AutoPilot AI Workspace

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  Landing page of AutoPilot

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  Authication by Firebase (OAuth)

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  Dashboard Page it includes Active Pipeline,Neural Suggestions,Workspace Output,Session History,Neural Core V5 Engine

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  Main command page , It includes 5 agents to communicate the best business plain and goals

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  Agnets MeDo V6 Core agent actions

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  It is node to node simulation workflow

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  It is the business insights goal and plans and other social networks

Inspiration The idea came from watching talented people — founders, developers, and marketers — spend more time managing work than actually building.

Every project follows the same exhausting cycle: Research -> Plan -> Build -> Market -> Measure -> Repeat

We asked: What if AI could handle that entire workflow automatically? We were also inspired by how elite teams operate. Great companies succeed because specialized experts collaborate efficiently. We wanted to recreate that experience using AI agents working together inside one synchronized workspace.

What It Does AutoPilot AI Workspace transforms a single idea into a fully executed strategy using autonomous AI agents. You simply describe your goal, and specialized agents begin working instantly: Orchestrator — coordinates workflows and task execution Product Manager — creates prioritized features and user stories Developer — designs architecture and implementation plans Marketer — generates GTM strategies and growth plans Analyst — tracks metrics and optimizes execution

Everything operates inside a unified real-time workspace with live workflows, dashboards, and synchronized collaboration.

How We Built It Frontend Next.js 14 TypeScript Tailwind CSS Framer Motion React Flow Firebase Authentication Backend FastAPI (Python) WebSockets for real-time communication REST APIs for workflows and tasks AI Architecture Custom orchestration engine for synchronized AI collaboration Infrastructure Render Deployment PostgreSQL Database Real-time event streaming system

Challenges We Faced Multi-Agent Coordination Ensuring agents collaborate without duplicating work or conflicting with each other required advanced orchestration and shared context handling.

Real-Time Synchronization Streaming live workflow updates, dashboards, and agent activities simultaneously demanded efficient WebSocket architecture.

Context Management Maintaining awareness across agents while optimizing token usage required careful summarization and memory strategies.

Interactive UI Design Balancing real-time information density with a clean and usable interface took multiple design iterations.

Achievements Built a real-time multi-agent collaboration system Developed a complete AI workspace during the hackathon Designed a live workflow visualization system Delivered a polished production-style UI experience Integrated frontend, backend, AI, auth, and database end-to-end

What We Learned Multi-agent systems require strong orchestration design Real-time AI experiences dramatically improve usability Prompt architecture is as important as software architecture Focused execution beats overbuilding during hackathons

Future Plans Agent Memory Persistent memory for personalized workflows and long-term learning.

Third-Party Integrations GitHub, Notion, Slack, Linear, and Analytics integrations.

Custom AI Agents Allow users to create specialized agents with custom behaviors.

Team Collaboration Shared multi-user AI workspaces for collaborative execution.

Voice Interface Users can describe goals using voice while agents execute automatically.

Built With

• dagre
• fastapi
• firebase(auth)
• framer
• langchain
• langgraph
• lucidereact
• mermaid
• next.js
• passlib
• pydantic
• python
• pythonjose
• react
• reacthottoast
• reactmarkdown
• render
• supabase
• tailwind
• typescript
• uvicorn
• vercel
• websockets
• zustand