diff --git a/surfsense_backend/app/agents/researcher/nodes.py b/surfsense_backend/app/agents/researcher/nodes.py
index c5ec229..5f153d6 100644
--- a/surfsense_backend/app/agents/researcher/nodes.py
+++ b/surfsense_backend/app/agents/researcher/nodes.py
@@ -26,6 +26,77 @@ from .sub_section_writer.graph import graph as sub_section_writer_graph
 from .utils import AnswerOutline, get_connector_emoji, get_connector_friendly_name
 
 
+def extract_sources_from_documents(
+    all_documents: list[dict[str, Any]],
+) -> list[dict[str, Any]]:
+    """
+    Extract sources from all_documents and group them by document type.
+
+    Args:
+        all_documents: List of document chunks from user-selected documents and connector-fetched documents
+
+    Returns:
+        List of source objects grouped by type for streaming
+    """
+    # Group documents by their source type
+    documents_by_type = {}
+
+    for doc in all_documents:
+        # Get source type from the document
+        source_type = doc.get("source", "UNKNOWN")
+        document_info = doc.get("document", {})
+        document_type = document_info.get("document_type", source_type)
+
+        # Use document_type if available, otherwise use source
+        group_type = document_type if document_type != "UNKNOWN" else source_type
+
+        if group_type not in documents_by_type:
+            documents_by_type[group_type] = []
+        documents_by_type[group_type].append(doc)
+
+    # Create source objects for each document type
+    source_objects = []
+    source_id_counter = 1
+
+    for doc_type, docs in documents_by_type.items():
+        sources_list = []
+
+        for doc in docs:
+            document_info = doc.get("document", {})
+            metadata = document_info.get("metadata", {})
+
+            # Create source entry based on document structure
+            source = {
+                "id": doc.get("chunk_id", source_id_counter),
+                "title": document_info.get("title", "Untitled Document"),
+                "description": doc.get("content", "")[:100] + "..."
+                if len(doc.get("content", "")) > 100
+                else doc.get("content", ""),
+                "url": metadata.get("url", metadata.get("page_url", "")),
+            }
+
+            source_id_counter += 1
+            sources_list.append(source)
+
+        # Create group object
+        group_name = (
+            get_connector_friendly_name(doc_type)
+            if doc_type != "UNKNOWN"
+            else "Unknown Sources"
+        )
+
+        source_object = {
+            "id": len(source_objects) + 1,
+            "name": group_name,
+            "type": doc_type,
+            "sources": sources_list,
+        }
+
+        source_objects.append(source_object)
+
+    return source_objects
+
+
 async def fetch_documents_by_ids(
     document_ids: list[int], user_id: str, db_session: AsyncSession
 ) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
@@ -1169,16 +1240,6 @@ async def fetch_relevant_documents(
             }
         )
 
-    # After all sources are collected and deduplicated, stream them
-    if streaming_service and writer:
-        writer(
-            {
-                "yield_value": streaming_service.format_sources_delta(
-                    deduplicated_sources
-                )
-            }
-        )
-
     # Deduplicate raw documents based on chunk_id or content
     seen_chunk_ids = set()
     seen_content_hashes = set()
@@ -1355,6 +1416,13 @@ async def process_sections(
     )
     print(f"Total documents for sections: {len(all_documents)}")
 
+    # Extract and stream sources from all_documents
+    if all_documents:
+        sources_to_stream = extract_sources_from_documents(all_documents)
+        writer(
+            {"yield_value": streaming_service.format_sources_delta(sources_to_stream)}
+        )
+
     writer(
         {
             "yield_value": streaming_service.format_terminal_info_delta(
@@ -1781,6 +1849,13 @@ async def handle_qna_workflow(
     print(f"Added {len(user_selected_documents)} user-selected documents for QNA")
     print(f"Total documents for QNA: {len(all_documents)}")
 
+    # Extract and stream sources from all_documents
+    if all_documents:
+        sources_to_stream = extract_sources_from_documents(all_documents)
+        writer(
+            {"yield_value": streaming_service.format_sources_delta(sources_to_stream)}
+        )
+
     writer(
         {
             "yield_value": streaming_service.format_terminal_info_delta(