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LYRAIOS MCP Server

@GalaxyLLMCI

RepositoryHomepage

Basic information

Category

Other

License

Apache-2.0

Transports

stdio

Author

GalaxyLLMCI

About LYRAIOS MCP Server

LYRAI is a Model Context Protocol (MCP) operating system for multi-AI AGENTs designed to extend the functionality of AI applications by enabling them to interact with financial networks and blockchain public chains. The server offers a range of advanced AI assistants, including b

LYRAIOS MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "lyraios": {
      "command": "python3",
      "args": [
        "-m",
        "venv",
        "aienv"
      ]
    }
  }
}

LYRAIOS MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is LYRAIOS?

LYRAIOS (LLM-based Your Reliable AI Operating System) is a Model Context Protocol (MCP) operating system designed for multi-AI agents. It extends AI applications like Claude Desktop and Cursor to interact with financial networks and blockchain public chains (SOLANA, ETH, etc.) and provides specialized assistants for blockchain operations, fintech analysis, and educational training.

How to use LYRAIOS?

Deploy LYRAIOS using Docker for containerized development or production. Access the system through its Streamlit web UI, command-line interface (CLI), or REST API (FastAPI backend). Configure environment variables for API keys, database connections, and tool integrations. Tool developers can define JSON manifests and register them via the Tool Integration Protocol.

Key features of LYRAIOS

  • Open Protocol Architecture with plug-and-play third-party tool integration
  • Multi-Agent Collaboration Engine for distributed task orchestration
  • Cross-Platform Runtime Environment for seamless AI agent migration
  • Built-in tools: Calculator, Web Search, Financial Analysis, File Management
  • Specialized assistant teams: Python Assistant and Research Assistant
  • AI Memory System with short-term and long-term vector database storage
  • Security & Access Control with authentication, authorization, and rate limiting

Use cases of LYRAIOS

  • Execute blockchain operations: retrieve wallet balances, transfer funds, deploy smart contracts, manage tokens on SOLANA/ETH
  • Generate fintech market analysis and summary reports using financial data tools
  • Power multi-agent learning and training systems for the education sector
  • Automate enterprise-grade complex workflows with dynamic agent collaboration
  • Build personal or enterprise digital employees that migrate across platforms

Frequently asked questions

1

What are the runtime dependencies of LYRAIOS?

LYRAIOS requires Python, Streamlit, FastAPI, PostgreSQL with PGVector for vector storage, Docker for containerization, and integration with OpenAI and other LLMs.

2

Which blockchains and financial networks does LYRAIOS support?

LYRAIOS supports blockchain public chains including SOLANA and ETH for operations like retrieving wallet addresses, listing balances, transferring funds, deploying smart contracts, on-chain lending, calling contract functions, and managing tokens.

3

How does LYRAIOS handle data storage and memory?

LYRAIOS uses PostgreSQL for persistent storage of conversations and assistant states, PGVector for vector-based semantic search, and supports short-term conversation memory as well as long-term knowledge base integration.

4

What transport and authentication mechanisms does LYRAIOS use?

LYRAIOS communicates via the MCP protocol, REST API, Web UI, and CLI. Security features include TLS for remote connections, API key management, authentication and authorization, rate limiting, and input validation.

5

Is LYRAIOS open source and how can I extend it?

LYRAIOS is open source (GitHub). Developers can integrate custom tools using the standardized Tool Integration Protocol with JSON manifests, pluggable adapters (REST API, Python plugins), and a secure execution environment.

Comments

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