Slife

Terminal-based AI agent — a function-calling loop with minimum harness. Chat with an LLM that calls tools, remembers every turn, and orchestrates other agents.

You: "Find all TODO comments and create GitHub issues"
  → LLM calls search_content("TODO")
  → LLM calls github__create_issue(...) for each one
  → LLM: "Created 7 issues. All linked above."

One TUI window around an LLM tool loop: 54 native tools in 12 categories, external MCP servers, always-on memory with hybrid search, inline images, runtime model switching across three API backends, and an agent-to-agent mesh — everything presented to the LLM as uniform OpenAI-style function definitions.

Requires Python 3.13+. Runs on Windows (native & WSL), macOS, and Linux.

Install

Zero prerequisites. The install script auto-installs uv, Node.js, and bun if needed. On WSL, Linux-native versions are installed (Windows executables cannot receive custom env vars via WSL interop). Mosquitto (only needed for the A2A MQTT mesh) is offered interactively.

macOS / Linux / WSL

# Global
curl -fsSL https://raw.githubusercontent.com/juzcn/slife/main/install.sh | bash
# China mainland
curl -fsSL https://gitee.com/juzcn/slife/raw/main/install.sh | bash

Windows PowerShell

# Global
powershell -ExecutionPolicy Bypass -Command "irm https://raw.githubusercontent.com/juzcn/slife/main/install.ps1 | iex"
# China mainland
powershell -ExecutionPolicy Bypass -Command "irm https://gitee.com/juzcn/slife/raw/main/install.ps1 | iex"

Try without installing

uvx --from git+https://github.com/juzcn/slife.git slife

Update

Re-run the install script — it auto-preserves optional packages (llama-cpp-python, sentence-transformers) by diffing the previous venv and re-adding them.

Uninstall

# macOS / Linux / WSL
curl -fsSL https://raw.githubusercontent.com/juzcn/slife/main/uninstall.sh | bash
# China mainland
curl -fsSL https://gitee.com/juzcn/slife/raw/main/uninstall.sh | bash

# Windows PowerShell
powershell -ExecutionPolicy Bypass -Command "irm https://raw.githubusercontent.com/juzcn/slife/main/uninstall.ps1 | iex"
# China mainland
powershell -ExecutionPolicy Bypass -Command "irm https://gitee.com/juzcn/slife/raw/main/uninstall.ps1 | iex"

User data (~/.slife/, ~/.credstore/) is not removed — delete manually for a full reset.

Quick Start

credstore set-password              # first time — encrypted backup
credstore set DEEPSEEK_API_KEY      # store API key (masked input)
slife

To share the same API key across multiple providers:

credstore copy DEEPSEEK_API_KEY BAILIAN_API_KEY

Configuration

Secrets in the OS keyring, config in JSON5:

env: {
  DEEPSEEK_API_KEY: "${DEEPSEEK_API_KEY}",   // → resolved from keyring at runtime
}

models: {
  providers: {
    deepseek: {
      base_url: "https://api.deepseek.com",
      api_key: "${DEEPSEEK_API_KEY}",
      api: "openai-completions",
      models: [{ model: "deepseek-v4-pro", name: "DeepSeek V4 Pro", reasoning: true }],
    },
  },
},
active_model: "deepseek/deepseek-v4-pro",

${VAR:-default} fallback syntax is supported. Secrets can also be referenced as keyring:service/key URIs.

Three first-class API backends:

Switch at runtime: list_models → switch_model(ref="bailian/qwen3.8-max").

Secrets never reach the LLM. User input, tool-call arguments, and every tool result pass through a pattern-based sanitizer before entering the conversation — API key shapes (sk-*, ghp_*, Bearer tokens, …) are auto-masked.

Features

Tools

All unified as OpenAI function definitions. The LLM sees no difference between native and MCP tools.

54 native tools in 12 categories — auto-discovered from slife/tools/:

Every tool additionally accepts two harness meta-parameters: _timeout (per-call override) and _async (run in background, poll with check_async).

Five managed categories (MCP / Skills / CLI / REST API / Models) support list / add / remove / set — all add tools are idempotent upserts.

Plus built-in MemDB tools: memory_search, memory_open, memory_summarize, memory_count, memory_list_recent, memory_check_embedding, memory_set_embedding, memory_set_enabled.

Memory — Always On

Every conversation turn is permanently recorded in SQLite (~/.slife/<agent>.db). Hybrid search across four modes:

Embedding backends: local GGUF (BGE-M3, offline), HuggingFace transformers, or OpenAI-compatible API. Keyword search works without any embedding backend.

Image & Vision

Attach images with @path / @url syntax (quotes supported for paths with spaces), displayed inline in the terminal:

Check this screenshot @D:\Downloads\error.png

Two-tier rendering: Sixel (full-colour on Windows Terminal / WezTerm / iTerm2 / Kitty) → HalfcellImage (coloured Unicode half-blocks on any true-colour terminal) → text placeholder. Vision-capable models receive local files as base64 data URIs and HTTP(S) URLs as-is; the include_image tool lets the agent attach images mid-conversation, and expose_file publishes any local file as a public HTTPS link via the ngrok tunnel (only available when the tunnel is active).

Plugins

Four built-in plugins as independent child processes:

External MCP servers configured in slife.json5 → mcp.servers. Any stdio or HTTP MCP server works — no Slife SDK required. Per-server option require_approval: true adds a human approval gate before each of its tool calls.

All plugins run with a watchdog that auto-restarts them on crash (exponential backoff 1s→30s, max 3 retries). The MCP wrapper watchdog also reconnects external servers after restart. Runtime health checks — check_memdb, check_wechat, check_memfiles, check_mcp — monitor application-level state and are surfaced via system_health; the watchdog is purely process-level.

A2A — Agent-to-Agent

Two working transports plus local workers, unified behind one tool surface: MQTT (remote peers over a Mosquitto broker — presence, heartbeat, task routing), Subagent (local child-process workers over JSON-RPC, always available), and an experimental HTTP Streamable transport. All messages — human, WeChat, MQTT, subagent results — flow through a single inbox queue and are processed one turn at a time.

Keyboard Shortcuts

CLI

Optional Extras

Linux / macOS — builds from source:

uv tool install "slife[gguf]" --reinstall

Windows — pre-built wheels (no C++ compiler needed); uv is configured to use the llama-cpp-python CPU wheel index. See install docs for wheel selection and first-use instructions.

Development

git clone https://github.com/juzcn/slife.git
cd slife
uv sync --all-extras

uv run credstore set-password
uv run credstore set DEEPSEEK_API_KEY
uv run slife

# Tests
uv run pytest
uv run pytest --cov=slife --cov=credstore --cov-report=term-missing

Dev mode auto-detects (via pyproject.toml in CWD): data files stay in the project directory. Production installs use ~/.slife/. CI runs the test suite on Ubuntu, macOS, and Windows with Python 3.13.

Architecture

See DESIGN.md — philosophy, agent loop, tool system, plugin contract, MCP gateway, memory database, A2A mesh, credential security model, and full project structure.

Known issues and improvement proposals from the latest code review: REVIEW.md.

License

MIT