car_inference/

registry.rs

//! Unified model registry — local and remote models under one schema.
//!
//! Replaces the hardcoded `ModelRegistry` from `models.rs` with a schema-driven
//! registry that treats all models as first-class typed resources. Users can
//! register custom models (fine-tuned endpoints, private APIs) alongside the
//! built-in catalog.

use crate::schema::reasoning_params;
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use std::time::SystemTime;

use serde::{Deserialize, Serialize};
use tracing::info;

use crate::download::{DownloadEvent, ProgressSink};
use crate::schema::*;
use crate::InferenceError;

/// Filter for querying the registry.
#[derive(Debug, Clone, Default)]
pub struct ModelFilter {
    /// Required capabilities (model must have ALL of these).
    pub capabilities: Vec<ModelCapability>,
    /// Maximum on-disk / RAM size in MB.
    pub max_size_mb: Option<u64>,
    /// Maximum expected latency in ms (from declared envelope).
    pub max_latency_ms: Option<u64>,
    /// Maximum cost per 1M output tokens in USD.
    pub max_cost_per_mtok: Option<f64>,
    /// Required tags (model must have ALL of these).
    pub tags: Vec<String>,
    /// Filter by provider.
    pub provider: Option<String>,
    /// Only local models.
    pub local_only: bool,
    /// Only models that are currently available.
    pub available_only: bool,
}

/// A curated replacement for a local model that is installed but no longer
/// the preferred model in its line.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelUpgrade {
    pub from_id: String,
    pub from_name: String,
    pub to_id: String,
    pub to_name: String,
    pub reason: String,
    pub target_runtime: Option<String>,
    pub target_runtime_requirement: Option<String>,
    pub minimum_runtimes: Vec<ModelRuntimeRequirement>,
    pub target_available: bool,
    pub target_pullable: bool,
    pub remove_old_supported: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelRuntimeRequirement {
    pub name: String,
    pub minimum_version: String,
}

/// Unified registry of all known models.
pub struct UnifiedRegistry {
    models_dir: PathBuf,
    /// All registered models, keyed by id.
    models: HashMap<String, ModelSchema>,
    /// User-added model config file path (~/.car/models.json).
    user_config_path: PathBuf,
}

#[derive(Debug, Clone, Deserialize)]
struct ModelUpgradeRule {
    from_ids: Vec<String>,
    to_id: String,
    reason: String,
    target_runtime: Option<String>,
    target_runtime_requirement: Option<String>,
    #[serde(default)]
    minimum_runtimes: Vec<ModelRuntimeRequirement>,
    #[serde(default = "default_remove_old_after_available")]
    remove_old_after_available: bool,
}

fn default_remove_old_after_available() -> bool {
    true
}

fn model_upgrade_rules() -> Vec<ModelUpgradeRule> {
    serde_json::from_str(include_str!("../assets/model-upgrades.json"))
        .expect("built-in model-upgrades.json should parse")
}

impl UnifiedRegistry {
    pub fn new(models_dir: PathBuf) -> Self {
        let user_config_path = models_dir
            .parent()
            .unwrap_or(&models_dir)
            .join("models.json");

        let mut registry = Self {
            models_dir,
            models: HashMap::new(),
            user_config_path,
        };
        registry.load_builtin_catalog();
        registry.refresh_availability();
        // Load user config on top (silently ignore if missing)
        let _ = registry.load_user_config();
        registry
    }

    /// Register a model at runtime.
    pub fn register(&mut self, mut schema: ModelSchema) {
        // Check availability for local models
        if schema.is_mlx() {
            // MLX requires Apple Silicon Metal. On any other build target —
            // Intel Mac, Linux, Windows, or `car_skip_mlx` — the backend
            // is not compiled in and any execution attempt would fail at
            // dispatch with a "model not found" or backend-missing error.
            // Mirror the AppleFoundationModels cfg-gating below: the
            // registry must reflect that the model is *not* runnable here
            // so the adaptive router doesn't add it to fallback chains
            // (Parslee-ai/car#231 — §7.1, the "fresh Windows install has
            // no usable inference path" finding). Same shape as the
            // `refresh_availability` MLX branch.
            #[cfg(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)))]
            {
                schema.available = if schema.tags.contains(&"speech".to_string()) {
                    speech_mlx_available()
                } else if let ModelSource::Mlx { ref hf_repo, .. } = schema.source {
                    // Available if cached locally OR has an hf_repo —
                    // ensure_local() lazy-downloads on first use, so a
                    // declared hf_repo is "functionally available" the
                    // same way Ollama/RemoteApi entries are. Mirrors the
                    // refresh_availability() check below; see #164.
                    let mlx_dir = self.models_dir.join(&schema.name);
                    mlx_dir.join("config.json").exists() || !hf_repo.is_empty()
                } else {
                    let mlx_dir = self.models_dir.join(&schema.name);
                    mlx_dir.join("config.json").exists()
                };
            }
            #[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
            {
                schema.available = false;
            }
        } else if schema.is_vllm_mlx() {
            // vLLM-MLX: available if endpoint env var set or was manually marked available
            schema.available = std::env::var("VLLM_MLX_ENDPOINT").is_ok() || schema.available;
        } else if schema.is_local() {
            let local_path = self.models_dir.join(&schema.name).join("model.gguf");
            schema.available = local_path.exists();
        } else if schema.is_remote() {
            // Remote models are assumed available if the env var exists
            if let ModelSource::RemoteApi {
                ref api_key_env, ..
            } = schema.source
            {
                schema.available = std::env::var(api_key_env).is_ok();
            }
        }
        info!(id = %schema.id, name = %schema.name, available = schema.available, "registered model");
        self.models.insert(schema.id.clone(), schema);
    }

    /// Unregister a model by id. Returns the removed schema if found.
    pub fn unregister(&mut self, id: &str) -> Option<ModelSchema> {
        let removed = self.models.remove(id);
        if let Some(ref m) = removed {
            info!(id = %m.id, "unregistered model");
        }
        removed
    }

    /// List all models.
    pub fn list(&self) -> Vec<&ModelSchema> {
        let mut models: Vec<&ModelSchema> = self.models.values().collect();
        models.sort_by(|a, b| a.id.cmp(&b.id));
        models
    }

    /// Query models matching a filter.
    pub fn query(&self, filter: &ModelFilter) -> Vec<&ModelSchema> {
        self.models
            .values()
            .filter(|m| {
                // Capability check: model must have ALL required capabilities
                if !filter.capabilities.iter().all(|c| m.has_capability(*c)) {
                    return false;
                }
                // Size check
                if let Some(max) = filter.max_size_mb {
                    if m.size_mb() > max && m.is_local() {
                        return false;
                    }
                }
                // Latency check (declared envelope)
                if let Some(max) = filter.max_latency_ms {
                    if let Some(p50) = m.performance.latency_p50_ms {
                        if p50 > max {
                            return false;
                        }
                    }
                }
                // Cost check
                if let Some(max) = filter.max_cost_per_mtok {
                    if let Some(cost) = m.cost.output_per_mtok {
                        if cost > max {
                            return false;
                        }
                    }
                }
                // Tag check
                if !filter.tags.iter().all(|t| m.tags.contains(t)) {
                    return false;
                }
                // Provider check
                if let Some(ref p) = filter.provider {
                    if &m.provider != p {
                        return false;
                    }
                }
                // Local only
                if filter.local_only && !m.is_local() {
                    return false;
                }
                // Available only
                if filter.available_only && !m.available {
                    return false;
                }
                true
            })
            .collect()
    }

    /// Query models by a single capability.
    pub fn query_by_capability(&self, cap: ModelCapability) -> Vec<&ModelSchema> {
        self.query(&ModelFilter {
            capabilities: vec![cap],
            ..Default::default()
        })
    }

    /// Report installed local models with curated newer replacements.
    pub fn available_upgrades(&self) -> Vec<ModelUpgrade> {
        let mut upgrades = Vec::new();
        for rule in model_upgrade_rules() {
            let Some(from) = rule
                .from_ids
                .iter()
                .find_map(|id| self.models.get(id.as_str()))
                .filter(|schema| schema.available)
            else {
                continue;
            };
            let Some(to) = self.models.get(rule.to_id.as_str()) else {
                continue;
            };
            upgrades.push(ModelUpgrade {
                from_id: from.id.clone(),
                from_name: from.name.clone(),
                to_id: to.id.clone(),
                to_name: to.name.clone(),
                reason: rule.reason.clone(),
                target_runtime: rule.target_runtime.clone(),
                target_runtime_requirement: rule.target_runtime_requirement.clone(),
                minimum_runtimes: rule.minimum_runtimes.clone(),
                target_available: to.available,
                target_pullable: matches!(
                    to.source,
                    ModelSource::Local { .. } | ModelSource::Mlx { .. }
                ),
                remove_old_supported: matches!(
                    from.source,
                    ModelSource::Local { .. } | ModelSource::Mlx { .. }
                ) && rule.remove_old_after_available,
            });
        }
        upgrades.sort_by(|a, b| a.from_id.cmp(&b.from_id).then(a.to_id.cmp(&b.to_id)));
        upgrades.dedup_by(|a, b| a.from_id == b.from_id && a.to_id == b.to_id);
        upgrades
    }

    /// Get a specific model by id.
    pub fn get(&self, id: &str) -> Option<&ModelSchema> {
        self.models.get(id)
    }

    /// Find a model by name (case-insensitive). For backward compatibility
    /// with the old registry that used short names like "Qwen3-4B".
    pub fn find_by_name(&self, name: &str) -> Option<&ModelSchema> {
        #[cfg(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)))]
        if !name.to_ascii_lowercase().ends_with("-mlx") {
            if let Some(mlx_variant) = self
                .models
                .values()
                .find(|m| m.name.eq_ignore_ascii_case(&format!("{name}-MLX")))
            {
                return Some(mlx_variant);
            }
        }

        self.models
            .values()
            .find(|m| m.name.eq_ignore_ascii_case(name))
    }

    /// On Apple Silicon, resolve a GGUF/Candle model to its MLX equivalent.
    /// Returns the MLX model schema if one exists with the same family and
    /// matching capabilities; otherwise returns None.
    #[cfg(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)))]
    pub fn resolve_mlx_equivalent(&self, schema: &ModelSchema) -> Option<&ModelSchema> {
        // Already MLX — no redirect needed.
        if schema.is_mlx() || schema.is_vllm_mlx() {
            return None;
        }
        // Only redirect local GGUF models.
        if !matches!(schema.source, ModelSource::Local { .. }) {
            return None;
        }
        // Find an MLX model in the same family with at least the same primary capability.
        let primary_cap = schema.capabilities.first()?;
        self.models.values().find(|m| {
            m.is_mlx() && m.family == schema.family && m.capabilities.contains(primary_cap)
        })
    }

    /// Ensure a local model is downloaded, returning its local directory path.
    pub async fn ensure_local(&self, id: &str) -> Result<PathBuf, InferenceError> {
        self.ensure_local_with_progress(id, &ProgressSink::none())
            .await
    }

    /// Like [`ensure_local`](Self::ensure_local) but drives a progress sink and
    /// enforces the acquisition lifecycle: a per-model lock (so this can't race
    /// a concurrent pull / remove / upgrade of the same model), a disk-space
    /// preflight, and `Started`/`Completed`/`Failed` events around the work.
    pub async fn ensure_local_with_progress(
        &self,
        id: &str,
        sink: &ProgressSink,
    ) -> Result<PathBuf, InferenceError> {
        let schema = self
            .get(id)
            .or_else(|| self.find_by_name(id))
            .ok_or_else(|| InferenceError::ModelNotFound(id.to_string()))?;
        let model_name = schema.name.clone();
        let model_id = schema.id.clone();
        let needed_mb = schema.size_mb();
        let model_dir = self.models_dir.join(&schema.name);

        // Serialize acquisition of this model id against other pull/remove tasks.
        let _guard = crate::download::acquire_model_lock(&model_id).await;

        // Preflight: fail fast if there isn't room, before touching disk.
        if let Err(e) = crate::download::check_disk_space(&model_dir, needed_mb) {
            sink.emit(DownloadEvent::Failed { error: e.clone() });
            return Err(InferenceError::DownloadFailed(e));
        }

        sink.emit(DownloadEvent::Started {
            model: model_name.clone(),
            total_files: 0,
            total_mb: needed_mb,
        });
        let result = self.ensure_local_inner(id, sink).await;
        match &result {
            Ok(_) => sink.emit(DownloadEvent::Completed { model: model_name }),
            Err(e) => sink.emit(DownloadEvent::Failed {
                error: e.to_string(),
            }),
        }
        result
    }

    async fn ensure_local_inner(
        &self,
        id: &str,
        sink: &ProgressSink,
    ) -> Result<PathBuf, InferenceError> {
        let schema = self
            .get(id)
            .or_else(|| self.find_by_name(id))
            .ok_or_else(|| InferenceError::ModelNotFound(id.to_string()))?;

        match &schema.source {
            ModelSource::Local {
                hf_repo,
                hf_filename,
                tokenizer_repo,
            } => {
                let model_dir = self.models_dir.join(&schema.name);
                let model_path = model_dir.join("model.gguf");
                let tokenizer_path = model_dir.join("tokenizer.json");

                if model_path.exists() && tokenizer_path.exists() {
                    return Ok(model_dir);
                }

                std::fs::create_dir_all(&model_dir)?;

                if !model_path.exists() {
                    info!(model = %schema.name, repo = %hf_repo, "downloading model weights");
                    sink.emit(DownloadEvent::FileStarted {
                        filename: "model weights".into(),
                        index: 1,
                        total_files: 2,
                        size_mb: schema.size_mb(),
                    });
                    download_file(hf_repo, hf_filename, &model_path).await?;
                    sink.emit(DownloadEvent::FileCompleted {
                        filename: "model weights".into(),
                    });
                }
                if !tokenizer_path.exists() {
                    info!(model = %schema.name, repo = %tokenizer_repo, "downloading tokenizer");
                    sink.emit(DownloadEvent::FileStarted {
                        filename: "tokenizer".into(),
                        index: 2,
                        total_files: 2,
                        size_mb: 0,
                    });
                    download_file(tokenizer_repo, "tokenizer.json", &tokenizer_path).await?;
                    sink.emit(DownloadEvent::FileCompleted {
                        filename: "tokenizer".into(),
                    });
                }

                Ok(model_dir)
            }
            ModelSource::Mlx {
                hf_repo,
                hf_weight_file,
            } => {
                let model_dir = self.models_dir.join(&schema.name);
                let config_path = model_dir.join("config.json");

                if config_path.exists() {
                    ensure_auxiliary_mlx_files(&schema.name, hf_repo, &model_dir).await?;
                    info!(model = %schema.name, path = %model_dir.display(), "using managed local MLX model");
                    return Ok(model_dir);
                }

                if let Some(snapshot_dir) = latest_huggingface_repo_snapshot(hf_repo) {
                    ensure_auxiliary_mlx_files(&schema.name, hf_repo, &snapshot_dir).await?;
                    info!(model = %schema.name, path = %snapshot_dir.display(), "using cached MLX snapshot");
                    return Ok(snapshot_dir);
                }

                if requires_full_mlx_snapshot(&schema) {
                    info!(
                        model = %schema.name,
                        repo = %hf_repo,
                        "downloading full MLX snapshot"
                    );
                    let (snapshot_dir, _files_downloaded) =
                        download_hf_repo_snapshot(hf_repo).await?;
                    ensure_auxiliary_mlx_files(&schema.name, hf_repo, &snapshot_dir).await?;
                    return Ok(snapshot_dir);
                }

                std::fs::create_dir_all(&model_dir)?;

                info!(model = %schema.name, repo = %hf_repo, "downloading MLX model");

                // Download config, tokenizer, and weight files. total_files is
                // not known up front for MLX (single vs sharded), so events
                // carry total_files = 0 ("unknown") and the UI shows names.
                emit_file(sink, "config", 0, schema.size_mb());
                download_file(hf_repo, "config.json", &config_path).await?;
                let tok_path = model_dir.join("tokenizer.json");
                if !tok_path.exists() {
                    emit_file(sink, "tokenizer", 0, 0);
                    download_file(hf_repo, "tokenizer.json", &tok_path).await?;
                }
                let tok_config_path = model_dir.join("tokenizer_config.json");
                if !tok_config_path.exists() {
                    let _ = download_file(hf_repo, "tokenizer_config.json", &tok_config_path).await;
                }

                // Download weight files
                if let Some(ref wf) = hf_weight_file {
                    let wf_path = model_dir.join(wf);
                    if !wf_path.exists() {
                        emit_file(sink, "model weights", 0, schema.size_mb());
                        download_file(hf_repo, wf, &wf_path).await?;
                    }
                } else {
                    // Try single file first, then sharded
                    let single = model_dir.join("model.safetensors");
                    if !single.exists() {
                        emit_file(sink, "model weights", 0, schema.size_mb());
                        match download_file(hf_repo, "model.safetensors", &single).await {
                            Ok(()) => {}
                            Err(_) => {
                                // Sharded: download index and then each shard
                                let index_path = model_dir.join("model.safetensors.index.json");
                                download_file(hf_repo, "model.safetensors.index.json", &index_path)
                                    .await?;

                                let index_json: serde_json::Value =
                                    serde_json::from_str(&std::fs::read_to_string(&index_path)?)
                                        .map_err(|e| {
                                            InferenceError::InferenceFailed(format!(
                                                "parse index: {e}"
                                            ))
                                        })?;

                                if let Some(weight_map) =
                                    index_json.get("weight_map").and_then(|m| m.as_object())
                                {
                                    let mut files: std::collections::HashSet<String> =
                                        std::collections::HashSet::new();
                                    for filename in weight_map.values() {
                                        if let Some(f) = filename.as_str() {
                                            files.insert(f.to_string());
                                        }
                                    }
                                    let shard_total = files.len() as u32;
                                    for (i, file) in files.iter().enumerate() {
                                        let dest = model_dir.join(file);
                                        if !dest.exists() {
                                            info!(file = %file, "downloading weight shard");
                                            sink.emit(DownloadEvent::FileStarted {
                                                filename: format!("weights part {}", i + 1),
                                                index: (i + 1) as u32,
                                                total_files: shard_total,
                                                size_mb: 0,
                                            });
                                            download_file(hf_repo, file, &dest).await?;
                                            sink.emit(DownloadEvent::FileCompleted {
                                                filename: format!("weights part {}", i + 1),
                                            });
                                        }
                                    }
                                }
                            }
                        }
                    }
                }

                ensure_auxiliary_mlx_files(&schema.name, hf_repo, &model_dir).await?;
                Ok(model_dir)
            }
            _ => Err(InferenceError::InferenceFailed(format!(
                "model {} is not local",
                id
            ))),
        }
    }

    /// Remove a downloaded local model.
    pub fn remove_local(&mut self, id: &str) -> Result<(), InferenceError> {
        let schema = self
            .get(id)
            .or_else(|| self.find_by_name(id))
            .ok_or_else(|| InferenceError::ModelNotFound(id.to_string()))?;

        let model_dir = self.models_dir.join(&schema.name);
        if model_dir.exists() {
            std::fs::remove_dir_all(&model_dir)?;
            info!(model = %schema.name, "removed model");
        }

        match &schema.source {
            ModelSource::Mlx { hf_repo, .. } => {
                let repo_dir = huggingface_repo_dir(hf_repo);
                if repo_dir.exists() {
                    std::fs::remove_dir_all(&repo_dir)?;
                    info!(model = %schema.name, repo = %hf_repo, "removed Hugging Face cache");
                }
            }
            ModelSource::Local {
                hf_repo,
                tokenizer_repo,
                ..
            } => {
                for repo in [hf_repo, tokenizer_repo] {
                    let repo_dir = huggingface_repo_dir(repo);
                    if repo_dir.exists() {
                        std::fs::remove_dir_all(&repo_dir)?;
                        info!(model = %schema.name, repo = %repo, "removed Hugging Face cache");
                    }
                }
            }
            _ => {}
        }

        // Update availability
        let id = schema.id.clone();
        if let Some(m) = self.models.get_mut(&id) {
            m.available = false;
        }
        Ok(())
    }

    /// Refresh availability flags for all models.
    ///
    /// Runtime-true vs catalog-says: this is what closes the gap
    /// `models.list_unified` callers rely on. If a model is listed
    /// as `available: true` here, an `infer` call against it should
    /// reach the backend, not bail with `UnsupportedMode { ...
    /// "mlx-vlm CLI not found on PATH" }` (the #137 trap).
    pub fn refresh_availability(&mut self) {
        // `models_dir` is consumed only inside the MLX and Local arms.
        // On non-MLX targets the MLX arm is a cfg-gated `available = false`
        // and never touches `models_dir`; the Local arm still needs it.
        let models_dir = self.models_dir.clone();
        // mlx-vlm CLI is the same probe call no matter which model
        // requires it; do it once per refresh, not per-model. On
        // non-MLX targets the variable is unused (the consuming arm
        // is cfg-gated out), so suppress the unused-variable warning.
        #[cfg(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)))]
        let mlx_vlm_cli_present = crate::backend::mlx_vlm_cli::is_available();
        #[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
        #[allow(unused_variables)]
        let mlx_vlm_cli_present = false;

        for m in self.models.values_mut() {
            match &m.source {
                ModelSource::Mlx { hf_repo, .. } => {
                    // MLX requires Apple Silicon Metal. On any other
                    // build target — Intel Mac, Linux, Windows, or
                    // `car_skip_mlx` — the backend is not compiled in
                    // and the adaptive router must not select these
                    // models. Cfg-gate identical to the
                    // AppleFoundationModels branch below and the
                    // `register()` MLX branch above. Closes the §7.1
                    // arm of Parslee-ai/car#231.
                    #[cfg(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)))]
                    {
                        // Models tagged `requires-mlx-vlm` shell out to the
                        // mlx_vlm Python CLI for image inference (#115).
                        // If the CLI isn't on PATH, the runtime reaches it
                        // anyway and bails — the registry MUST reflect that
                        // by marking such entries unavailable until the
                        // user installs `uv tool install mlx-vlm`. #137.
                        let needs_mlx_vlm = m.tags.iter().any(|t| t == "requires-mlx-vlm");

                        m.available = if needs_mlx_vlm {
                            mlx_vlm_cli_present
                        } else if m.tags.contains(&"speech".to_string()) {
                            speech_mlx_available()
                        } else {
                            // Available if cached locally OR has an hf_repo —
                            // the native MLX path's ensure_local() lazy-
                            // downloads on first use, so a declared hf_repo
                            // is "functionally available" the same way
                            // Ollama and RemoteApi entries are ("should
                            // work in principle" not "physically cached").
                            // Closes #164: mlx/ltx-2.3:q4 reported
                            // unavailable even though `car video` would
                            // just download-and-run successfully.
                            let mlx_dir = models_dir.join(&m.name);
                            mlx_dir.join("config.json").exists() || !hf_repo.is_empty()
                        };
                    }
                    #[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
                    {
                        let _ = hf_repo; // unused on non-MLX targets
                        m.available = false;
                    }
                }
                ModelSource::Local { .. } => {
                    let local_path = models_dir.join(&m.name).join("model.gguf");
                    m.available = local_path.exists();
                }
                ModelSource::RemoteApi { api_key_env, .. } => {
                    m.available = std::env::var(api_key_env).is_ok();
                }
                ModelSource::Ollama { .. } => {
                    // Assume available; health check is async and done lazily
                    m.available = true;
                }
                ModelSource::VllmMlx { .. } => {
                    // vLLM-MLX availability checked via health endpoint lazily
                    // Mark as available if VLLM_MLX_ENDPOINT env var is set or default endpoint assumed
                    m.available = std::env::var("VLLM_MLX_ENDPOINT").is_ok() || m.available;
                    // preserve manual registration
                }
                ModelSource::Proprietary { auth, .. } => {
                    // Check auth availability
                    m.available = match auth {
                        crate::schema::ProprietaryAuth::ApiKeyEnv { env_var } => {
                            std::env::var(env_var).is_ok()
                        }
                        crate::schema::ProprietaryAuth::BearerTokenEnv { env_var } => {
                            std::env::var(env_var).is_ok()
                        }
                        crate::schema::ProprietaryAuth::OAuth2Pkce { .. } => {
                            // OAuth2 availability determined at runtime by token provider
                            true
                        }
                    };
                }
                ModelSource::AppleFoundationModels { .. } => {
                    // Apple Silicon macOS 26+ AND iOS 26+ both expose
                    // the FoundationModels framework. The shim's
                    // runtime probe handles per-device availability
                    // (Apple Intelligence may be off, the device may
                    // be pre-A17, etc.); cfg-gating here just hides
                    // the call on targets where the shim isn't built.
                    #[cfg(any(
                        all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)),
                        all(target_os = "ios", target_arch = "aarch64")
                    ))]
                    {
                        m.available = crate::backend::foundation_models::is_available();
                    }
                    #[cfg(not(any(
                        all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)),
                        all(target_os = "ios", target_arch = "aarch64")
                    )))]
                    {
                        m.available = false;
                    }
                }
                ModelSource::Delegated { .. } => {
                    // Availability tracks whether a runner is registered.
                    // Hosts call `registerInferenceRunner` (or its
                    // language equivalent) at startup; until then the
                    // model is unavailable.
                    m.available = crate::runner::current_inference_runner().is_some();
                }
            }
        }
    }

    /// Persist user-registered (non-builtin) models to disk.
    pub fn save_user_config(&self) -> Result<(), InferenceError> {
        let user_models: Vec<&ModelSchema> = self
            .models
            .values()
            .filter(|m| !m.tags.contains(&"builtin".to_string()))
            .collect();

        if user_models.is_empty() {
            return Ok(());
        }

        let json = serde_json::to_string_pretty(&user_models)
            .map_err(|e| InferenceError::InferenceFailed(format!("serialize: {e}")))?;
        std::fs::write(&self.user_config_path, json)?;
        Ok(())
    }

    /// Load user-registered models from disk.
    pub fn load_user_config(&mut self) -> Result<(), InferenceError> {
        if !self.user_config_path.exists() {
            return Ok(());
        }

        let json = std::fs::read_to_string(&self.user_config_path)?;
        let models: Vec<ModelSchema> = serde_json::from_str(&json)
            .map_err(|e| InferenceError::InferenceFailed(format!("parse models.json: {e}")))?;

        for m in models {
            self.register(m);
        }
        Ok(())
    }

    /// Get the models directory path.
    pub fn models_dir(&self) -> &Path {
        &self.models_dir
    }

    /// Load the built-in Qwen3 catalog as ModelSchema objects.
    fn load_builtin_catalog(&mut self) {
        for schema in builtin_catalog() {
            self.models.insert(schema.id.clone(), schema);
        }
    }
}

#[cfg(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)))]
fn speech_mlx_available() -> bool {
    // On Apple Silicon, speech uses native MLX backends — no Python CLI needed.
    // Models are available if we're on the right platform (weights are downloaded on demand).
    #[cfg(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)))]
    {
        true
    }

    // On other platforms, check for the Python mlx-audio CLI.
    #[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
    {
        let runtime_root = speech_runtime_root();
        runtime_root
            .join("bin")
            .join("mlx_audio.stt.generate")
            .exists()
            || runtime_root
                .join("bin")
                .join("mlx_audio.tts.generate")
                .exists()
    }
}

#[cfg(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)))]
#[allow(dead_code)] // conditionally compiled — used only on MLX-backend (macOS) speech-runtime paths
fn speech_runtime_root() -> PathBuf {
    if let Ok(path) = std::env::var("CAR_SPEECH_RUNTIME_DIR") {
        if !path.trim().is_empty() {
            return PathBuf::from(path);
        }
    }
    std::env::var("HOME")
        .map(PathBuf::from)
        .unwrap_or_else(|_| PathBuf::from("."))
        .join(".car")
        .join("speech-runtime")
}

/// Backward-compatible ModelInfo for listing (used by CLI and old callers).
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelInfo {
    pub id: String,
    pub name: String,
    pub provider: String,
    pub capabilities: Vec<ModelCapability>,
    pub param_count: String,
    pub size_mb: u64,
    pub context_length: usize,
    pub available: bool,
    pub is_local: bool,
    /// Public benchmark scores carried straight through from `ModelSchema`.
    /// The built-in catalog ships this empty; populating it is a curation
    /// step (see `BenchmarkScore` in the schema for shape and conventions).
    #[serde(default)]
    pub public_benchmarks: Vec<crate::schema::BenchmarkScore>,
}

impl From<&ModelSchema> for ModelInfo {
    fn from(s: &ModelSchema) -> Self {
        ModelInfo {
            id: s.id.clone(),
            name: s.name.clone(),
            provider: s.provider.clone(),
            capabilities: s.capabilities.clone(),
            param_count: s.param_count.clone(),
            size_mb: s.size_mb(),
            context_length: s.context_length,
            available: s.available,
            is_local: s.is_local(),
            public_benchmarks: s.public_benchmarks.clone(),
        }
    }
}

/// Emit a lightweight `FileStarted` marker for an MLX auxiliary/weight file.
/// MLX pulls don't know `total_files` up front (single vs sharded), so these
/// carry `total_files = 0` ("unknown") and the UI shows the file name. The
/// sharded weight loop, which knows its count, pairs Started/Completed itself.
fn emit_file(sink: &ProgressSink, name: &str, index: u32, size_mb: u64) {
    sink.emit(DownloadEvent::FileStarted {
        filename: name.to_string(),
        index,
        total_files: 0,
        size_mb,
    });
}

/// Download a single file from a HuggingFace repo.
async fn download_file(repo: &str, filename: &str, dest: &Path) -> Result<(), InferenceError> {
    let api = hf_hub::api::tokio::Api::new()
        .map_err(|e| InferenceError::DownloadFailed(e.to_string()))?;

    let repo = api.model(repo.to_string());
    let path = repo
        .get(filename)
        .await
        .map_err(|e| InferenceError::DownloadFailed(format!("{filename}: {e}")))?;

    if dest.exists() {
        return Ok(());
    }

    // Try symlink first, fall back to copy
    #[cfg(unix)]
    {
        if std::os::unix::fs::symlink(&path, dest).is_ok() {
            return Ok(());
        }
    }

    std::fs::copy(&path, dest)
        .map_err(|e| InferenceError::DownloadFailed(format!("copy to {}: {e}", dest.display())))?;
    Ok(())
}

async fn ensure_auxiliary_mlx_files(
    model_name: &str,
    hf_repo: &str,
    model_dir: &Path,
) -> Result<(), InferenceError> {
    if hf_repo == "mlx-community/Flux-1.lite-8B-MLX-Q4" || model_name == "Flux-1.lite-8B-MLX-Q4" {
        let t5_tokenizer_path = model_dir.join("tokenizer_2").join("tokenizer.json");
        if !t5_tokenizer_path.exists() {
            std::fs::create_dir_all(t5_tokenizer_path.parent().ok_or_else(|| {
                InferenceError::InferenceFailed("invalid tokenizer path".into())
            })?)?;
            info!(
                path = %t5_tokenizer_path.display(),
                "downloading missing Flux tokenizer_2/tokenizer.json from base model"
            );
            download_file(
                "Freepik/flux.1-lite-8B",
                "tokenizer_2/tokenizer.json",
                &t5_tokenizer_path,
            )
            .await?;
        }
    }
    Ok(())
}

fn requires_full_mlx_snapshot(schema: &ModelSchema) -> bool {
    match &schema.source {
        ModelSource::Mlx { hf_repo, .. } => {
            hf_repo == "ckurasek/Yume-1.5-5B-720P-MLX-4bit"
                || schema.family.starts_with("yume")
                || schema.tags.iter().any(|tag| {
                    matches!(
                        tag.as_str(),
                        "wan2.2" | "ti2v" | "world-model" | "image-to-video"
                    )
                })
        }
        _ => false,
    }
}

#[allow(dead_code)] // conditionally compiled — used only on MLX-backend (macOS) snapshot-resolution paths
fn huggingface_repo_has_snapshot(repo_id: &str) -> bool {
    latest_huggingface_repo_snapshot(repo_id).is_some()
}

fn huggingface_cache_root() -> PathBuf {
    std::env::var("HF_HOME")
        .map(PathBuf::from)
        .unwrap_or_else(|_| {
            std::env::var("HOME")
                .map(PathBuf::from)
                .unwrap_or_else(|_| PathBuf::from("."))
                .join(".cache")
                .join("huggingface")
        })
        .join("hub")
}

fn huggingface_repo_dir(repo_id: &str) -> PathBuf {
    huggingface_cache_root().join(format!("models--{}", repo_id.replace('/', "--")))
}

fn resolve_huggingface_ref_snapshot(repo_dir: &Path, name: &str) -> Option<PathBuf> {
    let sha = std::fs::read_to_string(repo_dir.join("refs").join(name))
        .ok()?
        .trim()
        .to_string();
    if sha.is_empty() {
        return None;
    }

    let snapshot = repo_dir.join("snapshots").join(sha);
    if snapshot_looks_ready(&snapshot) {
        Some(snapshot)
    } else {
        None
    }
}

fn latest_huggingface_repo_snapshot(repo_id: &str) -> Option<PathBuf> {
    let repo_dir = huggingface_repo_dir(repo_id);
    if let Some(snapshot) = resolve_huggingface_ref_snapshot(&repo_dir, "main") {
        return Some(snapshot);
    }

    let snapshots = repo_dir.join("snapshots");
    let mut candidates: Vec<(SystemTime, PathBuf)> = std::fs::read_dir(snapshots)
        .ok()?
        .filter_map(Result::ok)
        .map(|e| e.path())
        .filter(|p| p.is_dir() && snapshot_looks_ready(p))
        .map(|path| {
            let modified = path
                .metadata()
                .and_then(|metadata| metadata.modified())
                .unwrap_or(SystemTime::UNIX_EPOCH);
            (modified, path)
        })
        .collect();
    candidates.sort();
    candidates.pop().map(|(_, path)| path)
}

fn snapshot_looks_ready(path: &Path) -> bool {
    if path.join("config.json").exists() || path.join("model_index.json").exists() {
        return true;
    }
    snapshot_contains_ext(path, "safetensors")
}

fn snapshot_contains_ext(root: &Path, ext: &str) -> bool {
    let Ok(entries) = std::fs::read_dir(root) else {
        return false;
    };
    entries.filter_map(Result::ok).any(|entry| {
        let path = entry.path();
        if path.is_dir() {
            snapshot_contains_ext(&path, ext)
        } else {
            path.extension()
                .and_then(|value| value.to_str())
                .map(|value| value.eq_ignore_ascii_case(ext))
                .unwrap_or(false)
        }
    })
}

async fn download_hf_repo_snapshot(repo_id: &str) -> Result<(PathBuf, usize), InferenceError> {
    let api = hf_hub::api::tokio::ApiBuilder::from_env()
        .with_progress(false)
        .build()
        .map_err(|e| InferenceError::DownloadFailed(format!("init hf api: {e}")))?;
    let repo = api.model(repo_id.to_string());
    let info = repo
        .info()
        .await
        .map_err(|e| InferenceError::DownloadFailed(format!("{repo_id}: {e}")))?;

    let snapshot_path = std::env::var("HF_HOME")
        .map(PathBuf::from)
        .unwrap_or_else(|_| {
            std::env::var("HOME")
                .map(PathBuf::from)
                .unwrap_or_else(|_| PathBuf::from("."))
                .join(".cache")
                .join("huggingface")
        })
        .join("hub")
        .join(format!("models--{}", repo_id.replace('/', "--")))
        .join("snapshots")
        .join(&info.sha);
    let mut downloaded = 0usize;
    for sibling in &info.siblings {
        let local_path = snapshot_path.join(&sibling.rfilename);
        if local_path.exists() {
            downloaded += 1;
            continue;
        }
        repo.download(&sibling.rfilename).await.map_err(|e| {
            InferenceError::DownloadFailed(format!("{repo_id}/{}: {e}", sibling.rfilename))
        })?;
        downloaded += 1;
    }

    Ok((snapshot_path, downloaded))
}

/// Built-in catalog parsed from `builtin_catalog.json`.
///
/// Adding, removing, or editing a model is a JSON-only change — Rust
/// source stays put. The JSON is embedded at compile time via
/// `include_str!`, parsed once into a `LazyLock`, and cloned on each
/// call. A malformed JSON file fails the integration test
/// `builtin_catalog_json_parses` so the binary never ships unable
/// to load its own catalog.
const BUILTIN_CATALOG_JSON: &str = include_str!("builtin_catalog.json");

static BUILTIN_CATALOG: std::sync::LazyLock<Vec<ModelSchema>> = std::sync::LazyLock::new(|| {
    serde_json::from_str(BUILTIN_CATALOG_JSON)
        .expect("builtin_catalog.json failed to parse — fix the JSON, not this code")
});

fn builtin_catalog() -> Vec<ModelSchema> {
    BUILTIN_CATALOG.clone()
}

#[cfg(test)]
mod tests {
    use super::*;
    use tempfile::TempDir;

    fn test_registry() -> (UnifiedRegistry, TempDir) {
        let tmp = TempDir::new().unwrap();
        let reg = UnifiedRegistry::new(tmp.path().join("models"));
        (reg, tmp)
    }

    #[test]
    fn builtin_catalog_loads() {
        let (reg, _tmp) = test_registry();
        let all = reg.list();
        assert_eq!(all.len(), builtin_catalog().len());
    }

    /// #137: a model tagged `requires-mlx-vlm` must report
    /// `available: true` if and only if `mlx_vlm_cli::is_available()`
    /// returns true. Without this, registry consumers (FFI
    /// `listModelsUnified`, the tray Models submenu, agent routing)
    /// see a model as available, the user picks it, and inference
    /// bails with `mlx-vlm CLI not found on PATH`.
    ///
    /// The probe is environmental — runs the same check on the host
    /// the test executes on. CI usually doesn't have `mlx_vlm`
    /// installed → expected unavailable; a dev box with it installed
    /// → expected available. Either way, the registry tracks the
    /// runtime probe.
    #[test]
    fn mlx_vlm_models_reflect_runtime_availability() {
        let (reg, _tmp) = test_registry();
        let mlx_vlm_models: Vec<&ModelSchema> = reg
            .list()
            .into_iter()
            .filter(|m| m.tags.iter().any(|t| t == "requires-mlx-vlm"))
            .collect();
        assert!(
            !mlx_vlm_models.is_empty(),
            "catalog should contain at least one model tagged \
             `requires-mlx-vlm` — otherwise this regression has \
             nothing to guard"
        );

        #[cfg(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)))]
        let expected = crate::backend::mlx_vlm_cli::is_available();
        #[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
        let expected = false;

        for m in mlx_vlm_models {
            assert_eq!(
                m.available, expected,
                "model {} `available` field should reflect \
                 mlx_vlm CLI presence (expected {expected}, got {})",
                m.id, m.available
            );
        }
    }

    /// F1 (Parslee-ai/car#231 §7.1): MLX models must report
    /// `available: false` on any non-Apple-Silicon-with-MLX build target.
    /// Without this, the Windows / Linux / Intel-Mac router happily
    /// adds MLX entries to fallback chains and inference fails at
    /// dispatch with "model not found", producing a 7-deep cascade
    /// of useless errors on a fresh install.
    ///
    /// The test runs on every platform. On macOS arm64 (default-features),
    /// at least one MLX model with an `hf_repo` should report available;
    /// on Linux / Windows / Intel-Mac / `car_skip_mlx`, every plain MLX
    /// model must report unavailable.
    #[test]
    fn mlx_models_unavailable_on_non_mlx_targets() {
        let (reg, _tmp) = test_registry();
        let mlx_models: Vec<&ModelSchema> = reg
            .list()
            .into_iter()
            .filter(|m| {
                m.is_mlx()
                    // Exclude `requires-mlx-vlm` and `speech` — those have
                    // their own availability logic (mlx_vlm CLI probe,
                    // speech_mlx_available). The plain MLX models are
                    // what §7.1 surfaced as broken on Windows.
                    && !m.tags.iter().any(|t| t == "requires-mlx-vlm")
                    && !m.tags.contains(&"speech".to_string())
            })
            .collect();
        assert!(
            !mlx_models.is_empty(),
            "catalog should contain at least one plain MLX model — \
             otherwise this F1 regression guard has nothing to guard"
        );

        #[cfg(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)))]
        {
            // On a real MLX target, models with an `hf_repo` should be
            // marked available (the `ensure_local()` lazy-download path).
            // At least one must qualify.
            let any_available = mlx_models.iter().any(|m| m.available);
            assert!(
                any_available,
                "on macOS arm64 with MLX enabled, at least one plain MLX \
                 model with hf_repo should be available — none were"
            );
        }
        #[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
        {
            // On every other target, MLX models cannot execute, so the
            // registry must mark them all unavailable.
            for m in &mlx_models {
                assert!(
                    !m.available,
                    "MLX model {} is marked available on a non-MLX target — \
                     the adaptive router will add it to fallback chains \
                     and dispatch will fail (Parslee-ai/car#231 §7.1)",
                    m.id
                );
            }
        }
    }

    /// Embedded JSON must parse cleanly — if it doesn't, the runtime
    /// would panic on first registry load. Catch it in CI instead.
    #[test]
    fn builtin_catalog_json_parses() {
        let catalog: Vec<ModelSchema> = serde_json::from_str(BUILTIN_CATALOG_JSON)
            .expect("builtin_catalog.json must be valid ModelSchema array");
        assert!(
            !catalog.is_empty(),
            "embedded catalog has no entries — that's almost certainly wrong"
        );

        let mut seen = std::collections::HashSet::new();
        for entry in &catalog {
            assert!(
                seen.insert(entry.id.clone()),
                "duplicate id in builtin_catalog.json: {}",
                entry.id
            );
        }
    }

    #[test]
    fn public_benchmarks_round_trip_through_model_info() {
        use crate::schema::BenchmarkScore;
        let (mut reg, _tmp) = test_registry();
        let mut schema = reg
            .find_by_name("Qwen3-4B")
            .expect("catalog has Qwen3-4B")
            .clone();
        schema.id = "test/qwen3-4b-with-bench".into();
        schema.public_benchmarks = vec![
            BenchmarkScore {
                name: "MMLU-Pro".into(),
                score: 0.482,
                harness: Some("5-shot CoT".into()),
                source_url: Some("https://example.invalid/qwen3-4b-card".into()),
                measured_at: Some("2025-08-12".into()),
            },
            BenchmarkScore {
                name: "HumanEval".into(),
                score: 0.713,
                harness: Some("pass@1".into()),
                source_url: None,
                measured_at: None,
            },
        ];
        reg.register(schema);

        let stored = reg
            .get("test/qwen3-4b-with-bench")
            .expect("registered model is retrievable");
        let info = ModelInfo::from(stored);
        assert_eq!(info.public_benchmarks.len(), 2);

        // The serialized JSON shape is what the WS / FFI clients consume.
        let json = serde_json::to_string(&info).unwrap();
        assert!(json.contains("\"public_benchmarks\""));
        assert!(json.contains("\"MMLU-Pro\""));
        assert!(json.contains("\"5-shot CoT\""));

        // Round-trip back through serde to confirm deserialization works.
        let decoded: ModelInfo = serde_json::from_str(&json).unwrap();
        assert_eq!(decoded.public_benchmarks.len(), 2);
        assert_eq!(decoded.public_benchmarks[0].name, "MMLU-Pro");
        assert_eq!(decoded.public_benchmarks[1].name, "HumanEval");
    }

    #[test]
    fn public_benchmarks_default_to_empty_when_absent_in_json() {
        // Older user-config JSON written before this field existed must
        // still deserialize cleanly into the new ModelSchema shape.
        let legacy_json = r#"{
            "id": "legacy/test:1",
            "name": "Legacy Test",
            "provider": "test",
            "family": "test",
            "version": "",
            "capabilities": ["generate"],
            "context_length": 4096,
            "param_count": "1B",
            "quantization": null,
            "performance": {},
            "cost": {},
            "source": { "type": "ollama", "model_tag": "legacy:1" },
            "tags": [],
            "supported_params": []
        }"#;
        let schema: ModelSchema = serde_json::from_str(legacy_json).unwrap();
        assert!(schema.public_benchmarks.is_empty());
    }

    #[test]
    fn find_by_name() {
        let (reg, _tmp) = test_registry();
        let m = reg.find_by_name("Qwen3-4B").unwrap();
        #[cfg(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)))]
        assert_eq!(m.id, "mlx/qwen3-4b:4bit");
        #[cfg(not(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx))))]
        assert_eq!(m.id, "qwen/qwen3-4b:q4_k_m");
        assert!(m.has_capability(ModelCapability::Code));
    }

    #[test]
    fn query_by_capability() {
        let (reg, _tmp) = test_registry();
        let embed_models = reg.query_by_capability(ModelCapability::Embed);
        assert_eq!(embed_models.len(), 2);
        assert!(embed_models
            .iter()
            .any(|model| model.name == "Qwen3-Embedding-0.6B"));
        assert!(embed_models
            .iter()
            .any(|model| model.name == "Qwen3-Embedding-0.6B-MLX"));
    }

    #[test]
    fn query_with_filter() {
        let (reg, _tmp) = test_registry();
        let code_small = reg.query(&ModelFilter {
            capabilities: vec![ModelCapability::Code],
            max_size_mb: Some(3000),
            local_only: true,
            ..Default::default()
        });
        // Qwen3-1.7B, Qwen3-1.7B-MLX, Qwen3-4B, and Qwen3-4B-MLX fit and have Code capability.
        assert_eq!(code_small.len(), 4);
    }

    #[test]
    fn register_remote() {
        let (mut reg, _tmp) = test_registry();
        let initial_len = reg.list().len();
        let initial_reasoning_len = reg
            .query(&ModelFilter {
                capabilities: vec![ModelCapability::Reasoning, ModelCapability::ToolUse],
                ..Default::default()
            })
            .len();
        let remote = ModelSchema {
            id: "anthropic/claude-sonnet-4-6:latest".into(),
            name: "Claude Sonnet 4.6".into(),
            provider: "anthropic".into(),
            family: "claude-4".into(),
            version: "latest".into(),
            capabilities: vec![
                ModelCapability::Generate,
                ModelCapability::Code,
                ModelCapability::Reasoning,
                ModelCapability::ToolUse,
            ],
            context_length: 200000,
            param_count: String::new(),
            quantization: None,
            performance: PerformanceEnvelope {
                latency_p50_ms: Some(2000),
                ..Default::default()
            },
            cost: CostModel {
                input_per_mtok: Some(3.0),
                output_per_mtok: Some(15.0),
                ..Default::default()
            },
            source: ModelSource::RemoteApi {
                endpoint: "https://api.anthropic.com/v1/messages".into(),
                api_key_env: "ANTHROPIC_API_KEY".into(),
                api_key_envs: vec![],
                api_version: Some("2023-06-01".into()),
                protocol: ApiProtocol::Anthropic,
            },
            tags: vec![],
            supported_params: vec![],
            public_benchmarks: vec![],
            trust_tier: crate::schema::TrustTier::Curated,
            deprecated: false,
            available: false,
        };

        reg.register(remote);
        // Same ID as builtin claude-sonnet-4-6 — replaces, count stays same
        assert_eq!(reg.list().len(), initial_len);

        let reasoning = reg.query(&ModelFilter {
            capabilities: vec![ModelCapability::Reasoning, ModelCapability::ToolUse],
            ..Default::default()
        });
        // Replacing an existing remote slot should not change the reasoning/tool-use lineup size.
        assert_eq!(reasoning.len(), initial_reasoning_len);
    }

    #[test]
    fn unregister() {
        let (mut reg, _tmp) = test_registry();
        let initial_len = reg.list().len();
        let removed = reg.unregister("qwen/qwen3-0.6b:q8_0");
        assert!(removed.is_some());
        assert_eq!(reg.list().len(), initial_len - 1);
    }

    #[test]
    fn speech_models_are_curated() {
        let (reg, _tmp) = test_registry();
        let stt = reg.query_by_capability(ModelCapability::SpeechToText);
        let tts = reg.query_by_capability(ModelCapability::TextToSpeech);
        assert_eq!(stt.len(), 2);
        assert_eq!(tts.len(), 4);
    }

    #[test]
    fn qwen_8b_variants_keep_tool_use_consistent() {
        let (reg, _tmp) = test_registry();
        for name in ["Qwen3-8B", "Qwen3-8B-MLX"] {
            let model = reg.find_by_name(name).expect("model should exist");
            assert!(model.has_capability(ModelCapability::ToolUse));
            assert!(model.has_capability(ModelCapability::MultiToolCall));
        }
    }

    #[test]
    fn mac_name_resolution_prefers_mlx_siblings() {
        // Only used inside the aarch64-macos cfg below; non-mac targets
        // keep the test as a smoke compile.
        #[allow(unused_variables)]
        let (reg, _tmp) = test_registry();
        #[cfg(all(target_os = "macos", target_arch = "aarch64", not(car_skip_mlx)))]
        {
            assert_eq!(
                reg.find_by_name("Qwen3-0.6B").unwrap().id,
                "mlx/qwen3-0.6b:6bit"
            );
            assert_eq!(
                reg.find_by_name("Qwen3-1.7B").unwrap().id,
                "mlx/qwen3-1.7b:3bit"
            );
            assert_eq!(
                reg.find_by_name("Qwen3-Embedding-0.6B").unwrap().id,
                "mlx/qwen3-embedding-0.6b:mxfp8"
            );
        }
    }

    #[test]
    fn remote_multimodal_models_are_curated_as_vision_capable() {
        let (reg, _tmp) = test_registry();
        for name in [
            "claude-opus-4-7",
            "claude-opus-4-6",
            "claude-sonnet-4-6",
            "claude-haiku-4-5",
            "gpt-5.4",
            "gpt-5.4-mini",
            "o3",
            "o4-mini",
            "gpt-4.1-mini",
            "gemini-2.5-pro",
            "gemini-2.5-flash",
        ] {
            let model = reg.find_by_name(name).expect("model should exist");
            assert!(
                model.has_capability(ModelCapability::Vision),
                "{name} should be curated as vision-capable"
            );
        }
    }

    #[test]
    fn qwen25vl_entries_are_replaced_by_qwen3vl_in_builtin_catalog() {
        let (reg, _tmp) = test_registry();

        let stale_ids = [
            // Native MLX text tower can't tokenize images — never advertise.
            "mlx/qwen2.5-vl-3b:4bit",
            "mlx/qwen2.5-vl-7b:4bit",
            // Qwen2.5-VL is superseded by Qwen3-VL; drop the mlx-vlm CLI
            // catalog entries so callers route to the upgraded family.
            "mlx-vlm/qwen2.5-vl-3b:4bit",
            "mlx-vlm/qwen2.5-vl-7b:4bit",
            // Same supersession applies to the vLLM-MLX route.
            "vllm-mlx/qwen2.5-vl-3b:4bit",
        ];
        for id in stale_ids {
            assert!(
                reg.get(id).is_none(),
                "{id} is superseded by Qwen3-VL; the catalog must not advertise it"
            );
        }

        let vision_ids: Vec<&str> = reg
            .query_by_capability(ModelCapability::Vision)
            .into_iter()
            .map(|model| model.id.as_str())
            .collect();
        for stale in stale_ids {
            assert!(
                !vision_ids.contains(&stale),
                "{stale} must not be reachable through the Vision capability index"
            );
        }
        assert!(
            vision_ids.contains(&"mlx-vlm/qwen3-vl-2b:bf16"),
            "Qwen3-VL is the supported local VL family and must route as Vision"
        );
    }

    #[test]
    fn gemini_models_are_curated_for_multimodal_tool_use() {
        let (reg, _tmp) = test_registry();
        for name in ["gemini-2.5-pro", "gemini-2.5-flash"] {
            let model = reg.find_by_name(name).expect("model should exist");
            assert!(model.has_capability(ModelCapability::Vision));
            assert!(model.has_capability(ModelCapability::ToolUse));
            assert!(model.has_capability(ModelCapability::MultiToolCall));
        }
    }

    #[test]
    fn visual_generation_models_are_curated() {
        let (reg, _tmp) = test_registry();
        assert_eq!(
            reg.query_by_capability(ModelCapability::ImageGeneration)
                .len(),
            1
        );
        assert_eq!(
            reg.query_by_capability(ModelCapability::VideoGeneration)
                .len(),
            2
        );
        let yume = reg
            .get("mlx/yume-1.5-5b-720p:q4")
            .expect("Yume MLX should be in the built-in catalog");
        assert!(yume.has_capability(ModelCapability::VideoGeneration));
        assert!(yume.tags.contains(&"text-to-video".to_string()));
        assert!(yume.tags.contains(&"image-to-video".to_string()));
        assert!(yume.tags.contains(&"world-model".to_string()));
    }
}