docs: add pre/post processing docs for langchain python (#2378)

## Description

Trigger has been tested corresponding to local changes. Latest
successful run:
https://pantheon.corp.google.com/cloud-build/builds;region=global/1c37031f-95f1-4c6c-9ef8-0452277599d5?e=13802955&mods=-autopush_coliseum&project=toolbox-testing-438616

Note: After merging, update python pre and post processing sample
testing trigger.

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🛠️ Fixes #<issue_number_goes_here>

---------

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Yuan Teoh <45984206+Yuan325@users.noreply.github.com>
Co-authored-by: Averi Kitsch <akitsch@google.com>
This commit is contained in:
Twisha Bansal
2026-02-10 22:11:02 +05:30
committed by GitHub
parent 7f88caa985
commit 1664a69dfd
7 changed files with 340 additions and 0 deletions

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---
title: "Python"
type: docs
weight: 1
description: >
How to add pre- and post- processing to your Agents using Python.
---
## Prerequisites
This tutorial assumes that you have set up 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 %}}
Coming soon.
{{% /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.
```py
{{< include "python/langchain/agent.py" >}}
```
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. Note that exact responses may vary due to the non-deterministic nature of LLMs and differences between orchestration frameworks.
```
AI: Booking Confirmed! You earned 500 Loyalty Points with this stay.
AI: Error: Maximum stay duration is 14 days.
```