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.

Forge MCP Server is a tool that automatically optimizes PyTorch models into high-performance CUDA/Triton kernels through 32 parallel AI agents, increasing inference speed by up to 14x and supporting multiple MCP-compatible AI programming assistants.

An intelligent agent system running locally, combining inference models and tool - calling models

eShopLite is a lightweight .NET-based eCommerce platform that offers features such as semantic search, cloud platform integration, and intelligent inference models to help developers quickly build and scale online stores.