neon-system
dev nayakBuilding a system that orchestrates multiple AI agents—one to research and another to refine your creative intent—is a great way to move beyond simple chat interactions. Since you are already working with Python and integrating NLP into your projects, you can use a Multi-Agent Orchestration approach.To get the "desirable result" you described, you need a System Prompt that defines the behavior of the orchestrator. Below is a refined, comprehensive prompt designed to turn a standard LLM into the multi-agent system you envisioned.The Master System PromptCopy and paste this into your AI tool to initialize the environment:Act as a Multi-Agent Orchestrator. Your goal is to process my requests by coordinating two distinct internal agents: Explorer and Synergy.1. Agent Definitions:Explorer (The Researcher): This agent specializes in real-time information retrieval. When a query requires current data, Explorer must simulate a web search, analyze the findings, and provide a concise, factual summary with sources.Synergy (The Prompt Master): This agent is an expert in generative AI (Images, Video, Music). Synergy’s job is to take the user’s intent and the Explorer’s data to craft the "Perfect Prompt." If the user's intent is vague, Synergy must ask 3–5 clarifying questions regarding style, mood, perspective, and technical specs before generating the final prompt.2. Operating Instructions:Machine Logic Translation: Treat all inputs as high-level instructions to be converted into actionable "machine-understandable" tasks (embeddings-ready logic).Instantaneous Output Generation: Once the prompt is finalized, provide the exact text ready for an Image Generator (e.g., Imagen/DALL-E) or Video Generator (e.g., Veo/Sora).UI Features Integration: Act as if you have access to the "Plus Menu" features: Upload, Drive, Music, Video, and Image generation.3. Workflow Protocol:Analyze the request.Call Explorer if facts are needed.Call Synergy to refine the creative vision.Interaction: If I ask to create something, Synergy must respond first with: "I am ready to craft this. To make it perfect, please tell me: [Question 1, 2, 3]..."Where should we start?Understanding the Technical ArchitectureFor your research paper on AI-powered detection or your phishing project, understanding how these agents communicate is vital. In a real-world application, this is often handled via Retrieval-Augmented Generation (RAG).Implementation Strategy for DevelopersIf you are building this as a standalone application (using Python/JavaScript), follow this structural map:ComponentResponsibilityRecommended TechOrchestratorRoutes tasks between agents.LangChain or CrewAIExplorerFetches live data/API results.Tavily API or Google Search APISynergyRefines and formats prompts.Gemini 1.5 Pro (System Instructions)Output ToolsGenerates the actual media.DALL-E 3 API (Images), Veo (Video)How to use this for your projectWhen you test the prompt above, try an input like:"I want to create a video about the history of cybersecurity in Mumbai."The system will then trigger the Explorer to find facts about Mumbai's cyber history and Synergy to ask you what visual style the video should have (e.g., "Cyberpunk," "Documentary," or "3D Animation").
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System Requirements
neon-system
Introduction
The neon-system is designed to orchestrate multiple AI agents to enhance creative processes and real-time data retrieval. This system leverages a Multi-Agent Orchestration approach, utilizing distinct agents to fulfill complex tasks beyond simple chat interactions. The primary goal is to coordinate these agents to achieve a seamless integration of research and creative intent refinement.
System Overview
The neon-system is an advanced AI orchestration platform that integrates multiple agents to perform specialized tasks. The system is built on a foundation of Python and NLP technologies, utilizing a Multi-Agent Orchestration approach. The core components include:
- Explorer (The Researcher): Specializes in real-time information retrieval, simulating web searches, analyzing findings, and providing concise summaries with sources.
- Synergy (The Prompt Master): Focuses on generative AI, crafting perfect prompts based on user intent and Explorer's data, while asking clarifying questions to refine the creative vision.
- Orchestrator: Routes tasks between Explorer and Synergy, ensuring smooth communication and task execution.
- Output Tools: Generate media outputs such as images and videos using APIs like DALL-E 3 and Veo.
The system employs Retrieval-Augmented Generation (RAG) for efficient communication between agents, enhancing the overall functionality and responsiveness.
Functional Requirements
- As a User, I should be able to initiate a request that triggers the Explorer to fetch real-time data.
- As a User, I should be able to receive a refined creative prompt from Synergy based on my initial input and Explorer's data.
- As a User, I should be able to specify the type of media output I desire, such as images or videos.
- As a Developer, I should be able to integrate the orchestrator with LangChain or CrewAI for task routing.
- As a Developer, I should be able to utilize APIs like Tavily or Google Search for data retrieval by Explorer.
- As a Developer, I should be able to use DALL-E 3 API for image generation and Veo for video creation.
User Personas
- User: Individuals seeking to create media content with enhanced AI-driven research and prompt refinement.
- Developer: Technologists integrating and maintaining the neon-system, ensuring seamless operation and feature expansion.
Visuals Colors and Theme
- primary: #1A73E8 (a vibrant blue for brand identity)
- primary_light: #4D90FE (a lighter blue for hover states)
- secondary: #FF6F61 (a coral hue for emphasis and links)
- accent: #FFD700 (a bright gold for CTAs and active states)
- highlight: #FFA500 (a warm orange for notifications and hover states)
- bg: #F5F5F5 (a soft gray for the background)
- surface: rgba(255, 255, 255, 0.8) (a translucent white for cards and panels)
- text: #333333 (a dark gray for primary text)
- text_muted: #777777 (a muted gray for secondary text)
- border: rgba(200, 200, 200, 0.5) (a subtle gray for borders)
Signature Design Concept
The neon-system's homepage will feature an interactive galaxy map where each star represents a feature or section of the system. Users can click on stars to open detailed task cards, drag to rotate the galaxy cluster, and hover to highlight connections between features. This dynamic and engaging interface will be built using @react-three/fiber and @react-three/drei for 3D interactions, providing a vivid and memorable first impression.
Interaction Model & Motion Direction
The landing page will utilize a "parallax" interaction model, creating a layered depth effect as users scroll. Decorative elements like atmospheric blobs and distant shapes will move at different speeds, while core content remains in the natural flow. This approach is ideal for storytelling and creating a visually rich first impression. Internal pages will adopt a "static" model for clarity and ease of use.
Non-Functional Requirements
- The system must handle concurrent requests efficiently.
- The system should provide responses within a reasonable time frame to ensure user satisfaction.
- The system must be scalable to accommodate future expansions and additional features.
Tech Stack
- Frontend: React for Web
- Backend: Python, FastAPI
- Database: MongoDB
- AI Models: Gemini 1.5 Pro for prompt refinement
- AI Tools: LangChain for orchestration
- Local Orchestration: Docker, docker-compose
- Server-side Orchestration: Kubernetes
Assumptions and Constraints
- The system assumes users have a basic understanding of AI-driven content creation.
- The system is constrained by the availability and reliability of third-party APIs for data retrieval and media generation.
Glossary
- Multi-Agent Orchestration: A system design approach that coordinates multiple specialized agents to perform complex tasks.
- RAG (Retrieval-Augmented Generation): A method for enhancing AI communication by integrating real-time data retrieval into the generation process.
- API (Application Programming Interface): A set of protocols for building and interacting with software applications.
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