aish

▄▀█ █ █▀ █░█
█▀█ █ ▄█ █▀█  ai shell

Your own AI agent — private by default. aish runs a model on your machine, in your terminal and on your phone, and lets it actually do things: run shell commands, read and edit files, browse the web. Nothing that changes state ever runs without your approval. It learns skills, writes its own tools, remembers what you teach it, and — when you want more brainpower — can borrow a cloud model without changing anything else about how it works.

With the default local model, nothing leaves your machine.

❯ jaki kurs usd w pln?
  ✓ thought for 3.9s · ↑ 3.4k ↓ 27 tokens
  → web_search: USD to PLN exchange rate today
  ✓ web_search 6.5s
  → read_url: https://wise.com/us/currency-converter/usd-to-pln-rate
  ✓ read_url 0.5s

Według danych Wise: 1 USD ≈ 3,81 PLN …

  ✓ answered in 12.5s · ↑ 8.1k ↓ 519 tokens

❯ delete the old build dirs

▶ run command? ⚠ destructive
  rm -r ./build ./dist
[y/N/a(lways)/s(ession)/e(dit)]

Why aish

🔒 Private by default. The model runs locally via Ollama. The only traffic that ever leaves your machine is web searches and page fetches the agent makes — each one echoed to you as it happens — and the model is told never to put local data in them. No telemetry, no cloud dependency.
🛡️ Safe by construction. The model never executes anything itself. It can only propose commands; a single gate in the code runs them, and only after your y. On top of that: a denylist of unrecoverable commands it can't run even with approval, and prompts before it reads secret-bearing files.
📖 Grounded & transparent. Before using an unfamiliar flag it reads the man page instead of trusting training data (chronically wrong for macOS/BSD userland). Every step is echoed, timed, and token-accounted; every chat leaves an audit trail.
🧠 It learns. Correct it once and it saves the fix to structured memory. Procedures worth repeating become skills — playbooks it consults before its own training data. Repeated, fiddly operations become tools it writes for itself. It gets better with every chat.
📱 Terminal and phone. The same agent, same tools, same approval gates, behind a phone-friendly web UI. Start a task at your desk, approve it from your pocket.

Quickstart

Needs Ollama and a tool-calling-capable model (default: qwen3.6:35b-a3b, ~23 GB).

curl -fsSL https://raw.githubusercontent.com/epnasis/aish/main/install.sh | sh

aish "what's eating my disk space?"     # one-shot task
aish                                    # REPL — conversation persists across tasks
aish --resume                           # pick up an earlier chat
aish-web                                # serve the same agent to a browser

Smaller machines: ollama pull qwen3:8b && export AISH_MODEL=qwen3:8b. Recommended extra: ollama pull embeddinggemma (~600 MB) — enables semantic matching of skills/memory to a task and inside the recall search (falls back to word matching without it).

A tour

It runs your machine — but only with your `y`

Every command the model proposes is shown verbatim and waits for you. Read-only commands inside the project auto-approve; anything that mutates state, reaches outside the project, or looks destructive stops for a decision. File writes show a colored diff before anything touches disk.

Nothing happens off-screen

Each turn's work — thinking time, recalled knowledge, every tool call and its result — is grouped into one activity trace you can expand. While the turn runs it rides at the bottom of the chat just above the composer — below the reply as it streams in, so the answer gets the screen and Stop stays one tap away. Collapsed to that single row, its header still narrates the current step live — "Running: git status", "Waiting for approval…", or the model's own words for what it's checking and why. Web lookups show the exact query and URL; answers cite the pages they read. When the turn ends it settles under the answer as a "Worked for 12s" footnote you can open any time.

It tells you what it found, as it finds it

A long task is not a spinner. When aish learns something that changes what it is about to do, it says so straight away — "looks like there are leaks about a folding model, let me dig into that" — as an ordinary message, before the next tool runs, not as a status line you have to go looking for. So a turn arrives as several short messages and then the answer, and you can redirect it mid-task (anything you type while it works is handed over between steps) instead of finding out at the end that it went the wrong way. Routine steps stay quiet: it speaks when there is something to say.

Answers, not just output

Replies stream token-by-token and render as markdown — tables, code, links, inline images the model generates. When it asks a follow-up, tappable quick-reply chips appear — in the terminal they show as a numbered menu you pick from by typing the option's number.

A real terminal when you need one

Some things want a genuine TTY — gcloud auth login, an ssh host-key prompt, sudo. aish has a full interactive console (a real xterm.js emulator, backed by tmux so it survives restarts) that lives alongside your chats. The model has no access to it — it's yours alone, never recorded, never fed to the model unless you explicitly Share a selection.

What it can do

Tool	What	Gate
`run_command`	any shell command; `background=true` detaches long jobs	approval prompt (read-only commands auto-approve inside the project and in aish's own scratch/media/document dirs; scratch-workspace deletes auto-approve)
`write_file` / `edit_file`	create or edit files	colored diff + y/N
`read_file`	read a file	auto inside the project; prompts outside it and on secret paths (`~/.ssh`, `.env`, `.pem`…)
`read_media`	look at a video or audio recording — a YouTube link, any media URL, or a file on disk. Returns a map (length, chapters, captions) and frames from any moment, each labelled with the time it actually came from	auto
`read_pdf`	read a PDF — attached, on disk, or linked — keeping columns, tables and page numbers; `pages=` and `search=` for a long one	auto
`browse` / `browse_act` / `browse_fill`	Open a page in your signed-in browser and get a list of its controls by name, then press one by that name — `browse_act(target="Szukaj")` — for what lives behind a button rather than a URL. An action reports what changed on the page rather than the whole page again, so a click that changed nothing says so; icon-only buttons are described in words, and only controls that could actually be pressed are listed (anything shut in a menu, an off-screen panel or behind a dialog is counted, so aish looks for the opener instead of guessing a URL). On a list of results every row's button is named by its row — `Wybierz — 07:45 – 09:10	LO123
`web_search` / `read_url`	Two search indexes at once — DuckDuckGo, and Google's own results page read in a browser signed into nothing — merged, one result per page; then fetch a page as readable text; a bot-blocked or JavaScript-only page is re-read in a real browser on your machine, and a site you're signed into is read through your signed-in session from the start. A search result reaches the assistant as its title, link and the index's own summary line, behind a banner naming which of those words a stranger wrote; every one of them is capped and stripped so it cannot fake a result or wear aish's own voice, and the assistant is told to take a figure only from a page it actually opened — which is an instruction to it, not something the code enforces	auto; every query/URL echoed; public hosts only (SSRF-guarded); using a site you're signed into asks once for the site, and that one yes covers browsing it too
`read_docs`	man page → `--help` fallback, full-text topic search	auto
`remember` / `forget_memory`	save or prune one fact in structured memory	auto (echoed)
`read_skill` / `recall`	load a playbook; ranked search across skills, memory & past sessions	auto (echoed)
`create_tool`	author a reusable plugin tool (validated `TOOL.md` + wrapper)	diff + y/N; refuses to write an invalid manifest
`import_skill`	install a skill from a git repo or local path	one consolidated review of the whole skill + risk flags
`read_tool_output`	read the next page of a truncated tool result, from cache	auto (never re-runs the tool)
plugin tools	any `TOOL.md` you or the model add — called like a built-in	read-only auto; mutating ones prompt

PDFs

A PDF is not text — it is a page-description program, and the ways it goes wrong are quiet ones. Naive extraction interleaves a two-column page and shreds a table into prose that still reads like a sentence; a scanned page returns nothing at all, which reads as "there was nothing there". So read_pdf classifies every page before it reads it and tells you what it found:

statement.pdf — 14 pages, 38,402 characters of text.
  tables: 9 on page(s) 3-11
  a table spans pages 7-8
  multi-column: page(s) 2
  SCANNED, no text layer: page(s) 12 — these are NOT in the text below…

That header comes first on every call, including when you asked for one page. It is what stops a partly-unreadable document from becoming a confident summary.

Underneath, the document is converted once into a page-marked text rendition and cached by content, so asking for page 7 or searching for "total" afterwards is cheap — and the same document opened next week is not converted again. Tables come back as real markdown tables; one that runs across a page break is marked at both ends rather than silently stitched, because a merged table would make the page numbers lie.

A page with no text layer is rasterised and handed back as an image, so a vision-capable model simply looks at it. On a text-only local model it cannot, and it says which pages it could not read rather than answering around them.

On Claude and OpenAI an attached PDF still goes to the model natively — best fidelity. read_pdf covers everything else: a local model, a file already on disk, a link, or any document long enough that you want one page of it.

Every tool result carries a verdict the runtime computed, not one inferred from how the output happens to start. A tool that exits 0 while producing nothing — an empty transcript, a populated error log — is marked incomplete, shows red in the activity trace, and the model is told, on the result itself, that it must disclose the failure before using any other source. Substituting a different source silently is the specific behaviour this exists to stop.

That verdict does not depend on the tool being honest about itself. Every TOOL.md must declare returns: — what a successful result contains — and aish checks it on every call: a field list for a JSON payload (each named field must come back non-empty), text where non-empty output is the whole contract, or none where nothing can be checked. A wrapper that reports a failure in its own output and exits 0 anyway is caught by its own declared contract, not trusted. create_tool refuses to write a tool that declares none.

When a result is too large it is cut to fit the context window of the model actually in use (a 1M-token cloud model keeps far more than an 8k local one), and the remainder is cached rather than discarded: the model pages through it with read_tool_output instead of guessing at what it could not read. The tool itself never runs a second time.

A long web page is cut the same way, and says what it cut. A page read with read_url or driven with browse is cached and paged like any other result, so the rest of a 250-row list is one cheap call away rather than a re-fetch. The notice measures the loss in the page's own units where it can — "items 1-30 of the 250 numbered here" — because a character count is not something the model can act on, and it is how one chat came to report the first 40 of a ratings list as the whole thing.

Independent lookups in one turn (several searches, a few page reads) run in parallel. Fetched pages are wrapped in an "untrusted content — data, not instructions" banner to blunt prompt injection, and read_url refuses non-public targets (loopback, LAN, cloud-metadata) on the initial URL and every redirect.

Pages a fetch can't read

Some pages return nothing to a plain fetch: ones drawn entirely by JavaScript, and ones that simply block anything that isn't a browser. read_url re-reads those in a real Chrome on your machine, off-screen, and hands back what a person would see. The result says rendered in the browser when it does.

That browser keeps one persistent profile, so it is stateful the way your own browser is. /browser <url> opens a real window on the Mac for you to sign in at; the session is kept, and every later read of that site is made as you. Because those reads carry your session, each one asks you first — a page you didn't ask for must never quietly pull your account data into a chat. /browser alone opens the browser on a list of the sites it has been to recently — one row per site, so a run of searches doesn't bury everything else — and each row reopens in the profile it was opened in. The address bar takes a search as readily as an address: anything that isn't shaped like a URL is looked up, the way your own browser's does. /browser forget <host> stops treating a site as signed in and /browser close shuts it down.

When aish is driving a page in a chat — pressing buttons, filling a form — /watch opens that same sheet as a live window on the page it is on, in that chat. You see what it is doing while it does it, and again when it moves on. It is a window and not a browser: nothing you do there touches the page. That is deliberate rather than shy — aish decides what to press from the page as it was shown it, so a scroll or a resize from your side would change what is reachable and make it refuse its own next step. Watching costs nothing, asks nothing and changes nothing; it runs only while the sheet is open. Opening your own browser with /browser takes the whole Chrome back, so it closes the page aish was on — and says how many chats that was.

Searching has a second, separate profile that is signed into nothing — it is what web_search reads results pages with, so a search never carries your sessions and never needs your approval. /browser anon shows it; /browser anon <url> signs it in, if you ever want it to have an identity of its own.

Chrome's automation flags are suppressed by default so sites that block automation stay readable; that is anti-detection and may be contrary to a site's terms, so it is one switch — AISH_BROWSER_STEALTH=0 turns it off, and AISH_BROWSER=0 disables the browser entirely.

Models: local first, cloud when you want it

The default is local-only. When a task needs a stronger model, the same agent — same tools, same approval gates — runs on a cloud backend instead. Everything in that conversation then leaves your machine, so it's an explicit choice (--model at launch or /model mid-chat).

`--model`	Runs on	Cost
(default)	local Ollama model	free — nothing leaves the machine
`gemini[:model]`	Google Gemini API	free tier — `export GEMINI_API_KEY=…`
`claude[:model]`	Anthropic API	pay per token — `export ANTHROPIC_API_KEY=…`
`claude-max[:opus\|sonnet]`	Claude Agent SDK via the local `claude` CLI	your Claude Pro/Max subscription — no API key
`openai[:model]`	OpenAI API	pay per token — `export OPENAI_API_KEY=…`

Bare provider names pick a sensible default model. claude-max strips Claude Code down to bare inference and hands it aish's own tools, so every command still goes through aish's approval gate and denylist.

Rate limits and quotas

A cloud API has a ceiling, and an agent loop resends its whole conversation on every step — so a long task can spend millions of input tokens a minute and hit a 429 that has nothing to do with the task being hard.

aish paces itself against that ceiling, process-wide, across every chat and every backend. It cannot know which billing tier your key is on, so out of the box it enforces nothing and learns the limit from the first 429 — once, not once per restart: what it learned is remembered on disk.

And it treats that limit as a belief rather than a law, because a real one moves. A pay-as-you-go key on a shared quota has neighbours, so the ceiling at 23:00 is not the ceiling at 09:00. After a quiet stretch aish loosens what it believes, and snaps back the moment the server says no — the server's answer is the only thing that counts as evidence. There is no probe traffic; what tests the loosened ceiling is the next call you were making anyway.

If you know your tier you can just say so, and aish will not second-guess it:

export AISH_RATE_LIMIT_GEMINI="rpm=10,tpm=250000"     # per provider
export AISH_RATE_LIMIT_GEMINI:GEMINI-3.5-FLASH="rpm=10,tpm=250000"   # or per model

Once a limit is known, aish also sizes the conversation to it — history is capped so a handful of steps fit in a minute, rather than one enormous step filling the whole budget. Waiting is shown while it happens, Stop works during it, and a quota that is spent rather than merely busy says so instead of retrying into a wall it cannot get past.

To see where the tokens went:

aish usage                      # last 7 days: spend by day and by model
aish usage --week --all         # everything, by week
aish usage --chat <name>        # what filled THAT chat's context, by tool
aish usage --window 60000       # how many calls would not fit a local window

Every period is also measured against a window you name — how many calls exceeded it, the median and the tail, and sys+menu: the characters every call carries besides the conversation. The tool menu half is the same on every task; the system half is composed per task, from the prompt, aish's own usage notes, the knowledge index, and the rules and skills that task selected.

For one turn, aish explain <chat> <turn> gained a what it cost section — each model call's reported tokens, what was added to the context since the previous call, and, at the fullest call, what the context was actually made of:

at call 17, the fullest — the provider reported 66,147 input tokens
  read_url        this turn      7    76,001 chars  35.1%
  tool menu       every call    42    55,566 chars  25.7%
  system text     every call     2    47,701 chars  22.0%
  web_search      this turn      7    20,618 chars   9.5%

Both report what was recorded — not a billing statement. The parts are measured characters; the totals are the provider's reported tokens; the two are never added, and a part nothing sized reads as not recorded rather than as zero.

How it learns

Everything is progressive disclosure: a small, capped index of one-line descriptions goes into the prompt each task (rescanned live, so new entries appear immediately — no restart), full bodies load on demand, and the long tail is reachable through the ranked recall search. The library can grow to thousands of entries without bloating the context.

Skills — playbooks for anything worth repeating. A markdown file <name>.md, or an agentskills.io-compatible folder <name>/SKILL.md bundling scripts/, references/, assets/. Live in ~/.config/aish/skills/ (global; per-project ./.aish/skills/ discovery is disabled pending a per-directory trust mechanism — a cloned repository must not get to inject prompt text or executables). The skills matching a task are preloaded into context automatically — selected by embedding similarity — before the model's first turn, so it doesn't have to remember to look. Selection can abstain: nothing is injected unless it clears a strict similarity floor (naming an entry outright always works, and short follow-ups are matched with recent conversation context, not alone). Retrieval also audits itself: every injection is logged with its score, and a weekly aish-curate pass scans those logs for entries that keep getting injected but never used (or keep being reached for but never surfaced). The pass is a scripted loop — the script orchestrates, a local model answers one bounded repair/pin/disable/skip verdict per entry (plus a merge-or-distinct question on embedding-detected duplicate pairs) — so it works with small local models and nothing private ever leaves the machine. All changes are frontmatter-only and reversible; deletion has no code path. Import skills from the ecosystem with import_skill (e.g. anthropics/skills): a read-only clone, then one consolidated review of every file plus deterministic risk flags before anything installs.
Plugin tools — where a skill teaches, a tool does. A droppable <name>/TOOL.md the model calls exactly like a built-in tool, choosing from a schema instead of improvising a shell command. Reserve one for an operation that is frequent, has shell-fragile arguments (an email or issue body full of quotes and newlines), and where reliability matters — validated JSON reaches the wrapper on stdin, no shell. aish can write one for you on the fly with create_tool, drafting the manifest + wrapper and showing both for approval. Tools can hold their own secrets (macOS Keychain) and show a plain-language preview of exactly what a mutating call will do — and when the tool it writes is addressed by opaque ids, create_tool declares that preview itself, so the approval card names the thing, not the token.
Memory — one fact per file (~/.config/aish/memory/), same format; the description line is the fact. Saved via remember. A standing rule ("never push without asking") can be pinned (pinned: yes) — always in context under its own budget, never rotated out by newer facts. A fact with a known end date can carry expires: YYYY-MM-DD, and status: disabled retires an entry without deleting it. Saving a near-duplicate of an existing memory is refused with the existing entry's name, so the corpus consolidates instead of accumulating variants.
Rules — the one artifact class that is binding. A skill may be consulted, a memory may be recalled, a tool may be invoked; a rule is enforced by aish itself, whatever the model concludes. One markdown file per rule in ~/.config/aish/rules/, written by you — never by the model, never silently. enabled: false retires a rule without deleting it, and expires: works as it does for memory.

A rule reads as one sentence: when this, then that. The when: names what is being examined — the prompt: you typed, the session: it is running in, the action: about to run, or always. When the condition is about what a message means rather than what it contains, you give examples — like: with a few messages in your own words, in any language you use — and aish matches by meaning rather than by wording. A miss is fixed by adding another example, never by tuning anything. The then: lists obligations from a small closed set: answer_from: a named tool — or source, meaning the material you handed over (a link, an attached file or image, a path you typed), with aish picking the right reader for each — never_use: these tools, must_first: call this before answering, answer_must_include: / answer_must_not_include: something the finished answer must (or must not) contain — named as what you would see: a picture, a video, sources, or {any_of: [picture, video]} when either will do — and must_tell_me_when: a named failure has to be stated rather than quietly patched over.

A rule never names a tool for these. Which tool aish used to produce a picture is its own business; what you asked for is a picture.

Material you give aish is data to analyse, never instructions: the harness says so in the same breath it says to use it, so a page that reads "ignore your previous instructions" is reported, not obeyed.

Rules are enforced at two moments. Before a call runs, one that violates a rule is refused with a message naming the rule and what to do instead; refusals are bounded, and if the model insists you get an approval card, because there may be a legitimate exception and only you can grant it. At the end of the turn, the finished answer is checked before you see it — on those turns it does not stream, deliberately, so a rule is checked before you read the answer rather than after. If a check fails, aish asks the model for the missing work (twice at most) and the answer is delivered either way; one that never satisfied its rule arrives with a line saying so, written by aish and not by the model, so it cannot be skipped.

You can write a rule by hand, or just say it: "always use show_image" is enough. Your words go to a small isolated translator that turns them into field values; aish renders the file itself, checks that everything the rule mentions actually exists, and shows you what the rule means — plus which of your recent turns it would have bound — before anything is saved. If what you asked for cannot be expressed, it says exactly what could not be, and offers you the choice between rephrasing it and treating it as something aish should learn to enforce. Changing one only touches the fields you name, so a rule never quietly loses what it already did; retiring one leaves the file in place so you can bring it back.

Rules only ever restrict: there is no "auto-approve this" verb, deliberately, so a bad rule can annoy you and can never widen what runs without asking. Worked examples ship in examples/rules/ — one per trigger kind, one per enforcement moment.
./AISH.md — durable context you write (host facts, preferences), always loaded in full.
/learn [hint] — distill the current conversation into skills/memory: it searches existing entries first, updates rather than duplicates, and you approve every file diff.

The safety model

Approval gate. Every proposed command is shown verbatim and waits for y / n / always / session / edit. The prompt (and the web card) also shows why — what aish said it was doing on the step that proposed the action, quoted whole and labelled as its claim, so you decide on a stated reason instead of reverse-engineering one from the command. When it said nothing, the card says that too. The reason is recorded alongside your decision, so aish explain can be asked what was claimed, not just what was allowed. a saves the command's prefix (e.g. gh issue create, never a blanket gh) to a persistent allowlist; s allows it for this chat only — /new, /clear or resuming another chat forgets it, and it is never written to disk. Both are offered only when a prefix could actually silence the command: some commands are refused before the allowlist is even read (see below), and a rule saved for one of those would sit in the file doing nothing. Auto-approval covers only a conservatively parsed set of read-only commands, and is scoped to the project directory — commands whose paths escape it (absolute, ~, .., resolved symlinks) prompt anyway, with a trust option to widen the scope one directory at a time. Execution is stateless for the model: every command runs in the project directory, a bare cd is rejected, and excursions are cd x && … subshells — the model's anchor can never silently drift. Launching from your home directory re-anchors to ~/aish so your home tree never becomes the auto-approval root.
Denylist. Unrecoverable classes (rm -rf, shred, mkfs, dd to raw devices, git push --force…) are blocked outright, even if you'd approve them; edited commands are re-checked. Only you can run them, via !. Extend in ~/.config/aish/deny.txt.
A private scratch workspace. A per-chat directory the model may freely create, edit, delete and read throwaway files in without prompting — it belongs to the chat, survives reconnects and restarts, and is deleted when you delete the chat. The same applies to aish's other own directories (the media store, the converted-document store, the tool-output cache): reading back what the process already writes unprompted grants nothing new. Your files are unaffected — anywhere else still prompts, and a secret path (~/.ssh, .env*, *.pem…) prompts even inside these.
Audit trail. Every command and decision (approved / denied / edited / auto) is logged with the chat in ~/.local/state/aish/.

Note on what the logs contain. Session logs are plaintext JSONL, and they record command output as well as commands — so anything a command printed is in them, including a token an env dump or a misfired cat put on screen. The same goes for what /learn distills into ~/.config/aish/. They are yours alone (owner-readable, never uploaded), but they are not encrypted and they are exactly the kind of thing a ~/.config backup repo sweeps up. If you work with credentials, either point that backup somewhere private or prune the state directory periodically. Secrets aish holds itself are not affected: aish secret keeps those in the macOS Keychain precisely so they can't land in a file.

The web UI

aish-web serves the same agent to a browser — built phone-first (iOS-styled, installable as a PWA via "Add to Home Screen"). The screenshots above are all this UI. Highlights:

Tap-able approvals. Approve / This chat / Always / Deny, with a pencil to edit a command first and an optional comment field whose text travels with whichever button you press — approve + comment means "rework it this way and re-propose", deny + comment means "stop and explain". The explanation that follows a deny has to mark anything aish inferred but never got to verify, and name the step it was stopped from taking.
Nothing lost on a locked phone. Reconnecting replays the transcript, including any approval still waiting. Survives server restarts too. Every turn carries the time it happened — including chats from long before the feature existed, since the logs always recorded it.
Parallel chats. Several chats open at once, each with its own agent, model, directory, and running task; live badges for running / needs-approval, a toast when a background task finishes. Chats live in a chat rail — swipe right anywhere on the conversation (or tap the button) and it slides over the chat; on a wide screen it simply stays docked as a sidebar. New chat sits in the rail's bottom bar, in thumb reach. One list, most-recently- used, where "used" means a turn happened: reading a chat never reshuffles it, so looking for something can't move what you're looking for. The top band is Needs you — anything holding an approval, plus anything that has moved since you last looked at it on this device — and it cuts across who started the chat, so an overnight email-triggered chat and your own long-running task queue up together. Automation is a glyph on the row, not a separate tab. Rows show state, not decoration: a spinner while it works, an alert when it needs approving, a glyph when a schedule or an email started it. Pin a chat to keep it above the fold and permanently on the device.
Global interactive console. The real TTY shell described above, openable from any chat (⌘/Ctrl+\), tmux-backed for restart survival. URLs in its output are tappable — including the ones an auth CLI wraps across several lines — and hold a link to choose where it opens: your own browser, or aish's remote browser, where you can see the page and sign in to it. The choice sticks as that device's default for a plain tap, and the menu's own row changes it back.
Voice. Mic dictation into the composer and hands-free read-aloud of replies — device-native speech, no cloud audio API, English/Polish. In the console, double-tap the arrows key for a dictation scratchpad: speech is staged there (correctable, with the last 10 sends recallable) and only reaches the terminal when you tap Send — and an unsent line survives an app update or relaunch, so a restart mid-sentence doesn't cost you the sentence.
Pictures in answers. Ask what something looks like and aish shows you. Behind it is one tool, show_image: the picture is fetched by the server, verified to actually be an image (by its bytes, not its file extension — a block page served as .jpg is caught, not displayed as a broken glyph), stored where the chat can render it, and handed back as a ready markdown line. Fetching server-side is what makes arbitrary image sources work while the page itself still loads nothing remote: the browser only ever sees same-origin bytes, so the zero-click exfiltration channel a remote <img> would open stays closed. When something does fail to render, the browser says so and the model is told — so it tries another source instead of leaving you looking at a broken picture.
Tap an attachment to look at it. A picture opens full-screen with the gestures a photo viewer has — double-tap to zoom, pinch, drag to pan, swipe between the pictures in one message, swipe down to dismiss. A PDF opens the same way, page by page: swipe to turn the page, pinch to read the small print, and the counter tells you where you are in the document. It opens in place rather than in a new tab, so looking at your own file never means leaving the app. Share it with the ⇪ or save it with the ⤓ in the preview bar — share opens your device's own share sheet (Messages, Mail, Photos, AirDrop) with the file in it, save puts it in Files; both take the file (for a PDF, the whole document, not the page you were reading). The share button appears only where the browser can hand a file to a share sheet, which on a phone it can. Attachments with nothing to show them in, like a spreadsheet or a zip, save straight from their chip.
Export to PDF, copy anything. Per-answer or whole-chat PDF export (rendered locally), copy chips on every code block / table / answer. A single-answer PDF is titled and filed after what the answer is about — the model that wrote it writes the title — not after the prompt that asked for it.
Native vision. On vision-capable backends (Gemini, OpenAI, Claude, Ollama vision models), images you attach are actually seen by the model.
Attach by ＋, paste, or drag. Paste a screenshot straight into the composer (Cmd/Ctrl+V, or the paste button on a phone, which reads copied images too) or drag files anywhere onto the window. Pasting is additive: an image and text on the same clipboard both arrive, the image as an attachment and the text in the box. From an iPhone, the system share sheet can hand things to aish as well — see Share to aish from iOS.
Works offline. The installed app opens and reads your past conversations with no connection at all — on a plane, abroad, or with the server simply off. Chats are mirrored to the device automatically (newest first), search still works across everything cached, and the last chat paints from local storage before the socket is even open, so it is faster online too. Sending is paused while offline; reading is not. See below.

aish-web                      # http://127.0.0.1:8787, config-default model
aish-web --host 0.0.0.0       # expose to your LAN (see Security)
aish-web --model gemini       # same --model forms as aish

Security: whoever reaches this UI can drive an agent that runs approved commands on the host, so it binds to 127.0.0.1 unless you opt into --host 0.0.0.0, and access always requires a token via ?token=<secret>: set AISH_WEB_TOKEN=<secret> for a stable one, or a random token is generated at startup and printed in the launch URL. Cross-origin browser requests (WebSocket and /trigger) are rejected outright, so a page you happen to visit can't drive the agent from your browser. Direct ! commands are deliberately not available from the web.

Offline

The web UI keeps a local copy of your conversations, so the installed app is useful with no connection — the case it was built for is looking up what aish recommended while travelling.

It always opens. The app shell is cached by a service worker, so launching offline gives you the real UI, never a browser error page. New versions still reach you: the page is fetched network-first when there is a network, and a reload throttle makes an update loop impossible.
Everything is mirrored, newest first. No "download this chat" step. Chats from your other devices are included, because the mirror syncs from the server's list, not from what you happened to open here.
Search works offline, over message contents and not just titles, using the same ranking as online.
Keep one forever. The ⤓ icon in the header pins the chat you're reading — one tap, and it survives the storage sweep no matter how old it gets. Filled means kept. Nothing else to manage: the mirror caps itself and drops the least useful copies first, so there is no cache to clear by hand.
Sending is paused, deliberately. Queuing a prompt to fire later would dispatch an agent that runs shell commands with nobody watching and an approval gate answered by someone who has moved on. Offline is read-only.

Bulk command output is trimmed in the local copy (the conversation itself is kept verbatim), which is what lets a whole archive fit on a phone. Storage is capped, and least-recently-useful unpinned chats are dropped first.

Opening straight into a new chat

Add ?new to the URL and aish opens a fresh chat instead of resuming the last one — https://your-aish-host/?new. The parameter is consumed on load and removed from the address bar, so reloading afterwards stays in that chat rather than minting another.

Useful as a second Home Screen icon: in Safari, go to …/?new&token=YOUR_TOKEN, then Share → Add to Home Screen and name it "New aish chat". Include the token — an installed web app has its own storage and will not inherit the one your existing icon holds.

It does not work from a Shortcut. iOS has no supported way to open an installed web app at an address of your choosing: webapp://your-aish-host/?new launches the app but drops the query, and an https:// URL opens Safari rather than the installed app. To put a shared item in its own chat, mark the share instead — see below.

Share to aish from iOS

Share a photo, a PDF or a link from any iOS app straight to aish. It takes one Shortcut to set up, because iOS cannot register a web app as a share destination — the Web Share Target API is Chromium-only, and Safari implements only the outbound half — so no amount of work inside aish can put it in the share sheet on its own. A Shortcut can be there, and can post to aish.

A shared item is parked, not run. Next time you open aish it is already in the composer — a file attached (✕ takes it back out), a link or note on a new line under whatever you had typed. Write your prompt and send. Nothing starts an agent, which is the point: the share sheet is reachable from every app on the phone, and it stages work rather than dispatching it. A file you never send stays waiting for next time; it is consumed when the message goes.

In the Shortcuts app: new shortcut → in its settings turn on Show in Share Sheet and accept Images, Files, URLs and Text → add one Get Contents of URL action:

field	value
URL	`https://your-aish-host/share?token=YOUR_TOKEN&name=shared.jpg&source=iPhone`
Method	`POST`
Request Body	`File` → Shortcut Input

Name it "Share to aish". That is the whole shortcut.

Two refinements worth making:

Photos. iOS shares photos as HEIC, which vision models do not read. Put a Convert Image action (to JPEG) before the POST, and set name=shared.jpg.
A chat of its own. Add &chat=new to the URL and that shared item opens a fresh chat when you next open aish, instead of landing on top of the last conversation. This is the one that works from a Shortcut, because the intent travels with the item rather than with a URL iOS will not honour. A batch shared together opens one chat between them, and an already-empty chat is reused rather than left behind.
Links and text. Safari shares a URL, not a file. Drop the name= from the URL and a body with no name is read as text — so one If action inside the shortcut (POST to …/share?token=…&source=Safari for a link, to the name= URL above for a file) covers both. Keep the link in the body, not the query string: a shared URL containing & does not survive being pasted into one.

The token is the same one in your aish-web URL. Anyone holding it can post to this endpoint, so treat the shortcut as you would the URL itself.

Automation (optional)

aish can also act on its own — for jobs you set up and own. A token-gated POST /trigger endpoint launches a task in a background session (from a schedule, an incoming email, a webhook), observable when you open it. The ingress is hardened against delivery storms: a repeat POST carrying the same meta.dedup_key reuses the already-opened session instead of firing again, each origin is rate-limited, and concurrently running automated sessions are capped — over either limit the endpoint answers 429 with a Retry-After, and a well-behaved source just retries later. The bundled aish-email-poll command is the email source: it polls the bot mailbox via gws, accepts only DMARC-authenticated mail from the owner's addresses, and fires /trigger with the Gmail message id as the dedup key; a message is marked processed only after a successful trigger, so a refused or failed delivery retries on the next poll instead of being lost. The capability policy is draft-and-hold: in an automated session, safe actions run unattended, but anything that reaches the outside world (a send or draft addressed to anyone but you — recipients are strictly parsed and validated, not regex-matched — a delete, a share, and even a web fetch or search naming a host you never mentioned) holds — pausing indefinitely until you open the session and approve. Writing to memory holds the same way: in your own chats saving a fact stays free, but a memory an automated session wants to keep is shown to you first, because it would otherwise persist into every future session — and deleting memory is refused outright there, with the entry named in the session's report instead so you retire it yourself. Automated sessions also cannot search your past-session archive (saved skills and memory stay available), so an injected instruction cannot chain "read everything ever discussed" into "ship it to a new host". A push notification (Pushover) tells you when a task needs approval or finishes, with a deep link straight to it. This is how the same agent safely runs, say, an email assistant while you're asleep.

Interrupted work is picked up again. If aish-web goes down mid-task — a deploy, a crash — the next start reopens that session and tells the model to continue from where it stopped instead of redoing what already succeeded. This matters most for automated sessions: an email trigger fires once, so without recovery an interrupted one would simply never answer. It applies to your own chats too, and is bounded — only tasks interrupted in the last 12 hours, at most three per start, and a task that keeps dying is left alone after three attempts.

Run it as an always-on service (launchd on a Mac mini / home server) with the scripts in scripts/, and reach it from your phone anytime.

Day-to-day reference

Escapes: !<command> runs directly — no model, no approval. !cd <dir> (= /cd) moves the project and re-anchors this chat's root (user-only).

While a command runs: Ctrl-C cancels it (not aish itself); Ctrl-B detaches it into a background job that survives aish exiting (/jobs lists them).

Slash commands (Tab completes): /resume, /delete, /rename, /new (or /clear), /model [name] (--save to persist), /learn [hint], /feedback [text] (files a GitHub issue), /fork (branch the conversation), /cd, /add-dir, /aliases, /jobs, /chat (this chat's log file — the older /session still works), /help, /quit.

Config — ~/.config/aish/config.toml: model, num_ctx, max_steps, vi_mode, and an [aliases] table (aish-level aliases, since commands run through a non-interactive shell that never sources your ~/.zshrc). CLI flags override config; $AISH_MODEL overrides the model. Paths override via $AISH_CONFIG, $AISH_STATE_DIR, $AISH_ALLOWLIST, $AISH_DENYLIST.

Step budget is progress-gated: a task making progress runs past the base max_steps (25) up to a hard ceiling; a stalled one stops early. Issuing the exact same tool call with the exact same output five times is treated as looping and stopped with a diagnostic. However it ends, the model gets one final turn to report the finished answer — or what's done, what remains, and the next step.

Chats are the same JSONL files for terminal and web, so aish --resume picks up one you started in the browser and vice versa. Because it is the same chat either way, both surfaces call it a chat; session is the machine's word for the log file it is written to, and it stays on the file.

Chats name themselves. A web chat is named after what it's about, not after the words you happened to open with — the model that answers it writes the name, and rewrites it if the conversation genuinely moves on (rarely: a name you navigate by shouldn't keep changing). A fork gets its own name at its first new turn, so branching a tangent doesn't leave two rows with the same label. Rename one yourself and that's final — the automatic naming stands down for good.

A chat has an eraser. The trash chip under a prompt in the web UI deletes that exchange — the prompt, everything it made the model do, and the answer. It asks first, in a dialog that says what is lost, because it is irreversible: the text is deleted from the session log, dropped from the model's context (so it stops being quoted), and gone from every device's offline copy at the next sync. What stays is a quiet "Message deleted" marker where the exchange was, so the deletion itself is on the record. For a message sent to the wrong chat, a half-typed one an autocorrect Return sent, or a secret pasted into the composer — previously the only options were deleting the whole chat or editing JSONL by hand. Deleting a chat asks the same way.

Resuming always switches, never merges. aish --resume, /resume in the terminal and the web chat list all mean the same thing: the chosen chat becomes the current one — its conversation, its log file, the model it last used and the directory it was working in. The chat you leave is untouched and can be resumed back the same way. Nothing is ever copied from one chat's log into another's.

Development

uv run pytest       # full suite — no model, network, or real commands needed
uv run ruff check .
uv run mypy
make ship           # reinstall the local build and restart aish-web

Tests script the model side (a fake chat client returns canned tool-call responses), so the whole suite runs offline with nothing executed for real. See CLAUDE.md for architecture notes.

make ship refuses on a dirty tree. uv tool install builds the wheel from the working tree, not from HEAD, so uncommitted files ship without a word — make ship-check shows exactly what would go out, and --dirty overrides when that is what you want.

License

aish is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later). You are free to use, study, modify, and share it, but any modified version — including one you run as a network service (e.g. aish-web) — must offer its complete source under the same license to the people who use it. This deliberately keeps a hosted fork of the agent open.

Name		Name	Last commit message	Last commit date
.claude/skills		.claude/skills
.github/workflows		.github/workflows
aish		aish
docs		docs
examples/rules		examples/rules
scripts		scripts
tests		tests
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
install.sh		install.sh
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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aish

Why aish

Quickstart

A tour

It runs your machine — but only with your y

Nothing happens off-screen

It tells you what it found, as it finds it

Answers, not just output

A real terminal when you need one

What it can do

PDFs

Pages a fetch can't read

Models: local first, cloud when you want it

Rate limits and quotas

How it learns

The safety model

The web UI

Offline

Opening straight into a new chat

Share to aish from iOS

Automation (optional)

Day-to-day reference

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It runs your machine — but only with your `y`