标签 · Knowledge Graph — Glean

Recent picks

5picks · chronological

07-23

Turn any codebase into a queryable knowledge graph, built for AI coding assistants

Graphify is an open-source tool that transforms codebases, docs, PDFs, images, etc. into a queryable knowledge graph. It uses tree-sitter for local deterministic AST parsing to extract code relationships (calls, imports, inheritance) without any LLM calls. Non-code files are semantically extracted via your AI assistant's model. The output includes an interactive HTML visualization, CLI queries (query/path/explain), and an MCP server for team use. Every edge is tagged EXTRACTED or INFERRED, providing transparency. Ideal for engineers navigating large monorepos, tracing dependencies, or understanding architecture.

github.com · 59 min · AI Agents · Developer Tools · Knowledge Graph

07-20

Graph Engineering Replaces RAG: Insights from Microsoft, Stanford, Anthropic

Traditional RAG struggles with complex multi-entity questions. Microsoft, Stanford, and Anthropic independently discovered that graph engineering (knowledge graphs) can improve accuracy by 18% and reduce costs by 85%. Microsoft's GraphRAG converts unstructured text into a knowledge graph, achieving 18% higher accuracy and 85% lower token costs. Stanford's DSPy and STORM treat the model as a node in a graph, and scaling law research shows a small model with a good graph outperforms a large model with a poor graph. Anthropic's Claude, combined with MCP, enabled LaunchNotes to detect incidents 5x faster and cut meeting time by 50%. The article details the full pipeline, five prompt templates, and five business applications. Ideal for engineers advancing beyond basic RAG.

x.com · 16 min · AI Engineering · GraphRAG · Knowledge Graph

07-17

Graphify: Turn Any Codebase into a Queryable Knowledge Graph for AI Coding Assistants

Graphify is an open-source tool that transforms codebases, docs, PDFs, images, and videos into a knowledge graph for AI coding assistants like Claude Code, Cursor, and Gemini CLI. It uses tree-sitter AST for deterministic, local-only code parsing, and delegates semantic extraction for non-code assets to the assistant's model. The output includes an interactive HTML visualization, a Markdown report, and a reusable graph.json, enabling natural-language queries, path traversal, and concept explanations. Every edge is tagged EXTRACTED or INFERRED, so users always know what was read vs. guessed. Ideal for engineers onboarding large unfamiliar codebases or augmenting long-tail maintenance workflows.

github.com · 47 min · Agent Engineering · Ai Tooling · Code Intelligence

06-18

A Structured Cybersecurity Skills Library Purpose-Built for AI Agents

This is not another collection of security scripts or checklists. It’s an AI-native knowledge base that encodes 754 practitioner-grade cybersecurity workflows into a structured, agent-readable format. Each skill carries YAML frontmatter for sub-second discovery and step-by-step Markdown procedures, essentially giving any LLM-based agent the decision-making playbook of a senior analyst. The library spans 26 domains—from DFIR and threat hunting to cloud security and OT/ICS—and maps every skill to MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, and NIST AI RMF, making it uniquely suited for security professionals integrating AI into real operational workflows.

github.com · 28 min · AI Agents · Claude Code · Cybersecurity

06-03

Meta-Meta-Prompting: The Secret to Making AI Agents Work

Garry Tan, CEO of Y Combinator, presents GBrain, his personal AI agent system built on 100,000 pages of structured knowledge and over 100 modular skills. The core architecture follows a “thin harness, fat skills, fat data” philosophy: a lightweight runtime like OpenClaw routes messages to self-contained skill files, which are themselves created and improved by a meta-skill called Skillify. Tan illustrates the compounding value through the “book-mirror” pipeline, which cross-references a book’s ideas with his actual life events, journal entries, and meeting notes. He details the evolution from an error-prone first version to a reliable workflow using multi-model cross-modal evaluation and deep brain retrieval. Other examples include automated meeting preparation that synthesizes months of accumulated context and entity propagation that updates every related person or company page after a conversation. The article provides a concrete architecture overview, evidence of iterative improvement, and a four-step starting guide for developers building personal compounding AI systems.

x.com · 16 min · Agents · Ai Tooling · Knowledge Graph