k7/tutorials/langchain-react-agent/README.md

LangChain ReAct Agent with K7 Sandbox Tool

This tutorial shows a minimal LangChain ReAct-style agent equipped with a tool that executes shell commands inside a K7 sandbox.

Prerequisites

• K7 API deployed and reachable (use k7 start-api and check k7 api-status for the public URL)
• API key generated: k7 generate-api-key <name>
• Python 3.10+
• uv (recommended): https://docs.astral.sh/uv/

Setup

0. Install uv (if not installed):

curl -LsSf https://astral.sh/uv/install.sh | sh

1. Create a .env file in this directory with:

K7_ENDPOINT=https://your-k7-endpoint
K7_API_KEY=your-api-key
K7_SANDBOX_NAME=lc-agent
K7_SANDBOX_IMAGE=alpine:latest
K7_NAMESPACE=default
OPENAI_API_KEY=sk-your-openai-key
OPENAI_MODEL=gpt-4o-mini

2. Create an isolated environment and install dependencies (using uv):

# from this tutorial directory
uv venv .venv-lc
. .venv-lc/bin/activate

# core deps for the tutorial
uv pip install -r requirements.txt

# install the local K7 SDK from the repo source
# (two levels up from this tutorial dir)
uv pip install -e ../..

# or from the PyPI registry:
uv pip install k7-sdk

Run

python agent.py

Ask the agent to perform simple shell actions, e.g., "List files in /". The agent will decide to use the sandbox tool and return the output.

In parallel if you want you can shell into its sandbox:

k7 shell lc-agent

or replace lc-agent with the sandbox name you chose.