harn-vm 0.8.24

use std::rc::Rc;

use crate::stdlib::{json_to_vm_value, schema_result_value};
use crate::value::{VmError, VmValue};

use super::helpers::extract_llm_options;
use super::trace::{emit_agent_event, AgentTraceEvent};
use super::{agent_config, agent_observe, api, helpers, routing, structural_experiments};

fn output_validation_mode(opts: &api::LlmCallOptions) -> &str {
    opts.output_validation.as_deref().unwrap_or("off")
}

fn schema_validation_errors(result: &VmValue) -> Vec<String> {
    match result {
        VmValue::EnumVariant {
            enum_name,
            variant,
            fields,
        } if enum_name.as_ref() == "Result" && variant.as_ref() == "Err" => fields
            .first()
            .and_then(|payload| payload.as_dict())
            .and_then(|payload| payload.get("errors"))
            .and_then(|errors| match errors {
                VmValue::List(items) => Some(items.iter().map(|err| err.display()).collect()),
                _ => None,
            })
            .unwrap_or_else(|| vec!["schema validation failed".to_string()]),
        _ => Vec::new(),
    }
}

/// Compute schema validation errors against `opts.output_schema` without
/// deciding disposition (warn vs error vs off). Returns an empty vec when
/// no schema is configured or the data validates. Used by the schema-retry
/// loop in `llm_call`.
pub(super) fn compute_validation_errors(data: &VmValue, opts: &api::LlmCallOptions) -> Vec<String> {
    let Some(schema_json) = &opts.output_schema else {
        return Vec::new();
    };
    let schema_vm = json_to_vm_value(schema_json);
    let validation = schema_result_value(data, &schema_vm, false);
    schema_validation_errors(&validation)
}

pub(crate) fn structured_output_errors(
    result: &VmValue,
    opts: &api::LlmCallOptions,
) -> Vec<String> {
    let Some(dict) = result.as_dict() else {
        return vec!["structured output result was not a dict".to_string()];
    };
    if let Some(data) = dict.get("data") {
        return compute_validation_errors(data, opts);
    }

    let mut errors = vec!["response did not contain parseable JSON".to_string()];
    if let Some(VmValue::List(violations)) = dict.get("protocol_violations") {
        let joined = violations
            .iter()
            .map(VmValue::display)
            .collect::<Vec<_>>()
            .join("; ");
        if !joined.is_empty() {
            errors.push(format!("protocol violations: {joined}"));
        }
    }
    if let Some(stop_reason) = dict.get("stop_reason").map(VmValue::display) {
        if matches!(stop_reason.as_str(), "length" | "max_tokens") {
            errors.push("response hit the token limit before producing complete JSON".to_string());
        }
    }
    errors
}

/// How `llm_call` should nudge the model when `output_schema` validation
/// fails and `schema_retries > 0`.
#[derive(Debug, Clone)]
pub(crate) enum SchemaNudge {
    /// Build a default corrective user message from the schema's top-level
    /// `required` / `properties` keys plus the validation errors. This is
    /// the default when `schema_retry_nudge` is unset or `true`.
    Auto,
    /// Use the caller's string verbatim (plus a short tail listing the
    /// validation errors).
    Verbatim(String),
    /// Retry without appending any corrective message (bare retry).
    /// Selected when `schema_retry_nudge: false`.
    Disabled,
}

pub(crate) fn parse_schema_nudge(
    options: &Option<std::collections::BTreeMap<String, VmValue>>,
) -> SchemaNudge {
    let Some(opts) = options.as_ref() else {
        return SchemaNudge::Auto;
    };
    match opts.get("schema_retry_nudge") {
        None | Some(VmValue::Nil) => SchemaNudge::Auto,
        Some(VmValue::Bool(true)) => SchemaNudge::Auto,
        Some(VmValue::Bool(false)) => SchemaNudge::Disabled,
        Some(VmValue::String(s)) => SchemaNudge::Verbatim(s.to_string()),
        Some(other) => SchemaNudge::Verbatim(other.display()),
    }
}

/// Build the corrective user message appended before a schema-retry
/// attempt. Callers that want full control pass a string via
/// `schema_retry_nudge`; the `Auto` variant summarizes the schema shape
/// so small / local models can fix nested object mistakes without seeing
/// the whole JSON Schema again (see `docs/llm/harn-quickref.md` "Schema
/// retries").
pub(crate) fn build_schema_nudge(
    errors: &[String],
    schema: Option<&serde_json::Value>,
    mode: &SchemaNudge,
) -> String {
    let errors_line = if errors.is_empty() {
        String::from("(no detailed errors)")
    } else {
        errors.join("; ")
    };
    match mode {
        SchemaNudge::Disabled => String::new(),
        SchemaNudge::Verbatim(s) => {
            format!("{s}\n\nValidation errors: {errors_line}")
        }
        SchemaNudge::Auto => {
            let mut required_keys: Vec<String> = Vec::new();
            let mut property_keys: Vec<String> = Vec::new();
            let mut shape_lines: Vec<String> = Vec::new();
            if let Some(schema) = schema {
                if let Some(req) = schema.get("required").and_then(|v| v.as_array()) {
                    for r in req {
                        if let Some(k) = r.as_str() {
                            required_keys.push(k.to_string());
                        }
                    }
                }
                if let Some(props) = schema.get("properties").and_then(|v| v.as_object()) {
                    for k in props.keys() {
                        property_keys.push(k.clone());
                    }
                }
                collect_schema_shape_lines(schema, "root", 0, &mut shape_lines);
            }
            let mut msg =
                String::from("Your previous response did not match the required JSON schema.");
            msg.push_str(&format!("\nValidation errors: {errors_line}."));
            if !required_keys.is_empty() {
                msg.push_str(&format!("\nRequired keys: {}.", required_keys.join(", ")));
            }
            if !property_keys.is_empty() {
                msg.push_str(&format!(
                    "\nAllowed top-level keys: {}.",
                    property_keys.join(", ")
                ));
            }
            if !shape_lines.is_empty() {
                msg.push_str("\nExpected JSON schema shape:");
                for line in shape_lines {
                    msg.push_str("\n- ");
                    msg.push_str(&line);
                }
            }
            msg.push_str(
                "\nRespond again with ONLY valid JSON conforming to the schema. No prose, no markdown fences.",
            );
            msg
        }
    }
}

const SCHEMA_NUDGE_MAX_DEPTH: usize = 3;
const SCHEMA_NUDGE_MAX_LINES: usize = 8;
const SCHEMA_NUDGE_MAX_KEYS: usize = 16;

fn collect_schema_shape_lines(
    schema: &serde_json::Value,
    path: &str,
    depth: usize,
    lines: &mut Vec<String>,
) {
    if depth > SCHEMA_NUDGE_MAX_DEPTH || lines.len() >= SCHEMA_NUDGE_MAX_LINES {
        return;
    }

    let object_like = schema
        .get("type")
        .and_then(|value| value.as_str())
        .is_some_and(|kind| kind == "object")
        || schema.get("properties").is_some();
    if object_like {
        if let Some(props) = schema.get("properties").and_then(|value| value.as_object()) {
            let mut keys = props.keys().cloned().collect::<Vec<_>>();
            keys.sort();
            if !keys.is_empty() {
                let mut line = format!("{path} object allowed keys: {}", join_limited_keys(&keys));
                let required = schema_required_keys(schema);
                if !required.is_empty() {
                    line.push_str(&format!(
                        "; required keys: {}",
                        join_limited_keys(&required)
                    ));
                }
                lines.push(line);
            }

            for key in keys {
                if lines.len() >= SCHEMA_NUDGE_MAX_LINES {
                    break;
                }
                if let Some(child_schema) = props.get(&key) {
                    let child_path = if path == "root" {
                        key
                    } else {
                        format!("{path}.{key}")
                    };
                    collect_schema_shape_lines(child_schema, &child_path, depth + 1, lines);
                }
            }
        }
    }

    let array_like = schema
        .get("type")
        .and_then(|value| value.as_str())
        .is_some_and(|kind| kind == "array")
        || schema.get("items").is_some();
    if array_like {
        if let Some(items) = schema.get("items") {
            collect_schema_shape_lines(items, &format!("{path}[]"), depth + 1, lines);
        }
    }
}

fn schema_required_keys(schema: &serde_json::Value) -> Vec<String> {
    let mut keys = schema
        .get("required")
        .and_then(|value| value.as_array())
        .map(|items| {
            items
                .iter()
                .filter_map(|item| item.as_str().map(ToString::to_string))
                .collect::<Vec<_>>()
        })
        .unwrap_or_default();
    keys.sort();
    keys
}

fn join_limited_keys(keys: &[String]) -> String {
    if keys.len() <= SCHEMA_NUDGE_MAX_KEYS {
        return keys.join(", ");
    }
    format!(
        "{}, ... (+{} more)",
        keys[..SCHEMA_NUDGE_MAX_KEYS].join(", "),
        keys.len() - SCHEMA_NUDGE_MAX_KEYS
    )
}

/// Shared implementation of `llm_call` / `llm_call_safe`. Runs the
/// full schema-retry loop; on success returns the LLM result dict, on
/// failure returns the underlying `VmError`. `llm_call` propagates the
/// error (re-wrapped as a thrown `{kind, reason, category, message,
/// retry_after_ms?, provider, model}` dict so catch blocks can dispatch
/// on the LLM error taxonomy);
/// `llm_call_safe` wraps it in a `{ok: false, error: …}` envelope with
/// the same fields.
pub(super) async fn llm_call_impl(args: Vec<VmValue>) -> Result<VmValue, VmError> {
    let options = args.get(2).and_then(|a| a.as_dict()).cloned();
    let opts = extract_llm_options(&args)?;
    let provider = opts.provider.clone();
    let model = opts.model.clone();
    // Publish the resolved provider/model/capabilities to templates
    // rendered while this `llm_call` frame is on the stack — e.g.
    // schema-retry prompts or middleware that re-renders a partial
    // for a corrective second pass.
    let _llm_render_guard = crate::stdlib::template::LlmRenderContextGuard::enter(
        crate::stdlib::template::LlmRenderContext::resolve(&provider, &model),
    );
    match execute_llm_call(opts, options, None).await {
        Ok(v) => Ok(v),
        Err(err) => Err(VmError::Thrown(build_llm_error_dict(
            &err, &provider, &model,
        ))),
    }
}

/// Build the `{kind, reason, category, message, retry_after_ms?, provider, model}`
/// dict that `llm_call` throws on failure. `retry_after_ms` is only
/// set when the underlying error carries a parseable `retry-after:`
/// hint, so callers can pattern-match on its presence:
///
/// ```harn
/// try { llm_call(prompt, nil, opts) } catch (e) {
///   if e.kind == "transient" && e.reason == "rate_limit" {
///     sleep(e.retry_after_ms ?? 1000)
///   }
/// }
/// ```
pub(crate) fn build_llm_error_dict(err: &VmError, provider: &str, model: &str) -> VmValue {
    let category = crate::value::error_to_category(err);
    let message = llm_error_message(err);
    let llm_error = api::classify_llm_error(category.clone(), &message);
    if let VmError::Thrown(VmValue::Dict(existing)) = err {
        let mut dict = existing.as_ref().clone();
        dict.entry("category".to_string())
            .or_insert_with(|| VmValue::String(Rc::from(category.as_str())));
        dict.entry("kind".to_string())
            .or_insert_with(|| VmValue::String(Rc::from(llm_error.kind.as_str())));
        dict.entry("reason".to_string())
            .or_insert_with(|| VmValue::String(Rc::from(llm_error.reason.as_str())));
        dict.entry("message".to_string())
            .or_insert_with(|| VmValue::String(Rc::from(message.as_str())));
        dict.insert("provider".to_string(), VmValue::String(Rc::from(provider)));
        dict.insert("model".to_string(), VmValue::String(Rc::from(model)));
        return VmValue::Dict(Rc::new(dict));
    }
    let mut dict = std::collections::BTreeMap::new();
    dict.insert(
        "category".to_string(),
        VmValue::String(Rc::from(category.as_str())),
    );
    dict.insert(
        "kind".to_string(),
        VmValue::String(Rc::from(llm_error.kind.as_str())),
    );
    dict.insert(
        "reason".to_string(),
        VmValue::String(Rc::from(llm_error.reason.as_str())),
    );
    dict.insert("message".to_string(), VmValue::String(Rc::from(message)));
    if let Some(ms) = agent_observe::extract_retry_after_ms(err) {
        dict.insert("retry_after_ms".to_string(), VmValue::Int(ms as i64));
    }
    dict.insert("provider".to_string(), VmValue::String(Rc::from(provider)));
    dict.insert("model".to_string(), VmValue::String(Rc::from(model)));
    VmValue::Dict(Rc::new(dict))
}

fn llm_error_message(err: &VmError) -> String {
    match err {
        VmError::CategorizedError { message, .. } => message.clone(),
        VmError::Thrown(VmValue::String(s)) => s.to_string(),
        VmError::Thrown(VmValue::Dict(d)) => d
            .get("message")
            .map(|v| v.display())
            .unwrap_or_else(|| err.to_string()),
        _ => err.to_string(),
    }
}

pub(crate) async fn execute_llm_call(
    opts: api::LlmCallOptions,
    options: Option<std::collections::BTreeMap<String, VmValue>>,
    bridge: Option<&Rc<crate::bridge::HostBridge>>,
) -> Result<VmValue, VmError> {
    if let Some(policy) = opts.routing_policy.clone() {
        return execute_with_routing_policy(policy, opts, bridge).await;
    }
    let outcome = execute_schema_retry_loop(opts, options, bridge).await?;
    if outcome.errors.is_empty() {
        return Ok(outcome.vm_result);
    }
    // Schema retries exhausted — honor the caller's output_validation mode.
    let hint = if outcome.schema_retries_budget == 0 {
        " (hint: set `schema_retries: N` in the llm_call options to automatically re-prompt the model with a corrective nudge)"
    } else {
        " (hint: schema_retries budget exhausted — the model did not produce conforming output after the configured retries; consider raising `schema_retries` or relaxing the schema)"
    };
    let message = format!(
        "LLM output failed schema validation: {}{hint}",
        outcome.errors.join("; ")
    );
    match outcome.output_validation_mode.as_str() {
        "error" => Err(crate::value::VmError::CategorizedError {
            message,
            category: crate::value::ErrorCategory::SchemaValidation,
        }),
        "warn" => {
            crate::events::log_warn("llm", &message);
            Ok(outcome.vm_result)
        }
        _ => Ok(outcome.vm_result),
    }
}

/// Dispatch through the first-class routing policy executor. Each
/// chain link is tried in order with failover, optional latency-aware
/// racing, and per-call / session budget enforcement; the winning
/// link's result is wrapped in the standard `llm_call` envelope plus
/// a `routing` block summarizing every attempt.
async fn execute_with_routing_policy(
    policy: Rc<routing::RoutingPolicyConfig>,
    mut opts: api::LlmCallOptions,
    bridge: Option<&Rc<crate::bridge::HostBridge>>,
) -> Result<VmValue, VmError> {
    let (result, trace) = routing::execute_with_routing(&policy, opts.clone(), bridge).await?;
    // Snap option metadata to the winning link so transcript / portal
    // payloads describe the call that actually ran.
    opts.provider = result.provider.clone();
    opts.model = result.model.clone();
    opts.routing_decision = Some(routing::trace_to_decision(&trace, &policy));
    let envelope = agent_config::build_llm_call_result(&result, &opts);
    Ok(attach_routing_block(envelope, &trace, &policy))
}

fn attach_routing_block(
    envelope: VmValue,
    trace: &routing::RoutingTrace,
    policy: &routing::RoutingPolicyConfig,
) -> VmValue {
    let VmValue::Dict(dict) = envelope else {
        return envelope;
    };
    let mut dict = dict.as_ref().clone();
    let mut routing_dict = std::collections::BTreeMap::new();
    let label = if trace.label.is_empty() {
        policy.label.clone()
    } else {
        trace.label.clone()
    };
    routing_dict.insert("policy".to_string(), VmValue::String(Rc::from(label)));
    routing_dict.insert("attempts".to_string(), routing::trace_to_vm_attempts(trace));
    if let Some(selected) = trace.selected {
        routing_dict.insert("selected".to_string(), VmValue::Int(selected as i64));
    }
    routing_dict.insert(
        "session_cost_usd".to_string(),
        VmValue::Float(trace.session_cost_usd),
    );
    dict.insert("routing".to_string(), VmValue::Dict(Rc::new(routing_dict)));
    VmValue::Dict(Rc::new(dict))
}

/// Outcome of the schema-retry loop, exposing both the final attempt's
/// payload and the telemetry that envelope-shaped callers (e.g.
/// `llm_call_structured_result`) need to surface diagnostics. Transport
/// errors short-circuit the loop and propagate as `Err`; schema failures
/// after exhaustion return `Ok(...)` with `errors` populated so the
/// caller can decide between throwing, warning, or building a result
/// envelope.
pub(crate) struct SchemaLoopOutcome {
    /// Final attempt's vm_result dict (regardless of validation success).
    pub vm_result: VmValue,
    /// Final attempt's raw model text — preserved for diagnostics and
    /// repair input even when JSON couldn't be extracted.
    pub raw_text: String,
    /// Validation errors from the final attempt (empty = success).
    pub errors: Vec<String>,
    /// Number of model calls made (1-indexed; 1 = no retries used).
    pub attempts: usize,
    /// Configured `schema_retries` budget (0 means "no retries").
    pub schema_retries_budget: usize,
    /// `output_validation` mode the caller configured (off / warn / error).
    pub output_validation_mode: String,
}

pub(crate) async fn execute_schema_retry_loop(
    mut opts: api::LlmCallOptions,
    options: Option<std::collections::BTreeMap<String, VmValue>>,
    bridge: Option<&Rc<crate::bridge::HostBridge>>,
) -> Result<SchemaLoopOutcome, VmError> {
    let _ = structural_experiments::apply_structural_experiment(&mut opts, None).await?;
    let retry_config = agent_observe::LlmRetryConfig {
        retries: helpers::opt_int(&options, "llm_retries")
            .unwrap_or(agent_observe::DEFAULT_LLM_CALL_RETRIES as i64)
            .max(0) as usize,
        backoff_ms: helpers::opt_int(&options, "llm_backoff_ms")
            .unwrap_or(agent_observe::DEFAULT_LLM_CALL_BACKOFF_MS as i64)
            .max(0) as u64,
    };
    // Schema retry loop is orthogonal to transient retries. Each
    // schema retry gets a fresh transient budget. Small/local models
    // often need the corrective nudge to produce conforming JSON.
    let schema_retries = helpers::opt_int(&options, "schema_retries")
        .unwrap_or(1)
        .max(0) as usize;
    let nudge_mode = parse_schema_nudge(&options);

    let tool_format = helpers::opt_str(&options, "tool_format");
    let bridged = bridge.is_some();
    let user_visible = bridged && helpers::opt_bool(&options, "user_visible");
    let output_validation_mode = output_validation_mode(&opts).to_string();
    let expects_structured = helpers::expects_structured_output(&opts);
    // Snapshot the caller's original messages once. Each schema retry
    // replays this snapshot plus a single corrective user message, so
    // the invalid response never pollutes subsequent attempts — the
    // retry is a single-turn correction rather than a multi-turn
    // conversation.
    let original_messages = opts.messages.clone();
    for attempt in 0..=schema_retries {
        let result = agent_observe::observed_llm_call(
            &opts,
            tool_format.as_deref(),
            bridge,
            &retry_config,
            None,
            user_visible,
            bridged, // offthread=true on the bridge path, local set otherwise
            // Top-level `llm_call` host calls don't have a session in the
            // sense the streaming detector needs (no agent loop, no
            // session_id), so skip candidate detection here. The agent
            // loop's `run_llm_call` is the integration point that owns it.
            None,
        )
        .await?;

        let raw_text = result.text.clone();
        // Non-bridge path runs schema validation; bridge path
        // delegates validation to the host.
        let vm_result = agent_config::build_llm_call_result(&result, &opts);
        if !expects_structured {
            return Ok(SchemaLoopOutcome {
                vm_result,
                raw_text,
                errors: Vec::new(),
                attempts: attempt + 1,
                schema_retries_budget: schema_retries,
                output_validation_mode,
            });
        }
        let errors = structured_output_errors(&vm_result, &opts);
        if errors.is_empty() {
            return Ok(SchemaLoopOutcome {
                vm_result,
                raw_text,
                errors,
                attempts: attempt + 1,
                schema_retries_budget: schema_retries,
                output_validation_mode,
            });
        }

        let more_attempts = attempt < schema_retries;
        if more_attempts {
            let nudge = build_schema_nudge(&errors, opts.output_schema.as_ref(), &nudge_mode);
            emit_agent_event(AgentTraceEvent::SchemaRetry {
                attempt: attempt + 1,
                errors: errors.clone(),
                nudge_used: !nudge.is_empty(),
                correction_prompt: nudge.clone(),
            });
            // Replay the original messages with a single corrective
            // user turn appended. The invalid assistant response is
            // deliberately dropped — smaller / local models get
            // confused by a user→assistant(bad)→user(nudge)→assistant
            // shape, and the verbatim bad response otherwise sits in
            // context for every subsequent retry.
            opts.messages = original_messages.clone();
            if !nudge.is_empty() {
                opts.messages.push(serde_json::json!({
                    "role": "user",
                    "content": nudge,
                }));
            }
            continue;
        }

        // Attempts exhausted with errors. Surface the failure to the caller.
        return Ok(SchemaLoopOutcome {
            vm_result,
            raw_text,
            errors,
            attempts: attempt + 1,
            schema_retries_budget: schema_retries,
            output_validation_mode,
        });
    }
    unreachable!("schema retry loop exited without returning");
}

pub(super) fn llm_safe_envelope_ok(response: VmValue) -> VmValue {
    let mut dict = std::collections::BTreeMap::new();
    dict.insert("ok".to_string(), VmValue::Bool(true));
    dict.insert("response".to_string(), response);
    dict.insert("error".to_string(), VmValue::Nil);
    VmValue::Dict(Rc::new(dict))
}

pub(super) fn llm_safe_envelope_err(err: &VmError) -> VmValue {
    // `llm_call_impl` pre-wraps its errors into a
    // `VmError::Thrown(VmValue::Dict{kind, reason, category, message,
    // retry_after_ms?, provider, model})`. Pass that dict through verbatim so
    // `llm_call_safe` callers see the same fields as `try/catch`
    // users — with `category` / `message` always populated.
    if let VmError::Thrown(VmValue::Dict(d)) = err {
        let mut dict = std::collections::BTreeMap::new();
        dict.insert("ok".to_string(), VmValue::Bool(false));
        dict.insert("response".to_string(), VmValue::Nil);
        dict.insert("error".to_string(), VmValue::Dict(d.clone()));
        return VmValue::Dict(Rc::new(dict));
    }
    let category = crate::value::error_to_category(err);
    let message = llm_error_message(err);
    let llm_error = api::classify_llm_error(category.clone(), &message);
    let mut err_dict = std::collections::BTreeMap::new();
    err_dict.insert(
        "category".to_string(),
        VmValue::String(Rc::from(category.as_str())),
    );
    err_dict.insert(
        "kind".to_string(),
        VmValue::String(Rc::from(llm_error.kind.as_str())),
    );
    err_dict.insert(
        "reason".to_string(),
        VmValue::String(Rc::from(llm_error.reason.as_str())),
    );
    err_dict.insert("message".to_string(), VmValue::String(Rc::from(message)));
    let mut dict = std::collections::BTreeMap::new();
    dict.insert("ok".to_string(), VmValue::Bool(false));
    dict.insert("response".to_string(), VmValue::Nil);
    dict.insert("error".to_string(), VmValue::Dict(Rc::new(err_dict)));
    VmValue::Dict(Rc::new(dict))
}

/// Rewrite `(prompt, schema, options?)` — the ergonomic
/// `llm_call_structured` argument shape — into the canonical
/// `(prompt, system, options)` arg list that `extract_llm_options` and
/// `llm_call_impl` expect. Schema is installed as `output_schema`; the
/// JSON-schema-validated-output defaults (`response_format: "json"`,
/// `output_validation: "error"`, `schema_retries: 3`) are applied
/// unless the caller already set them. The caller's `system` key
/// (when present) is lifted out of the options dict into the second
/// positional slot. Built as a standalone helper so the non-bridge
/// and bridge-aware paths share one definition.
pub(crate) fn rewrite_structured_args(args: Vec<VmValue>) -> Result<Vec<VmValue>, VmError> {
    if args.len() < 2 {
        return Err(VmError::Runtime(
            "llm_call_structured: missing required `schema` argument (expected \
             (prompt, schema, options?))"
                .to_string(),
        ));
    }
    let prompt = args.first().cloned().unwrap_or(VmValue::Nil);
    let schema = match args.get(1) {
        Some(VmValue::Dict(_)) => args.get(1).cloned().unwrap(),
        Some(other) => {
            return Err(VmError::Runtime(format!(
                "llm_call_structured: `schema` must be a dict (JSON Schema), got {}",
                other.type_name()
            )));
        }
        None => unreachable!("len check above guarantees arg index 1"),
    };
    let mut options = args
        .get(2)
        .and_then(|a| a.as_dict())
        .cloned()
        .unwrap_or_default();

    // Pull `system` out of the options dict (ergonomic alias — the
    // canonical llm_call path takes system as the second positional
    // arg). Nil values are treated as absence so `{system: nil, ...}`
    // still lets the default apply.
    let system = options
        .remove("system")
        .filter(|v| !matches!(v, VmValue::Nil));

    // Public ergonomic alias: `retries` maps to `schema_retries`. Honor
    // the long form if the caller passes it explicitly; otherwise
    // default to 3 (enough to recover from small-model JSON drift while
    // staying cheap on frontier models that rarely miss).
    let retries_alias = options.remove("retries").and_then(|v| v.as_int());
    if let Some(n) = retries_alias {
        options
            .entry("schema_retries".to_string())
            .or_insert(VmValue::Int(n));
    } else {
        options
            .entry("schema_retries".to_string())
            .or_insert(VmValue::Int(3));
    }

    options
        .entry("output_schema".to_string())
        .or_insert(schema.clone());
    options
        .entry("json_schema".to_string())
        .or_insert(schema.clone());
    options
        .entry("output_format".to_string())
        .or_insert_with(|| {
            let mut fmt = std::collections::BTreeMap::new();
            fmt.insert("kind".to_string(), VmValue::String(Rc::from("json_schema")));
            fmt.insert("schema".to_string(), schema);
            fmt.insert("strict".to_string(), VmValue::Bool(true));
            VmValue::Dict(Rc::new(fmt))
        });
    options
        .entry("response_format".to_string())
        .or_insert(VmValue::String(Rc::from("json")));
    options
        .entry("output_validation".to_string())
        .or_insert(VmValue::String(Rc::from("error")));

    Ok(vec![
        prompt,
        system.unwrap_or(VmValue::Nil),
        VmValue::Dict(Rc::new(options)),
    ])
}

/// Extract the `.data` field from a canonical `llm_call` result dict.
/// Used by `llm_call_structured` to surface just the validated payload.
pub(crate) fn extract_structured_data(response: VmValue) -> VmValue {
    match response {
        VmValue::Dict(d) => d.get("data").cloned().unwrap_or(VmValue::Nil),
        other => other,
    }
}

/// Build the `{ok: true, data, error: nil}` envelope for
/// `llm_call_structured_safe`. Mirrors `llm_safe_envelope_ok` but keys
/// the payload on `data` (the validated schema-parsed value) instead
/// of `response` (the full result dict), matching the issue shape.
pub(crate) fn structured_safe_envelope_ok(data: VmValue) -> VmValue {
    let mut dict = std::collections::BTreeMap::new();
    dict.insert("ok".to_string(), VmValue::Bool(true));
    dict.insert("data".to_string(), data);
    dict.insert("error".to_string(), VmValue::Nil);
    VmValue::Dict(Rc::new(dict))
}

pub(crate) fn structured_safe_envelope_err(err: &VmError) -> VmValue {
    if let VmError::Thrown(VmValue::Dict(d)) = err {
        let mut dict = std::collections::BTreeMap::new();
        dict.insert("ok".to_string(), VmValue::Bool(false));
        dict.insert("data".to_string(), VmValue::Nil);
        dict.insert("error".to_string(), VmValue::Dict(d.clone()));
        return VmValue::Dict(Rc::new(dict));
    }
    let category = crate::value::error_to_category(err);
    let message = llm_error_message(err);
    let llm_error = api::classify_llm_error(category.clone(), &message);
    let mut err_dict = std::collections::BTreeMap::new();
    err_dict.insert(
        "category".to_string(),
        VmValue::String(Rc::from(category.as_str())),
    );
    err_dict.insert(
        "kind".to_string(),
        VmValue::String(Rc::from(llm_error.kind.as_str())),
    );
    err_dict.insert(
        "reason".to_string(),
        VmValue::String(Rc::from(llm_error.reason.as_str())),
    );
    err_dict.insert("message".to_string(), VmValue::String(Rc::from(message)));
    let mut dict = std::collections::BTreeMap::new();
    dict.insert("ok".to_string(), VmValue::Bool(false));
    dict.insert("data".to_string(), VmValue::Nil);
    dict.insert("error".to_string(), VmValue::Dict(Rc::new(err_dict)));
    VmValue::Dict(Rc::new(dict))
}