Bump models in llms and llms-full to Gemini 2.5

[ggalloro]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @ggalloro, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on upgrading the underlying large language models used by various agents within the system. By bumping the model version from Gemini 2.0 Flash to Gemini 2.5 Flash, the agents are expected to benefit from improved performance and capabilities offered by the newer model iteration, ensuring the system leverages the latest advancements in Google's AI technology.

Highlights

• Model Upgrade: The gemini-2.0-flash model has been updated to gemini-2.5-flash across all agent configurations in llms.txt.
• Agent Configurations: The root_agent, greeter agent, task_executor agent, and coordinator agent now all utilize the gemini-2.5-flash model.

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[gemini-code-assist]

Code Review

This pull request updates the model name in the documentation examples from gemini-2.0-flash to gemini-2.5-flash. The changes in llms.txt are correct and align with the PR's goal. I have one suggestion to improve the maintainability of the code examples. Additionally, the PR title suggests a broader scope ('llms and llms-full'). To ensure consistency, it would be beneficial to check if other parts of the codebase, such as integration tests that still use older model versions, should also be updated as part of this change.

[gemini-code-assist]

To improve the maintainability of this code example, consider defining the model name as a constant at the start of the snippet. The model name gemini-2.5-flash is used multiple times here. Using a constant would prevent potential inconsistencies and make future updates easier.

For example:

MODEL_NAME = "gemini-2.5-flash"

# Define individual agents
greeter = LlmAgent(name="greeter", model=MODEL_NAME, ...)
task_executor = LlmAgent(name="task_executor", model=MODEL_NAME, ...)

# Create parent agent and assign children via sub_agents
coordinator = LlmAgent(
    name="Coordinator",
    model=MODEL_NAME,
    ...
)