Runs automated, parallel SEO audits inside Claude Code and emits prioritized, testable action plans across technical SEO, content quality (E‑E‑A‑T), Schema.org markup, AI-search (GEO/AEO), local and e-commerce SEO. Operates with 25 sub-skills and 18 specialist agents; optional MCP extensions add live data.
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.
Local integration layer that lets AI agents discover and securely call OpenAPI, MCP, GraphQL, or custom JavaScript functions. Centralizes a shared tool catalog, auth, and policy surface across multiple agents, with a local web UI and CLI for runtime control.
Provides a minimal web and desktop GUI for coding agents (Codex and Claude), letting you run LLM-driven code workflows through a lightweight interface. Emphasizes quick provider switching, desktop packaging, and an opinionated minimal UX; early-stage project, expect bugs.
Provides cross-platform semantic memory for AI coding agents by turning human-editable Markdown logs into a rebuildable Milvus “shadow” index and syncing memories across plugins (Claude Code, OpenClaw, OpenCode, Codex). Supports progressive retrieval, hybrid dense+BM25+RRF search, smart deduplication, live sync, and local ONNX embeddings.
A fast, local document parser that extracts spatial text with bounding boxes from PDFs and other formats. Bundles Tesseract OCR and supports HTTP OCR servers, multi-language bindings (Rust, Node, Python, WASM) and screenshot generation; best for lightweight local pipelines but less suited to very complex or heavily scanned documents.
Provides a local MCP server that returns precise, symbol-level code (functions, classes, imports) via tree-sitter parsing so AI agents send only the bytes they need—commonly cutting code-reading token usage 95%+ and enabling compact packed responses for further savings.
Runs a persistent, self-modifying AI agent locally with durable identity, memory, and versioned history across tasks. Provides native desktop and headless CLI runtimes, coordinated subagent swarms, configurable remote or local GGUF models, and reviewed self-evolution via Git.
Turns authorized ad-account exports or reads into dated, source-grounded audits, channel and budget plans, creative briefs, experiments, monitoring, and versioned JSON reports. Read-only by default; supports twelve ad platforms and drafts safe account changes behind capability, verification, and rollback gates.
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.
Scans a React codebase and produces a 0–100 health score plus actionable diagnostics across state & effects, performance, architecture, security, accessibility, and dead code. Auto-adapts to framework and React version, supports Next.js/Vite/React Native, a CLI, GitHub Action, and agent integrations to teach coding agents.