$ npm install -g hanabi-cli

$ hanabi --help

$ hanabi

$ hanabi reset

$ hanabi ask "how's the weather tomorrow?"
$ hanabi ask "generate a react todo app" > ./todo-app-instructions.md

{
	"llms": [
		{
      "id": "fdd1abc5-6791-4c29-b754-4f1174692c22",
      "provider": "OpenAI",
      "apiKey": "your-api-key",
      "apiVersion": "2025-01-01-preview"
    },
	],
	"defaultModel": {
		"provider": "openai",
		"model": "gpt-4",
		"temperature": 0.7
	}
}

{
	"llms": [
		// ...
	],
	"defaultModel": {
		// ...
	},
	"mcpServers": {
		"home-ai": {
			"name": "Home AI",
			"transport": "stdio",
			"command": "node",
			"args": ["c:/folder/home-mcp.js"]
		},
		"context7": {
			"name": "context7",
			"transport": "stdio",
			"command": "npx",
			"args": ["-y", "@upstash/context7-mcp@latest"]
		},
		"browser-use": {
			"name": "Browser-use automation",
			"transport": "sse",
			"url": "http://172.17.0.1:3003/sse",
			"headers": {
				"authentication": "Bearer api-token"
			}
		},
		// npx stdio approach is flaky & slow. highly recommend
		// to npm install -g  and use the following.
		// see https://github.com/modelcontextprotocol/servers/issues/64
        // "file-system": {
        // 	"name": "file system",
        // 	"transport": "stdio",
        // 	"command": "path/to/your/node.exe",
        // 	"args": [
		// "path/to/global/node_modules/@modelcontextprotocol/server-filesystem/dist/index.js", "."]
        // },
		"tavily": {
			"name": "Tavily Search",
			"transport": "stdio",
			"command": "npx",
			"env": {
				"TAVILY_API_KEY": "your-api-key"
			},
			"args": ["-y", "tavily-mcp@0.1.4"]
		},
		// npx is slow! use above recommendation
		"file-system": {
			"name": "file system",
			"transport": "stdio",
			"command": "npx",
			"args": ["-y", "@modelcontextprotocol/server-filesystem", "."]
		},
		"my-calendar": {
			"name": "My Calendar",
			"transport": "streamable_http",
			"url": "http://172.17.0.1:3001/mcp",
			"headers": {
				"authentication": "Bearer my-auth-token"
			}
		}
	}
}

// /.hanabi.json
{
	"exclude": ["certificates", "screenshots/**/*", "passwords/*", "*.pid"],
	"llms": [
		// ...
	],
	"defaultModel": {
		// ...
	}
}

// .hanabi.json
{
	"envs": {
		"FOO": "bar",
		"MY_DOC: "file://./README.md",
		"ALLOWED_ORIGIN": "http://localhost:3042"
	},
	"llms": [
		// ...
	],
	"defaultModel": {
		// ...
	}
}

OPENAI_API_KEY=xxx
GOOGLE_GENERATIVE_AI_API_KEY=xxx
DEEPSEEK_API_KEY=xxx
ANTHROPIC_API_KEY=xxx
GROQ_API_KEY=xxx
XAI_API_KEY=xxx

# MCP keys
TAVILY_API_KEY=xxx

# act as a polite chat bot collecting user feedback via conversational loop.

## context

Product name is ${PRODUCT_NAME}

## ask user the follwing questions one by one and prints a well formatted report

- What is your name
- How do you feel about our product? (classify answer as "Bad" | "OK" | "great")
- What is your company

// .hanabi.json
{
	"answerSchema": {
		"type": "object",
		"required": ["answer"],
		"properties": {
			"reason": {
				"type": "string",
				"description": "detailed reasoning for the final output."
			},
			"answer": {
				"type": "string",
				"description": "the final output without reasoning details. For math related question, this is the final output number."
			}
		}
	},
	"serve": {
 		...
	},
	"llms": [
		// ...
	],
	"defaultModel": {
		// ...
	}
}

// .hanabi.json
{
	"serve": {
 		"mcpKeys": ["home-ai"],
    	"port": 3041,
		/** name of the agent */
		name?: string;
		/** disable chat UI and only expose API endpoints */
		apiOnly?: boolean;
	},
	"llms": [
		// ...
	],
	"defaultModel": {
		// ...
	}
}

// .hanabi.json
{
	"multiAgents": {
		"strategy": "routing",
		/** default false - question with no classification
		* will be passed through to routing agent */
		"force": false,
		"agents": [
			{
				"name": "calendars",
				"apiUrl": "http://localhost:3051/api",
				"classification": "school calendar events and UK public holiday"
			},
			{
				"name": "math",
				"apiUrl": "http://localhost:3052/api",
				"classification": "math problem"
			},
			{
				"name": "api-doc",
				"apiUrl": "http://localhost:3053/api",
				"classification": "API document"
			}
		]
	},
	"llms": [
		// ...
	],
	"defaultModel": {
		// ...
	}
}

// .hanabi.json
{
	"multiAgents": {
		"strategy": "workflow",
		"steps": [
			{
				"apiUrl": "http://localhost:3051/api",
				"name": "process user email into trade instruction"
			},
			{
				"apiUrl": "http://localhost:3052/api",
				"name": "trade booking with payload"
			}
		]
	},
	"llms": [
		// ...
	],
	"defaultModel": {
		// ...
	}
}

// .hanabi.json
{
	"multiAgents": {
		strategy: 'parallel',
		agents: [
			{
				name: 'code quality agent',
				apiUrl: 'http://localhost:3051/api',
				prompt:
					'Review code structure, readability, and adherence to best practices.',
			},
			{
				name: 'code performance agent',
				apiUrl: 'http://localhost:3052/api',
				prompt: 'Identify performance bottlenecks & memory leaks.',
			},
			{
				name: 'code security agent',
				apiUrl: 'http://localhost:3053/api',
				prompt:
					'Identify security vulnerabilities, injection risks, and authentication issues',
			},
		],
	},
	"llms": [
		// ...
	],
	"defaultModel": {
		// ...
	}
}