Provides a persistent, dependency-aware structured memory for coding agents — replacing scattered markdown plans with a versioned task/issue graph backed by Dolt. Agent-optimized features include JSON output, dependency tracking, zero-conflict IDs, and semantic compaction for long-horizon workflows.
Provides a set of Claude Code skills that let an LLM-driven agent control Browserbase via browser automation and the official bb CLI — includes browser automation with anti-bot/solver support, cookie sync, fetch/tracing, site-debugging, and serverless function workflows.
Transforms enterprise architecture governance into a structured, AI-assisted workflow covering principles, requirements, risk, procurement and design reviews. Bundles templates, CLI/plugins and autonomous research agents (MCP integrations) to keep traceability and compliance.
Terminal-based coding agent that reads and edits code, runs shell commands, and fetches web pages while planning multi-step tasks autonomously. A Ctrl-X toggle drops into a raw shell, and ACP support plugs it into Zed and JetBrains IDEs.
Orchestrates multiple AI providers to generate context-aware attack payloads, scan web targets for 45+ vulnerability types, and produce compliance-mapped reports. Supports dynamic provider failover, RAG-indexed CVE intelligence, browser automation, and AI triage; requires API keys and authorized testing.
Provides 223 production-ready agent skills and plugins plus 298 stdlib Python CLI tools to add domain expertise to coding agents. Includes cross-tool conversion (11 platforms supported), personas, orchestration patterns, and a skill security auditor.
Enforces filesystem and network limits on arbitrary processes at the OS level, no container required. Uses macOS Seatbelt, Linux bubblewrap, and the Windows Filtering Platform; built to sandbox MCP servers and AI agents under a secure-by-default model.
Automates multi-step web tasks by perceiving webpages as pixels and issuing low-level mouse, keyboard and scroll actions. A 7B-parameter multimodal agent trained on 145K synthetic trajectories (FaraGen), designed for on-device deployment and efficient task completion (~16 steps/task).
Drives an LLM-powered agent to autonomously research, write, and ship ML code by accessing Hugging Face docs, datasets, repos, and cloud compute. Provides interactive CLI and headless modes, approval gates, tool routing, and integrations for HF, GitHub, and Anthropic models.
Discovers MCP servers already configured in Cursor, Claude, Codex and other editors, then calls their tools from TypeScript or the CLI. Can also turn any server into a standalone command-line tool or a typed TypeScript client.
Bundles your prompt and project files into a single context package and submits that bundle to one or multiple LLMs (GPT‑5.x, Gemini, Claude, etc.) via API or optional browser automation. Key features: multi-model runs, file-globbing and token-aware bundles, session lineage and replay, and a CLI-first workflow for code reviews, audits, and multi-model comparisons.
Shows per-provider usage meters, credit balances, and reset countdowns for AI coding providers directly in the macOS menu bar. Privacy-first design reuses existing sessions (OAuth, cookies, API keys) and includes a CLI, widgets, live status badges, and optional cost/spend charts across 50+ providers.