Inject current date into deep research planning and query prompts (#1347)

Deep research generated search queries from the LLM's training-cutoff
knowledge, so it emitted stale-year queries like "best Python tutorials
2025" when the actual year is later (issue #1341). The chat/agent path
already grounds the model with "Today is ..." (src/agent_loop.py); the
deep research planning and query-generation prompts had no equivalent.

Add a small current_date_context() helper and prepend it at the plan and
query-generation prompt sites (and the research_handler plan preview path
that reuses RESEARCH_PLAN_PROMPT). System-TZ local, portable strftime.

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

src/deep_research.py

@@ -11,6 +11,7 @@ import json
 import logging
 import re
 import time
+from datetime import datetime
 from typing import Callable, Dict, List, Optional, Set
 
 from src.research_utils import strip_thinking, is_low_quality
@@ -19,6 +20,20 @@ from src.goal_based_extractor import EXTRACTOR_PROMPT
 
 logger = logging.getLogger(__name__)
 
+
+def current_date_context() -> str:
+    """Preamble that grounds query-generation/planning LLMs in the real current
+    date. Without it the model falls back to its training-cutoff year and emits
+    queries like "best Python tutorials 2025" when the year is actually 2026.
+    System TZ-local so it matches what the user sees. Portable strftime only."""
+    now = datetime.now().astimezone()
+    return (
+        f"Today's date is {now.strftime('%B %d, %Y')} ({now.strftime('%Y-%m-%d')}). "
+        f"When a search query needs a year or refers to 'latest'/'current'/"
+        f"'this year', use {now.strftime('%Y')} or relative wording — never a "
+        f"year inferred from training data.\n\n"
+    )
+
 # ---------------------------------------------------------------------------
 # Prompts
 # ---------------------------------------------------------------------------
@@ -364,7 +379,7 @@ class DeepResearcher:
     # ------------------------------------------------------------------
     async def _create_plan(self, question: str) -> str:
         """LLM analyzes the question and creates a research plan."""
-        prompt = RESEARCH_PLAN_PROMPT.format(question=question)
+        prompt = current_date_context() + RESEARCH_PLAN_PROMPT.format(question=question)
         try:
             response = await self._llm(
                 [{"role": "user", "content": prompt}],
@@ -439,7 +454,7 @@ class DeepResearcher:
                 "that the report doesn't yet cover well."
             )
 
-        prompt = QUERY_GEN_PROMPT.format(
+        prompt = current_date_context() + QUERY_GEN_PROMPT.format(
             question=question,
             research_plan=self.research_plan or "(No plan — search broadly.)",
             report=report or "(No findings yet.)",

src/research_handler.py

@@ -161,10 +161,10 @@ class ResearchHandler:
     ) -> Optional[dict]:
         """Generate a research plan for user review before starting research."""
         try:
-            from src.deep_research import RESEARCH_PLAN_PROMPT
+            from src.deep_research import RESEARCH_PLAN_PROMPT, current_date_context
             from src.llm_core import llm_call_async
 
-            prompt = RESEARCH_PLAN_PROMPT.format(question=query)
+            prompt = current_date_context() + RESEARCH_PLAN_PROMPT.format(question=query)
             response = await llm_call_async(
                 url=llm_endpoint,
                 model=llm_model,