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Local Tool Installation ​

Before you can engineer a high-performance workspace, you must install the foundational toolbelt. This guide provides the baseline installation steps for Python, Node.js, PostgreSQL, and Docker, the four pillars of Agentic AI Engineering.

🐍 1. Python Environment (Miniconda) ​

We use Miniconda to manage isolated Python environments. This prevents dependency conflicts between different AI projects.

Installation ​

Follow these steps to pull the Miniconda installer and configure it within your shell.

bash
# Download the installer
curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

# Run the installer
bash Miniconda3-latest-Linux-x86_64.sh

# Initialize shell (restart terminal after)
source ~/miniconda3/bin/activate
conda init

Core Commands ​

  • conda create -n ai_dev python=3.11 -y: Create a new environment.
  • conda activate ai_dev: Enter your sandbox.
  • conda env list: See all available environments.

🟢 2. Node.js & NPM (NVM) ​

Many AI gateways (like OpenClaw) and developer tools (like claude-code) run on Node.js. We use NVM (Node Version Manager) to manage versions without needing sudo.

Installation ​

Fetch the Node Version Manager script and activate the latest stable Node release.

bash
# Install NVM
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash

# Load NVM and install the latest LTS version
nvm install --lts
nvm use --lts

🐘 3. Database Layer (PostgreSQL & pgvector) ​

PostgreSQL acts as the "Long-Term Memory" for your agents. pgvector adds the ability to store and search embedding vectors.

Installation (Ubuntu/Debian) ​

Update your package lists and install both the core database engine and the vector extension.

bash
sudo apt update
sudo apt install postgresql postgresql-contrib

# Install the pgvector extension
sudo apt install postgresql-16-pgvector # (adjust '16' to your version)

Service Management ​

  • sudo systemctl start postgresql: Start the database.
  • sudo systemctl enable postgresql: Ensure it starts on boot.

🐳 4. Containerization (Docker & Compose) ​

Docker allows you to run complex services (like n8n, Redis, or local LLM servers) in isolated containers.

Installation ​

Configure Docker's official repository and install the necessary engine and compose plugins.

bash
# Add Docker's official GPG key:
sudo apt update
sudo apt install ca-certificates curl gnupg
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg

# Add the repository to Apt sources:
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

sudo apt update
sudo apt install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

Post-Install (Run without Sudo) ​

Grant your user account permission to run containers without needing root privileges.

bash
sudo usermod -aG docker $USER
# (Log out and back in for changes to take effect)

🔗 Next Steps: Optimization ​

Once these tools are installed, proceed to the Linux Workspace Engineering Guide to optimize them for 18ms shell latency, Rust-based package resolution, and kernel-level LLM tuning.