Gortex is a high-performance, 100% local code-intelligence engine for AI agents and IDEs that indexes code into a persistent provenance-tiered knowledge graph via tree-sitter AST analysis and compiler-grade resolvers for 257 languages, with multi-repository support by default. It exposes the graph through CLI, MCP server, API and web UI, offering semantic search, cross-repo contract detection, speculative execution and live editor overlays aimed at cutting token usage up to 50x by surfacing only needed information.
- Ships as a single static binary for macOS, Linux and Windows with zero external dependencies
- Provides 175 configurable MCP tools, 16 resources and 3 prompts for symbol lookup, blast radius and refactoring
- Ships as a single static binary for macOS, Linux and Windows with zero external dependencies
- Provides 175 configurable MCP tools, 16 resources and 3 prompts for symbol lookup, blast radius and refactoring
graphify is an AI coding assistant skill that transforms codebases, documents, and images into a structured, queryable knowledge graph. By utilizing deterministic AST parsing via tree-sitter for code and multimodal LLM capabilities for unstructured data like PDFs and screenshots, it creates a comprehensive map of concepts and relationships. This allows developers to understand complex architectures faster and find the "why" behind design decisions. A key advantage is its massive reduction in token usage per query compared to reading raw files, making it highly efficient for large-scale projects. The tool supports 19 programming languages and integrates seamlessly with platforms like Claude Code and Codex, providing an interactive, persistent, and highly organized way to navigate any codebase or research corpus.
An end-to-end raw text-to-graph pipelines. This blog explores the limitations of LangChain extraction when using smaller quantized models, and how BAML can improve extraction success rates.
This tutorial details how to implement persistent memory in Claude Desktop using a local knowledge graph. It covers installation of dependencies (Node.js and Claude Desktop), configuration of `mcp.json` and Claude settings, and how to leverage the Knowledge Graph Memory Server for personalized and consistent responses.
This article details a step-by-step guide on building a knowledge graph from plain text using an LLM-powered pipeline. It covers concepts like Subject-Predicate-Object triples, text chunking, and LLM prompting to extract structured information.
The article discusses the process of preparing PDFs for use in Retrieval-Augmented Generation (RAG) systems, with a focus on creating graph-based RAGs from annual reports containing tables. It highlights the benefits of Graph RAGs over vector store-backed RAGs, particularly in terms of reasoning capabilities, and explores the construction of knowledge graphs for better information retrieval. The author shares insights into the challenges and solutions involved in building an enterprise-ready graph data store for RAG applications.
The article explores how Retrieval-Augmented Generation (RAG) and knowledge graphs can be used together to break down data silos and enable more accurate, context-aware, and insightful AI systems.
Turn your Pandas data frame into a knowledge graph using LLMs. Learn how to build your own LLM graph-builder, implement LLMGraphTransformer by LangChain, and perform QA on your knowledge graph.
A Python hands-on guide to understand the principles of generating new knowledge by following logical processes in knowledge graphs. Discusses the limitations of LLMs in structured reasoning compared to the rigorous logical processes needed in certain fields.
This article introduces Graph RAG, a method for enhancing Language Model (LLM) applications by incorporating knowledge graphs. It explains the limitations of traditional text embedding-based retrieval and how Graph RAG addresses them by providing a global understanding of the knowledge base through community detection and report generation.
