chore: import upstream snapshot with attribution

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

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+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+# This file makes the 'pre_post_processing/python' directory a Python package.
+
+# You can include any package-level initialization logic here if needed.
+# For now, this file is empty.

docs/en/documentation/configuration/pre-post-processing/python/_index.md

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+---
+title: "Python: Pre & Post Processing"
+type: docs
+weight: 1
+description: >
+  How to add pre- and post- processing to your Agents using Python.
+sample_filters: ["Pre & Post Processing", "Python", "ADK", "LangChain"]
+is_sample: true
+---
+
+## Prerequisites
+
+This tutorial assumes that you have set up MCP Toolbox with a basic agent as described in the [local quickstart](../../../getting-started/local_quickstart.md).
+
+This guide demonstrates how to implement these patterns in your Toolbox applications.
+
+## Implementation
+
+{{< tabpane persist=header >}}
+{{% tab header="ADK" text=true %}}
+The following example demonstrates how to use `ToolboxToolset` with ADK's pre and post processing hooks to implement pre and post processing for tool calls.
+
+{{< include "adk/agent.py" "python">}}
+
+You can also add model-level (`before_model_callback`, `after_model_callback`) and agent-level (`before_agent_callback`, `after_agent_callback`) hooks to intercept messages at different stages of the execution loop.
+
+For more information, see the [ADK Callbacks documentation](https://google.github.io/adk-docs/callbacks/types-of-callbacks/).
+{{% /tab %}}
+{{% tab header="Langchain" text=true %}}
+The following example demonstrates how to use `ToolboxClient` with LangChain's middleware to implement pre- and post- processing for tool calls.
+
+{{< include "langchain/agent.py" "python" >}}
+
+You can also add model-level (`wrap_model`) and agent-level (`before_agent`, `after_agent`) hooks to intercept messages at different stages of the execution loop. See the [LangChain Middleware documentation](https://docs.langchain.com/oss/python/langchain/middleware/custom#wrap-style-hooks) for details on these additional hook types.
+{{% /tab %}}
+{{< /tabpane >}}
+
+## Results
+
+The output should look similar to the following.
+
+{{< notice note >}}
+The exact responses may vary due to the non-deterministic nature of LLMs and differences between orchestration frameworks.
+{{< /notice >}}
+
+```
+AI: Booking Confirmed! You earned 500 Loyalty Points with this stay.
+
+AI: Error: Maximum stay duration is 14 days.
+```

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

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+import asyncio
+from datetime import datetime
+from typing import Any, Dict, Optional
+from copy import deepcopy
+
+from google.adk import Agent
+from google.adk.apps import App
+from google.adk.runners import Runner
+from google.adk.sessions.in_memory_session_service import InMemorySessionService
+from google.adk.tools.tool_context import ToolContext
+from google.genai import types
+from toolbox_adk import CredentialStrategy, ToolboxToolset, ToolboxTool
+
+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
+async def enfore_business_rules(
+    tool: ToolboxTool, args: Dict[str, Any], tool_context: ToolContext
+) -> Optional[Dict[str, Any]]:
+    """
+    Callback fired before a tool is executed.
+    Enforces business logic: Max stay duration is 14 days.
+    """
+    tool_name = tool.name
+    print(f"POLICY CHECK: Intercepting '{tool_name}'")
+
+    if tool_name == "update-hotel" and "checkin_date" in args and "checkout_date" in args:
+        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 {"result": "Error: Maximum stay duration is 14 days."}
+    return None
+
+
+# Post processing
+async def enrich_response(
+    tool: ToolboxTool,
+    args: Dict[str, Any],
+    tool_context: ToolContext,
+    tool_response: Any,
+) -> Optional[Any]:
+    """
+    Callback fired after a tool execution.
+    Enriches response for successful bookings.
+    """
+    if isinstance(tool_response, dict):
+        result = tool_response.get("result", "")
+    elif isinstance(tool_response, str):
+        result = tool_response
+    else:
+        return None
+
+    tool_name = tool.name
+    if isinstance(result, str) and "Error" not in result:
+        if tool_name == "book-hotel":
+            loyalty_bonus = 500
+            enriched_result = f"Booking Confirmed!\n You earned {loyalty_bonus} Loyalty Points with this stay.\n\nSystem Details: {result}"
+
+            if isinstance(tool_response, dict):
+                modified_response = deepcopy(tool_response)
+                modified_response["result"] = enriched_result
+                return modified_response
+            else:
+                return enriched_result
+    return None
+
+
+async def run_chat_turn(
+    runner: Runner, session_id: str, user_id: str, message_text: str
+):
+    """Executes a single chat turn and prints the interaction."""
+    print(f"\nUSER: '{message_text}'")
+    response_text = ""
+    async for event in runner.run_async(
+        user_id=user_id,
+        session_id=session_id,
+        new_message=types.Content(role="user", parts=[types.Part(text=message_text)]),
+    ):
+        if event.content and event.content.parts:
+            for part in event.content.parts:
+                if part.text:
+                    response_text += part.text
+
+    print(f"AI: {response_text}")
+
+
+async def main():
+    toolset = ToolboxToolset(
+        server_url="http://127.0.0.1:5000",
+        toolset_name="my-toolset",
+        credentials=CredentialStrategy.toolbox_identity(),
+    )
+    tools = await toolset.get_tools()
+    root_agent = Agent(
+        name="root_agent",
+        model="gemini-3-flash-preview",
+        instruction=SYSTEM_PROMPT,
+        tools=tools,
+        # add any pre and post processing callbacks
+        before_tool_callback=enfore_business_rules,
+        after_tool_callback=enrich_response,
+    )
+    app = App(root_agent=root_agent, name="my_agent")
+    runner = Runner(app=app, session_service=InMemorySessionService())
+    session_id = "test-session"
+    user_id = "test-user"
+    await runner.session_service.create_session(
+        app_name=app.name, user_id=user_id, session_id=session_id
+    )
+
+    # First turn: Successful booking
+    await run_chat_turn(runner, session_id, user_id, "Book hotel with id 3.")
+    print("-" * 50)
+    # Second turn: Policy violation (stay > 14 days)
+    await run_chat_turn(
+        runner,
+        session_id,
+        user_id,
+        "Update hotel with id 5 with checkin date 2025-01-18 and checkout date 2025-02-10",
+    )
+    await toolset.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

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+google-adk[toolbox]==1.28.1
+google-genai==2.3.0

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

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+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import asyncio
+import importlib
+import os
+from pathlib import Path
+
+import pytest
+
+ORCH_NAME = os.environ.get("ORCH_NAME")
+module_path = f"python.{ORCH_NAME}.agent"
+agent = importlib.import_module(module_path)
+
+GOLDEN_KEYWORDS = [
+    "AI:",
+    "Loyalty Points",
+    "POLICY CHECK: Intercepting 'update-hotel'",
+]
+
+# --- Execution Tests ---
+class TestExecution:
+    """Test framework execution and output validation."""
+
+    @pytest.fixture(scope="function")
+    def script_output(self, capsys):
+        """Run the agent function and return its output."""
+        asyncio.run(agent.main())
+        return capsys.readouterr()
+
+    def test_script_runs_without_errors(self, script_output):
+        """Test that the script runs and produces no stderr."""
+        assert script_output.err == "", f"Script produced stderr: {script_output.err}"
+
+    def test_keywords_in_output(self, script_output):
+        """Test that expected keywords are present in the script's output."""
+        output = script_output.out
+        print(f"\nAgent Output:\n{output}\n")
+        missing_keywords = [kw for kw in GOLDEN_KEYWORDS if kw not in output]
+        assert not missing_keywords, f"Missing keywords in output: {missing_keywords}"

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