Create minimax_example.py with the code below, or save it directly from your editor.
"""MiniMax AI Research AgentA Bindu agent powered by MiniMax's M2.7 model via OpenAI-compatible API.MiniMax offers high-performance models with up to 1M context window.Features:- MiniMax M2.7 model (1M context)- Web search integration via DuckDuckGo- Research and summarization capabilitiesUsage: python minimax_example.pyEnvironment: Requires MINIMAX_API_KEY in .env file Get your API key at https://platform.minimaxi.com"""import osfrom bindu.penguin.bindufy import bindufyfrom agno.agent import Agentfrom agno.tools.duckduckgo import DuckDuckGoToolsfrom agno.models.openai import OpenAILikefrom dotenv import load_dotenvload_dotenv()# MiniMax API configurationMINIMAX_API_KEY = os.getenv("MINIMAX_API_KEY")MINIMAX_BASE_URL = "https://api.minimax.io/v1"# Define your agent with MiniMax M2.7agent = Agent( instructions="You are a research assistant that finds and summarizes information.", model=OpenAILike( id="MiniMax-M2.7", api_key=MINIMAX_API_KEY, base_url=MINIMAX_BASE_URL, ), tools=[DuckDuckGoTools()],)# Configurationconfig = { "author": "your.email@example.com", "name": "minimax_research_agent", "description": "A research assistant agent powered by MiniMax AI", "deployment": { "url": os.getenv("BINDU_DEPLOYMENT_URL", "http://localhost:3773"), "expose": True, "cors_origins": ["http://localhost:5173"] },}# Handler functiondef handler(messages: list[dict[str, str]]): """Process messages and return agent response. Args: messages: List of message dictionaries containing conversation history Returns: Agent response result """ result = agent.run(input=messages) return result# Bindu-fy itif __name__ == "__main__": bindufy(config, handler)
# Clone the Bindu repositorygit clone https://github.com/GetBindu/Bindu# Navigate to frontend directorycd frontend# Install dependenciesnpm install# Start frontend development servernpm run dev