent memory concrete as a compiled, inspectable knowledge layer rather than learned or repeatedly re-ingested context. It reinforces selective retrieval and structured navigation, while narrowing trust: generated wiki pages need source links, source-only fallback, and review because persistence can preserve distortions as readily as useful synthesis. The reported recall gain remains directional without evaluation details.

thedotmack/claude-memBoth keep persistent memory external and selectively retrievable; LLM Wiki adds a maintained schema and source-linked wiki layer, while leaving similar governance and contradiction risks.Citation Needed: Provenance for LLM-Built Knowledge Graphs — Daniel Chalef, Zep AIThe provenance warning directly qualifies LLM Wiki’s generated layer: simple source links may be insufficient when a page synthesizes, changes, or merges claims.Scaling Compute on Context — Jack Morris, EngramLLM Wiki supplies a retrieval-and-compression alternative to training on a private corpus, consistent with the finding that corpus memorization alone does not make knowledge usable.SheetCompass: Hierarchical Relation Graphs for Agentic Spreadsheet ReasoningBoth preserve structure instead of flattening source material, but SheetCompass specializes that principle for spreadsheet relations while LLM Wiki applies a general wiki and schema.

Context Map

contextresearchdata#retrieval#context-engineering #agent-memory

Uncertainty

Vector retrieval is optional and reportedly raises recall from **58.2% to 71.4%**, but the material does not describe the benchmark dataset or evaluation method. Generated pages can still distort sources, so source-only answering, traceability, linting, and human review remain important.