feat(agents)✨: Add notebook demonstrating AgentBot usage with tools and models

- Create 'agents.ipynb' showing how to use AgentBot with write_and_execute_script and search_internet_and_summarize.
- Include examples for summarizing album ratings, training a classifier, and sports predictions.
- Add 'devstral' model to ollama_model_names.txt for new model support.

Author: ericmjl

llamabot/bot/ollama_model_names.txt

@@ -1,3 +1,4 @@
+devstral
 qwen2.5vl
 phi4-reasoning
 phi4-mini-reasoning

notebooks/agents.ipynb

@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import llamabot as lmb\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llamabot.components.tools import (\n",
+    "    search_internet_and_summarize,\n",
+    "    write_and_execute_script,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "agent = lmb.AgentBot(\n",
+    "    tools=[write_and_execute_script],\n",
+    "    model_name=\"gpt-4.1-mini\",\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = agent(\"Summarize for me the latest ratings of Taylor Swfit's latest album.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's try providing explicitly the `search_internet_and_summarize` tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = lmb.AgentBot(\n",
+    "    tools=[write_and_execute_script, search_internet_and_summarize],\n",
+    "    model_name=\"gpt-4.1-mini\",\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = agent(\"Summarize for me the latest ratings of Taylor Swfit's latest album.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = agent(\"Download the red wine quality dataset from https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv. Use the provided column headers. Train a random forest classifier with 100 trees and a maximum depth of 5 to predict the 'quality' column using the other features. Perform 5-fold cross-validation and return the mean and standard deviation of the accuracy.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(response.content)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = agent(\"What are the predictions for Man Utd's europa league final game?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = agent(\"What were the predictions for Man Utd's 1999 Champions League final?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "default",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}