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The Machine (TM)

A local AI agent that can control your machine, extensible and permission-gated.

TM is a Python agent harness inspired by the architecture of pi, rebuilt around explicit permission approval for filesystem, shell, and network access.

Status

Core agent, providers, built-in tools, permissions, and CLI are working. See the phase table below.

Installation

TM is not on PyPI yet, so install it from the repository. uv is required (it can also install Python for you). Once released, the install will simply be uv tool install "the-machine[all]".

Install uv (once):

# Windows (PowerShell)
irm https://astral.sh/uv/install.ps1 | iex

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

Install the tm command (recommended)

uv tool install "the-machine[all] @ git+https://github.com/beyondalbert/tm"
uv tool update-shell   # add the tool bin dir to PATH; restart the terminal afterwards
tm --help

[all] pulls in the Anthropic, Google, and TUI extras. Drop it for a minimal install (OpenAI-compatible providers only; --tui then needs textual). git must be installed for the git+https source. Without git, install from the source archive instead:

uv tool install "the-machine[all] @ https://github.com/beyondalbert/tm/archive/refs/heads/main.tar.gz"

From a clone (development)

git clone https://github.com/beyondalbert/tm
cd tm
uv sync --all-extras
uv run tm --help

First run

tm --login deepseek        # store an API key (hidden input)
tm "list the files in this folder"

Requires Python 3.11+. uv installs a suitable interpreter automatically.

Providers

Multi-provider via official SDK adapters. Domestic providers first (DeepSeek, Qwen, Kimi, Zhipu GLM), then OpenAI and any OpenAI-compatible endpoint (Ollama, vLLM, LM Studio).

Set the API key for the provider you want, then run:

$env:DEEPSEEK_API_KEY = "sk-..."
python -m uv run tm "list the files in this folder"

Where to set the API key

Three options, in order of convenience:

1. Store it once (recommended). Prompts with hidden input and saves to the credentials file:

   python -m uv run tm --login deepseek
   

   Saved to <config>/credentials.toml:

   • Windows: %APPDATA%\the-machine\credentials.toml
   • Linux/macOS: ~/.config/the-machine/credentials.toml

   The file looks like:

   [providers]
   deepseek = "sk-..."
   qwen = "sk-..."
   

2. Environment variable (session or persistent):

   # current shell only
   $env:DEEPSEEK_API_KEY = "sk-..."
   # persistent for your user (restart the terminal afterwards)
   [Environment]::SetEnvironmentVariable("DEEPSEEK_API_KEY", "sk-...", "User")
   

3. Override the config location with TM_CONFIG_DIR if you do not want the default directory.

Stored credentials take precedence over environment variables.

Providers and env vars:

Anthropic and Google use their official SDKs; the rest use the OpenAI-compatible adapter. ollama and other local servers need no key.

List models: tm --list-models.

Modes

Context files (AGENTS.md / CLAUDE.md, walking up from cwd, plus the global config dir) are appended to the system prompt. Disable with --no-context-files.

Permissions

Every file, shell, and network action is evaluated against a policy. Deny rules win, then allow rules, then the default decision (ask by default). Undecided actions prompt the user; a remembers the decision for the session. All decisions are appended to <config>/audit.jsonl.

Policy files are merged from <config>/policy.toml (global) and <cwd>/.aiagent/policy.toml (project). Example:

default = "ask"

[files.read]
allow = ["**"]
deny = ["**/.env", "**/id_rsa"]

[files.write]
allow = ["src/**", "tests/**"]
deny = [".git/**"]

[shell]
allow = ["git *", "ls *", "pytest *"]
deny = ["rm -rf *", "shutdown*", "format *"]

[network]
allow = ["api.deepseek.com"]

Note: shell commands are matched textually. TM cannot reliably stop a shell command from making network calls; use a sandbox/container when you need a hard network boundary.

Interactive commands

Inside tm (agent REPL or TUI) type / for commands:

Sessions

Sessions are appended to JSONL files under <config>/sessions/, tagged with the working directory. Resume them in several ways:

• tm -c — continue the most recent session for this directory.
• tm -r — list saved sessions (number, message count, updated time, preview) and pick one; --all-sessions includes other directories.
• tm --session <file> — open a specific file.
• tm --no-session — run without persisting.

Inside a session, /resume [n|id] switches sessions (a picker modal in the TUI), /tree navigates conversation points, and /fork copies a branch to a new file.

Settings

<config>/settings.toml (Windows: %APPDATA%\the-machine\settings.toml):

provider = "deepseek"
model = "deepseek-chat"
temperature = 0.2
max_tokens = 8192
system_prompt = "Extra instructions appended to the system prompt."
auto_compact = true      # summarize older context when nearing the limit
compact_threshold = 0.8  # fraction of the context window that triggers it
compact_keep_recent = 6  # recent messages kept verbatim

Automatic compaction runs before a prompt when the estimated context exceeds context_window * compact_threshold. Disable per run with --no-auto-compact.

Customization

Context files. AGENTS.md / CLAUDE.md (and AGENTS.override.md) are loaded from the global config dir and walking up from the working directory, and appended to the system prompt. Disable with --no-context-files.

Skills. A skill is a directory with a SKILL.md (optional frontmatter with name and description). Load from <config>/skills/, .agents/skills/, or .aiagent/skills/. Their names/descriptions are advertised in the system prompt; load one with /skill:<name>.

---
name: code-review
description: Review code for bugs and security issues
---
Review the target for bugs, security issues, and missing tests.

Prompt templates. Markdown files in <config>/prompts/ or .aiagent/prompts/, invoked as /<filename>; {{input}} is replaced by the text after the command.

<!-- prompts/review.md -->
Review this for bugs, security, and performance: {{input}}

Extensions. Python modules in <config>/extensions/ or .aiagent/extensions/. Each defines setup(api) and can register tools, event listeners, and slash commands. Extensions run with full process permissions.

from tm.tools.base import Tool, text_result

def setup(api):
    api.register_tool(MyTool())
    api.on("tool_execution_end", lambda event: None)
    api.register_command("greet", lambda arg: f"hello {arg}")

Architecture

• tm/ai – unified multi-provider streaming LLM layer (types, event stream, OpenAI-compatible + Anthropic + Google adapters, registry/catalog)
• tm/core – agent loop, high-level Agent, events, sessions, compaction
• tm/tools – built-in machine-control tools (read/write/edit/shell/grep/find/ls)
• tm/permissions – policy, allow/deny/ask checker, approval, audit log
• tm/skills.py, tm/prompts.py, tm/extensions.py, tm/context – resources
• tm/tui – terminal UI (Textual)
• tm/cli – Typer entry point and slash commands

Development

python -m uv sync --all-extras   # install dev + provider extras
python -m uv run pytest -q       # tests
python -m uv run ruff check .    # lint
python -m uv run mypy            # types
python -m uv build               # wheel + sdist

uv.lock is committed. CI (.github/workflows/ci.yml) runs ruff and mypy on Linux, and the test suite on Linux, Windows, and macOS for Python 3.11 and 3.12. See RELEASE.md for publishing to PyPI.

Roadmap

All planned phases are implemented. Possible future work: more providers, provider-side prompt caching controls, a web UI, and multi-agent orchestration.

License

MIT, see LICENSE.