Live — Serving 10K+ Generations

Mixture of Agents5 NVIDIA models. Parallel execution. One superior result.

10,342
CAM Generations
5
AI Models
1,287
Video Generations
99.8%
Uptime
⚡ Try 5.1 — Free 🧠 Try 5.2 — Chat Mode API Docs ↓
Proven at Scale

What We've Built With MOA

Real projects powered by our 5-model parallel pipeline — from batch image generation to video model deployment.

Image Generation

10K+ CAM Family Scene Generations

Batch-rendered over 10,000 cinematic family animation scenes using MOA-orchestrated prompt engineering across all 5 models. Nemotron handled structure, GLM 5.2 managed bilingual style prompts, DeepSeek V4 Pro handled long-context scene continuity.

10,342
Scenes Generated
5
Models Used
~2.4s
Per Prompt
Video AI

Video Model Deployment Pipeline

Deployed multi-model video generation workflows serving 1,287 renders. MOA aggregates model-specific parameters (duration, style, resolution) into unified generation configs. Step 3.7 Flash handles real-time parameter validation.

1,287
Videos Rendered
3
Video Models
99.1%
Success Rate
Code Generation

Production Code Synthesis

Advanced Fibonacci with memoization, FastAPI microservices, Rust CLI tools, GraphQL APIs — all generated by 5-model consensus. Nemotron writes, Minimax optimizes, GLM 5.2 documents, DeepSeek V4 Pro DeepSeek V4 reasons deeply.

4/5
Models Successful
4,754
Avg Response Chars
~12s
Full Pipeline
Multi-Model

E-Commerce Dashboard Design

Designed and prototyped a full merchant onboarding dashboard using MOA for UI structure generation. GLM 5.2 handled bilingual labels, DeepSeek V4 Pro managed long-form layout specs, Nemotron aggregated into production-ready HTML.

63
UI Screens
5
Models
97KB
HTML Output
Live Output

See MOA in Action

Real aggregated output from our 5-model pipeline — Fibonacci with memoization, generated by Nemotron, Minimax, GLM 5.2, DeepSeek V4 Pro, and Step 3.7 Flash.

Output: Fibonacci with Memoization (Aggregated)

from functools import lru_cache # Method 1: Pythonic — @lru_cache (Recommended) @lru_cache(maxsize=None) def fib_lru(n: int) -> int: if n < 2: return n return fib_lru(n - 1) + fib_lru(n - 2) # Method 2: Manual dictionary memoization def fib_manual(n: int, memo: dict = None) -> int: if memo is None: memo = {} if n < 2: return n if n in memo: return memo[n] memo[n] = fib_manual(n - 1, memo) + fib_manual(n - 2, memo) return memo[n] # Method 3: Iterative O(1) space (Best for large n) def fib_iterative(n: int) -> int: a, b = 0, 1 for _ in range(n): a, b = b, a + b return a # Performance: O(2ⁿ) naive → O(n) memoized → O(n)/O(1) iterative # fib(100) computes instantly with any memoized approach
Model Consensus

4 of 5 Models Agreed

Nemotron, Minimax, GLM 5.2, and Step 3.7 all converged on @lru_cache as the recommended approach. DeepSeek V4 Pro was offline during this run.

Pipeline Stats

Parallel Execution: ~12s

All 5 models received the prompt simultaneously. Aggregation via Nemotron 550B synthesized 4 responses into one comprehensive answer.

Quality

3 Implementation Methods

Aggregator identified consensus: @lru_cache (Pythonic), manual dict (educational), iterative (production). All consistent across models.

Developer Feedback

What Teams Are Saying

Real feedback from developers using CUTEADMOA in production workflows.

★★★★★
The 5-model parallel execution is a game-changer for our code review pipeline. Nemotron catches edge cases that single-model systems miss. The auto-failover across 3 API keys means zero downtime.
AK
Alex K.
Senior Engineer, Fintech Startup
★★★★★
We used CUTEADMOA-5.2's conversation memory for a multi-turn code migration from Django to FastAPI. 17 turns, each building on the last. The aggregated results were consistently better than any single model.
ML
Maria L.
Backend Lead, SaaS Platform
★★★★★
Batch-generated 10K+ Midjourney prompts for our animation studio using all 5 models. GLM 5.2 handled bilingual prompts flawlessly. The aggregation layer meant we got one unified creative direction per scene.
RJ
Rahul J.
Creative Director, Animation Studio
★★★★☆
Solid platform. The sequential mode is great for iterative design work — each model builds on the previous. Would love to see streaming support in the next version.
SC
Sarah C.
Product Designer, Web3 Company
Two Flavors

Choose Your Workflow

5.1 for one-shot generation. 5.2 for multi-turn conversations with memory.

Stable · v5.1.0

CUTEADMOA-5.1

Production-ready one-shot MOA pipeline.

  • 5 models parallel or sequential
  • Nemotron 550B aggregator
  • 3-key auto-failover
  • REST API + CLI + Python SDK
  • Timestamped JSON output for traceability
Open Generator API Docs Architecture
Latest · v5.2.0

CUTEADMOA-5.2

Conversation-aware MOA with chat memory.

  • All 5.1 features + conversation memory
  • Chat-style UI with model sidebar
  • Multi-turn context retention
  • Per-conversation history tracking
  • Same 5-model pipeline, persistent memory
Open Chat API Docs PDF Guide
Get Started in 30 Seconds

API Quickstart

One POST request. Five models. One aggregated response.

# CUTEADMOA-5.1 — One-shot generation curl -X POST https://cuteadmoa.site/CUTEADMOA5.1/api/generate \ -H "Content-Type: application/json" \ -d '{ "task": "Build a REST API with FastAPI and JWT auth", "mode": "parallel", "models": ["nemotron", "minimax", "glm52", "deepseek", "step37"] }' # Response includes: aggregated result + all 5 individual responses
# CUTEADMOA-5.2 — Multi-turn with conversation memory curl -X POST https://cuteadmoa.site/CUTEADMOA5.2/api/generate \ -H "Content-Type: application/json" \ -d '{ "task": "Design a PostgreSQL schema for a blog", "conversation_id": "blog-project", "mode": "parallel", "models": ["nemotron", "deepseek", "step37"] }' # Second turn — context is preserved! curl -X POST https://cuteadmoa.site/CUTEADMOA5.2/api/generate \ -H "Content-Type: application/json" \ -d '{ "task": "Add full-text search and performance indexes", "conversation_id": "blog-project" }'
import requests # 5.1 — One-shot r = requests.post( "https://cuteadmoa.site/CUTEADMOA5.1/api/generate", json={"task": "Build a React component for a data table"} ) print(r.json()["result"]["aggregated"]) # 5.2 — Multi-turn conversation conv_id = "my-project" r1 = requests.post( "https://cuteadmoa.site/CUTEADMOA5.2/api/generate", json={"task": "Design system architecture", "conversation_id": conv_id} ) r2 = requests.post( "https://cuteadmoa.site/CUTEADMOA5.2/api/generate", json={"task": "Add auth layer", "conversation_id": conv_id} ) print(r2.json()["memory_turns"]) # 2
const generate = async (task, convId) => { const res = await fetch('https://cuteadmoa.site/CUTEADMOA5.2/api/generate', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ task, mode: 'parallel', conversation_id: convId // optional for 5.2 memory }) }); return (await res.json()).result.aggregated; }; // Usage const answer = await generate('Write a sorting algorithm in Rust'); console.log(answer);