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Transform PDF documents into structured knowledge graphs with citation provenance

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MalimGraph (v0.2.0)

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PyPI version PyPI Downloads Python 3.10+ License: MIT MCP Compatible CI

Claude Code

From documents to interactive knowledge discovery.

MalimGraph is an agentic knowledge graph engine for Claude Code and Claude Desktop. Extract entities, build graphs, and visualize discovery maps — all orchestrated by Claude using its own intelligence. No ANTHROPIC_API_KEY required.


🚀 Claude Code Integration (Native)

MalimGraph v0.2.0 is a first-class plugin for Claude Code. It features auto-discovery and a native orchestration command.

Installation

pip install malimgraph
claude mcp add malimgraph -- malimgraph-plugin

The /kg Command

Initialize a full discovery workflow by typing /kg in your Claude terminal:

  1. Extract: Parses PDF text via read_pdf.
  2. Analyze: Claude identifies entities and relationships with verbatim evidence.
  3. Build: Generates a standard Knowledge Graph (.json, .cypher, .sql).
  4. Visualize: Launches the Premium Visual Discovery Map immediately.

🎨 Visual Discovery Suite

Explore your data through high-fidelity, interactive browser visualizations.

1. Premium Visualizer (vis.js) — Recommended

A robust, self-contained explorer with a pitch-black neon aesthetic.

python skills/graph-visualizer/scripts/visualize_graph.py --input output/knowledge_graph.json
  • Robust Mapping: Reliable handling of id, label, and relationship IDs.
  • Physics-Powered: ForceAtlas2 layout for optimal spatial clarity.
  • Detail Inspector: Click nodes to see verbatim citations and confidence.

2. High-Performance Explorer (Sigma.js)

WebGL-powered rendering for exceptionally large graphs (thousands of nodes).

python skills/graph-visualizer-sigma/scripts/visualize_sigma.py --input output/knowledge_graph.json

🕸️ Skill Triggers

MalimGraph provides specialized agentic skills. Trigger them via natural language:

  • "knowledge graph" — Full extraction and export
  • "visualise graph" — Launch visual explorer
  • "chunk for RAG" — Prepare document for vector embeddings
  • "load into Neo4j" — Import to local graph database

🛠️ Tools & Capabilities

  • read_pdf: PDF → text + rule-extracted metadata.
  • save_knowledge_graph: Stable CID-based graph construction.
  • chunk_document: Token-aware overlapping chunks for RAG.
  • manage_graph_db: Load and query Neo4j or PostgreSQL (Apache AGE).
  • embed_and_store_chunks: Native pgvector integration.

☁️ MCP Server Endpoint

Deploy MalimGraph as a serverless MCP endpoint on: https://mcpserver.malim.my/mcp (Streamable HTTP)


Credits

Built by Malim AI Labs — AI-powered knowledge infrastructure. MIT License © 2026.

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