Runway 1.0.2-dev.1

Runway

What runway short-video produces

Five storyboard keyframes generated end-to-end by Runway.Cli with --keyframes gemini-image3-pro, planned by Claude from the repo's MARKETING.md brief. These are the exact stills that anchor the linked motion demo below — each was rendered with Nano Banana Pro at 768:1344 and then animated by veo3.1_fast image-to-video.

BEFORE	the pain	the pivot	the work	AFTER
Five glowing Runway dashboard tabs, sticky-note prompt fragments, scattered clip files	The hand-typed ffmpeg -f concat -safe 0 -i list.txt -c copy output.mp4 while a wall clock reads 15 minutes lost	The pivot: same developer types runway short-video launch-our-new-feature in one focused terminal	Plan JSON streams (urban_night / noir / shot_id / type) while three shot progress bars climb	A single polished vertical mp4 tile centered on the desktop, hand drifting up to upload

▶ Watch the 30-second motion versions (audio on):

Reproduce locally:

dotnet tool install -g Runway.Cli --prerelease
echo "RUNWAY_API_KEY=..." > .env

# Plan + generate the same 30-second short (text-to-video, demo-promo-v1)
runway short-video "developer types one command and the promo for Runway.Cli assembles itself" \
  --shots 5 --duration 6 --ratio 720:1280 --audio --planner auto

# Higher fidelity: anchor each shot on a Nano Banana Pro keyframe (demo-promo-v2)
runway short-video "..." \
  --shots 5 --duration 6 --ratio 720:1280 --audio --planner auto \
  --keyframes gemini-image3-pro

Use as an agent skill

The CLI also ships as an installable agent skill via the skills.sh ecosystem. The skill bundles guidance for Claude Code, Cursor, Codex, and 50+ other agents on how to drive dnx Runway.Cli for image, video, and audio generation, multi-shot short videos, and the named custom workflows.

# Install into the current project (auto-detects your agent)
npx skills add tryAGI/Runway

# Or globally for all agents
npx skills add tryAGI/Runway -g

# Target a specific agent and skip prompts (CI-friendly)
npx skills add tryAGI/Runway -a claude-code -y

After install, the skill's instructions live at .agents/skills/runway-cli/SKILL.md. Listing on skills.sh/tryAGI/Runway is updated automatically from anonymous install telemetry.

Features 🔥

• Fully generated C# SDK based on official Runway OpenAPI specification using AutoSDK
• Same day update to support new features
• Updated and supported automatically if there are no breaking changes
• All modern .NET features - nullability, trimming, NativeAOT, etc.

Usage

Avatar Video

Generate a talking-avatar video from text using a Runway preset avatar.

using Runway;

using var client = new RunwayClient(apiKey);

var response = await client.AvatarVideos.CreateAvatarVideosAsync(
    xRunwayVersion: "2024-11-06",
    request: new CreateAvatarVideosRequest
    {
        Avatar = new CreateAvatarVideosRequestAvatarRunwayPresetAvatar
        {
            PresetId = CreateAvatarVideosRequestAvatarRunwayPresetAvatarPresetId.Influencer,
        },
        Speech = new CreateAvatarVideosRequestSpeechTextInput
        {
            Text = "Welcome to the Runway API hackathon. Here is a quick prototype walkthrough.",
            Voice = new CreateAvatarVideosRequestSpeechTextInputVoiceRunwayPresetVoice
            {
                PresetId = CreateAvatarVideosRequestSpeechTextInputVoiceRunwayPresetVoicePresetId.Clara,
            },
        },
    });

Create Avatar

Create a custom realtime avatar from a reference image, personality prompt, and voice.

var avatar = await client.Avatars.CreateAvatarsAsync(
    xRunwayVersion: "2024-11-06",
    request: new CreateAvatarsRequest
    {
        Name = "Hackathon Producer",
        ReferenceImage = "https://example.com/reference.jpg",
        Personality = "You are a concise creative producer who helps teams turn rough ideas into practical video plans.",
        StartScript = "Tell me what you want to make and I will help shape the shot list.",
        Voice = new CreateAvatarsRequestVoiceRunwayLivePresetVoice
        {
            PresetId = CreateAvatarsRequestVoiceRunwayLivePresetVoicePresetId.Adrian,
        },
        ImageProcessing = CreateAvatarsRequestImageProcessing.Optimize,
    });

Realtime Avatar Session

Use this path for live Characters calls and backend bridges. The session ID is also the conversation ID for transcript and recording retrieval.

var session = await client.RealtimeSessions.CreateRealtimeSessionsAsync(
    request: new CreateRealtimeSessionsRequest
    {
        Avatar = new CreateRealtimeSessionsRequestAvatarRunwayPresetAvatar
        {
            PresetId = CreateRealtimeSessionsRequestAvatarRunwayPresetAvatarPresetId.Influencer,
        },
        MaxDuration = 60,
    });

GetRealtimeSessionsResponse status;
do
{
    status = await client.RealtimeSessions.GetRealtimeSessionsByIdAsync(id: session.Id);
    await Task.Delay(TimeSpan.FromSeconds(1));
}
while (status.IsNotReady);

if (!status.IsReady)
{
    throw new InvalidOperationException("Realtime session did not become ready.");
}

var credentials = await client.RealtimeSessions.ConsumeRealtimeSessionAsync(
    id: session.Id,
    sessionKey: status.Ready!.SessionKey);

For watchOS or other clients without WebRTC support, terminate WebRTC in your backend using the returned LiveKit credentials and relay optimized media over your own transport. AvatarVideos.CreateAvatarVideosAsync and StartGenerating.CreateCharacterPerformanceAsync are asynchronous generation endpoints, not live conversation transports.

The watch bridge controls the client-facing media shape after it receives the realtime avatar track. Set only watchVideoWidth to preserve the source aspect ratio, or set both width and height for a fixed player surface:

{
  "presetAvatar": "influencer",
  "maxDurationSeconds": 60,
  "audioEnabled": true,
  "videoEnabled": true,
  "watchVideoMode": "h264-fmp4",
  "watchVideoWidth": 256,
  "watchVideoHeight": 256,
  "watchVideoResizeMode": "cover",
  "watchVideoCropTop": 0,
  "watchVideoFps": 8,
  "watchVideoBitrateKbps": 220,
  "watchFmp4FragmentMs": 500
}

watchVideoResizeMode can be cover for a center-cropped exact size, fit for letterboxed/padded exact size, or stretch for exact dimensions with distortion. If a generated avatar includes an unwanted top border, set watchVideoCropTop to crop output pixels from the top before encoding. For watchOS, start with H.264/fMP4 at 256x256, cover, 6-8 FPS, and roughly 160-300 kbps. H.265 can reduce bandwidth if your watchOS playback path supports HEVC; JPEG mode is simpler but usually costs more bandwidth, so keep it around 45-60 quality for small watch previews. PNG mode preserves alpha when the upstream avatar track has transparent pixels, but it is much larger and should be treated as a diagnostic or overlay-specific mode.

Image to Video

using Runway;

using var client = new RunwayClient(apiKey);

var response = await client.StartGenerating.CreateImageToVideoAsync(
    xRunwayVersion: "2024-11-06",
    request: new CreateImageToVideoRequestGen3aTurbo
    {
        PromptImage = "https://example.com/photo.jpg",
        PromptText = "The girl smiles a little",
        Seed = 999999999,
        Model = "gen3a_turbo",
        Duration = 5,
        Ratio = CreateImageToVideoRequestGen3aTurboRatio.x1280_768,
    });

// Poll until complete
GetTasksResponse taskDetail;
do
{
    taskDetail = await client.TaskManagement.GetTasksByIdAsync(
        id: response.Id,
        xRunwayVersion: "2024-11-06");
    await Task.Delay(TimeSpan.FromSeconds(10));
}
while (!taskDetail.IsFailed && !taskDetail.IsSucceeded && !taskDetail.IsCancelled);

foreach (var output in taskDetail.Succeeded!.Output)
{
    Console.WriteLine($"Video URL: {output}");
}

Text to Video

Generate video from a text prompt using Veo 3.1 Fast.

var response = await client.StartGenerating.CreateTextToVideoAsync(
    xRunwayVersion: "2024-11-06",
    request: new CreateTextToVideoRequestVeo31Fast
    {
        PromptText = "A calm ocean with gentle waves under a starlit sky",
        Ratio = CreateTextToVideoRequestVeo31FastRatio.x1280_720,
        Duration = 4,
    });

Text to Image

Generate images from text using Gemini 2.5 Flash.

var response = await client.StartGenerating.CreateTextToImageAsync(
    xRunwayVersion: "2024-11-06",
    request: new CreateTextToImageRequestGemini25Flash
    {
        PromptText = "A vibrant coral reef teeming with tropical fish",
        Ratio = CreateTextToImageRequestGemini25FlashRatio.x1024_1024,
    });

Text to Speech

Choose from 48 preset voices including Maya, Arjun, Eleanor, Bernard, and more.

var response = await client.StartGenerating.CreateTextToSpeechAsync(
    xRunwayVersion: "2024-11-06",
    request: new CreateTextToSpeechRequestElevenMultilingualV2
    {
        PromptText = "Hello! Welcome to Runway's text-to-speech API.",
        Voice = new CreateTextToSpeechRequestElevenMultilingualV2VoiceRunwayPresetVoice
        {
            PresetId = CreateTextToSpeechRequestElevenMultilingualV2VoiceRunwayPresetVoicePresetId.Maya,
        },
    });

Sound Effects

Generate sound effects from text descriptions (0.5–30 seconds, optional seamless looping).

var response = await client.StartGenerating.CreateSoundEffectAsync(
    xRunwayVersion: "2024-11-06",
    request: new CreateSoundEffectRequestElevenTextToSoundV2
    {
        PromptText = "A thunderstorm with heavy rain and distant thunder rumbling",
        Duration = 10.0,
        Loop = false,
    });

Voice Dubbing

Dub audio to a target language with automatic voice cloning.

var response = await client.StartGenerating.CreateVoiceDubbingAsync(
    xRunwayVersion: "2024-11-06",
    request: new CreateVoiceDubbingRequestElevenVoiceDubbing
    {
        AudioUri = "https://example.com/audio.mp3",
        TargetLang = CreateVoiceDubbingRequestElevenVoiceDubbingTargetLang.Es,
        DisableVoiceCloning = false,
        DropBackgroundAudio = false,
    });

Speech to Speech

Transform the voice in an audio file while preserving the original speech content.

var response = await client.StartGenerating.CreateSpeechToSpeechAsync(
    xRunwayVersion: "2024-11-06",
    request: new CreateSpeechToSpeechRequestElevenMultilingualStsV2
    {
        Media = new CreateSpeechToSpeechRequestElevenMultilingualStsV2MediaSpeechToSpeechAudio
        {
            Uri = "https://example.com/speech.mp3",
        },
        Voice = new CreateSpeechToSpeechRequestElevenMultilingualStsV2VoiceRunwayPresetVoice
        {
            PresetId = CreateSpeechToSpeechRequestElevenMultilingualStsV2VoiceRunwayPresetVoicePresetId.Eleanor,
        },
        RemoveBackgroundNoise = true,
    });

Error Handling and Cancellation

All generation APIs return async tasks. Check for failures or cancel running tasks.

// Check for failures with machine-readable error codes
if (taskDetail.IsFailed)
{
    Console.WriteLine($"Failure: {taskDetail.Failed!.Failure}");
    Console.WriteLine($"Code: {taskDetail.Failed.FailureCode}");
}

// Cancel a running task or delete a completed one
await client.TaskManagement.DeleteTasksByIdAsync(
    id: taskId,
    xRunwayVersion: "2024-11-06");

AI Agent Tools

The SDK exposes Runway generation endpoints as Microsoft.Extensions.AI tools for use with any IChatClient.

using Microsoft.Extensions.AI;
using Runway;

using var runway = new RunwayClient(apiKey);

var tools = runway.AsTools();

GPT Image 2

Runway's current model list includes gpt_image_2. The SDK also keeps a typed convenience helper for GPT Image 2 text/image-to-image calls.

using Runway;

using var client = new RunwayClient(apiKey);

var response = await client.StartGenerating.CreateGptImage2TextToImageAsync(
    request: new CreateGptImage2TextToImageRequest
    {
        PromptText = "A clean poster that says RUNWAY CLI in precise typography",
        Ratio = "1920:1088",
        Quality = GptImage2Quality.Low,
        OutputCount = 1,
    });

Model Schema

The Runway OpenAPI spec is embedded in the SDK assembly, and RunwayModelSchema exposes per-model endpoint metadata so apps can discover model capabilities without hand-writing a catalog or shelling out to the CLI.

using Runway;

foreach (var entry in RunwayModelSchema.Lookup("gen4_turbo"))
{
    Console.WriteLine($"{entry.Endpoint}: required={string.Join(",", entry.RequiredParameters)}");
}

// Validate a chosen model against an endpoint before issuing a request:
RunwayModelSchema.EnsureModelSupportsEndpoint("gpt_image_2", "text_to_image");

// Validate that the user's inputs satisfy the spec's required-set for the chosen model:
RunwayModelSchema.EnsureRequiredParametersProvided(
    modelId: "gen4_image_turbo",
    endpoint: "text_to_image",
    providedFlags: new Dictionary<string, bool>
    {
        ["promptText"] = !string.IsNullOrEmpty(prompt),
        ["ratio"] = !string.IsNullOrEmpty(ratio),
        ["referenceImages"] = referenceImages.Length > 0,
    });

Unknown model ids pass through silently so brand-new spec entries don't break existing callers. The CLI uses this same API to power runway models schema <model> and the pre-flight validators on every generation command.

Built-in workflows (SDK)

The SDK ships 18 hardcoded workflow commands as a public API — the same set the CLI exposes (fabric-color-texture-swap, ai-hair-salon, virtual-try-on, video-style-transfer, …). Library consumers (a Telegram bot, a desktop app, a backend service) can enumerate and invoke them without going through the CLI shell:

using Runway;

using var client = new RunwayClient(apiKey);

// 1. Discover what's available — useful for inline-button menus.
foreach (var w in BuiltInWorkflows.All)
{
    Console.WriteLine($"{w.CommandName}  ({w.Category.DisplayName()})  {w.DisplayName}");
}

// 2. Run one end-to-end with whatever inputs the user gave you.
var inputs = BuiltInWorkflowInputs.Create()
    .SetStream("--image", uploadedPhotoStream, "photo.png")
    .Set("--hairstyle", userMessage)
    .Set("--background", "soft sunset gradient backdrop");

var result = await client.RunBuiltInWorkflowAsync(
    BuiltInWorkflows.AiHairSalon,
    inputs,
    waitForCompletion: true,
    pollInterval: TimeSpan.FromSeconds(15),
    progress: new Progress<double>(p => Console.WriteLine($"{p:P0}")),
    cancellationToken: cancellationToken);

foreach (var url in result.AllOutputUris)
{
    // result.Status is BuiltInWorkflowStatus.Succeeded; URLs expire in 24-48h.
    await SendToUser(url);
}

Inputs accept Stream / byte[] / Uri / local file path / smart-resolved string. Local assets are auto-uploaded as ephemeral assets before the workflow runs; https://, runway://, and data: URIs pass through unchanged. RunBuiltInWorkflowAsync handles upload + submit + poll + terminal-state dispatch in one call. Use waitForCompletion: false to fire-and-forget and re-poll later via the invocation ID. Look up workflows by command name (BuiltInWorkflows.FindByCommandName("ai-hair-salon")), strongly-typed kind (BuiltInWorkflows.FindByKind(BuiltInWorkflowKind.AiHairSalon)), or convenience accessor (BuiltInWorkflows.AiHairSalon).

CLI

This repository includes a local .NET tool project for Runway generation, task management, uploads, documents, voices, realtime sessions, organization usage, workflows, and avatar workflows. It reads RUNWAY_API_KEY, RUNWAYML_API_SECRET, or the nearest .env file by default.

cat > .env <<'EOF'
RUNWAY_API_KEY=...
EOF

# One-shot via dnx. The installed tool command is `runway`; dnx uses the package ID.
# Add `--prerelease --yes` after `Runway.Cli` until the first stable CLI package is tagged.
dnx Runway.Cli video a cinematic drone shot over a neon desert highway

dnx Runway.Cli image a product photo of a translucent glass speaker on a steel table

dnx Runway.Cli image "a clean app icon for a studio camera tool" \
  --model gemini-2.5-flash \
  --ratio 1024:1024 \
  --output ./runway-image

dnx Runway.Cli image-to-video "a slow push in on the reference image" \
  --image ./reference.png \
  --model gen4-turbo \
  --output ./runway-video

dnx Runway.Cli short-video "a tiny robot finds a glowing seed and turns a rooftop into a garden" \
  --shots 3 \
  --output ./runway-short-video

# Normal generation also saves the exact plan beside the final video as *.plan.json.

dnx Runway.Cli short-video "a calm product launch film for a transparent speaker" \
  --shots 3 \
  --plan-only > ./short-video-plan.json

dnx Runway.Cli short-video "a three-shot cyberpunk food commercial" \
  --planner claude \
  --planner-model opus \
  --planner-tools read-only \
  --plan-only

dnx Runway.Cli short-video run \
  --plan ./short-video-plan.json \
  --output ./runway-short-video

dnx Runway.Cli product-photoshoot create \
  --prompt "transparent speaker on a brushed steel table" \
  --mode social_carousel \
  --plan-only > ./product-photoshoot-plan.json

dnx Runway.Cli marketplace-cards create \
  --prompt "compact travel kettle" \
  --scope full-set \
  --plan-only > ./marketplace-cards-plan.json

dnx Runway.Cli ad-video create \
  --prompt "hands-free camera strap for travel creators" \
  --mode ugc \
  --shots 3 \
  --plan-only > ./ad-video-plan.json

# Run a published custom workflow (Fabric/Color/Texture Swap) by friendly flags
# instead of remembering workflow + node UUIDs.
dnx Runway.Cli fabric-color-texture-swap \
  --image ./assets/sample-dress.jpg \
  --change-1 "Change: color to teal" \
  --output ./out

# Restyle a clip via the Video Style Transfer workflow.
dnx Runway.Cli video-style-transfer \
  --video ./assets/sample-clip.mp4 \
  --style "modern Pixar 3D style with vibrant colors" \
  --output ./out

# More named workflows: AI Hair Salon, Virtual Try On, JSON to Manga, B-Roll, Storyboard to Film.
dnx Runway.Cli ai-hair-salon --image ./portrait.jpg --hairstyle "soft pixie cut" --output ./out
dnx Runway.Cli virtual-try-on --person ./model.jpg --garment ./jacket.png --scene "downtown at dusk" --output ./out
dnx Runway.Cli json-to-manga --character ./protagonist.png --storyboard @./storyboard.json --output ./out
dnx Runway.Cli b-roll-generator --image ./scene.jpg --b-roll "wide establishing shot" --output ./out
dnx Runway.Cli storyboard-to-film --character ./lead.png --script @./treatment.txt --output ./out

# 11 more: story-panels, storyboard-creator, character-creator, image-variations, mockup-generator,
# build-system-prompt, asset-reversioning, backplate-generator, wine-label-generator, game-item-generator,
# human-pose-replication. Each surfaces only the user-facing inputs of its published workflow.
dnx Runway.Cli storyboard-creator --prompt "a quiet detective scene in a rainy alley" --output ./out
dnx Runway.Cli mockup-generator --image ./logo.png --mockup "Mockup details: subway billboard, glossy paper" --output ./out
dnx Runway.Cli human-pose-replication --character ./hero.jpg --pose ./pose-ref.jpg --output ./out

# Register any other published workflow as a top-level subcommand on the fly.
dnx Runway.Cli workflow register <workflow-uuid> --name my-tool
dnx Runway.Cli my-tool --help

# Installed tool form.
dotnet tool install --global Runway.Cli
runway video a cinematic drone shot over a neon desert highway

# Local development from the repo.
dotnet run --project src/cli/Runway.Cli -- video a cinematic drone shot over a neon desert highway

dotnet run --project src/cli/Runway.Cli -- image a product photo of a translucent glass speaker on a steel table

dotnet run --project src/cli/Runway.Cli -- image a logo-style glass speaker icon \
  --model gemini-2.5-flash \
  --ratio 1024:1024 \
  --output ./runway-image

dotnet run --project src/cli/Runway.Cli -- image "a sharp product poster with readable text" \
  --model gpt-image-2 \
  --ratio 1920:1088 \
  --quality low \
  --output-count 1 \
  --output ./runway-gpt-image

dotnet run --project src/cli/Runway.Cli -- video a slow push in on the reference image \
  --image ./reference.png \
  --output ./runway-output

dotnet run --project src/cli/Runway.Cli -- short-video "a tiny robot finds a glowing seed and turns a rooftop into a garden" \
  --shots 3 \
  --output ./runway-short-video

dotnet run --project src/cli/Runway.Cli -- short-video run \
  --plan ./short-video-plan.json \
  --output ./runway-short-video

dotnet run --project src/cli/Runway.Cli -- models

dotnet run --project src/cli/Runway.Cli -- text-to-speech "A warm, concise product walkthrough voiceover." \
  --voice-preset clara \
  --output ./runway-audio

dotnet run --project src/cli/Runway.Cli -- task get 17f20503-6c24-4c16-946b-35dbbce2af2f \
  --wait \
  --download \
  --kind video

dotnet run --project src/cli/Runway.Cli -- avatar presets

dotnet run --project src/cli/Runway.Cli -- avatar list --limit 20

dotnet run --project src/cli/Runway.Cli -- avatar video \
  --preset-avatar influencer \
  --voice-preset clara \
  --text "Welcome to the Runway API hackathon." \
  --wait

dotnet run --project src/cli/Runway.Cli -- upload create --file ./reference.png

dotnet run --project src/cli/Runway.Cli -- workflow list

dotnet run --project src/cli/Runway.Cli -- gallery create \
  --input ./runway-short-video \
  --output ./runway-short-video/gallery.html

CLI endpoint and model coverage:

The CLI short-video command can plan with external agents before using Runway: --planner auto (default) tries Claude Code first, Codex CLI second, then the deterministic planner; --planner deterministic keeps output fully local and CI-safe. --planner-model, --planner-tools, and --planner-timeout-seconds also support RUNWAY_SHORT_VIDEO_PLANNER_MODEL, RUNWAY_SHORT_VIDEO_PLANNER_TOOLS, and RUNWAY_SHORT_VIDEO_PLANNER_TIMEOUT_SECONDS. short-video run --plan is execution-only and never invokes a planner. The bundled planner prompt is Runway-owned and was shaped by storyboard-creation workflows; no external storyboard skill is installed or required. Normal short-video generation writes the exact executed plan next to the final video as *.plan.json, and logs which planner source was used.

The creative recipe commands are Runway-native. product-photoshoot create, marketplace-cards create, and ad-video create bundle product/ad/storyboard prompt guidance inspired by compact creator workflows: concise sensory prompts, camera and motion structure, lighting, positive phrasing, mode routing, reference-image handling, and model-fit defaults. They do not install or call Higgsfield, and marketplace-card plans are creative asset bundles rather than marketplace compliance claims. For face-faithful identity reuse, the CLI ships a local soul-id registry (see "Higgsfield Parity" below) that auto-attaches reference photos to every generation call that supports them; it does not train a server-side identity model. For presenter-like talking videos, use the existing avatar and character-performance commands.

Higgsfield Parity

The CLI ships a Higgsfield-parity surface so an agent trained on higgsfield-ai/skills verbs can drive Runway with the same vocabulary. Mapping:

Pre-flight Validation

The CLI consults the embedded Runway OpenAPI spec before sending generation requests. Two checks run automatically — wrong-endpoint pairings and missing required parameters — both with spec-derived error messages so the user gets feedback before paying for an API round-trip. Unknown model IDs always pass through (brand-new spec entries work without a CLI release).

$ runway video "a cinematic shot" --model gpt_image_2
# Model `gpt_image_2` is not supported by `text_to_video` per the Runway OpenAPI spec.
# Supported endpoints: text_to_image.

$ runway image "a poster" --model gen4_image_turbo
# Model `gen4_image_turbo` on `text_to_image` requires referenceImages (--reference-image) per the Runway OpenAPI spec.
# Run `runway models schema gen4_image_turbo` for the full parameter list.

$ runway image-to-video --image ./pic.png --model gen3a_turbo
# Model `gen3a_turbo` on `image_to_video` requires promptText (--prompt) per the Runway OpenAPI spec.
# Run `runway models schema gen3a_turbo` for the full parameter list.

$ runway text-to-video "a cinematic shot" --model gen4.5
# Model `gen4.5` on `text_to_video` requires duration (--duration) per the Runway OpenAPI spec.
# Run `runway models schema gen4.5` for the full parameter list.

$ runway product-photoshoot create --prompt "x" --model gen4_image_turbo --plan-only
# Model `gen4_image_turbo` on `text_to_image` requires referenceImages (--reference-image) per the Runway OpenAPI spec.
# Run `runway models schema gen4_image_turbo` for the full parameter list.

Each missing required parameter is rendered as <spec-name> (<cli-flag>) so humans see which flag to add; agents and scripts that grep for the spec name still match. Validation runs even with --plan-only, so recipe commands (product-photoshoot create, marketplace-cards create, ad-video create) catch wrong-input combinations before any per-job API call. Audio, avatar, character-performance, and realtime commands hardcode their model id; the validator runs as a sentinel that surfaces spec drift if the upstream Runway spec ever drops a model.

Soul-id example flow:

# Create a soul-id from 5+ reference photos
dnx Runway.Cli soul-id create --name "alice" \
  --image ./refs/1.jpg --image ./refs/2.jpg --image ./refs/3.jpg \
  --image ./refs/4.jpg --image ./refs/5.jpg

# List entries
dnx Runway.Cli soul-id list

# Generate a product shot using the soul-id (auto-attaches the photos)
dnx Runway.Cli image "alice holding a vintage camera in golden-hour light" \
  --model gpt-image-2 --soul-id <id> --output ./runway-output

# Soul-id flows through every reference-aware recipe
dnx Runway.Cli product-photoshoot create \
  --prompt "alice in a quiet studio kitchen" \
  --mode closeup_product_with_person --soul-id <id> --plan-only

Auth and account:

dnx Runway.Cli auth set --api-key "$RUNWAY_API_KEY"
dnx Runway.Cli auth show
dnx Runway.Cli auth clear

dnx Runway.Cli account
dnx Runway.Cli models schema gen4_turbo

Marketing-studio webproducts dossier:

dnx Runway.Cli marketing-studio webproducts fetch --url https://example.com/product

The short-video workflow is also available from the SDK through RunwayShortVideoExtensions.CreateShortVideoPlan(...), IChatClient.CreateShortVideoPlanAsync(...), client.CreateShortVideoAsync(...), and client.CreateShortVideoAsync(plan, ...). Backend code can use the deterministic planner, supply a custom RunwayShortVideoPlanner, ask any Microsoft.Extensions.AI IChatClient for richer storyboard JSON, review or edit the plan, then execute the edited plan. RunwayShortVideoJsonSerializerContext provides AOT-safe JSON metadata for serializing plans and results.

Agent Skill

The repo includes a compact Codex-compatible skill at .agents/skills/runway-cli/SKILL.md. It mirrors the official runwayml/skills agent-skill flow, but uses dnx Runway.Cli as the runtime instead of bundled Python or Node scripts. The skill covers direct media generation, scenario-to-short-video planning, creative product/ad recipes, resource inspection, uploads, task polling/downloads, and simple multi-step recipes such as concept image to video and batch product-image animation.

Support

Priority place for bugs: https://github.com/tryAGI/Runway/issues
Priority place for ideas and general questions: https://github.com/tryAGI/Runway/discussions
Discord: https://discord.gg/Ca2xhfBf3v

Acknowledgments

This project is supported by JetBrains through the Open Source Support Program.