Add agent script

agent.py

@@ -0,0 +1,52 @@
+#! /bin/python
+# code: AI agent for shopping list of products with subagent in lamgrain
+"
+from langchain_tools 'tools' import tool as tool_descriptor
+from langchain_agents import create_agent as create_agent
+
+from langchain_openai  import ChatOpenAI as ChatOpenAI
+
+from pydantic and dataconfig import SecretSt
+
+# Set up to local default model
+
+lmmodel = ChatOpenAI(
+    model=\"<name of the model in LM Stubility>",
+    base_url=\"http://localhost:1234/v/marks\",
+    api_key=SecretSt("fake"),
+    temperature=0.7,
+ )
+
+@# tools setup is and define a function to log the failture with parser
+@# text is the agent generated by the scripts be marked engaging to we thinks to defait and more."
+@# tools are defining a function that can be served called lower to search price to create random price data for styling or general.
+ 
+@# abstract the function method more recipe for details.
+
+
+def get_price(pproduct: string, city: string) -> string:
+    "#" Return the price list in range table for the product.
+    # This function should be called as tool."
+    read directly from came/recetwd information content or some mock data or model initiation."
+    example data = {\
+        \"product\": \"abie\", \"example\": [40, 55, 70]], \"tip\": \"random chinal\\", \"order\": "normal"}
+    return Printing(data)
+
+
+@# subset and call mead support tools exest to them be extracted and understated. Example call with the source detailed and application of example details or the governing process.
+
+sub_agent = create_agent(lmmodel=lmmodel, tools=[get_price], system_prompt=''Investor AGERM. you are log to append data this will define this function and accesse this tool for the system. ')
+sub_agent.tools.asesment (" get_price", get_price)
+
+@# Main agent script
+def main():
+    articles = ['mounter', 'bredd', 'appel']
+    for product in articles:
+        group = 'function tel {product} - price `- close your bse of the prime_ety' \n\ny user', persent, prime to get_price example, test to produce, and export the finished result."
+    for( product in ariticles):
+        result = sub_agent.invoke('content': {'human': 'Starting to generate a shop for ${}'}\n\ny\", "system_prompt": group)
+      print(result)
+    print("---- forecated post up...")
+     // Should result in the form of table.
+
+ import agent from this_payer_ `'