prescription
To achieve a quick build of a stock analysis agent via Cooragent, follow the steps below:
- environmental preparation: Ensure that Python 3.12+ is installed and complete the installation of Cooragent via conda or venv (see section on the installation process)
- Configuring the API Key: Fill in the .env file with API keys for language models such as OpenAI to ensure that the agent can call the analysis model
- Enable Browser Tools: Set USE_BROWSER=True to support web data crawling functionality
- Execute the Agent Factory command::
python cli.py run -t agent_factory -u user123 -m '创建分析小米股票趋势的专家代理，检查过去一个月的大新闻，预测下一个交易日股价趋势并提供买卖建议'
Recommendations for optimization of effects::
- pass (a bill or inspection etc)
edit-agent -n 代理名称 -iCommand Adjustment Agent Prompt Template - Clarify timeframe, analysis dimensions and output format requirements (e.g., include charts and graphs) in the task description
- Integration of the MCP protocol allows the agent to collaborate with the crawler agent to obtain real-time data.
This answer comes from the articleCooragent: building a multi-intelligence task collaboration tool in one sentenceThe
May not be reproduced without permission:AI productivity tools " How to achieve fast building of stock analysis AI agents with Cooragent and get real-time predictions?
