MACCP Framework

Multi-Agent Platform

Our production-ready platform for intelligent agent orchestration, featuring advanced pruning strategies and real-time optimization for enterprise-scale AI systems.

Live Products

Try Suanfamama's AI products, running in production today.

📄

AI Paper Reviewer

Upload a paper and receive an AI peer review instantly - quality scores across 10 dimensions, likelihood of acceptance, reference verification, and a ready-to-share PPTX report.

  • 10-dimension quality score
  • Acceptance likelihood & feedback
  • Reference verification
  • PPTX report export
Try the Reviewer
🧥

Fashion Agent

Your AI companion for fashion buying and design - generate images and videos, explore trend looks, and map runway inspiration to real products.

  • Image & video generation
  • Runway look exploration
  • Runway-to-product mapping
  • Mobile-first AI-native app
Open Fashion Agent

Platform Architecture

A modular, scalable architecture designed for enterprise deployment.

1

Capability Index

Vector-based agent scoring with domain-specific sharding for optimal performance

  • Vector Search (HNSW/IVF)
  • Domain Sharding
  • Real-time Updates
  • Scalable Storage
2

Orchestrator

Central coordination layer for intelligent agent selection and request routing

  • Multi-strategy Support
  • Load Balancing
  • Fault Tolerance
  • Metrics Collection
3

Pruning Engine

Advanced pruning strategies with static and dynamic policy selection

  • Static Threshold
  • Static Ratio
  • Dynamic RL Policy
  • Safety Constraints

How It Works

From query to response, our platform optimizes every step of the agent selection process.

1

Query Reception

Incoming queries are analyzed to extract task requirements and context.

2

Capability Matching

The Capability Index quickly identifies relevant agents based on task requirements.

3

Intelligent Pruning

Pruning strategies filter candidates to optimal subset using static or dynamic policies.

4

Agent Execution

Selected agents process the query in parallel or sequential mode based on requirements.

5

Response Aggregation

Results are collected, validated, and returned with quality metrics.

Platform Performance

Enterprise-grade performance metrics from production deployments.

<50ms

Average Latency

Query response time

99.9%

Availability

System uptime

10K+

Concurrent Queries

Requests per second

1M+

Agents Supported

Scalable architecture

Seamless Integration

Deploy our platform with your existing infrastructure using our comprehensive SDKs and APIs.

P

Python

Native Python SDK with async support and comprehensive type hints

from maccp import Orchestrator

orch = Orchestrator(
  pruning_mode="dynamic"
)
results = await orch.select(task)
J

JavaScript

Modern JavaScript/TypeScript SDK for Node.js and browser environments

import { Orchestrator } from "@maccp/sdk";

const orch = new Orchestrator({
  pruningMode: "dynamic"
});
const results = await orch.select(task);
R

REST API

Simple REST API for easy integration with any programming language

POST /api/v1/select
{
  "task": "your query",
  "pruning_mode": "dynamic"
}

Ready to Deploy?

Get started with our platform today and transform your AI agent infrastructure.