diff --git a/backend/app/core/graph/checkpoint/utils.py b/backend/app/core/graph/checkpoint/utils.py
index beefbf1a..1629c1c4 100644
--- a/backend/app/core/graph/checkpoint/utils.py
+++ b/backend/app/core/graph/checkpoint/utils.py
@@ -1,6 +1,8 @@
 import json
+from typing import Any
 from uuid import uuid4
 
+from langchain_core.documents import Document
 from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, ToolMessage
 from langgraph.checkpoint.base import CheckpointTuple
 from psycopg import AsyncConnection
@@ -59,12 +61,23 @@ def convert_checkpoint_tuple_to_messages(
                 )
             )
         elif isinstance(message, ToolMessage) and message.name:
+            documents: list[dict[str, Any]] = []
+            if message.name == "KnowledgeBase":
+                docs: list[Document] = message.artifact
+                for doc in docs:
+                    documents.append(
+                        {
+                            "score": doc.metadata["score"],
+                            "content": doc.page_content,
+                        }
+                    )
             formatted_messages.append(
                 ChatResponse(
                     type="tool",
                     id=message.tool_call_id,
                     name=message.name,
                     tool_output=json.dumps(message.content),
+                    documents=json.dumps(documents),
                 )
             )
         else:
diff --git a/backend/app/core/graph/members.py b/backend/app/core/graph/members.py
index 0334973e..1d1e5f52 100644
--- a/backend/app/core/graph/members.py
+++ b/backend/app/core/graph/members.py
@@ -2,7 +2,6 @@
 from typing import Annotated, Any
 
 from langchain.chat_models import init_chat_model
-from langchain.tools.retriever import create_retriever_tool
 from langchain_core.messages import AIMessage, AnyMessage
 from langchain_core.output_parsers.openai_tools import JsonOutputKeyToolsParser
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
@@ -21,6 +20,7 @@
 from app.core.graph.rag.qdrant import QdrantStore
 from app.core.graph.skills import managed_skills
 from app.core.graph.skills.api_tool import dynamic_api_tool
+from app.core.graph.skills.retriever_tool import create_retriever_tool
 
 
 class GraphSkill(BaseModel):
@@ -49,9 +49,7 @@ class GraphUpload(BaseModel):
     @property
     def tool(self) -> BaseTool:
         retriever = QdrantStore().retriever(self.owner_id, self.upload_id)
-        return create_retriever_tool(
-            retriever, name=self.name, description=self.description
-        )
+        return create_retriever_tool(retriever)
 
 
 class GraphPerson(BaseModel):
diff --git a/backend/app/core/graph/messages.py b/backend/app/core/graph/messages.py
index c9008d4f..7ae3dd8a 100644
--- a/backend/app/core/graph/messages.py
+++ b/backend/app/core/graph/messages.py
@@ -75,27 +75,25 @@ def event_to_response(event: StreamEvent) -> ChatResponse | None:
     elif kind == "on_tool_end":
         tool_output: ToolMessage | None = event["data"].get("output")
         tool_name = event["name"]
+        # If tool is , KnowledgeBase then serialise the documents in artifact
+        documents: list[dict[str, Any]] = []
+        if tool_output and tool_output.name == "KnowledgeBase":
+            docs: list[Document] = tool_output.artifact
+            for doc in docs:
+                documents.append(
+                    {
+                        "score": doc.metadata["score"],
+                        "content": doc.page_content,
+                    }
+                )
         if tool_output:
             return ChatResponse(
                 type="tool",
                 id=id,
                 name=tool_name,
                 tool_output=json.dumps(tool_output.content),
+                documents=json.dumps(documents),
             )
-    elif kind == "on_retriever_end":
-        name = "documents"
-        docs: list[Document] = event["data"]["output"]
-        documents: list[dict[str, Any]] = []
-        for doc in docs:
-            documents.append(
-                {
-                    "score": doc.metadata["score"],
-                    "content": doc.page_content,
-                }
-            )
-        return ChatResponse(
-            type="retriever", id=id, name=name, documents=json.dumps(documents)
-        )
     # elif kind == "on_parser_end":
     #     content: str = event["data"]["output"].get("task")
     #     next = event["data"]["output"].get("next")
diff --git a/backend/app/core/graph/skills/retriever_tool.py b/backend/app/core/graph/skills/retriever_tool.py
new file mode 100644
index 00000000..3dccd0be
--- /dev/null
+++ b/backend/app/core/graph/skills/retriever_tool.py
@@ -0,0 +1,40 @@
+from typing import Annotated, Literal
+
+from langchain_core.documents import Document
+from langchain_core.prompts import BasePromptTemplate, PromptTemplate, format_document
+from langchain_core.retrievers import BaseRetriever
+from langchain_core.tools import BaseTool
+
+
+class RetrieverTool(BaseTool):
+    name: str = "KnowledgeBase"
+    description: str = "Query documents for answers."
+    response_format: Literal["content", "content_and_artifact"] = "content_and_artifact"
+
+    retriever: BaseRetriever
+    document_prompt: BasePromptTemplate | PromptTemplate  # type: ignore [type-arg]
+    document_separator: str
+
+    def _run(
+        self, query: Annotated[str, "query to look up in retriever"]
+    ) -> tuple[str, list[Document]]:
+        """Retrieve documents from knowledge base."""
+        docs = self.retriever.invoke(query, config={"callbacks": self.callbacks})
+        result_string = self.document_separator.join(
+            [format_document(doc, self.document_prompt) for doc in docs]
+        )
+        return result_string, docs
+
+
+def create_retriever_tool(
+    retriever: BaseRetriever,
+    document_prompt: BasePromptTemplate | None = None,  # type: ignore [type-arg]
+    document_separator: str = "\n\n",
+) -> BaseTool:
+    document_prompt = document_prompt or PromptTemplate.from_template("{page_content}")
+
+    return RetrieverTool(
+        retriever=retriever,
+        document_prompt=document_prompt,
+        document_separator=document_separator,
+    )