slife 0.2.0

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

Slife

Terminal-based AI agent — chat with an LLM that can execute shell commands, read and write files, search the web, call REST APIs, connect to MCP servers, spawn subagents for parallel work, communicate with other Slife instances over MQTT, and remember everything permanently.

┌────────────────────────────────────────────────────────────┐
│  Terminal UI (Textual)                                     │
│  ─────────────────────────────────────────────────────────  │
│  Agent Service — LLM + Tools + Loop + MCP + A2A + Inbox   │
│  ┌──────────┬─────────────┬──────────┬──────────────────┐  │
│  │ MCP Tool │ A2A + MQTT  │ Subagent │ Built-in Plugins │  │
│  │  Proxy   │ Mesh        │ Workers  │ ┌────┬────┬────┐ │  │
│  │          │             │          │ │MCP │Mem │WX  │ │  │
│  └──────────┴─────────────┴──────────┴─┴────┴────┴────┘─┘  │
│  Permanent Memory — hybrid search (grep + FTS5 + semantic)  │
└────────────────────────────────────────────────────────────┘

Quick Start

uv sync                                      # install dependencies
cp slife.json5.example slife.json5           # copy + edit: set your API key
uv run slife                                 # launch the TUI

The example config includes three pre-configured MCP servers (filesystem, web fetch, DuckDuckGo search) — you're productive immediately after setting your model key.

How It Works

Slife is a function-calling loop. You type a message → the LLM decides what tools to call → Slife executes them and returns results → the LLM responds → repeat.

You: "Find all TODO comments and create GitHub issues for them"
  → LLM calls execute_shell("rg TODO")
  → LLM calls github__create_issue(...) for each one
  → LLM: "Created 7 issues. All linked in the description above."

Configuration

Edit slife.json5. The only required setting is a provider + API key:

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

${ENV_VAR} and ${ENV_VAR:-default} syntax is supported throughout — values are resolved at startup and injected into os.environ.

Features

Tools

All tools are unified as OpenAI function definitions — the LLM sees no difference between a native shell command, an MCP tool, or a REST API endpoint.

Category	Examples	Location
Native	execute_shell, run_python_script, get_os_info	slife/tools/*.py
MCP / REST	filesystem__read_file, fetch__get, serper__search	Via slife-mcp proxy
Skills	On-demand plugins with list_skills / use_skill	skills/ directory
CLI	Auto-discovered external commands, persisted with cli_add_tool	Runtime registration
A2A	13 protocol tools — discovery, routing, lifecycle, broadcast	slife/tools/a2a.py

Memory

Every conversation turn is permanently recorded. Hybrid search (grep + FTS5 + semantic via vec0) lets the LLM recall past work. Memory runs as a built-in plugin (slife/plugins/memory/) — a separate process so crashes never race with writes.

memory_search("ConnectionError")            → exact error trace
memory_search("MCP config", mode="fts5")    → topic search
memory_search("that bug fix", mode="hybrid")→ semantic recall
memory_search(mode="time", since="2026-07") → browse by date

User isolation via --user alice. Embedding via local GGUF (offline) or OpenAI-compatible API. See DESIGN.md § Permanent Memory for the full architecture.

Plugins

Three built-in plugins ship with Slife, all using the same MCP stdio protocol:

Plugin	Role
slife-mcp	Proxy for external MCP servers (stdio + HTTP) — 10 management tools
slife-memory	Diary database with hybrid search (FTS5 + vec0 RRF)
slife-wechat	Bidirectional WeChat messaging via iLink ClawBot API

Third-party plugin auto-loading is on the roadmap — the infrastructure is ready.

A2A — Agent-to-Agent

Two transports, one interface: MQTT (remote peers, enable with --agent <id>) and Subagent (local child processes, always available). The unified inbox serializes human keyboard, WeChat, MQTT, and subagent messages through a single queue — only one AgentLoop runs at a time.

Progressive Disclosure

Not all tools are in every LLM request. Three categories use lightweight summaries first:

Category	Browse	Load
Memory	memory_search / memory_list_recent	memory_open
Skills	list_skills	use_skill
MCP	mcp_list_servers / mcp_list_tools	mcp_set_disclosure("eager")

Shortcuts

Key	Action
Ctrl+C	Quit
Esc	Cancel agent loop
Ctrl+L	Focus input field
Home / End	Scroll to top / bottom
Any key	Auto-focus input + type

CLI Flags

Flag	Default	Description
--agent <id>	(off)	Enable A2A — join the MQTT mesh with this identity
--user <id>	default	Memory isolation key — separate diary per user

Requirements

• Python ≥ 3.13
• uv — Python package manager
• Node.js — only if using npx-based MCP servers
• Windows + GGUF embeddings: see DESIGN.md § Embeddings for pre-built wheel instructions

Design

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

See DESIGN.md for the full architecture, component-level documentation, and design rationale.

License

MIT