An agentic framework that analyzes, plans, and executes multi-step video understanding and editing workflows using multimodal LLM-driven agents—features intent decomposition, graph-based workflow orchestration, and automated shot planning for long-form video tasks.
Adds a lightweight, spec-driven workflow so AI coding assistants agree on requirements before code is produced — creates per-change artifacts (proposal, specs, design, tasks), exposes CLI slash-commands, and integrates with 20+ tools for repeatable AI-driven development.
Write repository automation as natural-language markdown that compiles into deterministic GitHub Actions workflows running AI agents. Agents run read-only by default and write only via sanitized safe-outputs. Works with Copilot, Claude, Codex, or Gemini.
Wraps the OpenCode CLI with a plan-first workflow: agents propose a plan you approve before any code is written, and a ContextScout step loads your repo's existing patterns so output matches house style, not generic boilerplate.
Makes the spec an executable artifact: you write intent in structured markdown and AI agents generate the plan, task breakdown, and code from it. A specify CLI and slash commands drive a constitution-plan-tasks-implement workflow across 30+ coding agents.
Composes AI agent teams from a Ghost+Shell+Model formula: each Bot pairs a prompt/MCP/Skills Ghost with a Chat, ClaudeCode, or Dify shell and a model like Claude or DeepSeek. Bots form Teams that run as traceable Tasks, wired to GitHub and DingTalk.
Converts your goals and context into verifiable agent workflows that hill-climb your Current State → Ideal State across life and work. Bundles an ISA-based criteria system, persistent memory and a Pulse dashboard into a single AI-native skill designed to run inside an AI coding harness.
Build and self-host production voice agents with a drag-and-drop workflow builder, real-time telephony integration, and pluggable LLM/STT/TTS backends. Docker-first with an optional managed cloud offering for teams that want faster onboarding.
Extends vLLM beyond text to serve omni-modal models — Qwen3-Omni, TTS like CosyVoice3, and diffusion image/video/audio generators — in one engine, adding the non-autoregressive Diffusion Transformer support the core project never targeted.
Turns papers, repositories, or natural-language research goals into local-first executable 'quests' that automate reproducible experiments, branching, and result-to-paper workflows. Preserves experiment history, supports web/TUI/connectors, and keeps human takeover and inspection simple.
Packages Every's 'compound engineering' workflow into 26 slash commands for AI coding agents like Claude Code — brainstorm, plan, work, review, debug. Skews effort toward planning and review, and saves each run's lessons so the next task is easier.
A dependency-aware issue tracker for AI coding agents that stores tasks as a version-controlled graph in a Dolt database, so agents keep context across long-horizon work. Content-hash IDs prevent merge conflicts when multiple agents edit in parallel.