BioContextAI x BioChatter example

This repository provides a simple example of how MCPs from the BioContextAI ecosystem can be used with the biochatter Python package using a Chainlint interface.

Installation

First, configure an API key in a .env file at the project root. E.g.:

GOOGLE_API_KEY="YOUR_GOOGLE_AISTUDIO_API_KEY"

when using the default Google LLM provider.

Then install and run:

uv venv
source .venv/bin/activate
uv sync
chainlit run app.py

Then try it out with a query like: "Get the STRING protein-protein interactions for human MKI67."

Note that this is a minimal example. In practice, you would integrate biochatter directly into your applications or use BioContextAI-provided MCP servers through BioContextAI Chat or any agentic client with MCP support.