chore: import upstream snapshot with attribution

docs/en/documentation/configuration/pre-post-processing/python/langchain/agent.py

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+import asyncio
+from datetime import datetime
+
+from langchain.agents import create_agent
+from langchain.agents.middleware import wrap_tool_call
+from langchain_core.messages import ToolMessage
+from langchain_google_genai import ChatGoogleGenerativeAI
+from toolbox_langchain import ToolboxClient
+
+system_prompt = """
+  You're a helpful hotel assistant. You handle hotel searching, booking and
+  cancellations. When the user searches for a hotel, mention it's name, id,
+  location and price tier. Always mention hotel ids while performing any
+  searches. This is very important for any operations. For any bookings or
+  cancellations, please provide the appropriate confirmation. Be sure to
+  update checkin or checkout dates if mentioned by the user.
+  Don't ask for confirmations from the user.
+"""
+
+
+# Pre processing
+@wrap_tool_call
+async def enforce_business_rules(request, handler):
+    """
+    Business Logic Validation:
+    Enforces max stay duration (e.g., max 14 days).
+    """
+    tool_call = request.tool_call
+    name = tool_call["name"]
+    args = tool_call["args"]
+
+    print(f"POLICY CHECK: Intercepting '{name}'")
+
+    if name == "update-hotel":
+        if "checkin_date" in args and "checkout_date" in args:
+            try:
+                start = datetime.fromisoformat(args["checkin_date"])
+                end = datetime.fromisoformat(args["checkout_date"])
+                duration = (end - start).days
+
+                if duration > 14:
+                    print("BLOCKED: Stay too long")
+                    return ToolMessage(
+                        content="Error: Maximum stay duration is 14 days.",
+                        tool_call_id=tool_call["id"],
+                    )
+            except ValueError:
+                pass  # Ignore invalid date formats
+
+    # PRE: Code here runs BEFORE the tool execution
+    
+    # EXEC: Execute the tool (or next middleware)
+    result = await handler(request)
+
+    # POST: Code here runs AFTER the tool execution
+    return result
+
+
+# Post processing
+@wrap_tool_call
+async def enrich_response(request, handler):
+    """
+    Post-Processing & Enrichment:
+    Adds loyalty points information to successful bookings.
+    Standardizes output format.
+    """
+    # PRE: Code here runs BEFORE the tool execution
+    
+    # EXEC: Execute the tool (or next middleware)
+    result = await handler(request)
+
+    # POST: Code here runs AFTER the tool execution
+    if isinstance(result, ToolMessage):
+        content = str(result.content)
+        tool_name = request.tool_call["name"]
+
+        if tool_name == "book-hotel" and "Error" not in content:
+            loyalty_bonus = 500
+            result.content = f"Booking Confirmed!\n You earned {loyalty_bonus} Loyalty Points with this stay.\n\nSystem Details: {content}"
+
+    return result
+
+
+async def main():
+    async with ToolboxClient("http://127.0.0.1:5000") as client:
+        tools = await client.aload_toolset("my-toolset")
+        model = ChatGoogleGenerativeAI(model="gemini-3-flash-preview")
+        agent = create_agent(
+            system_prompt=system_prompt,
+            model=model,
+            tools=tools,
+            # add any pre and post processing methods
+            middleware=[enforce_business_rules, enrich_response],
+        )
+        # Test post-processing
+        user_input = "Book hotel with id 3."
+        print(f"\n[INPUT] User: {user_input}")
+        response = await agent.ainvoke(
+            {"messages": [{"role": "user", "content": user_input}]}
+        )
+
+        print("-" * 50)
+        last_ai_msg = response["messages"][-1].content
+        print(f"[OUTPUT] AI: {last_ai_msg}")
+
+        # Test Pre-processing
+        print("-" * 50)
+        user_input = "Update my hotel with id 3 with checkin date 2025-01-18 and checkout date 2025-02-20."
+        print(f"\n[INPUT] User: {user_input}")
+        response = await agent.ainvoke(
+            {"messages": [{"role": "user", "content": user_input}]}
+        )
+        last_ai_msg = response["messages"][-1].content
+        print(f"[OUTPUT] AI: {last_ai_msg}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/en/documentation/configuration/pre-post-processing/python/langchain/requirements.txt

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+langchain==1.3.9
+langchain-google-genai==4.2.1
+toolbox-langchain==1.0.0