diff --git a/src/interface.jl b/src/interface.jl
index 333bb8a..f2df26b 100755
--- a/src/interface.jl
+++ b/src/interface.jl
@@ -92,102 +92,6 @@ function removeLatestMsg(a::T)  where {T<:agent}
   end
 end
 
-# function generatePrompt_mistral_openorca(a::T, usermsg::String, role::Symbol) where {T<:agent}
-#   prompt = 
-#   """
-#     <|im_start|>system
-#     {systemMsg}
-#     <|im_end|>
-#     Here are the context for the question:
-#     {context}
-#   """
-#   prompt = replace(prompt, "{systemMsg}" => a.roles[role])
-  
-#   toolnames = ""
-#   toollines = ""
-#   for (toolname, v) in a.tools
-#     toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
-#     toollines *= toolline
-#     toolnames *= "$toolname,"
-#   end
-#   prompt = replace(prompt, "{toolnames}" => toolnames)
-#   prompt = replace(prompt, "{tools}" => toollines)
-
-#   prompt = replace(prompt, "{context}" => a.context)
-
-#   prompt *= "<|im_start|>user\n" * usermsg * "\n<|im_end|>\n"
-#   prompt *= "<|im_start|>assistant\n"
-
-#   return prompt
-# end
-
-# function generatePrompt_mistral_openorca(a::T, usermsg::String,
-#           thinkingMode::Symbol=:nothinking) where {T<:agent}
-
-#   prompt = 
-#   """
-#     <|im_start|>system
-#     {systemMsg}
-#     You have access to the following tools:
-#     {tools}
-#     {thinkingMode}
-#     <|im_end|>
-#     Here are the context for the question:
-#     {context}
-#   """
-#   prompt = replace(prompt, "{systemMsg}" => a.roles[a.role])
-#   prompt = replace(prompt, "{thinkingMode}" => a.thinkingMode[thinkingMode])
-#   toolnames = ""
-#   toollines = ""
-#   for (toolname, v) in a.tools
-#     toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
-#     toollines *= toolline
-#     toolnames *= "$toolname,"
-#   end
-#   prompt = replace(prompt, "{toolnames}" => toolnames)
-#   prompt = replace(prompt, "{tools}" => toollines)
-
-#   prompt = replace(prompt, "{context}" => a.context)
-
-#   prompt *= "<|im_start|>user\nQuestion: " * usermsg * "\n<|im_end|>\n"
-#   prompt *= "<|im_start|>assistant\n"
-  
-#   return prompt
-# end
-
-
-function generatePrompt_mistral_openorca(a::T, usermsg::String,
-          thinkingMode::Symbol=:nothinking) where {T<:agent}
-
-  prompt = 
-  """
-    <|im_start|>system
-    {systemMsg}
-    {tools}
-    {thinkingMode}
-    <|im_end|>
-    Here are the context for the stimulus:
-    {context}
-  """
-  prompt = replace(prompt, "{systemMsg}" => a.roles[a.role])
-  prompt = replace(prompt, "{thinkingMode}" => a.thinkingMode[thinkingMode])
-  toolnames = ""
-  toollines = ""
-  for (toolname, v) in a.tools
-    toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
-    toollines *= toolline
-    toolnames *= "$toolname,"
-  end
-  prompt = replace(prompt, "{toolnames}" => toolnames)
-
-  prompt = replace(prompt, "{context}" => a.context)
-
-  prompt *= "<|im_start|>user\nStimulus: " * usermsg * "\n<|im_end|>\n"
-  prompt *= "<|im_start|>assistant\n"
-  
-  return prompt
-end
-
 function chat_mistral_openorca(a::agentReflex)
   """
     general prompt format:
@@ -266,7 +170,7 @@ function planner_mistral_openorca(a::agentReflex)
   conversation = messagesToString(a.messages)
   toollines = ""
   for (toolname, v) in a.tools
-    if toolname ∉ ["chatbox"]
+    if toolname ∉ [""]
       toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
       toollines *= toolline
     end
@@ -313,93 +217,50 @@ function planner_mistral_openorca(a::agentReflex)
   return objective, plan
 end
 
-
-# function planner_mistral_openorca(a::agentReflex)
-#   """
-#     general prompt format:
-
-#     "
-#     <|im_start|>system
-#     {role}
-#     {tools}
-#     {thinkingFormat}
-#     <|im_end|>
-#     {context}
-#     <|im_start|>user
-#     {usermsg}
-#     <|im_end|>
-#     <|im_start|>assistant
-
-#     "
-
-#     Note:
-#       {context} = 
-#         "
-#         {earlierConversation}
-#         {env state}
-#         {shortterm memory}
-#         {longterm memory}
-#         "
-#   """
-
-#   prompt = 
-#   """
-#     <|im_start|>system
-#     {role}
-#     {roleSpecificKnowledge}
-#     {tools}
-#     {thinkingFormat}
-#     {context}
-#     <|im_end|>
-#     <|im_start|>user
-#     {usermsg}
-#     <|im_end|>
-#     <|im_start|>assistant
-#     Plan:
-#   """
-#   prompt = replace(prompt, "{role}" => a.roles[a.role])
-#   prompt = replace(prompt, "{thinkingFormat}" => a.thinkingFormat[:planner])
-#   roleSpecificKnowledge = 
-#   """
-#     Info you need from the user to be able to help them selecting their best wine:
-#       - type of food
-#       - occasion
-#       - user's personal taste of wine
-#       - wine price range
-#       - ambient temperature at the serving location
-#       - wines we have in stock
-#       You job is to provide a personalized recommendation of up to two wines based on the user's info above, and you describe the benefits of each wine in detail.
-#   """
-#   prompt = replace(prompt, "{roleSpecificKnowledge}" => roleSpecificKnowledge)
-#   toolnames = ""
-#   toollines = ""
-#   for (toolname, v) in a.tools
-#     toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
-#     toollines *= toolline
-#     toolnames *= "$toolname,"
-#   end
-  
-#   prompt = replace(prompt, "{toolnames}" => toolnames)
-#   prompt = replace(prompt, "{tools}" => "You have access to the following tools:\n$toollines")
+""" Update the current plan.
+""" #WORKING
+function updatePlan()
+  conversation = messagesToString_nomark(a.messages)
   
+  toollines = ""
+  for (toolname, v) in a.tools
+    if toolname ∉ ["chatbox"]
+      toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
+      toollines *= toolline
+    end
+  end
 
-#   # prepare contex
-#   context = 
-#   """
-#   My earlier talk with the user:
-#   $(a.earlierConversation)
+  work = dictToString(a.memory[:shortterm])
 
-#   My earlier experience
-#   $(experience(a.memory[:longterm]))
-#   """
+  prompt =
+    """
+    <|im_start|>system
+    $(a.roles[a.role])
+    The required info you need for wine recommendation:
+        - type of food: ask the user
+        - occasion: ask the user
+        - user's personal taste of wine: ask the user
+        - wine price range: ask the user
+        - ambient temperature at the serving location: ask the user
+        - wines we have in stock
+    You provide a personalized recommendation of up to two wines based on the user's info above, and you describe the benefits of each wine in detail.
+    
+    You have access to the following tools:
+    $toollines
 
-#   prompt = replace(prompt, "{context}" => context)
+    Your talk with the user:
+    $conversation
 
-#   # initialize short term memory
-#   prompt = replace(prompt, "{usermsg}" => "Stimulus: $(a.memory[:shortterm]["user:"])")
+    Your work:
+    $work
 
-#   return prompt
-# end
+    You job is to use info from your conversation with the user and your work to up date the plan.
+    <|im_end|>
+    New plan: 
+    """
+
+
+end
 
 function actor_mistral_openorca(a::agentReflex)
   """
@@ -429,22 +290,6 @@ function actor_mistral_openorca(a::agentReflex)
         "
   """
 
-
-  prompt = 
-  """
-    <|im_start|>system
-    $(a.roles[a.role])
-    {tools}
-    $(a.thinkingFormat[:actor])
-    {context}
-    <|im_end|>
-    {shorttermMemory}
-    Thought $(a.step): 
-  """
-  
-  s = dictToString(a.memory[:shortterm], skiplist=["user:", "Plan 1:"])
-  prompt = replace(prompt, "{shorttermMemory}" => s)
-
   toolnames = ""
   toollines = ""
   for (toolname, v) in a.tools
@@ -452,8 +297,8 @@ function actor_mistral_openorca(a::agentReflex)
     toollines *= toolline
     toolnames *= "$toolname, "
   end
-  prompt = replace(prompt, "{toolnames}" => toolnames)
-  prompt = replace(prompt, "{tools}" => "You have access to the following tools:\n$toollines")
+
+  shorttermMemory = dictToString(a.memory[:shortterm], skiplist=["user:"])
 
   conversation = messagesToString_nomark(a.messages, addressAIas="I")
   
@@ -461,93 +306,28 @@ function actor_mistral_openorca(a::agentReflex)
   """
   Your talk with the user:
   $conversation
-  {env state}
-  {longterm memory}
-  {plan}
   """
-  # context = replace(context, "{earlierConversation}" => "My earlier talk with the user:\n$(a.earlierConversation)")
-  context = replace(context, "{env state}" => "")
-  context = replace(context, "{longterm memory}" => "")
-  context = replace(context, "{plan}" => "My plan:\n$(a.memory[:shortterm]["Plan $(a.attempt):"])")
-  prompt = replace(prompt, "{context}" => context)
+
+  prompt = 
+  """
+    <|im_start|>system
+    $(a.roles[a.role])
+    You have access to the following tools:
+    $toollines
+    $(a.thinkingFormat[:actor])
+    $context
+    <|im_end|>
+    $shorttermMemory
+    Thought $(a.step): 
+  """
+  prompt = replace(prompt, "{toolnames}" => toolnames)
+  
+
+
 
   return prompt
 end
 
-# function actor_mistral_openorca(a::agentReflex)
-#   """
-#     general prompt format:
-
-#     "
-#     <|im_start|>system
-#     {role}
-#     {tools}
-#     {thinkingFormat}
-#     <|im_end|>
-#     {context}
-#     <|im_start|>user
-#     {usermsg}
-#     <|im_end|>
-#     <|im_start|>assistant
-
-#     "
-
-#     Note:
-#       {context} = 
-#         "
-#         {earlierConversation}
-#         {env state}
-#         {shortterm memory}
-#         {longterm memory}
-#         "
-#   """
-
-#   mark = "$(a.step)"
-
-#   prompt = 
-#   """
-#     <|im_start|>system
-#     {role} 
-#     {tools}
-#     {thinkingFormat}
-#     {context}
-#     <|im_end|>
-#     {shorttermMemory}
-#     Thought $(a.step): 
-#   """
-  
-#   prompt = replace(prompt, "{role}" => a.roles[a.role])
-#   prompt = replace(prompt, "{thinkingFormat}" => a.thinkingFormat[:actor])
-#   prompt = replace(prompt, "{step}" => a.step)
-  
-#   s = dictToString(a.memory[:shortterm], skiplist=["user:", "Plan 1:"])
-#   prompt = replace(prompt, "{shorttermMemory}" => s)
-
-#   toolnames = ""
-#   toollines = ""
-#   for (toolname, v) in a.tools
-#     toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
-#     toollines *= toolline
-#     toolnames *= "$toolname, "
-#   end
-#   prompt = replace(prompt, "{toolnames}" => toolnames)
-#   prompt = replace(prompt, "{tools}" => "You have access to the following tools:\n$toollines")
-  
-#   context = 
-#   """
-#   {env state}
-#   {longterm memory}
-#   {plan}
-#   """
-#   # context = replace(context, "{earlierConversation}" => "My earlier talk with the user:\n$(a.earlierConversation)")
-#   context = replace(context, "{env state}" => "")
-#   context = replace(context, "{longterm memory}" => "")
-#   context = replace(context, "{plan}" => "My plan:\n$(a.memory[:shortterm]["Plan $(a.attempt):"])")
-#   prompt = replace(prompt, "{context}" => context)
-
-#   return prompt
-# end
-
 
 
 """
@@ -603,7 +383,7 @@ function conversation(a::agentReflex, usermsg::String; attemptlimit::Int=3)
   end
 
   # if LLM using chatbox, use returning msg form chatbox as conversation response
-  if workstate == "chatbox"
+  if workstate == "chatbox" || workstate == "formulatedUserResponse"
     #TODO paraphrase msg so that it is human friendlier word.
   else
     response = chat_mistral_openorca(a)
@@ -653,7 +433,7 @@ function work(a::agentReflex)
       @show a.attempt
 
       # enter actor loop
-      actorstate, msgToUser = actor(a)  #WORKING
+      actorstate, msgToUser = actor(a)
 
       if actorstate == "chatbox"
         response = msgToUser
@@ -680,6 +460,10 @@ function work(a::agentReflex)
         score = grading(a, guideline, response)
         @show score
         if score > 5 # good enough answer
+          println("")
+          formulatedresponse_final = response
+          @show formulatedresponse_final
+          workstate = "formulatedUserResponse"
           a.memory[:shortterm] = OrderedDict{String, Any}()
           a.memory[:log] = OrderedDict{String, Any}()
           break
@@ -722,239 +506,6 @@ function work(a::agentReflex)
 end
 
 
-# function work(a::agentReflex, usermsg::String)
-#   response = nothing
-
-#   a.memory[:shortterm] = OrderedDict{String, Any}()
-#   a.memory[:log] = OrderedDict{String, Any}()
-#   a.memory[:shortterm]["user:"] = usermsg
-#   a.memory[:log]["user:"] = usermsg
-#   a.newplan = true
-
-#   while true  # Work loop
-#     # plan
-#     if a.attempt <= a.attemptlimit
-#       toolname = nothing
-#       toolinput = nothing
-#       if a.newplan == true
-#         a.attempt += 1
-#         a.step = 0
-#         prompt_plan = planner_mistral_openorca(a)
-#         println("")
-#         @show prompt_plan
-#         response = sendReceivePrompt(a, prompt_plan, max_tokens=1024)
-
-#         # sometimes LLM add not-need word I don't want
-#         plan = splittext(response, ["Step 1", "<|im_end|>", "Response", "Execution", 
-#                                   "Result", "Recommendation", "My response"])
-#         # plan = replace(plan, "Plan:"=>"")
-#         println("")
-#         @show plan
-        
-#         a.newplan = false
-#         a.memory[:shortterm]["Plan $(a.attempt):"] = plan
-#         a.memory[:log]["Plan $(a.attempt):"] = plan
-#       end
-      
-#       println("")
-#       @show a.attempt
-
-#       # enter actor loop
-#       actorstate, msgToUser = actor(a)
-
-#       if actorstate == "chatbox"
-#         response = msgToUser
-#         break
-#       elseif actorstate == "all steps done" ||  actorstate == "formulateUserresponse"
-#         println("all steps done")
-
-#         response = formulateUserresponse(a)
-
-#         println("")
-#         formulatedresponse = response
-#         @show formulatedresponse
-        
-#         a.memory[:shortterm]["response $(a.attempt):"] = response
-#         a.memory[:log]["response $(a.attempt):"] = response
-
-#         # evaluate. if score < 8/10 try again.
-#         guideline = writeEvaluationGuideline(a, a.memory[:shortterm]["user:"])
-
-#         println("")
-#         @show guideline
-
-#         score = grading(a, guideline, response)
-#         @show score
-#         if score >= 6 # good enough answer
-#           break
-#         else  # self evaluate and reflect then try again
-#           analysis = analyze(a)
-#           println("")
-#           @show analysis
-          
-#           lessonwithcontext = selfReflext(a, analysis)
-          
-#           println("")
-#           @show lessonwithcontext
-
-#           newdict = OrderedDict()
-
-#           a.memory[:shortterm] = keepOnlyKeys(a.memory[:shortterm], ["user:"])
-
-#           headerToDetect = ["Lesson:", "Context:", ]
-#           headers = detectCharacters(lessonwithcontext, headerToDetect)
-#           chunkedtext = chunktext(lessonwithcontext, headers)
-
-#           a.memory[:longterm][chunkedtext["Context:"]] = chunkedtext["Lesson:"]
-#           a.newplan = true
-#           println("")
-#           println("RETRY $(a.attempt +1)")
-#           println("")
-          
-#         end
-#       else
-#         error("undefied condition, actorstate $actorstate $(@__LINE__)")
-#         break
-#       end
-#     else
-#       error("attempt limit reach")
-#       break
-#     end
-#   end
-
-#   # good enough answer
-
-
-#   # communicates with user
-#   _ = addNewMessage(a, "assistant", response)
-#   return  response
-# end
-
-# function work(a::agentReflex, usermsg::String)
-#   response = nothing
-
-#   if a.thinkingmode == :new_thinking
-#     _ = addNewMessage(a, "user", usermsg)
-#     a.memory[:shortterm] = OrderedDict{String, Any}()
-#     a.memory[:log] = OrderedDict{String, Any}()
-#     a.memory[:shortterm]["user:"] = usermsg
-#     a.memory[:log]["user:"] = usermsg
-#     a.newplan = true
-#   elseif a.thinkingmode == :continue_thinking
-#     println("continue_thinking!!")
-#     _ = addNewMessage(a, "user", usermsg)
-#     a.memory[:shortterm]["Obs $(a.step):"] = usermsg
-#     a.memory[:log]["Obs $(a.step):"] = usermsg
-#   else
-#     error("undefined condition thinkingmode = $thinkingmode $(@__LINE__)")
-#   end
-
-#   while true  # Work loop
-#     # plan
-#     if a.attempt <= a.attemptlimit
-#       toolname = nothing
-#       toolinput = nothing
-#       if a.newplan == true
-#         a.attempt += 1
-#         a.step = 0
-#         prompt_plan = planner_mistral_openorca(a)
-#         println("")
-#         @show prompt_plan
-#         response = sendReceivePrompt(a, prompt_plan, max_tokens=1024)
-
-#         # sometimes LLM add not-need word I don't want
-#         plan = splittext(response, ["Step 1", "<|im_end|>", "Response", "Execution", 
-#                                   "Result", "Recommendation", "My response"])
-#         # plan = replace(plan, "Plan:"=>"")
-#         println("")
-#         @show plan
-        
-#         a.newplan = false
-#         a.memory[:shortterm]["Plan $(a.attempt):"] = plan
-#         a.memory[:log]["Plan $(a.attempt):"] = plan
-#       end
-      
-#       println("")
-#       @show a.attempt
-
-#       # enter actor loop
-#       actorstate, msgToUser = actor(a)
-
-#       if actorstate == "chatbox"
-#         response = msgToUser
-#         break
-#       elseif actorstate == "all steps done" ||  actorstate == "formulateUserresponse"
-#         println("all steps done")
-
-#         response = formulateUserresponse(a)
-
-#         println("")
-#         formulatedresponse = response
-#         @show formulatedresponse
-        
-#         a.memory[:shortterm]["response $(a.attempt):"] = response
-#         a.memory[:log]["response $(a.attempt):"] = response
-
-#         # evaluate. if score < 8/10 try again.
-#         guideline = writeEvaluationGuideline(a, a.memory[:shortterm]["user:"])
-
-#         println("")
-#         @show guideline
-
-#         score = grading(a, guideline, response)
-#         @show score
-#         if score >= 6 # good enough answer
-#           break
-#         else  # self evaluate and reflect then try again
-#           analysis = analyze(a)
-#           println("")
-#           @show analysis
-          
-#           lessonwithcontext = selfReflext(a, analysis)
-          
-#           println("")
-#           @show lessonwithcontext
-
-#           newdict = OrderedDict()
-
-#           a.memory[:shortterm] = keepOnlyKeys(a.memory[:shortterm], ["user:"])
-
-#           headerToDetect = ["Lesson:", "Context:", ]
-#           headers = detectCharacters(lessonwithcontext, headerToDetect)
-#           chunkedtext = chunktext(lessonwithcontext, headers)
-
-#           a.memory[:longterm][chunkedtext["Context:"]] = chunkedtext["Lesson:"]
-#           a.newplan = true
-#           println("")
-#           println("RETRY $(a.attempt +1)")
-#           println("")
-          
-#         end
-#       else
-#         error("undefied condition, actorstate $actorstate $(@__LINE__)")
-#         break
-#       end
-#     else
-#       error("attempt limit reach")
-#       break
-#     end
-#   end
-
-#   # good enough answer
-
-
-#   # communicates with user
-#   _ = addNewMessage(a, "assistant", response)
-#   return  response
-# end
-
-
-# function evaluate()
-  
-# end
-
-
-
 """
   Actor function. 
   
@@ -1004,7 +555,7 @@ function actor(a::agentReflex)
     @show a.step
     if a.step < totalsteps -1   # the last step of the plan is responding, let work() do this part
 
-      #WORKING check whether LLM already complete the current step
+      # check whether LLM already complete the current step
       iscomplete = checkStepCompletion(a)
 
       if iscomplete == false
@@ -1048,18 +599,18 @@ function actor(a::agentReflex)
         a.memory[:log] = addShortMem!(a.memory[:log], chunkedtext)
 
         toolname = toolNameBeingCalled(chunkedtext["Act $(a.step):"], a.tools)
-        toolinput = chunkedtext["Actinput $(a.step):"]
         @show toolname
-        @show toolinput
+
 
         if toolname == "chatbox"  # chat with user
-          msgToUser = toolinput
+          msgToUser = chunkedtext["Actinput $(a.step):"]
           actorState = toolname
           break
-        elseif toolname == "skipstep"
+        elseif toolname == "N/A"
           a.step += 1
         else  # function call
           f = a.tools[toolname][:func]
+          toolinput = chunkedtext["Actinput $(a.step):"]
           toolresult = f(a, toolinput)
           @show toolresult
           a.memory[:shortterm]["Obs $(a.step):"] = toolresult