LLM Wiki

WeKnora is an open-source, LLM-powered knowledge platform from Tencent for turning documents into queryable knowledge bases, retrieval-augmented Q&A, autonomous reasoning workflows, and self-maintaining Markdown wikis. It supports RAG-based quick Q&A and a ReAct agent that orchestrates retrieval, MCP tools, sandbox skills, and web search for multi-step tasks. Its Wiki Mode distills source documents into structured, interlinked Markdown pages with an interactive knowledge graph, manual editing, revision history, line-level diffs, and rollback. The platform ingests formats including PDF, Word, Markdown, HTML, images, spreadsheets, presentations, and XMind, and can synchronize sources such as Feishu, GitLab, Tencent IMA, Notion, Yuque, and RSS. Its modular pipeline supports interchangeable parsers, LLMs, embedding providers, vector databases, and storage backends, with dense, sparse, hybrid, parent-child, and graph-based retrieval strategies. WeKnora provides a web interface, REST API, command-line client, MCP server, website embed widget, and integrations with messaging channels. It can be deployed locally, with Docker, or on Kubernetes, including private and offline deployments. The repository states that it is licensed under the MIT License and supports workspace RBAC, scoped API keys, audit logs, and Langfuse-based observability.

LLM Space v4 is a local-first desktop workbench for prototyping and developing AI agents. It lets users version prompts, system messages, tools, and model settings; trace each model call and tool run in an agent loop; replay historical runs to debug failures; and evaluate agent performance across runs. It can generate prompts and tools with AI and turn a thread into a runnable LangGraph agent. Threads, project files, and API keys remain on the user's computer, while the project describes the app as cloud-ready for managed agents. The desktop application uses Electrobun with a React, Tailwind CSS, and shadcn/ui interface, and is built as a Bun monorepo around Pi Agent Core.

Awesome LLM Apps is an open-source GitHub collection of Python templates for AI agents, agent skills, and retrieval-augmented generation (RAG) applications. Its examples include starter and advanced agents, multi-agent teams, voice and multimodal agents, MCP-based agents, memory-enabled agents, data analysis, web scraping, and other application patterns. The repository provides runnable source projects and step-by-step tutorials. Individual agents can be cloned and run with an API key, while agent skills can be installed with a command such as `npx skills add` and used with coding agents including Claude Code, Codex, and Cursor. The projects support models from providers and ecosystems including Claude, Gemini, GPT, DeepSeek, Llama, and Qwen. The repository describes the collection as free and open source under the Apache-2.0 license.

WebLLM is an in-browser large language model inference engine developed by the MLC community. It runs model inference directly in web browsers using WebGPU hardware acceleration, without server-side processing, and can be used as an npm, Yarn, or CDN package for building web applications. Its MLCEngine exposes an OpenAI-compatible chat-completions interface with streaming, JSON-mode structured generation, seeding, and preliminary function-calling support. It supports model families including Llama, Phi, Gemma, Mistral, and Qwen, and can load custom MLC-format models consisting of model artifacts and WebAssembly model libraries. WebLLM provides Web Worker and Service Worker integrations, Chrome-extension examples, browser caching through Cache API, IndexedDB, or OPFS, and optional Subresource Integrity verification for downloaded model artifacts.

Lexicon is a free, open-source, local-first writing assistant and distraction-free rich-text editor for Windows, macOS, and Linux. It provides inline proofreading through the separate LanguageTool engine, while local AI tools handle rewriting, tone adjustment, summarization, and other writing tasks without requiring an account or cloud writing service. Users can review suggestions in a dedicated panel, format drafts with rich text and LaTeX, autosave work, and export it to Markdown, HTML, text, or PDF. The app can download and run a local AI model or use an existing local Ollama server; the editor and proofreading tools work without an AI-model download. It is MIT-licensed, and the repository notes that its Windows builds are unsigned and its macOS build is pre-release and unsigned.