codexwatch
codexwatch's MCP server lets an agent inspect local Codex usage, plan limits, quota cost, sessions, daily trends and budgets, all read from local session logs.
usage_summary — token totals, cache hit rate, API-equivalent dollar value, and top models/projects for
today,7d,30d,monthorall.plan_limits — current 5-hour and weekly window percent used, reset time, burn rate and projected fill time; plus per-account readings and the roomiest account when codexhop is installed.
quota_forensics — dollars and tokens per percentage point of the plan per window/day, and flags rises the local logs cannot explain; filter by
5horweek.sessions — the most recent Codex sessions with title, project, models, turns, tokens and dollar value (limit 1–100).
daily — tokens and API-equivalent dollars per local day, oldest first, including empty days (1–365 days).
budget_status — configured dollar, token or plan-window budgets with usage and near/over-limit state.
Provides tools for querying OpenAI Codex usage from local session logs, including usage summaries, live plan limits, quota forensics, session details, daily usage, and budget status.
codexwatch shows where your OpenAI Codex usage goes: tokens and their API-equivalent cost per day, project, model, session and tool, your plan's 5-hour and weekly limits as they stand now, and what each percent of those limits cost you. By default it reads only the session logs Codex already writes on your machine, never opens your login, and makes no network request; fetching prices and refreshing codexhop accounts are things you ask for.
pipx install codexwatch-cli # or: pip install --user codexwatch-cli
codexwatch # the overview in your terminal
codexwatch dashboard # the web dashboardYou need nothing beyond the Codex logs already on this machine, so the first command already has your numbers. On Linux, ./install.sh --tray --desktop from a clone also adds an app-menu entry, a desktop shortcut and the tray.
What it does differently
Every response is counted once
Codex gives each model response an id. codexwatch keys every row on it, so a forked session that copies its parent's history, a subagent, a repeated token event or the same logs synced from another machine never add tokens twice. Those are the four ways Codex usage has been overcounted, and each one has a test that reproduces it.
Your plan limits come from your own logs
After every response Codex writes the plan's rate-limit reading into the session log: how full the 5-hour and weekly windows are and when they reset. codexwatch reads those, so the live figures need no network call and no login.
Quota forensics
Because each reading follows a known response, codexwatch can set the tokens spent in a window against how far the window moved. You get an exchange rate (dollars of API usage per percent of your plan, per window and per day) and a flag on any rise your local logs do not account for, such as usage from another machine or from ChatGPT itself.
Fast on large logs
Lines are filtered on their first bytes, so tool output and reasoning text are never decoded, and a growing session is read from where the last scan stopped. On the machine it was built on, 1.85 GB of logs in 59 sessions took about 2 seconds to scan the first time (measured, 2026-11-04); later scans read only new lines.
Prices you can check
Every bundled price was copied from OpenAI's pricing pages, with the date and the page. A model with no published price is shown as unpriced and left out of dollar totals, never counted as $0. Fast mode is priced at the Fast tier, and prompts over 272K tokens at the long-context rate.
Related MCP server: Tallybook
The dashboard
codexwatch dashboard opens a page on 127.0.0.1. It works offline, follows your system's light or dark setting, and refreshes as you work.
The top of the page is your plan: both windows with their reset times, the burn rate over the last hour, the time the window fills at that pace, and what you have spent since it reset. The red band below compares the last 30 days of usage with what your plan costs. After that come the totals for the period you pick, a daily chart, models and projects, quota forensics, recent sessions, a weekday-by-hour map, tool calls, and how your work ran (CLI, IDE or codex exec, Standard or Fast, main sessions or subagents).
Share card
codexwatch card writes a 1200 × 630 image of your month, and the dashboard's Share card button saves it as SVG or PNG. It leads with what your plan returned. Project names stay off the card unless you pass --projects, because a project name can be a client's name.
Plan prices come from OpenAI's Codex pricing page. Enterprise and Edu have no public price, so set yours with codexwatch config set plan_price 60.
In your menu bar, tray, status bar and prompt
codexwatch bar -f FORMAT prints one line, or a small menu, in the shape each bar expects. It reads only new log lines, so calling it every few seconds is cheap.
Where | How |
macOS menu bar | Install SwiftBar or xbar, then copy |
Linux tray |
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waybar |
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tmux |
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polybar, i3blocks |
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Shell prompt |
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Desktop notifications fire as a window passes 75%, 90% and 100%, once each per window. The tray sends them, or run codexwatch notify from a timer.
Ask about it from Codex or Claude
codexwatch mcp is an MCP server, so an agent can check your usage mid-session:
codex mcp add codexwatch -- codexwatch mcp # OpenAI Codex
claude mcp add codexwatch -- codexwatch mcp # Claude CodeThen ask "how much of my 5-hour window is left?" or "what did this week cost?". The tools are usage_summary, plan_limits, quota_forensics, sessions, daily and budget_status. The server is in the official MCP Registry as io.github.psychofict/codexwatch.
Several ChatGPT accounts
codexhop keeps several ChatGPT logins for Codex and switches between them. When it is installed, codexwatch shows every account's latest reading and the one with the most room, names the account behind each plan window in the quota report, and codexwatch limits --refresh asks codexhop for fresh readings of all of them. codexwatch itself never opens a login file; it runs codexhop for anything that needs one.
codexwatch autohop moves Codex to another of your saved accounts when the live one is nearly spent. It is for accounts that are yours, such as a personal plan and your own seat in a company workspace, never a login someone shared with you; each account keeps its own limits, and OpenAI's terms of use apply to each one. It is off until you run it, and without --apply it only says what it would do:
$ codexwatch autohop
15:41:56 stay work is at 31% of its 5-hour window and 29% weekly; below 92% / 97%, so stayWith --apply it hops when the live account reaches 92% of its 5-hour window or 97% of its week, only to an account with at least 25% of its 5-hour window free, and at most once every 20 minutes. Every number can be changed in the autohop block of config.json. extras/systemd runs it every 3 minutes, and creating a file called autohop.off in the codexwatch folder stops it.
Budgets
codexwatch budget add --name "Monthly" --usd 300 --every month
codexwatch budget add --name "atlas daily" --usd 20 --every day --scope project:atlas-api
codexwatch budget add --name "5h headroom" --pct 80 --window 5h
codexwatch budget listA dollar or token budget adds up API-equivalent usage over the local calendar day, week (from Monday) or month, for everything or one project or model. A percent budget watches the plan's own window. Each sends a notification at 80% of its limit (--notify-pct) and again when it is spent. codexwatch budget check exits 1 when any budget is over, for scripts and CI.
Several machines
Copy or sync another machine's ~/.codex (or just its sessions folder) somewhere and add it:
codexwatch sources add ~/sync/laptop-codex --machine laptop
codexwatch machinesResponses that appear in both places are counted once, by their response id.
Commands
Every report takes -p/--period (today, 7d, 30d, week, month, year, all, 2026-10, 2026-10-31), --since and --until, filters (--project, --model, --client, --machine, --account) and --json.
Command | What it shows |
| Plan limits, totals for today, 7 days, this month and all time, a 30-day strip, top models and projects |
| Tokens (input, cached, output, reasoning) and API value per period; |
| The same, grouped another way |
| Recent sessions with title, model, turns, tokens, value and compactions; subagents under their parent |
| Which tools Codex called and how often |
| Median and 90th-percentile turn time and time to first token, per model |
| A weekday-by-hour map of when you use Codex |
| Every plan window, burn rate, when it fills, and codexhop accounts; |
| Dollars per percent of each window and day, and rises the logs cannot explain |
| A full-screen view that redraws every few seconds |
| The web dashboard on |
| The share card; |
| One line for a status bar or prompt |
| The Linux tray icon |
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| Send any due notification once |
| Move to another of your own codexhop accounts when the live one is nearly spent (dry run unless |
| The MCP server |
| The dashboard on invented data; |
| Every response as CSV or JSON |
| The price list; |
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| Read new log lines now; |
| Settings, a setup check, and re-costing after a price change |
How the numbers are made
Dollar figures are what the same tokens would cost on the OpenAI API at the published Standard price. ChatGPT plans bill a flat fee, so treat them as a measure of how much you used, not a bill. Input tokens include cached and cache-written tokens, and output tokens include reasoning, as in OpenAI's usage object.
The plan figures come from the readings in your logs, so they are as fresh as your last Codex request on this machine. Usage on another machine, in ChatGPT or in cloud tasks counts against the same plan but reaches these logs only with your next request here; that is what the quota report's "rises these logs cannot explain" catches. Fast mode spends plan limits at 2.5 times the Standard rate, and the quota report weights it that way.
docs/how-it-works.md covers the log format, every counting rule and the forensics in detail.
Prices
The bundled list covers the models Codex has used, from gpt-5 and o3 to gpt-6-astra, as of 2026-11-04. codexwatch prices lists them with their source. To change one, put it in prices.json in the codexwatch folder (codexwatch prices path):
{"models": {"gpt-6-astra": {"input": 10.0, "cached": 1.0, "cache_write": 12.5, "output": 50.0}}}Prices are USD per million tokens. codexwatch prices update downloads LiteLLM's list to fill gaps; your own file still wins, and codexwatch reprice re-costs your history after any change.
Privacy
codexwatch reads ~/.codex/sessions (and archived_sessions) and writes only to its own folder: ~/.config/codexwatch on Linux, ~/Library/Application Support/codexwatch on macOS, %APPDATA%\codexwatch on Windows. It makes no network request unless you run prices update, or ask codexhop for readings with limits --refresh, the dashboard's button or autohop. The dashboard listens on 127.0.0.1 only and answers data requests only from its own page, with a token a web page elsewhere cannot read. SECURITY.md has the details.
Platforms
Linux | macOS | Windows | |
CLI, live view, dashboard, card, MCP, budgets | yes | yes | yes |
Menu bar or tray | tray (GTK) | SwiftBar or xbar | no |
Autohop (needs codexhop) | yes | yes | no |
Python 3.9 or newer; no packages beyond the standard library.
FAQ
Why is the package called codexwatch-cli? PyPI treats codexwatch as the same name as an unrelated project called codex-watch, so the package carries a suffix. The command is codexwatch, and codexwatch-cli works too. To run it once without installing: uvx codexwatch-cli.
Why do my dollar figures differ from another tool's? Most often because that tool counted forked sessions, subagents or repeated events twice, priced fast mode at the Standard rate, or treated a model with no price as $0. codexwatch doctor lists unpriced models, and export gives you every response to check against.
Does it work with Codex in VS Code, codex exec and the desktop app? Yes, they all write the same logs; codexwatch clients splits usage by where it ran. Codex cloud tasks do not write local logs, so they appear only through their effect on your plan windows.
I use an API key, not a ChatGPT plan. Then there are no plan windows, and the dollar figures are close to your actual bill.
Can I try it without Codex logs? codexwatch demo opens the dashboard on two months of invented usage, kept apart from your real history.
Contributing
Issues and pull requests are welcome. CONTRIBUTING.md explains how to run the tests (python -m pytest) and how to add a log format or a price.
codexwatch is built by Ebenworks, which also makes codexhop and claudehop, and, with Statotech Systems, the Claude Usage Widget & Token Tracker. The dashboard uses the Geist fonts under the SIL Open Font License.
MIT licence. Not affiliated with, endorsed by, or sponsored by OpenAI. "OpenAI", "Codex" and "ChatGPT" are trademarks of OpenAI, used here only to say what this works with.
Available Tools
6 toolsbudget_statusB
The user's codexwatch budgets (dollar, token or plan-window caps) with how much of each is used and whether it is near or over its limit.
Inputs: no named parameters.
dailyB
Tokens and API-equivalent dollars per local day, oldest first, with empty days included.
Inputs: days.
plan_limitsB
Where the ChatGPT plan's Codex limits stand now: each window's percent used, reset time, burn rate per hour and projected time it fills, read from the newest session log. With codexhop installed, also every saved account's last reading and the account with the most 5-hour room.
Inputs: no named parameters.
quota_forensicsA
What each percentage point of the plan cost in API-equivalent dollars and tokens, per window and per day, plus rises in a window that the local logs cannot explain (usage from another machine, ChatGPT itself or cloud tasks, or a metering change).
Inputs: window.
sessionsB
The most recent Codex sessions with title, project, models, turns, tokens and dollar value.
Inputs: limit.
usage_summaryB
Codex token usage and its API-equivalent dollar value for a period: totals, cache hit rate, and the top models and projects. Dollar figures are estimates at OpenAI API prices; tokens of models with no published price are counted separately.
Inputs: period.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
TDQS
Scored across 6 tools
Each tool targets a distinct slice of Codex usage monitoring: period summary, plan-window status, forensic cost-per-point analysis, session listing, daily time series, and budget status. There is minor overlap between usage_summary, daily, and quota_forensics since all report token/dollar usage, but their descriptions clarify the different granularities and purposes.
All tools use lowercase snake_case and are noun-based metric/resource names, which is predictable. Minor deviation: some are single words (sessions, daily) while others are compound noun phrases (usage_summary, plan_limits), but the style remains consistent overall.
Six tools is well-scoped for a read-only Codex usage and budget monitoring server. Each tool earns its place by covering a different analytical view without excessive granularity.
The surface covers summaries, daily breakdowns, sessions, plan limits, forensic cost analysis, and budget status, giving strong read coverage of the monitoring domain. A minor gap is the absence of tools to create or edit budgets (only status is exposed), but agents can work around this if budgets are configured elsewhere.
Maintenance
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