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## Description > Should include a concise description of the changes (bug or feature), it's > impact, along with a summary of the solution ## PR Checklist > Thank you for opening a Pull Request! Before submitting your PR, there are a > few things you can do to make sure it goes smoothly: - [ ] Make sure you reviewed [CONTRIBUTING.md](https://github.com/googleapis/genai-toolbox/blob/main/CONTRIBUTING.md) - [ ] Make sure to open an issue as a [bug/issue](https://github.com/googleapis/genai-toolbox/issues/new/choose) before writing your code! That way we can discuss the change, evaluate designs, and agree on the general idea - [ ] Ensure the tests and linter pass - [ ] Code coverage does not decrease (if any source code was changed) - [ ] Appropriate docs were updated (if necessary) - [ ] Make sure to add `!` if this involve a breaking change 🛠️ Fixes #<issue_number_goes_here>
45 lines
1.4 KiB
Markdown
45 lines
1.4 KiB
Markdown
---
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title: "Python"
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type: docs
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weight: 1
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description: >
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How to add pre- and post- processing to your Agents using Python.
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---
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## Prerequisites
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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).
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This guide demonstrates how to implement these patterns in your Toolbox applications.
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## Implementation
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{{< tabpane persist=header >}}
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{{% tab header="ADK" text=true %}}
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Coming soon.
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{{% /tab %}}
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{{% tab header="Langchain" text=true %}}
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The following example demonstrates how to use `ToolboxClient` with LangChain's middleware to implement pre- and post- processing for tool calls.
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```py
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{{< include "python/langchain/agent.py" >}}
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```
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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.
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{{% /tab %}}
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{{< /tabpane >}}
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## Results
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The output should look similar to the following.
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{{< notice note >}}
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The exact responses may vary due to the non-deterministic nature of LLMs and differences between orchestration frameworks.
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{{< /notice >}}
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```
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AI: Booking Confirmed! You earned 500 Loyalty Points with this stay.
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AI: Error: Maximum stay duration is 14 days.
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```
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