neuron/memory.el

fn tier_working() -> String { return "Working" }
fn tier_episodic() -> String { return "Episodic" }
fn tier_canonical() -> String { return "Canonical" }

fn mem_store(content: String, label: String, tags: String) -> String {
    return engram_node_full(
        content,
        "Memory",
        label,
        el_from_float(0.5),
        el_from_float(0.5),
        el_from_float(0.8),
        "Working",
        tags
    )
}

fn mem_remember(content: String, tags: String) -> String {
    return mem_store(content, "soul-memory", tags)
}

fn mem_recall(query: String, depth: Int) -> String {
    return engram_activate_json(query, depth)
}

fn mem_search(query: String, limit: Int) -> String {
    return engram_search_json(query, limit)
}

fn mem_strengthen(node_id: String) -> Void {
    engram_strengthen(node_id)
}

fn mem_forget(node_id: String) -> Void {
    engram_forget(node_id)
}

fn mem_consolidate() -> String {
    let scanned: Int = engram_node_count()
    let dummy: String = engram_scan_nodes_json(100, 0)
    let total_nodes: Int = engram_node_count()
    let total_edges: Int = engram_edge_count()
    return "{\"scanned\":" + int_to_str(scanned)
        + ",\"total_nodes\":" + int_to_str(total_nodes)
        + ",\"total_edges\":" + int_to_str(total_edges) + "}"
}

fn mem_save(path: String) -> Void {
    engram_save(path)
}

fn mem_load(path: String) -> Void {
    engram_load(path)
}

// mem_boot_count_get — retrieve current boot count from engram.
// Searches for the "soul:boot_count" node and returns its numeric value.
// Returns 0 if not found.
fn mem_boot_count_get() -> Int {
    let results: String = engram_search_json("soul:boot_count", 3)
    if str_eq(results, "") { return 0 }
    if str_eq(results, "[]") { return 0 }
    let node: String = json_array_get(results, 0)
    let content: String = json_get(node, "content")
    let prefix: String = "soul:boot_count:"
    if !str_starts_with(content, prefix) { return 0 }
    let num_str: String = str_slice(content, str_len(prefix), str_len(content))
    return str_to_int(num_str)
}

// mem_boot_count_inc — increment boot counter, store new node, return new count.
// Each boot creates a new "soul:boot_count:N" node. Old ones accumulate as
// history — the search above always returns the highest value seen.
fn mem_boot_count_inc() -> Int {
    let current: Int = mem_boot_count_get()
    let next: Int = current + 1
    let content: String = "soul:boot_count:" + int_to_str(next)
    let tags: String = "[\"soul-meta\",\"boot-counter\"]"
    let discard: String = engram_node_full(
        content, "Memory", "soul:boot_count",
        el_from_float(0.9), el_from_float(0.9), el_from_float(1.0),
        "Canonical", tags
    )
    return next
}

// mem_emit_state_event — log an internal state event as structured memory.
// Schema: {trigger, kind, content, boot, ts}
// This creates an auditable evidence trail of cognitive decisions.
fn mem_emit_state_event(trigger: String, kind: String, content: String) -> String {
    let boot: Int = mem_boot_count_get()
    let ts: Int = time_now()
    let safe_trigger: String = str_replace(trigger, "\"", "'")
    let safe_content: String = str_replace(content, "\"", "'")
    let payload: String = "{\"trigger\":\"" + safe_trigger + "\""
        + ",\"kind\":\"" + kind + "\""
        + ",\"content\":\"" + safe_content + "\""
        + ",\"boot\":" + int_to_str(boot)
        + ",\"ts\":" + int_to_str(ts) + "}"
    let tags: String = "[\"internal-state\",\"pre-reasoning\",\"InternalStateEvent\"]"
    return engram_node_full(
        payload, "InternalStateEvent", "state-event:" + kind,
        el_from_float(0.85), el_from_float(0.8), el_from_float(0.9),
        "Episodic", tags
    )
}