gemini-flow

Gemini-Flow is an enterprise-grade AI orchestration platform that provides both A2A (agent-to-agent) and MCP (machine coordination protocol) capabilities. It ships with a production-ready architecture, real Google AI services integration, and a robust agent swarm framework designed for enterprise-scale deployments. The MCP support enables coordinated multi-agent workflows with fault tolerance and consensus validation, making it suitable for complex orchestration tasks across diverse AI services. Use the Gemini CLI to initialize your topology, spawn agent swarms, and manage workflows across distributed agents, all while benefiting from automatic failover and optimized routing.

To get started, install the Gemini-Flow CLI (via npm) and initialize your environment to enable MCP and A2A protocols. You can spawn hundreds of specialized agents, coordinate tasks with the Jules Tools integration, and deploy Google AI services through a unified API. The platform provides commands for creating agent swarms, launching workflows, and managing extensions that enhance orchestration capabilities. This combination empowers teams to build scalable AI applications with enterprise-grade reliability.

Prerequisites:

• Node.js (recommended LTS) and npm installed
• Basic familiarity with CLI tools

Step-by-step installation:

1. Verify Node.js and npm are installed node -v npm -v

2. Install the Gemini-Flow CLI globally (this pulls the MCP-capable package andCLI helpers) npm install -g @clduab11/gemini-flow

3. Initialize your Gemini-Flow environment with MCP and A2A support gemini-flow init --protocols a2a,mcp --topology hierarchical

4. (Optional) Start spawning agent swarms and deploy workflows gemini-flow agents spawn --count 50 --specialization "enterprise-ready"

5. If you need to use the MCP server directly via npx (for environments without a global install) npx -y @clduab11/gemini-flow

Notes:

• The project repository is archived/read-only as of January 29, 2026. Ensure you are using a copy or fork suitable for your needs.

Tips and caveats:

• This MCP server is provided via the Gemini-Flow package, which is archived. Consider forking or using a running instance that remains compatible with MCP workflows.
• If you encounter environment-related issues, ensure your Node.js and npm versions are compatible with the package and that network access to npm registries is allowed.
• The MCP configuration can be adjusted via the mcp_config object to point to different package sources or runners if you adapt the deployment in your environment. For example, you can switch to a local node-based setup if you export a server.js wrapper.
• Common issues include permission errors when installing global packages; use npx or local installations as alternatives.
• When integrating Google AI services, ensure API keys and service accounts are configured in your environment according to Gemini-Flow documentation.
• If you plan to deploy at scale, monitor latency (<75 ms routing) and ensure Byzantine fault tolerance settings align with your reliability requirements.