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Effort Agent

TechnologiesDjango, Python, OpenAI, LangChain, React, Next.js, AWS
Live ProjectVisit Website

Architecture at a Glance

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flowchart TD
  user((User))

  subgraph frontendLayer [Frontend & Collaboration]
    ui[Next.js / React]
    collab[Tiptap / Yjs / Hocuspocus]
  end

  subgraph logicLayer [Backend & AI Orchestration]
    api[Django / Python]
    langGraph[LangGraph Agent Engine]
  end

  subgraph dataAiLayer [Data & AI Services]
    ragDB[PostgreSQL / pgvector]
    llm[OpenAI / LangChain]
  end

  cloud[AWS Infrastructure]

  user --> ui
  ui <--> collab
  ui <--> api
  api <--> langGraph
  langGraph <--> llm
  langGraph <--> ragDB
  api <--> ragDB
  api --> cloud

The Problem

Traditional AI tools feel like disconnected chatbots, forcing users to manage context, latency, and fragmented workflows manually.

The Solution

We engineered a stateful multi-agent system that orchestrates specialized reasoning graphs, coupled with a real-time collaborative editor. By masking latency through dynamic UI feedback and granular state disclosure, the platform turns computational wait times into a polished, high-trust user journey.

The Impact

This architecture bridges the gap between raw LLM power and professional utility, enabling users to generate, refine, and co-create complex documents with unprecedented precision.

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