slife 0.1.0

Terminal-based AI agent — a function-calling loop with minimum harness

slife

Terminal-based AI agent — a function-calling loop with minimum harness. Chat with an LLM that can execute shell commands, search the web, and load on-demand skills.

Quick Start

# Install
uv sync

# Configure
cp slife.json5.example slife.json5
# Edit slife.json5 — set your API keys via ${ENV_VAR} references

# Run
uv run slife

Configuration

Edit slife.json5. Key sections:

{
  models: {
    providers: {
      deepseek: {
        base_url: "https://api.deepseek.com",
        api_key: "${DEEPSEEK_API_KEY}",
        models: [
          { model: "deepseek-v4-flash", name: "DeepSeek V4 Flash" },
          { model: "deepseek-v4-pro", name: "DeepSeek V4 Pro", reasoning: true },
        ],
      },
    },
  },
  active_model: "deepseek/deepseek-v4-pro",
  agent: { max_iterations: 10 },
  tools: [
    { type: "platform" },
    { type: "shell", timeout: 30 },
    { type: "serper" },
    { type: "skill", skills_dir: "skills" },
  ],
}

API keys use ${ENV_VAR} syntax — set them in your environment, not in the config file.

Tools

Tool	Type	What it does
execute_shell	shell	Run shell commands on the host machine
get_shell_command	platform	Translate intent into OS-correct shell syntax
web_search	serper	Google Search via Serper.dev API
list_skills	skill	List available skill plugins
use_skill	skill	Load a skill's documentation into context

Add or remove tools from the tools[] list to control what the agent can do.

Skills

Skills are on-demand documentation plugins. The agent loads them only when needed, keeping the context lean.

skills/baidu-search/
  SKILL.md              # Instructions the agent reads
  scripts/search.py     # Supporting code

Flow: the agent calls list_skills → sees what's available → calls use_skill("baidu-search") to load full instructions.

To add a skill, create a directory under skills/ with a SKILL.md file.

Tips

• /file image.png — attach an image for vision models
• Ctrl+C — clear the conversation
• Ctrl+Q — quit

Design

slife is a minimum-harness agent. The harness only does three things the LLM cannot: execute tools, maintain conversation state, and stream responses. Everything else — reasoning, planning, tool selection, error recovery — is the LLM's job.

The system prompt is intentionally lean. It only contains project-specific information not in the LLM's training data. The LLM already knows how function calling works.

See docs/design.md for the full design rationale.

Project Structure

slife/
  agent/           # Core agent loop, LLM client, conversation
    loop.py        #   Function-calling while-loop
    llm_client.py  #   OpenAI-compatible streaming client
    conversation.py#   Message history (OpenAI format)
    service.py     #   Wiring: client + tools + loop
    system_prompt.py#  Jinja2 template rendering
  tools/           # Extensible tool system
    base.py        #   Tool ABC
    registry.py    #   Name → Tool lookup
    factory.py     #   Config type → Tool instances
    shell.py       #   execute_shell
    shell_command.py#  get_shell_command (platform-aware)
    serper.py      #   web_search (Serper.dev)
    skill.py       #   list_skills / use_skill
  ui/              # Textual TUI
    app.py         #   Main application
    chat.py        #   Message widgets
    handler.py     #   Streaming event → UI bridge
    tool_display.py#   Tool call rendering
  config.py        # JSON5 config loading
  env.py           # ${ENV_VAR} resolution
  platform.py      # OS detection, shell syntax
  skills/          # Skill plugins
  docs/            # Design documents

License

MIT