diff --git a/src/interface.jl b/src/interface.jl
index bcebbb3..a031c3c 100755
--- a/src/interface.jl
+++ b/src/interface.jl
@@ -83,12 +83,11 @@ function clearMessage(a::T)  where {T<:agent}
       break
     end
   end
-  memory::Dict{Symbol, Any} = Dict(
-    :shortterm=> OrderedDict{String, Any}(),
-    :longterm=> OrderedDict{String, Any}(),
-    :log=> OrderedDict{String, Any}(),  # span from user stimulus -> multiples attempts -> final respond
-  )
+  
+  a.memory = newAgentMemory()
+
   @show a.messages
+  @show a.memory
 end
 
 function removeLatestMsg(a::T)  where {T<:agent}
@@ -350,7 +349,7 @@ function selfAwareness(a::agentReflex)
       </About yourself>
       <Your job> 
       Use the following format strictly:
-      Info extraction: from the latest observed result, breakdown all info into smaller one and repeat every important info thoroughly (PS. check <Required wine info> for more details)
+      Info extraction: from the latest observed result, breakdown each info into smaller one and repeat every one of them (PS. check <Required wine info> for more details)
       Info mapping: based on extracted info, explicitly state what each info could match which keyword memory's key
       Info matching: using JSON format, what key in my memory matches which info (Donot use nested JSON)
       </Your job>
@@ -386,7 +385,7 @@ function selfAwareness(a::agentReflex)
   <|assistant|>
   Info extraction: 
   """
-  response = sendReceivePrompt(a, prompt, max_tokens=1024, temperature=0.2, timeout=180,
+  response = sendReceivePrompt(a, prompt, max_tokens=1024, temperature=0.4, timeout=180,
                                   stopword=["/n/n", "END", "End", "Obs", "<|", "</"])
   response = split(response, "<|")[1]
   response = split(response, "</")[1]
diff --git a/src/type.jl b/src/type.jl
index 9922de0..df88f87 100644
--- a/src/type.jl
+++ b/src/type.jl
@@ -1,12 +1,22 @@
 module type
   
-export agent, agentReflex
+export agent, agentReflex, newAgentMemory
 
 using Dates, UUIDs, DataStructures
 using CommUtils
 
 #------------------------------------------------------------------------------------------------100
 
+function newAgentMemory()
+  memory::Dict{Any, Any} = Dict(
+    :shortterm=> OrderedDict{String, Any}(),
+    :longterm=> OrderedDict{String, Any}(),
+    :log=> OrderedDict{String, Any}(),  # span from user stimulus -> multiples attempts -> final respond
+    :keyword=> Dict{String, Any}(),
+    )
+    return memory
+end
+
 
 abstract type agent end
 
@@ -99,12 +109,7 @@ julia> agent = ChatAgent.agentReflex(
   env::AbstractString = "N/A"
   thinkingFormat::Union{Dict, Nothing} = nothing
   roleSpecificInstruction::Union{Dict, Nothing} = nothing
-  memory::Dict{Any, Any} = Dict(
-    :shortterm=> OrderedDict{String, Any}(),
-    :longterm=> OrderedDict{String, Any}(),
-    :log=> OrderedDict{String, Any}(),  # span from user stimulus -> multiples attempts -> final respond
-    :keyword=> Dict{String, Any}(),
-    )
+  memory = newAgentMemory()
 
   # LLM function related
   winestockResult = ""
@@ -152,13 +157,13 @@ function agentReflex(
       """
       <Required wine info>
         You need to gather all of the following info sequentially before you can recommend wine:
-          1. wine budget 
-          2. wine type (rose, white, red, sparkling, dessert)  
-          3. paring food that will be served with wine
-          4. wine sweetness level (dry to very sweet)
-          5. wine intensity level (light to full bodied)
-          6. wine tannin level (low to high)
-          7. wine acidity level (low to high)
+          1. "wine budget" 
+          2. "wine type" (rose, white, red, sparkling, dessert)  
+          3. "food pairing" that will be served with wine
+          4. "wine sweetness level" (dry to very sweet)
+          5. "wine intensity level" (light to full bodied)
+          6. "wine tannin level" (low to high)
+          7. "wine acidity level" (low to high)
           8. wine you have in stock that match the user preferences
       </Required wine info>
       """