LlamaIndex is a comprehensive Python framework designed for building Large Language Model (LLM) applications, with a primary focus on Retrieval-Augmented Generation (RAG) and agentic workflows. It functions as a modular library rather than a monolithic application, providing abstractions for data ingestion, indexing, retrieval, and query orchestration. The architecture is split between a core package (`llama-index-core`) containing base interfaces and logic, and a vast ecosystem of integration packages (`llama-index-integrations`) that connect to external LLM providers, vector databases, and data sources. The codebase demonstrates a strong plugin-based design, allowing developers to swap components like embeddings, retrievers, and LLMs without rewriting the core orchestration logic.
Zep is a long-term memory platform for AI agents, providing integration libraries for multiple agent frameworks (Google ADK, AutoGen, CrewAI, LangGraph, Pydantic AI, Vercel AI SDK, Mastra, LiveKit, Microsoft Agent Framework, Strands, AG2) across Python, TypeScript, and Go. The repository contains the core ingestion pipeline (zep-ingest) for bulk loading documents, Slack exports, emails, transcripts, and fact triples into Zep's temporal knowledge graph, an MCP server for direct graph access, benchmarks (LoCoMo, LongMemEval), and extensive examples. The architecture is a set of client libraries that communicate with the Zep Cloud API (external dependency). Key entry points include integration packages (e.g., zep_adk, zep_autogen), the ingestion pipeline (Pipeline, ingest functions), and the MCP server (cmd/server/main.go). Data flows: user messages are persisted to the Zep graph, context is retrieved and injected into agent prompts via hooks/callbacks or search tools. External dependencies: Zep Cloud API, optional LLM clients (OpenAI, Anthropic) for ingestion transforms and contextualization.
This repository is a comprehensive survey paper on the intersection of knowledge graphs and multi-modal learning. It organizes the literature into two main directions: KG-driven multi-modal learning (KG4MM) and multi-modal knowledge graphs (MM4KG). The survey covers various tasks such as visual question answering, image classification, multi-modal named entity recognition, and knowledge graph completion, among others. Each section includes a list of relevant papers with links to their sources (e.g., arXiv, conference proceedings). The repository is maintained through updates that include adding new papers and fixing links, as evidenced by the recent commit in 2026 and the news items up to 2024-12.
VeritasGraph is a comprehensive, governed GraphRAG and agent framework for building enterprise-grade knowledge graphs with verifiable attribution. The repository contains a core Python library (VeritasReason) for knowledge graph construction, semantic extraction, reasoning, provenance tracking, and export, along with multiple domain-specific applications: a HIPAA-safe clinical knowledge graph pipeline (clinical-kg), a floorplan extraction and validation web app (floorplan-kg), a municipality incident reporting chatbot, and a Model Context Protocol (MCP) server for IDE integration. The architecture is modular, with a plugin-based method registry pattern, unified configuration management, and a pipeline orchestration engine. While the primary character is an application framework, it also includes research-oriented components such as clinical benchmarks (MedKG-Signal) and evaluation suites, making it a hybrid project with a strong application focus.
This repository is a comprehensive educational resource and framework for Context Engineering - the discipline of optimizing how information is structured and presented to Large Language Models within their context window limitations. It moves beyond basic prompt engineering to treat context as a dynamic system that can be engineered for improved AI performance. The repository combines theoretical foundations with practical implementations, offering a structured learning path from basic concepts (atoms, molecules, cells) to advanced applications (neural fields, cognitive tools, multi-agent systems). It provides runnable examples, templates, and reusable components including protocol systems for context operations, multimodal processing frameworks, cognitive architectures, and token budgeting techniques. While primarily educational in nature, the repository contains numerous reusable components and patterns that could be extracted and applied in production AI applications. These include protocol shells for defining context operations, multimodal fusion components, cognitive architecture templates, and specialized agent systems for tasks like research, ethical analysis, and grant writing.
DeepOnto is a Python package for ontology engineering with deep learning, centered on ontology alignment (BERTMap) and subsumption inference (BERTSubs, OntoLAMA). It wraps the OWLAPI Java library through JPype to load, query, reason over, and prune OWL ontologies, and uses PyTorch/Hugging Face Transformers to train or apply BERT-based classifiers for mapping prediction and refinement. The package exposes config-driven pipelines (BERTMapPipeline, BERTSubs pipelines, OntoLAMA inference) plus evaluation utilities for OAEI-style benchmarks.