diff --git a/chat/services/prompt.py b/chat/services/prompt.py
new file mode 100644
index 0000000..89b416e
--- /dev/null
+++ b/chat/services/prompt.py
@@ -0,0 +1,554 @@
+"""Narrative-prompt assembly with must/should/nice trim tiers.
+
+Implements Task 18 (Phase 1D). See Requirements §3.2 (token budgets and
+trim tiers) and §6.3 (speaker prompt assembly order). The function
+:func:`assemble_narrative_prompt` returns a list of
+:class:`chat.llm.client.Message` objects ready to feed to
+``LLMClient.generate``.
+
+Trim policy when the assembled prompt exceeds the soft target:
+
+- **MUST-include** (never trimmed): system / speaker identity, the
+  speaker→addressee edge, the activity snapshot for all present
+  entities, the current scene description, and the last 4 turns of
+  dialogue.
+- **SHOULD-include** (trim when over budget): other edges of the
+  speaker. (Group nodes, active threads, and active events / props are
+  Phase 3 — skipped here.)
+- **NICE-include** (trim first): retrieved memories beyond top-2,
+  dialogue turns beyond the last 4 (replaced with a one-line elision
+  placeholder), per-POV summary of the previous scene.
+
+Token counting uses ``tiktoken.get_encoding("cl100k_base")`` per the
+requirements. Mistral / Llama tokenizers diverge ~5%; we accept the
+drift.
+
+The function is intentionally deterministic (no LLM call) so it is
+testable with synthetic state and so T29's regenerate flow can rebuild
+prompts without re-running classifiers.
+"""
+
+from __future__ import annotations
+
+from sqlite3 import Connection
+
+import tiktoken
+
+from chat.llm.client import Message
+from chat.state.edges import get_edge, list_edges_for
+from chat.state.entities import get_bot, get_you
+from chat.state.memory import search_memories
+from chat.state.world import (
+    active_scene,
+    get_activity,
+    get_chat,
+    get_container,
+    get_scene,
+)
+
+
+# Cache the encoder once at import-time. tiktoken's encoder load is
+# non-trivial (~tens of ms) and the encoding is process-wide stable.
+_ENCODER = tiktoken.get_encoding("cl100k_base")
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _count_tokens(text: str, encoding=_ENCODER) -> int:
+    """Return the cl100k_base token count for ``text`` (0 for falsy)."""
+    if not text:
+        return 0
+    return len(encoding.encode(text))
+
+
+def _build_speaker_identity(bot: dict) -> str:
+    """Render the bot identity block. Skips empty optional fields."""
+    lines = [f"You are {bot['name']}."]
+    if bot.get("persona"):
+        lines.append("")
+        lines.append("PERSONA:")
+        lines.append(bot["persona"])
+    voice_samples = bot.get("voice_samples") or []
+    if voice_samples:
+        lines.append("")
+        lines.append("VOICE REFERENCE:")
+        lines.append("\n---\n".join(voice_samples))
+    traits = bot.get("traits") or []
+    if traits:
+        lines.append("")
+        lines.append(f"TRAITS: {', '.join(traits)}")
+    if bot.get("backstory"):
+        lines.append("")
+        lines.append("BACKSTORY:")
+        lines.append(bot["backstory"])
+    return "\n".join(lines)
+
+
+def _build_edge_block(edge: dict | None, addressee_name: str) -> str | None:
+    """Render the speaker → addressee edge. Returns None when no edge exists."""
+    if edge is None:
+        return None
+    lines = [f"YOUR EDGE TO {addressee_name}:"]
+    lines.append(f"- Affinity: {edge.get('affinity', 50)}/100")
+    lines.append(f"- Trust: {edge.get('trust', 50)}/100")
+    summary = edge.get("summary") or ""
+    if summary:
+        lines.append(f"- Summary: {summary}")
+    knowledge = edge.get("knowledge") or []
+    if knowledge:
+        lines.append(f"- What you know about {addressee_name}:")
+        for fact in knowledge:
+            lines.append(f"  * {fact}")
+    return "\n".join(lines)
+
+
+def _build_activity_block(activities: list[dict]) -> str | None:
+    """Render the activity snapshot for all present entities."""
+    rendered: list[str] = []
+    for a in activities:
+        if a is None:
+            continue
+        label = a.get("_display_name") or a.get("entity_id", "?")
+        parts: list[str] = []
+        posture = a.get("posture") or ""
+        if posture:
+            parts.append(posture)
+        action = a.get("action") or {}
+        verb = action.get("verb") if isinstance(action, dict) else None
+        if verb:
+            parts.append(verb)
+        attention = a.get("attention") or ""
+        if attention:
+            parts.append(f"attention: {attention}")
+        holding = a.get("holding") or []
+        if holding:
+            parts.append(f"holding: {', '.join(holding)}")
+        if parts:
+            rendered.append(f"- {label}: " + ", ".join(parts))
+        else:
+            rendered.append(f"- {label}: (no activity recorded)")
+    if not rendered:
+        return None
+    return "ACTIVITIES:\n" + "\n".join(rendered)
+
+
+def _build_scene_block(chat: dict, container: dict | None, scene: dict | None) -> str | None:
+    """Render the current-scene block. Always present when chat exists."""
+    lines = ["CURRENT SCENE:"]
+    if container is not None:
+        lines.append(f"- Container: {container['name']} ({container['type']})")
+    chat_time = chat.get("time") if chat else None
+    if chat_time:
+        lines.append(f"- Time: {chat_time}")
+    if scene is not None and scene.get("started_at"):
+        lines.append(f"- Active scene started: {scene['started_at']}")
+    if len(lines) == 1:
+        return None
+    return "\n".join(lines)
+
+
+def _format_dialogue_turn(turn: dict) -> str:
+    speaker = turn.get("speaker") or "?"
+    text = turn.get("text") or ""
+    return f"{speaker}: {text}"
+
+
+def _build_dialogue_block(
+    recent: list[dict],
+    earlier_summary: str | None,
+) -> str | None:
+    """Render the recent-dialogue block. The ``recent`` list is the
+    *kept* tail of the dialogue (already trimmed to the last-N turns).
+    ``earlier_summary``, when non-None, is rendered as the first line as
+    ``earlier: <text>`` to flag elided context.
+    """
+    if not recent and not earlier_summary:
+        return None
+    lines = ["RECENT DIALOGUE:"]
+    if earlier_summary:
+        lines.append(f"earlier: {earlier_summary}")
+    for turn in recent:
+        lines.append(_format_dialogue_turn(turn))
+    return "\n".join(lines)
+
+
+def _build_memories_block(memory_summaries: list[str]) -> str | None:
+    if not memory_summaries:
+        return None
+    lines = ["RELEVANT MEMORIES:"]
+    for m in memory_summaries:
+        lines.append(f"- {m}")
+    return "\n".join(lines)
+
+
+def _build_other_edges_block(edges: list[dict]) -> str | None:
+    """Render edges to entities other than the addressee."""
+    if not edges:
+        return None
+    lines = ["OTHER EDGES:"]
+    for e in edges:
+        target = e.get("_display_name") or e.get("target_id", "?")
+        affinity = e.get("affinity", 50)
+        trust = e.get("trust", 50)
+        lines.append(f"- {target}: affinity {affinity}/100, trust {trust}/100")
+        summary = e.get("summary") or ""
+        if summary:
+            lines.append(f"  summary: {summary}")
+    return "\n".join(lines)
+
+
+def _build_previous_scene_block(pov_summary: str | None) -> str | None:
+    if not pov_summary:
+        return None
+    return "PREVIOUS SCENE SUMMARY:\n" + pov_summary
+
+
+def _closing_instruction(speaker_name: str, addressee_name: str) -> str:
+    return (
+        f"Continue the scene as {speaker_name}, in their voice, responding "
+        "naturally. Use *asterisks* for actions and quotes for dialogue. "
+        f"Stay in character. Do not narrate {addressee_name}'s actions or "
+        "thoughts."
+    )
+
+
+def _join_blocks(blocks: list[str | None]) -> str:
+    """Join non-empty blocks with double newlines."""
+    return "\n\n".join(b for b in blocks if b)
+
+
+def _earlier_summary_placeholder(elided_count: int) -> str:
+    """Phase 1 placeholder. Real summarization is a downstream concern."""
+    plural = "turn" if elided_count == 1 else "turns"
+    return f"{elided_count} earlier {plural} elided for brevity"
+
+
+def _resolve_previous_scene_summary(
+    conn: Connection, chat_id: str, speaker_bot_id: str
+) -> str | None:
+    """Return ``pov_summary`` of the most recent ended scene, owned by
+    the speaker. None if no closed scene exists or no matching memory.
+    """
+    row = conn.execute(
+        "SELECT id FROM scenes WHERE chat_id = ? AND ended_at IS NOT NULL "
+        "ORDER BY ended_at DESC LIMIT 1",
+        (chat_id,),
+    ).fetchone()
+    if not row:
+        return None
+    scene_id = row[0]
+    mem = conn.execute(
+        "SELECT pov_summary FROM memories WHERE scene_id = ? AND owner_id = ? "
+        "ORDER BY id DESC LIMIT 1",
+        (scene_id, speaker_bot_id),
+    ).fetchone()
+    if not mem:
+        return None
+    return mem[0]
+
+
+def _resolve_addressee(
+    conn: Connection, addressee: str, you: dict | None
+) -> tuple[str, str]:
+    """Return ``(addressee_id, addressee_display_name)``.
+
+    The function is permissive: ``addressee="you"`` resolves to the
+    you-entity (display name is its authored name, falling back to
+    "you" if no entity exists yet). Other ids resolve as bot ids.
+    """
+    if addressee == "you":
+        name = (you or {}).get("name") or "you"
+        return "you", name
+    bot = get_bot(conn, addressee)
+    if bot is not None:
+        return addressee, bot["name"]
+    return addressee, addressee
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+
+def assemble_narrative_prompt(
+    conn: Connection,
+    *,
+    chat_id: str,
+    speaker_bot_id: str,
+    addressee: str = "you",
+    user_turn_prose: str | None = None,
+    recent_dialogue: list[dict] | None = None,
+    retrieved_memory_summaries: list[str] | None = None,
+    budget_soft: int = 6000,
+    budget_hard: int = 8000,
+    encoding_name: str = "cl100k_base",
+) -> list[Message]:
+    """Assemble the narrative prompt for ``speaker_bot_id`` to respond.
+
+    Returns a list of :class:`Message` objects: one ``system`` message
+    carrying the assembled context, optionally followed by a single
+    ``user`` message containing ``user_turn_prose`` (when provided).
+
+    Trimming proceeds in tiers (NICE → SHOULD) once the total token
+    count exceeds ``budget_soft``; the function refuses to exceed
+    ``budget_hard``. If the MUST-include block alone is already over
+    ``budget_hard``, :class:`ValueError` is raised — the caller should
+    surface the failure rather than ship a malformed prompt.
+    """
+    encoding = (
+        _ENCODER if encoding_name == "cl100k_base"
+        else tiktoken.get_encoding(encoding_name)
+    )
+
+    bot = get_bot(conn, speaker_bot_id)
+    if bot is None:
+        raise ValueError(f"speaker_bot_id {speaker_bot_id!r} not found")
+
+    chat = get_chat(conn, chat_id)
+    if chat is None:
+        raise ValueError(f"chat_id {chat_id!r} not found")
+
+    you = get_you(conn)
+    addressee_id, addressee_name = _resolve_addressee(conn, addressee, you)
+
+    # ---- Build all components as text strings ------------------------------
+
+    speaker_identity = _build_speaker_identity(bot)
+
+    edge_to_addressee = _build_edge_block(
+        get_edge(conn, speaker_bot_id, addressee_id),
+        addressee_name,
+    )
+
+    # Activity for present entities. Phase 1: you + speaker bot. (When a
+    # guest is added in Phase 1+, callers that know about it can pass
+    # extra activities via a future hook; for now we keep it strict.)
+    activities: list[dict] = []
+    you_act = get_activity(conn, "you")
+    if you_act is not None:
+        you_act = dict(you_act)
+        you_act["_display_name"] = (you or {}).get("name") or "you"
+        activities.append(you_act)
+    bot_act = get_activity(conn, speaker_bot_id)
+    if bot_act is not None:
+        bot_act = dict(bot_act)
+        bot_act["_display_name"] = bot["name"]
+        activities.append(bot_act)
+    activity_block = _build_activity_block(activities)
+
+    container = None
+    if chat.get("active_scene_id"):
+        scene = get_scene(conn, chat["active_scene_id"])
+        if scene and scene.get("container_id"):
+            container = get_container(conn, scene["container_id"])
+    else:
+        scene = active_scene(conn, chat_id)
+    if container is None and scene and scene.get("container_id"):
+        container = get_container(conn, scene["container_id"])
+    scene_block = _build_scene_block(chat, container, scene)
+
+    # Other edges: speaker → non-addressee.
+    all_outgoing = list_edges_for(conn, speaker_bot_id)
+    other_edges_raw = [e for e in all_outgoing if e.get("target_id") != addressee_id]
+    for e in other_edges_raw:
+        tid = e.get("target_id")
+        if tid == "you":
+            e["_display_name"] = (you or {}).get("name") or "you"
+        else:
+            tb = get_bot(conn, tid) if tid else None
+            e["_display_name"] = tb["name"] if tb else (tid or "?")
+    other_edges_block = _build_other_edges_block(other_edges_raw)
+
+    # Memories: caller override wins; otherwise FTS5 search keyed on the
+    # scene's container/posture as a coarse query proxy.
+    if retrieved_memory_summaries is not None:
+        memory_summaries = list(retrieved_memory_summaries)
+    else:
+        query = (container or {}).get("name") or chat.get("narrative_anchor") or ""
+        memory_summaries = []
+        if query:
+            try:
+                hits = search_memories(conn, speaker_bot_id, "host", query, k=4)
+                memory_summaries = [h["pov_summary"] for h in hits]
+            except Exception:
+                memory_summaries = []
+
+    # Dialogue: caller override only (no event_log read in Phase 1).
+    dialogue_full = list(recent_dialogue or [])
+
+    previous_scene_summary = _resolve_previous_scene_summary(
+        conn, chat_id, speaker_bot_id
+    )
+
+    closing = _closing_instruction(bot["name"], addressee_name)
+
+    # ---- Build the MUST core ----------------------------------------------
+
+    last4 = dialogue_full[-4:] if dialogue_full else []
+    must_dialogue_block = _build_dialogue_block(last4, earlier_summary=None)
+
+    must_blocks: list[str | None] = [
+        speaker_identity,
+        edge_to_addressee,
+        scene_block,
+        activity_block,
+        must_dialogue_block,
+        closing,
+    ]
+    must_text = _join_blocks(must_blocks)
+    must_tokens = _count_tokens(must_text, encoding)
+    if must_tokens > budget_hard:
+        raise ValueError(
+            f"MUST-include block ({must_tokens} tokens) exceeds budget_hard "
+            f"({budget_hard}). Cannot assemble prompt."
+        )
+
+    # ---- Stage SHOULD additions, then NICE additions -----------------------
+
+    # We carry a running "components" list and rebuild the body as we go
+    # so token accounting reflects join-overhead. Order in the final
+    # prompt follows §6.3: identity → edge → other edges → scene →
+    # activities → previous scene summary → memories → dialogue → close.
+
+    def assemble(
+        *,
+        include_other_edges: bool,
+        include_previous_scene: bool,
+        include_memories_top_k: int,
+        dialogue_keep: int,
+    ) -> tuple[str, int, list[dict]]:
+        # dialogue: keep the last `dialogue_keep` turns verbatim; older
+        # turns become an "earlier:" placeholder line.
+        kept_dialogue = (
+            dialogue_full[-dialogue_keep:] if dialogue_keep > 0 else []
+        )
+        elided = max(0, len(dialogue_full) - len(kept_dialogue))
+        earlier_summary = (
+            _earlier_summary_placeholder(elided) if elided > 0 else None
+        )
+        dialogue_block = _build_dialogue_block(kept_dialogue, earlier_summary)
+
+        memories_subset = memory_summaries[:include_memories_top_k]
+        memories_block = _build_memories_block(memories_subset)
+
+        prev_block = (
+            _build_previous_scene_block(previous_scene_summary)
+            if include_previous_scene else None
+        )
+
+        body = _join_blocks([
+            speaker_identity,
+            edge_to_addressee,
+            other_edges_block if include_other_edges else None,
+            scene_block,
+            activity_block,
+            prev_block,
+            memories_block,
+            dialogue_block,
+            closing,
+        ])
+        return body, _count_tokens(body, encoding), kept_dialogue
+
+    # Start with the MUST baseline: last 4 turns of dialogue, no
+    # SHOULD/NICE extras.
+    baseline_keep = min(4, len(dialogue_full))
+
+    # Try the most generous configuration first; trim greedily.
+    nice_dialogue_keep = len(dialogue_full)  # all turns, no elision
+    nice_memories_k = min(4, len(memory_summaries))
+    include_prev = previous_scene_summary is not None
+    include_other = other_edges_block is not None
+
+    body, total, _ = assemble(
+        include_other_edges=include_other,
+        include_previous_scene=include_prev,
+        include_memories_top_k=nice_memories_k,
+        dialogue_keep=nice_dialogue_keep,
+    )
+
+    # If under soft, we're done.
+    if total <= budget_soft:
+        return _emit(body, user_turn_prose)
+
+    # Drop NICE in order: previous scene → memories beyond top-2 →
+    # older dialogue turns (collapse to 4).
+    if include_prev:
+        body, total, _ = assemble(
+            include_other_edges=include_other,
+            include_previous_scene=False,
+            include_memories_top_k=nice_memories_k,
+            dialogue_keep=nice_dialogue_keep,
+        )
+        include_prev = False
+        if total <= budget_soft:
+            return _emit(body, user_turn_prose)
+
+    if nice_memories_k > 2:
+        nice_memories_k = 2
+        body, total, _ = assemble(
+            include_other_edges=include_other,
+            include_previous_scene=False,
+            include_memories_top_k=nice_memories_k,
+            dialogue_keep=nice_dialogue_keep,
+        )
+        if total <= budget_soft:
+            return _emit(body, user_turn_prose)
+
+    if nice_dialogue_keep > baseline_keep:
+        nice_dialogue_keep = baseline_keep
+        body, total, _ = assemble(
+            include_other_edges=include_other,
+            include_previous_scene=False,
+            include_memories_top_k=nice_memories_k,
+            dialogue_keep=nice_dialogue_keep,
+        )
+        if total <= budget_soft:
+            return _emit(body, user_turn_prose)
+
+    # Drop more NICE until we're under hard: memories all the way to 0.
+    while nice_memories_k > 0 and total > budget_hard:
+        nice_memories_k = max(0, nice_memories_k - 1)
+        body, total, _ = assemble(
+            include_other_edges=include_other,
+            include_previous_scene=False,
+            include_memories_top_k=nice_memories_k,
+            dialogue_keep=nice_dialogue_keep,
+        )
+
+    # Drop SHOULD: other edges.
+    if include_other and total > budget_hard:
+        include_other = False
+        body, total, _ = assemble(
+            include_other_edges=False,
+            include_previous_scene=False,
+            include_memories_top_k=nice_memories_k,
+            dialogue_keep=nice_dialogue_keep,
+        )
+
+    if total > budget_hard:
+        # We've stripped everything optional and we still overflow.
+        # MUST alone fits (we checked at the top), so this means our
+        # last-4 dialogue + must blocks together exceed hard. Fall back
+        # to the bare MUST core.
+        body = must_text
+        total = must_tokens
+        if total > budget_hard:
+            raise ValueError(
+                f"Prompt cannot fit budget_hard={budget_hard}; MUST core "
+                f"is {total} tokens"
+            )
+
+    return _emit(body, user_turn_prose)
+
+
+def _emit(system_body: str, user_turn_prose: str | None) -> list[Message]:
+    msgs: list[Message] = [Message(role="system", content=system_body)]
+    if user_turn_prose is not None:
+        msgs.append(Message(role="user", content=user_turn_prose))
+    return msgs
+
+
+__all__ = ["assemble_narrative_prompt"]
diff --git a/tests/test_prompt.py b/tests/test_prompt.py
new file mode 100644
index 0000000..bef8dc0
--- /dev/null
+++ b/tests/test_prompt.py
@@ -0,0 +1,255 @@
+"""Tests for chat.services.prompt.assemble_narrative_prompt.
+
+Covers Task 18 — must/should/nice trim tiers (Requirements §3.2) and
+the speaker prompt assembly order (§6.3). Tests use direct event-log
+seeding so the projector populates state exactly the way the runtime
+will at play-time. No LLM is invoked: prompt assembly is deterministic.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from chat.db.connection import open_db
+from chat.db.migrate import apply_migrations
+from chat.eventlog.log import append_event
+from chat.eventlog.projector import project
+import chat.state.entities  # noqa: F401  (registers handlers)
+import chat.state.edges  # noqa: F401
+import chat.state.memory  # noqa: F401
+import chat.state.world  # noqa: F401
+from chat.llm.client import Message
+from chat.services.prompt import assemble_narrative_prompt
+
+
+def _seed_basic(conn) -> None:
+    """Seed bot, you-entity, edge, chat, container, scene, activities."""
+    append_event(conn, kind="bot_authored", payload={
+        "id": "bot_a",
+        "name": "Aria",
+        "persona": "reserved coworker who notices things",
+        "voice_samples": ["I — sorry, I didn't mean to.", "Right. Of course."],
+        "traits": ["introverted", "observant"],
+        "backstory": "An archivist who joined the firm last spring.",
+        "initial_relationship_to_you": "coworker; mild crush; never voiced",
+        "kickoff_prose": "you stay late at the office",
+    })
+    append_event(conn, kind="you_authored", payload={
+        "name": "Sam",
+        "pronouns": "they/them",
+        "persona": "tired analyst",
+    })
+    append_event(conn, kind="chat_created", payload={
+        "id": "chat_bot_a",
+        "host_bot_id": "bot_a",
+        "guest_bot_id": None,
+        "initial_time": "2026-04-26T20:00:00+00:00",
+        "narrative_anchor": "Day 1 evening",
+        "weather": "clear",
+    })
+    append_event(conn, kind="container_created", payload={
+        "chat_id": "chat_bot_a",
+        "name": "office bullpen",
+        "type": "workplace",
+        "properties": {"public": False, "moving": False, "audible_range": "room"},
+    })
+    append_event(conn, kind="edge_update", payload={
+        "source_id": "bot_a",
+        "target_id": "you",
+        "affinity_delta": 12,
+        "trust_delta": 5,
+        "knowledge_facts": [
+            "they work on the same floor",
+            "they've stayed late twice this week",
+        ],
+    })
+    append_event(conn, kind="activity_change", payload={
+        "entity_id": "you",
+        "container_id": 1,
+        "posture": "sitting at your desk",
+        "action": {"verb": "finishing emails"},
+        "attention": "the screen",
+        "holding": ["coffee mug"],
+    })
+    append_event(conn, kind="activity_change", payload={
+        "entity_id": "bot_a",
+        "container_id": 1,
+        "posture": "sitting at her desk",
+        "action": {"verb": "pretending to work"},
+        "attention": "you, in glances",
+    })
+    append_event(conn, kind="scene_opened", payload={
+        "chat_id": "chat_bot_a",
+        "container_id": 1,
+        "started_at": "2026-04-26T20:00:00+00:00",
+        "participants": ["you", "bot_a"],
+    })
+    project(conn)
+
+
+def test_basic_assembly_returns_system_message_with_all_must_blocks(tmp_path):
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    with open_db(db) as conn:
+        _seed_basic(conn)
+        msgs = assemble_narrative_prompt(
+            conn,
+            chat_id="chat_bot_a",
+            speaker_bot_id="bot_a",
+            recent_dialogue=[],
+            retrieved_memory_summaries=[],
+        )
+    assert isinstance(msgs, list)
+    assert len(msgs) == 1
+    sys_msg = msgs[0]
+    assert isinstance(sys_msg, Message)
+    assert sys_msg.role == "system"
+    body = sys_msg.content
+    # Must-include markers
+    assert "Aria" in body
+    assert "PERSONA" in body
+    assert "ACTIVITIES" in body
+    assert "CURRENT SCENE" in body
+    # Edge to addressee — name + numeric values (default affinity 50, +12 = 62)
+    assert "Sam" in body
+    assert "62/100" in body
+
+
+def test_user_turn_appended_as_user_message(tmp_path):
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    with open_db(db) as conn:
+        _seed_basic(conn)
+        msgs = assemble_narrative_prompt(
+            conn,
+            chat_id="chat_bot_a",
+            speaker_bot_id="bot_a",
+            user_turn_prose="*looks up* Hey.",
+            recent_dialogue=[],
+            retrieved_memory_summaries=[],
+        )
+    assert len(msgs) == 2
+    assert msgs[0].role == "system"
+    assert msgs[1].role == "user"
+    assert msgs[1].content == "*looks up* Hey."
+
+
+def test_must_only_succeeds_with_empty_optional_blocks(tmp_path):
+    """No dialogue, memories, other edges, or previous scene summary — should not raise."""
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    with open_db(db) as conn:
+        _seed_basic(conn)
+        msgs = assemble_narrative_prompt(
+            conn,
+            chat_id="chat_bot_a",
+            speaker_bot_id="bot_a",
+            recent_dialogue=None,  # default → nothing
+            retrieved_memory_summaries=None,
+            user_turn_prose=None,
+        )
+    assert len(msgs) == 1
+    body = msgs[0].content
+    # Must blocks present
+    assert "PERSONA" in body
+    assert "ACTIVITIES" in body
+    # Optional blocks not in body (nothing to render)
+    assert "OTHER EDGES" not in body
+    assert "PREVIOUS SCENE SUMMARY" not in body
+    assert "RELEVANT MEMORIES" not in body
+
+
+def test_long_dialogue_keeps_last_4_verbatim_and_summarizes_earlier(tmp_path):
+    """Stuff a huge dialogue history under budget pressure; older turns
+    must be elided to a placeholder, the last 4 verbatim, and earlier
+    unique markers gone.
+    """
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    with open_db(db) as conn:
+        _seed_basic(conn)
+        dialogue = []
+        for i in range(20):
+            speaker = "you" if i % 2 == 0 else "bot_a"
+            # Each line ~250 tokens of filler => 20 turns ≈ 5000 tokens,
+            # which together with MUST blocks pushes over soft (1500).
+            dialogue.append({
+                "speaker": speaker,
+                "text": f"unique-line-marker-{i:02d} " + ("filler " * 200),
+            })
+        msgs = assemble_narrative_prompt(
+            conn,
+            chat_id="chat_bot_a",
+            speaker_bot_id="bot_a",
+            recent_dialogue=dialogue,
+            retrieved_memory_summaries=[],
+            # Soft small enough to force NICE trim but hard fits MUST + 4.
+            budget_soft=1200,
+            budget_hard=8000,
+        )
+    body = msgs[0].content
+    # The last 4 unique markers (16, 17, 18, 19) must be present verbatim.
+    for i in range(16, 20):
+        assert f"unique-line-marker-{i:02d}" in body, f"expected last-4 marker {i} in body"
+    # Older markers must be dropped (replaced by elision placeholder).
+    for i in range(0, 16):
+        assert f"unique-line-marker-{i:02d}" not in body
+    # An "earlier" summary line must be present.
+    assert "earlier" in body.lower()
+    # Token count of system message respects hard budget.
+    import tiktoken
+    enc = tiktoken.get_encoding("cl100k_base")
+    assert len(enc.encode(body)) <= 8000
+
+
+def test_memories_drop_to_top_2_under_budget_pressure(tmp_path):
+    """4 memory summaries, each large; under tight soft budget only 2 should appear."""
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    with open_db(db) as conn:
+        _seed_basic(conn)
+        # Each ~1500 tokens of repeated text; drop tier should kick in.
+        long_chunk = "alpha beta gamma delta " * 400
+        memories = [
+            f"MEMORY-A {long_chunk}",
+            f"MEMORY-B {long_chunk}",
+            f"MEMORY-C {long_chunk}",
+            f"MEMORY-D {long_chunk}",
+        ]
+        msgs = assemble_narrative_prompt(
+            conn,
+            chat_id="chat_bot_a",
+            speaker_bot_id="bot_a",
+            recent_dialogue=[],
+            retrieved_memory_summaries=memories,
+            # Pressure: budgets that allow MUST + 2 memories but not 4.
+            budget_soft=4000,
+            budget_hard=5000,
+        )
+    body = msgs[0].content
+    # MEMORY-A and MEMORY-B are the top-2 and should remain; C & D dropped.
+    assert "MEMORY-A" in body
+    assert "MEMORY-B" in body
+    assert "MEMORY-C" not in body
+    assert "MEMORY-D" not in body
+    # Token count fits the hard budget.
+    import tiktoken
+    enc = tiktoken.get_encoding("cl100k_base")
+    assert len(enc.encode(body)) <= 5000
+
+
+def test_must_exceeds_budget_hard_raises_value_error(tmp_path):
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    with open_db(db) as conn:
+        _seed_basic(conn)
+        with pytest.raises(ValueError):
+            assemble_narrative_prompt(
+                conn,
+                chat_id="chat_bot_a",
+                speaker_bot_id="bot_a",
+                recent_dialogue=[],
+                retrieved_memory_summaries=[],
+                budget_soft=5,
+                budget_hard=10,
+            )