Skillget

memoriki

mcp
Security Audit
Pass
Health Pass
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 55 GitHub stars
Code Pass
  • Code scan — Scanned 1 files during light audit, no dangerous patterns found
Permissions Pass
  • Permissions — No dangerous permissions requested
Purpose
This project provides a personal knowledge base that combines structured wiki management with semantic search and an entity graph, designed to act as a "second brain" for MCP-compatible AI agents like Claude Code.

Security Assessment
Overall Risk: Low. The tool does not request dangerous permissions, and the light code scan found no hardcoded secrets or dangerous execution patterns. It acts primarily as a structural file manager for a local directory and interfaces with a Python package (MemPalace). While it processes your personal notes and files (which you should always be mindful of when feeding data to LLMs), the tool itself does not appear to make unauthorized network requests or execute hidden shell commands.

Quality Assessment
Overall Quality: Good. The project is very new but actively maintained, with its most recent push occurring today. It has a clear MIT license and is building noticeable early traction within the community (55 GitHub stars). The documentation is excellent, providing straightforward setup instructions and explaining its architecture clearly. It cleanly integrates concepts from established developers (Andrej Karpathy) rather than reinventing the wheel.

Verdict
Safe to use.
SUMMARY

Memoriki - LLM Wiki + MemPalace. Personal knowledge base with real memory.

README.md

Memoriki

Personal knowledge base with real memory. Combines LLM Wiki (Andrej Karpathy) + MemPalace (MCP server).

Wiki gives structure. MemPalace gives memory.

The Problem

  • LLM Wiki without MemPalace = library without a catalog. Search is just grep.
  • MemPalace without Wiki = search engine without books. Semantic search over raw chunks.
  • Together = structured knowledge + semantic search + entity graph.

Three Layers of Knowledge

Layer What it does Tool
Wiki Structure, [[wiki-links]], YAML frontmatter, index Markdown + Obsidian
MemPalace Drawers Semantic search over all content mempalace_search
MemPalace KG Entity relationship graph with timestamps mempalace_kg_query

Architecture

memoriki/
  raw/                    # Your sources (articles, notes, transcripts)
  wiki/                   # LLM-generated wiki (LLM owns this entirely)
    index.md              # Page catalog - updated on every ingest
    log.md                # Operation log (append-only)
    entities/             # People, companies, products
    concepts/             # Ideas, patterns, frameworks
    sources/              # Summary page per source
    synthesis/            # Cross-cutting analysis, comparisons
  mempalace.yaml          # MemPalace config
  CLAUDE.md               # Schema and rules for the LLM
  idea-file.md            # Karpathy's original idea (reference)

Quick Start

# 1. Clone
git clone https://github.com/AyanbekDos/memoriki.git my-knowledge-base
cd my-knowledge-base

# 2. Install MemPalace
pip install mempalace
mempalace init .

# 3. Connect MemPalace to Claude Code
claude mcp add mempalace -- python -m mempalace.mcp_server

# 4. Drop your first source
cp ~/some-article.md raw/

# 5. Launch Claude Code and start ingesting
claude
# > Read raw/some-article.md and ingest it into the wiki

Operations

  • Ingest - drop a file into raw/, tell the LLM to read and integrate it into the wiki
  • Query - ask a question, LLM finds relevant pages and synthesizes an answer
  • Lint - health check: contradictions, orphans, knowledge gaps

Works With

Any MCP-compatible LLM agent:

  • Claude Code - use CLAUDE.md as-is
  • OpenAI Codex - rename CLAUDE.md to AGENTS.md
  • Cursor, Gemini CLI and other MCP-compatible tools

Use Cases

  • Founders: customer discovery, interviews, competitors, pivots - all in one place
  • Researchers: papers, articles, notes - wiki with compounding synthesis
  • Students: lecture notes, books, projects - structured "second brain"
  • Teams: Slack threads, meetings, decisions - AI-maintained wiki

License

MIT

Credits

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