Auto Causal Inference is a project that uses large language models (LLMs) to automatically perform causal inference. Users only need to specify the treatment variable and the outcome variable, and the system can automatically complete the full - process analysis, including variable role identification, causal graph construction, effect estimation, and model validation. The project provides two agent architectures (LangGraph and MCP) to achieve this function, which is particularly suitable for causal problem analysis in the banking scenario.

The Eka MCP Server is a medical knowledge base interaction service developed by Eka Care, focusing on the Indian medical scenario. It provides a structured query function for drug brands and treatment plans to improve the accuracy and reliability of AI medical responses.