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Pre-indexes a project's code into a local semantic knowledge graph that Claude Code can query—fewer tool calls and faster exploration with all data kept on-device. Features FTS5 search, call/impact analysis, auto-sync watcher, and multi-language support.
Desktop-first agent client that composes LLM-driven agents into document-centric, multi-session workflows; it wires APIs, MCPs and local tools into shareable sessions, supports multiple LLM providers, and exposes a headless server + CLI for automation.
Desktop + CLI agent-native client for managing multi-session conversations, connecting to multiple LLM providers and external data sources, and creating shareable agent skills and automations without editing code.
Fetches multi-source content (webpages, YouTube, PDFs, WeChat, paywalled articles, podcasts), uploads it to Google NotebookLM, and generates outputs such as podcasts, PPTs, mind maps, or quizzes. Differentiators: automatic paywall-bypass pipeline, Claude Code Skill integration, and CLI + MCP components for WeChat and document scraping.
Native Windows companion suite for OpenClaw that provides a system tray app, shared gateway libraries, and CLI utilities for quick chat, node control, diagnostics, and gateway pairing/observability.
Local-first AI desktop app that combines multi-model chat and a proactive Agent workspace to embed agent workflows into daily work. Features per-workspace Skills, MCP support, Feishu/remote-robot bridges, and local JSON/JSONL storage for privacy and portability.
Self-hosted personal AI agent runtime that runs chats, tools, automations and long-term memory for persistent workflows. Small, readable core with a bundled WebUI, multi-chat integrations, an OpenAI-compatible API and a Python SDK for easy extension and deployment.
A TypeScript framework for building programmable, headless autonomous agents with a harness-centric runtime. Includes an SDK and CLI, virtual sandboxes (just-bash) with optional full container sandboxes, provider-agnostic model settings, and connectors for CI/Daytona/MCP—suited for deployable agent runtimes.
Desktop app for managing markdown-based knowledge bases with a files-first, git-first workflow. Works offline, uses plain markdown + YAML frontmatter for portability, and includes AI-agent integrations and agent configuration to organize context, memory, and procedures for assistants.
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 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.