Desktop AI agent that automates tasks against your real working environment—local files, terminals, browser workflows, slides/docs, data analysis and scheduled jobs. It pairs a local app layer (sessions, permissions, SQLite persistence) with the OpenClaw runtime to run multi-agent workflows, built-in skills, MCP integrations, and IM remote control (WeChat, Feishu, Telegram, etc.).
Local LLM inference server for Apple Silicon that exposes an OpenAI-compatible API and a macOS menubar app. Uses continuous batching and a two-tier KV cache (RAM + SSD in safetensors) to persist context across restarts, enabling practical multi-model serving and fast local coding workflows.
Acts as an OpenAI‑compatible local and cloud gateway that routes requests across 100+ LLM providers with smart routing, load balancing, retries and fallbacks. Adds policies, rate limits, semantic caching and observability for reliable, cost‑aware inference in Docker, Electron or npm installs.
Lets any LLM operate a ComfyUI instance: generate and iterate images/video/audio, manage models and custom nodes, and edit the live graph in natural language. Local-first control plane with a sidebar agent, multi-provider LLM support, and installer packs for ready workflows.
Provides a workspace-first, Kanban-backed multi-agent coordination platform that routes goals through specialist lanes (Backlog→Todo→Dev→Review→Done), enforces evidence-based review gates and traces, and runs on both web and desktop runtimes.
Provides reusable “skill” instruction bundles that teach AI coding tools how to author, query, and operate Microsoft Fabric workloads via REST APIs, T-SQL, KQL and notebooks. Includes Copilot CLI/Claude/Cursor integrations, workload-focused bundles, and optional MCP configurations for live data access.
Aggregates and deduplicates stories from Hacker News, Reddit, RSS, Telegram, GitHub and more, then uses LLMs to score, enrich, and produce bilingual (EN/CN) daily briefings. Supports customizable sources, comment summarization, multi-provider scoring, and delivery via GitHub Pages, email, or webhooks — designed for self-hosted, configurable news digests.
Dramatically reduces AI agents' context usage by sandboxing large tool outputs and indexing only relevant snippets into a searchable SQLite FTS5 (BM25) knowledge base, improving session continuity and privacy. Deploys cross-platform hooks and sandbox tools to cut context size by ~98% and avoid dumping raw logs into the model's window. ([github.com](https://github.com/mksglu/context-mode/blob/main/README.md?utm_source=openai))
Provides named agents, reusable skills, and MCP data connectors for common financial‑services workflows (investment banking, equity research, private equity, wealth). Available as Claude Cowork plugins or deployable Claude Managed Agents templates—designed as enterprise-ready templates, not turnkey investment advice.
Indexes codebases into a persistent, queryable knowledge graph for AI coding agents, enabling full-repo indexing in minutes and sub-millisecond structural queries. Bundles 158 vendored tree-sitter grammars, a Hybrid LSP resolver, built-in embeddings, and 14 MCP tools for search, trace, and architecture analysis.
One-command installer that gives AI agents the ability to read and search the web and social platforms (web pages, Twitter/X, Reddit, YouTube, GitHub, Bilibili, XiaohongShu) by installing and wiring upstream CLIs and MCP connectors while keeping credentials local.
Provides a set of task-focused agent “skills” — small folders of instructions that teach agents how to perform common Flutter development workflows (integration tests, widget previews, routing, localization). Maintained by the Flutter team to reduce mistakes and make repeatable dev tasks reliable.