diff --git a/src/backend/langflow/api/v1/callback.py b/src/backend/langflow/api/v1/callback.py
index b58393d7b..03f76543e 100644
--- a/src/backend/langflow/api/v1/callback.py
+++ b/src/backend/langflow/api/v1/callback.py
@@ -1,22 +1,107 @@
 import asyncio
-from typing import Any
 
 from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
 
 from langflow.api.v1.schemas import ChatResponse
 
 
+from typing import Any, Dict, List, Union
+from fastapi import WebSocket
+
+
+from langchain.schema import AgentAction, LLMResult, AgentFinish
+
+
 # https://github.com/hwchase17/chat-langchain/blob/master/callback.py
 class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
     """Callback handler for streaming LLM responses."""
 
-    def __init__(self, websocket):
+    def __init__(self, websocket: WebSocket):
         self.websocket = websocket
 
     async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
         resp = ChatResponse(message=token, type="stream", intermediate_steps="")
         await self.websocket.send_json(resp.dict())
 
+    async def on_llm_start(
+        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
+    ) -> Any:
+        """Run when LLM starts running."""
+
+    async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any:
+        """Run when LLM ends running."""
+
+    async def on_llm_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> Any:
+        """Run when LLM errors."""
+
+    async def on_chain_start(
+        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+    ) -> Any:
+        """Run when chain starts running."""
+
+    async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> Any:
+        """Run when chain ends running."""
+
+    async def on_chain_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> Any:
+        """Run when chain errors."""
+
+    async def on_tool_start(
+        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
+    ) -> Any:
+        """Run when tool starts running."""
+        resp = ChatResponse(
+            message="",
+            type="stream",
+            intermediate_steps=f"Tool input: {input_str}",
+        )
+        await self.websocket.send_json(resp.dict())
+
+    async def on_tool_end(self, output: str, **kwargs: Any) -> Any:
+        """Run when tool ends running."""
+        resp = ChatResponse(
+            message="",
+            type="stream",
+            intermediate_steps=f"Tool output: {output}",
+        )
+        await self.websocket.send_json(resp.dict())
+
+    async def on_tool_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> Any:
+        """Run when tool errors."""
+
+    async def on_text(self, text: str, **kwargs: Any) -> Any:
+        """Run on arbitrary text."""
+        # This runs when first sending the prompt
+        # to the LLM, adding it will send the final prompt
+        # to the frontend
+
+    async def on_agent_action(self, action: AgentAction, **kwargs: Any):
+        log = f"Thought: {action.log}"
+        # if there are line breaks, split them and send them
+        # as separate messages
+        if "\n" in log:
+            logs = log.split("\n")
+            for log in logs:
+                resp = ChatResponse(message="", type="stream", intermediate_steps=log)
+                await self.websocket.send_json(resp.dict())
+        else:
+            resp = ChatResponse(message="", type="stream", intermediate_steps=log)
+            await self.websocket.send_json(resp.dict())
+
+    async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
+        """Run on agent end."""
+        resp = ChatResponse(
+            message="",
+            type="stream",
+            intermediate_steps=finish.log,
+        )
+        await self.websocket.send_json(resp.dict())
+
 
 class StreamingLLMCallbackHandler(BaseCallbackHandler):
     """Callback handler for streaming LLM responses."""