surfkit 0.1.219

A toolkit for building AI agents that use devices

Surfkit

A toolkit for building AI agents that use devices
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Installation

pip install surfkit

Usage

Building Agents

Initialize a new project

surfkit new [NAME]

Build a docker container for the agent

surfkit build

Running Agents

Create an agent locally

surfkit create agent --name foo

Create an agent on kubernetes

surfkit create agent --runtime kube

List running agents

surfkit list agents

Get details about a specific agent

surfkit get agent --name foo

Fetch logs for a specific agent

surfkit logs --name foo

Delete an agent

surfkit delete agent --name foo

Managing Devices

Create a device

surfkit create device --type desktop --provicer gce --name bar

List devices

surfkit list devices

View device in UI

surfkit view --name bar

Delete a device

surfkit delete device --name bar

Tracking Tasks

Create a tracker

surfkit create tracker

List trackers

surfkit list trackers

Delete a tracker

surfkit delete tracker -n foo

Solving Tasks

Solve a task with an existing setup

surfkit solve --description "search for common french ducks" --agent foo --device bar

Solve a task creating the agent ad hoc

surfkit solve --description "search for alpaca sweaters" \
--device bar --agent-file ./agent.yaml

Solve a task and kill the agent post-execution

surfkit solve --description "search for the meaning of life" \
--device bar --agent-file ./agent.yaml --kill

List tasks

surfkit list tasks

Publishing Agents

Login to the hub

surfkit login

Publish the agent

surfkit publish

List published agent types

surfkit list types

Run a published agent

surfkit create agent --type SurfPizza --runtime kube

Integrations

Skillpacks is integrated with:

• MLLM A prompt management, routing, and schema validation library for multimodal LLMs
• Taskara A task management library for AI agents
• Skillpacks A library to fine tune AI agents on tasks.
• Threadmem A thread management library for AI agents

Community

Come join us on Slack

Developing

Add the following function to your ~/.zshrc (or similar)

function sk() {
  local project_dir="/path/to/surfkit/repo"
  local venv_dir="$project_dir/.venv"
  local ssh_auth_sock="$SSH_AUTH_SOCK"
  local ssh_agent_pid="$SSH_AGENT_PID"

  export SSH_AUTH_SOCK="$ssh_auth_sock"
  export SSH_AGENT_PID="$ssh_agent_pid"

  # Add the Poetry environment's bin directory to the PATH
  export PATH="$venv_dir/bin:$PATH"

  # Execute the surfkit.cli.main module using python -m
  surfkit "$@"
}

Replacing /path/to/surfkit/repo with the absolute path to your local repo.

Then calling sk will execute the working code in your repo from any location.