fix: normalize home task/timeline responses to tag-only lines

app/core/deep_agent.py

@@ -5,6 +5,7 @@ from __future__ import annotations
 import json
 import logging
 import re
+from datetime import date
 from collections.abc import AsyncGenerator
 from typing import Any, Literal
 
@@ -29,8 +30,12 @@ _HOME_SINGLE_AGENT_SYSTEM = (
     "Always use tools for factual data retrieval before answering. "
     "When the user asks to remember, forget, or update what you know about them, use memory tools. "
     "If context.context.resolved_project_id exists, use it as project_id for scoped list calls. "
-    "Return markdown and embed inline tags when relevant: <project>[ids]</project>, <task>[ids]</task>, "
-    "<note>[ids]</note>, <timeline>[ids]</timeline>, <chart>{json}</chart>."
+    "Return markdown and use tags when relevant: <project>[ids]</project>, <task>[ids]</task>, "
+    "<note>[ids]</note>, <timeline>[ids]</timeline>, <chart>{json}</chart>. "
+    "When listing tasks or timelines, each id tag must be on its own line with no prefix/suffix text. "
+    "Never put titles, priorities, or dates on the same line as <task> or <timeline> tags. "
+    "For questions about upcoming timelines (e.g. 'prossimi eventi'), include only future items in the current month unless the user asks a different range. "
+    "For upcoming tasks, after tag lines add a short recommendation based on due date and priority."
 )
 
 _FLOATING_SINGLE_AGENT_SYSTEM = (
@@ -136,6 +141,75 @@ def _context_for_model(context: dict[str, Any]) -> dict[str, Any]:
     return sanitized
 
 
+_TAG_LINE_RE = re.compile(r"<(task|timeline)>\[[^\]]+\]</\1>")
+_TIMELINE_DMY_RE = re.compile(r"(?P<d>\d{2})/(?P<m>\d{2})/(?P<y>\d{4})")
+
+
+def _is_upcoming_timeline_query(message: str) -> bool:
+    lowered = message.lower()
+    has_upcoming = "prossim" in lowered or "upcoming" in lowered or "next" in lowered
+    has_timeline_topic = any(
+        token in lowered
+        for token in ("event", "evento", "eventi", "timeline", "milestone", "scaden")
+    )
+    return has_upcoming and has_timeline_topic
+
+
+def _timeline_date_in_current_month_or_future(dmy: str) -> bool:
+    match = _TIMELINE_DMY_RE.search(dmy)
+    if not match:
+        return True
+    try:
+        parsed = date(
+            int(match.group("y")),
+            int(match.group("m")),
+            int(match.group("d")),
+        )
+    except ValueError:
+        return True
+
+    today = date.today()
+    return parsed >= today and parsed.year == today.year and parsed.month == today.month
+
+
+def _normalize_tagged_list_lines(text: str, message: str) -> str:
+    if not text:
+        return text
+
+    upcoming_timeline_only = _is_upcoming_timeline_query(message)
+    output_lines: list[str] = []
+
+    for line in text.splitlines():
+        matches = list(_TAG_LINE_RE.finditer(line))
+        if not matches:
+            output_lines.append(line)
+            continue
+
+        had_non_tag_text = _TAG_LINE_RE.sub("", line).strip(" -\t0123456789.*:)")
+        if not had_non_tag_text and len(matches) == 1:
+            tag_text = matches[0].group(0)
+            if (
+                upcoming_timeline_only
+                and "<timeline>" in tag_text
+                and not _timeline_date_in_current_month_or_future(line)
+            ):
+                continue
+            output_lines.append(tag_text)
+            continue
+
+        for match in matches:
+            tag_text = match.group(0)
+            if (
+                upcoming_timeline_only
+                and "<timeline>" in tag_text
+                and not _timeline_date_in_current_month_or_future(line)
+            ):
+                continue
+            output_lines.append(tag_text)
+
+    return "\n".join(output_lines)
+
+
 def _normalize_memory_label(path_or_label: str) -> str:
     value = path_or_label.strip()
     if value.startswith("/memories/"):
@@ -475,12 +549,13 @@ async def _run_single_agent_stream(
 
 async def run_home(user_id: str, message: str, context: dict[str, Any]) -> str:
     prepared_context = await _prepare_context(message, context)
-    return await _run_single_agent(
+    response = await _run_single_agent(
         user_id=user_id,
         system_prompt=_HOME_SINGLE_AGENT_SYSTEM,
         message=message,
         context=prepared_context,
     )
+    return _normalize_tagged_list_lines(response, message)
 
 
 async def run_floating(user_id: str, message: str, context: dict[str, Any]) -> tuple[str, str]:
@@ -501,13 +576,22 @@ async def run_home_stream(
     context: dict[str, Any],
 ) -> AsyncGenerator[tuple[str, Any], None]:
     prepared_context = await _prepare_context(message, context)
+    text_chunks: list[str] = []
     async for event in _run_single_agent_stream(
         user_id=user_id,
         system_prompt=_HOME_SINGLE_AGENT_SYSTEM,
         message=message,
         context=prepared_context,
     ):
-        yield event
+        event_type, data = event
+        if event_type != "token":
+            yield event
+            continue
+        text_chunks.append(str(data or ""))
+
+    normalized = _normalize_tagged_list_lines("".join(text_chunks), message)
+    if normalized:
+        yield "token", normalized
 
 
 async def run_floating_stream(