hermes-agent/hermes_cli/model_setup_flows.py

"""Per-provider model-selection wizard flows for ``hermes setup`` / ``hermes model``.

Extracted from ``hermes_cli/main.py`` as part of the god-file decomposition
campaign (``~/.hermes/plans/god-file-decomposition.md``, Phase 2 — splitting
main.py handler/flow bodies out of the module). These 18 ``_model_flow_*``
functions are the interactive provider-setup branches dispatched by
``select_provider_and_model`` (which stays in main.py).

Behavior-neutral: each function is lifted verbatim. ``select_provider_and_model``
in main.py re-imports them (``from hermes_cli.model_setup_flows import *``-style
explicit import) so existing call sites — and test monkeypatches that target
``hermes_cli.main._model_flow_*`` — keep resolving against main.py's namespace.

main.py-internal helpers the flows call (``_prompt_api_key``, ``_save_custom_provider``,
the reasoning-effort/stepfun/qwen helpers, ``_run_anthropic_oauth_flow``, …) are
imported lazily inside the flows (``from hermes_cli.main import ...`` resolves at
call time, when main.py is fully loaded) so this module never imports
``hermes_cli.main`` at import time -> no import cycle.
"""

from __future__ import annotations

import argparse
import os
import subprocess


def _model_flow_openrouter(config, current_model=""):
    """OpenRouter provider: ensure API key, then pick model."""
    from hermes_cli.main import _prompt_api_key
    from hermes_constants import OPENROUTER_BASE_URL
    from hermes_cli.auth import (
        ProviderConfig,
        _prompt_model_selection,
        _save_model_choice,
        deactivate_provider,
    )
    from hermes_cli.config import get_env_value

    # Route through _prompt_api_key so users can replace a stale/broken key
    # in-flow (K/R/C) instead of having to edit ~/.hermes/.env by hand. The
    # previous bypass-when-key-exists branch left no way to recover from a
    # bad paste short of re-running `hermes setup` from scratch. OpenRouter
    # isn't in PROVIDER_REGISTRY so we synthesize a minimal pconfig.
    pconfig = ProviderConfig(
        id="openrouter",
        name="OpenRouter",
        auth_type="api_key",
        api_key_env_vars=("OPENROUTER_API_KEY",),
    )
    existing_key = get_env_value("OPENROUTER_API_KEY") or ""
    if not existing_key:
        print("Get one at: https://openrouter.ai/keys")
        print()
    _resolved, abort = _prompt_api_key(pconfig, existing_key, provider_id="openrouter")
    if abort:
        return

    from hermes_cli.models import model_ids, get_pricing_for_provider

    openrouter_models = model_ids(force_refresh=True)

    # Fetch live pricing (non-blocking — returns empty dict on failure)
    pricing = get_pricing_for_provider("openrouter", force_refresh=True)

    selected = _prompt_model_selection(
        openrouter_models, current_model=current_model, pricing=pricing
    )
    if selected:
        _save_model_choice(selected)

        # Update config provider and deactivate any OAuth provider
        from hermes_cli.config import load_config, save_config

        cfg = load_config()
        model = cfg.get("model")
        if not isinstance(model, dict):
            model = {"default": model} if model else {}
            cfg["model"] = model
        model["provider"] = "openrouter"
        model["base_url"] = OPENROUTER_BASE_URL
        model["api_mode"] = "chat_completions"
        save_config(cfg)
        deactivate_provider()
        print(f"Default model set to: {selected} (via OpenRouter)")
    else:
        print("No change.")

def _model_flow_nous(config, current_model="", args=None):
    """Nous Portal provider: ensure logged in, then pick model."""
    from hermes_cli.auth import (
        get_provider_auth_state,
        _prompt_model_selection,
        _save_model_choice,
        _update_config_for_provider,
        resolve_nous_runtime_credentials,
        AuthError,
        format_auth_error,
        _login_nous,
        PROVIDER_REGISTRY,
    )
    from hermes_cli.config import (
        get_env_value,
        load_config,
        save_config,
        save_env_value,
    )
    from hermes_cli.nous_subscription import prompt_enable_tool_gateway

    state = get_provider_auth_state("nous")
    if not state or not state.get("access_token"):
        print("Not logged into Nous Portal. Starting login...")
        print()
        try:
            mock_args = argparse.Namespace(
                portal_url=getattr(args, "portal_url", None),
                inference_url=getattr(args, "inference_url", None),
                client_id=getattr(args, "client_id", None),
                scope=getattr(args, "scope", None),
                no_browser=bool(getattr(args, "no_browser", False)),
                timeout=getattr(args, "timeout", None) or 15.0,
                ca_bundle=getattr(args, "ca_bundle", None),
                insecure=bool(getattr(args, "insecure", False)),
            )
            _login_nous(mock_args, PROVIDER_REGISTRY["nous"])
            # Offer Tool Gateway enablement for paid subscribers
            try:
                _refreshed = load_config() or {}
                prompt_enable_tool_gateway(_refreshed)
            except Exception:
                pass
        except SystemExit:
            print("Login cancelled or failed.")
            return
        except Exception as exc:
            print(f"Login failed: {exc}")
            return
        # login_nous already handles model selection + config update
        return

    # Already logged in — use curated model list (same as OpenRouter defaults).
    # The live /models endpoint returns hundreds of models; the curated list
    # shows only agentic models users recognize from OpenRouter.
    from hermes_cli.models import (
        get_curated_nous_model_ids,
        get_pricing_for_provider,
        check_nous_free_tier,
        partition_nous_models_by_tier,
        union_with_portal_free_recommendations,
        union_with_portal_paid_recommendations,
    )

    model_ids = get_curated_nous_model_ids()
    if not model_ids:
        print("No curated models available for Nous Portal.")
        return

    # Verify credentials are still valid (catches expired sessions early)
    try:
        creds = resolve_nous_runtime_credentials()
    except Exception as exc:
        relogin = isinstance(exc, AuthError) and exc.relogin_required
        msg = format_auth_error(exc) if isinstance(exc, AuthError) else str(exc)
        if relogin:
            print(f"Session expired: {msg}")
            print("Re-authenticating with Nous Portal...\n")
            try:
                mock_args = argparse.Namespace(
                    portal_url=None,
                    inference_url=None,
                    client_id=None,
                    scope=None,
                    no_browser=False,
                    timeout=15.0,
                    ca_bundle=None,
                    insecure=False,
                )
                _login_nous(mock_args, PROVIDER_REGISTRY["nous"])
            except Exception as login_exc:
                print(f"Re-login failed: {login_exc}")
            return
        print(f"Could not verify credentials: {msg}")
        return

    # Fetch live pricing (non-blocking — returns empty dict on failure)
    pricing = get_pricing_for_provider("nous")

    # Force fresh account data for model selection so recent credit purchases
    # are reflected immediately.
    free_tier = check_nous_free_tier(force_fresh=True)
    if not free_tier:
        try:
            refreshed_creds = resolve_nous_runtime_credentials(
                force_refresh=True,
            )
            if refreshed_creds:
                creds = refreshed_creds
        except Exception:
            # Runtime inference has its own paid-entitlement recovery path; do
            # not block model selection if this opportunistic refresh fails.
            pass

    # Resolve portal URL early — needed both for upgrade links and for the
    # freeRecommendedModels endpoint below.
    _nous_portal_url = ""
    try:
        _nous_state = get_provider_auth_state("nous")
        if _nous_state:
            _nous_portal_url = _nous_state.get("portal_base_url", "")
    except Exception:
        pass

    # For free users: partition models into selectable/unavailable based on
    # whether they are free per the Portal-reported pricing.  First augment
    # with the Portal's freeRecommendedModels list so newly-launched free
    # models show up even if this CLI build's hardcoded curated list and
    # docs-hosted manifest haven't caught up yet.
    #
    # For paid users: mirror the same idea with paidRecommendedModels so
    # newly-launched paid models surface in the picker too — independent
    # of CLI release cadence.
    unavailable_models: list[str] = []
    unavailable_message = ""
    if free_tier:
        try:
            from hermes_cli.nous_account import (
                format_nous_portal_entitlement_message,
                get_nous_portal_account_info,
            )

            _account_info = get_nous_portal_account_info(force_fresh=True)
            unavailable_message = (
                format_nous_portal_entitlement_message(
                    _account_info,
                    capability="paid Nous models",
                )
                or ""
            )
        except Exception:
            unavailable_message = ""
        model_ids, pricing = union_with_portal_free_recommendations(
            model_ids, pricing, _nous_portal_url,
        )
        model_ids, unavailable_models = partition_nous_models_by_tier(
            model_ids, pricing, free_tier=True
        )
    else:
        model_ids, pricing = union_with_portal_paid_recommendations(
            model_ids, pricing, _nous_portal_url,
        )

    if not model_ids and not unavailable_models:
        print("No models available for Nous Portal after filtering.")
        return

    if free_tier and not model_ids:
        print("No free models currently available.")
        if unavailable_models:
            from hermes_cli.auth import DEFAULT_NOUS_PORTAL_URL

            _url = (_nous_portal_url or DEFAULT_NOUS_PORTAL_URL).rstrip("/")
            print(unavailable_message or f"Upgrade at {_url} to access paid models.")
        return

    print(
        f'Showing {len(model_ids)} curated models — use "Enter custom model name" for others.'
    )

    selected = _prompt_model_selection(
        model_ids,
        current_model=current_model,
        pricing=pricing,
        unavailable_models=unavailable_models,
        portal_url=_nous_portal_url,
        unavailable_message=unavailable_message,
    )
    if selected:
        _save_model_choice(selected)
        # Reactivate Nous as the provider and update config
        inference_url = creds.get("base_url", "")
        _update_config_for_provider("nous", inference_url)
        current_model_cfg = config.get("model")
        if isinstance(current_model_cfg, dict):
            model_cfg = dict(current_model_cfg)
        elif isinstance(current_model_cfg, str) and current_model_cfg.strip():
            model_cfg = {"default": current_model_cfg.strip()}
        else:
            model_cfg = {}
        model_cfg["provider"] = "nous"
        model_cfg["default"] = selected
        if inference_url and inference_url.strip():
            model_cfg["base_url"] = inference_url.rstrip("/")
        else:
            model_cfg.pop("base_url", None)
        config["model"] = model_cfg
        # Clear any custom endpoint that might conflict
        if get_env_value("OPENAI_BASE_URL"):
            save_env_value("OPENAI_BASE_URL", "")
            save_env_value("OPENAI_API_KEY", "")
        save_config(config)
        print(f"Default model set to: {selected} (via Nous Portal)")
        # Offer Tool Gateway enablement for paid subscribers
        prompt_enable_tool_gateway(config)
    else:
        print("No change.")

def _model_flow_openai_codex(config, current_model=""):
    """OpenAI Codex provider: ensure logged in, then pick model."""
    from hermes_cli.auth import (
        get_codex_auth_status,
        _prompt_model_selection,
        _save_model_choice,
        _update_config_for_provider,
        _login_openai_codex,
        PROVIDER_REGISTRY,
        DEFAULT_CODEX_BASE_URL,
    )
    from hermes_cli.codex_models import get_codex_model_ids

    status = get_codex_auth_status()
    if status.get("logged_in"):
        print("  OpenAI Codex credentials: ✓")
        print()
        print("    1. Use existing credentials")
        print("    2. Reauthenticate (new OAuth login)")
        print("    3. Cancel")
        print()
        try:
            choice = input("  Choice [1/2/3]: ").strip()
        except (KeyboardInterrupt, EOFError):
            choice = "1"

        if choice == "2":
            print("Starting a fresh OpenAI Codex login...")
            print()
            try:
                mock_args = argparse.Namespace()
                _login_openai_codex(
                    mock_args,
                    PROVIDER_REGISTRY["openai-codex"],
                    force_new_login=True,
                )
            except SystemExit:
                print("Login cancelled or failed.")
                return
            except Exception as exc:
                print(f"Login failed: {exc}")
                return
            status = get_codex_auth_status()
            if not status.get("logged_in"):
                print("Login failed.")
                return
        elif choice == "3":
            return
    else:
        print("Not logged into OpenAI Codex. Starting login...")
        print()
        try:
            mock_args = argparse.Namespace()
            _login_openai_codex(mock_args, PROVIDER_REGISTRY["openai-codex"])
        except SystemExit:
            print("Login cancelled or failed.")
            return
        except Exception as exc:
            print(f"Login failed: {exc}")
            return

    _codex_token = None
    # Prefer credential pool (where `hermes auth` stores device_code tokens),
    # fall back to legacy provider state.
    try:
        _codex_status = get_codex_auth_status()
        if _codex_status.get("logged_in"):
            _codex_token = _codex_status.get("api_key")
    except Exception:
        pass
    if not _codex_token:
        try:
            from hermes_cli.auth import resolve_codex_runtime_credentials

            _codex_creds = resolve_codex_runtime_credentials()
            _codex_token = _codex_creds.get("api_key")
        except Exception:
            pass

    codex_models = get_codex_model_ids(access_token=_codex_token)

    selected = _prompt_model_selection(codex_models, current_model=current_model)
    if selected:
        _save_model_choice(selected)
        _update_config_for_provider("openai-codex", DEFAULT_CODEX_BASE_URL)
        print(f"Default model set to: {selected} (via OpenAI Codex)")
    else:
        print("No change.")

def _model_flow_xai_oauth(_config, current_model="", *, args=None):
    """xAI Grok OAuth (SuperGrok / Premium+) provider: ensure logged in, then pick model."""
    from hermes_cli.auth import (
        get_xai_oauth_auth_status,
        _prompt_model_selection,
        _save_model_choice,
        _update_config_for_provider,
        resolve_xai_oauth_runtime_credentials,
        _login_xai_oauth,
        DEFAULT_XAI_OAUTH_BASE_URL,
        PROVIDER_REGISTRY,
    )
    from hermes_cli.models import _PROVIDER_MODELS

    status = get_xai_oauth_auth_status()
    if status.get("logged_in"):
        print("  xAI Grok OAuth (SuperGrok / Premium+) credentials: ✓")
        print()
        print("    1. Use existing credentials")
        print("    2. Reauthenticate (new OAuth login)")
        print("    3. Cancel")
        print()
        try:
            choice = input("  Choice [1/2/3]: ").strip()
        except (KeyboardInterrupt, EOFError):
            choice = "1"

        if choice == "2":
            print("Starting a fresh xAI OAuth login...")
            print()
            try:
                # Forward CLI flags from ``hermes model --manual-paste``
                # / ``--no-browser`` / ``--timeout`` into the loopback
                # login. Without this, browser-only remotes (#26923)
                # can't reach the manual-paste path via ``hermes model``.
                mock_args = argparse.Namespace(
                    manual_paste=bool(getattr(args, "manual_paste", False)),
                    no_browser=bool(getattr(args, "no_browser", False)),
                    timeout=getattr(args, "timeout", None),
                )
                _login_xai_oauth(
                    mock_args,
                    PROVIDER_REGISTRY["xai-oauth"],
                    force_new_login=True,
                )
            except SystemExit:
                print("Login cancelled or failed.")
                return
            except Exception as exc:
                print(f"Login failed: {exc}")
                return
        elif choice == "3":
            return
    else:
        print("Not logged into xAI Grok OAuth (SuperGrok / Premium+). Starting login...")
        print()
        try:
            mock_args = argparse.Namespace(
                manual_paste=bool(getattr(args, "manual_paste", False)),
                no_browser=bool(getattr(args, "no_browser", False)),
                timeout=getattr(args, "timeout", None),
            )
            _login_xai_oauth(mock_args, PROVIDER_REGISTRY["xai-oauth"])
        except SystemExit:
            print("Login cancelled or failed.")
            return
        except Exception as exc:
            print(f"Login failed: {exc}")
            return

    # Resolve a usable base URL.  ``resolve_xai_oauth_runtime_credentials``
    # only reads from the auth.json singleton — but credentials may legitimately
    # live only in the pool (e.g. after ``hermes auth add xai-oauth``).  Fall
    # back to the default base URL in that case so the model picker still
    # completes successfully instead of bailing out with
    # ``Could not resolve xAI OAuth credentials``.
    base_url = DEFAULT_XAI_OAUTH_BASE_URL
    try:
        creds = resolve_xai_oauth_runtime_credentials()
        base_url = (creds.get("base_url") or "").strip().rstrip("/") or base_url
    except Exception:
        pass

    models = list(_PROVIDER_MODELS.get("xai-oauth") or _PROVIDER_MODELS.get("xai") or [])
    selected = _prompt_model_selection(models, current_model=current_model or (models[0] if models else "grok-4.3"))
    if selected:
        _save_model_choice(selected)
        _update_config_for_provider("xai-oauth", base_url)
        print(f"Default model set to: {selected} (via xAI Grok OAuth — SuperGrok / Premium+)")
    else:
        print("No change.")

def _model_flow_qwen_oauth(_config, current_model=""):
    """Qwen OAuth provider: reuse local Qwen CLI login, then pick model."""
    from hermes_cli.main import _DEFAULT_QWEN_PORTAL_MODELS
    from hermes_cli.auth import (
        get_qwen_auth_status,
        resolve_qwen_runtime_credentials,
        _prompt_model_selection,
        _save_model_choice,
        _update_config_for_provider,
        DEFAULT_QWEN_BASE_URL,
    )
    from hermes_cli.models import fetch_api_models

    status = get_qwen_auth_status()
    if not status.get("logged_in"):
        print("Not logged into Qwen CLI OAuth.")
        print("Run: qwen auth qwen-oauth")
        auth_file = status.get("auth_file")
        if auth_file:
            print(f"Expected credentials file: {auth_file}")
        if status.get("error"):
            print(f"Error: {status.get('error')}")
        return

    # Try live model discovery, fall back to curated list.
    models = None
    try:
        creds = resolve_qwen_runtime_credentials(refresh_if_expiring=True)
        models = fetch_api_models(creds["api_key"], creds["base_url"])
    except Exception:
        pass
    if not models:
        models = list(_DEFAULT_QWEN_PORTAL_MODELS)

    default = current_model or (models[0] if models else "qwen3-coder-plus")
    selected = _prompt_model_selection(models, current_model=default)
    if selected:
        _save_model_choice(selected)
        _update_config_for_provider("qwen-oauth", DEFAULT_QWEN_BASE_URL)
        print(f"Default model set to: {selected} (via Qwen OAuth)")
    else:
        print("No change.")

def _model_flow_minimax_oauth(config, current_model="", args=None):
    """MiniMax OAuth provider: ensure logged in, then pick model."""
    from hermes_cli.auth import (
        get_provider_auth_state,
        _prompt_model_selection,
        _save_model_choice,
        _update_config_for_provider,
        resolve_minimax_oauth_runtime_credentials,
        AuthError,
        format_auth_error,
        _login_minimax_oauth,
        PROVIDER_REGISTRY,
    )

    state = get_provider_auth_state("minimax-oauth")
    if not state or not state.get("access_token"):
        print("Not logged into MiniMax. Starting OAuth login...")
        print()
        try:
            mock_args = argparse.Namespace(
                region=getattr(args, "region", None) or "global",
                no_browser=bool(getattr(args, "no_browser", False)),
                timeout=getattr(args, "timeout", None) or 15.0,
            )
            _login_minimax_oauth(mock_args, PROVIDER_REGISTRY["minimax-oauth"])
        except SystemExit:
            print("Login cancelled or failed.")
            return
        except Exception as exc:
            print(f"Login failed: {exc}")
            return

    try:
        creds = resolve_minimax_oauth_runtime_credentials()
    except AuthError as exc:
        print(format_auth_error(exc))
        return

    from hermes_cli.models import _PROVIDER_MODELS

    model_ids = _PROVIDER_MODELS.get("minimax-oauth", [])
    selected = _prompt_model_selection(model_ids, current_model)
    if not selected:
        return
    _save_model_choice(selected)
    _update_config_for_provider("minimax-oauth", creds["base_url"])
    print(f"\u2713 Using MiniMax model: {selected}")

def _model_flow_google_gemini_cli(_config, current_model=""):
    """Google Gemini OAuth (PKCE) via Cloud Code Assist — supports free AND paid tiers.

    Flow:
      1. Show upfront warning about Google's ToS stance (per opencode-gemini-auth).
      2. If creds missing, run PKCE browser OAuth via agent.google_oauth.
      3. Resolve project context (env -> config -> auto-discover -> free tier).
      4. Prompt user to pick a model.
      5. Save to ~/.hermes/config.yaml.
    """
    from hermes_cli.auth import (
        DEFAULT_GEMINI_CLOUDCODE_BASE_URL,
        get_gemini_oauth_auth_status,
        resolve_gemini_oauth_runtime_credentials,
        _prompt_model_selection,
        _save_model_choice,
        _update_config_for_provider,
    )
    from hermes_cli.models import _PROVIDER_MODELS

    print()
    print("⚠  Google considers using the Gemini CLI OAuth client with third-party")
    print("   software a policy violation. Some users have reported account")
    print("   restrictions. You can use your own API key via 'gemini' provider")
    print("   for the lowest-risk experience.")
    print()
    try:
        proceed = input("Continue with OAuth login? [y/N]: ").strip().lower()
    except (EOFError, KeyboardInterrupt):
        print("Cancelled.")
        return
    if proceed not in {"y", "yes"}:
        print("Cancelled.")
        return

    status = get_gemini_oauth_auth_status()
    if not status.get("logged_in"):
        try:
            from agent.google_oauth import resolve_project_id_from_env, start_oauth_flow

            env_project = resolve_project_id_from_env()
            start_oauth_flow(force_relogin=True, project_id=env_project)
        except Exception as exc:
            print(f"OAuth login failed: {exc}")
            return

    # Verify creds resolve + trigger project discovery
    try:
        creds = resolve_gemini_oauth_runtime_credentials(force_refresh=False)
        project_id = creds.get("project_id", "")
        if project_id:
            print(f"  Using GCP project: {project_id}")
        else:
            print(
                "  No GCP project configured — free tier will be auto-provisioned on first request."
            )
    except Exception as exc:
        print(f"Failed to resolve Gemini credentials: {exc}")
        return

    models = list(_PROVIDER_MODELS.get("google-gemini-cli") or [])
    default = current_model or (models[0] if models else "gemini-3-flash-preview")
    selected = _prompt_model_selection(models, current_model=default)
    if selected:
        _save_model_choice(selected)
        _update_config_for_provider(
            "google-gemini-cli", DEFAULT_GEMINI_CLOUDCODE_BASE_URL
        )
        print(
            f"Default model set to: {selected} (via Google Gemini OAuth / Code Assist)"
        )
    else:
        print("No change.")

def _model_flow_custom(config):
    """Custom endpoint: collect URL, API key, and model name.

    Automatically saves the endpoint to ``custom_providers`` in config.yaml
    so it appears in the provider menu on subsequent runs.
    """
    from hermes_cli.main import _auto_provider_name, _prompt_custom_api_mode_selection, _save_custom_provider
    from hermes_cli.auth import _save_model_choice, deactivate_provider
    from hermes_cli.config import get_env_value, load_config, save_config
    from hermes_cli.secret_prompt import masked_secret_prompt

    current_url = get_env_value("OPENAI_BASE_URL") or ""
    current_key = get_env_value("OPENAI_API_KEY") or ""

    print("Custom OpenAI-compatible endpoint configuration:")
    if current_url:
        print(f"  Current URL: {current_url}")
    if current_key:
        print(f"  Current key: {current_key[:8]}...")
    print()

    try:
        base_url = input(
            f"API base URL [{current_url or 'e.g. https://api.example.com/v1'}]: "
        ).strip()
        api_key = masked_secret_prompt(
            f"API key [{current_key[:8] + '...' if current_key else 'optional'}]: "
        ).strip()
    except (KeyboardInterrupt, EOFError):
        print("\nCancelled.")
        return

    if not base_url and not current_url:
        print("No URL provided. Cancelled.")
        return

    # Validate URL format
    effective_url = base_url or current_url
    if not effective_url.startswith(("http://", "https://")):
        print(f"Invalid URL: {effective_url} (must start with http:// or https://)")
        return

    effective_key = api_key or current_key

    # Hint: most local model servers (Ollama, vLLM, llama.cpp) require /v1
    # in the base URL for OpenAI-compatible chat completions.  Prompt the
    # user if the URL looks like a local server without /v1.
    _url_lower = effective_url.rstrip("/").lower()
    _looks_local = any(
        h in _url_lower
        for h in ("localhost", "127.0.0.1", "0.0.0.0", ":11434", ":8080", ":5000")
    )
    if _looks_local and not _url_lower.endswith("/v1"):
        print()
        print(f"  Hint: Did you mean to add /v1 at the end?")
        print(f"  Most local model servers (Ollama, vLLM, llama.cpp) require it.")
        print(f"  e.g. {effective_url.rstrip('/')}/v1")
        try:
            _add_v1 = input("  Add /v1? [Y/n]: ").strip().lower()
        except (KeyboardInterrupt, EOFError):
            _add_v1 = "n"
        if _add_v1 in {"", "y", "yes"}:
            effective_url = effective_url.rstrip("/") + "/v1"
            if base_url:
                base_url = effective_url
            print(f"  Updated URL: {effective_url}")
        print()

    from hermes_cli.models import probe_api_models

    probe = probe_api_models(effective_key, effective_url)
    if probe.get("used_fallback") and probe.get("resolved_base_url"):
        print(
            f"Warning: endpoint verification worked at {probe['resolved_base_url']}/models, "
            f"not the exact URL you entered. Saving the working base URL instead."
        )
        effective_url = probe["resolved_base_url"]
        if base_url:
            base_url = effective_url
    elif probe.get("models") is not None:
        print(
            f"Verified endpoint via {probe.get('probed_url')} "
            f"({len(probe.get('models') or [])} model(s) visible)"
        )
    else:
        print(
            f"Warning: could not verify this endpoint via {probe.get('probed_url')}. "
            f"Hermes will still save it."
        )
        if probe.get("suggested_base_url"):
            suggested = probe["suggested_base_url"]
            if suggested.endswith("/v1"):
                print(
                    f"  If this server expects /v1 in the path, try base URL: {suggested}"
                )
            else:
                print(f"  If /v1 should not be in the base URL, try: {suggested}")

    # Prompt for API compatibility mode explicitly so codex-compatible custom
    # providers don't silently fall back to chat_completions.
    current_model_cfg = config.get("model")
    current_api_mode = ""
    if isinstance(current_model_cfg, dict):
        current_api_mode = str(current_model_cfg.get("api_mode") or "").strip()
    api_mode = _prompt_custom_api_mode_selection(
        effective_url,
        current_api_mode=current_api_mode,
    )
    if api_mode:
        print(f"  API mode: {api_mode}")
    else:
        print("  API mode: auto-detect")

    # Select model — use probe results when available, fall back to manual input
    model_name = ""
    detected_models = probe.get("models") or []
    try:
        if len(detected_models) == 1:
            print(f"  Detected model: {detected_models[0]}")
            confirm = input("  Use this model? [Y/n]: ").strip().lower()
            if confirm in {"", "y", "yes"}:
                model_name = detected_models[0]
            else:
                model_name = input("Model name (e.g. gpt-4, llama-3-70b): ").strip()
        elif len(detected_models) > 1:
            print("  Available models:")
            for i, m in enumerate(detected_models, 1):
                print(f"    {i}. {m}")
            pick = input(
                f"  Select model [1-{len(detected_models)}] or type name: "
            ).strip()
            if pick.isdigit() and 1 <= int(pick) <= len(detected_models):
                model_name = detected_models[int(pick) - 1]
            elif pick:
                model_name = pick
        else:
            model_name = input("Model name (e.g. gpt-4, llama-3-70b): ").strip()

        context_length_str = input(
            "Context length in tokens [leave blank for auto-detect]: "
        ).strip()

        # Prompt for a display name — shown in the provider menu on future runs
        default_name = _auto_provider_name(effective_url)
        display_name = input(f"Display name [{default_name}]: ").strip() or default_name
    except (KeyboardInterrupt, EOFError):
        print("\nCancelled.")
        return

    context_length = None
    if context_length_str:
        try:
            context_length = int(
                context_length_str.replace(",", "")
                .replace("k", "000")
                .replace("K", "000")
            )
            if context_length <= 0:
                context_length = None
        except ValueError:
            print(f"Invalid context length: {context_length_str} — will auto-detect.")
            context_length = None

    if model_name:
        _save_model_choice(model_name)

        # Update config and deactivate any OAuth provider
        cfg = load_config()
        model = cfg.get("model")
        if not isinstance(model, dict):
            model = {"default": model} if model else {}
            cfg["model"] = model
        model["provider"] = "custom"
        model["base_url"] = effective_url
        if effective_key:
            model["api_key"] = effective_key
        if api_mode:
            model["api_mode"] = api_mode
        else:
            model.pop("api_mode", None)
        save_config(cfg)
        deactivate_provider()

        # Sync the caller's config dict so the setup wizard's final
        # save_config(config) preserves our model settings.  Without
        # this, the wizard overwrites model.provider/base_url with
        # the stale values from its own config dict (#4172).
        config["model"] = dict(model)

        print(f"Default model set to: {model_name} (via {effective_url})")
    else:
        if base_url or api_key:
            deactivate_provider()
        # Even without a model name, persist the custom endpoint on the
        # caller's config dict so the setup wizard doesn't lose it.
        _caller_model = config.get("model")
        if not isinstance(_caller_model, dict):
            _caller_model = {"default": _caller_model} if _caller_model else {}
        _caller_model["provider"] = "custom"
        _caller_model["base_url"] = effective_url
        if effective_key:
            _caller_model["api_key"] = effective_key
        if api_mode:
            _caller_model["api_mode"] = api_mode
        else:
            _caller_model.pop("api_mode", None)
        config["model"] = _caller_model
        print("Endpoint saved. Use `/model` in chat or `hermes model` to set a model.")

    # Auto-save to custom_providers so it appears in the menu next time
    _save_custom_provider(
        effective_url,
        effective_key,
        model_name or "",
        context_length=context_length,
        name=display_name,
        api_mode=api_mode,
    )

def _model_flow_azure_foundry(config, current_model=""):
    """Azure Foundry provider: configure endpoint, auth mode, API mode, and model.

    Azure Foundry supports both OpenAI-style (``/v1/chat/completions``) and
    Anthropic-style (``/v1/messages``) endpoints, and two authentication
    modes:

    * **API key** (default) — uses ``AZURE_FOUNDRY_API_KEY`` from .env.
    * **Microsoft Entra ID** — keyless, RBAC-based auth via the
      ``azure-identity`` SDK (Managed Identity / Workload Identity / az
      login / VS Code / azd / service principal env vars). Works on both
      OpenAI-style and Anthropic-style endpoints — Microsoft RBAC is
      per-resource and the same ``Azure AI User`` role grants
      both. For OpenAI-style the OpenAI SDK's native callable
      ``api_key=`` contract is used; for Anthropic-style an
      ``httpx.Client`` with a request event hook (built by
      :func:`agent.azure_identity_adapter.build_bearer_http_client`)
      mints a fresh JWT per request because the Anthropic SDK does not
      accept a callable ``auth_token`` natively.

    The wizard auto-detects the transport and available models when
    possible:

    * URLs ending in ``/anthropic`` → Anthropic Messages API.
    * Successful ``GET <base>/models`` probe → OpenAI-style + populates
      a picker with the returned deployment / model IDs.
    * Anthropic Messages probe fallback when ``/models`` fails.
    * Manual entry when every probe fails (private endpoints, etc.).

    Context lengths for the chosen model are resolved via the standard
    :func:`agent.model_metadata.get_model_context_length` chain
    (models.dev, provider metadata, hardcoded family fallbacks).
    """
    from hermes_cli.auth import _save_model_choice, deactivate_provider  # noqa: F401
    from hermes_cli.config import (
        get_env_value,
        save_env_value,
        load_config,
        save_config,
    )
    from hermes_cli import azure_detect

    # ── Load current Azure Foundry configuration ─────────────────────
    model_cfg = config.get("model", {})
    if isinstance(model_cfg, dict) and model_cfg.get("provider") == "azure-foundry":
        current_base_url = str(model_cfg.get("base_url", "") or "")
        current_api_mode = str(model_cfg.get("api_mode", "") or "")
        current_auth_mode = str(model_cfg.get("auth_mode") or "api_key").strip().lower() or "api_key"
        _cur_entra = model_cfg.get("entra") or {}
        current_entra = _cur_entra if isinstance(_cur_entra, dict) else {}
    else:
        current_base_url = ""
        current_api_mode = ""
        current_auth_mode = "api_key"
        current_entra = {}

    current_api_key = get_env_value("AZURE_FOUNDRY_API_KEY") or ""

    print()
    print("Azure Foundry Configuration")
    print("=" * 50)
    print()
    print("Azure Foundry can host models with either OpenAI-style or")
    print("Anthropic-style API endpoints.  Hermes will probe your")
    print("endpoint to auto-detect the transport and the deployed")
    print("models when possible.")
    print()

    if current_base_url:
        print(f"  Current endpoint:  {current_base_url}")
    if current_api_mode:
        _lbl = (
            "OpenAI-style"
            if current_api_mode == "chat_completions"
            else "Anthropic-style"
        )
        print(f"  Current API mode:  {_lbl}")
    if current_auth_mode == "entra_id":
        print(f"  Current auth mode: Microsoft Entra ID (keyless)")
    elif current_api_key:
        print(f"  Current auth mode: API key ({current_api_key[:8]}...)")
    print()

    # ── Step 1: endpoint URL ─────────────────────────────────────────
    try:
        _placeholder = (
            current_base_url
            or "e.g. https://<resource>.openai.azure.com/openai/v1 "
              "or https://<resource>.services.ai.azure.com/anthropic"
        )
        base_url = input(
            f"API endpoint URL [{_placeholder}]: "
        ).strip()
    except (KeyboardInterrupt, EOFError):
        print("\nCancelled.")
        return

    effective_url = (base_url or current_base_url).rstrip("/")
    if not effective_url:
        print("No endpoint URL provided. Cancelled.")
        return
    if not effective_url.startswith(("http://", "https://")):
        print(f"Invalid URL: {effective_url} (must start with http:// or https://)")
        return

    # ── Step 2: authentication mode ──────────────────────────────────
    print()
    print("Authentication:")
    print("  1. API key                  (AZURE_FOUNDRY_API_KEY in .env)")
    print("  2. Microsoft Entra ID       (managed identity / workload identity / az login)")
    print("     Recommended by Microsoft. Works for both OpenAI-style and Anthropic-style endpoints.")
    print("     Requires the 'Azure AI User' role on the Foundry resource.")
    try:
        _auth_default = "2" if current_auth_mode == "entra_id" else "1"
        auth_choice = (
            input(f"Authentication mode [1/2] ({_auth_default}): ").strip()
            or _auth_default
        )
    except (KeyboardInterrupt, EOFError):
        print("\nCancelled.")
        return
    use_entra = auth_choice == "2"
    auth_mode_label = "entra_id" if use_entra else "api_key"

    # ── Step 3: credentials (key OR Entra preflight) ─────────────────
    effective_key: str = ""
    entra_overrides: dict = {}
    token_provider = None  # callable when entra
    entra_scope = ""

    if use_entra:
        try:
            from agent.azure_identity_adapter import (
                EntraIdentityConfig,
                SCOPE_AI_AZURE_DEFAULT,
                build_token_provider,
                describe_active_credential,
                has_azure_identity_installed,
            )
        except ImportError as exc:
            print()
            print(f"⚠ Could not import azure-identity adapter: {exc}")
            print("  Falling back to API key auth.")
            use_entra = False
            auth_mode_label = "api_key"

    if use_entra:
        print()
        if not has_azure_identity_installed():
            print("◐ The 'azure-identity' package is not installed yet.")
            print(
                "  Hermes will install it now (the preflight below "
                "triggers the lazy-install). To skip lazy installs, "
                "run:  pip install azure-identity"
            )

        # Preserve only the optional scope override. Identity selection
        # (tenant, user-assigned MI, workload identity, service principal)
        # stays in Azure SDK env vars such as AZURE_CLIENT_ID.
        _persisted_scope_override = str(current_entra.get("scope") or "").strip()
        entra_scope = _persisted_scope_override or SCOPE_AI_AZURE_DEFAULT

        entra_overrides = {}
        if _persisted_scope_override:
            entra_overrides["scope"] = _persisted_scope_override

        print()
        print("◐ Probing Microsoft Entra ID credential chain (up to 10s)...")
        _config = EntraIdentityConfig(
            scope=entra_scope,
        )
        info = describe_active_credential(config=_config, timeout_seconds=10.0)
        if info.get("ok"):
            env_sources = info.get("env_sources") or []
            tag = ", ".join(env_sources) if env_sources else "default chain"
            print(f"✓ Entra ID token acquired ({tag}, scope={entra_scope})")
        else:
            err = info.get("error") or "credential chain exhausted"
            hint = info.get("hint") or (
                "Run `az login`, attach a managed identity to this VM, or "
                "set AZURE_TENANT_ID/AZURE_CLIENT_ID/AZURE_CLIENT_SECRET."
            )
            print(f"⚠ {err}")
            print(f"  Hint: {hint}")
            try:
                ans = input("Save Entra config anyway and validate later? [Y/n]: ").strip().lower()
            except (KeyboardInterrupt, EOFError):
                print("\nCancelled.")
                return
            if ans and ans not in ("y", "yes"):
                print("Cancelled.")
                return

        # Build the token provider for the detection probe (best-effort —
        # if the credential chain failed above, this will silently return
        # None inside azure_detect and the probe falls back to manual).
        try:
            token_provider = build_token_provider(config=_config)
        except Exception as exc:
            print(f"⚠ Could not build token provider for probing: {exc}")
            token_provider = None
    else:
        print()
        from hermes_cli.secret_prompt import masked_secret_prompt

        try:
            api_key = masked_secret_prompt(
                f"API key [{current_api_key[:8] + '...' if current_api_key else 'required'}]: "
            ).strip()
        except (KeyboardInterrupt, EOFError):
            print("\nCancelled.")
            return

        effective_key = api_key or current_api_key
        if not effective_key:
            print("No API key provided. Cancelled.")
            return

    # ── Step 4: auto-detect transport + models ───────────────────────
    print()
    print("◐ Probing endpoint to auto-detect transport and models...")
    detection = azure_detect.detect(
        effective_url,
        api_key=effective_key,
        token_provider=token_provider,
    )

    discovered_models: list[str] = list(detection.models)
    api_mode: str = detection.api_mode or ""

    if api_mode:
        mode_label = (
            "OpenAI-style" if api_mode == "chat_completions" else "Anthropic-style"
        )
        print(f"✓ Detected API transport: {mode_label}")
        if detection.reason:
            print(f"    ({detection.reason})")
        if discovered_models:
            print(
                f"✓ Found {len(discovered_models)} deployed model(s) on this endpoint"
            )
    else:
        print(f"⚠ Auto-detection incomplete: {detection.reason}")
        print()
        print("Select the API format your Azure Foundry endpoint uses:")
        print("  1. OpenAI-style  (POST /v1/chat/completions)")
        print("     For: GPT models, Llama, Mistral, and most open models")
        print("  2. Anthropic-style  (POST /v1/messages)")
        print("     For: Claude models deployed via Anthropic API format")
        try:
            default_choice = "2" if current_api_mode == "anthropic_messages" else "1"
            mode_choice = (
                input(f"API format [1/2] ({default_choice}): ").strip()
                or default_choice
            )
        except (KeyboardInterrupt, EOFError):
            print("\nCancelled.")
            return
        api_mode = "anthropic_messages" if mode_choice == "2" else "chat_completions"

    # ── Step 5: model name ───────────────────────────────────────────
    print()
    effective_model = ""
    if discovered_models:
        print("Available models on this endpoint:")
        for i, mid in enumerate(discovered_models[:30], start=1):
            print(f"  {i:>2}. {mid}")
        if len(discovered_models) > 30:
            print(
                f"  ... and {len(discovered_models) - 30} more (type name manually if not shown)"
            )
        print()
        try:
            pick = input(
                f"Pick by number, or type a deployment name [{current_model or discovered_models[0]}]: "
            ).strip()
        except (KeyboardInterrupt, EOFError):
            print("\nCancelled.")
            return
        if not pick:
            effective_model = current_model or discovered_models[0]
        elif pick.isdigit() and 1 <= int(pick) <= min(len(discovered_models), 30):
            effective_model = discovered_models[int(pick) - 1]
        else:
            effective_model = pick
    else:
        try:
            model_name = input(
                f"Model / deployment name [{current_model or 'e.g. gpt-5.4, claude-sonnet-4-6'}]: "
            ).strip()
        except (KeyboardInterrupt, EOFError):
            print("\nCancelled.")
            return
        effective_model = model_name or current_model

    if not effective_model:
        print("No model name provided. Cancelled.")
        return

    # ── Step 6: context-length lookup ────────────────────────────────
    ctx_len = azure_detect.lookup_context_length(
        effective_model,
        effective_url,
        api_key=effective_key,
        token_provider=token_provider,
    )

    # ── Step 7: persist ──────────────────────────────────────────────
    if not use_entra:
        save_env_value("AZURE_FOUNDRY_API_KEY", effective_key)

    cfg = load_config()
    model = cfg.get("model")
    if not isinstance(model, dict):
        model = {"default": model} if model else {}
        cfg["model"] = model

    model["provider"] = "azure-foundry"
    model["base_url"] = effective_url
    model["api_mode"] = api_mode
    model["default"] = effective_model
    model["auth_mode"] = auth_mode_label
    if use_entra:
        # Persist only the non-default Entra scope so config.yaml stays tidy.
        # Azure identity selection stays in standard AZURE_* env vars.
        clean_entra: dict = {}
        for key in ("scope",):
            val = entra_overrides.get(key)
            if val:
                clean_entra[key] = val
        if clean_entra:
            model["entra"] = clean_entra
        elif "entra" in model:
            del model["entra"]
    else:
        if "entra" in model:
            del model["entra"]
    if ctx_len:
        model["context_length"] = ctx_len

    save_config(cfg)
    deactivate_provider()
    config["model"] = dict(model)

    # Clear any conflicting env vars so auxiliary clients don't poison
    # themselves with a stale OpenAI base URL / key.
    if get_env_value("OPENAI_BASE_URL"):
        save_env_value("OPENAI_BASE_URL", "")
    if get_env_value("OPENAI_API_KEY"):
        save_env_value("OPENAI_API_KEY", "")

    mode_label = "OpenAI-style" if api_mode == "chat_completions" else "Anthropic-style"
    auth_label = (
        "Microsoft Entra ID (keyless)" if use_entra else "API key"
    )
    print()
    print("✓ Azure Foundry configured:")
    print(f"    Endpoint:       {effective_url}")
    print(f"    API mode:       {mode_label}")
    print(f"    Auth:           {auth_label}")
    print(f"    Model:          {effective_model}")
    if ctx_len:
        print(f"    Context length: {ctx_len:,} tokens")
    else:
        print("    Context length: not auto-detected (will fall back at runtime)")
    print()

def _model_flow_named_custom(config, provider_info):
    """Handle a named custom provider from config.yaml custom_providers list.

    Always probes the endpoint's /models API to let the user pick a model.
    If a model was previously saved, it is pre-selected in the menu.
    Falls back to the saved model if probing fails.
    """
    from hermes_cli.main import _custom_provider_api_key_config_value, _custom_provider_base_url_config_value, _save_custom_provider
    from hermes_cli.auth import _save_model_choice, deactivate_provider
    from hermes_cli.config import load_config, save_config
    from hermes_cli.models import fetch_api_models

    name = provider_info["name"]
    base_url = provider_info["base_url"]
    api_mode = provider_info.get("api_mode", "")
    api_key = provider_info.get("api_key", "")
    key_env = provider_info.get("key_env", "")
    saved_model = provider_info.get("model", "")
    provider_key = (provider_info.get("provider_key") or "").strip()

    # Resolve key from env var if api_key not set directly
    if not api_key and key_env:
        api_key = os.environ.get(key_env, "")
    config_api_key = _custom_provider_api_key_config_value(provider_info, api_key)

    # Honor ``discover_models: false`` (default True) — when discovery is
    # disabled, use the configured ``models:`` list verbatim and skip the
    # live /models probe. This lets operators restrict the picker to the
    # subset their plan actually serves instead of the endpoint's full
    # catalog (#18726: Baidu Qianfan returns 100+ models for a 2-3 model
    # plan). Same semantics as the slash-command picker (model_switch.py
    # sections 3 & 4): default discovers, false keeps the explicit list.
    discover = provider_info.get("discover_models", True)
    if isinstance(discover, str):
        discover = discover.lower() not in {"false", "no", "0"}
    configured_models: list[str] = []
    cfg_models = provider_info.get("models", {})
    if isinstance(cfg_models, dict):
        configured_models = [str(m) for m in cfg_models if str(m).strip()]
    elif isinstance(cfg_models, list):
        configured_models = [
            str(m) for m in cfg_models if isinstance(m, str) and m.strip()
        ]

    print(f"  Provider: {name}")
    print(f"  URL:      {base_url}")
    if saved_model:
        print(f"  Current:  {saved_model}")
    print()

    if not discover and configured_models:
        # Discovery disabled with an explicit list — use it verbatim, no probe.
        print(f"Using configured models (discover_models: false): {len(configured_models)}")
        models = configured_models
    else:
        print("Fetching available models...")
        fetch_kwargs = {"timeout": 8.0}
        if api_mode:
            fetch_kwargs["api_mode"] = api_mode
        models = fetch_api_models(api_key, base_url, **fetch_kwargs)
        # If the probe came back empty but the operator configured an explicit
        # list, fall back to it rather than forcing manual entry.
        if not models and configured_models:
            models = configured_models

    if models:
        default_idx = 0
        if saved_model and saved_model in models:
            default_idx = models.index(saved_model)

        print(f"Found {len(models)} model(s):\n")
        try:
            from hermes_cli.curses_ui import curses_radiolist

            menu_items = [
                f"{m} (current)" if m == saved_model else m for m in models
            ] + ["Cancel"]
            idx = curses_radiolist(
                f"Select model from {name}:",
                menu_items,
                selected=default_idx,
                cancel_returns=-1,
                searchable=True,
            )
            print()
            if idx < 0 or idx >= len(models):
                print("Cancelled.")
                return
            model_name = models[idx]
        except (ImportError, NotImplementedError, OSError, subprocess.SubprocessError):
            for i, m in enumerate(models, 1):
                suffix = " (current)" if m == saved_model else ""
                print(f"  {i}. {m}{suffix}")
            print(f"  {len(models) + 1}. Cancel")
            print()
            try:
                val = input(f"Choice [1-{len(models) + 1}]: ").strip()
                if not val:
                    print("Cancelled.")
                    return
                idx = int(val) - 1
                if idx < 0 or idx >= len(models):
                    print("Cancelled.")
                    return
                model_name = models[idx]
            except (ValueError, KeyboardInterrupt, EOFError):
                print("\nCancelled.")
                return
    elif saved_model:
        print("Could not fetch models from endpoint.")
        try:
            model_name = input(f"Model name [{saved_model}]: ").strip() or saved_model
        except (KeyboardInterrupt, EOFError):
            print("\nCancelled.")
            return
    else:
        print("Could not fetch models from endpoint. Enter model name manually.")
        try:
            model_name = input("Model name: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nCancelled.")
            return
        if not model_name:
            print("No model specified. Cancelled.")
            return

    # Activate and save the model to the custom_providers entry
    _save_model_choice(model_name)

    cfg = load_config()
    model = cfg.get("model")
    if not isinstance(model, dict):
        model = {"default": model} if model else {}
        cfg["model"] = model
    if provider_key:
        model["provider"] = provider_key
        model.pop("base_url", None)
        model.pop("api_key", None)
    else:
        model["provider"] = "custom"
        model["base_url"] = _custom_provider_base_url_config_value(
            provider_info, base_url
        )
        if config_api_key:
            model["api_key"] = config_api_key
    # Apply api_mode from custom_providers entry, or clear stale value
    custom_api_mode = provider_info.get("api_mode", "")
    if custom_api_mode:
        model["api_mode"] = custom_api_mode
    else:
        model.pop("api_mode", None)  # let runtime auto-detect from URL
    save_config(cfg)
    deactivate_provider()

    # Persist the selected model back to whichever schema owns this endpoint.
    if provider_key:
        cfg = load_config()
        providers_cfg = cfg.get("providers")
        if isinstance(providers_cfg, dict):
            provider_entry = providers_cfg.get(provider_key)
            if isinstance(provider_entry, dict):
                provider_entry["default_model"] = model_name
                # Only persist an inline api_key when the user originally had
                # one (either a literal secret or a ``${VAR}`` template). When
                # the entry relies on ``key_env``, do not synthesize a
                # ``${key_env}`` api_key — the runtime already resolves the
                # key from ``key_env`` directly, and writing the resolved
                # secret (or even a synthesized template) would silently
                # downgrade credential hygiene on entries that intentionally
                # keep plaintext out of ``config.yaml``. See issue #15803.
                original_api_key_ref = str(
                    provider_info.get("api_key_ref", "") or ""
                ).strip()
                original_api_key = str(provider_info.get("api_key", "") or "").strip()
                had_inline_api_key = bool(original_api_key_ref or original_api_key)
                if (
                    had_inline_api_key
                    and config_api_key
                    and not str(provider_entry.get("api_key", "") or "").strip()
                ):
                    provider_entry["api_key"] = config_api_key
                if key_env and not str(provider_entry.get("key_env", "") or "").strip():
                    provider_entry["key_env"] = key_env
                cfg["providers"] = providers_cfg
                save_config(cfg)
    else:
        # Save model name to the custom_providers entry for next time
        _save_custom_provider(base_url, config_api_key, model_name, api_mode=api_mode)

    print(f"\n✅ Model set to: {model_name}")
    print(f"   Provider: {name} ({base_url})")

def _model_flow_copilot(config, current_model=""):
    """GitHub Copilot flow using env vars, gh CLI, or OAuth device code."""
    from hermes_cli.main import _current_reasoning_effort, _prompt_reasoning_effort_selection, _set_reasoning_effort
    from hermes_cli.auth import (
        PROVIDER_REGISTRY,
        _prompt_model_selection,
        _save_model_choice,
        deactivate_provider,
        resolve_api_key_provider_credentials,
    )
    from hermes_cli.config import save_env_value, load_config, save_config
    from hermes_cli.models import (
        _PROVIDER_MODELS,
        fetch_api_models,
        fetch_github_model_catalog,
        github_model_reasoning_efforts,
        copilot_model_api_mode,
        normalize_copilot_model_id,
    )

    provider_id = "copilot"
    pconfig = PROVIDER_REGISTRY[provider_id]

    creds = resolve_api_key_provider_credentials(provider_id)
    api_key = creds.get("api_key", "")
    source = creds.get("source", "")

    if not api_key:
        print("No GitHub token configured for GitHub Copilot.")
        print()
        print("  Supported token types:")
        print(
            "    → OAuth token (gho_*)          via `copilot login` or device code flow"
        )
        print("    → Fine-grained PAT (github_pat_*)  with Copilot Requests permission")
        print("    → GitHub App token (ghu_*)     via environment variable")
        print("    ✗ Classic PAT (ghp_*)          NOT supported by Copilot API")
        print()
        print("  Options:")
        print("    1. Login with GitHub (OAuth device code flow)")
        print("    2. Enter a token manually")
        print("    3. Cancel")
        print()
        try:
            choice = input("  Choice [1-3]: ").strip()
        except (KeyboardInterrupt, EOFError):
            print()
            return

        if choice == "1":
            try:
                from hermes_cli.copilot_auth import copilot_device_code_login

                token = copilot_device_code_login()
                if token:
                    save_env_value("COPILOT_GITHUB_TOKEN", token)
                    print("  Copilot token saved.")
                    print()
                else:
                    print("  Login cancelled or failed.")
                    return
            except Exception as exc:
                print(f"  Login failed: {exc}")
                return
        elif choice == "2":
            from hermes_cli.secret_prompt import masked_secret_prompt

            try:
                new_key = masked_secret_prompt("  Token (COPILOT_GITHUB_TOKEN): ").strip()
            except (KeyboardInterrupt, EOFError):
                print()
                return
            if not new_key:
                print("  Cancelled.")
                return
            # Validate token type
            try:
                from hermes_cli.copilot_auth import validate_copilot_token

                valid, msg = validate_copilot_token(new_key)
                if not valid:
                    print(f"  ✗ {msg}")
                    return
            except ImportError:
                pass
            save_env_value("COPILOT_GITHUB_TOKEN", new_key)
            print("  Token saved.")
            print()
        else:
            print("  Cancelled.")
            return

        creds = resolve_api_key_provider_credentials(provider_id)
        api_key = creds.get("api_key", "")
        source = creds.get("source", "")
    else:
        if source in {"GITHUB_TOKEN", "GH_TOKEN"}:
            from hermes_cli.env_loader import format_secret_source_suffix
            bw_suffix = format_secret_source_suffix(source)
            print(f"  GitHub token: {api_key[:8]}... ✓ ({source}{bw_suffix})")
        elif source == "gh auth token":
            print("  GitHub token: ✓ (from `gh auth token`)")
        else:
            print("  GitHub token: ✓")
        print()

    effective_base = pconfig.inference_base_url

    catalog = fetch_github_model_catalog(api_key)
    live_models = (
        [item.get("id", "") for item in catalog if item.get("id")]
        if catalog
        else fetch_api_models(api_key, effective_base)
    )
    normalized_current_model = (
        normalize_copilot_model_id(
            current_model,
            catalog=catalog,
            api_key=api_key,
        )
        or current_model
    )
    if live_models:
        model_list = [model_id for model_id in live_models if model_id]
        print(f"  Found {len(model_list)} model(s) from GitHub Copilot")
    else:
        model_list = _PROVIDER_MODELS.get(provider_id, [])
        if model_list:
            print(
                "  ⚠ Could not auto-detect models from GitHub Copilot — showing defaults."
            )
            print('    Use "Enter custom model name" if you do not see your model.')

    if model_list:
        selected = _prompt_model_selection(
            model_list, current_model=normalized_current_model
        )
    else:
        try:
            selected = input("Model name: ").strip()
        except (KeyboardInterrupt, EOFError):
            selected = None

    if selected:
        selected = (
            normalize_copilot_model_id(
                selected,
                catalog=catalog,
                api_key=api_key,
            )
            or selected
        )
        initial_cfg = load_config()
        current_effort = _current_reasoning_effort(initial_cfg)
        reasoning_efforts = github_model_reasoning_efforts(
            selected,
            catalog=catalog,
            api_key=api_key,
        )
        selected_effort = None
        if reasoning_efforts:
            print(f"  {selected} supports reasoning controls.")
            selected_effort = _prompt_reasoning_effort_selection(
                reasoning_efforts, current_effort=current_effort
            )

        _save_model_choice(selected)

        cfg = load_config()
        model = cfg.get("model")
        if not isinstance(model, dict):
            model = {"default": model} if model else {}
            cfg["model"] = model
        model["provider"] = provider_id
        model["base_url"] = effective_base
        model["api_mode"] = copilot_model_api_mode(
            selected,
            catalog=catalog,
            api_key=api_key,
        )
        if selected_effort is not None:
            _set_reasoning_effort(cfg, selected_effort)
        save_config(cfg)
        deactivate_provider()

        print(f"Default model set to: {selected} (via {pconfig.name})")
        if reasoning_efforts:
            if selected_effort == "none":
                print("Reasoning disabled for this model.")
            elif selected_effort:
                print(f"Reasoning effort set to: {selected_effort}")
    else:
        print("No change.")

def _model_flow_copilot_acp(config, current_model=""):
    """GitHub Copilot ACP flow using the local Copilot CLI."""
    from hermes_cli.auth import (
        PROVIDER_REGISTRY,
        _prompt_model_selection,
        _save_model_choice,
        deactivate_provider,
        get_external_process_provider_status,
        resolve_api_key_provider_credentials,
        resolve_external_process_provider_credentials,
    )
    from hermes_cli.models import (
        _PROVIDER_MODELS,
        fetch_github_model_catalog,
        normalize_copilot_model_id,
    )
    from hermes_cli.config import load_config, save_config

    del config

    provider_id = "copilot-acp"
    pconfig = PROVIDER_REGISTRY[provider_id]

    status = get_external_process_provider_status(provider_id)
    resolved_command = (
        status.get("resolved_command") or status.get("command") or "copilot"
    )
    effective_base = status.get("base_url") or pconfig.inference_base_url

    print("  GitHub Copilot ACP delegates Hermes turns to `copilot --acp`.")
    print("  Hermes currently starts its own ACP subprocess for each request.")
    print("  Hermes uses your selected model as a hint for the Copilot ACP session.")
    print(f"  Command: {resolved_command}")
    print(f"  Backend marker: {effective_base}")
    print()

    try:
        creds = resolve_external_process_provider_credentials(provider_id)
    except Exception as exc:
        print(f"  ⚠ {exc}")
        print(
            "  Set HERMES_COPILOT_ACP_COMMAND or COPILOT_CLI_PATH if Copilot CLI is installed elsewhere."
        )
        return

    effective_base = creds.get("base_url") or effective_base

    catalog_api_key = ""
    try:
        catalog_creds = resolve_api_key_provider_credentials("copilot")
        catalog_api_key = catalog_creds.get("api_key", "")
    except Exception:
        pass

    catalog = fetch_github_model_catalog(catalog_api_key)
    normalized_current_model = (
        normalize_copilot_model_id(
            current_model,
            catalog=catalog,
            api_key=catalog_api_key,
        )
        or current_model
    )

    if catalog:
        model_list = [item.get("id", "") for item in catalog if item.get("id")]
        print(f"  Found {len(model_list)} model(s) from GitHub Copilot")
    else:
        model_list = _PROVIDER_MODELS.get("copilot", [])
        if model_list:
            print(
                "  ⚠ Could not auto-detect models from GitHub Copilot — showing defaults."
            )
            print('    Use "Enter custom model name" if you do not see your model.')

    if model_list:
        selected = _prompt_model_selection(
            model_list,
            current_model=normalized_current_model,
        )
    else:
        try:
            selected = input("Model name: ").strip()
        except (KeyboardInterrupt, EOFError):
            selected = None

    if not selected:
        print("No change.")
        return

    selected = (
        normalize_copilot_model_id(
            selected,
            catalog=catalog,
            api_key=catalog_api_key,
        )
        or selected
    )
    _save_model_choice(selected)

    cfg = load_config()
    model = cfg.get("model")
    if not isinstance(model, dict):
        model = {"default": model} if model else {}
        cfg["model"] = model
    model["provider"] = provider_id
    model["base_url"] = effective_base
    model["api_mode"] = "chat_completions"
    save_config(cfg)
    deactivate_provider()

    print(f"Default model set to: {selected} (via {pconfig.name})")

def _model_flow_kimi(config, current_model=""):
    """Kimi / Moonshot model selection with automatic endpoint routing.

    - sk-kimi-* keys   → api.kimi.com/coding/v1  (Kimi Coding Plan)
    - Other keys        → api.moonshot.ai/v1      (legacy Moonshot)

    No manual base URL prompt — endpoint is determined by key prefix.
    """
    from hermes_cli.main import _prompt_api_key
    from hermes_cli.auth import (
        PROVIDER_REGISTRY,
        KIMI_CODE_BASE_URL,
        _prompt_model_selection,
        _save_model_choice,
        deactivate_provider,
    )
    from hermes_cli.config import (
        get_env_value,
        save_env_value,
        load_config,
        save_config,
    )
    from hermes_cli.models import _PROVIDER_MODELS

    provider_id = "kimi-coding"
    pconfig = PROVIDER_REGISTRY[provider_id]
    key_env = pconfig.api_key_env_vars[0] if pconfig.api_key_env_vars else ""
    base_url_env = pconfig.base_url_env_var or ""

    # Step 1: Check / prompt for API key
    existing_key = ""
    for ev in pconfig.api_key_env_vars:
        existing_key = get_env_value(ev) or os.getenv(ev, "")
        if existing_key:
            break

    existing_key, abort = _prompt_api_key(
        pconfig, existing_key, provider_id=provider_id
    )
    if abort:
        return

    # Step 2: Auto-detect endpoint from key prefix
    is_coding_plan = existing_key.startswith("sk-kimi-")
    if is_coding_plan:
        effective_base = KIMI_CODE_BASE_URL
        print(f"  Detected Kimi Coding Plan key → {effective_base}")
    else:
        effective_base = pconfig.inference_base_url
        print(f"  Using Moonshot endpoint → {effective_base}")
    # Clear any manual base URL override so auto-detection works at runtime
    if base_url_env and get_env_value(base_url_env):
        save_env_value(base_url_env, "")
    print()

    # Step 3: Model selection — show appropriate models for the endpoint
    if is_coding_plan:
        # Coding Plan models (kimi-k2.6 first)
        model_list = [
            "kimi-k2.6",
            "kimi-k2.5",
            "kimi-for-coding",
            "kimi-k2-thinking",
            "kimi-k2-thinking-turbo",
        ]
    else:
        # Legacy Moonshot models (excludes Coding Plan-only models)
        model_list = _PROVIDER_MODELS.get("moonshot", [])

    if model_list:
        selected = _prompt_model_selection(model_list, current_model=current_model)
    else:
        try:
            selected = input("Enter model name: ").strip()
        except (KeyboardInterrupt, EOFError):
            selected = None

    if selected:
        _save_model_choice(selected)

        # Update config with provider and base URL
        cfg = load_config()
        model = cfg.get("model")
        if not isinstance(model, dict):
            model = {"default": model} if model else {}
            cfg["model"] = model
        model["provider"] = provider_id
        model["base_url"] = effective_base
        model.pop("api_mode", None)  # let runtime auto-detect from URL
        save_config(cfg)
        deactivate_provider()

        endpoint_label = "Kimi Coding" if is_coding_plan else "Moonshot"
        print(f"Default model set to: {selected} (via {endpoint_label})")
    else:
        print("No change.")

def _model_flow_stepfun(config, current_model=""):
    """StepFun Step Plan flow with region-specific endpoints."""
    from hermes_cli.main import _infer_stepfun_region, _prompt_api_key, _prompt_provider_choice, _stepfun_base_url_for_region
    from hermes_cli.auth import (
        PROVIDER_REGISTRY,
        _prompt_model_selection,
        _save_model_choice,
        deactivate_provider,
    )
    from hermes_cli.config import (
        get_env_value,
        save_env_value,
        load_config,
        save_config,
    )
    from hermes_cli.models import _PROVIDER_MODELS, fetch_api_models

    provider_id = "stepfun"
    pconfig = PROVIDER_REGISTRY[provider_id]
    key_env = pconfig.api_key_env_vars[0] if pconfig.api_key_env_vars else ""
    base_url_env = pconfig.base_url_env_var or ""

    existing_key = ""
    for ev in pconfig.api_key_env_vars:
        existing_key = get_env_value(ev) or os.getenv(ev, "")
        if existing_key:
            break

    existing_key, abort = _prompt_api_key(
        pconfig, existing_key, provider_id=provider_id
    )
    if abort:
        return

    current_base = ""
    if base_url_env:
        current_base = get_env_value(base_url_env) or os.getenv(base_url_env, "")
    if not current_base:
        model_cfg = config.get("model")
        if isinstance(model_cfg, dict):
            current_base = str(model_cfg.get("base_url") or "").strip()
    current_region = _infer_stepfun_region(current_base or pconfig.inference_base_url)

    region_choices = [
        (
            "international",
            f"International ({_stepfun_base_url_for_region('international')})",
        ),
        ("china", f"China ({_stepfun_base_url_for_region('china')})"),
    ]
    ordered_regions = []
    for region_key, label in region_choices:
        if region_key == current_region:
            ordered_regions.insert(0, (region_key, f"{label}  ← currently active"))
        else:
            ordered_regions.append((region_key, label))
    ordered_regions.append(("cancel", "Cancel"))

    region_idx = _prompt_provider_choice([label for _, label in ordered_regions])
    if region_idx is None or ordered_regions[region_idx][0] == "cancel":
        print("No change.")
        return

    selected_region = ordered_regions[region_idx][0]
    effective_base = _stepfun_base_url_for_region(selected_region)
    if base_url_env:
        save_env_value(base_url_env, effective_base)

    live_models = fetch_api_models(existing_key, effective_base)
    if live_models:
        model_list = live_models
        print(f"  Found {len(model_list)} model(s) from {pconfig.name} API")
    else:
        model_list = _PROVIDER_MODELS.get(provider_id, [])
        if model_list:
            print(
                f"  Could not auto-detect models from {pconfig.name} API — "
                "showing Step Plan fallback catalog."
            )

    if model_list:
        selected = _prompt_model_selection(model_list, current_model=current_model)
    else:
        try:
            selected = input("Model name: ").strip()
        except (KeyboardInterrupt, EOFError):
            selected = None

    if selected:
        _save_model_choice(selected)

        cfg = load_config()
        model = cfg.get("model")
        if not isinstance(model, dict):
            model = {"default": model} if model else {}
            cfg["model"] = model
        model["provider"] = provider_id
        model["base_url"] = effective_base
        model.pop("api_mode", None)
        save_config(cfg)
        deactivate_provider()

        config["model"] = dict(model)
        print(f"Default model set to: {selected} (via {pconfig.name})")
    else:
        print("No change.")

def _model_flow_bedrock_api_key(config, region, current_model=""):
    """Bedrock API Key mode — uses the OpenAI-compatible bedrock-mantle endpoint.

    For developers who don't have an AWS account but received a Bedrock API Key
    from their AWS admin. Works like any OpenAI-compatible endpoint.
    """
    from hermes_cli.auth import (
        _prompt_model_selection,
        _save_model_choice,
        deactivate_provider,
    )
    from hermes_cli.config import (
        load_config,
        save_config,
        get_env_value,
        save_env_value,
    )
    from hermes_cli.models import _PROVIDER_MODELS

    mantle_base_url = f"https://bedrock-mantle.{region}.api.aws/v1"

    # Prompt for API key
    existing_key = get_env_value("AWS_BEARER_TOKEN_BEDROCK") or ""
    if existing_key:
        from hermes_cli.env_loader import format_secret_source_suffix
        source_suffix = format_secret_source_suffix("AWS_BEARER_TOKEN_BEDROCK")
        print(f"  Bedrock API Key: {existing_key[:12]}... ✓{source_suffix}")
    else:
        print(f"  Endpoint: {mantle_base_url}")
        print()
        from hermes_cli.secret_prompt import masked_secret_prompt

        try:
            api_key = masked_secret_prompt("  Bedrock API Key: ").strip()
        except (KeyboardInterrupt, EOFError):
            print()
            return
        if not api_key:
            print("  Cancelled.")
            return
        save_env_value("AWS_BEARER_TOKEN_BEDROCK", api_key)
        existing_key = api_key
        print("  ✓ API key saved.")
    print()

    # Model selection — use static list (mantle doesn't need boto3 for discovery)
    model_list = _PROVIDER_MODELS.get("bedrock", [])
    print(f"  Showing {len(model_list)} curated models")

    if model_list:
        selected = _prompt_model_selection(model_list, current_model=current_model)
    else:
        try:
            selected = input("  Model ID: ").strip()
        except (KeyboardInterrupt, EOFError):
            selected = None

    if selected:
        _save_model_choice(selected)

        # Save as custom provider pointing to bedrock-mantle
        cfg = load_config()
        model = cfg.get("model")
        if not isinstance(model, dict):
            model = {"default": model} if model else {}
            cfg["model"] = model
        model["provider"] = "custom"
        model["base_url"] = mantle_base_url
        model.pop("api_mode", None)  # chat_completions is the default

        # Also save region in bedrock config for reference
        bedrock_cfg = cfg.get("bedrock", {})
        if not isinstance(bedrock_cfg, dict):
            bedrock_cfg = {}
        bedrock_cfg["region"] = region
        cfg["bedrock"] = bedrock_cfg

        # Save the API key env var name so hermes knows where to find it
        save_env_value("OPENAI_API_KEY", existing_key)
        save_env_value("OPENAI_BASE_URL", mantle_base_url)

        save_config(cfg)
        deactivate_provider()

        print(f"  Default model set to: {selected} (via Bedrock API Key, {region})")
        print(f"  Endpoint: {mantle_base_url}")
    else:
        print("  No change.")

def _model_flow_bedrock(config, current_model=""):
    """AWS Bedrock provider: verify credentials, pick region, discover models.

    Uses the native Converse API via boto3 — not the OpenAI-compatible endpoint.
    Auth is handled by the AWS SDK default credential chain (env vars, profile,
    instance role), so no API key prompt is needed.
    """
    from hermes_cli.auth import (
        _prompt_model_selection,
        _save_model_choice,
        deactivate_provider,
    )
    from hermes_cli.config import load_config, save_config
    from hermes_cli.models import _PROVIDER_MODELS

    # 1. Check for AWS credentials
    try:
        from agent.bedrock_adapter import (
            has_aws_credentials,
            resolve_aws_auth_env_var,
            resolve_bedrock_region,
            discover_bedrock_models,
        )
    except ImportError:
        print("  ✗ boto3 is not installed. Install it with:")
        print("    pip install boto3")
        print()
        return

    if not has_aws_credentials():
        print("  ⚠ No AWS credentials detected via environment variables.")
        print("  Bedrock will use boto3's default credential chain (IMDS, SSO, etc.)")
        print()

    auth_var = resolve_aws_auth_env_var()
    if auth_var:
        print(f"  AWS credentials: {auth_var} ✓")
    else:
        print("  AWS credentials: boto3 default chain (instance role / SSO)")
    print()

    # 2. Region selection
    current_region = resolve_bedrock_region()
    try:
        region_input = input(f"  AWS Region [{current_region}]: ").strip()
    except (KeyboardInterrupt, EOFError):
        print()
        return
    region = region_input or current_region

    # 2b. Authentication mode
    print("  Choose authentication method:")
    print()
    print("    1. IAM credential chain (recommended)")
    print("       Works with EC2 instance roles, SSO, env vars, aws configure")
    print("    2. Bedrock API Key")
    print("       Enter your Bedrock API Key directly — also supports")
    print("       team scenarios where an admin distributes keys")
    print()
    try:
        auth_choice = input("  Choice [1]: ").strip()
    except (KeyboardInterrupt, EOFError):
        print()
        return

    if auth_choice == "2":
        _model_flow_bedrock_api_key(config, region, current_model)
        return

    # 3. Model discovery — try live API first, fall back to static list
    print(f"  Discovering models in {region}...")
    live_models = discover_bedrock_models(region)

    if live_models:
        _EXCLUDE_PREFIXES = (
            "stability.",
            "cohere.embed",
            "twelvelabs.",
            "us.stability.",
            "us.cohere.embed",
            "us.twelvelabs.",
            "global.cohere.embed",
            "global.twelvelabs.",
        )
        _EXCLUDE_SUBSTRINGS = ("safeguard", "voxtral", "palmyra-vision")
        filtered = []
        for m in live_models:
            mid = m["id"]
            if any(mid.startswith(p) for p in _EXCLUDE_PREFIXES):
                continue
            if any(s in mid.lower() for s in _EXCLUDE_SUBSTRINGS):
                continue
            filtered.append(m)

        # Deduplicate: prefer inference profiles (us.*, global.*) over bare
        # foundation model IDs.
        profile_base_ids = set()
        for m in filtered:
            mid = m["id"]
            if mid.startswith(("us.", "global.")):
                base = mid.split(".", 1)[1] if "." in mid[3:] else mid
                profile_base_ids.add(base)

        deduped = []
        for m in filtered:
            mid = m["id"]
            if not mid.startswith(("us.", "global.")) and mid in profile_base_ids:
                continue
            deduped.append(m)

        _RECOMMENDED = [
            "us.anthropic.claude-sonnet-4-6",
            "us.anthropic.claude-opus-4-6",
            "us.anthropic.claude-haiku-4-5",
            "us.amazon.nova-pro",
            "us.amazon.nova-lite",
            "us.amazon.nova-micro",
            "deepseek.v3",
            "us.meta.llama4-maverick",
            "us.meta.llama4-scout",
        ]

        def _sort_key(m):
            mid = m["id"]
            for i, rec in enumerate(_RECOMMENDED):
                if mid.startswith(rec):
                    return (0, i, mid)
            if mid.startswith("global."):
                return (1, 0, mid)
            return (2, 0, mid)

        deduped.sort(key=_sort_key)
        model_list = [m["id"] for m in deduped]
        print(
            f"  Found {len(model_list)} text model(s) (filtered from {len(live_models)} total)"
        )
    else:
        model_list = _PROVIDER_MODELS.get("bedrock", [])
        if model_list:
            print(
                f"  Using {len(model_list)} curated models (live discovery unavailable)"
            )
        else:
            print(
                "  No models found. Check IAM permissions for bedrock:ListFoundationModels."
            )
            return

    # 4. Model selection
    if model_list:
        selected = _prompt_model_selection(model_list, current_model=current_model)
    else:
        try:
            selected = input("  Model ID: ").strip()
        except (KeyboardInterrupt, EOFError):
            selected = None

    if selected:
        _save_model_choice(selected)

        cfg = load_config()
        model = cfg.get("model")
        if not isinstance(model, dict):
            model = {"default": model} if model else {}
            cfg["model"] = model
        model["provider"] = "bedrock"
        model["base_url"] = f"https://bedrock-runtime.{region}.amazonaws.com"
        model.pop("api_mode", None)  # bedrock_converse is auto-detected

        bedrock_cfg = cfg.get("bedrock", {})
        if not isinstance(bedrock_cfg, dict):
            bedrock_cfg = {}
        bedrock_cfg["region"] = region
        cfg["bedrock"] = bedrock_cfg

        save_config(cfg)
        deactivate_provider()

        print(f"  Default model set to: {selected} (via AWS Bedrock, {region})")
    else:
        print("  No change.")

def _model_flow_api_key_provider(config, provider_id, current_model=""):
    """Generic flow for API-key providers (z.ai, MiniMax, OpenCode, etc.)."""
    from hermes_cli.main import _prompt_api_key
    from hermes_cli.auth import (
        PROVIDER_REGISTRY,
        _prompt_model_selection,
        _save_model_choice,
        deactivate_provider,
    )
    from hermes_cli.config import (
        get_env_value,
        save_env_value,
        load_config,
        save_config,
    )
    from hermes_cli.models import (
        _PROVIDER_MODELS,
        fetch_api_models,
        opencode_model_api_mode,
        normalize_opencode_model_id,
    )

    pconfig = PROVIDER_REGISTRY[provider_id]
    key_env = pconfig.api_key_env_vars[0] if pconfig.api_key_env_vars else ""
    base_url_env = pconfig.base_url_env_var or ""

    # Check / prompt for API key
    existing_key = ""
    for ev in pconfig.api_key_env_vars:
        existing_key = get_env_value(ev) or os.getenv(ev, "")
        if existing_key:
            break

    existing_key, abort = _prompt_api_key(
        pconfig, existing_key, provider_id=provider_id
    )
    if abort:
        return

    # Gemini free-tier gate: free-tier daily quotas (<= 250 RPD for Flash)
    # are exhausted in a handful of agent turns, so refuse to wire up the
    # provider with a free-tier key. Probe is best-effort; network or auth
    # errors fall through without blocking.
    if provider_id == "gemini" and existing_key:
        try:
            from agent.gemini_native_adapter import probe_gemini_tier
        except Exception:
            probe_gemini_tier = None
        if probe_gemini_tier is not None:
            print("  Checking Gemini API tier...")
            probe_base = (
                (get_env_value(base_url_env) if base_url_env else "")
                or os.getenv(base_url_env or "", "")
                or pconfig.inference_base_url
            )
            tier = probe_gemini_tier(existing_key, probe_base)
            if tier == "free":
                print()
                print(
                    "❌ This Google API key is on the free tier "
                    "(<= 250 requests/day for gemini-2.5-flash)."
                )
                print(
                    "   Hermes typically makes 3-10 API calls per user turn "
                    "(tool iterations + auxiliary tasks),"
                )
                print(
                    "   so the free tier is exhausted after a handful of "
                    "messages and cannot sustain"
                )
                print("   an agent session.")
                print()
                print(
                    "   To use Gemini with Hermes, enable billing on your "
                    "Google Cloud project and regenerate"
                )
                print(
                    "   the key in a billing-enabled project: "
                    "https://aistudio.google.com/apikey"
                )
                print()
                print(
                    "   Alternatives with workable free usage: DeepSeek, "
                    "OpenRouter (free models), Groq, Nous."
                )
                print()
                print("Not saving Gemini as the default provider.")
                return
            if tier == "paid":
                print("  Tier check: paid ✓")
            else:
                # "unknown" -- network issue, auth problem, unexpected response.
                # Don't block; the runtime 429 handler will surface free-tier
                # guidance if the key turns out to be free tier.
                print("  Tier check: could not verify (proceeding anyway).")
            print()

    # Optional base URL override.
    # Precedence: env var → config.yaml model.base_url → registry default.
    # Reading config.yaml prevents silently overwriting a saved remote URL
    # (e.g. a remote LM Studio endpoint) with localhost when the user just
    # presses Enter at the prompt below.
    current_base = ""
    if base_url_env:
        current_base = get_env_value(base_url_env) or os.getenv(base_url_env, "")
    if not current_base:
        try:
            _m = load_config().get("model") or {}
            if str(_m.get("provider") or "").strip().lower() == provider_id:
                current_base = str(_m.get("base_url") or "").strip()
        except Exception:
            pass
    effective_base = current_base or pconfig.inference_base_url

    try:
        override = input(f"Base URL [{effective_base}]: ").strip()
    except (KeyboardInterrupt, EOFError):
        print()
        override = ""
    if override and base_url_env:
        if not override.startswith(("http://", "https://")):
            print(
                "  Invalid URL — must start with http:// or https://. Keeping current value."
            )
        else:
            save_env_value(base_url_env, override)
            effective_base = override

    # Model selection — resolution order:
    #   1. models.dev registry (cached, filtered for agentic/tool-capable models)
    #   2. Curated static fallback list (offline insurance)
    #   3. Live /models endpoint probe (small providers without models.dev data)
    #
    # LM Studio: live /api/v1/models probe (no models.dev catalog).
    # Ollama Cloud: merged discovery (live API + models.dev + disk cache).
    if provider_id == "lmstudio":
        from hermes_cli.auth import AuthError
        from hermes_cli.models import fetch_lmstudio_models

        api_key_for_probe = existing_key or (get_env_value(key_env) if key_env else "")
        try:
            model_list = fetch_lmstudio_models(
                api_key=api_key_for_probe, base_url=effective_base
            )
        except AuthError as exc:
            print(f"  LM Studio rejected the request: {exc}")
            print("  Set LM_API_KEY (or update it) to match the server's bearer token.")
            model_list = []
        if model_list:
            print(f"  Found {len(model_list)} model(s) from LM Studio")
    elif provider_id == "ollama-cloud":
        from hermes_cli.models import fetch_ollama_cloud_models

        api_key_for_probe = existing_key or (get_env_value(key_env) if key_env else "")
        # During setup, force a live refresh so the picker reflects newly
        # released models (e.g. deepseek v4 flash, kimi k2.6) the moment
        # the user enters their key — not an hour later when the disk
        # cache TTL expires.
        model_list = fetch_ollama_cloud_models(
            api_key=api_key_for_probe,
            base_url=effective_base,
            force_refresh=True,
        )
        if model_list:
            print(f"  Found {len(model_list)} model(s) from Ollama Cloud")
    elif provider_id == "novita":
        from hermes_cli.models import fetch_api_models

        api_key_for_probe = existing_key or (get_env_value(key_env) if key_env else "")
        curated = _PROVIDER_MODELS.get(provider_id, [])
        live_models = fetch_api_models(api_key_for_probe, effective_base)
        if live_models:
            model_list = live_models
            print(f"  Found {len(model_list)} model(s) from {pconfig.name} API")
        else:
            mdev_models: list = []
            try:
                from agent.models_dev import list_agentic_models

                mdev_models = list_agentic_models(provider_id)
            except Exception:
                pass
            if mdev_models:
                seen = {m.lower() for m in mdev_models}
                model_list = list(mdev_models)
                for m in curated:
                    if m.lower() not in seen:
                        model_list.append(m)
                        seen.add(m.lower())
                print(f"  Found {len(model_list)} model(s) from models.dev registry")
            else:
                model_list = curated
                if model_list:
                    print(
                        f'  Showing {len(model_list)} curated models — use "Enter custom model name" for others.'
                    )
    else:
        curated = _PROVIDER_MODELS.get(provider_id, [])

        # Try models.dev first — returns tool-capable models, filtered for noise
        mdev_models: list = []
        try:
            from agent.models_dev import list_agentic_models

            mdev_models = list_agentic_models(provider_id)
        except Exception:
            pass

        if mdev_models:
            # Merge models.dev with curated list so newly added models
            # (not yet in models.dev) still appear in the picker.
            if curated:
                seen = {m.lower() for m in mdev_models}
                merged = list(mdev_models)
                for m in curated:
                    if m.lower() not in seen:
                        merged.append(m)
                        seen.add(m.lower())
                model_list = merged
            else:
                model_list = mdev_models
            print(f"  Found {len(model_list)} model(s) from models.dev registry")
        elif curated and len(curated) >= 8:
            # Curated list is substantial — use it directly, skip live probe
            model_list = curated
            print(
                f'  Showing {len(model_list)} curated models — use "Enter custom model name" for others.'
            )
        else:
            api_key_for_probe = existing_key or (
                get_env_value(key_env) if key_env else ""
            )
            live_models = fetch_api_models(api_key_for_probe, effective_base)
            if live_models and len(live_models) >= len(curated):
                model_list = live_models
                print(f"  Found {len(model_list)} model(s) from {pconfig.name} API")
            else:
                model_list = curated
                if model_list:
                    print(
                        f'  Showing {len(model_list)} curated models — use "Enter custom model name" for others.'
                    )
            # else: no defaults either, will fall through to raw input

    if provider_id in {"opencode-zen", "opencode-go"}:
        model_list = [
            normalize_opencode_model_id(provider_id, mid) for mid in model_list
        ]
        current_model = normalize_opencode_model_id(provider_id, current_model)
        model_list = list(dict.fromkeys(mid for mid in model_list if mid))

    if model_list:
        selected = _prompt_model_selection(model_list, current_model=current_model)
    else:
        try:
            selected = input("Model name: ").strip()
        except (KeyboardInterrupt, EOFError):
            selected = None

    if selected:
        if provider_id in {"opencode-zen", "opencode-go"}:
            selected = normalize_opencode_model_id(provider_id, selected)

        _save_model_choice(selected)

        # Update config with provider, base URL, and provider-specific API mode
        cfg = load_config()
        model = cfg.get("model")
        if not isinstance(model, dict):
            model = {"default": model} if model else {}
            cfg["model"] = model
        model["provider"] = provider_id
        model["base_url"] = effective_base
        if provider_id in {"opencode-zen", "opencode-go"}:
            model["api_mode"] = opencode_model_api_mode(provider_id, selected)
        else:
            model.pop("api_mode", None)
        save_config(cfg)
        deactivate_provider()

        print(f"Default model set to: {selected} (via {pconfig.name})")
    else:
        print("No change.")

def _model_flow_anthropic(config, current_model=""):
    """Flow for Anthropic provider — OAuth subscription, API key, or Claude Code creds."""
    from hermes_cli.main import _run_anthropic_oauth_flow
    from hermes_cli.auth import (
        _prompt_model_selection,
        _save_model_choice,
        deactivate_provider,
    )
    from hermes_cli.config import (
        save_env_value,
        load_config,
        save_config,
        save_anthropic_api_key,
    )
    from hermes_cli.models import _PROVIDER_MODELS

    # Check ALL credential sources
    from hermes_cli.auth import get_anthropic_key

    existing_key = get_anthropic_key()
    cc_available = False
    try:
        from agent.anthropic_adapter import (
            read_claude_code_credentials,
            is_claude_code_token_valid,
            _is_oauth_token,
        )

        cc_creds = read_claude_code_credentials()
        if cc_creds and is_claude_code_token_valid(cc_creds):
            cc_available = True
    except Exception:
        pass

    # Stale-OAuth guard: if the only existing cred is an expired OAuth token
    # (no valid cc_creds to fall back on), treat it as missing so the re-auth
    # path is offered instead of silently accepting a broken token.
    existing_is_stale_oauth = False
    if existing_key and _is_oauth_token(existing_key) and not cc_available:
        existing_is_stale_oauth = True

    has_creds = (bool(existing_key) and not existing_is_stale_oauth) or cc_available
    needs_auth = not has_creds

    if has_creds:
        # Show what we found
        if existing_key:
            from hermes_cli.env_loader import format_secret_source_suffix
            from hermes_cli.auth import PROVIDER_REGISTRY

            # Surface which env var supplied the key so users with
            # Bitwarden see "(from Bitwarden)" — without this, a detected
            # BSM key looks identical to a key in .env and users assume
            # nothing is wired up.
            source_suffix = ""
            for var in PROVIDER_REGISTRY["anthropic"].api_key_env_vars:
                if os.getenv(var, "").strip() == existing_key:
                    source_suffix = format_secret_source_suffix(var)
                    if source_suffix:
                        break
            print(
                f"  Anthropic credentials: {existing_key[:12]}... ✓{source_suffix}"
            )
        elif cc_available:
            print("  Claude Code credentials: ✓ (auto-detected)")
        print()
        print("    1. Use existing credentials")
        print("    2. Reauthenticate (new OAuth login)")
        print("    3. Cancel")
        print()
        try:
            choice = input("  Choice [1/2/3]: ").strip()
        except (KeyboardInterrupt, EOFError):
            choice = "1"

        if choice == "2":
            needs_auth = True
        elif choice == "3":
            return
        # choice == "1" or default: use existing, proceed to model selection

    if needs_auth:
        # Show auth method choice
        print()
        print("  Choose authentication method:")
        print()
        print("    1. Claude Pro/Max subscription (OAuth login)")
        print("    2. Anthropic API key (pay-per-token)")
        print("    3. Cancel")
        print()
        try:
            choice = input("  Choice [1/2/3]: ").strip()
        except (KeyboardInterrupt, EOFError):
            print()
            return

        if choice == "1":
            if not _run_anthropic_oauth_flow(save_env_value):
                return

        elif choice == "2":
            print()
            print("  Get an API key at: https://platform.claude.com/settings/keys")
            print()
            from hermes_cli.secret_prompt import masked_secret_prompt

            try:
                api_key = masked_secret_prompt("  API key (sk-ant-...): ").strip()
            except (KeyboardInterrupt, EOFError):
                print()
                return
            if not api_key:
                print("  Cancelled.")
                return
            save_anthropic_api_key(api_key, save_fn=save_env_value)
            print("  ✓ API key saved.")

        else:
            print("  No change.")
            return
    print()

    # Model selection
    model_list = _PROVIDER_MODELS.get("anthropic", [])
    if model_list:
        selected = _prompt_model_selection(model_list, current_model=current_model)
    else:
        try:
            selected = input("Model name (e.g., claude-sonnet-4-20250514): ").strip()
        except (KeyboardInterrupt, EOFError):
            selected = None

    if selected:
        _save_model_choice(selected)

        # Update config with provider — clear base_url since
        # resolve_runtime_provider() always hardcodes Anthropic's URL.
        # Leaving a stale base_url in config can contaminate other
        # providers if the user switches without running 'hermes model'.
        cfg = load_config()
        model = cfg.get("model")
        if not isinstance(model, dict):
            model = {"default": model} if model else {}
            cfg["model"] = model
        model["provider"] = "anthropic"
        model.pop("base_url", None)
        save_config(cfg)
        deactivate_provider()

        print(f"Default model set to: {selected} (via Anthropic)")
    else:
        print("No change.")