TokenMon

A fast, zero-dependency command-line monitor for local AI coding agents.

It measures how fast models actually generate tokens (Tokens Per Second, TPS) by tracking pure generation time—separating thinking and output from tool runs, file edits, and idle waiting.

Screenshots

Expand a screenshot below. Click the image to view it at full size.

Features

• Real Output Speed: Measures actual streaming speed (TPS), ignoring tool execution and network idle pauses.
• Zero Extra Dependencies: Runs on standard Python 3.10+ without installing third-party packages.
• Strictly Read-Only: Safely opens local files and SQLite databases in read-only mode (?mode=ro). Never locks or changes your logs.
• Meaningful Averages: Calculates true weighted speed ($\frac{\text{total tokens}}{\text{total time}}$), median speed, and min/max ranges over rolling time windows (30m, 1d, 7d, 30d, all).
• Supports Popular Agents: Auto-detects Codex (~/.codex), Claude Code (~/.claude/projects/), and Antigravity (~/.gemini/antigravity-cli).
• Session Timelines: Step-by-step history of user prompts, thinking, assistant responses, and tool calls.
• JSON Ready: Add --json to pipe clean data into jq or external dashboards.

Installation

Requires Python 3.10+.

# Clone the repository
git clone https://github.com/quanhua92/tokenmon.git
cd tokenmon

# Run directly with uv
uv run tokenmon

# Or run tests
uv run python -m unittest discover -s tests

Usage

TokenMon uses simple Docker-style subcommands: stats (default), ps (sessions), logs (timelines), and interactive (shell).

Running tokenmon by itself defaults directly to stats.

Generation Speed & Metrics (stats, top, default)

View token throughput (TPS), rolling averages, and recent generation outputs:

# Auto-detect local agents and show stats (default)
uv run tokenmon
# or explicitly
uv run tokenmon stats

# Filter to a specific time window (30m, 1d, 7d, 30d, all)
uv run tokenmon stats --window 1d

# Include full history beyond the default 30-day cutoff
uv run tokenmon stats --all

# Target a specific agent or custom folder
uv run tokenmon stats codex
uv run tokenmon stats claude --home ~/.claude

# Compact layout for narrow panes or wide full-detail table
uv run tokenmon stats --compact
uv run tokenmon stats --wide

Active & Recent Sessions (sessions, ps, ls)

See all recent sessions, message counts, tool runs, and idle status:

# List recent sessions
uv run tokenmon ps

# Filter sessions within a time window
uv run tokenmon ps --window 1d

# Filter to a specific agent
uv run tokenmon ps codex

Event Timelines (timeline, logs, log)

See the chronological step-by-step history of prompts, model thoughts, responses, and tool calls:

# View timeline of the latest session
uv run tokenmon logs

# View timeline of a specific session ID or prefix
uv run tokenmon logs 01a10275

# Export all session timelines from today in JSON format
uv run tokenmon timeline --window 1d --json

Interactive Shell (interactive, repl, shell, -i)

Open an interactive terminal shell with live auto-refresh and tab completion:

uv run tokenmon interactive
# or
uv run tokenmon -i

Available commands inside the shell:

(tokenmon) summary 30m        # view 30m throughput table
(tokenmon) sessions           # list active & inactive sessions
(tokenmon) timeline latest    # view step-by-step event timeline
(tokenmon) recent 15          # view latest generation speeds
(tokenmon) watch 2.0 1d       # live auto-refresh dashboard (Ctrl+C to stop)
(tokenmon) help               # list all commands

Machine-Readable JSON Output

Every command supports --json for easy scripting:

uv run tokenmon stats --json | jq .
uv run tokenmon ps --json | jq .
uv run tokenmon logs 01a10275 --json | jq .
uv run tokenmon timeline --window 1d --json | jq .

Adding New Adapters

TokenMon uses a base class in src/tokenmon/adapters/base.py:

class BaseAdapter(ABC):
    @property
    @abstractmethod
    def name(self) -> str: ...

    @abstractmethod
    def detect(self) -> bool: ...

    @abstractmethod
    def collect(self, max_sessions: int = 64, min_timestamp: float | None = None) -> list[GenerationSpan]: ...

    @abstractmethod
    def collect_sessions(self, max_sessions: int = 32, min_timestamp: float | None = None) -> list[SessionTimeline]: ...

To add support for a new agent (e.g. OpenCode):

1. Create src/tokenmon/adapters/opencode.py subclassing BaseAdapter.
2. Implement discovery (detect()), stream parsing (collect()), and timelines (collect_sessions()).
3. Register it in src/tokenmon/adapters/__init__.py.

CI and PyPI Releases

The CI workflow runs on pull requests and pushes to main. It tests Python 3.10–3.14 on Linux and 3.13 on macOS, builds a wheel and source distribution, checks package metadata and README rendering, and checks the installed CLI. Jobs use hosted runners, read-only permissions, and actions pinned to full commit SHAs.

The Publish to PyPI workflow is manual and only runs from main. It validates the requested version, runs tests, builds and checks distributions, then passes them to a separate publishing job. Only the publishing job has OIDC permissions. It uses PyPI trusted publishing without a stored API token.

Before the first release:

1. Rename the GitHub repository to tokenmon in Settings → General to match the repository and screenshot URLs, then update your local remote: git remote set-url origin git@github.com:quanhua92/tokenmon.git.
2. In GitHub repository Settings → Environments, create pypi. Allow deployments from main only and configure a required reviewer for release approval.
3. On PyPI, add a GitHub trusted publisher with owner quanhua92, repository tokenmon, workflow filename release.yml, and environment pypi. For a new project, register a pending publisher with project name tokenmon; for an existing project, use its Publishing settings.

To release:

1. Commit the desired version in both pyproject.toml and src/tokenmon/__init__.py, refresh uv.lock with uv lock, and merge to main after CI passes. The workflow checks versions; it does not change them.
2. Open Actions → Publish to PyPI → Run workflow, select main, and enter the exact version, such as 0.1.0.
3. Review the built distributions in the workflow artifact, then approve the pypi deployment. PyPI rejects uploading an already published distribution again.

Validate workflow edits locally with actionlint. Packaging tools are pinned in .github/requirements-build.txt; they are separate from application dependencies.

License

MIT