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.
A curated, security-first registry of verified, tested skills you can install into AI coding agents. Each skill is human-curated, scanned (Snyk/static analysis), and integrity-locked; delivered via a CLI and optional MCP server to multiple agents (Claude Code, Cursor, Copilot, etc.).
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.
Reusable skills—instructions plus helper scripts—that extend AI coding agents. Covers Vercel deployment audits, React performance rules, UI accessibility checks, and writing guidelines; each loads only when a relevant task appears.
Searches raw files with no vector DB or embedding step — drop documents in and query instantly, firing LLM calls only when a match needs reasoning. Adds Monte Carlo evidence sampling and self-evolving clusters as a low-overhead RAG alternative.
Provides a manifest-driven marketplace of official Cursor plugins for developer workflows and agent integrations. Each plugin lives in its own directory with a .cursor-plugin manifest; examples include agent skills, PR review canvases, SDK integrations, CI/team tooling, and orchestration for parallel agent work.
Runs background coding agents in isolated sandboxes to autonomously handle development tasks, create pull requests, and integrate with Slack, GitHub, Linear and webhooks. Supports multiplayer sessions, multiple LLM providers, fast startup via snapshots and prebuilt images; designed for single-tenant deployments.
Provides a REST server that lets AI agents browse sites while avoiding common bot-detection by running Camoufox (a Firefox fork with C++-level fingerprint spoofing). Returns compact accessibility snapshots, stable element refs, session isolation, proxy/geoIP support, and agent-friendly endpoints (click, type, snapshot, transcripts).
Provides a unified plugin suite that connects OpenClaw AI assistants to Chinese IM platforms (WeChat, WeCom, QQ, DingTalk, Feishu). Focuses on stable messaging, unified plugin API, streaming replies, media handling and configurable delivery modes.
Browser dashboard for OpenClaw Gateways that shows agents, streams runtime events, supports chat and exec approvals, and lets you configure jobs. Runs a small server process (Node + SQLite) and supports local or cloud setups with Tailscale or SSH access.
Provides a systematic, project-driven tutorial and runnable codebase for building AI agents, RAG pipelines, and multi-agent systems—focused on Python, LangChain/LangGraph, tooling, deployment, and an interview question bank for engineers aiming to ship production agent applications.