Pitroom

Why Pitroom?

Claude Code, Codex and friends spend premium tokens reading: grepping, opening files, scanning code they'll never quote.

Pitroom goes one step further:

Your agent stays in the driver's seat. A pit crew of cheap workers does the legwork in parallel, while your agent keeps going, and hands back only the answer, the exact diff and a receipt.

And more

• Receipts, not vibes. Tokens the worker burned, tokens returned to your agent, compression ratio, and an estimate of what your primary model would have charged. pitroom savings --card card.svg makes a shareable card.
• Self-healing worker chain. Free models get rate-limited or retired. List fallback workers once (fallback) and a run that hits "model not found", 429 or quota errors moves to the next model automatically. Still no model hardcoded: the chain is yours.
• A crew, not just one worker. pitroom crew starts several workers at once, pitroom watch --json streams one line per change so your agent follows them live, pitroom wait -g collects every answer, pitroom apply -g lands isolated patches in order. Up to 20 workers run at once by default (maxParallel, 30 at most), the rest queue, and the fallback chain absorbs free-tier rate limits; parallel writers are refused.
• Faster, not only cheaper. Workers run in the background and side by side (up to 20 at once) while your agent keeps working, so slow reading and review stop blocking it. In the benchmark, 470 s of worker time finished in 156 s of wall clock, about 3× faster than one question after another, and your agent's own context stayed small.
• Free first, dearer only when needed. Runs and plan tasks start on the cheap tier, OpenCode's free model; standard (Codex) and capable (Claude Code) are for tasks that need them, and an optional review tier names who reviews. Gemini CLI with a free Google AI Studio key is one more cheap worker (small daily quota per model). pitroom models shows what each worker offers, the effort levels (low … xhigh) each model accepts, what you say it costs (costs) and what it used; --effort sets the level per run. Details: Models, costs and effort.
• Exact changes, even in a dirty tree. Pitroom snapshots the working tree with a throwaway git index (your index, branches and stash are never touched), so it reports only the worker's edits and can undo exactly those: pitroom revert <id>.
• Your agent sets the permissions. It creates each worker with the permissions the task and your session allow (read-only, isolated copy, in-place edits, web), and a worker never widens its own. A fixed floor stays for every run: no git history changes, no sudo, no publishing, no secrets. A worker cannot land a deletion either: pitroom apply refuses a patch that deletes files unless your agent passes --allow-delete.
• Real isolation. --isolate runs the worker in a private copy of your current state (its own repository, sharing your objects read-only), uncommitted and untracked files included, then hands you a patch: pitroom apply <id> (checked, refuses on conflict) or pitroom discard <id>.
• Zero repo pollution. No .pitroom/ folder, no .gitignore edits, no branches. Records live in ~/.local/state/pitroom.
• Works with any agent. Fourteen Agent Skills: the superpowers development workflow run by workers, plus delegation (using-pitroom, pitroom-research, -crew, -implement), a CLI, and a Claude Code / Codex plugin whose session-start hook loads the workflow. Claude Code, Codex, Gemini CLI, Cursor, or anything that can run a shell command.
• A live dashboard and a searchable history. pitroom dash opens a local page of every run (the task, what the worker did step by step, its result and diff) with history and statistics, for the agent apps that show neither hooks nor a status line; pitroom history searches everything Pitroom ever ran. See Dashboard.
• Small. About 6,500 lines of TypeScript in the CLI, a ~210 KB CLI bundle (plus the ~1.9 MB of dashboard files it serves, with syntax highlighting for ten languages), zero runtime dependencies.

How it stays safe

Each run gets the worker's own safety mechanism set to the mode (for OpenCode, a permission profile injected through OPENCODE_CONFIG_CONTENT): no commit/push/reset/checkout/stash/clean/rebase, no bulk deletes, no sudo, a file-reading tool that refuses .env and key files (OpenCode and Claude Code workers), no web tools unless you pass --web, no subagents, no recursive delegation. Only allow/deny rules, so a headless run never stalls on a prompt. A git guard on the worker's PATH also stops sh -c "git push", env git reset, aliases and scripts from committing, pushing, resetting, stashing or touching your index. Your opencode.json is never touched. These safeguards reduce risk; they are not a security boundary (see Responsibility and SECURITY.md).

See it work

A real run on a free OpenCode Zen model: your agent reads ~554 tokens instead of 163,000.

$ pitroom run "Which function builds the read-only permission profile, and which bash commands does it allow?"pitroom ✔ done · read · 30s · run 20260930-003902-3270session ses_… · model opencode/muse-spark-1.3-contributor-free
SUMMARY: The read-only permission profile is built by `readProfile()` in src/profiles.ts:89.Its bash allow-list is `READ_ONLY_BASH` in src/profiles.ts:29-46.DETAILS:- Allowed (src/profiles.ts:31-41): `git status*`:31, `git diff*`:32, … `rg *`:40, `wc *`:41.- Explicit sub-denies: `rg *--pre*`:43, `git *--output*`:44, `git *--ext-diff*`:45.FILES CHANGED: none
── receipt: worker processed 163k tokens in 6 steps (7 tool calls) · worker cost $0.000   · returned ~554 tokens, 294× compression · est. saved $0.152 vs Claude Sonnet

Every run prints a receipt, and pitroom savings adds them up (--card writes this shareable card; the numbers here are sample data, and a real measurement follows in Benchmarks):

[Pitroom savings card]

Dashboard

pitroom dash --detach prints the address of a live page on 127.0.0.1 (read-only, this machine only). It is for the places where hook messages and status lines do not reach, such as the Claude Code and Codex apps: open it in a browser or in the app's own browser pane. The skills tell your agent to start it and give you the address when it runs workers in the background.

The screenshots below show sample data (an imaginary shop-api project), not a real one.

Live. What is running now, and the latest runs. Running cards show a timer, the worker's last words and its animated pixel mascot (a finished card shows the worker's logo); finished ones show the result and, for reviews, the findings.

[The Live tab: four running workers with timers and progress bars, then finished runs with approved and needs-fixes badges, step counts, tokens and savings]

Click a card and it grows into a panel with the task, what the worker did step by step (files read, searches, commands, edits, with times, failed ones marked), its result, the files and diff it changed, and the details (model, tokens, cost, fallbacks, reference check).

[An expanded run card: the task, a ten-step activity list, the result, five changed files with one opened to its highlighted diff, and the details]

Audits and skipped workers show on the card. A research answer that another worker re-checked (Audits) carries an audited · agrees / partly agrees / disagrees badge and, when it disagrees, the claims it disputes; a worker left out because its quota ran out is listed under the details ("Skipped … cooling down until 14:05", see pitroom cooldown).

[An expanded research card whose answer was audited by another worker, which disagrees and lists two disputed claims with file:line, and a note that a worker was skipped because its daily quota was used up]

History searches everything Pitroom ever ran (the full text of tasks, answers and steps), with filters by state, model and period; each run opens as the same card as on Live. Stats shows runs, success rate, time, tokens and savings, per worker and model and per day.

[The History tab: a search box, filters and every past run as a card] [The Stats tab: totals, runs per day, and per worker and model its logo, success rate and how many of its audited answers another worker confirmed]

The dashboard is a React app built once into dist/ui and served as two static files, so the CLI still has no runtime dependencies. It follows your system's light or dark theme (a button switches it).

Benchmarks

Measured on a real, public repository: PI-Desktop at commit c2bfe35, 2,349 tracked files and about 419,000 lines of TypeScript and Rust. Eight questions of the kind an agent asks before it changes code, run as one read-only crew (pitroom crew, at most four workers at a time) on OpenCode's free muse-spark-1.3-contributor-free model.

Workers read 5.5 million tokens of code and docs (96 steps, 179 tool calls) and handed the agent 7,684 tokens, about 960 per answer. The eight runs add up to 470 s of worker time but finished in 156 s of wall clock, because four ran at a time (the limit then; the default is now 20): about 3× faster than one after another. That compares the same workers with themselves, not with your agent doing the reading alone, which I did not measure. pitroom savings estimates $3.86 saved for these eight at Claude Sonnet list prices.

Were the answers right? Checked by hand against the repository: 46 key claims across all eight answers (the cited line and what it says) were all correct, and two completeness checks held (exactly 6 invoke and 3 event window channels; no tests in config_sync_rpc.rs). The i18n answer lists the 12 source files that import the package and leaves out 11 test and fixture files that do too, which the question did not ask about. Pitroom's own checker marks fewer references as verified than that: ¹ those two answers cite bare file names (transcripts.rs:651) that it cannot resolve to a path, and it counts them as unverified although the lines are right.

A change in an isolated copy: "add a one-line comment above resolveUpdateMode" finished in 16 s and 4 steps (142,491 tokens). The patch was one file and one line, the comment matched the code, and your working tree is untouched until you apply.

Free models against gpt-6-sol, on three large repositories. The same kind of work on React (7,252 files), Django (7,091) and Kubernetes (31,353 files), at pinned commits. Nine bounded questions, three per repository: the file and line that define a function, how many files contain a word, and which files contain it. Every answer is checked against git grep, not by opinion: an exact path:line (half a point for the right file on the wrong line), an exact count, and for the lists an F1 score that punishes both missed and invented files. Each worker ran the nine questions once, read-only, with no fallback, so a model that cannot do it fails instead of being swapped for another.

Four free models matched gpt-6-sol on these questions. Every model found every definition; the misses are count questions (a wrong number) and, for two models, list questions, one of them by adding docs/ files that are not under django/. gpt-6-sol was as fast as the quickest free models, and lowering its effort did not cost accuracy here. nemotron-3.5-lightning-free wrote an unrelated text for one list question and ran into the 10-minute limit on another. The scoring harness and every raw answer are in benchmarks/multi-repo.

Only workers that ran are in the table. Models that could not answer at all (a provider error, or the shared daily quota of OpenRouter's free tier) are left out, and so are the two gpt-oss-20b runs that ended in a provider error; a timeout is kept.

Does a review find a planted bug? A bug was changed into real code (an inverted check, a swapped &&/||, a flipped return) in React, Django, Kubernetes and Pitroom itself, committed as a bare "tidy", and pitroom review was asked to review the commit. 29 packages (14 with the bug alone, 15 with the bug and three comment-only edits around it). A review counts as a find when a Critical or Important finding names the changed file and either cites a line within 5 or names the changed identifier or its function; "exact line" is the strict version (within 3).

Almost every model found almost every bug, so this test separates them on speed and precision, not on whether they can review: muse-spark described the right bug but often quoted a wrong line number (57% exact), and gpt-6-sol and muse-spark were the fastest by a wide margin. Only three models finished all 29: the other four are scored on the reviews that ran before a provider limit (the free tier's usage limit, and the Codex plan's, which gpt-6-sol reached), and the rest are left out, not counted as misses, so their rows rest on 9 to 14 reviews. Every change is only 2 to 5 lines, which is easy; there is no false-alarm rate (the comment-only "clean" packages turned out to contain comments that were really wrong, so they cannot show one); and React, Django and Kubernetes may be in a model's training data. The harness and every raw review are in benchmarks/review-bugs.

What this does not show

• One run per question and model: there is no variance here. The questions are bounded lookups that grep can answer, so they do not show how a model handles design questions or large edits, and four free models and gpt-6-sol all scoring 100% says the test is easy at the top, not that they are equal.
• An open-ended task ("find up to five spelling mistakes in docs/") did not finish: it was stopped after 19 minutes and 57 tool calls. Give workers bounded tasks.
• "Tokens processed" is what the workers read and wrote. What your agent would have spent doing the same reading itself is an estimate, not a measurement: the savings figure assumes it would process about the same tokens. The $0 worker cost is because the model is free.
• The hand check covered key claims, not every citation.

To repeat it on your own repository: pitroom crew -d <repo> -g bench "<question 1>" "<question 2>" …, then pitroom wait -g bench, pitroom show <run> and pitroom savings --models.

Quick start

You need Node.js 22.13+ and at least one worker CLI: OpenCode v2+ (the default worker, with free models), Codex CLI, Claude Code or Gemini CLI (beta; it needs an API key from Google AI Studio, a Google account sign-in no longer works for it).

1. Install: pick your agent

Claude Code

claude plugin marketplace add ATASTECH/pitroomclaude plugin install pitroom@pitroom

_{Inside a session: /plugin marketplace add ATASTECH/pitroom, then /plugin install pitroom@pitroom.}

Codex

codex plugin marketplace add ATASTECH/pitroomcodex plugin add pitroom@pitroomnpm i -g pitroom

Gemini CLI

gemini extensions install https://github.com/ATASTECH/pitroomnpm i -g pitroom

Any agent that speaks MCP (Cursor, Claude Desktop, and the three above too)

Pitroom is also an MCP server on stdio: ten tools instead of shell commands and skills (pitroom_run, pitroom_wait, pitroom_show, pitroom_info, pitroom_review, pitroom_audit, pitroom_apply, pitroom_discard, pitroom_revert, pitroom_stop), the runs as resources, and four prompts. The tool definitions sit in the client's context for the whole session, so they are kept small: about 1.9k tokens. It is listed in the MCP Registry as io.github.ATASTECH/pitroom, so clients that browse the registry can find it. After npm i -g pitroom, let Pitroom register itself in the clients it finds (Claude Code, Codex, Gemini CLI, Cursor, Claude Desktop):

pitroom install --mcp               # also links the skills; add --no-skills to skip thatpitroom install --mcp --dry-run     # only say what it would dopitroom install --mcp --client cursor,claude-desktop

It registers the launcher by its full path (apps like Claude Desktop start without your PATH), changes Claude Code, Codex and Gemini CLI with their own mcp add command (user scope where it has one), merges the JSON of Cursor and Claude Desktop without touching their other servers (keeping a .bak-pitroom backup, and leaving a file that is not valid JSON alone), skips what is already registered, and pitroom uninstall removes it again. pitroom doctor shows where it is registered. Restart the client afterwards. Or do it by hand:

claude mcp add pitroom -- pitroom mcpcodex mcp add pitroom -- pitroom mcpgemini mcp add pitroom pitroom mcp

or, for Cursor (~/.cursor/mcp.json), Claude Desktop (claude_desktop_config.json) and others:

{ "mcpServers": { "pitroom": { "command": "pitroom", "args": ["mcp"] } } }

Each tool runs the matching pitroom command, so every rule of the CLI applies unchanged (permission profiles, git guard, isolation, read snapshots). A run is waited for up to waitSeconds (default 50), then comes back as "still running" with its id for pitroom_wait; keep it below your client's tool timeout. The server works in the directory it is started in: the client's for stdio, or the one -d DIR names (Claude Desktop starts it elsewhere, so give it "args": ["mcp", "-d", "/path/to/project"]); the tools' dir argument picks another per call.

• Progress. While a call waits for a run, a client that sent a progress token gets a notifications/progress every few seconds ("running · read · 12s · 3 steps, 2 tool calls · last: …"), and a client that resets its timeout when progress comes in (the protocol allows it; not every client does) does not give up on a long run. Cancelling a pitroom_run, pitroom_review or pitroom_audit call stops the run it started (a cancelled pitroom_wait only stops waiting).
• Resources. The latest runs are listed as pitroom://run/<id> (the report) and, for runs that changed files, pitroom://run/<id>/patch (the exact diff), so a client can attach one to a conversation. A client can subscribe to a run (resources/subscribe) and is told when it changes state (notifications/resources/updated), and every client is told when the newest run changes (notifications/resources/list_changed), so it need not poll; over HTTP these arrive on the session's event stream (a GET).
• Prompts. research, implement, review and crew say how to use Pitroom for that job (in Claude Code they appear as /mcp__pitroom__research, …), and every skill is a prompt of the same name (using-pitroom, pitroom-research, …), so a client without skills gets the same guidance. The server's instructions tell the agent to use the skills alongside the tools.
• More than runs: pitroom_run takes tasks (independent tasks in parallel, one worker each) and continue (a follow-up in the same worker session); pitroom_info reports without changing anything, by topic: runs, history (find an earlier answer before asking again), stats and models (pick a worker), savings, cooldown, config, doctor; pitroom_stop with cooldowns tries rate-limited models again.
• Over HTTP. pitroom mcp --http [--port N] (default 7117) serves the same tools at http://127.0.0.1:7117/mcp for clients that connect to a URL, for example claude mcp add --transport http pitroom http://127.0.0.1:7117/mcp --header "Authorization: Bearer $(cat ~/.local/state/pitroom/mcp-token)" (the command is printed when it starts). It is a local service: it listens on 127.0.0.1 only, needs the bearer token (made on first start, kept at <state dir>/mcp-token with owner-only permissions, or set with PITROOM_MCP_TOKEN), and refuses a Host or Origin that is not local, so a web page cannot use it. Anyone who has the token can run workers as you, so keep it private. It works in the directory it was started in (printed when it starts; -d DIR for another). Each client has its own session; a call that asks for progress is answered as an event stream. Ending a session or stopping the server ends the waiting, not the runs: collect them with pitroom_wait or the CLI. pitroom install --mcp registers the stdio command, which needs no token and no running process.

Any other agent (a plain shell) or just the CLI

npm i -g pitroompitroom install

Pick one path, not several: doctor warns if the skills load twice. The plugins alone do not put pitroom on your PATH, so install it from npm too if you want to run it yourself (watch, models, savings, the status line).

2. Check the setup

pitroom doctor --probe

It checks the worker CLIs, models, permissions and skills, and runs one live round trip.

3. Use it

Pitroom is a toolbox, not a procedure: your agent uses it when it helps, or you ask explicitly:

Use pitroom to map how sessions are created and invalidated, then propose a fix. Split this into a pitroom crew: audit auth, billing and uploads for missing input validation.

Or start a worker yourself and read its receipt:

pitroom "Which files read the session cookie? Cite file:line."pitroom -W gemini:gemini-3.8-flash "…"   # the same on another worker (see Workers below)pitroom savings          # what all your runs saved so farpitroom models           # what each worker offers, what it costs you, what it used

# updateclaude plugin update pitroom@pitroomcodex plugin marketplace upgrade pitroomnpm i -g pitroom@latest
# uninstallclaude plugin uninstall pitroom@pitroomcodex plugin remove pitroom@pitroompitroom uninstall && npm rm -g pitroom

Modes

Read snapshots. With secret-looking files in the directory, a read run reads a snapshot of your project's current state (uncommitted and untracked files included) without them and without git-ignored files, made from git objects, kept once per state and shared by every read run on it (twenty workers, one snapshot), and cleaned up after three days or by pitroom clean. Nothing is copied back: a read run has nothing to land. Its files are read-only, and the paths a worker cites are turned back into your project's. With no secret-looking files nothing changes: the worker runs in place. --in-place (or "readIn": "project" in the config) reads the directory itself, e.g. for a task that needs build output; "readIn": "snapshot" always uses one.

Answer cache. Ask the same read question on the same code and you get the earlier answer back at once, with no worker run: pitroom ⟲ cached answer · the same question on the same code as run … · no worker ran. "The same code" is exact: the commit and every working file, uncommitted and untracked ones included (git-ignored files are not code), so any edit makes it a new question. Only answers that held up are reused (the run finished, every file:line reference checked out, no audit disputed it) and only for 7 days ("cacheDays" in the config, 0 turns it off, or PITROOM_CACHE_DAYS). Follow-ups, web runs, plan tasks, reviews, audits, --verify runs, parallel work (crews) and changes are never answered from the cache, and --fresh asks a worker anyway (its answer is the one reused from then on). Not part of "the same code": git-ignored files (build output, installed dependencies) and the worker or model that answered; when a question depends on them, use --fresh.

pitroom run "Find every place we build SQL strings by hand; file:line and risk"pitroom run -i --link node_modules --verify "npm test" "Fix the off-by-one in paginate() and add a test"pitroom run -w "Rename getUserById to findUserById in src/ and fix imports"pitroom run --continue last "Now handle the empty-page case too"

Long jobs: --bg returns immediately; pitroom wait <id> blocks for up to 9 minutes (made for agents with 10-minute tool limits; exit code 75 means "call wait again").

Workflow

Pitroom ships a full development methodology as skills, adapted from superpowers so that its subagents are cheap workers: your agent brainstorms and plans with you, then executes the plan task by task while workers do the typing and a second model does the reviewing.

[Workflow: brainstorming, then a plan with a worker tier per task, then for every task a worker implements in an isolated copy, another model reviews, fix rounds repeat until approved, and your agent applies, tests and commits; then a whole-branch review and finishing.]

A review package holds the diff under review with 10 lines of context, and the reviewer's CLI sends it to that worker's model provider. Pitroom does not filter it: secrets committed in a reviewed range go along as they are.

Tiers map plan tasks to workers: "tiers": {"cheap": "opencode", "standard": "codex", "capable": "claude"}. pitroom plan status PLAN rebuilds where a plan stands from the run records (it survives context compaction), and pitroom plan note keeps completions and rulings outside the repo.

Audits

Pitroom checks every path:line a worker cites, which proves a line exists, not that the claim about it is true. An audit asks a second worker to verify an answer's key claims against your project and to say AGREE, PARTIAL or DISAGREE, with the claims it disputes.

pitroom audit <run>            # now: re-check one read run (-W picks the auditor)pitroom run --audit "…"        # re-check this run's answer when it finishes (--no-audit skips it)

or set "audit": 0.1 in the config (or PITROOM_AUDIT=0.1) to have about one read run in ten audited, in the background, without your agent asking. The same run is always in or out of the sample.

• Cost: off by default. An audit is a read run of the auditor, so about the rate times the worker's own tokens, on the audit tier (else cheap, i.e. a free model if that is your cheap tier). It never delays the run or fails it.
• Never itself: the auditor is never the worker and model that gave the answer; with no other worker configured nothing is audited (pitroom doctor says so). Only read runs are audited: a change has pitroom review.
• Where it shows: the answer's card and pitroom show carry the verdict and the disputed claims; the Stats tab counts, per worker, how many audited answers were confirmed. Audits are not counted as runs and save nothing.
• A sample, not a guarantee. The auditor is a model too: it can share a blind spot, and a few audits say little about a worker. Treat DISAGREE as a reason to look, and AGREE as one more signal.

Crews

A real crew over this repository, four questions at once on a free OpenCode Zen model:

pitroom crew -g v04-demo \  "Where does Pitroom enforce the maxParallel queue, and how does a waiting run react to pitroom stop? Cite file:line." \  "How does the git guard shim resolve git aliases? Cite file:line." \  "Which fields of OpenCode v2 events does the parser read for token usage, refused tool calls and errors? Cite file:line." \  "How does pitroom apply -g decide which patches to apply and in what order? Cite file:line."pitroom watch -g v04-demo --json   # what the primary agent follows, one line per change:

{"event":"started","run":"20260930-102812-a65d","mode":"read","task":"Where does Pitroom enforce the maxParallel queue, and how d…"}{"event":"progress","run":"20260930-102812-a65d","steps":3,"tools":4,"last":"grep SIGINT|SIGTERM|stop.*queued|stopped.*queued"}{"event":"done","run":"20260930-102812-a563","time":"21s","worker":"opencode (default model)","summary":"The shim resolves `alias.<sub>` via `real git config --get` in a loop …","refs":"5/5"}{"event":"all-done","runs":4,"ok":4,"failed":0}

$ pitroom status -g v04-demoRUN                   STATE  MODE  TIME  STEPS  WORKER                    NOW / RESULT20260930-102812-0c0d  done   read  30s   5      opencode (default model)  Parser `src/backends/opencode/events.ts` reads …20260930-102812-7642  done   read  27s   5      opencode (default model)  `pitroom apply -g NAME` applies finished isolat…20260930-102812-a563  done   read  21s   2      opencode (default model)  The shim resolves `alias.<sub>` via `real git c…20260930-102812-a65d  done   read  39s   7      opencode (default model)  maxParallel is enforced by file-based slots (`s…

Humans get the same table live with pitroom watch -g NAME; pitroom wait -g NAME --brief prints one line per worker, pitroom show <run> the full answer.

For changes, pitroom crew -i … gives every worker its own isolated copy of your current state and pitroom apply -g NAME lands the patches in order, stopping at the first conflict. Up to maxParallel workers (default 20, at most 30) run at once, the rest queue; only one --write run per repository is allowed. pitroom stop -g NAME stops running and queued workers.

Skills

Commands

pitroom run [-r|-w|-i] [-W WORKER] [-m MODEL] [-d DIR] [-f FILE]… [-t 30m] [--verify CMD] [--link a,b] [--bg] "task"pitroom run --continue <run|last> "follow-up"pitroom crew [-i] [-g NAME] "task 1" "task 2" …   (or --task-file with --- separators)pitroom run -i --plan PLAN --step N [--tier T] ["notes"]pitroom review [run | --range A..B [--plan PLAN]] [--tier T | -W T] [--bg]pitroom audit RUN [-W worker]pitroom mcp [-d DIR] [--http [--port N]]   # serve Pitroom as MCP tools on stdio, or over HTTP on 127.0.0.1 (bearer token)pitroom cooldown [--clear]pitroom plan status PLAN [--json] · pitroom plan note PLAN "Task N: …"pitroom status|wait|watch [run… | -g NAME]        wait: --any --brief --timeout · watch: --json|--brief --intervalpitroom dash [--detach] [--port N] [--open] [--stop]   a live page of the runs on 127.0.0.1pitroom history [TEXT] [--model M] [--state S] [--since 30d] [--limit N] [--json]   finished runs, searchablepitroom history stats [--since 30d] · pitroom history import   per worker and model · take in older runspitroom show [run]                                --patch --events --full --jsonpitroom apply [run | -g NAME] · pitroom discard|revert [run] · pitroom stop [run | -g NAME]pitroom ls [--running] [-g NAME] · pitroom clean [--days 14] [--yes]pitroom savings [--since 7d|30d|all] [--models] [--card file.svg] [--badge]pitroom models [worker] [--all] [--json]      models, effort levels, your costs, your usagepitroom statusline [--then CMD] · pitroom hook-cardpitroom doctor [--probe] · pitroom config · pitroom init [--model ID] [--fallback A,B] [--yes] [--force] · pitroom install [--copy] [--force] [--mcp [--client A,B] [--no-skills] [--dry-run]] · pitroom uninstall

History

Every finished run is also written to a SQLite database (history.db in Pitroom's state directory; Node's built-in node:sqlite, nothing to install). It keeps the task, worker and model, time, tokens, savings, the steps the worker took, its answer and the diff, and it is searchable:

pitroom history "login bug"            # full-text search over the task, the answer and the stepspitroom history --state problem --since 7dpitroom history stats --since 30d      # runs, success rate, average time and tokens per worker and model

While a run runs, its raw event stream is a plain file (the simplest thing that survives a crash). When it ends, the stream is compressed, and the stderr log is kept only for runs that did not succeed. pitroom clean removes old run directories but the history keeps them: pitroom show <run> still prints their report, steps, and patch. Runs from before the history existed are taken in by pitroom history import (it also runs on the first pitroom history and pitroom dash). The files remain the source: the database can be deleted and rebuilt with history import for the runs still on disk.

Seeing Pitroom at work

Two settings make every delegation visible, whether or not the agent mentions it. A status line shows running workers and this week's savings (--then keeps your own status line first); a card appears after each pitroom command the agent runs, once per phase (started, finished with its result, applied). The plugin registers the card hook itself; with pitroom install, add both to ~/.claude/settings.json:

{  "statusLine": { "type": "command", "command": "pitroom statusline --then 'your-own-statusline'" },  "hooks": {    "PostToolUse": [{ "matcher": "Bash", "hooks": [{ "type": "command", "command": "pitroom hook-card", "timeout": 10 }] }]  }}

The Claude Code and Codex apps show neither hook messages nor a status line. For them there are two things that need no setup:

• pitroom dash --detach prints the address of a live dashboard with three tabs: Live (every running and recent run as an animated card; click one for the task, what the worker did step by step, its result, the diff and the details), History (search and filter everything Pitroom ever ran; every run opens as the same card) and Stats (success rate, time, tokens and savings per worker and model). It is read-only, listens on 127.0.0.1 only and stops itself after an hour without a request (pitroom dash --stop ends it sooner). Open it in a browser or in the app's own browser pane. The skills tell the agent to start it and give you the address when it runs workers in the background.
• pitroom watch -g NAME --brief prints one card line when a worker starts and one when it ends. In Claude Code, the agent runs it through the Monitor tool and the lines appear in the app; in Codex the command's output block fills as it goes.

How it works

[How a run flows: the primary agent delegates to the Pitroom CLI, which starts a worker CLI in read, isolate or write mode; events, then the answer, an exact diff and a receipt come back.]

Workers

A worker is named by a target: backend[:model].

pitroom run "…"                                          # preferred worker (config "worker", default opencode)pitroom run -W opencode:opencode/space-bunny-free "…"    # a specific backend and modelpitroom run -W 'codex:#low' "…"                          # the model from config "models" (not Codex's default), low reasoning effortpitroom run --effort high "…"                           # model#level (Codex, Claude Code, OpenCode; Gemini CLI has no effort option)pitroom run -W claude:haiku "…"                          # Claude Code on a cheap modelpitroom run -W gemini:gemini-3.8-flash "…"               # Gemini CLI (beta; needs a Google AI Studio API key)pitroom run -m nvidia/z-ai/glm-5.3 "…"                   # another model on the preferred worker

Models, costs and effort

pitroom models lists what each worker offers (Codex from its own model cache, Claude Code's aliases, OpenCode's opencode models, Gemini CLI's built-in names), the reasoning-effort levels each model accepts, what you say it costs and what your own runs used:

worker  model        effort (* default)                 cost  runs  avg tokens  $/run   in usecodex   gpt-6.1-sol  low*/medium/high/xhigh/max/ultra   2     20    174k        -       tier standardcodex   gpt-6-sol    low/medium*/high/xhigh/max/ultra   1     1     24k         -       -claude  haiku        low/medium/high/xhigh/max          ?     -     -           -       -

Pitroom cannot know vendor prices and does not fetch them, so a cost is what you enter: "costs": {"codex:gpt-6-sol": 1, "codex:gpt-6.1-sol": 2} in the config, in any unit (they are only compared). pitroom doctor then prints the cost of the models in use and says when you priced a cheaper one of the same worker. A target that names only an effort (codex:#low) uses the model from models; a Codex or Claude Code worker with no pinned model gets a warning, because it would run the vendor's own default, which can change and cost more. --effort LEVEL sets the level for one run; your agent picks model and level from pitroom models (cheapest that fits: low for lookups, medium for ordinary changes, high for reviews).

Fallbacks cross backends (e.g. "fallback": ["codex:#low", "opencode"]): a worker that is rate-limited, logged out or missing its model hands the task to the next. Follow-ups (--continue) always stay on the worker that owns the session. Writing an adapter: docs/backends.md. A model that says "rate limited" (a daily quota used up, an overloaded provider) is remembered for the time its message gives, or a guess: the next runs skip it while a fallback is left, so they do not each wait for it to fail. pitroom cooldown lists what is skipped and until when, --clear tries it again, and doctor shows it too.

Configuration

Or put defaults in ~/.config/pitroom/config.json (flags and env still win); pitroom config shows every effective value and where it came from. pitroom init proposes a starter config from the worker CLIs and OpenCode models you have (a fallback chain of your free models, tiers when more than one CLI is installed) and writes it only with --yes; it never picks your model for you. models gives each worker a default model for targets that name none (-W codex, a "codex" fallback); a model in the target or -m still wins. tiers names workers for --tier and for plan tasks' **Worker:** lines:

{  "worker": "opencode",  "fallback": ["opencode:opencode/space-bunny-free", "codex"],  "models": { "codex": "gpt-6.1-sol", "claude": "claude-sonnet-5-5" },  "costs": { "codex:gpt-6-sol": 1, "codex:gpt-6.1-sol": 2 },  "tiers": { "cheap": "opencode", "standard": "codex", "capable": "claude", "review": "opencode:opencode/space-bunny-free" },  "timeout": "20m",  "primary": "opus",  "link": ["node_modules"],  "maxParallel": 20,  "audit": 0.1}

• worker and fallback: who runs a task when you name no one, and who takes over when it fails; models: the model each worker uses when a target names none.
• tiers: cheap, standard and capable name the workers for plan tasks and --tier; an optional review tier names who reviews a run (by default another worker than the implementer's, standard first).
• audit: the chance (0 to 1, default 0 = off) that a finished read run is re-checked in the background, see Audits. The audit tier (else cheap) names who does it.
• costs: your relative cost per worker:model, only compared with each other; maxParallel: workers at once (default 20, at most 30).

FAQ

Where does my code go? To whichever provider your worker's model uses. For private code, point the worker at a local model. The file-reading tools of OpenCode and Claude Code workers refuse .env files and private keys; Codex workers are not restricted that way and a shell command can still read them, so keep secrets out of the folder you delegate in.

How is "saved" computed? It assumes your primary agent would have processed about the same tokens the worker did, priced at your primary's list prices (PITROOM_PRIMARY / PITROOM_PRICE), minus the worker's cost and the cost of reading the report. It is an estimate and is labelled as one.

Can the worker break my repo? It can't touch git history, refs or your index (permission profile + git guard), can't run bulk deletes, and write-mode edits are revertible with one command. The guard is not an OS sandbox: git called by absolute path from inside a script, or non-git tools, are outside its reach, which is why --isolate exists: nothing reaches your tree until you apply it.

Can the worker read my .env? In a read run, no: when the directory holds secret-looking files (.env, prod.env, private keys and keystores, .netrc, credentials.json, secrets.json), the worker reads a clean snapshot of your project instead (see below), where they are simply not present, whatever the worker CLI would have allowed. Edits (-i, -w) and reviews are different: an isolated copy (-i) leaves out git-ignored files, so a git-ignored .env is not in it, but one that is not ignored would be copied, and Pitroom warns when it sees such files (PITROOM_NO_SECRET_WARNING=1 silences the warning). A free model may be hosted by a third party: keep real secrets out of what you delegate.

What if the worker fails? Pitroom exits non-zero with the real cause (for example a default model that no longer exists) and your agent simply continues on its own. pitroom doctor diagnoses setup problems.

Responsibility

Pitroom is provided as is, under the MIT License, and is an independent project: it is not affiliated with or endorsed by OpenAI, Anthropic, OpenCode or the other tools it can drive.

You are responsible for how you use it. You decide what you delegate and to which model provider, which permissions a worker gets, and what you apply to your projects. Workers are AI agents and can be wrong: review their changes and run your tests before you rely on them. Pitroom's permission profiles, git guard and isolated copies reduce risk, but they are not a security boundary against a determined attacker or a malicious repository. Keep secrets out of the folder you delegate in, prefer isolated mode for changes you have not reviewed, and follow the terms of the worker CLIs and model providers you connect. What reaches a provider is described in the privacy policy.

Contributing

Issues and pull requests are welcome. Start with CONTRIBUTING.md: setup, tests, the dist bundle and the commit style. Worker adapters are the easiest place to begin: docs/backends.md.

npm installnpm test          # end-to-end tests against a fake worker CLI + adapter contract tests on recorded real streamsnpm run typecheck

Security: please report vulnerabilities privately, as described in SECURITY.md, not in a public issue.

Report an issue · Open issues · Security policy · Code of conduct

Credits

The workflow skills (brainstorming, planning, worker-driven development, review, debugging, TDD, verification, worktrees, finishing) are adapted from superpowers by Jesse Vincent, under the MIT License; the notice is in THIRD_PARTY_NOTICES.md.

License

Pitroom is licensed under the MIT License. See LICENSE for details.