feat(memories): add get_recent_memories task and integrate recent memories into agent workflow

workflow/agent.py

@@ -3,7 +3,7 @@ from langgraph.func import entrypoint
 from langchain_core.messages import BaseMessage
 from langchain_core.runnables import RunnableConfig
 
-from .tasks import call_model, get_structued_output, manage_memories, call_tool
+from .tasks import call_model, get_structued_output, manage_memories, call_tool, get_recent_memories
 
 
 @entrypoint()
@@ -12,7 +12,10 @@ def agent(messages: list[BaseMessage], config: RunnableConfig):
     answer = None
     links = []
 
-    llm_future = call_model(messages)
+    # Fetch Recent User Information
+    recent_memories = get_recent_memories().result()
+
+    llm_future = call_model(messages, memories=recent_memories)
     memories_future = manage_memories(messages[-1].content)
 
     # Now, wait for both to complete
@@ -34,7 +37,7 @@ def agent(messages: list[BaseMessage], config: RunnableConfig):
         messages = add_messages(messages, [llm_response, *tool_results])
 
         # Call model again
-        llm_response = call_model(messages).result()
+        llm_response = call_model(messages, memories=recent_memories).result()
 
     if tool_calls_count > 0:
         # Structure the final output

workflow/prompts.py

@@ -37,9 +37,12 @@ If you used `search_vectorstore` tool to retrieve documents, you should:
 **Note:** Do not provide redundant sources.
 
 ---
-## Memories
-- When needed, use the `search_memories` tool to access information about the user's history and preferences
-- This tool provides context about past interactions and helps personalize responses
+## User Context
+1. You can use `search_memories` tool to retrieve stored user-related information such as personal details, hobbies, preferences, and background. This information evolves as the user continues to interact with you. Use it to personalize your responses.
+2. Current known information about the user:
+{memories}
+
+**Note:** DO NOT call `search_memories` tool if the section above indicates that no information is available yet. This usually means the user is new or hasn’t shared any information yet.
 
 ---
 ## Privacy

workflow/tasks.py

@@ -48,6 +48,25 @@ def manage_memories(user_message: str, config: RunnableConfig):
     )
     return memories
 
+@task
+def get_recent_memories(config: RunnableConfig):
+    """Retrieve the most recent user memories (max 10) based on `updated_at` timestamp. """
+    user_id = config.get("configurable", {}).get("user_id")
+
+    if not user_id:
+        raise ValueError("User Id not found in config")
+
+    memories = memory_client.get_all(version="v2", filters={"user_id": user_id})
+    
+    if not memories:
+        return "(No information is available about the user yet)"
+
+    # Sort memories by `updated_at` in descending order
+    sorted_memories = sorted(memories, key=lambda m: m["updated_at"], reverse=True)
+    recent_memories = sorted_memories[:10]
+
+    return "\n".join(f"- {mem['memory']}" for mem in recent_memories)
+
 @task
 def call_tool(tool_call, config: RunnableConfig):
     tool = tools_by_name[tool_call["name"]]