diff --git a/letta/agent.py b/letta/agent.py
index b276ed79..41fdf6dd 100644
--- a/letta/agent.py
+++ b/letta/agent.py
@@ -1269,6 +1269,119 @@ class Agent(BaseAgent):
             functions_definitions=available_functions_definitions,
         )
 
+    async def get_context_window_async(self) -> ContextWindowOverview:
+        """Get the context window of the agent"""
+        # Grab the in-context messages
+        # conversion of messages to OpenAI dict format, which is passed to the token counter
+        in_context_messages = await self.agent_manager.get_in_context_messages_async(agent_id=self.agent_state.id, actor=self.user)
+        in_context_messages_openai = [m.to_openai_dict() for m in in_context_messages]
+
+        # Extract system, memory and external summary
+        if (
+            len(in_context_messages) > 0
+            and in_context_messages[0].role == MessageRole.system
+            and in_context_messages[0].content
+            and len(in_context_messages[0].content) == 1
+            and isinstance(in_context_messages[0].content[0], TextContent)
+        ):
+            system_message = in_context_messages[0].content[0].text
+
+            external_memory_marker_pos = system_message.find("###")
+            core_memory_marker_pos = system_message.find("<", external_memory_marker_pos)
+            if external_memory_marker_pos != -1 and core_memory_marker_pos != -1:
+                system_prompt = system_message[:external_memory_marker_pos].strip()
+                external_memory_summary = system_message[external_memory_marker_pos:core_memory_marker_pos].strip()
+                core_memory = system_message[core_memory_marker_pos:].strip()
+            else:
+                # if no markers found, put everything in system message
+                system_prompt = system_message
+                external_memory_summary = ""
+                core_memory = ""
+        else:
+            # if no system message, fall back on agent's system prompt
+            system_prompt = self.agent_state.system
+            external_memory_summary = ""
+            core_memory = ""
+
+        num_tokens_system = count_tokens(system_prompt)
+        num_tokens_core_memory = count_tokens(core_memory)
+        num_tokens_external_memory_summary = count_tokens(external_memory_summary)
+
+        # Check if there's a summary message in the message queue
+        if (
+            len(in_context_messages) > 1
+            and in_context_messages[1].role == MessageRole.user
+            and in_context_messages[1].content
+            and len(in_context_messages[1].content) == 1
+            and isinstance(in_context_messages[1].content[0], TextContent)
+            # TODO remove hardcoding
+            and "The following is a summary of the previous " in in_context_messages[1].content[0].text
+        ):
+            # Summary message exists
+            text_content = in_context_messages[1].content[0].text
+            assert text_content is not None
+            summary_memory = text_content
+            num_tokens_summary_memory = count_tokens(text_content)
+            # with a summary message, the real messages start at index 2
+            num_tokens_messages = (
+                num_tokens_from_messages(messages=in_context_messages_openai[2:], model=self.model)
+                if len(in_context_messages_openai) > 2
+                else 0
+            )
+
+        else:
+            summary_memory = None
+            num_tokens_summary_memory = 0
+            # with no summary message, the real messages start at index 1
+            num_tokens_messages = (
+                num_tokens_from_messages(messages=in_context_messages_openai[1:], model=self.model)
+                if len(in_context_messages_openai) > 1
+                else 0
+            )
+
+        # tokens taken up by function definitions
+        agent_state_tool_jsons = [t.json_schema for t in self.agent_state.tools]
+        if agent_state_tool_jsons:
+            available_functions_definitions = [OpenAITool(type="function", function=f) for f in agent_state_tool_jsons]
+            num_tokens_available_functions_definitions = num_tokens_from_functions(functions=agent_state_tool_jsons, model=self.model)
+        else:
+            available_functions_definitions = []
+            num_tokens_available_functions_definitions = 0
+
+        num_tokens_used_total = (
+            num_tokens_system  # system prompt
+            + num_tokens_available_functions_definitions  # function definitions
+            + num_tokens_core_memory  # core memory
+            + num_tokens_external_memory_summary  # metadata (statistics) about recall/archival
+            + num_tokens_summary_memory  # summary of ongoing conversation
+            + num_tokens_messages  # tokens taken by messages
+        )
+        assert isinstance(num_tokens_used_total, int)
+
+        return ContextWindowOverview(
+            # context window breakdown (in messages)
+            num_messages=len(in_context_messages),
+            num_archival_memory=agent_manager_passage_size,
+            num_recall_memory=message_manager_size,
+            num_tokens_external_memory_summary=num_tokens_external_memory_summary,
+            external_memory_summary=external_memory_summary,
+            # top-level information
+            context_window_size_max=self.agent_state.llm_config.context_window,
+            context_window_size_current=num_tokens_used_total,
+            # context window breakdown (in tokens)
+            num_tokens_system=num_tokens_system,
+            system_prompt=system_prompt,
+            num_tokens_core_memory=num_tokens_core_memory,
+            core_memory=core_memory,
+            num_tokens_summary_memory=num_tokens_summary_memory,
+            summary_memory=summary_memory,
+            num_tokens_messages=num_tokens_messages,
+            messages=in_context_messages,
+            # related to functions
+            num_tokens_functions_definitions=num_tokens_available_functions_definitions,
+            functions_definitions=available_functions_definitions,
+        )
+
     def count_tokens(self) -> int:
         """Count the tokens in the current context window"""
         context_window_breakdown = self.get_context_window()