RAG y conocimiento
OpenKB
OpenKB compiles your documents into a persistent, interlinked wiki knowledge base — no vector DB
★ 4,8k estrellasApache-2.0
Ver la guía →llm-wiki-agent es un proyecto de RAG y conocimiento de código abierto de SamurAIGPT (SamurAIGPT/llm-wiki-agent en GitHub). Tiene 3.610 estrellas en GitHub y 418 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 5 de octubre de 2026.
A personal knowledge base that builds itself — drop in sources, get a persistent interlinked wiki.
Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.
Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.
Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.
Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.
The agent ingests sources and keeps entity, concept and synthesis pages updated and interlinked.
Claude Code, Codex, OpenCode and Gemini CLI are all supported — no new platform to learn.
It rides on the agent CLI you already have set up, per the project description.
Many source formats auto-convert at ingest, with advanced arXiv-paper and batch-directory conversion.
Build the graph and find the most connected nodes; contradictions and gaps across sources are surfaced.
Vault symlink pattern, graph-view filters and Dataview queries over the generated frontmatter.
Get the source so you can inspect, configure and run it locally.
git clone https://github.com/SamurAIGPT/llm-wiki-agent.git
cd llm-wiki-agentGet the project and open it with your agent CLI, which reads its instruction file automatically.
git clone https://github.com/SamurAIGPT/llm-wiki-agent.git
cd llm-wiki-agentStart your agent (claude, codex, opencode or gemini) and ask it to ingest a source, then query the wiki.
claude # reads CLAUDE.md + .claude/commands/
# then: "Ingest this paper: raw/papers/llama2.md"It is maintained by SamurAIGPT on GitHub (SamurAIGPT/llm-wiki-agent), where the source, issues and releases live.
Yes — it is free and open source under the MIT licence, which permits use, modification and redistribution, including commercially, with attribution.
No API key for the tool itself — it works through Claude Code, Codex, OpenCode or Gemini CLI, so you only need your existing access to one of those.
Compared with classic RAG over raw documents, llm-wiki-agent's distinguishing focus is: a personal knowledge base that builds itself — drop in sources, get a persistent interlinked wiki. Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.
Clone https://github.com/SamurAIGPT/llm-wiki-agent (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.
OpenKB compiles your documents into a persistent, interlinked wiki knowledge base — no vector DB
Agent-fed knowledge base that distills learnings into cited wiki pages.
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
AI agent operating system for Blender 5.2 — bpy knowledge base, execution contract, 3D-print production gate.
Starter template for AI agents that stay synced with your knowledge base.
Local knowledge-base RAG and agent app built on LangChain with ChatGLM, Qwen and Llama models
Los demás proyectos de RAG y conocimiento con más estrellas del catálogo de LibreAI: la lista realista de alternativas si llm-wiki-agent no es lo que buscas.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Proyectos de RAG y conocimiento cercanos en el catálogo, los más próximos en estrellas a llm-wiki-agent: sigue explorando a ambos lados.
Knowhere turns documents into structured, hierarchy-native chunks ready for agents and RAG.
VideoRAG — chat with your videos, even hundreds of hours of them, with graph-driven retrieval.
The open-source, permission-aware context layer that lets agents search all your company knowledge.
Fast, lightweight Python embeddings from Qdrant — dense, sparse, ColBERT, image and rerankers.
nano-graphrag: a small, hackable GraphRAG implementation in about 1,100 lines — no Microsoft GraphRAG bulk
seekdb — the AI-native search database unifying vector, text and structured data for agents.
Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.