fix: strip <think> tags from chat responses

Add thinking content cleaning to notebook and source chat graphs.
Previously, models that output <think>...</think> tags (like DeepSeek)
or malformed variants without opening tags (like Nemotron) would leak
reasoning content into user-visible responses.

Changes:
- chat.py: Clean AI response content before returning messages
- source_chat.py: Same fix for source-specific chat
- text_utils.py: Handle malformed output where opening <think> tag
  is missing but </think> is present

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

open_notebook/graphs/chat.py

@@ -3,8 +3,10 @@ import sqlite3
 from typing import Annotated, Optional
 
 from ai_prompter import Prompter
-from langchain_core.messages import SystemMessage
+from langchain_core.messages import AIMessage, SystemMessage
 from langchain_core.runnables import RunnableConfig
+
+from open_notebook.utils import clean_thinking_content
 from langgraph.checkpoint.sqlite import SqliteSaver
 from langgraph.graph import END, START, StateGraph
 from langgraph.graph.message import add_messages
@@ -66,7 +68,13 @@ def call_model_with_messages(state: ThreadState, config: RunnableConfig) -> dict
         )
 
     ai_message = model.invoke(payload)
-    return {"messages": ai_message}
+
+    # Clean thinking content from AI response (e.g., <think>...</think> tags)
+    content = ai_message.content if isinstance(ai_message.content, str) else str(ai_message.content)
+    cleaned_content = clean_thinking_content(content)
+    cleaned_message = AIMessage(content=cleaned_content)
+
+    return {"messages": cleaned_message}
 
 
 conn = sqlite3.connect(

open_notebook/graphs/source_chat.py

@@ -3,8 +3,10 @@ import sqlite3
 from typing import Annotated, Dict, List, Optional
 
 from ai_prompter import Prompter
-from langchain_core.messages import SystemMessage
+from langchain_core.messages import AIMessage, SystemMessage
 from langchain_core.runnables import RunnableConfig
+
+from open_notebook.utils import clean_thinking_content
 from langgraph.checkpoint.sqlite import SqliteSaver
 from langgraph.graph import END, START, StateGraph
 from langgraph.graph.message import add_messages
@@ -154,9 +156,14 @@ def call_model_with_source_context(
 
     ai_message = model.invoke(payload)
 
+    # Clean thinking content from AI response (e.g., <think>...</think> tags)
+    content = ai_message.content if isinstance(ai_message.content, str) else str(ai_message.content)
+    cleaned_content = clean_thinking_content(content)
+    cleaned_message = AIMessage(content=cleaned_content)
+
     # Update state with context information
     return {
-        "messages": ai_message,
+        "messages": cleaned_message,
         "source": source,
         "insights": insights,
         "context": formatted_context,

open_notebook/utils/text_utils.py

@@ -11,8 +11,11 @@ from langchain_text_splitters import RecursiveCharacterTextSplitter
 
 from .token_utils import token_count
 
-# Pattern for matching thinking content in AI responses
+# Patterns for matching thinking content in AI responses
+# Standard pattern: <think>...</think>
 THINK_PATTERN = re.compile(r"<think>(.*?)</think>", re.DOTALL)
+# Pattern for malformed output: content</think> (missing opening tag)
+THINK_PATTERN_NO_OPEN = re.compile(r"^(.*?)</think>", re.DOTALL)
 
 
 def split_text(txt: str, chunk_size=500):
@@ -77,6 +80,9 @@ def parse_thinking_content(content: str) -> Tuple[str, str]:
     """
     Parse message content to extract thinking content from <think> tags.
 
+    Handles both well-formed tags and malformed output where the opening
+    <think> tag is missing but </think> is present.
+
     Args:
         content (str): The original message content
 
@@ -101,22 +107,31 @@ def parse_thinking_content(content: str) -> Tuple[str, str]:
     if len(content) > 100000:
         return "", content
 
-    # Find all thinking blocks
+    # Find all well-formed thinking blocks
     thinking_matches = THINK_PATTERN.findall(content)
 
-    if not thinking_matches:
-        return "", content
+    if thinking_matches:
+        # Join all thinking content with double newlines
+        thinking_content = "\n\n".join(match.strip() for match in thinking_matches)
 
-    # Join all thinking content with double newlines
-    thinking_content = "\n\n".join(match.strip() for match in thinking_matches)
+        # Remove all <think>...</think> blocks from the original content
+        cleaned_content = THINK_PATTERN.sub("", content)
 
-    # Remove all <think>...</think> blocks from the original content
-    cleaned_content = THINK_PATTERN.sub("", content)
+        # Clean up extra whitespace
+        cleaned_content = re.sub(r"\n\s*\n\s*\n", "\n\n", cleaned_content).strip()
 
-    # Clean up extra whitespace
-    cleaned_content = re.sub(r"\n\s*\n\s*\n", "\n\n", cleaned_content).strip()
+        return thinking_content, cleaned_content
 
-    return thinking_content, cleaned_content
+    # Handle malformed output: content</think> (missing opening tag)
+    # Some models like Nemotron output thinking without the opening <think> tag
+    malformed_match = THINK_PATTERN_NO_OPEN.match(content)
+    if malformed_match:
+        thinking_content = malformed_match.group(1).strip()
+        # Remove the thinking content and </think> tag
+        cleaned_content = content[malformed_match.end():].strip()
+        return thinking_content, cleaned_content
+
+    return "", content
 
 
 def clean_thinking_content(content: str) -> str: