Turn your coding agent into a content farm.

Open-source runtime for agent-driven AI video. You chat with Claude Code / Cursor / Codex; the agent drives Ralphy. Fork-able, observable, reproducible. From a brief to an mp4 in ~8 minutes.

Ralphy is a tool for agents, not a CLI you operate by hand. You stay in chat — Claude Code, Cursor, Codex, or a future desktop surface — and describe what you want; the agent runs Ralphy for you. The CLI is the runtime that gives the agent what it needs to produce content at farm cadence: reproducible model calls, project state, quality gates, renders, logs, and memory.

Under the hood, two API keys (OPENROUTER_API_KEY + ELEVENLABS_API_KEY) wire up image / video / vision / LLM (OpenRouter), voice + music (ElevenLabs), HTML+GSAP composition (HyperFrames), and a local async-job queue (bun + SQLite). Direct ralphy <verb> commands stay available for setup, debugging, and power users — but driving them yourself is not the primary workflow.

See what Ralphy makes → — real rendered outputs from real projects.

Cost: ~$8–12 per 30s video. Speed: ~8 min cold-start, ~25 min for a 10-batch. Engine: HyperFrames (HTML + GSAP, deterministic Puppeteer + FFmpeg render).

All four ship the same binary.

Setup is a one-time step you run once so your agent can take over from there. These two commands are agent enablement + diagnostics, not the everyday workflow:

ralphy setup          # interactive wizard — paste the two API keys + install agent skill
ralphy doctor         # verify env is green (run this when the agent reports a problem)

Expected output:

✦ ralphy v0.3.0
▸ Dependencies          ✓ bun  ✓ ffmpeg
▸ API keys              ✓ OPENROUTER_API_KEY  ✓ ELEVENLABS_API_KEY
  ✓ ready

macOS Gatekeeper warning? You used the direct-download path. Brew / npm / install.sh bypass Gatekeeper automatically. If you hit it: xattr -d com.apple.quarantine /path/to/ralphy once and you're done.

Verify your install: every Release includes a SHA256SUMS file. shasum -a 256 -c SHA256SUMS (macOS / Linux) or Get-FileHash (Windows) confirms the binary matches.

In practice you say "make a spring espresso ad" in chat and the agent runs these verbs for you. Here's the surface it drives, so you can see what's happening under the hood:

# 1. Create a project
ralphy new "Spring espresso ad" --id espresso-001

# 2. Find a template by free-text utterance
ralphy template suggest "talking head rant about deadlines" -p

✦ Query: "talking head rant about deadlines"
  1. ✓ talking-head  ███████████████░  0.95  strong
  2. ✓ story-time    ███████████░░░░░  0.70  strong

# 3. Scaffold from the chosen template (sourced from the hosted library)
ralphy template use talking-head --id espresso-001

# 4. Cost-preview before spending a cent
ralphy generate image --project espresso-001 --slot scene-01-bg \
  --prompt "studio packshot, white seamless, 50mm, photoreal" --dry-run

# 5. Render the project to mp4
ralphy render espresso-001

That's it. Full CLI surface in docs/cli-surface.md.

What you actually get vs other ways to do this. The operator is your agent; you stay in chat.

The hard rule that makes the rest work: ralphy <verb> is the only entry-point. No ad-hoc ffmpeg shell-outs, no direct provider fetches, no orphan scripts. Every model call lands in generations.jsonl, every cost in the rollup, every failure in the postmortem.

graph LR
    A[Agent: Claude Code / Cursor / Codex] -->|playbooks| B[ralphy CLI]
    B --> C[Provider router]
    C --> D[OpenRouter<br/>Kling / Seedance / Veo / Sora / Nano-Banana]
    C --> E[ElevenLabs<br/>TTS + Music]
    B --> F[HyperFrames composer<br/>HTML + GSAP]
    F --> G[mp4 via Puppeteer + FFmpeg]
    B --> H[Project memory<br/>genlogs · postmortems · cost rollup]
    B --> I[Hosted template library<br/>+ guidelines in git]

5 agent roles (researcher / scenarist / art-director / editor / producer) routed via AGENTS.md. The router decides which playbook the agent reads before acting.

git clone https://github.com/alecs5am/ralphy.git
cd ralphy && bun install

bun test                       # unit + integration (1,000+ tests)
bun run lint                   # typecheck + project lints (errors / help-examples / skills / agents-md / cli-surface)
bun run docs:cli               # regenerate docs-mintlify/reference/cli/
bun run build:bin              # build cross-platform binaries

A pre-commit hook runs the test suite. CI runs the same on push/PR.

PRs welcome — especially:

• New model entries in MODELS.md with real cost numbers + known pitfalls.
• Bug fixes in cli/lib/providers/.
• New guidelines under guidelines/<slug>/ (image-prompt rules — tag-able from chat as @guideline:<slug>).

For non-trivial changes, open an issue first or start a discussion.

Apache 2.0. Use, fork, ship to prod — patent grant included.