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Morpheus

Morpheus Open Source Platform for AI Smart Agents.

Morpheus is an open-source, decentralized AI platform that aims to make Web3 accessible to everyone through natural language interactions.

The platform combines foundational AI models, embeddings, and smart agents to create a user-friendly interface for interacting with blockchain technology.

Tokenomics​

Fair launch model with the MOR token, distributing 24% each to community builders, coders, compute providers, and capital providers.

Participation​

Participate by providing staked ETH, contributing code, offering compute power, or building front-end applications. Morpheus emphasizes individual ownership and control of data, positioning itself as a personal AI assistant that can seamlessly integrate with various Web3 services and decentralized storage solutions like Filecoin.

  • Download the Morpheus full node and connect your Web3 wallet
  • Choose your participation method: community builder, coder, compute

For Developers:

  • Contribute code to the Morpheus software or build specialized agents
  • Register your contributions to earn MOR tokens
  • Explore opportunities to create front-end applications and tools

For Investors:

  • Consider providing staked ETH to earn MOR tokens
  • Understand the 90-day bootstrapping period and token claim process
  • Research the potential long-term value of the MOR token and its utility in the ecosystem

For Compute Providers:

  • Set up infrastructure to offer compute power to the network
  • Register as a compute provider to earn MOR tokens

Engineering​

The Morpheus platform is designed to be highly modular and customizable. Users can swap out foundational models, add custom embeddings, create new smart agents, and develop custom user interfaces. The compute layer can be provided by various decentralized providers, allowing for flexibility in resource allocation.

Developers can extend the platform's functionality through custom plugins and smart agents. The atomic governance model allows for a permissionless ecosystem where anyone can contribute new components or improvements without centralized approval.

Tech Stack​

Morpheus tech stack layers and components, along with details on which can be swapped out or customized:

  1. Foundational Models:
    • Base layer of general intelligence
    • Examples: LLaMA 2, Mixtral, New Hermes
    • Customizable: Users can choose different open-source models based on preference
  2. Embeddings:
    • Database files providing context to foundational models
    • Contains information about smart contracts and Web3 topics
    • Customizable: Users can add or modify embeddings to expand context
  3. Smart Agents:
    • Built on top of foundational models and embeddings
    • Perform specific tasks (e.g., token swaps, transactions)
    • Customizable: Developers can create and deploy new smart agents
  4. User Interfaces:
    • Front-end applications for user interaction
    • Customizable: Developers can create custom interfaces
  5. Compute Layer:
    • Provides processing power for running models and agents
    • Customizable: Users can choose different compute providers
  6. Wallet Integration:
    • Connects to Web3 wallets for transactions
    • Customizable: Can integrate with various wallet providers
  7. Smart Rank System:
    • Algorithm for ranking smart contracts based on intent and context
    • Likely customizable by developers
  8. Decentralized Storage:
    • Integration with systems like Filecoin for data storage
    • Customizable: Can potentially integrate with different storage solutions
  9. Layer 2 Integration:
    • Uses Arbitrum for lower fees and faster transactions
    • Potentially customizable to use other Layer 2 solutions
  10. Non-Fungible Agents (NFAs):
    • Unique, ownable AI agents within the Morpheus ecosystem
    • Customizable: Developers can create new types of NFAs

Purpose​

Explore use cases for using AI empowered by Crypto to explore arbitrage opportunities from asynchronous information.

Solving AI centralization: Prevent the centralization of power under a single entity by distributing the models, compute, data, and decision-making across decentralized networks.

Mantra

Not your model, not your mind

  1. Decentralize AI models by running open-source models on local devices ("Edge AI") instead of centralized cloud servers. This allows users to own the model and keep their data private.
  2. Use decentralized computing power to train smaller AI models or fine-tune large models, overcoming the hardware limitations of individual devices.
  3. Decentralize the training data for AI models to prevent political bias and ensure the outputs are not skewed.
  4. Leverage blockchain technology and zero-knowledge proofs to prove the authenticity of AI-generated content and prevent deep fakes, which will be crucial for events like elections.
  5. Incorporate AI into decentralized applications (dApps) and protocols, enabling features like programmatic rewards for creators, trend identification from on-chain data, and scalable content production.
  6. Explore using AI for automated decision-making and governance in decentralized autonomous organizations (DAOs), with transparent parameters that the community can adjust.
  7. Utilize crypto economic incentives to build a consumer-grade network where participants contribute computing power for decentralized AI training and inference.

Blockchain Data​

Engineering​

Planning​

  • Identify the specific AI capabilities needed for your blockchain application
  • Determine which blockchain platform and AI services are most suitable
  • Evaluate the integration method (e.g., Chainlink Functions for connecting blockchains with external AI services)
  • Consider data privacy and security implications of using external AI services
  • Plan for scalability and cost management of AI API calls

Delivery​

Execution and Testing Phase

  • Set up development environment with necessary tools (e.g., Chainlink Functions beta, OpenAI API)
  • Implement proper environment variable management for API keys and secrets
  • Use a request configuration file to define parameters for AI requests
  • Craft effective prompts for AI services to get desired outputs
  • Implement error handling and response parsing for AI API calls
  • Simulate AI requests off-chain before deploying to test functionality
  • Optimize token usage and API call frequency to manage costs
  • Test different AI models or services to find the best fit for your use case

Deployment​

  • Deploy smart contracts to the chosen blockchain network
  • Set up and fund a subscription for Chainlink Functions (if using)
  • Implement proper billing and payment mechanisms for AI service usage
  • Ensure secure storage and transmission of API keys and other secrets
  • Set up monitoring and logging for AI requests and responses
  • Implement fallback mechanisms in case of AI service failures
  • Consider implementing caching strategies to reduce redundant AI calls
  • Plan for updates and maintenance of both smart contracts and AI integration

Best Practices​

  • Always encrypt and securely manage API keys and sensitive data
  • Use simulation and testing environments before deploying to mainnet
  • Implement rate limiting and error handling for AI API calls
  • Regularly update and maintain both blockchain and AI components
  • Monitor and optimize gas costs for on-chain operations
  • Implement proper access controls for smart contract functions
  • Document the integration process and provide clear instructions for users
  • Stay informed about updates and changes in both blockchain and AI technologies

Attachments​

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