Skip to content

root / tags / graphe-de-connaissance

#graphe de connaissance

2 fiches

AI Coding Agents & Skills Auto-verified translation

graphify — « Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store. »

**Skill** entry (not an article): **graphify** by **Safi Shamsi** (Graphify Labs, **Y Combinator S26**) turns an entire project — code, docs, PDFs, images, videos — into a **queryable knowledge graph**, invoked via `/graphify` from Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, and about fifteen other clients. Observed on **August 6, 2026**: **103,187 stars**, **10,024 forks**, repository created on **April 3, 2026** — an extraordinary trajectory in four months. **Apache-2.0**, Python 3.10+, default branch **v8**. **The three design commitments fit in three lines of the README**: *« Code maps for free, fully local »* (code is parsed into **tree-sitter AST**, deterministic, no LLM, **nothing leaves the machine**); *« Every edge is explained »* (each edge is tagged **`EXTRACTED`** — explicit in the source — or **`INFERRED`** — resolved by graphify —, with a third value `AMBIGUOUS` appearing in the report); and *« Not a vector index »* — *« no embeddings, no vector store: a real graph you traverse »*. **Three outputs**: `graph.html` (interactive graph), `GRAPH_REPORT.md` (god nodes, surprising connections, suggested questions), and `graph.json` (persistent graph, queryable weeks later without re-reading the files). **Three query modes** replace grep: `query` (subgraph for a natural-language question), `path A B` (shortest path between two entities), and `explain` (neighborhood of a concept). **Coverage**: 36 tree-sitter grammars (~40 languages), plus Terraform, Apex, **MCP configurations**, package manifests, Office, Google Workspace, PDFs, images, and video/audio transcribed **locally** by faster-whisper. Communities detected via **Leiden**, labeled **without an LLM**. ⭐ **The most interesting benchmark result is not a win but a free one**: on LOCOMO, graphify achieves a **recall@10 of 0.497** versus 0.149 for supermemory and 0.048 for mem0, but **loses on QA accuracy** (45.3% versus 49.7%); on LongMemEval-S it scores **76%, tied with a dense RAG**; and the line that matters is *« Graph build — LLM credits: **0** »* where the field typically bills per token. ⚠️ **Points to record**: the `main` branch carries a v1-era README describing a different product (Claude Code skill only, the « 71.5× fewer tokens » claim); the PyPI package is named **`graphifyy`** with two *y*'s, until the `graphify` name is reclaimed; and a **query log** is written by default to `~/.cache/graphify-queries.log`, which can be disabled via an environment variable.

#skill#knowledge graph#knowledge graph

**Safi Shamsi** — créateur et mainteneur de graphify · et de **Graphify Labs** · société passée par **Y Combinator (promotion S26)** selon le badge du dépôt. Il maintient aussi le site d'annuaire `graphify.net` (cf. [[graphify-net-annuaire-ia-coding-2026-08-06]]) et publie un livre · *The Memory Layer* · sur les idées et l'architecture derrière le projet.

Architecture & Construction Auto-verified translation

New Engineering Disciplines for the AI Era Part 3: KDLC — Knowledge Development Life Cycle

Third installment of Ashish Singh's « New Engineering Disciplines for the AI Era » series, devoted to **KDLC — Knowledge Development Life Cycle**: an **8-stage** life cycle for turning enterprise knowledge into an **engineered asset**, on a par with code or data. Thesis: AI initiatives fail not for lack of the right LLM choice or a deployed RAG system, but because they **do not address the underlying structure of knowledge** — « AI is only as effective as the knowledge it can discover, understand, retrieve, and trust ». The KDLC chains Discovery → Extraction → Structuring → Knowledge Graph → Embedding → Index Optimization → Retrieval Evaluation → Refresh. It contrasts **traditional RAG** (isolated documents, keywords) with the **Enterprise Knowledge Fabric** (Knowledge Graphs + Semantic Search + Vector DB + Hybrid Search), where agents understand « relationships, context, and business meaning ». Signature line: « Models provide reasoning. Memory provides continuity. Knowledge provides understanding. » Three examples (finance/compliance, software engineering, healthcare) illustrate the impact.

#KDLC#knowledge development life cycle#knowledge life cycle

Ashish Singh