unsloth/unsloth_cli/commands/start.py

# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

"""`unsloth start` — launch a coding agent against a running Studio server."""

import atexit
import contextlib
import json
import os
import re
import shlex
import shutil
import signal
import subprocess
import sys
import tempfile
import time
import urllib.error
import urllib.request
from pathlib import Path
from typing import NamedTuple, NoReturn, Optional
from urllib.parse import urlparse

import click
import typer

from unsloth_cli._inference import (
    _USER_AGENT,
    _studio_token,
    ensure_studio_backend_path,
    find_studio_server,
    is_loopback_url,
    urlopen_no_redirect,
    verify_studio_identity,
)

start_app = typer.Typer(
    help = "Start a coding agent against a running Studio server.",
    no_args_is_help = True,
    context_settings = {"help_option_names": ["-h", "--help"]},
)

_CODEX_PROFILE = "unsloth_api"
_CODEX_ENV_KEY = "UNSLOTH_STUDIO_AUTH_TOKEN"
_HERMES_ENV_KEY = "UNSLOTH_API_KEY"
_HERMES_PROVIDER = "unsloth"
# Hermes refuses to initialize when the model window is under 64,000 tokens; its
# error message points at the model.context_length / auxiliary.compression
# overrides in config.yaml. write_hermes_config claims this value for smaller
# windows and scales the compaction threshold back down to the real window.
_HERMES_MIN_CONTEXT = 65536
_PI_PROVIDER = "unsloth"
_PROVIDER_HEADER = f"[model_providers.{_CODEX_PROFILE}]"
_PASSTHROUGH = {"allow_extra_args": True, "ignore_unknown_options": True}
_CLAUDE_ENV_UNSET = ("ANTHROPIC_API_KEY", "CLAUDE_CODE_OAUTH_TOKEN")

# Shared by every agent command; only the config/env/command differ.
_MODEL_OPTION = typer.Option(
    None, "--model", "-m", help = "Model for the agent; defaults to the one loaded in Studio."
)
_KEY_OPTION = typer.Option(
    None,
    "--api-key",
    envvar = "UNSLOTH_API_KEY",
    help = (
        "Studio API key. For a local Studio it is minted automatically and "
        "remembered per server. For a remote server, pass one with --api-key "
        "(or UNSLOTH_API_KEY); it is remembered for next time."
    ),
)
_LAUNCH_OPTION = typer.Option(
    True,
    "--launch/--no-launch",
    help = "--no-launch prints the env and command instead (remote shells, WSL).",
)
_SERVE_OPTION = typer.Option(
    True,
    "--serve/--no-serve",
    help = (
        "If no Studio server is running, auto-start one for --model and stop it when the "
        "agent exits. --no-serve keeps the old behavior of erroring out."
    ),
)
# Model-load knobs mirrored from `unsloth run`; only used when --model triggers a
# load on the server. Server-startup flags (--host/--port/--cloudflare/...) do not
# apply here because `unsloth start` attaches to an already-running server.
_GGUF_VARIANT_OPTION = typer.Option(
    None, "--gguf-variant", help = "GGUF quant variant to load (e.g. UD-Q4_K_XL)."
)
_CONTEXT_OPTION = typer.Option(
    0,
    "--max-seq-length",
    "--context-length",
    help = "Context length in tokens for the load (0 = model default).",
)
_LOAD_4BIT_OPTION = typer.Option(
    True, "--load-in-4bit/--no-load-in-4bit", help = "Load hub models in 4-bit (ignored for GGUF)."
)
_TENSOR_PARALLEL_OPTION = typer.Option(
    False,
    "--tensor-parallel/--no-tensor-parallel",
    help = "Split a GGUF across GPUs by tensor instead of by layer (multi-GPU only).",
)
# One normalized "run tools without prompting" switch. Each agent spells this
# differently and it's easy to forget which is which, so accept every spelling and
# route to the agent's own mechanism in _yolo_command_flags / the config writers.
_YOLO_OPTION = typer.Option(
    False,
    "--yolo",
    "--dangerously-skip-permissions",
    "--dangerously-bypass-approvals-and-sandbox",
    help = (
        "Auto-approve all tool actions for this session; routed to the agent's own "
        "flag/config. Any of the three spellings works for any agent."
    ),
)

# Per-agent CLI flag for "run tools without prompting". opencode and openclaw have no
# such flag (config only) and are handled in their config writers, so they are absent.
_YOLO_COMMAND_FLAGS = {
    "claude": ["--dangerously-skip-permissions"],
    "codex": ["--dangerously-bypass-approvals-and-sandbox"],
    "hermes": ["--yolo"],
    # Pi never prompts per tool call; its only approval gate is project trust, so -a
    # (trust project resources) is the closest "don't ask me" equivalent.
    "pi": ["--approve"],
}


def _yolo_command_flags(agent: str, yolo: bool) -> list:
    # .get so a config-based agent (or a typo) yields no flag instead of a KeyError.
    return _YOLO_COMMAND_FLAGS.get(agent, []) if yolo else []


class LoadOptions(NamedTuple):
    """Model-load knobs forwarded to /api/inference/load when --model triggers a load."""

    gguf_variant: Optional[str] = None
    max_seq_length: int = 0
    load_in_4bit: bool = True
    tensor_parallel: bool = False


def _split_repo_variant(model: str) -> tuple:
    """Split ``org/name:QUANT`` into ``(repo, variant)`` -> ``("org/name", "QUANT")``.

    ``unsloth run`` and llama.cpp accept ``--model org/name:QUANT`` as shorthand for
    ``--model org/name --gguf-variant QUANT``. Mirror that here so a ``:variant`` suffix
    resolves against the already-loaded ``org/name`` (which /v1/models lists without the
    suffix) instead of trying to load a repo id containing ``:`` -- which Hugging Face
    rejects, and which would evict a model another session is using. Local paths, Windows
    drive letters, and ids without a ``:`` pass through unchanged.
    """
    s = (model or "").strip()
    if not s or s.startswith(("/", "./", "../", "~")) or s == ".":
        return s, None
    if len(s) >= 2 and s[1] == ":" and s[0].isalpha():  # Windows drive, e.g. C:\models\x
        return s, None
    if ":" not in s:
        return s, None
    repo, _, variant = s.rpartition(":")
    if not repo or not variant or "/" in variant:
        return s, None
    return repo, variant


def _fail(message: str) -> NoReturn:
    typer.echo(message, err = True)
    raise typer.Exit(code = 1)


def _http_error_detail(exc: urllib.error.HTTPError) -> str:
    try:
        body = json.loads(exc.read().decode())
        return body.get("detail") or body["error"]["message"]
    except Exception:
        return str(exc)


def _http_json(
    method: str,
    url: str,
    token: str,
    payload = None,
    timeout = 30,
    error = None,
):
    """On HTTPError: raise if `error` is None, else fail with `error` plus the server's detail."""
    request = urllib.request.Request(
        url,
        data = None if payload is None else json.dumps(payload).encode(),
        headers = {
            "Authorization": f"Bearer {token}",
            "Content-Type": "application/json",
            "User-Agent": _USER_AGENT,
        },
        method = method,
    )
    try:
        # No redirects: a 3xx would leak this bearer token to an unvetted base.
        with urlopen_no_redirect(request, timeout = timeout) as response:
            return json.loads(response.read().decode() or "{}")
    except urllib.error.HTTPError as exc:
        if error is None:
            raise
        _fail(f"{error}: {_http_error_detail(exc)}")
    except (urllib.error.URLError, TimeoutError) as exc:
        if error is None:
            raise
        _fail(f"{error}: {getattr(exc, 'reason', None) or exc}")


# A server that WE auto-started (never one we merely found). Kept at module scope so
# _run's finally and the atexit backstop can tear it down without threading a handle
# through all six agent commands. Only one agent runs per process, so one slot is enough.
_auto_served_server: Optional[subprocess.Popen] = None
# Model download + load can be slow; give the auto-started server room before giving up.
_SERVER_START_TIMEOUT_S = 900


def _studio_healthy(base: str, timeout: float = 3.0) -> bool:
    request = urllib.request.Request(f"{base}/api/health", headers = {"User-Agent": _USER_AGENT})
    try:
        with urllib.request.urlopen(request, timeout = timeout) as response:
            return json.loads(response.read(65536).decode() or "{}").get("status") == "healthy"
    except Exception:
        return False


def _log_tail(path: Path, lines: int = 20) -> str:
    try:
        return "\n".join(path.read_text(encoding = "utf-8", errors = "replace").splitlines()[-lines:])
    except OSError:
        return "(no server log)"


def _shutdown_server(server: Optional[subprocess.Popen]) -> None:
    # Idempotent teardown of a server WE started, plus its own children (llama-server,
    # cloudflared). A no-op once the process is already gone.
    if server is None or server.poll() is not None:
        return
    if os.name == "nt":
        # terminate()/kill() reach only the parent `unsloth run`; taskkill /T walks the
        # whole tree so the llama-server child doesn't keep the port and GPU (matches the
        # taskkill /T /F pattern already used in unsloth/dataprep/synthetic.py).
        try:
            subprocess.run(
                ["taskkill", "/PID", str(server.pid), "/T", "/F"],
                capture_output = True,
                timeout = 15,
                check = False,
            )
            server.wait(timeout = 5)
        except Exception:
            with contextlib.suppress(Exception):
                server.kill()
        return
    try:
        os.killpg(os.getpgid(server.pid), signal.SIGTERM)
    except OSError:
        server.terminate()
    try:
        server.wait(timeout = 15)
    except Exception:
        try:
            os.killpg(os.getpgid(server.pid), signal.SIGKILL)
        except OSError:
            server.kill()


def _shutdown_auto_served() -> None:
    global _auto_served_server
    server, _auto_served_server = _auto_served_server, None
    if server is not None and server.poll() is None:
        typer.echo("Stopping the auto-started Studio server…")
        _shutdown_server(server)


def _start_studio_server(base: str, model: str, load: LoadOptions) -> subprocess.Popen:
    """Spawn `unsloth run` for `model`, wait until it is fully ready, and return it."""
    global _auto_served_server
    unsloth = shutil.which("unsloth") or "unsloth"
    parsed = urlparse(base)
    # --disable-tools = passthrough mode (relay the agent's own tools); --no-cloudflare =
    # loopback only, no tunnel. Mirrors .github/scripts/serve-unsloth-run.sh.
    command = [
        unsloth,
        "run",
        "-H",
        parsed.hostname or "127.0.0.1",
        "-p",
        str(parsed.port or 8888),
        "--disable-tools",
        "--no-cloudflare",
        "--model",
        model,
    ]
    if load.gguf_variant:
        command += ["--gguf-variant", load.gguf_variant]
    if load.max_seq_length:
        command += ["--context-length", str(load.max_seq_length)]
    if not load.load_in_4bit:
        command += ["--no-load-in-4bit"]
    if load.tensor_parallel:
        command += ["--tensor-parallel"]

    log_path = Path(tempfile.gettempdir()) / f"unsloth-start-server-{os.getpid()}.log"
    typer.echo(
        f"No Studio server at {base}. Starting one for {model} (loading the model can take a while)…"
    )
    typer.echo(f"Server log: {log_path}")
    # 0600: the `unsloth run` banner in this log carries the minted sk-unsloth- key, and
    # the tempdir is world-traversable. Unlink first so a stale looser-mode file (pid
    # reuse) can't survive with its old permissions.
    log_path.unlink(missing_ok = True)
    log = os.fdopen(os.open(log_path, os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0o600), "wb")
    # Own session/process group so a mid-session Ctrl+C (cancel a turn) doesn't reach the
    # server; we tear it down explicitly when the agent exits.
    kwargs: dict = {"stdout": log, "stderr": subprocess.STDOUT, "stdin": subprocess.DEVNULL}
    if os.name == "nt":
        kwargs["creationflags"] = subprocess.CREATE_NEW_PROCESS_GROUP
    else:
        kwargs["start_new_session"] = True
    try:
        server = subprocess.Popen(command, **kwargs)
    finally:
        log.close()  # Popen dup'd the fd; drop the parent's copy
    _auto_served_server = server
    atexit.register(_shutdown_auto_served)

    deadline = time.monotonic() + _SERVER_START_TIMEOUT_S
    while time.monotonic() < deadline:
        if server.poll() is not None:
            tail = _log_tail(log_path)
            _shutdown_auto_served()
            _fail(f"The Studio server stopped before it was ready. Last log lines:\n{tail}")
        # `unsloth run` prints the minted key only after the server is up AND the model is
        # loaded, so it is the fully-ready signal (same contract serve-unsloth-run.sh uses).
        if _studio_healthy(base) and "sk-unsloth-" in _log_tail(log_path, lines = 400):
            typer.echo(f"Studio server ready at {base}.")
            return server
        time.sleep(2.0)
    _shutdown_auto_served()
    _fail(
        f"The Studio server didn't become ready within {_SERVER_START_TIMEOUT_S}s. See {log_path}."
    )


def _effective_base(base: str) -> str:
    # `unsloth run` binds to `parsed.port or 8888` and serves at the root, so normalize
    # UNSLOTH_STUDIO_URL to plain scheme://host:port. A portless http://127.0.0.1 would
    # otherwise launch on 8888 but poll port 80, and a path like /studio would poll
    # /studio/api/health (404) -- either way hitting the startup timeout. IPv6 literals
    # stay bracketed.
    parsed = urlparse(base)
    host = parsed.hostname or "127.0.0.1"
    if ":" in host:  # bare IPv6 literal (urlparse strips the brackets)
        host = f"[{host}]"
    return f"{parsed.scheme or 'http'}://{host}:{parsed.port or 8888}"


def _require_studio(
    model: Optional[str] = None,
    load: Optional[LoadOptions] = None,
    *,
    serve: bool = False,
    launch: bool = True,
) -> tuple:
    """Return (base, server). server is a Popen only when WE auto-started it."""
    base = find_studio_server()
    if base is not None:
        return base, None
    expected = os.environ.get("UNSLOTH_STUDIO_URL", "http://127.0.0.1:8888").rstrip("/")
    # Auto-start a local server only for an interactive launch with a model to serve, and
    # only for a plain-HTTP loopback target: never stand in for an explicit remote
    # UNSLOTH_STUDIO_URL, and never for an https:// one -- `unsloth run` serves plain
    # HTTP, so the health poll against https would spin until the startup timeout.
    if (
        serve
        and launch
        and model
        and is_loopback_url(expected)
        and urlparse(expected).scheme == "http"
    ):
        # Normalize to the port unsloth run actually binds, so the health poll and the
        # returned base hit the same server we launch (not a portless :80).
        expected = _effective_base(expected)
        return expected, _start_studio_server(expected, model, load or LoadOptions())
    model_hint = "" if model else " Pass --model to have it start one for you, or"
    _fail(
        f"No running Studio server found at {expected}.{model_hint} start one with "
        "`unsloth studio`, or point UNSLOTH_STUDIO_URL at a remote server."
    )


def _key_cache_path() -> Path:
    ensure_studio_backend_path()
    from utils.paths import auth_root
    return auth_root() / "agent_api_key.json"


def _read_cache(cache: Path) -> dict:
    try:
        data = json.loads(cache.read_text(encoding = "utf-8"))
    except Exception:
        return {}
    return data if isinstance(data, dict) else {}


def _server_buckets(servers: dict, base: str) -> dict:
    # Normalise a server's entry to {"saved": [...], "minted": [...]}, tolerating a
    # corrupt/legacy value (bare string/list -> treated as minted, behind the handshake).
    entry = servers.get(base) if isinstance(servers, dict) else None
    if isinstance(entry, list):
        return {"saved": [], "minted": [k for k in entry if isinstance(k, str)]}
    if not isinstance(entry, dict):
        return {"saved": [], "minted": []}

    def _strs(name: str) -> list:
        value = entry.get(name)
        return [k for k in value if isinstance(k, str)] if isinstance(value, list) else []

    return {"saved": _strs("saved"), "minted": _strs("minted")}


def _cached_keys(cache: Path, base: str, source: str) -> list:
    # Keys are scoped per server. `source` splits user-supplied --api-key keys
    # ("saved", trusted for that base) from auto-minted ones ("minted", replayed
    # only after the identity check). Legacy unscoped caches are ignored.
    return _server_buckets(_read_cache(cache).get("servers", {}), base)[source]


def _write_private_json(path: Path, data: dict) -> None:
    # O_CREAT with 0o600 so a file holding an API key is never world-readable,
    # even briefly (existing files keep whatever perms the user set).
    path.parent.mkdir(parents = True, exist_ok = True, mode = 0o700)
    fd = os.open(path, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o600)
    with os.fdopen(fd, "w") as handle:
        handle.write(json.dumps(data, indent = 2) + "\n")


def _read_json_object(path: Path) -> Optional[dict]:
    # {} when missing, None when it can't be parsed as an object (so the caller
    # leaves a user-managed file untouched rather than clobbering it).
    if not path.exists():
        return {}
    try:
        data = json.loads(path.read_text(encoding = "utf-8"))
    except (ValueError, OSError):
        return None
    return data if isinstance(data, dict) else None


def _subdict(parent: dict, key: str) -> dict:
    child = parent.get(key)
    if not isinstance(child, dict):
        child = parent[key] = {}
    return child


def _remember_key(cache: Path, base: str, key: str, source: str) -> None:
    data = _read_cache(cache)
    servers = data.get("servers")
    if not isinstance(servers, dict):
        servers = data["servers"] = {}
    buckets = _server_buckets(servers, base)
    other = "minted" if source == "saved" else "saved"
    buckets[source] = ([key] + [k for k in buckets[source] if k != key])[:8]
    buckets[other] = [k for k in buckets[other] if k != key]  # a key has one provenance
    new_entry = {"saved": buckets["saved"], "minted": buckets["minted"]}
    if servers.get(base) == new_entry:
        return
    servers[base] = new_entry
    # Collapse legacy unscoped fields.
    data.pop("keys", None)
    data.pop("key", None)
    try:
        _write_private_json(cache, data)
    except OSError:
        pass  # worst case the next launch mints another key


def _key_accepted(base: str, key: str) -> bool:
    # Only a genuine auth rejection (401/403) means "this key is bad -- skip it and try
    # the next cached key or mint a fresh one". A 5xx or a network blip is a server-side
    # outage, not a bad key: fail with a clean message (never a traceback) instead of
    # silently discarding a working key and minting extras against a struggling server.
    try:
        _http_json("GET", f"{base}/v1/models", key)
        return True
    except urllib.error.HTTPError as exc:
        if exc.code in (401, 403):
            return False
        _fail(
            f"Studio server error while checking an API key ({exc.code}). "
            "The server may be starting up or unhealthy; try again shortly."
        )
    except (urllib.error.URLError, TimeoutError) as exc:
        _fail(
            "Couldn't reach the Studio server while checking an API key: "
            f"{getattr(exc, 'reason', None) or exc}"
        )


def _agent_api_key(
    base: str,
    explicit: Optional[str],
    *,
    auto_started: bool = False,
) -> str:
    cache = _key_cache_path()
    if explicit:
        if not auto_started or _key_accepted(base, explicit):
            _remember_key(cache, base, explicit, "saved")
            return explicit
        # The server was auto-started for this run, so an exported
        # UNSLOTH_API_KEY meant for some other server must not fail the
        # launch: the loopback mint path below is guaranteed to work.
        # (An explicit key that the fresh server accepts, e.g. one persisted
        # in this Studio home's auth db, is still honored above.)

    # Replay a key the user saved for *this exact* server first (scoped per base,
    # so it only goes back there -- including a remote/SSH-tunnelled Studio whose
    # secret the local handshake can't match). Skip ones the server rejects.
    for key in _cached_keys(cache, base, "saved"):
        if _key_accepted(base, key):
            _remember_key(cache, base, key, "saved")
            return key

    # Beyond here we auto-mint or replay an auto-minted key. find_studio_server()
    # trusts a base after only a health check, so both are limited to a loopback
    # server we can cryptographically confirm is ours.
    if not is_loopback_url(base):
        _fail(
            f"No saved API key for {base} and automatic minting only runs against "
            "a local Studio. Create an API key in Studio → Settings → API and "
            "pass it with --api-key (it is remembered per server), or set "
            "UNSLOTH_API_KEY."
        )
    if not verify_studio_identity(base):
        _fail(
            f"Couldn't verify that {base} is your Studio (it may be running as a "
            "different OS user, or another process took the port). Create an API "
            "key in Studio → Settings → API and pass it with --api-key, or set "
            "UNSLOTH_API_KEY."
        )

    # Identity verified: replay a previously auto-minted key, else mint a new one.
    for key in _cached_keys(cache, base, "minted"):
        if _key_accepted(base, key):
            _remember_key(cache, base, key, "minted")
            return key

    # Self-issue a JWT (signed with the local secret) and mint a key.
    token = _studio_token()
    if token is None:
        _fail(
            "Couldn't authenticate with the Studio server automatically. Create "
            "an API key in Studio → Settings → API and pass it with --api-key, "
            "or set UNSLOTH_API_KEY."
        )
    key = _http_json(
        "POST",
        f"{base}/api/auth/api-keys",
        token,
        {"name": "Coding agents (unsloth start)"},
        error = "Couldn't create an API key",
    )["key"]
    _remember_key(cache, base, key, "minted")
    return key


def _loaded_models(base: str, key: str) -> list:
    return _http_json("GET", f"{base}/v1/models", key, error = "Couldn't list models").get("data", [])


def _resolve_model(
    base: str,
    key: str,
    requested: Optional[str],
    load: LoadOptions = LoadOptions(),
) -> dict:
    models = _loaded_models(base, key)
    # /v1/models reports the model id but not the active GGUF variant or runtime load
    # settings, so an id match alone can hide the wrong quant (Q8_0 serving while the
    # user asked for UD-Q4_K_XL). When the user passed any explicit load knob, defer to
    # /api/inference/load: the server's already-loaded dedup answers "already_loaded"
    # without reloading when the variant AND settings match, so a second session running
    # the same command still attaches without evicting the first.
    load_has_overrides = bool(
        load.gguf_variant or load.max_seq_length or not load.load_in_4bit or load.tensor_parallel
    )
    match = (
        None
        if requested and load_has_overrides
        else next((m for m in models if m["id"] == requested), None)
    )
    if requested and match is None:
        typer.echo(
            f"Ensuring {requested} is loaded with the requested settings…"
            if load_has_overrides
            else f"Loading {requested} on the Studio server (this can take a while)…"
        )
        # Mirror `unsloth run`'s load knobs; keep the default payload as just
        # model_path so a bare `--model` load is unchanged.
        payload = {"model_path": requested}
        if load.gguf_variant:
            payload["gguf_variant"] = load.gguf_variant
        if load.max_seq_length:
            payload["max_seq_length"] = load.max_seq_length
        if not load.load_in_4bit:
            payload["load_in_4bit"] = False
        if load.tensor_parallel:
            payload["tensor_parallel"] = True
        loaded = _http_json(
            "POST",
            f"{base}/api/inference/load",
            key,
            payload,
            timeout = 3600,
            error = "Model load failed",
        )
        # Studio registers the model under a canonical id (resolved identifier,
        # casing) that /v1/models echoes but which may differ from the path we
        # passed; match on the id the load reports so we don't silently fall
        # through to models[0] and connect to a different loaded model.
        wanted = {requested}
        if isinstance(loaded, dict):
            wanted |= {loaded.get("model"), loaded.get("display_name")} - {None}
        models = _loaded_models(base, key)
        match = next((m for m in models if m["id"] in wanted), None)
    if match is not None:
        return match
    if requested:
        # We asked Studio to load it and it didn't surface in /v1/models; don't
        # silently hand back an unrelated loaded model.
        _fail(
            f"Studio didn't report '{requested}' as loaded. Double-check the model "
            "id, or load it from the model dropdown in the UI."
        )
    if not models:
        _fail(
            "No model is loaded in Studio. Load one from the model dropdown in "
            "the UI, or pass --model <hf-id-or-path> to load it from here."
        )
    return models[0]


def _require_gguf_for_codex(base: str, key: str, model_id: str) -> None:
    # Codex always streams, and Studio only streams /v1/responses from llama-server.
    try:
        status = _http_json("GET", f"{base}/api/inference/status", key)
    except urllib.error.HTTPError as exc:
        if exc.code == 404:
            return  # older server without the endpoint; don't block the launch
        raise
    if status.get("is_gguf"):
        return
    hint = model_id if "gguf" in model_id.lower() else f"{model_id}-GGUF"
    _fail(
        f"Codex needs a GGUF model served by llama-server, but {model_id} is on "
        f"the transformers backend. Try: unsloth start codex --model {hint}"
    )


_DYNAMIC_SECTIONS_FLAG = "--exclude-dynamic-system-prompt-sections"
# Session overlay applied via `claude --settings`; suppresses the attribution header
# for THIS run only (no ~/.claude write) so llama.cpp KV-cache reuse is preserved. It
# reinforces the CLAUDE_CODE_ATTRIBUTION_HEADER env var on builds that read the setting
# only from settings.json.
_CLAUDE_SETTINGS_OVERLAY = '{"env":{"CLAUDE_CODE_ATTRIBUTION_HEADER":"0"}}'


def _claude_version() -> Optional[tuple]:
    # None = no local `claude` (a --no-launch printout for another machine; assume a
    # current build). An unparseable version is treated as too old for the new flags.
    executable = shutil.which("claude")
    if executable is None:
        return None
    try:
        result = subprocess.run(
            [executable, "--version"], capture_output = True, text = True, timeout = 10
        )
        # Pull the X.Y.Z out of the output rather than assuming it is the first token.
        # claude prints it first today ("2.1.98 (Claude Code)"), but a format change
        # (e.g. "claude version 2.1.98") shouldn't silently drop the optimization flags;
        # no match falls through to "too old", same as an unparseable version.
        match = re.search(r"(\d+)\.(\d+)\.(\d+)", result.stdout)
        return tuple(int(part) for part in match.groups()) if match else (0,)
    except Exception:
        return (0,)


def _claude_flags() -> list:
    # Both knobs preserve llama.cpp KV-cache reuse: --exclude-dynamic-system-prompt-sections
    # moves per-session context out of the system prompt, and --settings suppresses the
    # attribution header for this session only (no persistent ~/.claude write; the env var
    # sets it too). Claude Code < 2.1.98 aborts on unknown flags, so gate on the version;
    # no local binary means a printout for another machine, so assume a current build.
    version = _claude_version()
    if version is not None and version < (2, 1, 98):
        return []
    return [_DYNAMIC_SECTIONS_FLAG, "--settings", _CLAUDE_SETTINGS_OVERLAY]


def _merge_codex_config(existing: str, base: str) -> str:
    chunks = re.split(r"(?m)^(?=\[)", existing)  # preamble, then one chunk per table
    if not re.search(r"(?m)^\s*oss_provider\s*=", chunks[0]):
        if chunks[0] and not chunks[0].endswith("\n"):
            chunks[0] += "\n"
        chunks[0] += f'oss_provider = "{_CODEX_PROFILE}"\n'
    # Drop the provider table and any stale [model_providers.unsloth_api.*] subtables.
    stale = (_PROVIDER_HEADER, _PROVIDER_HEADER[:-1] + ".")
    text = "".join(c for c in chunks if not c.startswith(stale))
    if not text.endswith("\n"):
        text += "\n"
    if not text.endswith("\n\n"):
        text += "\n"
    return text + (
        f"{_PROVIDER_HEADER}\n"
        'name = "Unsloth Studio"\n'
        f"base_url = {json.dumps(base + '/v1')}\n"
        f'env_key = "{_CODEX_ENV_KEY}"\n'
        'wire_api = "responses"\n'
        "requires_openai_auth = false\n"
    )


def write_codex_config(base: str, model: dict, home: Path) -> None:
    home.mkdir(parents = True, exist_ok = True)

    config = home / "config.toml"
    existing = config.read_text(encoding = "utf-8") if config.exists() else ""
    merged = _merge_codex_config(existing, base)
    if merged != existing:
        config.write_text(merged, encoding = "utf-8")
        typer.echo(f"Updated {config}")

    # oss_provider here too: codex --oss picks the provider from it, and the
    # profile layer must beat a user-set value (e.g. "ollama") in config.toml.
    profile_text = (
        f'oss_provider = "{_CODEX_PROFILE}"\n'
        f'model_provider = "{_CODEX_PROFILE}"\n'
        f"model = {json.dumps(model['id'])}\n"
    )
    window = model.get("context_length") or model.get("max_context_length")
    if window:
        profile_text += f"model_context_window = {int(window)}\n"
    profile = home / f"{_CODEX_PROFILE}.config.toml"
    if not profile.exists() or profile.read_text(encoding = "utf-8") != profile_text:
        profile.write_text(profile_text, encoding = "utf-8")
        typer.echo(f"Updated {profile}")


def _wsl_windows_executable(command: list) -> Optional[str]:
    if os.name == "nt" or not os.environ.get("WSL_DISTRO_NAME"):
        return None
    executable = shutil.which(command[0])
    if executable and executable.startswith("/mnt/"):
        return executable
    return None


def _looks_like_path(value: str) -> bool:
    # A var only wants the WSLENV /p flag if its value is a filesystem path: an
    # absolute POSIX path (/...), a UNC path (\\...), or a drive-qualified Windows
    # path (C:...). Scalar knobs (e.g. a numeric context window) must pass through
    # untranslated, so they get no flag.
    return bool(value) and (value.startswith(("/", "\\")) or (len(value) >= 2 and value[1] == ":"))


def _wsl_bridge_names(env: dict, unset_env: tuple) -> tuple:
    # Build the WSLENV share list for a Windows shim reached from WSL. Path-valued
    # vars get /p so WSLENV translates them to the Windows path the /mnt shim can
    # actually open; a cleared var carries no value to translate.
    names = [name + ("/p" if _looks_like_path(value) else "") for name, value in env.items()]
    names.extend(unset_env)
    return tuple(dict.fromkeys(names))


def _merge_wslenv(current: str, names: tuple) -> str:
    # Index WSLENV entries by bare var name, preserving first-seen order. The vars we
    # bridge are applied last so our entry wins: a user's pre-existing unflagged "HOME"
    # is upgraded to "HOME/p" (rather than left as-is), since WSLENV ignores a duplicate
    # name and a bare entry would leave the path untranslated for a Windows shim.
    ordered = []
    by_name = {}
    for entry in (*current.split(":"), *names):
        if not entry:
            continue
        base = entry.split("/", 1)[0]
        if base not in by_name:
            ordered.append(base)
        by_name[base] = entry
    return ":".join(by_name[base] for base in ordered)


def _powershell_quote(arg: str) -> str:
    # PowerShell reads single-quoted strings literally (an embedded ' is doubled), so
    # JSON args such as `--settings {"env":...}` survive intact. list2cmdline's
    # backslash-escaped double quotes are cmd.exe syntax and PowerShell mis-parses them.
    if arg and re.fullmatch(r"[A-Za-z0-9_./:=+-]+", arg):
        return arg
    return "'" + arg.replace("'", "''") + "'"


def _print_env(
    env: dict,
    command: list,
    unset_env: tuple = (),
    wsl_env_bridge: tuple = (),
) -> None:
    if os.name == "nt":
        for name in unset_env:
            typer.echo(f"Remove-Item Env:{name} -ErrorAction SilentlyContinue")
        for name, value in env.items():
            # PowerShell: ` is the escape char, and $ triggers expansion inside "".
            escaped = value.replace("`", "``").replace('"', '`"').replace("$", "`$")
            typer.echo(f'$env:{name} = "{escaped}"')
        typer.echo(" ".join(_powershell_quote(arg) for arg in command))
        return
    for name in unset_env:
        typer.echo(f"export {name}=" if wsl_env_bridge else f"unset {name}")
    for name, value in env.items():
        typer.echo(f"export {name}={shlex.quote(value)}")
    if wsl_env_bridge:
        typer.echo(
            f"export WSLENV={shlex.quote(_merge_wslenv(os.environ.get('WSLENV', ''), wsl_env_bridge))}"
        )
    # The final line is a SELF-CONTAINED one-liner (inline env, VAR=... cmd) rather than a
    # bare command. People copy just the last line, and a bare `codex`/`claude` would then
    # run against their real ~/.codex or Anthropic credentials with zero isolation -- e.g.
    # inheriting a pre-existing damaged ~/.codex state DB and blaming the recipe. Inline
    # assignments scope every var (and empty-string the conflicting ones) to this single
    # invocation, so a partial copy behaves the same as pasting the whole block.
    inline = [f"{name}=" for name in unset_env]
    inline += [f"{name}={shlex.quote(value)}" for name, value in env.items()]
    if wsl_env_bridge:
        inline.append(
            f"WSLENV={shlex.quote(_merge_wslenv(os.environ.get('WSLENV', ''), wsl_env_bridge))}"
        )
    typer.echo(" ".join((*inline, shlex.join(command))))


def _install_agent(name: str, install_hint: str) -> Optional[str]:
    # Missing agent under --launch: offer to run its documented install command, then
    # re-resolve it on PATH. Consent-based (we never auto-run a remote install script
    # silently), and a non-interactive stdin cannot answer the prompt, so both the
    # no-TTY and declined cases return None and let the caller print the hint and exit.
    if not sys.stdin.isatty():
        return None
    typer.echo(f"`{name}` is not installed.")
    if not typer.confirm(f"Install it now with `{install_hint}`?", default = False):
        return None
    # Run each hint through the shell it is written for: PowerShell (irm | iex, or npm)
    # on Windows, /bin/sh (curl | bash, or npm) everywhere else.
    if os.name == "nt":
        install_command = ["powershell", "-NoProfile", "-Command", install_hint]
    else:
        install_command = ["/bin/sh", "-c", install_hint]
    if subprocess.run(install_command).returncode != 0:
        _fail(f"Install command failed. Run it yourself, then re-run: {install_hint}")
    executable = shutil.which(name)
    if executable is None:
        _fail(
            f"`{name}` installed but isn't on PATH yet. Open a new shell (or add it to "
            f"PATH), then re-run. Install command: {install_hint}"
        )
    return executable


def _launch(
    command: list,
    env: dict,
    install_hint: str,
    unset_env: tuple = (),
) -> NoReturn:
    executable = shutil.which(command[0]) or _install_agent(command[0], install_hint)
    if executable is None:
        _fail(f"`{command[0]}` not found on PATH. Install it with: {install_hint}")
    wsl_env_bridge = _wsl_bridge_names(env, unset_env) if _wsl_windows_executable(command) else ()
    child_env = dict(os.environ)
    if wsl_env_bridge:
        child_env["WSLENV"] = _merge_wslenv(child_env.get("WSLENV", ""), wsl_env_bridge)
        for name in unset_env:
            child_env[name] = ""
    else:
        for name in unset_env:
            child_env.pop(name, None)
    child_env.update(env)
    # Ctrl+C cancels a turn inside the agent; don't let it kill this wrapper.
    previous = signal.signal(signal.SIGINT, signal.SIG_IGN)
    try:
        code = subprocess.run([executable, *command[1:]], env = child_env).returncode
    finally:
        signal.signal(signal.SIGINT, previous)
    # Negative returncode means killed by signal N; shells expect 128+N.
    raise typer.Exit(code = code if code >= 0 else 128 - code)


def _connect(
    api_key: Optional[str],
    model: Optional[str],
    load: LoadOptions = LoadOptions(),
    *,
    serve: bool = False,
    launch: bool = True,
) -> tuple:
    # `--model org/name:QUANT` is shorthand for `--model org/name --gguf-variant QUANT`.
    # Split it before we match/serve so the attach path resolves against the already-loaded
    # `org/name` (listed without the suffix) instead of reloading a `:`-suffixed repo id --
    # which Studio rejects and which would evict a model another session is using.
    if model:
        repo, variant = _split_repo_variant(model)
        if variant:
            model = repo
            if not load.gguf_variant:
                load = load._replace(gguf_variant = variant)
    base, server = _require_studio(model, load, serve = serve, launch = launch)
    try:
        key = _agent_api_key(base, api_key, auto_started = server is not None)
        # A server we just started has exactly the requested model loaded, so resolve to
        # whatever it is serving instead of re-matching the raw --model string.
        entry = _resolve_model(base, key, None if server is not None else model, load)
    except BaseException:
        _shutdown_auto_served()
        raise
    return base, key, entry


def _run(
    base: str,
    entry: dict,
    env: dict,
    command: list,
    *,
    launch: bool,
    install_hint: str,
    unset_env: tuple = (),
    clear_screen: bool = False,
) -> None:
    # Some agents (Pi) render inline from wherever the cursor sits: their first
    # paint assumes a clean screen rather than clearing or entering the
    # alternate screen themselves. Hand them one so the session doesn't start
    # mid-scroll under our connection output. click.clear() is cross-platform
    # and a no-op when stdout is not a terminal (piped/CI), so transcripts and
    # --no-launch recipes stay intact.
    if launch and clear_screen:
        click.clear()
    typer.echo(f"Studio {base} · model {entry['id']}")
    wsl_env_bridge = _wsl_bridge_names(env, unset_env) if _wsl_windows_executable(command) else ()
    if not launch:
        _print_env(env, command, unset_env = unset_env, wsl_env_bridge = wsl_env_bridge)
        return
    try:
        _launch(command, env, install_hint = install_hint, unset_env = unset_env)
    finally:
        # Tear down a server we auto-started once the agent session ends (no-op otherwise).
        _shutdown_auto_served()


def _agents_config_root() -> Path:
    ensure_studio_backend_path()
    from utils.paths import auth_root
    return auth_root() / "agents"


@contextlib.contextmanager
def _session_config(agent: str, launch: bool):
    """Yield a private directory for an agent's session config (never the user's own).

    launch: an ephemeral temp dir removed after the agent process exits, so nothing
    persists. no-launch: a stable Unsloth-owned dir (the printed recipe is run later
    on this machine), reused across runs. Either way the user's real ~/.<agent>
    config is left untouched.
    """
    if launch:
        path = Path(tempfile.mkdtemp(prefix = f"unsloth-{agent}-"))
        try:
            yield path
        finally:
            shutil.rmtree(path, ignore_errors = True)
    else:
        # Never wipe this dir: a previously printed recipe may still be running
        # an agent whose sessions/state live here, and every config writer
        # merges idempotently into an existing home anyway.
        path = _agents_config_root() / agent
        path.mkdir(parents = True, exist_ok = True, mode = 0o700)
        yield path


def write_openclaw_config(
    base: str,
    key: str,
    model: dict,
    path: Path,
    yolo: bool = False,
) -> None:
    config = _read_json_object(path)
    if config is None:
        typer.echo(
            f"Warning: couldn't parse {path} — add an 'unsloth' provider there "
            "yourself, or move the file aside and re-run.",
            err = True,
        )
        return
    before = json.dumps(config, sort_keys = True)
    # Studio is a generic OpenAI-compatible /v1 endpoint (the vLLM/LM Studio path).
    provider_model = {"id": model["id"], "name": model["id"]}
    window = model.get("context_length") or model.get("max_context_length")
    if window:
        provider_model["contextWindow"] = int(window)
    models = _subdict(config, "models")
    models.setdefault("mode", "merge")
    _subdict(models, "providers")["unsloth"] = {
        "baseUrl": f"{base}/v1",
        "apiKey": key,
        "api": "openai-completions",
        "models": [provider_model],
    }
    # Pin a default model, else OpenClaw drops into its setup agent ("no models available").
    defaults = _subdict(_subdict(config, "agents"), "defaults")
    _subdict(defaults, "model")["primary"] = f"unsloth/{model['id']}"
    # Unauthenticated loopback gateway: without auth.mode=none the client won't open
    # the websocket. The daemon must still be started separately (`openclaw gateway`).
    gateway = _subdict(config, "gateway")
    gateway.setdefault("mode", "local")
    _subdict(gateway, "auth").setdefault("mode", "none")
    if yolo:
        # OpenClaw has no --yolo flag, and it gates tool execution on BOTH the
        # tools.exec config AND a host-local approvals file (the stricter wins), so
        # setting only the config still lets the agent prompt/deny. Set both, mirroring
        # `openclaw exec-policy preset yolo`.
        exec_policy = _subdict(_subdict(config, "tools"), "exec")
        exec_policy["host"] = "gateway"
        exec_policy["security"] = "full"
        exec_policy["ask"] = "off"
        # Approvals file in OPENCLAW_STATE_DIR (== this config's dir). ask=off means
        # nothing is ever prompted, so the runtime socket block is unnecessary here.
        approvals = path.parent / "exec-approvals.json"
        _write_private_json(
            approvals,
            {"version": 1, "defaults": {"security": "full", "ask": "off", "askFallback": "full"}},
        )
        typer.echo(f"Updated {approvals}")
    if json.dumps(config, sort_keys = True) != before:
        _write_private_json(path, config)
        typer.echo(f"Updated {path}")


def write_opencode_config(
    base: str,
    key: str,
    model: dict,
    path: Path,
    yolo: bool = False,
) -> None:
    config = _read_json_object(path)
    if config is None:
        typer.echo(
            f"Warning: couldn't parse {path} — add an 'unsloth' provider there "
            "yourself, or move the file aside and re-run.",
            err = True,
        )
        return
    before = json.dumps(config, sort_keys = True)
    config.setdefault("$schema", "https://opencode.ai/config.json")
    model_entry = {"name": model["id"]}
    window = model.get("context_length") or model.get("max_context_length")
    if window:
        window = int(window)
        # A custom-provider model with no limit defaults to context 0, which silently
        # disables OpenCode's auto-compaction; declare the real window (and a sane
        # output cap) so it compacts instead of overflowing the server.
        model_entry["limit"] = {"context": window, "output": min(window // 4, 8192)}
    _subdict(config, "provider")["unsloth"] = {
        "npm": "@ai-sdk/openai-compatible",
        "name": "Unsloth Studio",
        "options": {"baseURL": f"{base}/v1", "apiKey": key},
        "models": {model["id"]: model_entry},
    }
    # OpenCode selects a model by "<providerID>/<modelID>".
    config["model"] = f"unsloth/{model['id']}"
    if window:
        # Compact with ~10% headroom (near 90% full). The fixed 20k-token default
        # buffer over-compacts, or never settles, on a small local context.
        compaction = _subdict(config, "compaction")
        compaction["auto"] = True
        compaction["reserved"] = max(1, window // 10)
    if yolo:
        # OpenCode has no --yolo flag; auto-approve is the config `permission` block
        # (singular). Allow the prompting tools so tool calls don't block on the TUI.
        config["permission"] = {"edit": "allow", "bash": "allow", "webfetch": "allow"}
    if json.dumps(config, sort_keys = True) != before:
        _write_private_json(path, config)
        typer.echo(f"Updated {path}")


def write_hermes_config(base: str, model: dict, path: Path) -> None:
    import yaml

    config: dict = {}
    if path.exists():
        try:
            loaded = yaml.safe_load(path.read_text(encoding = "utf-8"))
        except (yaml.YAMLError, OSError):
            typer.echo(
                f"Warning: couldn't parse {path} — configure the custom endpoint "
                "there yourself, or move the file aside and re-run.",
                err = True,
            )
            return
        if isinstance(loaded, dict):
            config = loaded
        elif loaded is not None:
            # Non-empty, non-mapping YAML is a user-managed file; leave it.
            typer.echo(
                f"Warning: couldn't parse {path} — configure the custom endpoint "
                "there yourself, or move the file aside and re-run.",
                err = True,
            )
            return
    # Hermes only reads the key for a *named* custom provider (a bare
    # `provider: custom` ignores it), so register it under providers.*.
    _subdict(config, "model").update(
        provider = f"custom:{_HERMES_PROVIDER}",
        default = model["id"],
        api_mode = "openai",
    )
    window = model.get("context_length") or model.get("max_context_length")
    if window:
        window = int(window)
        # Hermes auto-detects context from GET /v1/models, but OpenAI's schema has no
        # context field, so it can fall back to a 256k default that overflows a small
        # local model. Pin the real window (top-level model.context_length is the
        # highest-priority override) and compact at 90% of it (Hermes defaults to 50%).
        if window >= _HERMES_MIN_CONTEXT:
            _subdict(config, "model")["context_length"] = window
            _subdict(config, "compression").update(enabled = True, threshold = 0.9)
        else:
            # Below Hermes' 64,000-token floor it refuses to initialize, so claim
            # the floor and shrink the threshold so compaction still fires at 90%
            # of the REAL window (the threshold is a fraction of the claimed
            # context_length). The auxiliary override keeps the same floor check
            # from rejecting the compression model mid-session.
            _subdict(config, "model")["context_length"] = _HERMES_MIN_CONTEXT
            threshold = round(0.9 * window / _HERMES_MIN_CONTEXT, 4)
            _subdict(config, "compression").update(enabled = True, threshold = threshold)
            auxiliary = _subdict(_subdict(config, "auxiliary"), "compression")
            auxiliary["context_length"] = _HERMES_MIN_CONTEXT
    _subdict(config, "providers")[_HERMES_PROVIDER] = {
        "base_url": f"{base}/v1",
        "api_mode": "openai",
        "key_env": _HERMES_ENV_KEY,
    }
    text = yaml.safe_dump(config, sort_keys = False)
    if not path.exists() or path.read_text(encoding = "utf-8") != text:
        path.parent.mkdir(parents = True, exist_ok = True)
        path.write_text(text, encoding = "utf-8")
        typer.echo(f"Updated {path}")


def write_pi_config(base: str, key: str, model: dict, path: Path) -> None:
    config = _read_json_object(path)
    if config is None:
        typer.echo(
            f"Warning: couldn't parse {path} — add an 'unsloth' provider there "
            "yourself, or move the file aside and re-run.",
            err = True,
        )
        return
    before = json.dumps(config, sort_keys = True)
    # Pi reads custom providers from ~/.pi/agent/models.json (HOME-relocated for the
    # session). Studio is a generic OpenAI-compatible /v1 endpoint, and the key lives
    # in the config rather than the env (matching openclaw/opencode).
    provider_model = {"id": model["id"]}
    window = model.get("context_length") or model.get("max_context_length")
    if window:
        window = int(window)
        # An unspecified model defaults to contextWindow 128000 / maxTokens 16384,
        # far larger than a small Studio context, so Pi compacts too late and overflows
        # the server. Pin the real window and a sane output cap (mirrors OpenCode).
        provider_model["contextWindow"] = window
        provider_model["maxTokens"] = min(window // 4, 8192)
    _subdict(config, "providers")[_PI_PROVIDER] = {
        "api": "openai-completions",
        "baseUrl": f"{base}/v1",
        "apiKey": key,
        "models": [provider_model],
    }
    if json.dumps(config, sort_keys = True) != before:
        _write_private_json(path, config)
        typer.echo(f"Updated {path}")


@start_app.command("claude", context_settings = _PASSTHROUGH)
def claude(
    ctx: typer.Context,
    model: Optional[str] = _MODEL_OPTION,
    api_key: Optional[str] = _KEY_OPTION,
    launch: bool = _LAUNCH_OPTION,
    gguf_variant: Optional[str] = _GGUF_VARIANT_OPTION,
    max_seq_length: int = _CONTEXT_OPTION,
    load_in_4bit: bool = _LOAD_4BIT_OPTION,
    tensor_parallel: bool = _TENSOR_PARALLEL_OPTION,
    serve: bool = _SERVE_OPTION,
    yolo: bool = _YOLO_OPTION,
):
    """Point Claude Code at the running Studio server and start it."""
    base, key, entry = _connect(
        api_key,
        model,
        LoadOptions(gguf_variant, max_seq_length, load_in_4bit, tensor_parallel),
        serve = serve,
        launch = launch,
    )
    model_id = entry["id"]

    env = {
        "ANTHROPIC_BASE_URL": base,
        "ANTHROPIC_AUTH_TOKEN": key,
        "ANTHROPIC_MODEL": model_id,
        # Session-only (no ~/.claude write): suppress the attribution header so
        # llama.cpp KV-cache reuse is preserved; --settings below reinforces it.
        "CLAUDE_CODE_ATTRIBUTION_HEADER": "0",
        # Update checks, beta features, and other background requests either
        # stall against a local server or evict the conversation from
        # llama-server's KV-cache slots, so turn off everything nonessential.
        "CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC": "1",
        "CLAUDE_CODE_DISABLE_EXPERIMENTAL_BETAS": "1",
        # A local server streams in bursts; disable the full-screen TUI redraw so the
        # terminal doesn't flicker between tokens.
        "CLAUDE_CODE_NO_FLICKER": "1",
    }
    # Claude Code auto-compacts against its native (~600k token) window; a local
    # model's context is usually far smaller, so size the window to the loaded
    # model's real context length. Otherwise the conversation overflows the
    # server's window (silent truncation) long before Claude decides to compact.
    # codex/openclaw get the same value through their config (model_context_window
    # / contextWindow); Claude has no config file, so it rides on the env var.
    window = entry.get("context_length") or entry.get("max_context_length")
    if window:
        env["CLAUDE_CODE_AUTO_COMPACT_WINDOW"] = str(int(window))
        # Compact at 90% of that window; the override only takes effect once the
        # window is set, and it can only lower the threshold, so it just guarantees
        # headroom before the server's context limit instead of relying on Claude's
        # default (which is tuned for its native 200K/1M window).
        env["CLAUDE_AUTOCOMPACT_PCT_OVERRIDE"] = "90"
    # --yolo (or its aliases) maps to Claude's own --dangerously-skip-permissions.
    # IS_SANDBOX is left unset on purpose: Claude refuses bypass mode as root unless a
    # sandbox is detected, and we don't want to falsely claim one on the user's host.
    command = [
        "claude",
        "--model",
        model_id,
        *_claude_flags(),
        *_yolo_command_flags("claude", yolo),
        *ctx.args,
    ]
    install_hint = (
        "irm https://claude.ai/install.ps1 | iex"
        if os.name == "nt"
        else "curl -fsSL https://claude.ai/install.sh | bash"
    )
    _run(
        base,
        entry,
        env,
        command,
        launch = launch,
        install_hint = install_hint,
        unset_env = _CLAUDE_ENV_UNSET,
    )


@start_app.command("codex", context_settings = _PASSTHROUGH)
def codex(
    ctx: typer.Context,
    model: Optional[str] = _MODEL_OPTION,
    api_key: Optional[str] = _KEY_OPTION,
    launch: bool = _LAUNCH_OPTION,
    gguf_variant: Optional[str] = _GGUF_VARIANT_OPTION,
    max_seq_length: int = _CONTEXT_OPTION,
    load_in_4bit: bool = _LOAD_4BIT_OPTION,
    tensor_parallel: bool = _TENSOR_PARALLEL_OPTION,
    serve: bool = _SERVE_OPTION,
    yolo: bool = _YOLO_OPTION,
):
    """Point OpenAI Codex at the running Studio server and start it."""
    base, key, entry = _connect(
        api_key,
        model,
        LoadOptions(gguf_variant, max_seq_length, load_in_4bit, tensor_parallel),
        serve = serve,
        launch = launch,
    )
    # This preflight runs after _connect may have auto-started a server but before _run
    # installs its teardown finally, so tear the server down here if it rejects the model
    # (e.g. a transformers-backend model) rather than leaving it on the atexit backstop.
    try:
        _require_gguf_for_codex(base, key, entry["id"])
    except BaseException:
        _shutdown_auto_served()
        raise
    command = [
        "codex",
        "--oss",
        "--profile",
        _CODEX_PROFILE,
        *_yolo_command_flags("codex", yolo),
        *ctx.args,
    ]
    with _session_config("codex", launch) as home:
        write_codex_config(base, entry, home)
        env = {_CODEX_ENV_KEY: key, "CODEX_HOME": str(home)}
        _run(base, entry, env, command, launch = launch, install_hint = "npm install -g @openai/codex")


@start_app.command("openclaw", context_settings = _PASSTHROUGH)
def openclaw(
    ctx: typer.Context,
    model: Optional[str] = _MODEL_OPTION,
    api_key: Optional[str] = _KEY_OPTION,
    launch: bool = _LAUNCH_OPTION,
    gguf_variant: Optional[str] = _GGUF_VARIANT_OPTION,
    max_seq_length: int = _CONTEXT_OPTION,
    load_in_4bit: bool = _LOAD_4BIT_OPTION,
    tensor_parallel: bool = _TENSOR_PARALLEL_OPTION,
    serve: bool = _SERVE_OPTION,
    yolo: bool = _YOLO_OPTION,
):
    """Point OpenClaw at the running Studio server and start it."""
    base, key, entry = _connect(
        api_key,
        model,
        LoadOptions(gguf_variant, max_seq_length, load_in_4bit, tensor_parallel),
        serve = serve,
        launch = launch,
    )
    command = ["openclaw", *ctx.args]
    install_hint = (
        "iwr -useb https://openclaw.ai/install.ps1 | iex"
        if os.name == "nt"
        else "curl -fsSL https://openclaw.ai/install.sh | bash"
    )
    with _session_config("openclaw", launch) as cfg:
        config_path = cfg / "openclaw.json"
        # key lives in the config, not the env; --yolo writes the exec policy here too.
        write_openclaw_config(base, key, entry, config_path, yolo = yolo)
        # Scope both config and state so OpenClaw never touches the user's ~/.openclaw.
        env = {"OPENCLAW_CONFIG_PATH": str(config_path), "OPENCLAW_STATE_DIR": str(cfg)}
        _run(base, entry, env, command, launch = launch, install_hint = install_hint)


@start_app.command("opencode", context_settings = _PASSTHROUGH)
def opencode(
    ctx: typer.Context,
    model: Optional[str] = _MODEL_OPTION,
    api_key: Optional[str] = _KEY_OPTION,
    launch: bool = _LAUNCH_OPTION,
    gguf_variant: Optional[str] = _GGUF_VARIANT_OPTION,
    max_seq_length: int = _CONTEXT_OPTION,
    load_in_4bit: bool = _LOAD_4BIT_OPTION,
    tensor_parallel: bool = _TENSOR_PARALLEL_OPTION,
    serve: bool = _SERVE_OPTION,
    yolo: bool = _YOLO_OPTION,
):
    """Point OpenCode at the running Studio server and start it."""
    base, key, entry = _connect(
        api_key,
        model,
        LoadOptions(gguf_variant, max_seq_length, load_in_4bit, tensor_parallel),
        serve = serve,
        launch = launch,
    )
    command = ["opencode", *ctx.args]
    with _session_config("opencode", launch) as cfg:
        config_path = cfg / "opencode.json"
        # OPENCODE_CONFIG is an overlay (loaded between the user's global and project
        # configs), so this adds the Unsloth provider/model for the session without
        # changing the user's default model. Key lives in the config, not the env.
        write_opencode_config(base, key, entry, config_path, yolo = yolo)
        # A project's own opencode.json outranks OPENCODE_CONFIG, so the session model
        # pin (and --yolo permissions) would silently lose to a repo config. Carry the
        # settings that must win in OPENCODE_CONFIG_CONTENT, which outranks project
        # config; the API key stays in the private file, never in the printed env.
        inline_config: dict = {"model": f"unsloth/{entry['id']}"}
        if yolo:
            inline_config["permission"] = {"edit": "allow", "bash": "allow", "webfetch": "allow"}
        env = {
            "OPENCODE_CONFIG": str(config_path),
            "OPENCODE_CONFIG_CONTENT": json.dumps(inline_config),
        }
        _run(base, entry, env, command, launch = launch, install_hint = "npm install -g opencode-ai")


@start_app.command("hermes", context_settings = _PASSTHROUGH)
def hermes(
    ctx: typer.Context,
    model: Optional[str] = _MODEL_OPTION,
    api_key: Optional[str] = _KEY_OPTION,
    launch: bool = _LAUNCH_OPTION,
    gguf_variant: Optional[str] = _GGUF_VARIANT_OPTION,
    max_seq_length: int = _CONTEXT_OPTION,
    load_in_4bit: bool = _LOAD_4BIT_OPTION,
    tensor_parallel: bool = _TENSOR_PARALLEL_OPTION,
    serve: bool = _SERVE_OPTION,
    yolo: bool = _YOLO_OPTION,
):
    """Point Hermes (Nous Research) at the running Studio server and start it."""
    base, key, entry = _connect(
        api_key,
        model,
        LoadOptions(gguf_variant, max_seq_length, load_in_4bit, tensor_parallel),
        serve = serve,
        launch = launch,
    )
    command = ["hermes", *_yolo_command_flags("hermes", yolo), *ctx.args]
    install_hint = (
        "curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent"
        "/main/scripts/install.sh | bash"
    )
    with _session_config("hermes", launch) as home:
        # HERMES_HOME relocates hermes' whole home dir (config.yaml, sessions, state)
        # like CODEX_HOME, so the user's ~/.hermes is left untouched for the session.
        write_hermes_config(base, entry, home / "config.yaml")
        env = {_HERMES_ENV_KEY: key, "HERMES_HOME": str(home)}
        _run(base, entry, env, command, launch = launch, install_hint = install_hint)


@start_app.command("pi", context_settings = _PASSTHROUGH)
def pi(
    ctx: typer.Context,
    model: Optional[str] = _MODEL_OPTION,
    api_key: Optional[str] = _KEY_OPTION,
    launch: bool = _LAUNCH_OPTION,
    gguf_variant: Optional[str] = _GGUF_VARIANT_OPTION,
    max_seq_length: int = _CONTEXT_OPTION,
    load_in_4bit: bool = _LOAD_4BIT_OPTION,
    tensor_parallel: bool = _TENSOR_PARALLEL_OPTION,
    serve: bool = _SERVE_OPTION,
    yolo: bool = _YOLO_OPTION,
):
    """Point Pi (coding agent) at the running Studio server and start it."""
    base, key, entry = _connect(
        api_key,
        model,
        LoadOptions(gguf_variant, max_seq_length, load_in_4bit, tensor_parallel),
        serve = serve,
        launch = launch,
    )
    # Pi defaults to the google provider, so pin our provider/model on the command
    # line; the custom OpenAI-compatible endpoint itself is only configurable via
    # ~/.pi/agent/models.json.
    command = [
        "pi",
        "--provider",
        _PI_PROVIDER,
        "--model",
        entry["id"],
        *_yolo_command_flags("pi", yolo),
        *ctx.args,
    ]
    # --ignore-scripts matches Pi's documented install recipe (its README notes Pi needs
    # no install scripts), so accepting the prompt skips dependency lifecycle scripts.
    install_hint = "npm install -g --ignore-scripts @earendil-works/pi-coding-agent"
    with _session_config("pi", launch) as home:
        # Pi resolves its config dir from PI_CODING_AGENT_DIR first (getAgentDir() prefers
        # it over $HOME/.pi/agent), so pin it at the session dir: an inherited
        # PI_CODING_AGENT_DIR in the user's shell would otherwise send Pi to their real
        # config and skip our provider/key. HOME is relocated too so any other ~/.pi paths
        # stay in the session. The key rides in the config rather than the env.
        pi_agent_dir = home / ".pi" / "agent"
        write_pi_config(base, key, entry, pi_agent_dir / "models.json")
        env = {"HOME": str(home), "PI_CODING_AGENT_DIR": str(pi_agent_dir)}
        if os.name == "nt" or os.environ.get("WSL_DISTRO_NAME"):
            # Node resolves ~/.pi via USERPROFILE (then HOMEDRIVE + HOMEPATH) on Windows,
            # not HOME. Set them whenever Pi may run as a Windows process: native Windows,
            # or a /mnt Windows shim launched from WSL (the WSLENV bridge then translates
            # the path). Otherwise the Windows process falls back to the user's real
            # %USERPROFILE%\.pi. splitdrive yields no drive off a POSIX path, so
            # HOMEDRIVE/HOMEPATH stay unset there.
            env["USERPROFILE"] = str(home)
            drive, tail = os.path.splitdrive(str(home))
            if drive:
                env["HOMEDRIVE"], env["HOMEPATH"] = drive, tail
        # Pi paints inline from the current cursor position (no alternate screen,
        # no clear on first render), so give it the clean screen it assumes.
        _run(
            base,
            entry,
            env,
            command,
            launch = launch,
            install_hint = install_hint,
            clear_screen = True,
        )