integrate-characters

Original：🇺🇸 English

Translated

Help users create Runway Characters (GWM-1 avatars) and integrate real-time conversational sessions into their apps

11installs

Sourcerunwayml/skills

Added on2026-03-29

NPX Install

npx skill4agent add runwayml/skills integrate-characters

SKILL.md Content

View Translation Comparison →

Integrate Characters (GWM-1 Avatars)

PREREQUISITES:
+check-compatibility
— Project must have a server-side component (API key must NEVER be exposed to the client)
+fetch-api-reference
— Load the latest API reference from https://docs.dev.runwayml.com/api/ before integrating
+setup-api-key
— API credentials must be configured
OPTIONAL DEPENDENCIES:
+integrate-documents
— Add a knowledge base to your character
+integrate-character-embed
— Use the React SDK to embed the avatar call UI

Help users create Runway Characters — real-time conversational AI avatars powered by GWM-1.

Characters are generated from a single image (any visual style — photorealistic, animated, non-human) with full control over voice, personality, knowledge, and actions. No fine-tuning or training required.

Key Concepts

Avatars vs Sessions

Concept	Description
Avatar	A persistent persona with a defined appearance, voice, and personality. Created once, used many times.
Session	A live WebRTC connection for real-time conversation. Connects one user to one avatar. Max duration: 5 minutes.

Session Lifecycle

                    ┌───────────┐
         ┌──────────┤ NOT_READY ├──────────┐
         │          └─────┬─────┘          │
         │                │                │
         ▼                ▼                ▼
     CANCELLED          READY           FAILED
                       ┌──┴──┐
                       │     │
                       ▼     ▼
                    RUNNING FAILED
                    ┌──┴──┐
                    │     │
                    ▼     ▼
                COMPLETED CANCELLED

Status	Description
`NOT_READY`	Session is being provisioned. Poll until ready.
`READY`	Session is ready. The `sessionKey` is available.
`RUNNING`	WebRTC connection is active. Conversation in progress.
`COMPLETED`	Session ended normally.
`FAILED`	Error occurred. Check the `failure` field.
`CANCELLED`	Explicitly cancelled before completion.

Important: Session credentials can only be consumed once. If the WebRTC connection fails after credentials are consumed, you must create a new Session.

Architecture

The API key must stay server-side. The flow is:

Client (React)  →  Your Server  →  Runway API
                                      ↓
Client (React)  ←─── WebRTC ───← Runway (realtime)

Client requests a session from your server
Your server calls Runway API to create a session (
```
POST /v1/realtime_sessions
```
)
Your server polls until session is
```
READY
```
(
```
GET /v1/realtime_sessions/:id
```
)
Your server consumes credentials (
```
POST /v1/realtime_sessions/:id/consume
```
)
Your server returns credentials to the client
Client establishes a direct WebRTC connection to Runway

Step 1: Install Dependencies

bash

npm install @runwayml/sdk @runwayml/avatars-react

```
@runwayml/sdk
```
— Server-side SDK (session creation, avatar management)
```
@runwayml/avatars-react
```
— Client-side React components (WebRTC, UI)

Step 2: Create an Avatar

Avatars can be created via the Developer Portal (UI) or the API (programmatic).

Option A: Developer Portal (Recommended for first time)

Go to https://dev.runwayml.com/ → Characters tab
Click Create a Character
Upload a reference image (tips below)
Choose a voice preset
Write personality instructions (e.g., "You are a helpful customer support agent for Acme Corp...")
Optionally add a starting script (what the character says first)
Optionally upload knowledge documents (
```
.txt
```
files)
Click Create Character
Copy the Avatar ID (a UUID like
```
8be4df61-93ca-11d2-aa0d-00e098032b8c
```
)

Option B: API (Programmatic)

javascript

// Node.js
import RunwayML from '@runwayml/sdk';

const client = new RunwayML();

const avatar = await client.avatars.create({
  name: 'Support Agent',
  referenceImage: 'https://example.com/avatar.png',
  voice: {
    type: 'runway-live-preset',
    presetId: 'clara',
  },
  personality: 'You are a helpful customer support agent for Acme Corp. You help users with billing questions and technical issues.',
});

console.log('Avatar ID:', avatar.id);

python

# Python
from runwayml import RunwayML

client = RunwayML()

avatar = client.avatars.create(
    name='Support Agent',
    reference_image='https://example.com/avatar.png',
    voice={
        'type': 'runway-live-preset',
        'preset_id': 'clara',
    },
    personality='You are a helpful customer support agent for Acme Corp.',
)

print('Avatar ID:', avatar.id)

If the reference image is a local file, upload it first using

+integrate-uploads

javascript

import fs from 'fs';

const upload = await client.uploads.createEphemeral(
  fs.createReadStream('/path/to/avatar-image.png')
);

const avatar = await client.avatars.create({
  name: 'Support Agent',
  referenceImage: upload.runwayUri,
  voice: { type: 'runway-live-preset', presetId: 'clara' },
  personality: 'You are a helpful customer support agent...',
});

Reference Image Guidelines

Any visual style works: photorealistic humans, animated mascots, stylized brand characters
Use high-quality images with good lighting
Face should be clearly visible and centered
Avoid images with multiple people or obstructions
Recommended aspect ratio: 1088×704

Voice Presets

Preset ID	Name	Style
`clara`	Clara	Soft, approachable
`victoria`	Victoria	Firm, professional
`vincent`	Vincent	Knowledgeable, authoritative

Preview all voices in the Developer Portal.

Step 3: Create a Session (Server-Side)

This is the server-side API route that your client will call. It creates a session, polls until ready, consumes credentials, and returns them.

Next.js App Router

typescript

// app/api/avatar/session/route.ts
import RunwayML from '@runwayml/sdk';

const client = new RunwayML();

export async function POST(request: Request) {
  const { avatarId } = await request.json();

  // 1. Create session
  const { id: sessionId } = await client.realtimeSessions.create({
    model: 'gwm1_avatars',
    avatar: { type: 'custom', avatarId },
  });

  // 2. Poll until ready
  let sessionKey: string | undefined;
  for (let i = 0; i < 60; i++) {
    const session = await client.realtimeSessions.retrieve(sessionId);

    if (session.status === 'READY') {
      sessionKey = session.sessionKey;
      break;
    }
    if (session.status === 'FAILED') {
      return Response.json({ error: session.failure }, { status: 500 });
    }

    await new Promise(r => setTimeout(r, 1000));
  }

  if (!sessionKey) {
    return Response.json({ error: 'Session timed out' }, { status: 504 });
  }

  // 3. Consume session to get WebRTC credentials
  const consumeResponse = await fetch(
    `${client.baseURL}/v1/realtime_sessions/${sessionId}/consume`,
    {
      method: 'POST',
      headers: {
        Authorization: `Bearer ${sessionKey}`,
        'X-Runway-Version': '2024-11-06',
      },
    }
  );
  const credentials = await consumeResponse.json();

  return Response.json({
    sessionId,
    serverUrl: credentials.url,
    token: credentials.token,
    roomName: credentials.roomName,
  });
}

Express.js

typescript

import RunwayML from '@runwayml/sdk';
import express from 'express';

const client = new RunwayML();
const app = express();
app.use(express.json());

app.post('/api/avatar/session', async (req, res) => {
  const { avatarId } = req.body;

  try {
    // 1. Create session
    const { id: sessionId } = await client.realtimeSessions.create({
      model: 'gwm1_avatars',
      avatar: { type: 'custom', avatarId },
    });

    // 2. Poll until ready
    let sessionKey: string | undefined;
    for (let i = 0; i < 60; i++) {
      const session = await client.realtimeSessions.retrieve(sessionId);

      if (session.status === 'READY') {
        sessionKey = session.sessionKey;
        break;
      }
      if (session.status === 'FAILED') {
        return res.status(500).json({ error: session.failure });
      }

      await new Promise(r => setTimeout(r, 1000));
    }

    if (!sessionKey) {
      return res.status(504).json({ error: 'Session timed out' });
    }

    // 3. Consume credentials
    const consumeResponse = await fetch(
      `${client.baseURL}/v1/realtime_sessions/${sessionId}/consume`,
      {
        method: 'POST',
        headers: {
          Authorization: `Bearer ${sessionKey}`,
          'X-Runway-Version': '2024-11-06',
        },
      }
    );
    const credentials = await consumeResponse.json();

    res.json({
      sessionId,
      serverUrl: credentials.url,
      token: credentials.token,
      roomName: credentials.roomName,
    });
  } catch (error) {
    console.error('Session creation failed:', error);
    res.status(500).json({ error: error instanceof Error ? error.message : 'Unknown error' });
  }
});

FastAPI (Python)

python

import time
import httpx
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from runwayml import RunwayML

app = FastAPI()
client = RunwayML()

class SessionRequest(BaseModel):
    avatar_id: str

@app.post("/api/avatar/session")
async def create_session(req: SessionRequest):
    # 1. Create session
    result = client.realtime_sessions.create(
        model='gwm1_avatars',
        avatar={'type': 'custom', 'avatar_id': req.avatar_id},
    )
    session_id = result.id

    # 2. Poll until ready
    session_key = None
    for _ in range(60):
        session = client.realtime_sessions.retrieve(session_id)

        if session.status == 'READY':
            session_key = session.session_key
            break
        if session.status == 'FAILED':
            raise HTTPException(status_code=500, detail=str(session.failure))

        time.sleep(1)

    if not session_key:
        raise HTTPException(status_code=504, detail='Session timed out')

    # 3. Consume credentials
    async with httpx.AsyncClient() as http:
        resp = await http.post(
            f"{client.base_url}/v1/realtime_sessions/{session_id}/consume",
            headers={
                "Authorization": f"Bearer {session_key}",
                "X-Runway-Version": "2024-11-06",
            },
        )
    credentials = resp.json()

    return {
        "session_id": session_id,
        "server_url": credentials["url"],
        "token": credentials["token"],
        "room_name": credentials["roomName"],
    }

Step 4: Connect from the Client

See

+integrate-character-embed

for the React SDK components that handle WebRTC connection and rendering. The simplest approach:

tsx

'use client';
import { AvatarCall } from '@runwayml/avatars-react';
import '@runwayml/avatars-react/styles.css';

export default function CharacterPage() {
  return (
    <AvatarCall
      avatarId="your-avatar-id"
      connectUrl="/api/avatar/session"
      onEnd={() => console.log('Call ended')}
      onError={(error) => console.error('Error:', error)}
    />
  );
}

Troubleshooting

API key errors: Key starts with
```
key_
```
followed by 128 hex chars. Ensure it's active.
No credits: Account must have prepaid credits before starting a call.
Session timeout: The 60-iteration poll loop waits ~60 seconds. If sessions consistently time out, check your tier's concurrency limits.
Credentials already consumed: Session credentials are one-time use. If WebRTC fails after consume, create a new session.

Debug logging

tsx

<AvatarCall
  avatarId="your-avatar-id"
  connectUrl="/api/avatar/session"
  onError={(error) => {
    console.error('Avatar error:', error);
    console.error('Error name:', error.name);
    console.error('Error message:', error.message);
    if (error.cause) console.error('Cause:', error.cause);
  }}
/>

Monitor session state

tsx

import { useAvatarSession } from '@runwayml/avatars-react';

function DebugInfo() {
  const { state, sessionId, error } = useAvatarSession();
  return (
    <pre>
      {JSON.stringify({ state, sessionId, error: error?.message }, null, 2)}
    </pre>
  );
}

Test with minimal setup