Html To Markdown
kreuzberg-dev
High performance and CommonMark compliant HTML to Markdown converter. Maintained by the Kreuzberg team. Kreuzberg is a fast, polyglot document intelligence engine with a…
OPENAGENTSKILL / DIRECTORY
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Results: 567
kreuzberg-dev
High performance and CommonMark compliant HTML to Markdown converter. Maintained by the Kreuzberg team. Kreuzberg is a fast, polyglot document intelligence engine with a…
jonfairbanks
Ingest files for retrieval augmented generation (RAG) with open-source Large Language Models (LLMs), all without 3rd parties or sensitive data leaving your network.
nuclia
NucliaDB, The AI Search database for RAG
om-ai-lab
[EMNLP-2024] Build multimodal language agents for fast prototype and production
bosun-ai
Fast, streaming indexing, query, and agentic LLM applications in Rust
kingjulio8238
The Open Source Memory Layer For Autonomous Agents
athina-ai
This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems.
xynehq
AI-first Search & Answer Engine for work. Open-source alternative to Glean.
NVIDIA-AI-Blueprints
This NVIDIA RAG blueprint serves as a reference solution for a foundational Retrieval Augmented Generation (RAG) pipeline.
TIGER-AI-Lab
This repo contains the code for "VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks" [ICLR 2025]
Ai00-X
The all-in-one RWKV runtime box with embed, RAG, AI agents, and more.
osovv
GRACE (Graph-RAG Anchored Code Engineering): open Agent Skills for contract-driven AI code generation with semantic markup, knowledge graphs, and support for Claude Code…
framersai
Build autonomous AI agents with adaptive intelligence and emergent behaviors, included with multimodal RAG and optional HEXACO personalities.
felladrin
Minimalist web-searching platform with an AI assistant that runs directly from your browser. Demo: https://felladrin-minisearch.hf.space
skaldlabs
Context layer platform in your infrastructure
hhy-huang
[EMNLP'25 findings] This is the official repo for the paper, HiRAG: Retrieval-Augmented Generation with Hierarchical Knowledge.