Stata-MCP

# Agent Examples Here is some examples of how to use Stata-MCP in agents. > If you are finding how to make the agent perform better, read file: [How to write a task prompt](task_prompt/README.md) ## Catalog - [OpenAI-Agent](#openai-agent) - [Langchain-ReAct-Agent](#langchain-react-agent) ## OpenAI-Agent The OpenAI-Agent is the most traditional agent here (I think), it's also the most simple one. If you just hope to find out that there is something AI could do, have a try here, it is a good start. You can `cd` to the `OpenAI-Agent` directory and run `main.py` to start the agent. ```bash git clone https://github.com/sepinetam/stata-mcp.git cd stata-mcp # If you did not make your environment, make one. # uv sync uv run agent_examples/openai/main.py # before that, you can change your task message, just provide the minimal task description, and don’t forget to include your data path and the output path. ``` If there is timeout error, do not worry, you can install it before running the agent, like this: ```bash git clone https://github.com/sepinetam/stata-mcp.git cd stata-mcp pip install -e . # You can find whether it is installed successfully by: stata-mcp --version ``` and, edit the agent file `agent_examples/openai/main.py`, from ```python mcp_server = MCPServerStdio( name="Stata-MCP", params={ "command": "uvx", "args": [ "stata-mcp" ], "env": { "stata_cli": "stata-mp" } } ) ``` to ```python mcp_server = MCPServerStdio( name="Stata-MCP", params={ "command": "stata-mcp", "args": [], "env": {"stata_cli": "stata-mp"} } ) ``` ## Langchain-ReAct-Agent Similar to the OpenAI-Agent, the Langchain-ReAct-Agent is also a simple agent. With ReAct framework, it could perform better than the common Agent. More information could read this paper: [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629). How to run? ```bash git clone https://github.com/sepinetam/stata-mcp.git cd stata-mcp uv run agent_examples/langchain/main.py ```