Fix directory expansion tracking in conversation memory

When directories were provided to tools, only the directory path was stored
in conversation history instead of the individual expanded files. This caused
file filtering to incorrectly skip files in continued conversations.

Changes:
- Modified _prepare_file_content_for_prompt to return (content, processed_files)
- Updated all tools to track actually processed files for conversation memory
- Ensures directories are tracked as their expanded individual files

Fixes issue where Swift directory with 46 files was not properly embedded
in conversation continuations.

tools/analyze.py

@@ -137,7 +137,8 @@ class AnalyzeTool(BaseTool):
 
         # Use centralized file processing logic
         continuation_id = getattr(request, "continuation_id", None)
-        file_content = self._prepare_file_content_for_prompt(request.files, continuation_id, "Files")
+        file_content, processed_files = self._prepare_file_content_for_prompt(request.files, continuation_id, "Files")
+        self._actually_processed_files = processed_files
 
         # Build analysis instructions
         analysis_focus = []

tools/base.py

@@ -544,7 +544,7 @@ class BaseTool(ABC):
         reserve_tokens: int = 1_000,
         remaining_budget: Optional[int] = None,
         arguments: Optional[dict] = None,
-    ) -> str:
+    ) -> tuple[str, list[str]]:
         """
         Centralized file processing for tool prompts.
 
@@ -563,10 +563,13 @@ class BaseTool(ABC):
             arguments: Original tool arguments (used to extract _remaining_tokens if available)
 
         Returns:
-            str: Formatted file content string ready for prompt inclusion
+            tuple[str, list[str]]: (formatted_file_content, actually_processed_files)
+                - formatted_file_content: Formatted file content string ready for prompt inclusion
+                - actually_processed_files: List of individual file paths that were actually read and embedded
+                  (directories are expanded to individual files)
         """
         if not request_files:
-            return ""
+            return "", []
 
         # Note: Even if conversation history is already embedded, we still need to process
         # any NEW files that aren't in the conversation history yet. The filter_new_files
@@ -705,6 +708,7 @@ class BaseTool(ABC):
             )
 
         content_parts = []
+        actually_processed_files = []
 
         # Read content of new files only
         if files_to_embed:
@@ -713,6 +717,11 @@ class BaseTool(ABC):
                 f"[FILES] {self.name}: Starting file embedding with token budget {effective_max_tokens + reserve_tokens:,}"
             )
             try:
+                # Before calling read_files, expand directories to get individual file paths
+                from utils.file_utils import expand_paths
+                expanded_files = expand_paths(files_to_embed)
+                logger.debug(f"[FILES] {self.name}: Expanded {len(files_to_embed)} paths to {len(expanded_files)} individual files")
+                
                 file_content = read_files(
                     files_to_embed,
                     max_tokens=effective_max_tokens + reserve_tokens,
@@ -721,6 +730,9 @@ class BaseTool(ABC):
                 )
                 self._validate_token_limit(file_content, context_description)
                 content_parts.append(file_content)
+                
+                # Track the expanded files as actually processed
+                actually_processed_files.extend(expanded_files)
 
                 # Estimate tokens for debug logging
                 from utils.token_utils import estimate_tokens
@@ -730,6 +742,7 @@ class BaseTool(ABC):
                     f"{self.name} tool successfully embedded {len(files_to_embed)} files ({content_tokens:,} tokens)"
                 )
                 logger.debug(f"[FILES] {self.name}: Successfully embedded files - {content_tokens:,} tokens used")
+                logger.debug(f"[FILES] {self.name}: Actually processed {len(actually_processed_files)} individual files")
             except Exception as e:
                 logger.error(f"{self.name} tool failed to embed files {files_to_embed}: {type(e).__name__}: {e}")
                 logger.debug(f"[FILES] {self.name}: File embedding failed - {type(e).__name__}: {e}")
@@ -759,8 +772,8 @@ class BaseTool(ABC):
                 logger.debug(f"[FILES] {self.name}: No skipped files to note")
 
         result = "".join(content_parts) if content_parts else ""
-        logger.debug(f"[FILES] {self.name}: _prepare_file_content_for_prompt returning {len(result)} chars")
+        logger.debug(f"[FILES] {self.name}: _prepare_file_content_for_prompt returning {len(result)} chars, {len(actually_processed_files)} processed files")
-        return result
+        return result, actually_processed_files
 
     def get_websearch_instruction(self, use_websearch: bool, tool_specific: Optional[str] = None) -> str:
         """
@@ -1408,7 +1421,9 @@ When recommending searches, be specific about what information you need and why
             )
 
             # Add this response as the first turn (assistant turn)
-            request_files = getattr(request, "files", []) or []
+            # Use actually processed files from file preparation instead of original request files
+            # This ensures directories are tracked as their individual expanded files
+            request_files = getattr(self, "_actually_processed_files", []) or getattr(request, "files", []) or []
             # Extract model metadata
             model_provider = None
             model_name = None

tools/chat.py

@@ -124,9 +124,10 @@ class ChatTool(BaseTool):
 
         # Add context files if provided (using centralized file handling with filtering)
         if request.files:
-            file_content = self._prepare_file_content_for_prompt(
+            file_content, processed_files = self._prepare_file_content_for_prompt(
                 request.files, request.continuation_id, "Context files"
             )
+            self._actually_processed_files = processed_files
             if file_content:
                 user_content = f"{user_content}\n\n=== CONTEXT FILES ===\n{file_content}\n=== END CONTEXT ===="
 

tools/codereview.py

@@ -196,7 +196,8 @@ class CodeReviewTool(BaseTool):
 
         # Use centralized file processing logic
         continuation_id = getattr(request, "continuation_id", None)
-        file_content = self._prepare_file_content_for_prompt(request.files, continuation_id, "Code")
+        file_content, processed_files = self._prepare_file_content_for_prompt(request.files, continuation_id, "Code")
+        self._actually_processed_files = processed_files
 
         # Build customized review instructions based on review type
         review_focus = []

tools/debug.py

@@ -166,7 +166,8 @@ class DebugIssueTool(BaseTool):
         if request.files:
             # Use centralized file processing logic
             continuation_id = getattr(request, "continuation_id", None)
-            file_content = self._prepare_file_content_for_prompt(request.files, continuation_id, "Code")
+            file_content, processed_files = self._prepare_file_content_for_prompt(request.files, continuation_id, "Code")
+            self._actually_processed_files = processed_files
 
             if file_content:
                 context_parts.append(f"\n=== RELEVANT CODE ===\n{file_content}\n=== END CODE ===")

tools/precommit.py

@@ -408,13 +408,14 @@ class Precommit(BaseTool):
             remaining_tokens = max_tokens - total_tokens
 
             # Use centralized file handling with filtering for duplicate prevention
-            file_content = self._prepare_file_content_for_prompt(
+            file_content, processed_files = self._prepare_file_content_for_prompt(
                 translated_files,
                 request.continuation_id,
                 "Context files",
                 max_tokens=remaining_tokens + 1000,  # Add back the reserve that was calculated
                 reserve_tokens=1000,  # Small reserve for formatting
             )
+            self._actually_processed_files = processed_files
 
             if file_content:
                 context_tokens = estimate_tokens(file_content)

tools/refactor.py

@@ -330,13 +330,14 @@ class RefactorTool(BaseTool):
         # Use standard file content preparation with dynamic token budget and line numbers
         try:
             logger.debug(f"[REFACTOR] Preparing file content for {len(examples_to_process)} style examples")
-            content = self._prepare_file_content_for_prompt(
+            content, processed_files = self._prepare_file_content_for_prompt(
                 examples_to_process,
                 continuation_id,
                 "Style guide examples",
                 max_tokens=style_examples_budget,
                 reserve_tokens=1000,
             )
+            # Store processed files for tracking - style examples are tracked separately from main code files
 
             # Determine how many files were actually included
             if content:
@@ -478,9 +479,10 @@ class RefactorTool(BaseTool):
 
         # Use centralized file processing logic for main code files (with line numbers enabled)
         logger.debug(f"[REFACTOR] Preparing {len(code_files_to_process)} code files for analysis")
-        code_content = self._prepare_file_content_for_prompt(
+        code_content, processed_files = self._prepare_file_content_for_prompt(
             code_files_to_process, continuation_id, "Code to analyze", max_tokens=remaining_tokens, reserve_tokens=2000
         )
+        self._actually_processed_files = processed_files
 
         if code_content:
             from utils.token_utils import estimate_tokens

tools/testgen.py

@@ -214,13 +214,14 @@ class TestGenTool(BaseTool):
         # Use standard file content preparation with dynamic token budget
         try:
             logger.debug(f"[TESTGEN] Preparing file content for {len(examples_to_process)} test examples")
-            content = self._prepare_file_content_for_prompt(
+            content, processed_files = self._prepare_file_content_for_prompt(
                 examples_to_process,
                 continuation_id,
                 "Test examples",
                 max_tokens=test_examples_budget,
                 reserve_tokens=1000,
             )
+            # Store processed files for tracking - test examples are tracked separately from main code files
 
             # Determine how many files were actually included
             if content:
@@ -358,9 +359,10 @@ class TestGenTool(BaseTool):
 
         # Use centralized file processing logic for main code files (after deduplication)
         logger.debug(f"[TESTGEN] Preparing {len(code_files_to_process)} code files for analysis")
-        code_content = self._prepare_file_content_for_prompt(
+        code_content, processed_files = self._prepare_file_content_for_prompt(
             code_files_to_process, continuation_id, "Code to test", max_tokens=remaining_tokens, reserve_tokens=2000
         )
+        self._actually_processed_files = processed_files
 
         if code_content:
             from utils.token_utils import estimate_tokens

tools/thinkdeep.py

@@ -148,7 +148,8 @@ class ThinkDeepTool(BaseTool):
         if request.files:
             # Use centralized file processing logic
             continuation_id = getattr(request, "continuation_id", None)
-            file_content = self._prepare_file_content_for_prompt(request.files, continuation_id, "Reference files")
+            file_content, processed_files = self._prepare_file_content_for_prompt(request.files, continuation_id, "Reference files")
+            self._actually_processed_files = processed_files
 
             if file_content:
                 context_parts.append(f"\n=== REFERENCE FILES ===\n{file_content}\n=== END FILES ===")