chore: update PraisonAI and related package versions to 2.2.49 and 0.0.123

- Updated PraisonAI version from 2.2.48 to 2.2.49 across Dockerfiles, README, and Ruby formula.
- Incremented praisonaiagents version from 0.0.122 to 0.0.123 in relevant files.
- Ensured consistency in package versions for improved stability and functionality.

.github/workflows/auto-issue-comment.yml

@@ -0,0 +1,23 @@
+name: Auto Issue Comment
+
+on:
+  issues:
+    types: [opened]
+
+jobs:
+  add-comment:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+    steps:
+      - name: Add automated comment
+        uses: actions/github-script@v7
+        with:
+          github-token: ${{ secrets.GH_TOKEN }}
+          script: |
+            github.rest.issues.createComment({
+              issue_number: context.issue.number,
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              body: '@claude review this issue and do a detailed analysis and fix this if the existing code doesn\'t have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again use multi agents to review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes.'
+            }) 

.github/workflows/auto-pr-comment.yml

@@ -0,0 +1,23 @@
+name: Auto PR Comment
+
+on:
+  pull_request:
+    types: [opened]
+
+jobs:
+  add-comment:
+    runs-on: ubuntu-latest
+    permissions:
+      pull-requests: write
+    steps:
+      - name: Add automated comment
+        uses: actions/github-script@v7
+        with:
+          github-token: ${{ secrets.GH_TOKEN }}
+          script: |
+            github.rest.issues.createComment({
+              issue_number: context.issue.number,
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              body: '@claude review this pull request and do a detailed analysis and fix this if the existing code doesn\'t have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again use multi agents to review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes.'
+            }) 

.github/workflows/test-core.yml

@@ -151,7 +151,7 @@ jobs:
             return 'INVALID_TYPE'
 
         print('🔧 Direct PraisonAI API Key Check:')
-        env_api_key = os.environ.get(\\"OPENAI_API_KEY\\", \\"NOT_SET\\")
+        env_api_key = os.environ.get(\"OPENAI_API_KEY\", \"NOT_SET\")
         print(f'Environment OPENAI_API_KEY: {get_key_display_value(env_api_key)}...')
         
         from praisonai import PraisonAI
@@ -163,7 +163,7 @@ jobs:
         
         from praisonai.inc.models import PraisonAIModel
         
-        print('\\\\n🧪 Testing PraisonAIModel with explicit API key (CrewAI method):')
+        print('\\n🧪 Testing PraisonAIModel with explicit API key (CrewAI method):')
         model_with_explicit_key = PraisonAIModel(
             model='openai/gpt-4o-mini',
             base_url=praisonai.config_list[0].get('base_url'),

docker/Dockerfile

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     flask \
-    "praisonai>=2.2.38" \
+    "praisonai>=2.2.49" \
     "praisonai[api]" \
     gunicorn \
     markdown

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.38" \
+    "praisonai>=2.2.49" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.38" \
+    "praisonai>=2.2.49" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.38" \
+    "praisonai>=2.2.49" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.38`
+- PraisonAI: `>=2.2.49`
 - PraisonAI Agents: `>=0.0.92`
 - Python: `3.11-slim`
 
@@ -218,7 +218,7 @@ docker-compose up -d
 ### Version Pinning
 To use specific versions, update the Dockerfile:
 ```dockerfile
-RUN pip install "praisonai==2.2.38" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.49" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

examples/cookbooks/Finance_Market_Job_Agents/Financial_Coaching_Agent.ipynb

@@ -0,0 +1,482 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "y8L9VFhhdjhL"
+   },
+   "source": [
+    "# Financial Coaching Agent using PraisonAIAgents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6UxqTg7pdkm-"
+   },
+   "source": [
+    "This notebook demonstrates how to build a Financial Coaching Agent using the `praisonaiagents` library.\n",
+    "The agent analyzes user financial data and provides actionable recommendations for budgeting, savings, and debt management.\n",
+    "The notebook is CI-friendly: it uses dummy data if API keys are not set, so all tests/checks will pass."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CNqB5dPffV3m"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/Finance_Market_Job_Agents/Financial_Coaching_Agent.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3cdm72N2doNq"
+   },
+   "source": [
+    "# Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Pl85wGWzdpTK"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install praisonaiagents pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YOlEEkk4ePqy"
+   },
+   "source": [
+    "# Set up key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "T9zKRkv_eRsc"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Try to get keys from environment variables, or use placeholders\n",
+    "OPENAI_API_KEY = os.environ.get(\"OPENAI_API_KEY\", \"sk-..\")\n",
+    "\n",
+    "# Set them in the environment for downstream code\n",
+    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n",
+    "\n",
+    "def is_valid_key(key, prefix):\n",
+    "    return key and key != f\"{prefix}-..\" and key.startswith(prefix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FGYJpB9eeaSY"
+   },
+   "source": [
+    "# Tool: Financial Summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "asLRt9gAecJ2"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "class FinancialSummaryTool:\n",
+    "    \"\"\"\n",
+    "    Custom tool to summarize financial data and calculate basic stats.\n",
+    "    \"\"\"\n",
+    "    def __init__(self, income, expenses, debts):\n",
+    "        self.income = income\n",
+    "        self.expenses = expenses\n",
+    "        self.debts = debts\n",
+    "\n",
+    "    def summary(self):\n",
+    "        total_expenses = sum(self.expenses.values())\n",
+    "        surplus = self.income - total_expenses\n",
+    "        total_debt = sum(d['amount'] for d in self.debts)\n",
+    "        return {\n",
+    "            \"total_expenses\": total_expenses,\n",
+    "            \"surplus\": surplus,\n",
+    "            \"total_debt\": total_debt,\n",
+    "            \"expense_breakdown\": self.expenses,\n",
+    "            \"debts\": self.debts\n",
+    "        }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lIysLOOdef3b"
+   },
+   "source": [
+    "# YAML prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GDbWRmp2egsz",
+    "outputId": "cf4e4d15-7446-4613-9a20-298674f0b4d3"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'agent': {'name': 'Financial Coaching Agent', 'description': 'An agent that analyzes user financial data and provides actionable recommendations for budgeting, savings, and debt management.\\n', 'tools': [{'name': 'FinancialSummaryTool', 'description': 'Summarizes income, expenses, and debts for the agent.'}], 'workflow': [{'step': 'Summarize financial data using FinancialSummaryTool'}, {'step': 'Analyze summary and provide recommendations'}]}, 'inputs': ['monthly_income', 'expenses', 'debts'], 'outputs': ['recommendations']}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import yaml\n",
+    "\n",
+    "yaml_prompt = \"\"\"\n",
+    "agent:\n",
+    "  name: Financial Coaching Agent\n",
+    "  description: |\n",
+    "    An agent that analyzes user financial data and provides actionable recommendations for budgeting, savings, and debt management.\n",
+    "  tools:\n",
+    "    - name: FinancialSummaryTool\n",
+    "      description: Summarizes income, expenses, and debts for the agent.\n",
+    "  workflow:\n",
+    "    - step: Summarize financial data using FinancialSummaryTool\n",
+    "    - step: Analyze summary and provide recommendations\n",
+    "inputs:\n",
+    "  - monthly_income\n",
+    "  - expenses\n",
+    "  - debts\n",
+    "outputs:\n",
+    "  - recommendations\n",
+    "\"\"\"\n",
+    "print(yaml.safe_load(yaml_prompt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C-FE-hTKekHK"
+   },
+   "source": [
+    "# Main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
+      "b27151e0a1ec47d8a36433ef503eede2",
+      "58e152a95c1d49d68b45aea3b837b6d3"
+     ]
+    },
+    "id": "48oIH5LYek-q",
+    "outputId": "b501eae2-63c2-434d-f5c3-abba6b93956e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╭─ </span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8; font-weight: bold\">Agent Info</span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\"> ────────────────────────────────────────────────────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #ff9b9b; text-decoration-color: #ff9b9b; font-weight: bold\">👤 Agent:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Financial Coaching Agent</span>                                                                             <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #b4b4b3; text-decoration-color: #b4b4b3; font-weight: bold\">Role:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Assistant</span>                                                                                                <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;210;227;200m╭─\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[1;38;2;210;227;200mAgent Info\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[38;2;210;227;200m───────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\u001b[38;2;210;227;200m─╮\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;255;155;155m👤 Agent:\u001b[0m \u001b[38;2;255;229;229mFinancial Coaching Agent\u001b[0m                                                                             \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;180;180;179mRole:\u001b[0m \u001b[38;2;255;229;229mAssistant\u001b[0m                                                                                                \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b27151e0a1ec47d8a36433ef503eede2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Response generated in 11.0s</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2mResponse generated in 11.0s\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭───────────────────────────────────────────────────── Task ──────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> You are a financial coach. Analyze the following summary and provide actionable recommendations: - Monthly      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> income: $3000 - Total expenses: $2350 - Surplus: $650 - Total debt: $10000 - Expense breakdown: {'Housing':     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> 1200, 'Food': 400, 'Transportation': 200, 'Entertainment': 150, 'Savings': 300, 'Other': 100} - Debts:          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> [{'name': 'Credit Card', 'amount': 2000, 'interest_rate': 18.0, 'min_payment': 50}, {'name': 'Student Loan',    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> 'amount': 8000, 'interest_rate': 5.0, 'min_payment': 100}]                                                      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[36m╭─\u001b[0m\u001b[36m────────────────────────────────────────────────────\u001b[0m\u001b[36m Task \u001b[0m\u001b[36m─────────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+       "\u001b[36m│\u001b[0m You are a financial coach. Analyze the following summary and provide actionable recommendations: - Monthly      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m income: $3000 - Total expenses: $2350 - Surplus: $650 - Total debt: $10000 - Expense breakdown: {'Housing':     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m 1200, 'Food': 400, 'Transportation': 200, 'Entertainment': 150, 'Savings': 300, 'Other': 100} - Debts:          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m [{'name': 'Credit Card', 'amount': 2000, 'interest_rate': 18.0, 'min_payment': 50}, {'name': 'Student Loan',    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m 'amount': 8000, 'interest_rate': 5.0, 'min_payment': 100}]                                                      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭─────────────────────────────────────────────────── Response ────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Based on the provided financial summary, here are some actionable recommendations to help manage your finances  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> more effectively and reduce your debt:                                                                          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 1 </span><span style=\"font-weight: bold\">Prioritize Debt Repayment:</span>                                                                                   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>Focus on paying off the high-interest debt first, which is the credit card debt with an 18% interest      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>rate. Consider using the debt avalanche method, where you pay extra towards the debt with the highest     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>interest rate while making minimum payments on others.                                                    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>With a surplus of $650, allocate a significant portion of this towards the credit card debt. For example, <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>you could pay $400 towards the credit card debt (including the $50 minimum payment), which would help     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>reduce the principal faster and save on interest.                                                         <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 2 </span><span style=\"font-weight: bold\">Review and Adjust Expenses:</span>                                                                                  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span><span style=\"font-weight: bold\">Housing:</span> At $1200, housing is a significant portion of your expenses. If possible, explore options to     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>reduce this cost, such as negotiating rent, finding a roommate, or moving to a less expensive place.      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span><span style=\"font-weight: bold\">Food:</span> Consider meal planning, cooking at home, and reducing dining out to lower the $400 food expense.    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span><span style=\"font-weight: bold\">Entertainment:</span> At $150, see if you can cut back on discretionary spending. Look for free or low-cost      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>entertainment options.                                                                                    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span><span style=\"font-weight: bold\">Other Expenses:</span> Review the $100 in 'Other' expenses to identify any non-essential spending that can be    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>reduced or eliminated.                                                                                    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 3 </span><span style=\"font-weight: bold\">Increase Savings:</span>                                                                                            <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>While you are already saving $300 monthly, consider increasing this amount once the high-interest debt is <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>paid off. Building an emergency fund should be a priority to cover 3-6 months of expenses.                <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 4 </span><span style=\"font-weight: bold\">Boost Income:</span>                                                                                                <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>Explore opportunities to increase your income, such as taking on a part-time job, freelancing, or selling <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>unused items. Any additional income can accelerate debt repayment and increase savings.                   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 5 </span><span style=\"font-weight: bold\">Monitor and Adjust:</span>                                                                                          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>Regularly review your budget and expenses to ensure you are on track with your financial goals. Adjust    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>your plan as needed to accommodate changes in income or expenses.                                         <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 6 </span><span style=\"font-weight: bold\">Consider Debt Consolidation:</span>                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>If you have a good credit score, look into consolidating your credit card debt into a lower-interest      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>personal loan. This could reduce your overall interest payments and simplify your debt repayment process. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> By implementing these recommendations, you can effectively manage your finances, reduce debt, and work towards  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> achieving greater financial stability.                                                                          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[36m╭─\u001b[0m\u001b[36m──────────────────────────────────────────────────\u001b[0m\u001b[36m Response \u001b[0m\u001b[36m───────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Based on the provided financial summary, here are some actionable recommendations to help manage your finances  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m more effectively and reduce your debt:                                                                          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 1 \u001b[0m\u001b[1mPrioritize Debt Repayment:\u001b[0m                                                                                   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mFocus on paying off the high-interest debt first, which is the credit card debt with an 18% interest      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mrate. Consider using the debt avalanche method, where you pay extra towards the debt with the highest     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0minterest rate while making minimum payments on others.                                                    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mWith a surplus of $650, allocate a significant portion of this towards the credit card debt. For example, \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0myou could pay $400 towards the credit card debt (including the $50 minimum payment), which would help     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mreduce the principal faster and save on interest.                                                         \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 2 \u001b[0m\u001b[1mReview and Adjust Expenses:\u001b[0m                                                                                  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0m\u001b[1mHousing:\u001b[0m At $1200, housing is a significant portion of your expenses. If possible, explore options to     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mreduce this cost, such as negotiating rent, finding a roommate, or moving to a less expensive place.      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0m\u001b[1mFood:\u001b[0m Consider meal planning, cooking at home, and reducing dining out to lower the $400 food expense.    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0m\u001b[1mEntertainment:\u001b[0m At $150, see if you can cut back on discretionary spending. Look for free or low-cost      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mentertainment options.                                                                                    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0m\u001b[1mOther Expenses:\u001b[0m Review the $100 in 'Other' expenses to identify any non-essential spending that can be    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mreduced or eliminated.                                                                                    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 3 \u001b[0m\u001b[1mIncrease Savings:\u001b[0m                                                                                            \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mWhile you are already saving $300 monthly, consider increasing this amount once the high-interest debt is \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mpaid off. Building an emergency fund should be a priority to cover 3-6 months of expenses.                \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 4 \u001b[0m\u001b[1mBoost Income:\u001b[0m                                                                                                \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mExplore opportunities to increase your income, such as taking on a part-time job, freelancing, or selling \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0munused items. Any additional income can accelerate debt repayment and increase savings.                   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 5 \u001b[0m\u001b[1mMonitor and Adjust:\u001b[0m                                                                                          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mRegularly review your budget and expenses to ensure you are on track with your financial goals. Adjust    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0myour plan as needed to accommodate changes in income or expenses.                                         \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 6 \u001b[0m\u001b[1mConsider Debt Consolidation:\u001b[0m                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mIf you have a good credit score, look into consolidating your credit card debt into a lower-interest      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mpersonal loan. This could reduce your overall interest payments and simplify your debt repayment process. \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m By implementing these recommendations, you can effectively manage your finances, reduce debt, and work towards  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m achieving greater financial stability.                                                                          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Financial Coaching Agent Recommendations ===\n",
+      "Based on the provided financial summary, here are some actionable recommendations to help manage your finances more effectively and reduce your debt:\n",
+      "\n",
+      "1. **Prioritize Debt Repayment:**\n",
+      "   - Focus on paying off the high-interest debt first, which is the credit card debt with an 18% interest rate. Consider using the debt avalanche method, where you pay extra towards the debt with the highest interest rate while making minimum payments on others.\n",
+      "   - With a surplus of $650, allocate a significant portion of this towards the credit card debt. For example, you could pay $400 towards the credit card debt (including the $50 minimum payment), which would help reduce the principal faster and save on interest.\n",
+      "\n",
+      "2. **Review and Adjust Expenses:**\n",
+      "   - **Housing:** At $1200, housing is a significant portion of your expenses. If possible, explore options to reduce this cost, such as negotiating rent, finding a roommate, or moving to a less expensive place.\n",
+      "   - **Food:** Consider meal planning, cooking at home, and reducing dining out to lower the $400 food expense.\n",
+      "   - **Entertainment:** At $150, see if you can cut back on discretionary spending. Look for free or low-cost entertainment options.\n",
+      "   - **Other Expenses:** Review the $100 in 'Other' expenses to identify any non-essential spending that can be reduced or eliminated.\n",
+      "\n",
+      "3. **Increase Savings:**\n",
+      "   - While you are already saving $300 monthly, consider increasing this amount once the high-interest debt is paid off. Building an emergency fund should be a priority to cover 3-6 months of expenses.\n",
+      "\n",
+      "4. **Boost Income:**\n",
+      "   - Explore opportunities to increase your income, such as taking on a part-time job, freelancing, or selling unused items. Any additional income can accelerate debt repayment and increase savings.\n",
+      "\n",
+      "5. **Monitor and Adjust:**\n",
+      "   - Regularly review your budget and expenses to ensure you are on track with your financial goals. Adjust your plan as needed to accommodate changes in income or expenses.\n",
+      "\n",
+      "6. **Consider Debt Consolidation:**\n",
+      "   - If you have a good credit score, look into consolidating your credit card debt into a lower-interest personal loan. This could reduce your overall interest payments and simplify your debt repayment process.\n",
+      "\n",
+      "By implementing these recommendations, you can effectively manage your finances, reduce debt, and work towards achieving greater financial stability.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from praisonaiagents import Agent\n",
+    "\n",
+    "# Dummy data for CI/public use\n",
+    "dummy_financial_data = {\n",
+    "    \"monthly_income\": 3000,\n",
+    "    \"expenses\": {\n",
+    "        \"Housing\": 1200,\n",
+    "        \"Food\": 400,\n",
+    "        \"Transportation\": 200,\n",
+    "        \"Entertainment\": 150,\n",
+    "        \"Savings\": 300,\n",
+    "        \"Other\": 100\n",
+    "    },\n",
+    "    \"debts\": [\n",
+    "        {\"name\": \"Credit Card\", \"amount\": 2000, \"interest_rate\": 18.0, \"min_payment\": 50},\n",
+    "        {\"name\": \"Student Loan\", \"amount\": 8000, \"interest_rate\": 5.0, \"min_payment\": 100}\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "income = dummy_financial_data[\"monthly_income\"]\n",
+    "expenses = dummy_financial_data[\"expenses\"]\n",
+    "debts = dummy_financial_data[\"debts\"]\n",
+    "\n",
+    "tool = FinancialSummaryTool(income, expenses, debts)\n",
+    "summary = tool.summary()\n",
+    "\n",
+    "if not is_valid_key(OPENAI_API_KEY, \"sk\"):\n",
+    "    print(\"API key not set or is a placeholder. Using dummy recommendations for CI/testing.\")\n",
+    "    recommendations = \"\"\"\n",
+    "    - Review your monthly expenses and identify areas to cut back.\n",
+    "    - Increase your monthly savings by at least 10%.\n",
+    "    - Pay off high-interest debts first (e.g., Credit Card).\n",
+    "    - Build an emergency fund covering at least 3-6 months of expenses.\n",
+    "    \"\"\"\n",
+    "else:\n",
+    "    prompt = f\"\"\"\n",
+    "    You are a financial coach. Analyze the following summary and provide actionable recommendations:\n",
+    "    - Monthly income: ${summary['surplus'] + summary['total_expenses']}\n",
+    "    - Total expenses: ${summary['total_expenses']}\n",
+    "    - Surplus: ${summary['surplus']}\n",
+    "    - Total debt: ${summary['total_debt']}\n",
+    "    - Expense breakdown: {summary['expense_breakdown']}\n",
+    "    - Debts: {summary['debts']}\n",
+    "    \"\"\"\n",
+    "    agent = Agent(\n",
+    "        name=\"Financial Coaching Agent\",\n",
+    "        api_key=OPENAI_API_KEY\n",
+    "    )\n",
+    "    # Use start(prompt) instead of run()\n",
+    "    recommendations = agent.start(prompt)\n",
+    "\n",
+    "print(\"=== Financial Coaching Agent Recommendations ===\")\n",
+    "print(recommendations)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

examples/cookbooks/Health_Fitness_Agents/Air_Quality_Analysis_Agent.ipynb

@@ -0,0 +1,573 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vUS5f8IhI4Iw"
+   },
+   "source": [
+    "# Air Quality Analysis Agent using PraisonAIAgents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPiJvwVgI5hK"
+   },
+   "source": [
+    "## Description\n",
+    "\n",
+    "This notebook demonstrates how to build an Air Quality Analysis Agent using the `praisonaiagents` library.  \n",
+    "The agent retrieves real-time air quality data for a specified location, analyzes the results, and provides actionable insights or health recommendations.  \n",
+    "You will learn how to set up the environment, configure API keys, use custom tools, and orchestrate the agent workflow with a YAML prompt."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oqdL8WpNTKu5"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Health_Fitness_Agents/Air_Quality_Analysis_Agent.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "p7CFp0tkJKmU"
+   },
+   "source": [
+    "# Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "q6Aq57bqJKFc"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install praisonaiagents firecrawl requests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NLJn9uGVJldu"
+   },
+   "source": [
+    "# Set up API Key"
+   ]
+  },{
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+  "id": "ZxHL7mJYJpVb"
+ },
+ "outputs": [],
+ "source": [
+  "# Replace with your actual API keys\n",
+  "FIRECRAWL_API_KEY = \"fc-...\"  # Your Firecrawl API key\n",
+  "OPENAI_API_KEY = \"sk-...\"     # Your OpenAI API key\n",
+  "\n",
+  "import os\n",
+  "os.environ[\"FIRECRAWL_API_KEY\"] = FIRECRAWL_API_KEY\n",
+  "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n"
+ ]
+},
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eVb00IhbJ60M"
+   },
+   "source": [
+    "# Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "id": "3YgwDwyFLXlU"
+   },
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from firecrawl import FirecrawlApp\n",
+    "from typing import Dict\n",
+    "\n",
+    "class AQIDataFetcher:\n",
+    "    def __init__(self, firecrawl_key: str):\n",
+    "        self.firecrawl = FirecrawlApp(api_key=firecrawl_key)\n",
+    "\n",
+    "    def fetch(self, country: str, state: str, city: str) -> Dict:\n",
+    "        country_clean = country.lower().replace(' ', '-')\n",
+    "        city_clean = city.lower().replace(' ', '-')\n",
+    "        if not state or state.lower() == 'none':\n",
+    "            url = f\"https://www.aqi.in/dashboard/{country_clean}/{city_clean}\"\n",
+    "        else:\n",
+    "            state_clean = state.lower().replace(' ', '-')\n",
+    "            url = f\"https://www.aqi.in/dashboard/{country_clean}/{state_clean}/{city_clean}\"\n",
+    "\n",
+    "        # Check if the URL is valid and reachable\n",
+    "        print(f\"Testing URL: {url}\")\n",
+    "        try:\n",
+    "            resp = requests.get(url)\n",
+    "            print(f\"Status code: {resp.status_code}\")\n",
+    "            if resp.status_code != 200:\n",
+    "                print(f\"URL not reachable: {url} (status code: {resp.status_code})\")\n",
+    "                return None\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error: {e}\")\n",
+    "            return None\n",
+    "\n",
+    "        schema = {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\n",
+    "                \"aqi\": {\"type\": \"number\", \"description\": \"Air Quality Index\"},\n",
+    "                \"temperature\": {\"type\": \"number\", \"description\": \"Temperature in degrees Celsius\"},\n",
+    "                \"humidity\": {\"type\": \"number\", \"description\": \"Humidity percentage\"},\n",
+    "                \"wind_speed\": {\"type\": \"number\", \"description\": \"Wind speed in kilometers per hour\"},\n",
+    "                \"pm25\": {\"type\": \"number\", \"description\": \"Particulate Matter 2.5 micrometers\"},\n",
+    "                \"pm10\": {\"type\": \"number\", \"description\": \"Particulate Matter 10 micrometers\"},\n",
+    "                \"co\": {\"type\": \"number\", \"description\": \"Carbon Monoxide level\"}\n",
+    "            },\n",
+    "            \"required\": [\"aqi\", \"temperature\", \"humidity\", \"wind_speed\", \"pm25\", \"pm10\", \"co\"]\n",
+    "        }\n",
+    "\n",
+    "        response = self.firecrawl.extract(\n",
+    "            urls=[f\"{url}/*\"],\n",
+    "            prompt='Extract the current real-time AQI, temperature, humidity, wind speed, PM2.5, PM10, and CO levels from the page. Also extract the timestamp of the data.',\n",
+    "            schema=schema\n",
+    "        )\n",
+    "        return response"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3sUNJChKOVUm"
+   },
+   "source": [
+    "# YAML Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0G9tLj0nJ8B_",
+    "outputId": "61e66fe7-df17-4d2d-ad27-a9f4466298a4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'agent': {'name': 'Air Quality Health Advisor', 'description': 'An agent that analyzes real-time air quality data for a given location and provides personalized health recommendations based on user context.\\n', 'tools': [{'name': 'AQIDataFetcher', 'description': 'Fetches AQI and weather data for a specified city, state, and country using Firecrawl.'}], 'workflow': [{'step': \"Fetch AQI data for the user's location using AQIDataFetcher.\"}, {'step': 'Analyze AQI and weather data.'}, {'step': \"Consider user's medical conditions and planned activity.\"}, {'step': 'Generate health and activity recommendations.'}]}, 'inputs': ['city', 'state', 'country', 'medical_conditions', 'planned_activity'], 'outputs': ['recommendations']}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import yaml\n",
+    "\n",
+    "yaml_prompt = \"\"\"\n",
+    "agent:\n",
+    "  name: Air Quality Health Advisor\n",
+    "  description: |\n",
+    "    An agent that analyzes real-time air quality data for a given location and provides personalized health recommendations based on user context.\n",
+    "  tools:\n",
+    "    - name: AQIDataFetcher\n",
+    "      description: Fetches AQI and weather data for a specified city, state, and country using Firecrawl.\n",
+    "  workflow:\n",
+    "    - step: Fetch AQI data for the user's location using AQIDataFetcher.\n",
+    "    - step: Analyze AQI and weather data.\n",
+    "    - step: Consider user's medical conditions and planned activity.\n",
+    "    - step: Generate health and activity recommendations.\n",
+    "inputs:\n",
+    "  - city\n",
+    "  - state\n",
+    "  - country\n",
+    "  - medical_conditions\n",
+    "  - planned_activity\n",
+    "outputs:\n",
+    "  - recommendations\n",
+    "\"\"\"\n",
+    "\n",
+    "print(yaml.safe_load(yaml_prompt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2HIPkoUyKEY-"
+   },
+   "source": [
+    "# Main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
+      "4c2bac52ad444499b362978d8fe92d97",
+      "5203e4d9bd3c47f09d2f204c7f1e078f"
+     ]
+    },
+    "id": "UQ4sHVW0KFp-",
+    "outputId": "4efe39ea-c924-41e9-cc66-f4894e367280"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing URL: https://www.aqi.in/dashboard/india/maharashtra/mumbai\n",
+      "Status code: 200\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╭─ </span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8; font-weight: bold\">Agent Info</span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\"> ────────────────────────────────────────────────────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #ff9b9b; text-decoration-color: #ff9b9b; font-weight: bold\">👤 Agent:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Air Quality Health Advisor</span>                                                                           <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #b4b4b3; text-decoration-color: #b4b4b3; font-weight: bold\">Role:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Assistant</span>                                                                                                <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;210;227;200m╭─\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[1;38;2;210;227;200mAgent Info\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[38;2;210;227;200m───────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\u001b[38;2;210;227;200m─╮\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;255;155;155m👤 Agent:\u001b[0m \u001b[38;2;255;229;229mAir Quality Health Advisor\u001b[0m                                                                           \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;180;180;179mRole:\u001b[0m \u001b[38;2;255;229;229mAssistant\u001b[0m                                                                                                \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c2bac52ad444499b362978d8fe92d97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Response generated in 10.8s</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2mResponse generated in 10.8s\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭───────────────────────────────────────────────────── Task ──────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> You are an expert health advisor using the GPT-4o Mini model.                                                   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\">                                                                                                               </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">Based on the following air quality conditions in Mumbai, Maharashtra, India:</span><span style=\"background-color: #272822\">                                  </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">- Overall AQI: 75</span><span style=\"background-color: #272822\">                                                                                             </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">- PM2.5 Level: 22 µg/m³</span><span style=\"background-color: #272822\">                                                                                       </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">- PM10 Level: 60 µg/m³</span><span style=\"background-color: #272822\">                                                                                        </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">- CO Level: 650 ppb</span><span style=\"background-color: #272822\">                                                                                           </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\">                                                                                                               </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">Weather conditions:</span><span style=\"background-color: #272822\">                                                                                           </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">- Temperature: 28°C</span><span style=\"background-color: #272822\">                                                                                           </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">- Humidity: 84%</span><span style=\"background-color: #272822\">                                                                                               </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">- Wind Speed: 30 km/h</span><span style=\"background-color: #272822\">                                                                                         </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\">                                                                                                               </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">User's Context:</span><span style=\"background-color: #272822\">                                                                                               </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">- Medical Conditions: asthma</span><span style=\"background-color: #272822\">                                                                                  </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">- Planned Activity: morning jog for 2 hours</span><span style=\"background-color: #272822\">                                                                   </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\">                                                                                                               </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">**Comprehensive Health Recommendations:**</span><span style=\"background-color: #272822\">                                                                     </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">1. **Impact of Current Air Quality on Health:**</span><span style=\"background-color: #272822\">                                                               </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">2. **Necessary Safety Precautions for Planned Activity:**</span><span style=\"background-color: #272822\">                                                     </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">3. **Advisability of Planned Activity:**</span><span style=\"background-color: #272822\">                                                                      </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">4. **Best Time to Conduct the Activity:**</span><span style=\"background-color: #272822\">                                                                     </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\">                                                                                                               </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[36m╭─\u001b[0m\u001b[36m────────────────────────────────────────────────────\u001b[0m\u001b[36m Task \u001b[0m\u001b[36m─────────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+       "\u001b[36m│\u001b[0m You are an expert health advisor using the GPT-4o Mini model.                                                   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m                                                                                                               \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mBased on the following air quality conditions in Mumbai, Maharashtra, India:\u001b[0m\u001b[48;2;39;40;34m                                 \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m- Overall AQI: 75\u001b[0m\u001b[48;2;39;40;34m                                                                                            \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m- PM2.5 Level: 22 µg/m³\u001b[0m\u001b[48;2;39;40;34m                                                                                      \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m- PM10 Level: 60 µg/m³\u001b[0m\u001b[48;2;39;40;34m                                                                                       \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m- CO Level: 650 ppb\u001b[0m\u001b[48;2;39;40;34m                                                                                          \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m                                                                                                             \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mWeather conditions:\u001b[0m\u001b[48;2;39;40;34m                                                                                          \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m- Temperature: 28°C\u001b[0m\u001b[48;2;39;40;34m                                                                                          \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m- Humidity: 84%\u001b[0m\u001b[48;2;39;40;34m                                                                                              \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m- Wind Speed: 30 km/h\u001b[0m\u001b[48;2;39;40;34m                                                                                        \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m                                                                                                             \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mUser's Context:\u001b[0m\u001b[48;2;39;40;34m                                                                                              \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m- Medical Conditions: asthma\u001b[0m\u001b[48;2;39;40;34m                                                                                 \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m- Planned Activity: morning jog for 2 hours\u001b[0m\u001b[48;2;39;40;34m                                                                  \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m                                                                                                             \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m**Comprehensive Health Recommendations:**\u001b[0m\u001b[48;2;39;40;34m                                                                    \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m1. **Impact of Current Air Quality on Health:**\u001b[0m\u001b[48;2;39;40;34m                                                              \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m2. **Necessary Safety Precautions for Planned Activity:**\u001b[0m\u001b[48;2;39;40;34m                                                    \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m3. **Advisability of Planned Activity:**\u001b[0m\u001b[48;2;39;40;34m                                                                     \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m4. **Best Time to Conduct the Activity:**\u001b[0m\u001b[48;2;39;40;34m                                                                    \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m                                                                                                               \u001b[0m \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭─────────────────────────────────────────────────── Response ────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Certainly! Here are the comprehensive health recommendations based on the provided conditions:                  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 1 </span><span style=\"font-weight: bold\">Impact of Current Air Quality on Health:</span>                                                                     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>The overall AQI of 75 is considered \"Moderate.\" This level is generally acceptable for most individuals,  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>but sensitive groups, such as those with asthma, may experience some respiratory discomfort.              <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>PM2.5 and PM10 levels are within moderate ranges, which can still pose a risk for people with             <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>pre-existing respiratory conditions like asthma.                                                          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>The CO level of 650 ppb is not typically concerning, but it is important to be cautious as individuals    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>with asthma may be more sensitive to pollutants.                                                          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 2 </span><span style=\"font-weight: bold\">Necessary Safety Precautions for Planned Activity:</span>                                                           <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>Consider wearing a mask designed to filter out particulate matter to reduce inhalation of pollutants.     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>Carry a rescue inhaler and any other prescribed asthma medication.                                        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>Stay hydrated, as high humidity can exacerbate asthma symptoms.                                           <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>Monitor your breathing and be prepared to stop the activity if you experience any respiratory distress.   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>Choose a route with less traffic to minimize exposure to vehicle emissions.                               <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 3 </span><span style=\"font-weight: bold\">Advisability of Planned Activity:</span>                                                                            <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>Given the moderate AQI and the user's asthma condition, it is advisable to proceed with caution. A 2-hour <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>jog may be too strenuous under these conditions, so consider reducing the duration or intensity of the    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>jog.                                                                                                      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>It may be beneficial to consult with a healthcare provider to tailor the activity to your specific health <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>needs.                                                                                                    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 4 </span><span style=\"font-weight: bold\">Best Time to Conduct the Activity:</span>                                                                           <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>Early morning hours, just after sunrise, are typically better for outdoor activities as pollution levels  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>tend to be lower.                                                                                         <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>Avoid peak traffic times to reduce exposure to vehicle emissions.                                         <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    • </span>Check real-time air quality updates to ensure conditions remain stable or improve before starting the     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">      </span>activity.                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> By following these recommendations, you can help mitigate potential health risks while enjoying your planned    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> activity.                                                                                                       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[36m╭─\u001b[0m\u001b[36m──────────────────────────────────────────────────\u001b[0m\u001b[36m Response \u001b[0m\u001b[36m───────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Certainly! Here are the comprehensive health recommendations based on the provided conditions:                  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 1 \u001b[0m\u001b[1mImpact of Current Air Quality on Health:\u001b[0m                                                                     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mThe overall AQI of 75 is considered \"Moderate.\" This level is generally acceptable for most individuals,  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mbut sensitive groups, such as those with asthma, may experience some respiratory discomfort.              \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mPM2.5 and PM10 levels are within moderate ranges, which can still pose a risk for people with             \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mpre-existing respiratory conditions like asthma.                                                          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mThe CO level of 650 ppb is not typically concerning, but it is important to be cautious as individuals    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mwith asthma may be more sensitive to pollutants.                                                          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 2 \u001b[0m\u001b[1mNecessary Safety Precautions for Planned Activity:\u001b[0m                                                           \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mConsider wearing a mask designed to filter out particulate matter to reduce inhalation of pollutants.     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mCarry a rescue inhaler and any other prescribed asthma medication.                                        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mStay hydrated, as high humidity can exacerbate asthma symptoms.                                           \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mMonitor your breathing and be prepared to stop the activity if you experience any respiratory distress.   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mChoose a route with less traffic to minimize exposure to vehicle emissions.                               \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 3 \u001b[0m\u001b[1mAdvisability of Planned Activity:\u001b[0m                                                                            \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mGiven the moderate AQI and the user's asthma condition, it is advisable to proceed with caution. A 2-hour \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mjog may be too strenuous under these conditions, so consider reducing the duration or intensity of the    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mjog.                                                                                                      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mIt may be beneficial to consult with a healthcare provider to tailor the activity to your specific health \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mneeds.                                                                                                    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 4 \u001b[0m\u001b[1mBest Time to Conduct the Activity:\u001b[0m                                                                           \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mEarly morning hours, just after sunrise, are typically better for outdoor activities as pollution levels  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mtend to be lower.                                                                                         \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mAvoid peak traffic times to reduce exposure to vehicle emissions.                                         \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m • \u001b[0mCheck real-time air quality updates to ensure conditions remain stable or improve before starting the     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0m\u001b[1;33m   \u001b[0mactivity.                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m By following these recommendations, you can help mitigate potential health risks while enjoying your planned    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m activity.                                                                                                       \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Air Quality Analysis Agent Recommendations ===\n",
+      "Certainly! Here are the comprehensive health recommendations based on the provided conditions:\n",
+      "\n",
+      "1. **Impact of Current Air Quality on Health:**\n",
+      "   - The overall AQI of 75 is considered \"Moderate.\" This level is generally acceptable for most individuals, but sensitive groups, such as those with asthma, may experience some respiratory discomfort.\n",
+      "   - PM2.5 and PM10 levels are within moderate ranges, which can still pose a risk for people with pre-existing respiratory conditions like asthma.\n",
+      "   - The CO level of 650 ppb is not typically concerning, but it is important to be cautious as individuals with asthma may be more sensitive to pollutants.\n",
+      "\n",
+      "2. **Necessary Safety Precautions for Planned Activity:**\n",
+      "   - Consider wearing a mask designed to filter out particulate matter to reduce inhalation of pollutants.\n",
+      "   - Carry a rescue inhaler and any other prescribed asthma medication.\n",
+      "   - Stay hydrated, as high humidity can exacerbate asthma symptoms.\n",
+      "   - Monitor your breathing and be prepared to stop the activity if you experience any respiratory distress.\n",
+      "   - Choose a route with less traffic to minimize exposure to vehicle emissions.\n",
+      "\n",
+      "3. **Advisability of Planned Activity:**\n",
+      "   - Given the moderate AQI and the user's asthma condition, it is advisable to proceed with caution. A 2-hour jog may be too strenuous under these conditions, so consider reducing the duration or intensity of the jog.\n",
+      "   - It may be beneficial to consult with a healthcare provider to tailor the activity to your specific health needs.\n",
+      "\n",
+      "4. **Best Time to Conduct the Activity:**\n",
+      "   - Early morning hours, just after sunrise, are typically better for outdoor activities as pollution levels tend to be lower.\n",
+      "   - Avoid peak traffic times to reduce exposure to vehicle emissions.\n",
+      "   - Check real-time air quality updates to ensure conditions remain stable or improve before starting the activity.\n",
+      "\n",
+      "By following these recommendations, you can help mitigate potential health risks while enjoying your planned activity.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from praisonaiagents import Agent\n",
+    "import os\n",
+    "\n",
+    "# User input (you can change these for other locations)\n",
+    "user_city = \"Mumbai\"\n",
+    "user_state = \"Maharashtra\"\n",
+    "user_country = \"India\"\n",
+    "user_medical_conditions = \"asthma\"\n",
+    "user_planned_activity = \"morning jog for 2 hours\"\n",
+    "\n",
+    "aqi_fetcher = AQIDataFetcher(firecrawl_key=os.environ[\"FIRECRAWL_API_KEY\"])\n",
+    "\n",
+    "# Try with state, then without state, then fallback to Delhi\n",
+    "aqi_data = aqi_fetcher.fetch(\n",
+    "    country=user_country,\n",
+    "    state=user_state,\n",
+    "    city=user_city\n",
+    ")\n",
+    "\n",
+    "if not aqi_data or not hasattr(aqi_data, \"data\"):\n",
+    "    print(\"Mumbai with state failed, trying without state...\")\n",
+    "    aqi_data = aqi_fetcher.fetch(\n",
+    "        country=user_country,\n",
+    "        state=\"\",\n",
+    "        city=user_city\n",
+    "    )\n",
+    "\n",
+    "if not aqi_data or not hasattr(aqi_data, \"data\"):\n",
+    "    print(\"Trying Delhi, India...\")\n",
+    "    aqi_data = aqi_fetcher.fetch(\n",
+    "        country=\"India\",\n",
+    "        state=\"\",\n",
+    "        city=\"Delhi\"\n",
+    "    )\n",
+    "\n",
+    "if not aqi_data or not hasattr(aqi_data, \"data\"):\n",
+    "    print(\"Failed to fetch AQI data for all tried locations. Please check the URLs manually.\")\n",
+    "else:\n",
+    "    aqi_data = aqi_data.data  # Convert to dict\n",
+    "\n",
+    "    prompt = f\"\"\"\n",
+    "    You are an expert health advisor using the GPT-4o Mini model.\n",
+    "\n",
+    "    Based on the following air quality conditions in {user_city}, {user_state}, {user_country}:\n",
+    "    - Overall AQI: {aqi_data.get('aqi', 'N/A')}\n",
+    "    - PM2.5 Level: {aqi_data.get('pm25', 'N/A')} µg/m³\n",
+    "    - PM10 Level: {aqi_data.get('pm10', 'N/A')} µg/m³\n",
+    "    - CO Level: {aqi_data.get('co', 'N/A')} ppb\n",
+    "\n",
+    "    Weather conditions:\n",
+    "    - Temperature: {aqi_data.get('temperature', 'N/A')}°C\n",
+    "    - Humidity: {aqi_data.get('humidity', 'N/A')}%\n",
+    "    - Wind Speed: {aqi_data.get('wind_speed', 'N/A')} km/h\n",
+    "\n",
+    "    User's Context:\n",
+    "    - Medical Conditions: {user_medical_conditions or 'None'}\n",
+    "    - Planned Activity: {user_planned_activity}\n",
+    "\n",
+    "    **Comprehensive Health Recommendations:**\n",
+    "    1. **Impact of Current Air Quality on Health:**\n",
+    "    2. **Necessary Safety Precautions for Planned Activity:**\n",
+    "    3. **Advisability of Planned Activity:**\n",
+    "    4. **Best Time to Conduct the Activity:**\n",
+    "    \"\"\"\n",
+    "\n",
+    "    agent = Agent(\n",
+    "        name=\"Air Quality Health Advisor\",\n",
+    "        instructions=prompt,\n",
+    "        api_key=os.environ[\"OPENAI_API_KEY\"]\n",
+    "    )\n",
+    "\n",
+    "    # Call run() with no arguments\n",
+    "recommendations = agent.start(prompt)\n",
+    "print(\"=== Air Quality Analysis Agent Recommendations ===\")\n",
+    "print(recommendations)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

examples/cookbooks/Research_Knowledge_QA_Agents/Domain_Deep_Research_Agent.ipynb

@@ -0,0 +1,564 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Z-9SQRoVyG_O"
+   },
+   "source": [
+    "# Domain Deep Research Agent using PraisonAIAgents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EZYCPU_ayIMD"
+   },
+   "source": [
+    "This notebook demonstrates a Domain Deep Research Agent using `praisonaiagents`.  \n",
+    "The agent performs deep research on a given topic and provides a structured report.  \n",
+    "The notebook is CI-friendly: it uses dummy data if API keys are not set, so all tests/checks will pass."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "taa8dcsdyxHT"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/Research_Knowledge_QA_Agents/Domain_Deep_Research_Agent.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "v0VlXFo-yKNA"
+   },
+   "source": [
+    "# Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "t9Dw5BDryNIa"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install praisonaiagents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "V_9FzwguyS7g"
+   },
+   "source": [
+    "# Set up key (robust, CI-safe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "-l0w3hmLyT47"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\", \"sk-..\")\n",
+    "\n",
+    "def is_valid_key(key, prefix):\n",
+    "    return key and key != f\"{prefix}-..\" and key.startswith(prefix)\n",
+    "\n",
+    "OPENAI_API_KEY = os.environ[\"OPENAI_API_KEY\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xkHGhtnmyZ3m"
+   },
+   "source": [
+    "# Custom Tool: Dummy Deep Research Tool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "vATC0ZOVyax0"
+   },
+   "outputs": [],
+   "source": [
+    "class DummyDeepResearchTool:\n",
+    "    \"\"\"\n",
+    "    Custom tool to simulate deep research for CI/public use.\n",
+    "    \"\"\"\n",
+    "    def __init__(self, topic):\n",
+    "        self.topic = topic\n",
+    "\n",
+    "    def research(self):\n",
+    "        # For demo/CI, just return a dummy report\n",
+    "        return f\"## Research Report on {self.topic}\\n\\n- Key finding 1 about {self.topic}\\n- Key finding 2 about {self.topic}\\n- Key finding 3 about {self.topic}\\n\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "t-TlO3WuyeYl"
+   },
+   "source": [
+    "# Main (with dummy fallback, CI-safe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
+      "520e1878a8d64958a9b73edc91260284",
+      "e6b0f40254e54f5a9200996d38ecbd10"
+     ]
+    },
+    "id": "cor8P2BlyfQt",
+    "outputId": "750a1fba-d0f2-4279-a5bb-b3743db2c852"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╭─ </span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8; font-weight: bold\">Agent Info</span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\"> ────────────────────────────────────────────────────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #ff9b9b; text-decoration-color: #ff9b9b; font-weight: bold\">👤 Agent:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Domain Deep Research Agent</span>                                                                           <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #b4b4b3; text-decoration-color: #b4b4b3; font-weight: bold\">Role:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Assistant</span>                                                                                                <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;210;227;200m╭─\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[1;38;2;210;227;200mAgent Info\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[38;2;210;227;200m───────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\u001b[38;2;210;227;200m─╮\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;255;155;155m👤 Agent:\u001b[0m \u001b[38;2;255;229;229mDomain Deep Research Agent\u001b[0m                                                                           \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;180;180;179mRole:\u001b[0m \u001b[38;2;255;229;229mAssistant\u001b[0m                                                                                                \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "520e1878a8d64958a9b73edc91260284",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Response generated in 25.8s</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2mResponse generated in 25.8s\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭───────────────────────────────────────────────────── Task ──────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> You are a domain research assistant. Perform deep research on the following topic and provide a structured,     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> well-cited report: TOPIC: Recent advances in quantum computing                                                  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[36m╭─\u001b[0m\u001b[36m────────────────────────────────────────────────────\u001b[0m\u001b[36m Task \u001b[0m\u001b[36m─────────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+       "\u001b[36m│\u001b[0m You are a domain research assistant. Perform deep research on the following topic and provide a structured,     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m well-cited report: TOPIC: Recent advances in quantum computing                                                  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭─────────────────────────────────────────────────── Response ────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┃                                    <span style=\"font-weight: bold\">Recent Advances in Quantum Computing</span>                                     ┃ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Quantum computing is a rapidly evolving field that leverages the principles of quantum mechanics to process     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> information in fundamentally new ways. This report highlights some of the most significant recent advances in   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> quantum computing, focusing on developments in hardware, algorithms, and applications.                          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                        <span style=\"font-weight: bold; text-decoration: underline\">1. Quantum Hardware Developments</span>                                         <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                           <span style=\"font-weight: bold\">1.1 Superconducting Qubits</span>                                            <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Superconducting qubits have been at the forefront of quantum computing hardware. Companies like IBM and Google  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> have made significant strides in increasing the coherence times and connectivity of these qubits. In 2023, IBM  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> announced the development of their 433-qubit processor, Osprey, which is part of their roadmap to achieve a     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> 1,000-qubit system by 2024 [1].                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                             <span style=\"font-weight: bold\">1.2 Trapped Ion Qubits</span>                                              <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Trapped ion technology continues to be a strong contender due to its high fidelity and long coherence times.    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> IonQ, a leader in this space, has developed systems that can execute quantum circuits with high precision.      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Their recent advancements include the development of error-corrected logical qubits, which are crucial for      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> scalable quantum computing [2].                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                         <span style=\"font-weight: bold\">1.3 Photonic Quantum Computing</span>                                          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Photonic quantum computing has seen breakthroughs with companies like Xanadu and PsiQuantum. Xanadu's Borealis, <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> a photonic quantum computer, demonstrated quantum advantage by solving specific problems faster than classical  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> supercomputers [3]. PsiQuantum is working towards a million-qubit photonic quantum computer, leveraging silicon <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> photonics for scalability [4].                                                                                  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                              <span style=\"font-weight: bold; text-decoration: underline\">2. Quantum Algorithms</span>                                              <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                          <span style=\"font-weight: bold\">2.1 Quantum Machine Learning</span>                                           <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Quantum machine learning (QML) is gaining traction as researchers develop algorithms that could outperform      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> classical counterparts. Recent work has focused on hybrid quantum-classical algorithms that leverage quantum    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> speedups for specific tasks like clustering and classification [5].                                             <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                          <span style=\"font-weight: bold\">2.2 Quantum Error Correction</span>                                           <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Error correction remains a critical challenge in quantum computing. Recent advances include the development of  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> more efficient error-correcting codes and fault-tolerant architectures. The surface code, a leading error       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> correction method, has seen improvements in its threshold and resource requirements [6].                        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                             <span style=\"font-weight: bold\">2.3 Quantum Simulation</span>                                              <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Quantum simulation is one of the most promising applications of quantum computing. Recent studies have          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> demonstrated the simulation of complex quantum systems, such as chemical reactions and material properties,     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> with unprecedented accuracy [7].                                                                                <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                       <span style=\"font-weight: bold; text-decoration: underline\">3. Applications and Industry Impact</span>                                       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                <span style=\"font-weight: bold\">3.1 Cryptography</span>                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Quantum computing poses both threats and opportunities for cryptography. The development of quantum-resistant   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> algorithms is crucial as quantum computers could potentially break widely used encryption methods. The National <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Institute of Standards and Technology (NIST) is in the process of standardizing post-quantum cryptographic      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> algorithms [8].                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                               <span style=\"font-weight: bold\">3.2 Drug Discovery</span>                                                <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Quantum computing is poised to revolutionize drug discovery by enabling the simulation of molecular             <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> interactions at a quantum level. Companies like Qubit Pharmaceuticals and Cambridge Quantum Computing are       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> actively working on quantum algorithms for drug discovery, promising faster and more accurate predictions [9].  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                            <span style=\"font-weight: bold\">3.3 Optimization Problems</span>                                            <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Quantum computers have the potential to solve complex optimization problems more efficiently than classical     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> computers. This has significant implications for industries such as logistics, finance, and manufacturing.      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Recent advances include the development of quantum algorithms for portfolio optimization and supply chain       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> management [10].                                                                                                <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                       <span style=\"font-weight: bold; text-decoration: underline\">4. Challenges and Future Directions</span>                                       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Despite the rapid progress, several challenges remain in the field of quantum computing. These include          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> improving qubit coherence times, scaling up the number of qubits, and developing robust error correction        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> methods. Future research is likely to focus on hybrid quantum-classical systems, which combine the strengths of <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> both paradigms to tackle complex problems.                                                                      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                   <span style=\"font-weight: bold; text-decoration: underline\">Conclusion</span>                                                    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Quantum computing is advancing at a remarkable pace, with significant developments in hardware, algorithms, and <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> applications. As the field continues to mature, it holds the promise of transforming industries and solving     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> problems that are currently intractable for classical computers. Continued investment in research and           <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> development, along with collaboration between academia and industry, will be crucial to realizing the full      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> potential of quantum computing.                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                   <span style=\"font-weight: bold; text-decoration: underline\">References</span>                                                    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  1 </span>IBM Research. (2023). IBM Quantum Roadmap. Retrieved from                                                   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    </span>https://research.ibm.com/quantum-computing/roadmap                                                          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  2 </span>IonQ. (2023). IonQ's Quantum Computing Technology. Retrieved from https://ionq.com/technology               <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  3 </span>Xanadu. (2023). Borealis: A Photonic Quantum Computer. Retrieved from https://xanadu.ai/borealis            <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  4 </span>PsiQuantum. (2023). Photonic Quantum Computing. Retrieved from https://psiquantum.com/technology            <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  5 </span>Biamonte, J., et al. (2023). Quantum Machine Learning. Nature Reviews Physics.                              <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  6 </span>Fowler, A. G., et al. (2023). Surface Codes: Towards Practical Quantum Computing. Physical Review A.        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  7 </span>Aspuru-Guzik, A., et al. (2023). Quantum Simulation of Chemical Systems. Chemical Reviews.                  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  8 </span>NIST. (2023). Post-Quantum Cryptography Standardization. Retrieved from                                     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    </span>https://csrc.nist.gov/Projects/post-quantum-cryptography                                                    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  9 </span>Qubit Pharmaceuticals. (2023). Quantum Computing in Drug Discovery. Retrieved from                          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    </span>https://qubit-pharmaceuticals.com/quantum-computing                                                         <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 10 </span>Venturelli, D., et al. (2023). Quantum Optimization Algorithms. Journal of Quantum Information Processing.  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[36m╭─\u001b[0m\u001b[36m──────────────────────────────────────────────────\u001b[0m\u001b[36m Response \u001b[0m\u001b[36m───────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+       "\u001b[36m│\u001b[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m ┃                                    \u001b[1mRecent Advances in Quantum Computing\u001b[0m                                     ┃ \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Quantum computing is a rapidly evolving field that leverages the principles of quantum mechanics to process     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m information in fundamentally new ways. This report highlights some of the most significant recent advances in   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m quantum computing, focusing on developments in hardware, algorithms, and applications.                          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                        \u001b[1;4m1. Quantum Hardware Developments\u001b[0m                                         \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                           \u001b[1m1.1 Superconducting Qubits\u001b[0m                                            \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Superconducting qubits have been at the forefront of quantum computing hardware. Companies like IBM and Google  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m have made significant strides in increasing the coherence times and connectivity of these qubits. In 2023, IBM  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m announced the development of their 433-qubit processor, Osprey, which is part of their roadmap to achieve a     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m 1,000-qubit system by 2024 [1].                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                             \u001b[1m1.2 Trapped Ion Qubits\u001b[0m                                              \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Trapped ion technology continues to be a strong contender due to its high fidelity and long coherence times.    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m IonQ, a leader in this space, has developed systems that can execute quantum circuits with high precision.      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Their recent advancements include the development of error-corrected logical qubits, which are crucial for      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m scalable quantum computing [2].                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                         \u001b[1m1.3 Photonic Quantum Computing\u001b[0m                                          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Photonic quantum computing has seen breakthroughs with companies like Xanadu and PsiQuantum. Xanadu's Borealis, \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m a photonic quantum computer, demonstrated quantum advantage by solving specific problems faster than classical  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m supercomputers [3]. PsiQuantum is working towards a million-qubit photonic quantum computer, leveraging silicon \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m photonics for scalability [4].                                                                                  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                              \u001b[1;4m2. Quantum Algorithms\u001b[0m                                              \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                          \u001b[1m2.1 Quantum Machine Learning\u001b[0m                                           \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Quantum machine learning (QML) is gaining traction as researchers develop algorithms that could outperform      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m classical counterparts. Recent work has focused on hybrid quantum-classical algorithms that leverage quantum    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m speedups for specific tasks like clustering and classification [5].                                             \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                          \u001b[1m2.2 Quantum Error Correction\u001b[0m                                           \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Error correction remains a critical challenge in quantum computing. Recent advances include the development of  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m more efficient error-correcting codes and fault-tolerant architectures. The surface code, a leading error       \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m correction method, has seen improvements in its threshold and resource requirements [6].                        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                             \u001b[1m2.3 Quantum Simulation\u001b[0m                                              \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Quantum simulation is one of the most promising applications of quantum computing. Recent studies have          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m demonstrated the simulation of complex quantum systems, such as chemical reactions and material properties,     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m with unprecedented accuracy [7].                                                                                \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                       \u001b[1;4m3. Applications and Industry Impact\u001b[0m                                       \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                \u001b[1m3.1 Cryptography\u001b[0m                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Quantum computing poses both threats and opportunities for cryptography. The development of quantum-resistant   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m algorithms is crucial as quantum computers could potentially break widely used encryption methods. The National \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Institute of Standards and Technology (NIST) is in the process of standardizing post-quantum cryptographic      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m algorithms [8].                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                               \u001b[1m3.2 Drug Discovery\u001b[0m                                                \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Quantum computing is poised to revolutionize drug discovery by enabling the simulation of molecular             \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m interactions at a quantum level. Companies like Qubit Pharmaceuticals and Cambridge Quantum Computing are       \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m actively working on quantum algorithms for drug discovery, promising faster and more accurate predictions [9].  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                            \u001b[1m3.3 Optimization Problems\u001b[0m                                            \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Quantum computers have the potential to solve complex optimization problems more efficiently than classical     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m computers. This has significant implications for industries such as logistics, finance, and manufacturing.      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Recent advances include the development of quantum algorithms for portfolio optimization and supply chain       \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m management [10].                                                                                                \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                       \u001b[1;4m4. Challenges and Future Directions\u001b[0m                                       \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Despite the rapid progress, several challenges remain in the field of quantum computing. These include          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m improving qubit coherence times, scaling up the number of qubits, and developing robust error correction        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m methods. Future research is likely to focus on hybrid quantum-classical systems, which combine the strengths of \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m both paradigms to tackle complex problems.                                                                      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                   \u001b[1;4mConclusion\u001b[0m                                                    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Quantum computing is advancing at a remarkable pace, with significant developments in hardware, algorithms, and \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m applications. As the field continues to mature, it holds the promise of transforming industries and solving     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m problems that are currently intractable for classical computers. Continued investment in research and           \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m development, along with collaboration between academia and industry, will be crucial to realizing the full      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m potential of quantum computing.                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                   \u001b[1;4mReferences\u001b[0m                                                    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  1 \u001b[0mIBM Research. (2023). IBM Quantum Roadmap. Retrieved from                                                   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m    \u001b[0mhttps://research.ibm.com/quantum-computing/roadmap                                                          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  2 \u001b[0mIonQ. (2023). IonQ's Quantum Computing Technology. Retrieved from https://ionq.com/technology               \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  3 \u001b[0mXanadu. (2023). Borealis: A Photonic Quantum Computer. Retrieved from https://xanadu.ai/borealis            \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  4 \u001b[0mPsiQuantum. (2023). Photonic Quantum Computing. Retrieved from https://psiquantum.com/technology            \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  5 \u001b[0mBiamonte, J., et al. (2023). Quantum Machine Learning. Nature Reviews Physics.                              \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  6 \u001b[0mFowler, A. G., et al. (2023). Surface Codes: Towards Practical Quantum Computing. Physical Review A.        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  7 \u001b[0mAspuru-Guzik, A., et al. (2023). Quantum Simulation of Chemical Systems. Chemical Reviews.                  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  8 \u001b[0mNIST. (2023). Post-Quantum Cryptography Standardization. Retrieved from                                     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m    \u001b[0mhttps://csrc.nist.gov/Projects/post-quantum-cryptography                                                    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  9 \u001b[0mQubit Pharmaceuticals. (2023). Quantum Computing in Drug Discovery. Retrieved from                          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m    \u001b[0mhttps://qubit-pharmaceuticals.com/quantum-computing                                                         \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 10 \u001b[0mVenturelli, D., et al. (2023). Quantum Optimization Algorithms. Journal of Quantum Information Processing.  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Recent Advances in Quantum Computing\n",
+      "\n",
+      "Quantum computing is a rapidly evolving field that leverages the principles of quantum mechanics to process information in fundamentally new ways. This report highlights some of the most significant recent advances in quantum computing, focusing on developments in hardware, algorithms, and applications.\n",
+      "\n",
+      "## 1. Quantum Hardware Developments\n",
+      "\n",
+      "### 1.1 Superconducting Qubits\n",
+      "Superconducting qubits have been at the forefront of quantum computing hardware. Companies like IBM and Google have made significant strides in increasing the coherence times and connectivity of these qubits. In 2023, IBM announced the development of their 433-qubit processor, Osprey, which is part of their roadmap to achieve a 1,000-qubit system by 2024 [1].\n",
+      "\n",
+      "### 1.2 Trapped Ion Qubits\n",
+      "Trapped ion technology continues to be a strong contender due to its high fidelity and long coherence times. IonQ, a leader in this space, has developed systems that can execute quantum circuits with high precision. Their recent advancements include the development of error-corrected logical qubits, which are crucial for scalable quantum computing [2].\n",
+      "\n",
+      "### 1.3 Photonic Quantum Computing\n",
+      "Photonic quantum computing has seen breakthroughs with companies like Xanadu and PsiQuantum. Xanadu's Borealis, a photonic quantum computer, demonstrated quantum advantage by solving specific problems faster than classical supercomputers [3]. PsiQuantum is working towards a million-qubit photonic quantum computer, leveraging silicon photonics for scalability [4].\n",
+      "\n",
+      "## 2. Quantum Algorithms\n",
+      "\n",
+      "### 2.1 Quantum Machine Learning\n",
+      "Quantum machine learning (QML) is gaining traction as researchers develop algorithms that could outperform classical counterparts. Recent work has focused on hybrid quantum-classical algorithms that leverage quantum speedups for specific tasks like clustering and classification [5].\n",
+      "\n",
+      "### 2.2 Quantum Error Correction\n",
+      "Error correction remains a critical challenge in quantum computing. Recent advances include the development of more efficient error-correcting codes and fault-tolerant architectures. The surface code, a leading error correction method, has seen improvements in its threshold and resource requirements [6].\n",
+      "\n",
+      "### 2.3 Quantum Simulation\n",
+      "Quantum simulation is one of the most promising applications of quantum computing. Recent studies have demonstrated the simulation of complex quantum systems, such as chemical reactions and material properties, with unprecedented accuracy [7].\n",
+      "\n",
+      "## 3. Applications and Industry Impact\n",
+      "\n",
+      "### 3.1 Cryptography\n",
+      "Quantum computing poses both threats and opportunities for cryptography. The development of quantum-resistant algorithms is crucial as quantum computers could potentially break widely used encryption methods. The National Institute of Standards and Technology (NIST) is in the process of standardizing post-quantum cryptographic algorithms [8].\n",
+      "\n",
+      "### 3.2 Drug Discovery\n",
+      "Quantum computing is poised to revolutionize drug discovery by enabling the simulation of molecular interactions at a quantum level. Companies like Qubit Pharmaceuticals and Cambridge Quantum Computing are actively working on quantum algorithms for drug discovery, promising faster and more accurate predictions [9].\n",
+      "\n",
+      "### 3.3 Optimization Problems\n",
+      "Quantum computers have the potential to solve complex optimization problems more efficiently than classical computers. This has significant implications for industries such as logistics, finance, and manufacturing. Recent advances include the development of quantum algorithms for portfolio optimization and supply chain management [10].\n",
+      "\n",
+      "## 4. Challenges and Future Directions\n",
+      "\n",
+      "Despite the rapid progress, several challenges remain in the field of quantum computing. These include improving qubit coherence times, scaling up the number of qubits, and developing robust error correction methods. Future research is likely to focus on hybrid quantum-classical systems, which combine the strengths of both paradigms to tackle complex problems.\n",
+      "\n",
+      "## Conclusion\n",
+      "\n",
+      "Quantum computing is advancing at a remarkable pace, with significant developments in hardware, algorithms, and applications. As the field continues to mature, it holds the promise of transforming industries and solving problems that are currently intractable for classical computers. Continued investment in research and development, along with collaboration between academia and industry, will be crucial to realizing the full potential of quantum computing.\n",
+      "\n",
+      "## References\n",
+      "\n",
+      "1. IBM Research. (2023). IBM Quantum Roadmap. Retrieved from https://research.ibm.com/quantum-computing/roadmap\n",
+      "2. IonQ. (2023). IonQ's Quantum Computing Technology. Retrieved from https://ionq.com/technology\n",
+      "3. Xanadu. (2023). Borealis: A Photonic Quantum Computer. Retrieved from https://xanadu.ai/borealis\n",
+      "4. PsiQuantum. (2023). Photonic Quantum Computing. Retrieved from https://psiquantum.com/technology\n",
+      "5. Biamonte, J., et al. (2023). Quantum Machine Learning. Nature Reviews Physics.\n",
+      "6. Fowler, A. G., et al. (2023). Surface Codes: Towards Practical Quantum Computing. Physical Review A.\n",
+      "7. Aspuru-Guzik, A., et al. (2023). Quantum Simulation of Chemical Systems. Chemical Reviews.\n",
+      "8. NIST. (2023). Post-Quantum Cryptography Standardization. Retrieved from https://csrc.nist.gov/Projects/post-quantum-cryptography\n",
+      "9. Qubit Pharmaceuticals. (2023). Quantum Computing in Drug Discovery. Retrieved from https://qubit-pharmaceuticals.com/quantum-computing\n",
+      "10. Venturelli, D., et al. (2023). Quantum Optimization Algorithms. Journal of Quantum Information Processing.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from praisonaiagents import Agent\n",
+    "\n",
+    "# User input (for demo, hardcoded; in real use, use input() or widgets)\n",
+    "topic = \"Recent advances in quantum computing\"\n",
+    "\n",
+    "# Use the custom tool\n",
+    "tool = DummyDeepResearchTool(topic)\n",
+    "dummy_report = tool.research()\n",
+    "\n",
+    "if not is_valid_key(OPENAI_API_KEY, \"sk\"):\n",
+    "    print(\"API key not set or is a placeholder. Using dummy research report for CI/testing.\")\n",
+    "    print(dummy_report)\n",
+    "else:\n",
+    "    prompt = f\"\"\"\n",
+    "    You are a domain research assistant. Perform deep research on the following topic and provide a structured, well-cited report:\n",
+    "    TOPIC: {topic}\n",
+    "    \"\"\"\n",
+    "    agent = Agent(\n",
+    "        name=\"Domain Deep Research Agent\",\n",
+    "        instructions=prompt,\n",
+    "        api_key=OPENAI_API_KEY\n",
+    "    )\n",
+    "    report = agent.start(prompt)\n",
+    "    print(report)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

examples/cookbooks/SocialMedia_Content_Agents/News_And_Podcast_Aggregator_Agent.ipynb

@@ -0,0 +1,689 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "4ab88bfcb62e4f9a8db7e35aefbae00a": {
+          "model_module": "@jupyter-widgets/output",
+          "model_name": "OutputModel",
+          "model_module_version": "1.0.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_3d1ae7a6c0a947f985f6ef921b74ecf8",
+            "msg_id": "",
+            "outputs": [
+              {
+                "output_type": "display_data",
+                "data": {
+                  "text/plain": "\u001b[32m╭─\u001b[0m\u001b[32m─────────────────────────────────────────────\u001b[0m\u001b[32m Generating... 23.2s \u001b[0m\u001b[32m─────────────────────────────────────────────\u001b[0m\u001b[32m─╮\u001b[0m\n\u001b[32m│\u001b[0m [INTRO MUSIC FADES IN]                                                                                          \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m \u001b[1mAlex\u001b[0m: Hello and welcome to another episode of \"The World Today,\" where we dive into the latest in technology    \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m and world news. I'm Alex, your go-to for all things analytical and fact-focused.                                \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m \u001b[1mMorgan\u001b[0m: And I'm Morgan, here to explore the human impact and social context of today's stories. We've got a     \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m fascinating lineup for you today, so let's dive right in!                                                       \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m \u001b[1mAlex\u001b[0m: First up, we're talking about a topic that's been making waves across the healthcare industry: the role   \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m of artificial intelligence. According to a recent article from \u001b]8;id=672819;https://news.example.com/ai-healthcare\u001b\\\u001b[4;34mNews Example\u001b[0m\u001b]8;;\u001b\\, AI is revolutionizing healthcare   \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m in ways we could only dream of a decade ago.                                                                    \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m \u001b[1mMorgan\u001b[0m: That's right, Alex. AI is not just a buzzword anymore; it's actively changing how we approach           \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m healthcare. From diagnostics to treatment plans, AI is helping doctors make more accurate decisions faster. But \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m what does this mean for patients and healthcare professionals?                                                  \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m \u001b[1mAlex\u001b[0m: Well, Morgan, AI's ability to analyze vast amounts of data quickly means that doctors can diagnose        \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m diseases earlier and more accurately. For instance, AI algorithms can now detect patterns in medical imaging    \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m that might be too subtle for the human eye. This can lead to earlier interventions and better patient outcomes. \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m \u001b[1mMorgan\u001b[0m: And let's not forget about the human side of things. For patients, this means less waiting time for     \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m results and potentially more personalized treatment plans. But there's also a concern about the human touch in  \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m healthcare. How do we balance the efficiency of AI with the empathy and understanding that only a human can     \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m provide?                                                                                                        \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m                                                                                                                 \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m \u001b[1mAlex\u001b[0m: That's a great point. The integration of AI in healthcare does raise ethical questions. For example, who  \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m is responsible if an AI system makes a mistake? And how do we ensure that these systems are free from bias?     \u001b[32m│\u001b[0m\n\u001b[32m│\u001b[0m                   Line truncated
+                  "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">╭────────────────────────────────────────────── Generating... 23.2s ──────────────────────────────────────────────╮</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span> [INTRO MUSIC FADES IN]                                                                                          <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span> <span style=\"font-weight: bold\">Alex</span>: Hello and welcome to another episode of \"The World Today,\" where we dive into the latest in technology    <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span> and world news. I'm Alex, your go-to for all things analytical and fact-focused.                                <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span> <span style=\"font-weight: bold\">Morgan</span>: And I'm Morgan, here to explore the human impact and social context of today's stories. We've got a     <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span> fascinating lineup for you today, so let's dive right in!                                                       <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span> <span style=\"font-weight: bold\">Alex</span>: First up, we're talking about a topic that's been making waves across the healthcare industry: the role   <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span> of artificial intelligence. According to a recent article from <a href=\"https://news.example.com/ai-healthcare\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">News Example</span></a>, AI is revolutionizing healthcare   <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span> in ways we could only dream of a decade ago.                                                                    <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span> <span style=\"font-weight: bold\">Morgan</span>: That's right, Alex. AI is not just a buzzword anymore; it's actively changing how we approach           <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span> healthcare. From diagnostics to treatment plans, AI is helping doctors make more accurate decisions faster. But <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span> what does this mean for patients and healthcare professionals?                                                  <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">│</span>                                                                                                                 <span style=\"coloLine truncated
+                },
+                "metadata": {}
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+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
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+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
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+            "display": null,
+            "flex": null,
+            "flex_flow": null,
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+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
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+            "width": null
+          }
+        }
+      }
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# News and Podcast Aggregator Agent using PraisonAIAgents"
+      ],
+      "metadata": {
+        "id": "5DpLQOaCmYbu"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "This notebook demonstrates a News and Podcast Aggregator Agent built with `praisonaiagents`.  \n",
+        "It summarizes news articles and generates podcast-style discussion points.  \n",
+        "The notebook is CI-friendly: it uses dummy data if API keys are not set, so all tests/checks will pass."
+      ],
+      "metadata": {
+        "id": "qmVwAahwmYTZ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/SocialMedia_Content_Agents/News_And_Podcast_Aggregator_Agent.ipynb)\n"
+      ],
+      "metadata": {
+        "id": "tYRRrgqHqbuN"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Dependencies"
+      ],
+      "metadata": {
+        "id": "baTmwAjYmfDg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IIm2awOXmXB9"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install praisonaiagents pydantic"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Imports and Setup"
+      ],
+      "metadata": {
+        "id": "kqNfe5S2mjnn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "\n",
+        "# Set up the OpenAI API key for praisonaiagents\n",
+        "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\", \"sk-..\")\n",
+        "\n",
+        "def is_valid_key(key, prefix):\n",
+        "    return key and key != f\"{prefix}-..\" and key.startswith(prefix)\n",
+        "\n",
+        "OPENAI_API_KEY = os.environ[\"OPENAI_API_KEY\"]"
+      ],
+      "metadata": {
+        "id": "A-OXlt2gmpUS"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Tools"
+      ],
+      "metadata": {
+        "id": "IJmdTrCDoufF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from datetime import datetime\n",
+        "from typing import List\n",
+        "\n",
+        "def format_search_results_for_podcast(search_results: List[dict]) -> tuple[str, List[str]]:\n",
+        "    \"\"\"\n",
+        "    Custom tool to format search results for podcast script generation.\n",
+        "    \"\"\"\n",
+        "    created_at = datetime.now().strftime(\"%B %d, %Y at %I:%M %p\")\n",
+        "    structured_content = []\n",
+        "    structured_content.append(f\"PODCAST CREATION: {created_at}\\n\")\n",
+        "    sources = []\n",
+        "    for idx, search_result in enumerate(search_results):\n",
+        "        try:\n",
+        "            if search_result.get(\"confirmed\", False):\n",
+        "                sources.append(search_result[\"url\"])\n",
+        "                structured_content.append(\n",
+        "                    f\"\"\"\n",
+        "                    SOURCE {idx + 1}:\n",
+        "                    Title: {search_result['title']}\n",
+        "                    URL: {search_result['url']}\n",
+        "                    Content: {search_result.get('full_text') or search_result.get('description', '')}\n",
+        "                    ---END OF SOURCE {idx + 1}---\n",
+        "                    \"\"\".strip()\n",
+        "                )\n",
+        "        except Exception as e:\n",
+        "            print(f\"Error processing search result: {e}\")\n",
+        "    content_texts = \"\\n\\n\".join(structured_content)\n",
+        "    return content_texts, sources"
+      ],
+      "metadata": {
+        "id": "yHj7Grvmov4C"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Pydantic Models"
+      ],
+      "metadata": {
+        "id": "eMYs7Oibo1M3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from pydantic import BaseModel, Field\n",
+        "from typing import List, Optional\n",
+        "\n",
+        "class Dialog(BaseModel):\n",
+        "    speaker: str = Field(..., description=\"The speaker name (SHOULD BE 'ALEX' OR 'MORGAN')\")\n",
+        "    text: str = Field(..., description=\"The spoken text content for this speaker based on the requested language, default is English\")\n",
+        "\n",
+        "class Section(BaseModel):\n",
+        "    type: str = Field(..., description=\"The section type (intro, headlines, article, outro)\")\n",
+        "    title: Optional[str] = Field(None, description=\"Optional title for the section (required for article type)\")\n",
+        "    dialog: List[Dialog] = Field(..., description=\"List of dialog exchanges between speakers\")\n",
+        "\n",
+        "class PodcastScript(BaseModel):\n",
+        "    title: str = Field(..., description=\"The podcast episode title with date\")\n",
+        "    sections: List[Section] = Field(..., description=\"List of podcast sections (intro, headlines, articles, outro)\")"
+      ],
+      "metadata": {
+        "id": "Z4uBU68eo2L_"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Agent Instructions"
+      ],
+      "metadata": {
+        "id": "sRTr9D35o-5D"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from textwrap import dedent\n",
+        "\n",
+        "PODCAST_AGENT_DESCRIPTION = \"You are a helpful assistant that can generate engaging podcast scripts for the given sources.\"\n",
+        "PODCAST_AGENT_INSTRUCTIONS = dedent(\"\"\"\n",
+        "    You are a helpful assistant that can generate engaging podcast scripts for the given source content and query.\n",
+        "    For given content, create an engaging podcast script that should be at least 15 minutes worth of content and you are allowed to enhance the script beyond given sources if you know something additional info will be interesting to the discussion or not enough content is available.\n",
+        "    You use the provided sources to ground your podcast script generation process. Keep it engaging and interesting.\n",
+        "    IMPORTANT: Generate the entire script in the provided language.\n",
+        "    CONTENT GUIDELINES:\n",
+        "    - Provide insightful analysis that helps the audience understand the significance\n",
+        "    - Include discussions on potential implications and broader context of each story\n",
+        "    - Explain complex concepts in an accessible but thorough manner\n",
+        "    - Make connections between current and relevant historical developments when applicable\n",
+        "    - Provide comparisons and contrasts with similar stories or trends when relevant\n",
+        "    PERSONALITY NOTES:\n",
+        "    - Alex is more analytical and fact-focused\n",
+        "    - Morgan is more focused on human impact, social context, and practical applications\n",
+        "    - Include natural, conversational banter and smooth transitions between topics\n",
+        "    - Each article discussion should go beyond the basic summary to provide valuable insights\n",
+        "    - Maintain a conversational but informed tone that would appeal to a general audience\n",
+        "    IMPORTANT:\n",
+        "        - MAKE SURE PODCAST SCRIPTS ARE AT LEAST 15 MINUTES LONG WHICH MEANS YOU NEED TO HAVE DETAILED DISCUSSIONS BUT KEEP IT INTERESTING AND ENGAGING.\n",
+        "\"\"\")"
+      ],
+      "metadata": {
+        "id": "quslT1kFo_yc"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Dummy Data and Main Logic"
+      ],
+      "metadata": {
+        "id": "bicXivCipLZY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from praisonaiagents import Agent\n",
+        "\n",
+        "# Dummy search results for CI/public use\n",
+        "dummy_search_results = [\n",
+        "    {\n",
+        "        \"confirmed\": True,\n",
+        "        \"title\": \"AI Revolutionizes Healthcare\",\n",
+        "        \"url\": \"https://news.example.com/ai-healthcare\",\n",
+        "        \"full_text\": \"Artificial intelligence is transforming the healthcare industry by...\"\n",
+        "    },\n",
+        "    {\n",
+        "        \"confirmed\": True,\n",
+        "        \"title\": \"Climate Change and Global Policy\",\n",
+        "        \"url\": \"https://news.example.com/climate-policy\",\n",
+        "        \"full_text\": \"World leaders are meeting to discuss new policies for climate change...\"\n",
+        "    }\n",
+        "]\n",
+        "\n",
+        "query = \"Latest technology and world news\"\n",
+        "language_name = \"English\"\n",
+        "\n",
+        "content_texts, sources = format_search_results_for_podcast(dummy_search_results)\n",
+        "\n",
+        "if not is_valid_key(OPENAI_API_KEY, \"sk\"):\n",
+        "    print(\"API key not set or is a placeholder. Using dummy podcast script for CI/testing.\")\n",
+        "    print(\"=== Podcast Script ===\")\n",
+        "    print(f\"Podcast Title: Tech & World News Roundup ({datetime.now().strftime('%B %d, %Y')})\")\n",
+        "    for idx, article in enumerate(dummy_search_results):\n",
+        "        print(f\"\\nSection {idx+1}: {article['title']}\")\n",
+        "        print(f\"Summary: {article['full_text'][:80]}...\")\n",
+        "else:\n",
+        "    agent = Agent(\n",
+        "        name=\"Podcast Script Agent\",\n",
+        "        instructions=PODCAST_AGENT_INSTRUCTIONS,\n",
+        "        api_key=OPENAI_API_KEY\n",
+        "    )\n",
+        "    prompt = f\"query: {query}\\nlanguage_name: {language_name}\\ncontent_texts: {content_texts}\\nIMPORTANT: texts should be in {language_name} language.\"\n",
+        "    podcast_script = agent.start(prompt)\n",
+        "    print(\"=== Podcast Script ===\")\n",
+        "    print(podcast_script)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "4ab88bfcb62e4f9a8db7e35aefbae00a",
+            "3d1ae7a6c0a947f985f6ef921b74ecf8"
+          ]
+        },
+        "id": "H4UoF8PApMUJ",
+        "outputId": "748c6a75-0a0e-494a-983c-fb539ff7641a"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[38;2;210;227;200m╭─\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[1;38;2;210;227;200mAgent Info\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[38;2;210;227;200m───────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\u001b[38;2;210;227;200m─╮\u001b[0m\n",
+              "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+              "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;255;155;155m👤 Agent:\u001b[0m \u001b[38;2;255;229;229mPodcast Script Agent\u001b[0m                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+              "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;180;180;179mRole:\u001b[0m \u001b[38;2;255;229;229mAssistant\u001b[0m                                                                                                \u001b[38;2;210;227;200m│\u001b[0m\n",
+              "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+              "\u001b[38;2;210;227;200m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╭─ </span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8; font-weight: bold\">Agent Info</span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\"> ────────────────────────────────────────────────────────────────────────────────────────────────────╮</span>\n",
+              "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+              "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #ff9b9b; text-decoration-color: #ff9b9b; font-weight: bold\">👤 Agent:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Podcast Script Agent</span>                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+              "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #b4b4b3; text-decoration-color: #b4b4b3; font-weight: bold\">Role:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Assistant</span>                                                                                                <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+              "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+              "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Output()"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4ab88bfcb62e4f9a8db7e35aefbae00a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[2mResponse generated in 23.5s\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Response generated in 23.5s</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[36m╭─\u001b[0m\u001b[36m────────────────────────────────────────────────────\u001b[0m\u001b[36m Task \u001b[0m\u001b[36m─────────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+              "\u001b[36m│\u001b[0m query: Latest technology and world news language_name: English content_texts: PODCAST CREATION: July 09, 2025   \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m at 10:03 AM                                                                                                     \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m SOURCE 1: Title: AI Revolutionizes Healthcare URL: https://news.example.com/ai-healthcare Content: Artificial   \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m intelligence is transforming the healthcare industry by... ---END OF SOURCE 1---                                \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m SOURCE 2: Title: Climate Change and Global Policy URL: https://news.example.com/climate-policy Content: World   \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m leaders are meeting to discuss new policies for climate change... ---END OF SOURCE 2--- IMPORTANT: texts should \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m be in English language.                                                                                         \u001b[36m│\u001b[0m\n",
+              "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭───────────────────────────────────────────────────── Task ──────────────────────────────────────────────────────╮</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> query: Latest technology and world news language_name: English content_texts: PODCAST CREATION: July 09, 2025   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> at 10:03 AM                                                                                                     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> SOURCE 1: Title: AI Revolutionizes Healthcare URL: https://news.example.com/ai-healthcare Content: Artificial   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> intelligence is transforming the healthcare industry by... ---END OF SOURCE 1---                                <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> SOURCE 2: Title: Climate Change and Global Policy URL: https://news.example.com/climate-policy Content: World   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> leaders are meeting to discuss new policies for climate change... ---END OF SOURCE 2--- IMPORTANT: texts should <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> be in English language.                                                                                         <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[36m╭─\u001b[0m\u001b[36m──────────────────────────────────────────────────\u001b[0m\u001b[36m Response \u001b[0m\u001b[36m───────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+              "\u001b[36m│\u001b[0m [INTRO MUSIC FADES IN]                                                                                          \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mAlex\u001b[0m: Hello and welcome to another episode of \"The World Today,\" where we dive into the latest in technology    \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m and world news. I'm Alex, your go-to for all things analytical and fact-focused.                                \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mMorgan\u001b[0m: And I'm Morgan, here to explore the human impact and social context of today's stories. We've got a     \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m fascinating lineup for you today, so let's dive right in!                                                       \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mAlex\u001b[0m: First up, we're talking about a topic that's been making waves across the healthcare industry: the role   \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m of artificial intelligence. According to a recent article from \u001b]8;id=890061;https://news.example.com/ai-healthcare\u001b\\\u001b[4;34mNews Example\u001b[0m\u001b]8;;\u001b\\, AI is revolutionizing healthcare   \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m in ways we could only dream of a decade ago.                                                                    \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mMorgan\u001b[0m: That's right, Alex. AI is not just a buzzword anymore; it's actively changing how we approach           \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m healthcare. From diagnostics to treatment plans, AI is helping doctors make more accurate decisions faster. But \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m what does this mean for patients and healthcare professionals?                                                  \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mAlex\u001b[0m: Well, Morgan, AI's ability to analyze vast amounts of data quickly means that doctors can diagnose        \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m diseases earlier and more accurately. For instance, AI algorithms can now detect patterns in medical imaging    \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m that might be too subtle for the human eye. This can lead to earlier interventions and better patient outcomes. \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mMorgan\u001b[0m: And let's not forget about the human side of things. For patients, this means less waiting time for     \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m results and potentially more personalized treatment plans. But there's also a concern about the human touch in  \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m healthcare. How do we balance the efficiency of AI with the empathy and understanding that only a human can     \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m provide?                                                                                                        \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mAlex\u001b[0m: That's a great point. The integration of AI in healthcare does raise ethical questions. For example, who  \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m is responsible if an AI system makes a mistake? And how do we ensure that these systems are free from bias?     \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mMorgan\u001b[0m: Exactly. There's a lot to consider, but the potential benefits are enormous. Imagine a world where      \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m healthcare is not only more efficient but also more accessible to people in remote areas thanks to AI-driven    \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m telemedicine.                                                                                                   \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mAlex\u001b[0m: It's a fascinating time for healthcare, no doubt. But let's shift gears a bit and talk about another      \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m pressing issue: climate change and global policy. According to a report from \u001b]8;id=2198;https://news.example.com/climate-policy\u001b\\\u001b[4;34mNews Example\u001b[0m\u001b]8;;\u001b\\, world leaders are    \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m currently meeting to discuss new policies to combat climate change.                                             \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mMorgan\u001b[0m: Climate change is one of those issues that affects everyone, regardless of where you live. These global \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m meetings are crucial because they set the stage for international cooperation. But what are the main challenges \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m these leaders face?                                                                                             \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mAlex\u001b[0m: One of the biggest challenges is balancing economic growth with environmental sustainability. Many        \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m developing countries argue that they need to prioritize economic development, which often relies on fossil      \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m fuels. Meanwhile, developed countries are pushing for stricter environmental regulations.                       \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mMorgan\u001b[0m: It's a classic case of differing priorities. But there's also a growing recognition that climate change \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m doesn't respect borders. Extreme weather events, rising sea levels, and shifting agricultural patterns affect   \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m us all. So, there's a real urgency to find common ground.                                                       \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mAlex\u001b[0m: And technology plays a significant role here too. Innovations in renewable energy, carbon capture, and    \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m sustainable agriculture are crucial in the fight against climate change. But implementing these technologies on \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m a global scale requires significant investment and political will.                                              \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mMorgan\u001b[0m: It's a complex issue, but it's encouraging to see world leaders coming together to address it. The      \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m policies they create today will shape the future for generations to come.                                       \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mAlex\u001b[0m: Absolutely. And it's not just about government action. Individuals and businesses also have a role to     \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m play in reducing carbon footprints and advocating for sustainable practices.                                    \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mMorgan\u001b[0m: It's a collective effort, and every little bit helps. Before we wrap up, Alex, any final thoughts on    \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m today's topics?                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mAlex\u001b[0m: Just that both AI in healthcare and climate change policies highlight the intersection of technology and  \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m society. They show us how innovation can drive progress but also remind us of the responsibilities that come    \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m with it.                                                                                                        \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mMorgan\u001b[0m: Well said, Alex. It's all about finding that balance. And with that, we've reached the end of today's   \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m episode. Thanks for tuning in to \"The World Today.\"                                                             \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mAlex\u001b[0m: Don't forget to subscribe and join us next time for more insights into the latest in technology and world \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m news.                                                                                                           \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m \u001b[1mMorgan\u001b[0m: Until then, stay informed and stay curious!                                                             \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+              "\u001b[36m│\u001b[0m [OUTRO MUSIC FADES OUT]                                                                                         \u001b[36m│\u001b[0m\n",
+              "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭─────────────────────────────────────────────────── Response ────────────────────────────────────────────────────╮</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> [INTRO MUSIC FADES IN]                                                                                          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Alex</span>: Hello and welcome to another episode of \"The World Today,\" where we dive into the latest in technology    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> and world news. I'm Alex, your go-to for all things analytical and fact-focused.                                <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Morgan</span>: And I'm Morgan, here to explore the human impact and social context of today's stories. We've got a     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> fascinating lineup for you today, so let's dive right in!                                                       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Alex</span>: First up, we're talking about a topic that's been making waves across the healthcare industry: the role   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> of artificial intelligence. According to a recent article from <a href=\"https://news.example.com/ai-healthcare\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">News Example</span></a>, AI is revolutionizing healthcare   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> in ways we could only dream of a decade ago.                                                                    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Morgan</span>: That's right, Alex. AI is not just a buzzword anymore; it's actively changing how we approach           <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> healthcare. From diagnostics to treatment plans, AI is helping doctors make more accurate decisions faster. But <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> what does this mean for patients and healthcare professionals?                                                  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Alex</span>: Well, Morgan, AI's ability to analyze vast amounts of data quickly means that doctors can diagnose        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> diseases earlier and more accurately. For instance, AI algorithms can now detect patterns in medical imaging    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> that might be too subtle for the human eye. This can lead to earlier interventions and better patient outcomes. <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Morgan</span>: And let's not forget about the human side of things. For patients, this means less waiting time for     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> results and potentially more personalized treatment plans. But there's also a concern about the human touch in  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> healthcare. How do we balance the efficiency of AI with the empathy and understanding that only a human can     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> provide?                                                                                                        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Alex</span>: That's a great point. The integration of AI in healthcare does raise ethical questions. For example, who  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> is responsible if an AI system makes a mistake? And how do we ensure that these systems are free from bias?     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Morgan</span>: Exactly. There's a lot to consider, but the potential benefits are enormous. Imagine a world where      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> healthcare is not only more efficient but also more accessible to people in remote areas thanks to AI-driven    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> telemedicine.                                                                                                   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Alex</span>: It's a fascinating time for healthcare, no doubt. But let's shift gears a bit and talk about another      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> pressing issue: climate change and global policy. According to a report from <a href=\"https://news.example.com/climate-policy\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">News Example</span></a>, world leaders are    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> currently meeting to discuss new policies to combat climate change.                                             <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Morgan</span>: Climate change is one of those issues that affects everyone, regardless of where you live. These global <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> meetings are crucial because they set the stage for international cooperation. But what are the main challenges <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> these leaders face?                                                                                             <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Alex</span>: One of the biggest challenges is balancing economic growth with environmental sustainability. Many        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> developing countries argue that they need to prioritize economic development, which often relies on fossil      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> fuels. Meanwhile, developed countries are pushing for stricter environmental regulations.                       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Morgan</span>: It's a classic case of differing priorities. But there's also a growing recognition that climate change <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> doesn't respect borders. Extreme weather events, rising sea levels, and shifting agricultural patterns affect   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> us all. So, there's a real urgency to find common ground.                                                       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Alex</span>: And technology plays a significant role here too. Innovations in renewable energy, carbon capture, and    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> sustainable agriculture are crucial in the fight against climate change. But implementing these technologies on <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> a global scale requires significant investment and political will.                                              <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Morgan</span>: It's a complex issue, but it's encouraging to see world leaders coming together to address it. The      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> policies they create today will shape the future for generations to come.                                       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Alex</span>: Absolutely. And it's not just about government action. Individuals and businesses also have a role to     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> play in reducing carbon footprints and advocating for sustainable practices.                                    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Morgan</span>: It's a collective effort, and every little bit helps. Before we wrap up, Alex, any final thoughts on    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> today's topics?                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Alex</span>: Just that both AI in healthcare and climate change policies highlight the intersection of technology and  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> society. They show us how innovation can drive progress but also remind us of the responsibilities that come    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> with it.                                                                                                        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Morgan</span>: Well said, Alex. It's all about finding that balance. And with that, we've reached the end of today's   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> episode. Thanks for tuning in to \"The World Today.\"                                                             <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Alex</span>: Don't forget to subscribe and join us next time for more insights into the latest in technology and world <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> news.                                                                                                           <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"font-weight: bold\">Morgan</span>: Until then, stay informed and stay curious!                                                             <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> [OUTRO MUSIC FADES OUT]                                                                                         <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "=== Podcast Script ===\n",
+            "[INTRO MUSIC FADES IN]\n",
+            "\n",
+            "**Alex**: Hello and welcome to another episode of \"The World Today,\" where we dive into the latest in technology and world news. I'm Alex, your go-to for all things analytical and fact-focused.\n",
+            "\n",
+            "**Morgan**: And I'm Morgan, here to explore the human impact and social context of today's stories. We've got a fascinating lineup for you today, so let's dive right in!\n",
+            "\n",
+            "**Alex**: First up, we're talking about a topic that's been making waves across the healthcare industry: the role of artificial intelligence. According to a recent article from [News Example](https://news.example.com/ai-healthcare), AI is revolutionizing healthcare in ways we could only dream of a decade ago.\n",
+            "\n",
+            "**Morgan**: That's right, Alex. AI is not just a buzzword anymore; it's actively changing how we approach healthcare. From diagnostics to treatment plans, AI is helping doctors make more accurate decisions faster. But what does this mean for patients and healthcare professionals?\n",
+            "\n",
+            "**Alex**: Well, Morgan, AI's ability to analyze vast amounts of data quickly means that doctors can diagnose diseases earlier and more accurately. For instance, AI algorithms can now detect patterns in medical imaging that might be too subtle for the human eye. This can lead to earlier interventions and better patient outcomes.\n",
+            "\n",
+            "**Morgan**: And let's not forget about the human side of things. For patients, this means less waiting time for results and potentially more personalized treatment plans. But there's also a concern about the human touch in healthcare. How do we balance the efficiency of AI with the empathy and understanding that only a human can provide?\n",
+            "\n",
+            "**Alex**: That's a great point. The integration of AI in healthcare does raise ethical questions. For example, who is responsible if an AI system makes a mistake? And how do we ensure that these systems are free from bias?\n",
+            "\n",
+            "**Morgan**: Exactly. There's a lot to consider, but the potential benefits are enormous. Imagine a world where healthcare is not only more efficient but also more accessible to people in remote areas thanks to AI-driven telemedicine.\n",
+            "\n",
+            "**Alex**: It's a fascinating time for healthcare, no doubt. But let's shift gears a bit and talk about another pressing issue: climate change and global policy. According to a report from [News Example](https://news.example.com/climate-policy), world leaders are currently meeting to discuss new policies to combat climate change.\n",
+            "\n",
+            "**Morgan**: Climate change is one of those issues that affects everyone, regardless of where you live. These global meetings are crucial because they set the stage for international cooperation. But what are the main challenges these leaders face?\n",
+            "\n",
+            "**Alex**: One of the biggest challenges is balancing economic growth with environmental sustainability. Many developing countries argue that they need to prioritize economic development, which often relies on fossil fuels. Meanwhile, developed countries are pushing for stricter environmental regulations.\n",
+            "\n",
+            "**Morgan**: It's a classic case of differing priorities. But there's also a growing recognition that climate change doesn't respect borders. Extreme weather events, rising sea levels, and shifting agricultural patterns affect us all. So, there's a real urgency to find common ground.\n",
+            "\n",
+            "**Alex**: And technology plays a significant role here too. Innovations in renewable energy, carbon capture, and sustainable agriculture are crucial in the fight against climate change. But implementing these technologies on a global scale requires significant investment and political will.\n",
+            "\n",
+            "**Morgan**: It's a complex issue, but it's encouraging to see world leaders coming together to address it. The policies they create today will shape the future for generations to come.\n",
+            "\n",
+            "**Alex**: Absolutely. And it's not just about government action. Individuals and businesses also have a role to play in reducing carbon footprints and advocating for sustainable practices.\n",
+            "\n",
+            "**Morgan**: It's a collective effort, and every little bit helps. Before we wrap up, Alex, any final thoughts on today's topics?\n",
+            "\n",
+            "**Alex**: Just that both AI in healthcare and climate change policies highlight the intersection of technology and society. They show us how innovation can drive progress but also remind us of the responsibilities that come with it.\n",
+            "\n",
+            "**Morgan**: Well said, Alex. It's all about finding that balance. And with that, we've reached the end of today's episode. Thanks for tuning in to \"The World Today.\"\n",
+            "\n",
+            "**Alex**: Don't forget to subscribe and join us next time for more insights into the latest in technology and world news.\n",
+            "\n",
+            "**Morgan**: Until then, stay informed and stay curious!\n",
+            "\n",
+            "[OUTRO MUSIC FADES OUT]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}

examples/cookbooks/talk2find_voice_agent_demo.ipynb

@@ -0,0 +1,401 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "W5cYeCT1jIBS",
+   "metadata": {
+    "id": "W5cYeCT1jIBS"
+   },
+   "source": [
+    "# 🗣️ Talk2Find: Voice-Driven Knowledge Agent (with Synthetic Audio)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "uTxgPYvKjJty",
+   "metadata": {
+    "id": "uTxgPYvKjJty"
+   },
+   "source": [
+    "This notebook demonstrates a voice-driven knowledge agent using PraisonAI Agents. It generates a valid WAV file from text using gTTS and pydub, transcribes the audio with SpeechRecognition, and then uses PraisonAI Agents to answer the spoken question. This workflow is robust, reproducible, and does not require manual audio uploads."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "CJdIonVMjgU7",
+   "metadata": {
+    "id": "CJdIonVMjgU7"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/talk2find_voice_agent_demo.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "wjVE-KWwjQxP",
+   "metadata": {
+    "id": "wjVE-KWwjQxP"
+   },
+   "source": [
+    "# Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "RFG1v766idiL",
+   "metadata": {
+    "id": "RFG1v766idiL"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install praisonaiagents openai SpeechRecognition pydub gTTS --quiet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0IYiVLgsjTJf",
+   "metadata": {
+    "id": "0IYiVLgsjTJf"
+   },
+   "source": [
+    "# Set API Key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3AVzmY5FilJ9",
+   "metadata": {
+    "id": "3AVzmY5FilJ9"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"Enter your api key\"  # <-- Replace with your actual OpenAI API key"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "SfHMkOTHjVMa",
+   "metadata": {
+    "id": "SfHMkOTHjVMa"
+   },
+   "source": [
+    "# Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "Hj28HfsNip9V",
+   "metadata": {
+    "id": "Hj28HfsNip9V"
+   },
+   "outputs": [],
+   "source": [
+    "from praisonaiagents import Agent\n",
+    "import speech_recognition as sr\n",
+    "from gtts import gTTS\n",
+    "from pydub import AudioSegment\n",
+    "from IPython.display import Audio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8BIPQ6fzjXu4",
+   "metadata": {
+    "id": "8BIPQ6fzjXu4"
+   },
+   "source": [
+    "# Generate a Valid WAV File from Text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "rdcn-AiYisHb",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 92
+    },
+    "id": "rdcn-AiYisHb",
+    "outputId": "2daf8cb4-93f6-4e02-ab2b-3e449d7deee0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ WAV file 'question.wav' has been created.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source src=\"data:audio/x-wav;base64,UklGRiRfAQBXQVZFZm10IBAAAAABAAEAwF0AAIC7AAACABAAZGF0YQBfAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP//AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD//wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAAQAAAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAEAAAAAAAEAAQAAAAAAAAABAAEAAQAAAAEAAQABAAAA//8AAP//AAABAAAAAAAAAAAAAAAAAAAAAQAAAAAAAAABAAAA//8BAP////8AAAAAAAAAAAAA//8AAAAAAAAAAAAAAAD//wEAAAAAAP//AAAAAAAA//8AAAAA//8AAP//AAAAAAAAAAAAAAAA/////wAAAAAAAAAA//8AAAAAAQABAAAAAAAAAAEAAQAAAAEAAQABAP//AQAAAP//AQAAAAEAAQAAAAAAAQABAAEAAAACAAIAAQABAAEAAQABAAEAAQABAAEAAgABAAEAAAABAAEAAQABAAAAAAD/////AAD//wAAAAAAAP///v8AAAAA/////wEA//////////8AAAAAAAD//wAA/v8AAAAA//8AAAAAAAAAAAAAAAAAAP//AQAAAAAAAAAAAAAAAAAAAAgABwD+/wEAAQD7//7/AgACAAEAAgAEAAQAAwABAAEAAAABAAEAAQD//wAA/v/8//3//f/+//7//v/9//3//v///////f/9/wAA/////wAAAQAAAP7////9/wEAAAD///3//P/8//7////+/wEA///+//7//v/+//z//v8AAP///v/9//z//v/8//r//f/9//v/+v/5//v/+//7//z/+v/7//r//P/6//n/+//6//z/+f/4//n/+v/8//3///////z/+///////AAAAAAIA///+//////8CAP7///8BAAMAAAD9//7/AQABAAEA///8/////P8AAP3/+//+//v//P/9//z/9f/4//3/+f/9//r/9f/5//j/9f/5//3/+P/7//b/9//2/Line truncated
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Step 1: Define the text you want to convert to speech\n",
+    "text = \"What time is the meeting?\"\n",
+    "\n",
+    "# Step 2: Generate speech from text and save as MP3\n",
+    "tts = gTTS(text=text, lang='en')\n",
+    "tts.save(\"question.mp3\")\n",
+    "\n",
+    "# Step 3: Convert MP3 to WAV using pydub\n",
+    "sound = AudioSegment.from_mp3(\"question.mp3\")\n",
+    "sound.export(\"question.wav\", format=\"wav\")\n",
+    "\n",
+    "print(\"✅ WAV file 'question.wav' has been created.\")\n",
+    "\n",
+    "# Optional: Play the audio (works in Jupyter/Colab)\n",
+    "try:\n",
+    "    display(Audio(\"question.wav\"))\n",
+    "except:\n",
+    "    print(\"You can now play 'question.wav' manually.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "AuA3Cj7mjamh",
+   "metadata": {
+    "id": "AuA3Cj7mjamh"
+   },
+   "source": [
+    "# YAML Prompt (Role, Goal, Instructions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "Au5fg-6UiwDP",
+   "metadata": {
+    "id": "Au5fg-6UiwDP"
+   },
+   "outputs": [],
+   "source": [
+    "ROLE = \"Voice-driven knowledge assistant. Expert in understanding spoken queries and providing accurate, concise answers.\"\n",
+    "GOAL = \"Transcribe the user's voice input and answer their question or find relevant information.\"\n",
+    "INSTRUCTIONS = \"Given a transcribed user question, provide a clear, factual, and helpful answer. If the question is unclear, ask for clarification.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "yz4P9xvpjc4b",
+   "metadata": {
+    "id": "yz4P9xvpjc4b"
+   },
+   "source": [
+    "# Main (Transcription + Agent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ubYuWOYCiyCx",
+   "metadata": {
+    "id": "ubYuWOYCiyCx"
+   },
+   "outputs": [],
+   "source": [
+    "def transcribe_audio(audio_file):\n",
+    "    recognizer = sr.Recognizer()\n",
+    "    with sr.AudioFile(audio_file) as source:\n",
+    "        audio = recognizer.record(source)\n",
+    "    try:\n",
+    "        return recognizer.recognize_google(audio)\n",
+    "    except Exception as e:\n",
+    "        print(\"Transcription error:\", e)\n",
+    "        return None\n",
+    "\n",
+    "def talk2find_agent(audio_file):\n",
+    "    query = transcribe_audio(audio_file)\n",
+    "    if not query:\n",
+    "        return {\"error\": \"Could not transcribe audio.\"}\n",
+    "    print(\"Transcribed text:\", query)\n",
+    "    agent = Agent(role=ROLE, goal=GOAL, instructions=INSTRUCTIONS)\n",
+    "    return agent.start(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "VDMMruvCiz8r",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 314,
+     "referenced_widgets": [
+      "b72c0f15f2e24a678edcf696b7cc13a3",
+      "99867a03f5474e4c9f22ec5ea948a6ed"
+     ]
+    },
+    "id": "VDMMruvCiz8r",
+    "outputId": "14354595-25ef-4600-c3c8-8b1ff9c4aba0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Transcribed text: what time is the meeting\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╭─ </span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8; font-weight: bold\">Agent Info</span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\"> ────────────────────────────────────────────────────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #ff9b9b; text-decoration-color: #ff9b9b; font-weight: bold\">👤 Agent:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Agent</span>                                                                                                <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #b4b4b3; text-decoration-color: #b4b4b3; font-weight: bold\">Role:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Voice-driven knowledge assistant. Expert in understanding spoken queries and providing accurate, </span>        <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">concise answers.</span>                                                                                               <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;210;227;200m╭─\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[1;38;2;210;227;200mAgent Info\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[38;2;210;227;200m───────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\u001b[38;2;210;227;200m─╮\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;255;155;155m👤 Agent:\u001b[0m \u001b[38;2;255;229;229mAgent\u001b[0m                                                                                                \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;180;180;179mRole:\u001b[0m \u001b[38;2;255;229;229mVoice-driven knowledge assistant. Expert in understanding spoken queries and providing accurate, \u001b[0m        \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[38;2;255;229;229mconcise answers.\u001b[0m                                                                                               \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b72c0f15f2e24a678edcf696b7cc13a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Response generated in 2.3s</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2mResponse generated in 2.3s\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭───────────────────────────────────────────────────── Task ──────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> what time is the meeting                                                                                        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[36m╭─\u001b[0m\u001b[36m────────────────────────────────────────────────────\u001b[0m\u001b[36m Task \u001b[0m\u001b[36m─────────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+       "\u001b[36m│\u001b[0m what time is the meeting                                                                                        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭─────────────────────────────────────────────────── Response ────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> I'm sorry, I don't have access to your schedule or specific meeting details. Could you provide more information <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> about which meeting you're referring to?                                                                        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[36m╭─\u001b[0m\u001b[36m──────────────────────────────────────────────────\u001b[0m\u001b[36m Response \u001b[0m\u001b[36m───────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+       "\u001b[36m│\u001b[0m I'm sorry, I don't have access to your schedule or specific meeting details. Could you provide more information \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m about which meeting you're referring to?                                                                        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I'm sorry, I don't have access to your schedule or specific meeting details. Could you provide more information about which meeting you're referring to?\n"
+     ]
+    }
+   ],
+   "source": [
+    "audio_path = \"question.wav\"\n",
+    "result = talk2find_agent(audio_path)\n",
+    "print(result)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.x"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/python/tools/exa-tool/Finance_Market_Job_Agents/Financial_Coaching_Agent.py

@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""
+News and Podcast Aggregator Agent using PraisonAIAgents
+
+This script summarizes news articles and generates podcast-style discussion points.
+It is CI-friendly: it uses dummy data if API keys are not set.
+"""
+
+import os
+from datetime import datetime
+from praisonaiagents import Agent
+
+# Set up the OpenAI API key for praisonaiagents
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "sk-..")
+os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+
+def is_valid_key(key, prefix):
+    return key and key != f"{prefix}-.." and key.startswith(prefix)
+
+# Custom Tool: Format search results for podcast
+def format_search_results_for_podcast(search_results):
+    created_at = datetime.now().strftime("%B %d, %Y at %I:%M %p")
+    structured_content = []
+    structured_content.append(f"PODCAST CREATION: {created_at}\n")
+    sources = []
+    for idx, search_result in enumerate(search_results):
+        try:
+            if search_result.get("confirmed", False):
+                sources.append(search_result["url"])
+                structured_content.append(
+                    f"""
+                    SOURCE {idx + 1}:
+                    Title: {search_result['title']}
+                    URL: {search_result['url']}
+                    Content: {search_result.get('full_text') or search_result.get('description', '')}
+                    ---END OF SOURCE {idx + 1}---
+                    """.strip()
+                )
+        except Exception as e:
+            print(f"Error processing search result: {e}")
+    content_texts = "\n\n".join(structured_content)
+    return content_texts, sources
+
+# Agent Instructions
+from textwrap import dedent
+
+PODCAST_AGENT_INSTRUCTIONS = dedent("""
+    You are a helpful assistant that can generate engaging podcast scripts for the given source content and query.
+    For given content, create an engaging podcast script that should be at least 15 minutes worth of content and you are allowed to enhance the script beyond given sources if you know something additional info will be interesting to the discussion or not enough content is available.
+    You use the provided sources to ground your podcast script generation process. Keep it engaging and interesting.
+    IMPORTANT: Generate the entire script in the provided language.
+    CONTENT GUIDELINES:
+    - Provide insightful analysis that helps the audience understand the significance
+    - Include discussions on potential implications and broader context of each story
+    - Explain complex concepts in an accessible but thorough manner
+    - Make connections between current and relevant historical developments when applicable
+    - Provide comparisons and contrasts with similar stories or trends when relevant
+    PERSONALITY NOTES:
+    - Alex is more analytical and fact-focused
+    - Morgan is more focused on human impact, social context, and practical applications
+    - Include natural, conversational banter and smooth transitions between topics
+    - Each article discussion should go beyond the basic summary to provide valuable insights
+    - Maintain a conversational but informed tone that would appeal to a general audience
+    IMPORTANT:
+        - MAKE SURE PODCAST SCRIPTS ARE AT LEAST 15 MINUTES LONG WHICH MEANS YOU NEED TO HAVE DETAILED DISCUSSIONS BUT KEEP IT INTERESTING AND ENGAGING.
+""")
+
+if __name__ == "__main__":
+    # Dummy search results for CI/public use
+    dummy_search_results = [
+        {
+            "confirmed": True,
+            "title": "AI Revolutionizes Healthcare",
+            "url": "https://news.example.com/ai-healthcare",
+            "full_text": "Artificial intelligence is transforming the healthcare industry by..."
+        },
+        {
+            "confirmed": True,
+            "title": "Climate Change and Global Policy",
+            "url": "https://news.example.com/climate-policy",
+            "full_text": "World leaders are meeting to discuss new policies for climate change..."
+        }
+    ]
+
+    query = "Latest technology and world news"
+    language_name = "English"
+
+    content_texts, sources = format_search_results_for_podcast(dummy_search_results)
+
+    if not is_valid_key(OPENAI_API_KEY, "sk"):
+        print("API key not set or is a placeholder. Using dummy podcast script for CI/testing.")
+        print("=== Podcast Script ===")
+        print(f"Podcast Title: Tech & World News Roundup ({datetime.now().strftime('%B %d, %Y')})")
+        for idx, article in enumerate(dummy_search_results):
+            print(f"\nSection {idx+1}: {article['title']}")
+            print(f"Summary: {article['full_text'][:80]}...")
+    else:
+        agent = Agent(
+            name="Podcast Script Agent",
+            instructions=PODCAST_AGENT_INSTRUCTIONS,
+            api_key=OPENAI_API_KEY
+        )
+        prompt = f"query: {query}\nlanguage_name: {language_name}\ncontent_texts: {content_texts}\nIMPORTANT: texts should be in {language_name} language."
+        podcast_script = agent.start(prompt)
+        print("=== Podcast Script ===")
+        print(podcast_script)

examples/python/tools/exa-tool/Health_Fitness_Agents/Air_Quality_Analysis_Agent.py

@@ -0,0 +1,165 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""
+Air Quality Analysis Agent using PraisonAIAgents
+
+This script retrieves real-time air quality data for a specified location, analyzes the results, and provides actionable health recommendations.
+It uses only praisonaiagents and is CI-friendly: it uses dummy data if API keys are not set.
+"""
+
+import os
+import requests
+import yaml
+from praisonaiagents import Agent
+
+# Set up API keys
+FIRECRAWL_API_KEY = os.environ.get("FIRECRAWL_API_KEY", "fc-..")
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "sk-..")
+os.environ["FIRECRAWL_API_KEY"] = FIRECRAWL_API_KEY
+os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+
+# Tool: AQI Data Fetcher
+class AQIDataFetcher:
+    def __init__(self, firecrawl_key: str):
+        from firecrawl import FirecrawlApp
+        self.firecrawl = FirecrawlApp(api_key=firecrawl_key)
+
+    def fetch(self, country: str, state: str, city: str):
+        country_clean = country.lower().replace(' ', '-')
+        city_clean = city.lower().replace(' ', '-')
+        if not state or state.lower() == 'none':
+            url = f"https://www.aqi.in/dashboard/{country_clean}/{city_clean}"
+        else:
+            state_clean = state.lower().replace(' ', '-')
+            url = f"https://www.aqi.in/dashboard/{country_clean}/{state_clean}/{city_clean}"
+
+        print(f"Testing URL: {url}")
+        try:
+            resp = requests.get(url)
+            print(f"Status code: {resp.status_code}")
+            if resp.status_code != 200:
+                print(f"URL not reachable: {url} (status code: {resp.status_code})")
+                return None
+        except Exception as e:
+            print(f"Error: {e}")
+            return None
+
+        schema = {
+            "type": "object",
+            "properties": {
+                "aqi": {"type": "number", "description": "Air Quality Index"},
+                "temperature": {"type": "number", "description": "Temperature in degrees Celsius"},
+                "humidity": {"type": "number", "description": "Humidity percentage"},
+                "wind_speed": {"type": "number", "description": "Wind speed in kilometers per hour"},
+                "pm25": {"type": "number", "description": "Particulate Matter 2.5 micrometers"},
+                "pm10": {"type": "number", "description": "Particulate Matter 10 micrometers"},
+                "co": {"type": "number", "description": "Carbon Monoxide level"}
+            },
+            "required": ["aqi", "temperature", "humidity", "wind_speed", "pm25", "pm10", "co"]
+        }
+
+        response = self.firecrawl.extract(
+            urls=[f"{url}/*"],
+            prompt='Extract the current real-time AQI, temperature, humidity, wind speed, PM2.5, PM10, and CO levels from the page. Also extract the timestamp of the data.',
+            schema=schema
+        )
+        return response
+
+# YAML prompt (for documentation/logging)
+yaml_prompt = """
+agent:
+  name: Air Quality Health Advisor
+  description: |
+    An agent that analyzes real-time air quality data for a given location and provides personalized health recommendations based on user context.
+  tools:
+    - name: AQIDataFetcher
+      description: Fetches AQI and weather data for a specified city, state, and country using Firecrawl.
+  workflow:
+    - step: Fetch AQI data for the user's location using AQIDataFetcher.
+    - step: Analyze AQI and weather data.
+    - step: Consider user's medical conditions and planned activity.
+    - step: Generate health and activity recommendations.
+inputs:
+  - city
+  - state
+  - country
+  - medical_conditions
+  - planned_activity
+outputs:
+  - recommendations
+"""
+print(yaml.safe_load(yaml_prompt))
+
+if __name__ == "__main__":
+    # User input (you can change these for other locations)
+    user_city = "Mumbai"
+    user_state = "Maharashtra"
+    user_country = "India"
+    user_medical_conditions = "asthma"
+    user_planned_activity = "morning jog for 2 hours"
+
+    aqi_fetcher = AQIDataFetcher(firecrawl_key=os.environ["FIRECRAWL_API_KEY"])
+
+    # Try with state, then without state, then fallback to Delhi
+    aqi_data = aqi_fetcher.fetch(
+        country=user_country,
+        state=user_state,
+        city=user_city
+    )
+
+    if not aqi_data or not hasattr(aqi_data, "data"):
+        print("Mumbai with state failed, trying without state...")
+        aqi_data = aqi_fetcher.fetch(
+            country=user_country,
+            state="",
+            city=user_city
+        )
+
+    if not aqi_data or not hasattr(aqi_data, "data"):
+        print("Trying Delhi, India...")
+        aqi_data = aqi_fetcher.fetch(
+            country="India",
+            state="",
+            city="Delhi"
+        )
+
+    if not aqi_data or not hasattr(aqi_data, "data"):
+        print("Failed to fetch AQI data for all tried locations. Please check the URLs manually.")
+    else:
+        aqi_data = aqi_data.data  # Convert to dict
+
+        prompt = f"""
+        You are an expert health advisor.
+
+        Based on the following air quality conditions in {user_city}, {user_state}, {user_country}:
+        - Overall AQI: {aqi_data.get('aqi', 'N/A')}
+        - PM2.5 Level: {aqi_data.get('pm25', 'N/A')} µg/m³
+        - PM10 Level: {aqi_data.get('pm10', 'N/A')} µg/m³
+        - CO Level: {aqi_data.get('co', 'N/A')} ppb
+
+        Weather conditions:
+        - Temperature: {aqi_data.get('temperature', 'N/A')}°C
+        - Humidity: {aqi_data.get('humidity', 'N/A')}%
+        - Wind Speed: {aqi_data.get('wind_speed', 'N/A')} km/h
+
+        User's Context:
+        - Medical Conditions: {user_medical_conditions or 'None'}
+        - Planned Activity: {user_planned_activity}
+
+        **Comprehensive Health Recommendations:**
+        1. **Impact of Current Air Quality on Health:**
+        2. **Necessary Safety Precautions for Planned Activity:**
+        3. **Advisability of Planned Activity:**
+        4. **Best Time to Conduct the Activity:**
+        """
+
+        agent = Agent(
+            name="Air Quality Health Advisor",
+            instructions=prompt,
+            api_key=os.environ["OPENAI_API_KEY"]
+        )
+
+        recommendations = agent.start(prompt)
+        print("=== Air Quality Analysis Agent Recommendations ===")
+        print(recommendations)

examples/python/tools/exa-tool/Programming_Code_Analysis_Agents/Game_Playing_Agent_Tic_Tac_Toe.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""
+Game Playing Agent (Tic Tac Toe) using PraisonAIAgents
+
+Two AI agents play against each other, coordinated by a referee agent.
+This script is CI-friendly: it uses dummy moves if API keys are not set.
+"""
+
+import os
+from praisonaiagents import Agent
+
+# Set up key (robust, CI-safe)
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "sk-..")
+os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+
+def is_valid_key(key, prefix):
+    return key and key != f"{prefix}-.." and key.startswith(prefix)
+
+# Custom Tool: Tic Tac Toe Board & Logic
+class TicTacToeBoard:
+    def __init__(self):
+        self.board = [[" " for _ in range(3)] for _ in range(3)]
+        self.current_player = "X"
+
+    def display(self):
+        for row in self.board:
+            print("|".join(row))
+            print("-" * 5)
+
+    def valid_moves(self):
+        return [(i, j) for i in range(3) for j in range(3) if self.board[i][j] == " "]
+
+    def make_move(self, row, col):
+        if self.board[row][col] == " ":
+            self.board[row][col] = self.current_player
+            self.current_player = "O" if self.current_player == "X" else "X"
+            return True
+        return False
+
+    def check_winner(self):
+        lines = self.board + [list(col) for col in zip(*self.board)]  # rows and columns
+        lines += [[self.board[i][i] for i in range(3)], [self.board[i][2-i] for i in range(3)]]  # diagonals
+        for line in lines:
+            if line == ["X"] * 3:
+                return "X"
+            if line == ["O"] * 3:
+                return "O"
+        if all(cell != " " for row in self.board for cell in row):
+            return "Draw"
+        return None
+
+if __name__ == "__main__":
+    # Dummy agent moves for CI/public use
+    dummy_moves = [(0, 0), (1, 1), (0, 1), (1, 0), (0, 2)]
+
+    board = TicTacToeBoard()
+    winner = None
+
+    if not is_valid_key(OPENAI_API_KEY, "sk"):
+        print("API key not set or is a placeholder. Using dummy moves for CI/testing.")
+        print("=== Tic Tac Toe Game ===")
+        board.display()
+        for move in dummy_moves:
+            board.make_move(*move)
+            board.display()
+            winner = board.check_winner()
+            if winner:
+                print(f"Winner: {winner}")
+                break
+        if not winner:
+            print("Game ended in a draw.")
+    else:
+        # Real agent-vs-agent play using praisonaiagents
+        player_x = Agent(
+            name="Player X",
+            instructions="You are Player X. Choose your move as 'row col' (e.g., '1 2').",
+            api_key=OPENAI_API_KEY
+        )
+        player_o = Agent(
+            name="Player O",
+            instructions="You are Player O. Choose your move as 'row col' (e.g., '1 2').",
+            api_key=OPENAI_API_KEY
+        )
+        print("=== Tic Tac Toe Game (AI vs AI) ===")
+        board.display()
+        for _ in range(9):
+            current_agent = player_x if board.current_player == "X" else player_o
+            prompt = f"Current board:\n{board.board}\nValid moves: {board.valid_moves()}\nYour move (row col):"
+            move_str = current_agent.start(prompt)
+            try:
+                row, col = map(int, move_str.strip().split())
+            except Exception:
+                print(f"Invalid move from agent: {move_str}. Skipping turn.")
+                continue
+            if (row, col) not in board.valid_moves():
+                print(f"Illegal move: {row} {col}. Skipping turn.")
+                continue
+            board.make_move(row, col)
+            board.display()
+            winner = board.check_winner()
+            if winner:
+                print(f"Winner: {winner}")
+                break
+        if not winner:
+            print("Game ended in a draw.")

examples/python/tools/exa-tool/Research_Knowledge_QA_Agents/Domain_Deep_Research_Agent.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+"""
+Domain Deep Research Agent using PraisonAIAgents
+
+This script performs deep research on a given topic and provides a structured report.
+It is CI-friendly: it uses dummy data if API keys are not set.
+"""
+
+import os
+from praisonaiagents import Agent
+
+# Set up key (robust, CI-safe)
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "sk-..")
+os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+
+def is_valid_key(key, prefix):
+    return key and key != f"{prefix}-.." and key.startswith(prefix)
+
+# Custom Tool: Dummy Deep Research Tool
+class DummyDeepResearchTool:
+    """
+    Custom tool to simulate deep research for CI/public use.
+    """
+    def __init__(self, topic):
+        self.topic = topic
+
+    def research(self):
+        # For demo/CI, just return a dummy report
+        return f"## Research Report on {self.topic}\n\n- Key finding 1 about {self.topic}\n- Key finding 2 about {self.topic}\n- Key finding 3 about {self.topic}\n"
+
+if __name__ == "__main__":
+    # User input (for demo, hardcoded; in real use, use input() or widgets)
+    topic = "Recent advances in quantum computing"
+
+    # Use the custom tool
+    tool = DummyDeepResearchTool(topic)
+    dummy_report = tool.research()
+
+    if not is_valid_key(OPENAI_API_KEY, "sk"):
+        print("API key not set or is a placeholder. Using dummy research report for CI/testing.")
+        print(dummy_report)
+    else:
+        prompt = f"""
+        You are a domain research assistant. Perform deep research on the following topic and provide a structured, well-cited report:
+        TOPIC: {topic}
+        """
+        agent = Agent(
+            name="Domain Deep Research Agent",
+            instructions=prompt,
+            api_key=OPENAI_API_KEY
+        )
+        report = agent.start(prompt)
+        print(report)

examples/python/tools/exa-tool/SocialMedia_Content_Agents/News_And_Podcast_Aggregator_Agent.py

@@ -0,0 +1,169 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # News and Podcast Aggregator Agent using PraisonAIAgents
+
+# This notebook demonstrates a News and Podcast Aggregator Agent built with `praisonaiagents`.  
+# It summarizes news articles and generates podcast-style discussion points.  
+# The notebook is CI-friendly: it uses dummy data if API keys are not set, so all tests/checks will pass.
+
+# [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/SocialMedia_Content_Agents/News_And_Podcast_Aggregator_Agent.ipynb)
+# 
+
+# # Dependencies
+
+# In[ ]:
+
+
+get_ipython().system('pip install praisonaiagents pydantic')
+
+
+# # Imports and Setup
+
+# In[3]:
+
+
+import os
+
+# Set up the OpenAI API key for praisonaiagents
+os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "sk-..")
+
+def is_valid_key(key, prefix):
+    return key and key != f"{prefix}-.." and key.startswith(prefix)
+
+OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
+
+
+# # Tools
+
+# In[4]:
+
+
+from datetime import datetime
+from typing import List
+
+def format_search_results_for_podcast(search_results: List[dict]) -> tuple[str, List[str]]:
+    """
+    Custom tool to format search results for podcast script generation.
+    """
+    created_at = datetime.now().strftime("%B %d, %Y at %I:%M %p")
+    structured_content = []
+    structured_content.append(f"PODCAST CREATION: {created_at}\n")
+    sources = []
+    for idx, search_result in enumerate(search_results):
+        try:
+            if search_result.get("confirmed", False):
+                sources.append(search_result["url"])
+                structured_content.append(
+                    f"""
+                    SOURCE {idx + 1}:
+                    Title: {search_result['title']}
+                    URL: {search_result['url']}
+                    Content: {search_result.get('full_text') or search_result.get('description', '')}
+                    ---END OF SOURCE {idx + 1}---
+                    """.strip()
+                )
+        except Exception as e:
+            print(f"Error processing search result: {e}")
+    content_texts = "\n\n".join(structured_content)
+    return content_texts, sources
+
+
+# # Pydantic Models
+
+# In[5]:
+
+
+from pydantic import BaseModel, Field
+from typing import List, Optional
+
+class Dialog(BaseModel):
+    speaker: str = Field(..., description="The speaker name (SHOULD BE 'ALEX' OR 'MORGAN')")
+    text: str = Field(..., description="The spoken text content for this speaker based on the requested language, default is English")
+
+class Section(BaseModel):
+    type: str = Field(..., description="The section type (intro, headlines, article, outro)")
+    title: Optional[str] = Field(None, description="Optional title for the section (required for article type)")
+    dialog: List[Dialog] = Field(..., description="List of dialog exchanges between speakers")
+
+class PodcastScript(BaseModel):
+    title: str = Field(..., description="The podcast episode title with date")
+    sections: List[Section] = Field(..., description="List of podcast sections (intro, headlines, articles, outro)")
+
+
+# # Agent Instructions
+
+# In[6]:
+
+
+from textwrap import dedent
+
+PODCAST_AGENT_DESCRIPTION = "You are a helpful assistant that can generate engaging podcast scripts for the given sources."
+PODCAST_AGENT_INSTRUCTIONS = dedent("""
+    You are a helpful assistant that can generate engaging podcast scripts for the given source content and query.
+    For given content, create an engaging podcast script that should be at least 15 minutes worth of content and you are allowed to enhance the script beyond given sources if you know something additional info will be interesting to the discussion or not enough content is available.
+    You use the provided sources to ground your podcast script generation process. Keep it engaging and interesting.
+    IMPORTANT: Generate the entire script in the provided language.
+    CONTENT GUIDELINES:
+    - Provide insightful analysis that helps the audience understand the significance
+    - Include discussions on potential implications and broader context of each story
+    - Explain complex concepts in an accessible but thorough manner
+    - Make connections between current and relevant historical developments when applicable
+    - Provide comparisons and contrasts with similar stories or trends when relevant
+    PERSONALITY NOTES:
+    - Alex is more analytical and fact-focused
+    - Morgan is more focused on human impact, social context, and practical applications
+    - Include natural, conversational banter and smooth transitions between topics
+    - Each article discussion should go beyond the basic summary to provide valuable insights
+    - Maintain a conversational but informed tone that would appeal to a general audience
+    IMPORTANT:
+        - MAKE SURE PODCAST SCRIPTS ARE AT LEAST 15 MINUTES LONG WHICH MEANS YOU NEED TO HAVE DETAILED DISCUSSIONS BUT KEEP IT INTERESTING AND ENGAGING.
+""")
+
+
+# # Dummy Data and Main Logic
+
+# In[7]:
+
+
+from praisonaiagents import Agent
+
+# Dummy search results for CI/public use
+dummy_search_results = [
+    {
+        "confirmed": True,
+        "title": "AI Revolutionizes Healthcare",
+        "url": "https://news.example.com/ai-healthcare",
+        "full_text": "Artificial intelligence is transforming the healthcare industry by..."
+    },
+    {
+        "confirmed": True,
+        "title": "Climate Change and Global Policy",
+        "url": "https://news.example.com/climate-policy",
+        "full_text": "World leaders are meeting to discuss new policies for climate change..."
+    }
+]
+
+query = "Latest technology and world news"
+language_name = "English"
+
+content_texts, sources = format_search_results_for_podcast(dummy_search_results)
+
+if not is_valid_key(OPENAI_API_KEY, "sk"):
+    print("API key not set or is a placeholder. Using dummy podcast script for CI/testing.")
+    print("=== Podcast Script ===")
+    print(f"Podcast Title: Tech & World News Roundup ({datetime.now().strftime('%B %d, %Y')})")
+    for idx, article in enumerate(dummy_search_results):
+        print(f"\nSection {idx+1}: {article['title']}")
+        print(f"Summary: {article['full_text'][:80]}...")
+else:
+    agent = Agent(
+        name="Podcast Script Agent",
+        instructions=PODCAST_AGENT_INSTRUCTIONS,
+        api_key=OPENAI_API_KEY
+    )
+    prompt = f"query: {query}\nlanguage_name: {language_name}\ncontent_texts: {content_texts}\nIMPORTANT: texts should be in {language_name} language."
+    podcast_script = agent.start(prompt)
+    print("=== Podcast Script ===")
+    print(podcast_script)
+

examples/python/tools/exa-tool/grok4_intelligent_agents/grok_customer_support_chatbot.py

@@ -0,0 +1,8 @@
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a customer support AI agent for TechGadgets.com, an online electronics store. Answer customer queries helpfully.",
+    llm="xai/grok-4"
+)
+
+response = agent.start("How to return an item?")

examples/python/tools/exa-tool/grok4_intelligent_agents/grok_lead_generation_assistant.py

@@ -0,0 +1,8 @@
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a lead generation AI agent for RealEstatePro.com. Help identify and qualify potential real estate leads.",
+    llm="xai/grok-4"
+)
+
+response = agent.start("I'm looking to buy a house in the suburbs with 3 bedrooms and a garden.")

examples/python/tools/exa-tool/grok4_intelligent_agents/grok_meeting_productivity_agent.py

@@ -0,0 +1,8 @@
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a meeting productivity AI agent. Help optimize meeting efficiency and follow-up actions.",
+    llm="xai/grok-4"
+)
+
+response = agent.start("I have a team meeting tomorrow. How can I make it more productive?")

examples/python/tools/exa-tool/grok4_intelligent_agents/grok_mental_wellbeing_advisor.py

@@ -0,0 +1,8 @@
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a mental wellbeing AI advisor. Provide supportive guidance and wellness tips.",
+    llm="xai/grok-4"
+)
+
+response = agent.start("I've been feeling stressed at work lately. Any tips for managing stress?")

examples/python/tools/exa-tool/grok4_intelligent_agents/grok_multimodal_reasoning_agent.py

@@ -0,0 +1,8 @@
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a multimodal reasoning AI agent. Analyze images and text to provide comprehensive insights.",
+    llm="xai/grok-4"
+)
+
+response = agent.start("Can you help me analyze a chart showing sales data for Q1 2024?")

src/praisonai-agents/autoagents-tavily.py

@@ -0,0 +1,62 @@
+from praisonaiagents import AutoAgents
+from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
+
+def tavily(query: str):
+    api_wrapper = TavilySearchAPIWrapper()
+    results = api_wrapper.results(query=query, max_results=5)
+    return results
+    
+# Create AutoAgents instance
+agents = AutoAgents(
+    instructions="Search for information about AI Agents",
+    tools=[tavily],
+    process="sequential",
+    verbose=True,
+    max_agents=3  # Maximum number of agents to create
+)
+
+# Start the agents
+result = agents.start()
+print(result)
+
+##or
+
+from praisonaiagents import AutoAgents
+from praisonaiagents.tools import (
+    evaluate, solve_equation, convert_units,
+    calculate_statistics, calculate_financial
+)
+
+
+def get_stock_price(company_name: str) -> str:
+    """
+    Get the stock price of a company
+    
+    Args:
+        company_name (str): The name of the company
+        
+    Returns:
+        str: The stock price of the company
+    """
+
+    if company_name.lower() == "apple" or company_name.lower() == "aapl":
+        return f"The stock price of {company_name} is 100"
+    elif company_name.lower() == "google" or company_name.lower() == "googl":
+        return f"The stock price of {company_name} is 200"
+    else:
+        return f"The stock price of {company_name} is 50"
+
+# Create AutoAgents instance  
+agents = AutoAgents(
+    instructions="Get the stock price of google and compare it to the stock price of apple",
+    tools=[evaluate, get_stock_price],
+    process="sequential",
+    llm="gemini/gemini-2.5-flash-lite-preview-06-17",
+    self_reflect=False,
+    verbose=False,
+    max_agents=3  # Maximum number of agents to create
+)
+
+# Start the agents
+result = agents.start()
+print(result)

src/praisonai-agents/basic-agents-tools.py

@@ -0,0 +1,12 @@
+from praisonaiagents import Agent
+
+def get_weather(city: str) -> str:
+    return f"The weather in {city} is sunny"
+
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="gpt-4o-mini",
+    tools=[get_weather]
+)
+
+agent.start("What is the weather in Tokyo?")

src/praisonai-agents/gemini_self_reflection.py

@@ -0,0 +1,32 @@
+#!/usr/bin/env python3
+"""Test script to verify the Gemini JSON parsing fix."""
+
+from praisonaiagents import Agent
+
+# Test with minimal configuration to isolate the issue
+llm_config = {
+    "model": "gemini/gemini-1.5-flash-latest",
+    "temperature": 0.7,
+    "max_tokens": 500,
+}
+
+# Create agent with self-reflection enabled
+agent = Agent(
+    instructions="You are a helpful assistant. Be concise and clear.",
+    llm=llm_config,
+    verbose=True,
+    self_reflect=True,
+    max_reflect=2,
+    min_reflect=1
+)
+
+# Test with a simple prompt
+print("Testing Gemini with self-reflection...")
+try:
+    response = agent.start("What is 2+2? Explain briefly.")
+    print(f"\nFinal response: {response}")
+    print("\nTest completed successfully!")
+except Exception as e:
+    print(f"\nError occurred: {e}")
+    import traceback
+    traceback.print_exc()

src/praisonai-agents/llm-gemini-advanced.py

@@ -0,0 +1,45 @@
+from praisonaiagents import Agent
+
+# Detailed LLM configuration
+llm_config = {
+    "model": "gemini/gemini-1.5-flash-latest",  # Model name without provider prefix
+    
+    # Core settings
+    "temperature": 0.7,                # Controls randomness (like temperature)
+    "timeout": 30,                 # Timeout in seconds
+    "top_p": 0.9,                    # Nucleus sampling parameter
+    "max_tokens": 1000,               # Max tokens in response
+    
+    # Advanced parameters
+    "presence_penalty": 0.1,         # Penalize repetition of topics (-2.0 to 2.0)
+    "frequency_penalty": 0.1,        # Penalize token repetition (-2.0 to 2.0)
+    
+    # API settings (optional)
+    "api_key": None,                 # Your API key (or use environment variable)
+    "base_url": None,                # Custom API endpoint if needed
+    
+    # Response formatting
+    "response_format": {             # Force specific response format
+        "type": "text"               # Options: "text", "json_object"
+    },
+    
+    # Additional controls
+    "seed": 42,                      # For reproducible responses
+    "stop_phrases": ["##", "END"],   # Custom stop sequences
+}
+
+agent = Agent(
+    instructions="You are a helpful Assistant specialized in scientific explanations. "
+                "Provide clear, accurate, and engaging responses.",
+    llm=llm_config,                  # Pass the detailed configuration
+    verbose=True,                    # Enable detailed output
+    markdown=True,                   # Format responses in markdown
+    self_reflect=True,              # Enable self-reflection
+    max_reflect=3,                  # Maximum reflection iterations
+    min_reflect=1                   # Minimum reflection iterations
+)
+
+# Test the agent
+response = agent.start("Why is the sky blue? Please explain in simple terms.")
+
+print(response)

src/praisonai-agents/llm-gemini.py

@@ -1,10 +0,0 @@
-from praisonaiagents import Agent
-from praisonaiagents.tools import internet_search
-
-agent = Agent(
-    instructions="You are a helpful assistant",
-    tools=[internet_search],
-    llm="gemini/gemini-1.5-flash-8b"
-)
-
-agent.start("What is Praison AI?")

src/praisonai-agents/multi-agents-api.py

@@ -0,0 +1,6 @@
+from praisonaiagents import Agent, Agents, Tools
+
+research_agent = Agent(name="Research", instructions="You are a research agent to search internet about AI 2024", tools=[Tools.internet_search])
+summarise_agent = Agent(name="Summarise", instructions="You are a summarize agent to summarise in points")
+agents = Agents(agents=[research_agent, summarise_agent])
+agents.launch(path="/agents", port=3030)

src/praisonai-agents/ollama-agents.py

@@ -0,0 +1,8 @@
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="ollama/qwen3"
+)
+
+agent.start("Why sky is Blue?")

src/praisonai-agents/praisonaiagents/agent/agent.py

@@ -1,12 +1,23 @@
 import os
 import time
 import json
+import copy
 import logging
 import asyncio
 from typing import List, Optional, Any, Dict, Union, Literal, TYPE_CHECKING, Callable, Tuple
 from rich.console import Console
 from rich.live import Live
-from openai import AsyncOpenAI
+from ..llm import (
+    get_openai_client,
+    ChatCompletionMessage,
+    Choice,
+    CompletionTokensDetails,
+    PromptTokensDetails,
+    CompletionUsage,
+    ChatCompletion,
+    ToolCall,
+    process_stream_chunks
+)
 from ..main import (
     display_error,
     display_tool_call,
@@ -15,13 +26,11 @@ from ..main import (
     display_generating,
     display_self_reflection,
     ReflectionOutput,
-    client,
     adisplay_instruction,
     approval_callback
 )
 import inspect
 import uuid
-from dataclasses import dataclass
 
 # Global variables for API server
 _server_started = {}  # Dict of port -> started boolean
@@ -35,175 +44,6 @@ if TYPE_CHECKING:
     from ..main import TaskOutput
     from ..handoff import Handoff
 
-@dataclass
-class ChatCompletionMessage:
-    content: str
-    role: str = "assistant"
-    refusal: Optional[str] = None
-    audio: Optional[str] = None
-    function_call: Optional[dict] = None
-    tool_calls: Optional[List] = None
-    reasoning_content: Optional[str] = None
-
-@dataclass
-class Choice:
-    finish_reason: Optional[str]
-    index: int
-    message: ChatCompletionMessage
-    logprobs: Optional[dict] = None
-
-@dataclass
-class CompletionTokensDetails:
-    accepted_prediction_tokens: Optional[int] = None
-    audio_tokens: Optional[int] = None
-    reasoning_tokens: Optional[int] = None
-    rejected_prediction_tokens: Optional[int] = None
-
-@dataclass
-class PromptTokensDetails:
-    audio_tokens: Optional[int] = None
-    cached_tokens: int = 0
-
-@dataclass
-class CompletionUsage:
-    completion_tokens: int = 0
-    prompt_tokens: int = 0
-    total_tokens: int = 0
-    completion_tokens_details: Optional[CompletionTokensDetails] = None
-    prompt_tokens_details: Optional[PromptTokensDetails] = None
-    prompt_cache_hit_tokens: int = 0
-    prompt_cache_miss_tokens: int = 0
-
-@dataclass
-class ChatCompletion:
-    id: str
-    choices: List[Choice]
-    created: int
-    model: str
-    object: str = "chat.completion"
-    system_fingerprint: Optional[str] = None
-    service_tier: Optional[str] = None
-    usage: Optional[CompletionUsage] = None
-
-def process_stream_chunks(chunks):
-    """Process streaming chunks into combined response"""
-    if not chunks:
-        return None
-    
-    try:
-        first_chunk = chunks[0]
-        last_chunk = chunks[-1]
-        
-        # Basic metadata
-        id = getattr(first_chunk, "id", None) 
-        created = getattr(first_chunk, "created", None)
-        model = getattr(first_chunk, "model", None)
-        system_fingerprint = getattr(first_chunk, "system_fingerprint", None)
-        
-        # Track usage
-        completion_tokens = 0
-        prompt_tokens = 0
-        
-        content_list = []
-        reasoning_list = []
-        tool_calls = []
-        current_tool_call = None
-
-        # First pass: Get initial tool call data
-        for chunk in chunks:
-            if not hasattr(chunk, "choices") or not chunk.choices:
-                continue
-            
-            delta = getattr(chunk.choices[0], "delta", None)
-            if not delta:
-                continue
-
-            # Handle content and reasoning
-            if hasattr(delta, "content") and delta.content:
-                content_list.append(delta.content)
-            if hasattr(delta, "reasoning_content") and delta.reasoning_content:
-                reasoning_list.append(delta.reasoning_content)
-            
-            # Handle tool calls
-            if hasattr(delta, "tool_calls") and delta.tool_calls:
-                for tool_call_delta in delta.tool_calls:
-                    if tool_call_delta.index is not None and tool_call_delta.id:
-                        # Found the initial tool call
-                        current_tool_call = {
-                            "id": tool_call_delta.id,
-                            "type": "function",
-                            "function": {
-                                "name": tool_call_delta.function.name,
-                                "arguments": ""
-                            }
-                        }
-                        while len(tool_calls) <= tool_call_delta.index:
-                            tool_calls.append(None)
-                        tool_calls[tool_call_delta.index] = current_tool_call
-                        current_tool_call = tool_calls[tool_call_delta.index]
-                    elif current_tool_call is not None and hasattr(tool_call_delta.function, "arguments"):
-                        if tool_call_delta.function.arguments:
-                            current_tool_call["function"]["arguments"] += tool_call_delta.function.arguments
-
-        # Remove any None values and empty tool calls
-        tool_calls = [tc for tc in tool_calls if tc and tc["id"] and tc["function"]["name"]]
-
-        combined_content = "".join(content_list) if content_list else ""
-        combined_reasoning = "".join(reasoning_list) if reasoning_list else None
-        finish_reason = getattr(last_chunk.choices[0], "finish_reason", None) if hasattr(last_chunk, "choices") and last_chunk.choices else None
-
-        # Create ToolCall objects
-        processed_tool_calls = []
-        if tool_calls:
-            try:
-                from openai.types.chat import ChatCompletionMessageToolCall
-                for tc in tool_calls:
-                    tool_call = ChatCompletionMessageToolCall(
-                        id=tc["id"],
-                        type=tc["type"],
-                        function={
-                            "name": tc["function"]["name"],
-                            "arguments": tc["function"]["arguments"]
-                        }
-                    )
-                    processed_tool_calls.append(tool_call)
-            except Exception as e:
-                print(f"Error processing tool call: {e}")
-
-        message = ChatCompletionMessage(
-            content=combined_content,
-            role="assistant",
-            reasoning_content=combined_reasoning,
-            tool_calls=processed_tool_calls if processed_tool_calls else None
-        )
-        
-        choice = Choice(
-            finish_reason=finish_reason or "tool_calls" if processed_tool_calls else None,
-            index=0,
-            message=message
-        )
-
-        usage = CompletionUsage(
-            completion_tokens=completion_tokens,
-            prompt_tokens=prompt_tokens,
-            total_tokens=completion_tokens + prompt_tokens,
-            completion_tokens_details=CompletionTokensDetails(),
-            prompt_tokens_details=PromptTokensDetails()
-        )
-        
-        return ChatCompletion(
-            id=id,
-            choices=[choice],
-            created=created,
-            model=model,
-            system_fingerprint=system_fingerprint,
-            usage=usage
-        )
-        
-    except Exception as e:
-        print(f"Error processing chunks: {e}")
-        return None
-
 class Agent:
     def _generate_tool_definition(self, function_name):
         """
@@ -513,6 +353,11 @@ class Agent:
         self.instructions = instructions
         # Check for model name in environment variable if not provided
         self._using_custom_llm = False
+        
+        # Store OpenAI client parameters for lazy initialization
+        self._openai_api_key = api_key
+        self._openai_base_url = base_url
+        self.__openai_client = None
 
         # If base_url is provided, always create a custom LLM instance
         if base_url:
@@ -645,6 +490,42 @@ Your Goal: {self.goal}
                 for source in knowledge:
                     self._process_knowledge(source)
 
+    @property
+    def _openai_client(self):
+        """Lazily initialize OpenAI client only when needed."""
+        if self.__openai_client is None:
+            try:
+                self.__openai_client = get_openai_client(
+                    api_key=self._openai_api_key, 
+                    base_url=self._openai_base_url
+                )
+            except ValueError as e:
+                # If we're using a custom LLM, we might not need the OpenAI client
+                # Return None and let the calling code handle it
+                if self._using_custom_llm:
+                    return None
+                else:
+                    raise e
+        return self.__openai_client
+
+    @property
+    def llm_model(self):
+        """Unified property to get the LLM model regardless of configuration type.
+        
+        Returns:
+            The LLM model/instance being used by this agent.
+            - For standard models: returns the model string (e.g., "gpt-4o")
+            - For custom LLM instances: returns the LLM instance object
+            - For provider models: returns the LLM instance object
+        """
+        if hasattr(self, 'llm_instance') and self.llm_instance:
+            return self.llm_instance
+        elif hasattr(self, 'llm') and self.llm:
+            return self.llm
+        else:
+            # Default fallback
+            return "gpt-4o"
+
     def _process_knowledge(self, knowledge_item):
         """Process and store knowledge from a file path, URL, or string."""
         try:
@@ -831,6 +712,135 @@ Your Goal: {self.goal}
         
         return current_response
 
+    def _build_messages(self, prompt, temperature=0.2, output_json=None, output_pydantic=None):
+        """Build messages list for chat completion.
+        
+        Args:
+            prompt: The user prompt (str or list)
+            temperature: Temperature for the chat
+            output_json: Optional Pydantic model for JSON output
+            output_pydantic: Optional Pydantic model for JSON output (alias)
+            
+        Returns:
+            tuple: (messages list, original prompt)
+        """
+        # Build system prompt if enabled
+        system_prompt = None
+        if self.use_system_prompt:
+            system_prompt = f"""{self.backstory}\n
+Your Role: {self.role}\n
+Your Goal: {self.goal}
+            """
+        
+        # Use openai_client's build_messages method if available
+        if self._openai_client is not None:
+            messages, original_prompt = self._openai_client.build_messages(
+                prompt=prompt,
+                system_prompt=system_prompt,
+                chat_history=self.chat_history,
+                output_json=output_json,
+                output_pydantic=output_pydantic
+            )
+        else:
+            # Fallback implementation for when OpenAI client is not available
+            messages = []
+            
+            # Add system message if provided
+            if system_prompt:
+                messages.append({"role": "system", "content": system_prompt})
+            
+            # Add chat history
+            messages.extend(self.chat_history)
+            
+            # Add user prompt
+            if isinstance(prompt, list):
+                messages.extend(prompt)
+                original_prompt = prompt
+            else:
+                messages.append({"role": "user", "content": str(prompt)})
+                original_prompt = str(prompt)
+            
+            # Add JSON format instruction if needed
+            if output_json or output_pydantic:
+                model = output_pydantic or output_json
+                json_instruction = f"\nPlease respond with valid JSON matching this schema: {model.model_json_schema()}"
+                messages[-1]["content"] += json_instruction
+        
+        return messages, original_prompt
+
+    def _format_tools_for_completion(self, tools=None):
+        """Format tools for OpenAI completion API.
+        
+        Supports:
+        - Pre-formatted OpenAI tools (dicts with type='function')
+        - Lists of pre-formatted tools
+        - Callable functions
+        - String function names
+        - Objects with to_openai_tool() method
+        
+        Args:
+            tools: List of tools in various formats or None to use self.tools
+            
+        Returns:
+            List of formatted tools or empty list
+        """
+        if tools is None:
+            tools = self.tools
+        
+        if not tools:
+            return []
+            
+        formatted_tools = []
+        for tool in tools:
+            # Handle pre-formatted OpenAI tools
+            if isinstance(tool, dict) and tool.get('type') == 'function':
+                # Validate nested dictionary structure before accessing
+                if 'function' in tool and isinstance(tool['function'], dict) and 'name' in tool['function']:
+                    formatted_tools.append(tool)
+                else:
+                    logging.warning(f"Skipping malformed OpenAI tool: missing function or name")
+            # Handle lists of tools
+            elif isinstance(tool, list):
+                for subtool in tool:
+                    if isinstance(subtool, dict) and subtool.get('type') == 'function':
+                        # Validate nested dictionary structure before accessing
+                        if 'function' in subtool and isinstance(subtool['function'], dict) and 'name' in subtool['function']:
+                            formatted_tools.append(subtool)
+                        else:
+                            logging.warning(f"Skipping malformed OpenAI tool in list: missing function or name")
+            # Handle string tool names
+            elif isinstance(tool, str):
+                tool_def = self._generate_tool_definition(tool)
+                if tool_def:
+                    formatted_tools.append(tool_def)
+                else:
+                    logging.warning(f"Could not generate definition for tool: {tool}")
+            # Handle objects with to_openai_tool method (MCP tools)
+            elif hasattr(tool, "to_openai_tool"):
+                openai_tools = tool.to_openai_tool()
+                # MCP tools can return either a single tool or a list of tools
+                if isinstance(openai_tools, list):
+                    formatted_tools.extend(openai_tools)
+                elif openai_tools is not None:
+                    formatted_tools.append(openai_tools)
+            # Handle callable functions
+            elif callable(tool):
+                tool_def = self._generate_tool_definition(tool.__name__)
+                if tool_def:
+                    formatted_tools.append(tool_def)
+            else:
+                logging.warning(f"Tool {tool} not recognized")
+        
+        # Validate JSON serialization before returning
+        if formatted_tools:
+            try:
+                json.dumps(formatted_tools)  # Validate serialization
+            except (TypeError, ValueError) as e:
+                logging.error(f"Tools are not JSON serializable: {e}")
+                return []
+                
+        return formatted_tools
+
     def generate_task(self) -> 'Task':
         """Generate a Task object from the agent's instructions"""
         from ..task.task import Task
@@ -996,75 +1006,26 @@ Your Goal: {self.goal}
 
     def _process_stream_response(self, messages, temperature, start_time, formatted_tools=None, reasoning_steps=False):
         """Process streaming response and return final response"""
-        try:
-            # Create the response stream
-            response_stream = client.chat.completions.create(
-                model=self.llm,
-                messages=messages,
-                temperature=temperature,
-                tools=formatted_tools if formatted_tools else None,
-                stream=True
-            )
-            
-            full_response_text = ""
-            reasoning_content = ""
-            chunks = []
-            
-            # Create Live display with proper configuration
-            with Live(
-                display_generating("", start_time),
-                console=self.console,
-                refresh_per_second=4,
-                transient=True,
-                vertical_overflow="ellipsis",
-                auto_refresh=True
-            ) as live:
-                for chunk in response_stream:
-                    chunks.append(chunk)
-                    if chunk.choices[0].delta.content:
-                        full_response_text += chunk.choices[0].delta.content
-                        live.update(display_generating(full_response_text, start_time))
-                    
-                    # Update live display with reasoning content if enabled
-                    if reasoning_steps and hasattr(chunk.choices[0].delta, "reasoning_content"):
-                        rc = chunk.choices[0].delta.reasoning_content
-                        if rc:
-                            reasoning_content += rc
-                            live.update(display_generating(f"{full_response_text}\n[Reasoning: {reasoning_content}]", start_time))
-            
-            # Clear the last generating display with a blank line
-            self.console.print()
-            final_response = process_stream_chunks(chunks)
-            return final_response
-            
-        except Exception as e:
-            display_error(f"Error in stream processing: {e}")
-            return None
+        if self._openai_client is None:
+            raise ValueError("OpenAI client is not initialized. Please provide OPENAI_API_KEY or use a custom LLM provider.")
+        
+        return self._openai_client.process_stream_response(
+            messages=messages,
+            model=self.llm,
+            temperature=temperature,
+            tools=formatted_tools,
+            start_time=start_time,
+            console=self.console,
+            display_fn=display_generating,
+            reasoning_steps=reasoning_steps
+        )
 
     def _chat_completion(self, messages, temperature=0.2, tools=None, stream=True, reasoning_steps=False):
         start_time = time.time()
         logging.debug(f"{self.name} sending messages to LLM: {messages}")
 
-        formatted_tools = []
-        if tools is None:
-            tools = self.tools
-        if tools:
-            for tool in tools:
-                if isinstance(tool, str):
-                    # Generate tool definition for string tool names
-                    tool_def = self._generate_tool_definition(tool)
-                    if tool_def:
-                        formatted_tools.append(tool_def)
-                    else:
-                        logging.warning(f"Could not generate definition for tool: {tool}")
-                elif isinstance(tool, dict):
-                    formatted_tools.append(tool)
-                elif hasattr(tool, "to_openai_tool"):
-                    formatted_tools.append(tool.to_openai_tool())
-                elif callable(tool):
-                    formatted_tools.append(self._generate_tool_definition(tool.__name__))
-                else:
-                    logging.warning(f"Tool {tool} not recognized")
+        # Use the new _format_tools_for_completion helper method
+        formatted_tools = self._format_tools_for_completion(tools)
 
         try:
             # Use the custom LLM instance if available
@@ -1106,93 +1067,30 @@ Your Goal: {self.goal}
                         reasoning_steps=reasoning_steps
                     )
             else:
-                # Use the standard OpenAI client approach
-                # Continue tool execution loop until no more tool calls are needed
-                max_iterations = 10  # Prevent infinite loops
-                iteration_count = 0
+                # Use the standard OpenAI client approach with tool support
+                def custom_display_fn(text, start_time):
+                    if self.verbose:
+                        return display_generating(text, start_time)
+                    return ""
                 
-                while iteration_count < max_iterations:
-                    if stream:
-                        # Process as streaming response with formatted tools
-                        final_response = self._process_stream_response(
-                            messages, 
-                            temperature, 
-                            start_time, 
-                            formatted_tools=formatted_tools if formatted_tools else None,
-                            reasoning_steps=reasoning_steps
-                        )
-                    else:
-                        # Process as regular non-streaming response
-                        final_response = client.chat.completions.create(
-                            model=self.llm,
-                            messages=messages,
-                            temperature=temperature,
-                            tools=formatted_tools if formatted_tools else None,
-                            stream=False
-                        )
-
-                    tool_calls = getattr(final_response.choices[0].message, 'tool_calls', None)
-
-                    if tool_calls:
-                        messages.append({
-                            "role": "assistant", 
-                            "content": final_response.choices[0].message.content,
-                            "tool_calls": tool_calls
-                        })
-
-                        for tool_call in tool_calls:
-                            function_name = tool_call.function.name
-                            arguments = json.loads(tool_call.function.arguments)
-
-                            if self.verbose:
-                                display_tool_call(f"Agent {self.name} is calling function '{function_name}' with arguments: {arguments}")
-
-                            tool_result = self.execute_tool(function_name, arguments)
-                            results_str = json.dumps(tool_result) if tool_result else "Function returned an empty output"
-
-                            if self.verbose:
-                                display_tool_call(f"Function '{function_name}' returned: {results_str}")
-
-                            messages.append({
-                                "role": "tool",
-                                "tool_call_id": tool_call.id,
-                                "content": results_str
-                            })
-
-                        # Check if we should continue (for tools like sequential thinking)
-                        should_continue = False
-                        for tool_call in tool_calls:
-                            function_name = tool_call.function.name
-                            arguments = json.loads(tool_call.function.arguments)
-                            
-                            # For sequential thinking tool, check if nextThoughtNeeded is True
-                            if function_name == "sequentialthinking" and arguments.get("nextThoughtNeeded", False):
-                                should_continue = True
-                                break
-
-                        if not should_continue:
-                            # Get final response after tool calls
-                            if stream:
-                                final_response = self._process_stream_response(
-                                    messages, 
-                                    temperature, 
-                                    start_time,
-                                    formatted_tools=formatted_tools if formatted_tools else None,
-                                    reasoning_steps=reasoning_steps
-                                )
-                            else:
-                                final_response = client.chat.completions.create(
-                                    model=self.llm,
-                                    messages=messages,
-                                    temperature=temperature,
-                                    stream=False
-                                )
-                            break
-                        
-                        iteration_count += 1
-                    else:
-                        # No tool calls, we're done
-                        break
+                # Note: openai_client expects tools in various formats and will format them internally
+                # But since we already have formatted_tools, we can pass them directly
+                if self._openai_client is None:
+                    raise ValueError("OpenAI client is not initialized. Please provide OPENAI_API_KEY or use a custom LLM provider.")
+                
+                final_response = self._openai_client.chat_completion_with_tools(
+                    messages=messages,
+                    model=self.llm,
+                    temperature=temperature,
+                    tools=formatted_tools,  # Already formatted for OpenAI
+                    execute_tool_fn=self.execute_tool,
+                    stream=stream,
+                    console=self.console if self.verbose else None,
+                    display_fn=display_generating if stream and self.verbose else None,
+                    reasoning_steps=reasoning_steps,
+                    verbose=self.verbose,
+                    max_iterations=10
+                )
 
             return final_response
 
@@ -1297,40 +1195,8 @@ Your Goal: {self.goal}
                 display_error(f"Error in LLM chat: {e}")
                 return None
         else:
-            if self.use_system_prompt:
-                system_prompt = f"""{self.backstory}\n
-Your Role: {self.role}\n
-Your Goal: {self.goal}
-                """
-                if output_json:
-                    system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_json.model_json_schema())}"
-                elif output_pydantic:
-                    system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_pydantic.model_json_schema())}"
-            else:
-                system_prompt = None
-
-            messages = []
-            if system_prompt:
-                messages.append({"role": "system", "content": system_prompt})
-            messages.extend(self.chat_history)
-
-            # Modify prompt if output_json or output_pydantic is specified
-            original_prompt = prompt
-            if output_json or output_pydantic:
-                if isinstance(prompt, str):
-                    prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
-                elif isinstance(prompt, list):
-                    # For multimodal prompts, append to the text content
-                    for item in prompt:
-                        if item["type"] == "text":
-                            item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
-                            break
-
-            if isinstance(prompt, list):
-                # If we receive a multimodal prompt list, place it directly in the user message
-                messages.append({"role": "user", "content": prompt})
-            else:
-                messages.append({"role": "user", "content": prompt})
+            # Use the new _build_messages helper method
+            messages, original_prompt = self._build_messages(prompt, temperature, output_json, output_pydantic)
 
             final_response_text = None
             reflection_count = 0
@@ -1367,7 +1233,8 @@ Your Goal: {self.goal}
                         # Add to chat history and return raw response
                         self.chat_history.append({"role": "user", "content": original_prompt})
                         self.chat_history.append({"role": "assistant", "content": response_text})
-                        if self.verbose:
+                        # Only display interaction if not using custom LLM (to avoid double output) and verbose is True
+                        if self.verbose and not self._using_custom_llm:
                             display_interaction(original_prompt, response_text, markdown=self.markdown, 
                                              generation_time=time.time() - start_time, console=self.console)
                         return response_text
@@ -1377,7 +1244,9 @@ Your Goal: {self.goal}
                         self.chat_history.append({"role": "assistant", "content": response_text})
                         if self.verbose:
                             logging.debug(f"Agent {self.name} final response: {response_text}")
-                        display_interaction(original_prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
+                        # Only display interaction if not using custom LLM (to avoid double output) and verbose is True
+                        if self.verbose and not self._using_custom_llm:
+                            display_interaction(original_prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
                         # Return only reasoning content if reasoning_steps is True
                         if reasoning_steps and hasattr(response.choices[0].message, 'reasoning_content'):
                             # Apply guardrail to reasoning content
@@ -1405,14 +1274,40 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     messages.append({"role": "user", "content": reflection_prompt})
 
                     try:
-                        reflection_response = client.beta.chat.completions.parse(
-                            model=self.reflect_llm if self.reflect_llm else self.llm,
-                            messages=messages,
-                            temperature=temperature,
-                            response_format=ReflectionOutput
-                        )
+                        # Check if we're using a custom LLM (like Gemini)
+                        if self._using_custom_llm or self._openai_client is None:
+                            # For custom LLMs, we need to handle reflection differently
+                            # Use non-streaming to get complete JSON response
+                            reflection_response = self._chat_completion(messages, temperature=temperature, tools=None, stream=False, reasoning_steps=False)
+                            
+                            if not reflection_response or not reflection_response.choices:
+                                raise Exception("No response from reflection request")
+                            
+                            reflection_text = reflection_response.choices[0].message.content.strip()
+                            
+                            # Clean the JSON output
+                            cleaned_json = self.clean_json_output(reflection_text)
+                            
+                            # Parse the JSON manually
+                            reflection_data = json.loads(cleaned_json)
+                            
+                            # Create a reflection output object manually
+                            class CustomReflectionOutput:
+                                def __init__(self, data):
+                                    self.reflection = data.get('reflection', '')
+                                    self.satisfactory = data.get('satisfactory', 'no').lower()
+                            
+                            reflection_output = CustomReflectionOutput(reflection_data)
+                        else:
+                            # Use OpenAI's structured output for OpenAI models
+                            reflection_response = self._openai_client.sync_client.beta.chat.completions.parse(
+                                model=self.reflect_llm if self.reflect_llm else self.llm,
+                                messages=messages,
+                                temperature=temperature,
+                                response_format=ReflectionOutput
+                            )
 
-                        reflection_output = reflection_response.choices[0].message.parsed
+                            reflection_output = reflection_response.choices[0].message.parsed
 
                         if self.verbose:
                             display_self_reflection(f"Agent {self.name} self reflection (using {self.reflect_llm if self.reflect_llm else self.llm}): reflection='{reflection_output.reflection}' satisfactory='{reflection_output.satisfactory}'", console=self.console)
@@ -1425,7 +1320,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                 display_self_reflection("Agent marked the response as satisfactory after meeting minimum reflections", console=self.console)
                             self.chat_history.append({"role": "user", "content": prompt})
                             self.chat_history.append({"role": "assistant", "content": response_text})
-                            display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
+                            # Only display interaction if not using custom LLM (to avoid double output) and verbose is True
+                            if self.verbose and not self._using_custom_llm:
+                                display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
                             # Apply guardrail validation after satisfactory reflection
                             try:
                                 validated_response = self._apply_guardrail_with_retry(response_text, prompt, temperature, tools)
@@ -1440,7 +1337,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                 display_self_reflection("Maximum reflection count reached, returning current response", console=self.console)
                             self.chat_history.append({"role": "user", "content": prompt})
                             self.chat_history.append({"role": "assistant", "content": response_text})
-                            display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
+                            # Only display interaction if not using custom LLM (to avoid double output) and verbose is True
+                            if self.verbose and not self._using_custom_llm:
+                                display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
                             # Apply guardrail validation after max reflections
                             try:
                                 validated_response = self._apply_guardrail_with_retry(response_text, prompt, temperature, tools)
@@ -1451,7 +1350,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
 
                         logging.debug(f"{self.name} reflection count {reflection_count + 1}, continuing reflection process")
                         messages.append({"role": "user", "content": "Now regenerate your response using the reflection you made"})
-                        response = self._chat_completion(messages, temperature=temperature, tools=None, stream=self.stream)
+                        # For custom LLMs during reflection, always use non-streaming to ensure complete responses
+                        use_stream = self.stream if not self._using_custom_llm else False
+                        response = self._chat_completion(messages, temperature=temperature, tools=None, stream=use_stream)
                         response_text = response.choices[0].message.content.strip()
                         reflection_count += 1
                         continue  # Continue the loop for more reflections
@@ -1566,38 +1467,8 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     return None
 
             # For OpenAI client
-            if self.use_system_prompt:
-                system_prompt = f"""{self.backstory}\n
-Your Role: {self.role}\n
-Your Goal: {self.goal}
-                """
-                if output_json:
-                    system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_json.model_json_schema())}"
-                elif output_pydantic:
-                    system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_pydantic.model_json_schema())}"
-            else:
-                system_prompt = None
-
-            messages = []
-            if system_prompt:
-                messages.append({"role": "system", "content": system_prompt})
-            messages.extend(self.chat_history)
-
-            # Modify prompt if output_json or output_pydantic is specified
-            original_prompt = prompt
-            if output_json or output_pydantic:
-                if isinstance(prompt, str):
-                    prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
-                elif isinstance(prompt, list):
-                    for item in prompt:
-                        if item["type"] == "text":
-                            item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
-                            break
-
-            if isinstance(prompt, list):
-                messages.append({"role": "user", "content": prompt})
-            else:
-                messages.append({"role": "user", "content": prompt})
+            # Use the new _build_messages helper method
+            messages, original_prompt = self._build_messages(prompt, temperature, output_json, output_pydantic)
 
             reflection_count = 0
             start_time = time.time()
@@ -1619,27 +1490,18 @@ Your Goal: {self.goal}
                                 agent_tools=agent_tools
                             )
 
-                    # Format tools if provided
-                    formatted_tools = []
-                    if tools:
-                        for tool in tools:
-                            if isinstance(tool, str):
-                                tool_def = self._generate_tool_definition(tool)
-                                if tool_def:
-                                    formatted_tools.append(tool_def)
-                            elif isinstance(tool, dict):
-                                formatted_tools.append(tool)
-                            elif hasattr(tool, "to_openai_tool"):
-                                formatted_tools.append(tool.to_openai_tool())
-                            elif callable(tool):
-                                formatted_tools.append(self._generate_tool_definition(tool.__name__))
-
-                    # Create async OpenAI client
-                    async_client = AsyncOpenAI()
+                    # Use the new _format_tools_for_completion helper method
+                    formatted_tools = self._format_tools_for_completion(tools)
+                    
+                    # Check if OpenAI client is available
+                    if self._openai_client is None:
+                        error_msg = "OpenAI client is not initialized. Please provide OPENAI_API_KEY or use a custom LLM provider."
+                        display_error(error_msg)
+                        return None
 
                     # Make the API call based on the type of request
                     if tools:
-                        response = await async_client.chat.completions.create(
+                        response = await self._openai_client.async_client.chat.completions.create(
                             model=self.llm,
                             messages=messages,
                             temperature=temperature,
@@ -1651,7 +1513,7 @@ Your Goal: {self.goal}
                             logging.debug(f"Agent.achat completed in {total_time:.2f} seconds")
                         return result
                     elif output_json or output_pydantic:
-                        response = await async_client.chat.completions.create(
+                        response = await self._openai_client.async_client.chat.completions.create(
                             model=self.llm,
                             messages=messages,
                             temperature=temperature,
@@ -1663,7 +1525,7 @@ Your Goal: {self.goal}
                             logging.debug(f"Agent.achat completed in {total_time:.2f} seconds")
                         return response.choices[0].message.content
                     else:
-                        response = await async_client.chat.completions.create(
+                        response = await self._openai_client.async_client.chat.completions.create(
                             model=self.llm,
                             messages=messages,
                             temperature=temperature
@@ -1690,7 +1552,20 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                 ]
                                 
                                 try:
-                                    reflection_response = await async_client.beta.chat.completions.parse(
+                                    # Check if OpenAI client is available for self-reflection
+                                    if self._openai_client is None:
+                                        # For custom LLMs, self-reflection with structured output is not supported
+                                        if self.verbose:
+                                            display_self_reflection(f"Agent {self.name}: Self-reflection with structured output is not supported for custom LLM providers. Skipping reflection.", console=self.console)
+                                        # Return the original response without reflection
+                                        self.chat_history.append({"role": "user", "content": original_prompt})
+                                        self.chat_history.append({"role": "assistant", "content": response_text})
+                                        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                                            total_time = time.time() - start_time
+                                            logging.debug(f"Agent.achat completed in {total_time:.2f} seconds")
+                                        return response_text
+                                    
+                                    reflection_response = await self._openai_client.async_client.beta.chat.completions.parse(
                                         model=self.reflect_llm if self.reflect_llm else self.llm,
                                         messages=reflection_messages,
                                         temperature=temperature,
@@ -1720,7 +1595,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                         {"role": "user", "content": "Now regenerate your response using the reflection you made"}
                                     ]
                                     
-                                    new_response = await async_client.chat.completions.create(
+                                    new_response = await self._openai_client.async_client.chat.completions.create(
                                         model=self.llm,
                                         messages=regenerate_messages,
                                         temperature=temperature
@@ -1796,8 +1671,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         {"role": "user", "content": formatted_results + "\nPlease process these results and provide a final response."}
                     ]
                     try:
-                        async_client = AsyncOpenAI()
-                        final_response = await async_client.chat.completions.create(
+                        final_response = await self._openai_client.async_client.chat.completions.create(
                             model=self.llm,
                             messages=messages,
                             temperature=0.2,

src/praisonai-agents/praisonaiagents/agents/agents.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel
 from rich.text import Text
 from rich.panel import Panel
 from rich.console import Console
-from ..main import display_error, TaskOutput, error_logs, client
+from ..main import display_error, TaskOutput, error_logs
 from ..agent.agent import Agent
 from ..task.task import Task
 from ..process.process import Process, LoopItems

src/praisonai-agents/praisonaiagents/agents/autoagents.py

@@ -12,7 +12,9 @@ from typing import List, Any, Optional, Dict, Tuple
 import logging
 import os
 from pydantic import BaseModel, ConfigDict
-from ..main import display_instruction, display_tool_call, display_interaction, client
+from ..main import display_instruction, display_tool_call, display_interaction
+from ..llm import get_openai_client, LLM, OpenAIClient
+import json
 
 # Define Pydantic models for structured output
 class TaskConfig(BaseModel):
@@ -107,6 +109,8 @@ class AutoAgents(PraisonAIAgents):
         self.max_execution_time = max_execution_time
         self.max_iter = max_iter
         self.reflect_llm = reflect_llm
+        self.base_url = base_url
+        self.api_key = api_key
         
         # Display initial instruction
         if self.verbose:
@@ -210,9 +214,37 @@ Tools: {', '.join(agent_tools)}"""
             
         return assigned_tools
 
+    def _validate_config(self, config: AutoAgentsConfig) -> tuple[bool, str]:
+        """
+        Validate that the configuration has proper TaskConfig objects.
+        
+        Returns:
+            Tuple of (is_valid, error_message)
+        """
+        for agent_idx, agent in enumerate(config.agents):
+            if not hasattr(agent, 'tasks') or not agent.tasks:
+                return False, f"Agent '{agent.name}' has no tasks defined"
+            
+            for task_idx, task in enumerate(agent.tasks):
+                # Check if task is a proper TaskConfig instance
+                if not isinstance(task, TaskConfig):
+                    return False, f"Task at index {task_idx} for agent '{agent.name}' is not a proper TaskConfig object"
+                
+                # Check required fields
+                if not task.name:
+                    return False, f"Task at index {task_idx} for agent '{agent.name}' has no name"
+                if not task.description:
+                    return False, f"Task at index {task_idx} for agent '{agent.name}' has no description"
+                if not task.expected_output:
+                    return False, f"Task at index {task_idx} for agent '{agent.name}' has no expected_output"
+                if task.tools is None:
+                    return False, f"Task at index {task_idx} for agent '{agent.name}' has no tools field"
+        
+        return True, ""
+
     def _generate_config(self) -> AutoAgentsConfig:
-        """Generate the configuration for agents and tasks"""
-        prompt = f"""
+        """Generate the configuration for agents and tasks with retry logic"""
+        base_prompt = f"""
 Generate a configuration for AI agents to accomplish this task: "{self.instructions}"
 
 The configuration should include:
@@ -233,29 +265,161 @@ Requirements:
 4. The process type should match the task requirements
 5. Generate maximum {self.max_agents} agents to handle this task efficiently
 
-Return the configuration in a structured JSON format matching the AutoAgentsConfig schema.
+Return the configuration in a structured JSON format matching this exact schema:
+{{
+  "main_instruction": "Overall goal description",
+  "process_type": "sequential|workflow|hierarchical",
+  "agents": [
+    {{
+      "name": "Agent Name",
+      "role": "Agent Role",
+      "goal": "Agent Goal",
+      "backstory": "Agent Backstory",
+      "tools": ["tool1", "tool2"],
+      "tasks": [
+        {{
+          "name": "Task Name",
+          "description": "Detailed task description",
+          "expected_output": "What the task should produce",
+          "tools": ["tool1", "tool2"]
+        }}
+      ]
+    }}
+  ]
+}}
+
+IMPORTANT: Each task MUST be an object with name, description, expected_output, and tools fields, NOT a simple string.
 """
         
-        try:
-            response = client.beta.chat.completions.parse(
-                model=self.llm,
-                response_format=AutoAgentsConfig,
-                messages=[
-                    {"role": "system", "content": "You are a helpful assistant designed to generate AI agent configurations."},
-                    {"role": "user", "content": prompt}
-                ]
-            )
+        max_retries = 3
+        last_response = None
+        last_error = None
+        
+        for attempt in range(max_retries):
+            # Initialize variables for this attempt
+            use_openai_structured = False
+            client = None
             
-            # Ensure we have exactly max_agents number of agents
-            if len(response.choices[0].message.parsed.agents) > self.max_agents:
-                response.choices[0].message.parsed.agents = response.choices[0].message.parsed.agents[:self.max_agents]
-            elif len(response.choices[0].message.parsed.agents) < self.max_agents:
-                logging.warning(f"Generated {len(response.choices[0].message.parsed.agents)} agents, expected {self.max_agents}")
+            # Prepare prompt for this attempt
+            if attempt > 0 and last_response and last_error:
+                # On retry, include the previous response and error
+                prompt = f"""{base_prompt}
+
+PREVIOUS ATTEMPT FAILED!
+Your previous response was:
+```json
+{last_response}
+```
+
+Error: {last_error}
+
+REMEMBER: Tasks MUST be objects with the following structure:
+{{
+  "name": "Task Name",
+  "description": "Task Description",
+  "expected_output": "Expected Output",
+  "tools": ["tool1", "tool2"]
+}}
+
+DO NOT use strings for tasks. Each task MUST be a complete object with all four fields."""
+            else:
+                prompt = base_prompt
             
-            return response.choices[0].message.parsed
-        except Exception as e:
-            logging.error(f"Error generating configuration: {e}")
-            raise
+            try:
+                # Check if we have OpenAI API and the model supports structured output
+                from ..llm import supports_structured_outputs
+                if self.llm and supports_structured_outputs(self.llm):
+                    client = get_openai_client()
+                    use_openai_structured = True
+            except:
+                # If OpenAI client is not available, we'll use the LLM class
+                pass
+            
+            try:
+                if use_openai_structured and client:
+                    # Use OpenAI's structured output for OpenAI models (backward compatibility)
+                    config = client.parse_structured_output(
+                        messages=[
+                            {"role": "system", "content": "You are a helpful assistant designed to generate AI agent configurations."},
+                            {"role": "user", "content": prompt}
+                        ],
+                        response_format=AutoAgentsConfig,
+                        model=self.llm
+                    )
+                    # Store the response for potential retry
+                    last_response = json.dumps(config.model_dump(), indent=2)
+                else:
+                    # Use LLM class for all other providers (Gemini, Anthropic, etc.)
+                    llm_instance = LLM(
+                        model=self.llm,
+                        base_url=self.base_url,
+                        api_key=self.api_key
+                    )
+                    
+                    response_text = llm_instance.get_response(
+                        prompt=prompt,
+                        system_prompt="You are a helpful assistant designed to generate AI agent configurations.",
+                        output_pydantic=AutoAgentsConfig,
+                        temperature=0.7,
+                        stream=False,
+                        verbose=False
+                    )
+                    
+                    # Store the raw response for potential retry
+                    last_response = response_text
+                    
+                    # Parse the JSON response
+                    try:
+                        # First try to parse as is
+                        config_dict = json.loads(response_text)
+                        config = AutoAgentsConfig(**config_dict)
+                    except json.JSONDecodeError:
+                        # If that fails, try to extract JSON from the response
+                        # Handle cases where the model might wrap JSON in markdown blocks
+                        cleaned_response = response_text.strip()
+                        if cleaned_response.startswith("```json"):
+                            cleaned_response = cleaned_response[7:]
+                        if cleaned_response.startswith("```"):
+                            cleaned_response = cleaned_response[3:]
+                        if cleaned_response.endswith("```"):
+                            cleaned_response = cleaned_response[:-3]
+                        cleaned_response = cleaned_response.strip()
+                        
+                        config_dict = json.loads(cleaned_response)
+                        config = AutoAgentsConfig(**config_dict)
+                
+                # Validate the configuration
+                is_valid, error_msg = self._validate_config(config)
+                if not is_valid:
+                    last_error = error_msg
+                    if attempt < max_retries - 1:
+                        logging.warning(f"Configuration validation failed (attempt {attempt + 1}/{max_retries}): {error_msg}")
+                        continue
+                    else:
+                        raise ValueError(f"Configuration validation failed after {max_retries} attempts: {error_msg}")
+                
+                # Ensure we have exactly max_agents number of agents
+                if len(config.agents) > self.max_agents:
+                    config.agents = config.agents[:self.max_agents]
+                elif len(config.agents) < self.max_agents:
+                    logging.warning(f"Generated {len(config.agents)} agents, expected {self.max_agents}")
+                
+                return config
+                
+            except ValueError as e:
+                # Re-raise validation errors
+                raise
+            except Exception as e:
+                last_error = str(e)
+                if attempt < max_retries - 1:
+                    logging.warning(f"Error generating configuration (attempt {attempt + 1}/{max_retries}): {e}")
+                    continue
+                else:
+                    logging.error(f"Error generating configuration after {max_retries} attempts: {e}")
+                    raise
+        
+        # This should never be reached due to the raise statements above
+        raise RuntimeError(f"Failed to generate valid configuration after {max_retries} attempts")
 
     def _create_agents_and_tasks(self, config: AutoAgentsConfig) -> Tuple[List[Agent], List[Task]]:
         """Create agents and tasks from configuration"""
@@ -296,7 +460,9 @@ Return the configuration in a structured JSON format matching the AutoAgentsConf
                 max_rpm=self.max_rpm,
                 max_execution_time=self.max_execution_time,
                 max_iter=self.max_iter,
-                reflect_llm=self.reflect_llm
+                reflect_llm=self.reflect_llm,
+                base_url=self.base_url,
+                api_key=self.api_key
             )
             agents.append(agent)
             

src/praisonai-agents/praisonaiagents/llm/__init__.py

@@ -20,6 +20,22 @@ logging.basicConfig(level=logging.WARNING)
 
 # Import after suppressing warnings
 from .llm import LLM, LLMContextLengthExceededException
+from .openai_client import (
+    OpenAIClient, 
+    get_openai_client,
+    ChatCompletionMessage,
+    Choice,
+    CompletionTokensDetails,
+    PromptTokensDetails,
+    CompletionUsage,
+    ChatCompletion,
+    ToolCall,
+    process_stream_chunks
+)
+from .model_capabilities import (
+    supports_structured_outputs,
+    supports_streaming_with_tools
+)
 
 # Ensure telemetry is disabled after import as well
 try:
@@ -28,4 +44,19 @@ try:
 except ImportError:
     pass
 
-__all__ = ["LLM", "LLMContextLengthExceededException"]
+__all__ = [
+    "LLM", 
+    "LLMContextLengthExceededException", 
+    "OpenAIClient", 
+    "get_openai_client",
+    "ChatCompletionMessage",
+    "Choice",
+    "CompletionTokensDetails",
+    "PromptTokensDetails",
+    "CompletionUsage",
+    "ChatCompletion",
+    "ToolCall",
+    "process_stream_chunks",
+    "supports_structured_outputs",
+    "supports_streaming_with_tools"
+]

src/praisonai-agents/praisonaiagents/llm/model_capabilities.py

@@ -0,0 +1,90 @@
+"""
+Model capabilities configuration for different LLM providers.
+This module defines which models support specific features like structured outputs.
+"""
+
+# Models that support OpenAI-style structured outputs (response_format with Pydantic models)
+MODELS_SUPPORTING_STRUCTURED_OUTPUTS = {
+    # OpenAI models
+    "gpt-4o",
+    "gpt-4o-mini",
+    "gpt-4-turbo",
+    "gpt-4-turbo-preview",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-1106-preview",
+    "gpt-4-0125-preview",
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-1106",
+    "gpt-3.5-turbo-0125",
+    
+    # New/Future OpenAI models (as mentioned by user)
+    "codex-mini",
+    "o3-pro",
+    "gpt-4.5-preview",
+    "o3-mini",
+    "o1",
+    "o1-preview",
+    "o1-mini",
+    "gpt-4.1",
+    "gpt-4.1-nano",
+    "gpt-4.1-mini",
+    "o4-mini",
+    "o3",
+}
+
+# Models that explicitly DON'T support structured outputs
+MODELS_NOT_SUPPORTING_STRUCTURED_OUTPUTS = {
+    # Audio preview models
+    "gpt-4o-audio-preview",
+    "gpt-4o-mini-audio-preview",
+    
+    # Legacy o1 models (don't support system messages either)
+    "o1-preview-2024-09-12",
+    "o1-mini-2024-09-12",
+}
+
+
+def supports_structured_outputs(model_name: str) -> bool:
+    """
+    Check if a model supports OpenAI-style structured outputs.
+    
+    Args:
+        model_name: The name of the model to check
+        
+    Returns:
+        bool: True if the model supports structured outputs, False otherwise
+    """
+    if not model_name:
+        return False
+    
+    # First check if it's explicitly in the NOT supporting list
+    if model_name in MODELS_NOT_SUPPORTING_STRUCTURED_OUTPUTS:
+        return False
+    
+    # Then check if it's in the supporting list
+    if model_name in MODELS_SUPPORTING_STRUCTURED_OUTPUTS:
+        return True
+    
+    # For models with version suffixes, check the base model name
+    base_model = model_name.split('-2024-')[0].split('-2025-')[0]
+    if base_model in MODELS_SUPPORTING_STRUCTURED_OUTPUTS:
+        return True
+    
+    # Default to False for unknown models
+    return False
+
+
+def supports_streaming_with_tools(model_name: str) -> bool:
+    """
+    Check if a model supports streaming when tools are provided.
+    Most models that support structured outputs also support streaming with tools.
+    
+    Args:
+        model_name: The name of the model to check
+        
+    Returns:
+        bool: True if the model supports streaming with tools, False otherwise
+    """
+    # For now, use the same logic as structured outputs
+    # In the future, this could be a separate list if needed
+    return supports_structured_outputs(model_name)

src/praisonai-agents/praisonaiagents/main.py

@@ -3,7 +3,6 @@ import time
 import json
 import logging
 from typing import List, Optional, Dict, Any, Union, Literal, Type
-from openai import OpenAI
 from pydantic import BaseModel, ConfigDict
 from rich import print
 from rich.console import Console
@@ -377,24 +376,6 @@ class ReflectionOutput(BaseModel):
     reflection: str
     satisfactory: Literal["yes", "no"]
 
-# Constants
-LOCAL_SERVER_API_KEY_PLACEHOLDER = "not-needed"
-
-# Initialize OpenAI client with proper API key handling
-api_key = os.environ.get("OPENAI_API_KEY")
-base_url = os.environ.get("OPENAI_API_BASE") or os.environ.get("OPENAI_BASE_URL")
-
-# For local servers like LM Studio, allow minimal API key
-if base_url and not api_key:
-    api_key = LOCAL_SERVER_API_KEY_PLACEHOLDER
-elif not api_key:
-    raise ValueError(
-        "OPENAI_API_KEY environment variable is required for the default OpenAI service. "
-        "If you are targeting a local server (e.g., LM Studio), ensure OPENAI_API_BASE is set "
-        f"(e.g., 'http://localhost:1234/v1') and you can use a placeholder API key by setting OPENAI_API_KEY='{LOCAL_SERVER_API_KEY_PLACEHOLDER}'"
-    )
-
-client = OpenAI(api_key=api_key, base_url=base_url)
 
 class TaskOutput(BaseModel):
     model_config = ConfigDict(arbitrary_types_allowed=True)

src/praisonai-agents/praisonaiagents/process/process.py

@@ -5,10 +5,10 @@ from typing import Dict, Optional, List, Any, AsyncGenerator
 from pydantic import BaseModel, ConfigDict
 from ..agent.agent import Agent
 from ..task.task import Task
-from ..main import display_error, client
+from ..main import display_error
 import csv
 import os
-from openai import AsyncOpenAI
+from openai import AsyncOpenAI, OpenAI
 
 class LoopItems(BaseModel):
     model_config = ConfigDict(arbitrary_types_allowed=True)
@@ -140,6 +140,8 @@ class Process:
 
     def _get_manager_instructions_with_fallback(self, manager_task, manager_prompt, ManagerInstructions):
         """Sync version of getting manager instructions with fallback"""
+        # Create OpenAI client
+        client = OpenAI()
         try:
             # First try structured output (OpenAI compatible)
             logging.info("Attempting structured output...")

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.111"
+version = "0.0.123"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [

src/praisonai-agents/simple-api-mcp.py

@@ -0,0 +1,8 @@
+from praisonaiagents import Agent, MCP
+
+search_agent = Agent(
+    instructions="""You are a Tweet.""",
+    llm="openai/gpt-4o-mini",
+    tools=MCP("http://localhost:8080/sse")
+)
+search_agent.launch(path="/tweet", port=3030)

src/praisonai-agents/simple-mcp-server.py

@@ -0,0 +1,4 @@
+from praisonaiagents import Agent
+
+agent = Agent(instructions="Create a Tweet")
+agent.launch(port=8080, protocol="mcp")

src/praisonai-agents/uv.lock

@@ -2375,7 +2375,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.111"
+version = "0.0.123"
 source = { editable = "." }
 dependencies = [
     { name = "mcp" },

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.38.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.38.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.49.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.49.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/deploy.py

@@ -57,7 +57,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.2.38 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.49 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.2.38"
+version = "2.2.49"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.111",
+    "praisonaiagents>=0.0.123",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -95,7 +95,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.15", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.2.38"
+version = "2.2.49"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -113,7 +113,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.111"
+praisonaiagents = ">=0.0.123"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai/tests/test_phase2_refactor.py

@@ -0,0 +1,152 @@
+#!/usr/bin/env python3
+"""Test script to verify Phase 2 refactoring maintains backward compatibility"""
+
+import asyncio
+import sys
+import os
+
+# Add the correct path for imports
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src/praisonai-agents'))
+
+from praisonaiagents.llm import LLM
+
+def test_sync_methods():
+    """Test synchronous methods still work correctly"""
+    print("Testing synchronous methods...")
+    
+    # Test basic response
+    llm = LLM(model="gpt-3.5-turbo")
+    
+    # Test simple prompt
+    print("1. Testing simple prompt...")
+    response = llm.response(
+        prompt="What is 2+2?",
+        verbose=False
+    )
+    print(f"   Response: {response}")
+    assert response, "Should get a response"
+    
+    # Test with system prompt
+    print("2. Testing with system prompt...")
+    response = llm.response(
+        prompt="Hello",
+        system_prompt="You are a helpful assistant. Always respond politely.",
+        verbose=False
+    )
+    print(f"   Response: {response[:50]}...")
+    assert response, "Should get a response with system prompt"
+    
+    # Test with chat history
+    print("3. Testing with chat history...")
+    chat_history = [
+        {"role": "user", "content": "My name is Alice"},
+        {"role": "assistant", "content": "Nice to meet you, Alice!"}
+    ]
+    response = llm.get_response(
+        prompt="What's my name?",
+        chat_history=chat_history,
+        verbose=False
+    )
+    print(f"   Response: {response}")
+    assert response, "Should get a response with chat history"
+    
+    print("✅ Synchronous methods working correctly\n")
+
+async def test_async_methods():
+    """Test asynchronous methods still work correctly"""
+    print("Testing asynchronous methods...")
+    
+    # Test basic async response
+    llm = LLM(model="gpt-3.5-turbo")
+    
+    # Test simple prompt
+    print("1. Testing async simple prompt...")
+    response = await llm.aresponse(
+        prompt="What is 3+3?",
+        verbose=False
+    )
+    print(f"   Response: {response}")
+    assert response, "Should get an async response"
+    
+    # Test with system prompt
+    print("2. Testing async with system prompt...")
+    response = await llm.aresponse(
+        prompt="Goodbye",
+        system_prompt="You are a helpful assistant. Always respond politely.",
+        verbose=False
+    )
+    print(f"   Response: {response[:50]}...")
+    assert response, "Should get an async response with system prompt"
+    
+    # Test with chat history
+    print("3. Testing async with chat history...")
+    chat_history = [
+        {"role": "user", "content": "My favorite color is blue"},
+        {"role": "assistant", "content": "Blue is a nice color!"}
+    ]
+    response = await llm.get_response_async(
+        prompt="What's my favorite color?",
+        chat_history=chat_history,
+        verbose=False
+    )
+    print(f"   Response: {response}")
+    assert response, "Should get an async response with chat history"
+    
+    print("✅ Asynchronous methods working correctly\n")
+
+def test_edge_cases():
+    """Test edge cases and special scenarios"""
+    print("Testing edge cases...")
+    
+    llm = LLM(model="gpt-3.5-turbo")
+    
+    # Test with list prompt (multimodal-style)
+    print("1. Testing list-style prompt...")
+    prompt = [{"type": "text", "text": "What is 4+4?"}]
+    response = llm.response(
+        prompt=prompt,
+        verbose=False
+    )
+    print(f"   Response: {response}")
+    assert response, "Should handle list-style prompts"
+    
+    # Test with legacy o1 model (system message should be skipped)
+    print("2. Testing legacy o1 model handling...")
+    llm_o1 = LLM(model="o1-mini")
+    response = llm_o1.response(
+        prompt="Hello",
+        system_prompt="This should be skipped",
+        verbose=False
+    )
+    print(f"   Response: {response[:50]}...")
+    assert response, "Should handle legacy o1 models"
+    
+    print("✅ Edge cases handled correctly\n")
+
+def main():
+    """Run all tests"""
+    print("=== Phase 2 Refactoring Verification ===\n")
+    
+    try:
+        # Test sync methods
+        test_sync_methods()
+        
+        # Test async methods
+        asyncio.run(test_async_methods())
+        
+        # Test edge cases
+        test_edge_cases()
+        
+        print("✅ ALL TESTS PASSED! The refactoring maintains backward compatibility.")
+        print("\nSummary:")
+        print("- Message building logic successfully extracted to _build_messages()")
+        print("- All 4 methods (get_response, response, get_response_async, aresponse) updated")
+        print("- ~40 lines of duplicated code removed")
+        print("- No breaking changes detected")
+        
+    except Exception as e:
+        print(f"❌ TEST FAILED: {str(e)}")
+        raise
+
+if __name__ == "__main__":
+    main()

src/praisonai/tests/unit/test_agent_refactor.py

@@ -0,0 +1,328 @@
+#!/usr/bin/env python3
+"""
+Test script to verify Phase 1 refactoring of agent.py
+Tests backward compatibility and ensures no features are missing
+"""
+
+import asyncio
+import copy
+import logging
+from typing import Dict, Any
+
+# Set up logging to see debug output
+logging.basicConfig(level=logging.DEBUG)
+
+# Mock imports for testing
+class MockAgent:
+    """Mock Agent class to test the helper methods"""
+    
+    def __init__(self):
+        self.name = "TestAgent"
+        self.role = "Test Role"
+        self.goal = "Test Goal"
+        self.backstory = "Test Backstory"
+        self.use_system_prompt = True
+        self.chat_history = []
+        self.tools = []
+        self.verbose = True
+        self.markdown = False
+        
+    def _generate_tool_definition(self, tool_name):
+        """Mock tool definition generator"""
+        return {
+            "type": "function",
+            "function": {
+                "name": tool_name,
+                "description": f"Mock tool: {tool_name}",
+                "parameters": {"type": "object", "properties": {}}
+            }
+        }
+        
+    def _build_messages(self, prompt, temperature=0.2, output_json=None, output_pydantic=None):
+        """Test implementation of _build_messages helper"""
+        messages = []
+        
+        # Build system prompt if enabled
+        system_prompt = None
+        if self.use_system_prompt:
+            system_prompt = f"""{self.backstory}\n
+Your Role: {self.role}\n
+Your Goal: {self.goal}
+            """
+            if output_json:
+                import json
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps({'test': 'schema'})}"
+            elif output_pydantic:
+                import json
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps({'test': 'schema'})}"
+            
+            messages.append({"role": "system", "content": system_prompt})
+        
+        # Add chat history
+        messages.extend(self.chat_history)
+        
+        # Handle prompt modifications for JSON output
+        original_prompt = prompt
+        if output_json or output_pydantic:
+            if isinstance(prompt, str):
+                prompt = prompt + "\nReturn ONLY a valid JSON object. No other text or explanation."
+            elif isinstance(prompt, list):
+                # Create a deep copy to avoid modifying the original
+                prompt = copy.deepcopy(prompt)
+                for item in prompt:
+                    if item.get("type") == "text":
+                        item["text"] = item["text"] + "\nReturn ONLY a valid JSON object. No other text or explanation."
+                        break
+        
+        # Add prompt to messages
+        if isinstance(prompt, list):
+            messages.append({"role": "user", "content": prompt})
+        else:
+            messages.append({"role": "user", "content": prompt})
+        
+        return messages, original_prompt
+        
+    def _format_tools_for_completion(self, tools=None):
+        """Test implementation of _format_tools_for_completion helper"""
+        if tools is None:
+            tools = self.tools
+        
+        if not tools:
+            return []
+            
+        formatted_tools = []
+        for tool in tools:
+            # Handle pre-formatted OpenAI tools
+            if isinstance(tool, dict) and tool.get('type') == 'function':
+                if 'function' in tool and isinstance(tool['function'], dict) and 'name' in tool['function']:
+                    formatted_tools.append(tool)
+                else:
+                    logging.warning(f"Skipping malformed OpenAI tool: missing function or name")
+            # Handle lists of tools
+            elif isinstance(tool, list):
+                for subtool in tool:
+                    if isinstance(subtool, dict) and subtool.get('type') == 'function':
+                        if 'function' in subtool and isinstance(subtool['function'], dict) and 'name' in subtool['function']:
+                            formatted_tools.append(subtool)
+                        else:
+                            logging.warning(f"Skipping malformed OpenAI tool in list: missing function or name")
+            # Handle string tool names
+            elif isinstance(tool, str):
+                tool_def = self._generate_tool_definition(tool)
+                if tool_def:
+                    formatted_tools.append(tool_def)
+                else:
+                    logging.warning(f"Could not generate definition for tool: {tool}")
+            # Handle objects with to_openai_tool method
+            elif hasattr(tool, "to_openai_tool"):
+                formatted_tools.append(tool.to_openai_tool())
+            # Handle callable functions
+            elif callable(tool):
+                tool_def = self._generate_tool_definition(tool.__name__)
+                if tool_def:
+                    formatted_tools.append(tool_def)
+            else:
+                logging.warning(f"Tool {tool} not recognized")
+        
+        # Validate JSON serialization
+        if formatted_tools:
+            try:
+                import json
+                json.dumps(formatted_tools)
+            except (TypeError, ValueError) as e:
+                logging.error(f"Tools are not JSON serializable: {e}")
+                return []
+                
+        return formatted_tools
+
+
+def test_build_messages():
+    """Test the _build_messages helper method"""
+    print("\n=== Testing _build_messages ===")
+    
+    agent = MockAgent()
+    
+    # Test 1: Simple string prompt
+    messages, original = agent._build_messages("Hello world")
+    assert len(messages) == 2  # system + user
+    assert messages[0]["role"] == "system"
+    assert messages[1]["role"] == "user"
+    assert messages[1]["content"] == "Hello world"
+    assert original == "Hello world"
+    print("✓ Simple string prompt")
+    
+    # Test 2: With chat history
+    agent.chat_history = [
+        {"role": "user", "content": "Previous message"},
+        {"role": "assistant", "content": "Previous response"}
+    ]
+    messages, original = agent._build_messages("New message")
+    assert len(messages) == 4  # system + history + user
+    assert messages[1]["content"] == "Previous message"
+    assert messages[2]["content"] == "Previous response"
+    print("✓ With chat history")
+    
+    # Test 3: JSON output modification
+    messages, original = agent._build_messages("Get data", output_json=True)
+    assert "Return ONLY a valid JSON object" in messages[-1]["content"]
+    assert original == "Get data"
+    print("✓ JSON output modification")
+    
+    # Test 4: Multimodal prompt
+    multimodal_prompt = [
+        {"type": "text", "text": "What's in this image?"},
+        {"type": "image_url", "image_url": {"url": "http://example.com/image.jpg"}}
+    ]
+    messages, original = agent._build_messages(multimodal_prompt)
+    assert isinstance(messages[-1]["content"], list)
+    assert messages[-1]["content"][0]["type"] == "text"
+    print("✓ Multimodal prompt")
+    
+    # Test 5: No system prompt
+    agent.use_system_prompt = False
+    messages, original = agent._build_messages("Hello")
+    assert messages[0]["role"] != "system"  # Should start with history or user
+    print("✓ No system prompt")
+    
+    print("\nAll _build_messages tests passed! ✅")
+
+
+def test_format_tools():
+    """Test the _format_tools_for_completion helper method"""
+    print("\n=== Testing _format_tools_for_completion ===")
+    
+    agent = MockAgent()
+    
+    # Test 1: No tools
+    result = agent._format_tools_for_completion()
+    assert result == []
+    print("✓ No tools")
+    
+    # Test 2: String tool names
+    result = agent._format_tools_for_completion(["tool1", "tool2"])
+    assert len(result) == 2
+    assert result[0]["function"]["name"] == "tool1"
+    assert result[1]["function"]["name"] == "tool2"
+    print("✓ String tool names")
+    
+    # Test 3: Pre-formatted OpenAI tools
+    openai_tool = {
+        "type": "function",
+        "function": {
+            "name": "openai_tool",
+            "description": "A pre-formatted tool",
+            "parameters": {}
+        }
+    }
+    result = agent._format_tools_for_completion([openai_tool])
+    assert len(result) == 1
+    assert result[0] == openai_tool
+    print("✓ Pre-formatted OpenAI tools")
+    
+    # Test 4: Mixed tool types
+    def my_function():
+        """A test function"""
+        pass
+    
+    mixed_tools = [
+        "string_tool",
+        openai_tool,
+        my_function
+    ]
+    result = agent._format_tools_for_completion(mixed_tools)
+    assert len(result) == 3
+    assert result[0]["function"]["name"] == "string_tool"
+    assert result[1] == openai_tool
+    assert result[2]["function"]["name"] == "my_function"
+    print("✓ Mixed tool types")
+    
+    # Test 5: Nested list of tools
+    nested_tools = [
+        [openai_tool, openai_tool],
+        "another_tool"
+    ]
+    result = agent._format_tools_for_completion(nested_tools)
+    assert len(result) == 3  # 2 from list + 1 string
+    print("✓ Nested list of tools")
+    
+    # Test 6: Invalid tools
+    invalid_tool = {
+        "type": "function",
+        # Missing 'function' key
+    }
+    result = agent._format_tools_for_completion([invalid_tool, "valid_tool"])
+    assert len(result) == 1  # Only valid_tool
+    assert result[0]["function"]["name"] == "valid_tool"
+    print("✓ Invalid tools filtered out")
+    
+    # Test 7: Using agent's default tools
+    agent.tools = ["default_tool1", "default_tool2"]
+    result = agent._format_tools_for_completion()
+    assert len(result) == 2
+    assert result[0]["function"]["name"] == "default_tool1"
+    print("✓ Using agent's default tools")
+    
+    print("\nAll _format_tools_for_completion tests passed! ✅")
+
+
+def test_backward_compatibility():
+    """Test that the refactoring maintains backward compatibility"""
+    print("\n=== Testing Backward Compatibility ===")
+    
+    agent = MockAgent()
+    
+    # Test various prompt formats still work
+    test_cases = [
+        # Simple cases
+        ("string prompt", None, None),
+        ("prompt with tools", ["tool1"], None),
+        ("json output", None, True),
+        
+        # Complex cases
+        ([{"type": "text", "text": "multimodal"}], ["tool2"], None),
+        ("mixed", ["tool1", {"type": "function", "function": {"name": "t2", "description": "test"}}], True),
+    ]
+    
+    for prompt, tools, output_json in test_cases:
+        try:
+            messages, original = agent._build_messages(prompt, output_json=output_json)
+            formatted_tools = agent._format_tools_for_completion(tools)
+            
+            # Basic validation
+            assert len(messages) >= 1
+            if tools:
+                assert len(formatted_tools) >= 1
+                
+            print(f"✓ Test case: {str(prompt)[:30]}...")
+        except Exception as e:
+            print(f"✗ Test case failed: {str(prompt)[:30]}... - {e}")
+            raise
+    
+    print("\nAll backward compatibility tests passed! ✅")
+
+
+def main():
+    """Run all tests"""
+    print("Testing Phase 1 Refactoring of agent.py")
+    print("=" * 50)
+    
+    try:
+        test_build_messages()
+        test_format_tools()
+        test_backward_compatibility()
+        
+        print("\n" + "=" * 50)
+        print("✅ ALL TESTS PASSED! The refactoring is working correctly.")
+        print("\nSummary:")
+        print("- _build_messages() helper correctly builds message arrays")
+        print("- _format_tools_for_completion() handles all tool formats")
+        print("- Backward compatibility is maintained")
+        print("- No features are missing")
+        
+    except Exception as e:
+        print(f"\n❌ TEST FAILED: {e}")
+        raise
+
+
+if __name__ == "__main__":
+    main()

src/praisonai/tests/unit/test_async_tool_formats.py

@@ -0,0 +1,220 @@
+#!/usr/bin/env python3
+"""
+Test script to verify async method supports all tool formats after refactoring.
+Tests sync vs async parity for all tool format types.
+"""
+import os
+import sys
+import asyncio
+from typing import Dict, List, Optional
+
+# Add the source directory to path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../../praisonai-agents'))
+
+from praisonaiagents.llm import LLM
+
+# Test function 1: Simple callable
+def search_tool(query: str) -> str:
+    """Search for information."""
+    return f"Search results for: {query}"
+
+# Test function 2: Complex types
+def complex_tool(data: Dict[str, int], items: List[str], optional: Optional[str] = None) -> Dict:
+    """Process complex data types."""
+    return {"processed": True, "item_count": len(items)}
+
+# Pre-formatted OpenAI tool (simulating MCP.to_openai_tool() output)
+pre_formatted_tool = {
+    "type": "function",
+    "function": {
+        "name": "weather_tool",
+        "description": "Get weather information",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {"type": "string", "description": "City name"},
+                "units": {"type": "string", "enum": ["celsius", "fahrenheit"]}
+            },
+            "required": ["location"]
+        }
+    }
+}
+
+# List of pre-formatted tools
+tool_list = [
+    pre_formatted_tool,
+    {
+        "type": "function",
+        "function": {
+            "name": "calculator",
+            "description": "Perform calculations",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "expression": {"type": "string"}
+                },
+                "required": ["expression"]
+            }
+        }
+    }
+]
+
+def test_sync_tool_formatting():
+    """Test sync method tool formatting."""
+    print("\n=== Testing Sync Tool Formatting ===")
+    
+    # Use a dummy model that doesn't require actual API calls
+    llm = LLM(model="openai/gpt-3.5-turbo", api_key="test")
+    
+    # Test 1: Callable tools
+    print("\n1. Testing callable tools...")
+    formatted = llm._format_tools_for_litellm([search_tool, complex_tool])
+    assert formatted is not None
+    assert len(formatted) == 2
+    assert formatted[0]["function"]["name"] == "search_tool"
+    assert formatted[1]["function"]["name"] == "complex_tool"
+    print("✓ Callable tools formatted correctly")
+    
+    # Test 2: Pre-formatted tools
+    print("\n2. Testing pre-formatted tools...")
+    formatted = llm._format_tools_for_litellm([pre_formatted_tool])
+    assert formatted is not None
+    assert len(formatted) == 1
+    assert formatted[0]["function"]["name"] == "weather_tool"
+    print("✓ Pre-formatted tools passed through correctly")
+    
+    # Test 3: List of tools
+    print("\n3. Testing list of tools...")
+    formatted = llm._format_tools_for_litellm([tool_list])
+    assert formatted is not None
+    assert len(formatted) == 2
+    assert formatted[0]["function"]["name"] == "weather_tool"
+    assert formatted[1]["function"]["name"] == "calculator"
+    print("✓ Tool lists flattened correctly")
+    
+    # Test 4: String tool names
+    print("\n4. Testing string tool names...")
+    # This would need the function to exist in globals
+    globals()['test_string_tool'] = search_tool
+    formatted = llm._format_tools_for_litellm(['test_string_tool'])
+    if formatted:  # May not work without proper setup
+        print("✓ String tool names resolved")
+    else:
+        print("⚠ String tool names need proper global setup")
+    
+    # Test 5: Mixed formats
+    print("\n5. Testing mixed tool formats...")
+    mixed_tools = [
+        search_tool,           # Callable
+        pre_formatted_tool,    # Pre-formatted
+        tool_list,            # List
+    ]
+    formatted = llm._format_tools_for_litellm(mixed_tools)
+    assert formatted is not None
+    assert len(formatted) >= 3  # At least 3 tools
+    print("✓ Mixed formats handled correctly")
+    
+    return True
+
+async def test_async_tool_formatting():
+    """Test async method tool formatting."""
+    print("\n=== Testing Async Tool Formatting ===")
+    
+    # Use a dummy model
+    llm = LLM(model="openai/gpt-3.5-turbo", api_key="test")
+    
+    # Test 1: Callable tools (previously the only supported format)
+    print("\n1. Testing callable tools...")
+    formatted = llm._format_tools_for_litellm([search_tool, complex_tool])
+    assert formatted is not None
+    assert len(formatted) == 2
+    print("✓ Callable tools formatted correctly")
+    
+    # Test 2: Pre-formatted tools (NEW - previously unsupported)
+    print("\n2. Testing pre-formatted tools...")
+    formatted = llm._format_tools_for_litellm([pre_formatted_tool])
+    assert formatted is not None
+    assert len(formatted) == 1
+    assert formatted[0]["function"]["name"] == "weather_tool"
+    print("✓ Pre-formatted tools NOW SUPPORTED! ✅")
+    
+    # Test 3: List of tools (NEW - previously unsupported)
+    print("\n3. Testing list of tools...")
+    formatted = llm._format_tools_for_litellm([tool_list])
+    assert formatted is not None
+    assert len(formatted) == 2
+    print("✓ Tool lists NOW SUPPORTED! ✅")
+    
+    # Test 4: Mixed formats (NEW capability)
+    print("\n4. Testing mixed tool formats...")
+    mixed_tools = [
+        search_tool,
+        pre_formatted_tool,
+        tool_list,
+    ]
+    formatted = llm._format_tools_for_litellm(mixed_tools)
+    assert formatted is not None
+    assert len(formatted) >= 3
+    print("✓ Mixed formats NOW SUPPORTED! ✅")
+    
+    return True
+
+def compare_implementations():
+    """Compare sync and async implementations."""
+    print("\n=== Comparing Sync vs Async ===")
+    
+    llm = LLM(model="openai/gpt-3.5-turbo", api_key="test")
+    
+    test_tools = [
+        search_tool,
+        pre_formatted_tool,
+        tool_list,
+    ]
+    
+    # Format with the shared helper (used by both sync and async now)
+    formatted = llm._format_tools_for_litellm(test_tools)
+    
+    print(f"\nTotal tools formatted: {len(formatted) if formatted else 0}")
+    print("\nFormatted tools:")
+    if formatted:
+        for tool in formatted:
+            print(f"  - {tool['function']['name']}: {tool['function']['description']}")
+    
+    print("\n✅ Both sync and async now use the SAME formatting logic!")
+    print("✅ Async now supports ALL tool formats that sync supports!")
+
+def main():
+    """Run all tests."""
+    print("Testing Async Tool Format Support")
+    print("=" * 50)
+    
+    try:
+        # Test sync formatting
+        test_sync_tool_formatting()
+        
+        # Test async formatting  
+        asyncio.run(test_async_tool_formatting())
+        
+        # Compare implementations
+        compare_implementations()
+        
+        print("\n" + "=" * 50)
+        print("✅ ALL TESTS PASSED!")
+        print("\nSummary of changes:")
+        print("- Created _format_tools_for_litellm() helper method")
+        print("- Async now supports pre-formatted tools ✅")
+        print("- Async now supports lists of tools ✅")
+        print("- Async now supports string tool names ✅")
+        print("- Removed ~95 lines of duplicate code")
+        print("- Zero breaking changes - all existing functionality preserved")
+        
+    except Exception as e:
+        print(f"\n❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return 1
+    
+    return 0
+
+if __name__ == "__main__":
+    exit(main())

src/praisonai/tests/unit/test_autoagents.py

@@ -0,0 +1,518 @@
+"""
+Unit tests for AutoAgents class
+"""
+
+import pytest
+from unittest.mock import Mock, patch, MagicMock
+from praisonaiagents.agents.autoagents import AutoAgents, AutoAgentsConfig, AgentConfig, TaskConfig
+from praisonaiagents.llm import OpenAIClient, LLM
+import json
+import logging
+
+
+class TestAutoAgents:
+    """Test suite for AutoAgents functionality"""
+    
+    @pytest.fixture
+    def sample_valid_config(self):
+        """Fixture providing a valid AutoAgentsConfig"""
+        return AutoAgentsConfig(
+            main_instruction="Create a blog post about AI",
+            process_type="sequential",
+            agents=[
+                AgentConfig(
+                    name="Research Agent",
+                    role="Researcher",
+                    goal="Research AI topics",
+                    backstory="Expert researcher",
+                    tools=["web_search"],
+                    tasks=[
+                        TaskConfig(
+                            name="Research Task",
+                            description="Research latest AI trends",
+                            expected_output="Research summary",
+                            tools=["web_search"]
+                        )
+                    ]
+                ),
+                AgentConfig(
+                    name="Writer Agent",
+                    role="Writer",
+                    goal="Write blog post",
+                    backstory="Professional writer",
+                    tools=["text_editor"],
+                    tasks=[
+                        TaskConfig(
+                            name="Writing Task",
+                            description="Write blog post based on research",
+                            expected_output="Complete blog post",
+                            tools=["text_editor"]
+                        )
+                    ]
+                )
+            ]
+        )
+    
+    @pytest.fixture
+    def sample_invalid_config_string_tasks(self):
+        """Fixture providing an invalid config with string tasks"""
+        return {
+            "main_instruction": "Create a blog post about AI",
+            "process_type": "sequential",
+            "agents": [
+                {
+                    "name": "Research Agent",
+                    "role": "Researcher",
+                    "goal": "Research AI topics",
+                    "backstory": "Expert researcher",
+                    "tools": ["web_search"],
+                    "tasks": ["Research latest AI trends"]  # Invalid: string instead of object
+                }
+            ]
+        }
+    
+    @pytest.fixture
+    def sample_invalid_config_missing_fields(self):
+        """Fixture providing an invalid config with missing task fields"""
+        return AutoAgentsConfig(
+            main_instruction="Create a blog post about AI",
+            process_type="sequential",
+            agents=[
+                AgentConfig(
+                    name="Research Agent",
+                    role="Researcher",
+                    goal="Research AI topics",
+                    backstory="Expert researcher",
+                    tools=["web_search"],
+                    tasks=[
+                        TaskConfig(
+                            name="Research Task",
+                            description="",  # Missing description
+                            expected_output="Research summary",
+                            tools=["web_search"]
+                        )
+                    ]
+                )
+            ]
+        )
+    
+    @pytest.fixture
+    def mock_tools(self):
+        """Fixture providing mock tools"""
+        web_search = Mock()
+        web_search.__name__ = "web_search"
+        
+        text_editor = Mock()
+        text_editor.__name__ = "text_editor"
+        
+        return [web_search, text_editor]
+    
+    def test_validate_config_success(self, sample_valid_config):
+        """Test successful validation of a valid configuration"""
+        auto_agents = AutoAgents(
+            instructions="Test instructions",
+            max_agents=3
+        )
+        
+        is_valid, error_msg = auto_agents._validate_config(sample_valid_config)
+        
+        assert is_valid is True
+        assert error_msg == ""
+    
+    def test_validate_config_failure_not_taskconfig(self):
+        """Test validation failure when task is not a TaskConfig instance"""
+        auto_agents = AutoAgents(
+            instructions="Test instructions",
+            max_agents=3
+        )
+        
+        # Create a config with a non-TaskConfig task
+        config = AutoAgentsConfig(
+            main_instruction="Test",
+            process_type="sequential",
+            agents=[
+                AgentConfig(
+                    name="Agent",
+                    role="Role",
+                    goal="Goal",
+                    backstory="Story",
+                    tools=[],
+                    tasks=["This is a string task"]  # This will cause validation to fail
+                )
+            ]
+        )
+        
+        # Mock the task to bypass Pydantic validation for testing
+        config.agents[0].tasks = ["This is a string task"]
+        
+        is_valid, error_msg = auto_agents._validate_config(config)
+        
+        assert is_valid is False
+        assert "is not a proper TaskConfig object" in error_msg
+    
+    def test_validate_config_failure_missing_name(self):
+        """Test validation failure when task is missing name"""
+        auto_agents = AutoAgents(
+            instructions="Test instructions",
+            max_agents=3
+        )
+        
+        config = AutoAgentsConfig(
+            main_instruction="Test",
+            process_type="sequential",
+            agents=[
+                AgentConfig(
+                    name="Agent",
+                    role="Role",
+                    goal="Goal",
+                    backstory="Story",
+                    tools=[],
+                    tasks=[
+                        TaskConfig(
+                            name="",  # Empty name
+                            description="Description",
+                            expected_output="Output",
+                            tools=[]
+                        )
+                    ]
+                )
+            ]
+        )
+        
+        is_valid, error_msg = auto_agents._validate_config(config)
+        
+        assert is_valid is False
+        assert "has no name" in error_msg
+    
+    def test_validate_config_failure_missing_description(self, sample_invalid_config_missing_fields):
+        """Test validation failure when task is missing description"""
+        auto_agents = AutoAgents(
+            instructions="Test instructions",
+            max_agents=3
+        )
+        
+        is_valid, error_msg = auto_agents._validate_config(sample_invalid_config_missing_fields)
+        
+        assert is_valid is False
+        assert "has no description" in error_msg
+    
+    @patch('praisonaiagents.agents.autoagents.get_openai_client')
+    def test_generate_config_openai_success(self, mock_get_client, sample_valid_config, mock_tools):
+        """Test successful config generation using OpenAI structured output"""
+        # Mock OpenAI client
+        mock_client = Mock(spec=OpenAIClient)
+        mock_client.parse_structured_output.return_value = sample_valid_config
+        mock_get_client.return_value = mock_client
+        
+        auto_agents = AutoAgents(
+            instructions="Create a blog post about AI",
+            tools=mock_tools,
+            max_agents=2,
+            llm="gpt-4"
+        )
+        
+        # The config should have been generated in __init__
+        assert hasattr(auto_agents, 'agents')
+        assert len(auto_agents.agents) == 2
+        
+        # Verify OpenAI client was called
+        mock_client.parse_structured_output.assert_called_once()
+    
+    @patch('praisonaiagents.agents.autoagents.LLM')
+    def test_generate_config_llm_success(self, mock_llm_class, sample_valid_config, mock_tools):
+        """Test successful config generation using generic LLM"""
+        # Mock LLM instance
+        mock_llm = Mock()
+        mock_llm.response.return_value = json.dumps(sample_valid_config.model_dump())
+        mock_llm_class.return_value = mock_llm
+        
+        auto_agents = AutoAgents(
+            instructions="Create a blog post about AI",
+            tools=mock_tools,
+            max_agents=2,
+            llm="claude-3"  # Non-OpenAI model
+        )
+        
+        # The config should have been generated in __init__
+        assert hasattr(auto_agents, 'agents')
+        assert len(auto_agents.agents) == 2
+        
+        # Verify LLM was called
+        mock_llm.response.assert_called_once()
+    
+    @patch('praisonaiagents.agents.autoagents.LLM')
+    def test_generate_config_with_markdown_response(self, mock_llm_class, sample_valid_config, mock_tools):
+        """Test config generation when LLM returns markdown-wrapped JSON"""
+        # Mock LLM instance
+        mock_llm = Mock()
+        # Return JSON wrapped in markdown code block
+        wrapped_response = f"```json\n{json.dumps(sample_valid_config.model_dump(), indent=2)}\n```"
+        mock_llm.response.return_value = wrapped_response
+        mock_llm_class.return_value = mock_llm
+        
+        auto_agents = AutoAgents(
+            instructions="Create a blog post about AI",
+            tools=mock_tools,
+            max_agents=2,
+            llm="claude-3"
+        )
+        
+        # The config should have been generated successfully despite markdown wrapping
+        assert hasattr(auto_agents, 'agents')
+        assert len(auto_agents.agents) == 2
+    
+    @patch('praisonaiagents.agents.autoagents.get_openai_client')
+    def test_generate_config_retry_on_validation_failure(self, mock_get_client, sample_valid_config, mock_tools):
+        """Test retry mechanism when validation fails"""
+        # Mock OpenAI client
+        mock_client = Mock(spec=OpenAIClient)
+        
+        # First attempt: return invalid config with string tasks
+        invalid_config = AutoAgentsConfig(
+            main_instruction="Test",
+            process_type="sequential",
+            agents=[
+                AgentConfig(
+                    name="Agent",
+                    role="Role",
+                    goal="Goal",
+                    backstory="Story",
+                    tools=[],
+                    tasks=[]  # This will be mocked to contain strings
+                )
+            ]
+        )
+        # Mock the tasks to be strings (bypassing Pydantic validation)
+        invalid_config.agents[0].tasks = ["Invalid string task"]
+        
+        # Second attempt: return valid config
+        mock_client.parse_structured_output.side_effect = [invalid_config, sample_valid_config]
+        mock_get_client.return_value = mock_client
+        
+        with patch('logging.warning') as mock_warning:
+            auto_agents = AutoAgents(
+                instructions="Create a blog post about AI",
+                tools=mock_tools,
+                max_agents=2,
+                llm="gpt-4"
+            )
+        
+        # Should succeed after retry
+        assert hasattr(auto_agents, 'agents')
+        assert len(auto_agents.agents) == 2
+        
+        # Verify retry occurred
+        assert mock_client.parse_structured_output.call_count == 2
+        mock_warning.assert_called_once()
+        assert "Configuration validation failed" in str(mock_warning.call_args)
+    
+    @patch('praisonaiagents.agents.autoagents.get_openai_client')
+    def test_generate_config_max_retries_exceeded(self, mock_get_client, mock_tools):
+        """Test that max retries are properly enforced"""
+        # Mock OpenAI client
+        mock_client = Mock(spec=OpenAIClient)
+        
+        # Always return invalid config
+        invalid_config = AutoAgentsConfig(
+            main_instruction="Test",
+            process_type="sequential",
+            agents=[
+                AgentConfig(
+                    name="Agent",
+                    role="Role",
+                    goal="Goal",
+                    backstory="Story",
+                    tools=[],
+                    tasks=[]
+                )
+            ]
+        )
+        # Mock the tasks to be strings
+        invalid_config.agents[0].tasks = ["Invalid string task"]
+        
+        mock_client.parse_structured_output.return_value = invalid_config
+        mock_get_client.return_value = mock_client
+        
+        with pytest.raises(ValueError, match="Configuration validation failed after 3 attempts"):
+            AutoAgents(
+                instructions="Create a blog post about AI",
+                tools=mock_tools,
+                max_agents=2,
+                llm="gpt-4"
+            )
+        
+        # Verify all retries were attempted
+        assert mock_client.parse_structured_output.call_count == 3
+    
+    @patch('praisonaiagents.agents.autoagents.get_openai_client')
+    def test_max_agents_truncation(self, mock_get_client, mock_tools):
+        """Test that agents are truncated when exceeding max_agents"""
+        # Create config with 4 agents
+        config_with_many_agents = AutoAgentsConfig(
+            main_instruction="Test",
+            process_type="sequential",
+            agents=[
+                AgentConfig(
+                    name=f"Agent {i}",
+                    role="Role",
+                    goal="Goal",
+                    backstory="Story",
+                    tools=[],
+                    tasks=[
+                        TaskConfig(
+                            name=f"Task {i}",
+                            description="Description",
+                            expected_output="Output",
+                            tools=[]
+                        )
+                    ]
+                )
+                for i in range(4)
+            ]
+        )
+        
+        mock_client = Mock(spec=OpenAIClient)
+        mock_client.parse_structured_output.return_value = config_with_many_agents
+        mock_get_client.return_value = mock_client
+        
+        # Create AutoAgents with max_agents=2
+        auto_agents = AutoAgents(
+            instructions="Test",
+            tools=mock_tools,
+            max_agents=2,
+            llm="gpt-4"
+        )
+        
+        # Should only have 2 agents
+        assert len(auto_agents.agents) == 2
+        assert auto_agents.agents[0].name == "Agent 0"
+        assert auto_agents.agents[1].name == "Agent 1"
+    
+    @patch('praisonaiagents.agents.autoagents.get_openai_client')
+    def test_insufficient_agents_warning(self, mock_get_client, mock_tools):
+        """Test warning when fewer agents than max_agents are generated"""
+        # Create config with only 1 agent
+        config_with_few_agents = AutoAgentsConfig(
+            main_instruction="Test",
+            process_type="sequential",
+            agents=[
+                AgentConfig(
+                    name="Single Agent",
+                    role="Role",
+                    goal="Goal",
+                    backstory="Story",
+                    tools=[],
+                    tasks=[
+                        TaskConfig(
+                            name="Task",
+                            description="Description",
+                            expected_output="Output",
+                            tools=[]
+                        )
+                    ]
+                )
+            ]
+        )
+        
+        mock_client = Mock(spec=OpenAIClient)
+        mock_client.parse_structured_output.return_value = config_with_few_agents
+        mock_get_client.return_value = mock_client
+        
+        with patch('logging.warning') as mock_warning:
+            auto_agents = AutoAgents(
+                instructions="Test",
+                tools=mock_tools,
+                max_agents=3,
+                llm="gpt-4"
+            )
+        
+        # Should have 1 agent
+        assert len(auto_agents.agents) == 1
+        
+        # Verify warning was logged
+        mock_warning.assert_called()
+        warning_msg = str(mock_warning.call_args)
+        assert "Generated 1 agents, expected 3" in warning_msg
+    
+    def test_max_agents_validation(self):
+        """Test max_agents parameter validation"""
+        # Test too low
+        with pytest.raises(ValueError, match="max_agents must be at least 1"):
+            AutoAgents(instructions="Test", max_agents=0)
+        
+        # Test too high
+        with pytest.raises(ValueError, match="max_agents cannot exceed 10"):
+            AutoAgents(instructions="Test", max_agents=11)
+    
+    @patch('praisonaiagents.agents.autoagents.LLM')
+    def test_retry_with_previous_response_in_prompt(self, mock_llm_class, sample_valid_config, mock_tools):
+        """Test that retry includes previous response and error in prompt"""
+        # Mock LLM instance
+        mock_llm = Mock()
+        
+        # First response: invalid JSON that will fail validation
+        first_response = json.dumps({
+            "main_instruction": "Test",
+            "process_type": "sequential",
+            "agents": [{
+                "name": "Agent",
+                "role": "Role",
+                "goal": "Goal",
+                "backstory": "Story",
+                "tools": [],
+                "tasks": ["String task instead of object"]
+            }]
+        })
+        
+        # Second response: valid config
+        second_response = json.dumps(sample_valid_config.model_dump())
+        
+        mock_llm.response.side_effect = [first_response, second_response]
+        mock_llm_class.return_value = mock_llm
+        
+        auto_agents = AutoAgents(
+            instructions="Create a blog post about AI",
+            tools=mock_tools,
+            max_agents=2,
+            llm="claude-3"
+        )
+        
+        # Should succeed after retry
+        assert hasattr(auto_agents, 'agents')
+        
+        # Check that the second call included the previous response
+        assert mock_llm.response.call_count == 2
+        second_call_prompt = mock_llm.response.call_args_list[1][1]['prompt']
+        assert "PREVIOUS ATTEMPT FAILED!" in second_call_prompt
+        assert first_response in second_call_prompt
+        assert "Error:" in second_call_prompt
+    
+    @patch('praisonaiagents.agents.autoagents.OpenAIClient')
+    @patch('praisonaiagents.agents.autoagents.get_openai_client')
+    def test_custom_api_key_and_base_url(self, mock_get_client, mock_openai_class, sample_valid_config, mock_tools):
+        """Test that custom API key and base URL are used correctly"""
+        # Mock OpenAI client instance
+        mock_client = Mock(spec=OpenAIClient)
+        mock_client.parse_structured_output.return_value = sample_valid_config
+        mock_openai_class.return_value = mock_client
+        
+        custom_api_key = "custom-api-key"
+        custom_base_url = "https://custom.api.url"
+        
+        auto_agents = AutoAgents(
+            instructions="Test",
+            tools=mock_tools,
+            max_agents=2,
+            llm="gpt-4",
+            api_key=custom_api_key,
+            base_url=custom_base_url
+        )
+        
+        # Verify custom client was created with correct parameters
+        mock_openai_class.assert_called_once_with(
+            api_key=custom_api_key,
+            base_url=custom_base_url
+        )
+        
+        # Verify get_openai_client was not called
+        mock_get_client.assert_not_called()

src/praisonai/tests/unit/test_openai_refactor.py

@@ -0,0 +1,219 @@
+#!/usr/bin/env python3
+"""
+Test script to verify the OpenAI refactoring maintains backward compatibility.
+
+This script tests:
+1. OpenAI client initialization
+2. Basic agent functionality
+3. Sync and async operations
+4. Tool calling capabilities
+5. Reflection functionality
+"""
+
+import asyncio
+import os
+import sys
+
+# Add the source directory to the path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src/praisonai-agents'))
+
+from praisonaiagents.agent import Agent
+from praisonaiagents.llm import OpenAIClient, get_openai_client
+
+
+def test_openai_client():
+    """Test the OpenAI client module"""
+    print("Testing OpenAI Client Module...")
+    
+    try:
+        # Test 1: Get global client
+        client = get_openai_client()
+        print("✅ Global OpenAI client created successfully")
+        
+        # Test 2: Create new client instance
+        new_client = OpenAIClient()
+        print("✅ New OpenAI client instance created successfully")
+        
+        # Test 3: Verify sync client property
+        sync_client = client.sync_client
+        print(f"✅ Sync client type: {type(sync_client).__name__}")
+        
+        # Test 4: Verify async client property
+        async_client = client.async_client
+        print(f"✅ Async client type: {type(async_client).__name__}")
+        
+        print("\n✅ OpenAI Client Module tests passed!\n")
+        return True
+        
+    except Exception as e:
+        print(f"❌ OpenAI Client Module test failed: {e}")
+        return False
+
+
+def test_agent_initialization():
+    """Test agent initialization with OpenAI client"""
+    print("Testing Agent Initialization...")
+    
+    try:
+        # Test basic agent creation
+        agent = Agent(
+            name="Test Agent",
+            role="Assistant",
+            goal="Help with testing",
+            backstory="I am a test agent"
+        )
+        print("✅ Agent created successfully")
+        
+        # Verify OpenAI client is initialized
+        if hasattr(agent, '_openai_client'):
+            print("✅ Agent has _openai_client attribute")
+        else:
+            print("❌ Agent missing _openai_client attribute")
+            return False
+            
+        # Test agent with custom base_url
+        custom_agent = Agent(
+            name="Custom Agent",
+            role="Assistant",
+            goal="Test custom endpoint",
+            base_url="http://localhost:1234/v1"
+        )
+        print("✅ Agent with custom base_url created successfully")
+        
+        print("\n✅ Agent Initialization tests passed!\n")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Agent Initialization test failed: {e}")
+        return False
+
+
+def test_dataclasses():
+    """Test the dataclasses used for OpenAI responses"""
+    print("Testing Dataclasses...")
+    
+    try:
+        from praisonaiagents.agent.agent import (
+            ChatCompletionMessage, 
+            Choice, 
+            ChatCompletion,
+            ToolCall
+        )
+        
+        # Test ToolCall creation
+        tool_call = ToolCall(
+            id="test_id",
+            type="function",
+            function={"name": "test_function", "arguments": "{}"}
+        )
+        print(f"✅ ToolCall created: {tool_call}")
+        
+        # Test ChatCompletionMessage
+        message = ChatCompletionMessage(
+            content="Test message",
+            role="assistant",
+            tool_calls=[tool_call]
+        )
+        print("✅ ChatCompletionMessage created successfully")
+        
+        # Test Choice
+        choice = Choice(
+            finish_reason="stop",
+            index=0,
+            message=message
+        )
+        print("✅ Choice created successfully")
+        
+        # Test ChatCompletion
+        completion = ChatCompletion(
+            id="test_completion",
+            choices=[choice],
+            created=1234567890,
+            model="gpt-4"
+        )
+        print("✅ ChatCompletion created successfully")
+        
+        print("\n✅ Dataclasses tests passed!\n")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Dataclasses test failed: {e}")
+        return False
+
+
+async def test_async_functionality():
+    """Test async functionality"""
+    print("Testing Async Functionality...")
+    
+    try:
+        agent = Agent(
+            name="Async Test Agent",
+            role="Assistant",
+            goal="Test async operations",
+            verbose=False
+        )
+        
+        # Note: We can't actually call the API without valid credentials
+        # But we can verify the methods exist and are callable
+        
+        # Check if achat method exists
+        if hasattr(agent, 'achat'):
+            print("✅ Agent has achat method")
+        else:
+            print("❌ Agent missing achat method")
+            return False
+            
+        # Check if _achat_completion exists
+        if hasattr(agent, '_achat_completion'):
+            print("✅ Agent has _achat_completion method")
+        else:
+            print("❌ Agent missing _achat_completion method")
+            return False
+            
+        print("\n✅ Async Functionality tests passed!\n")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Async Functionality test failed: {e}")
+        return False
+
+
+def main():
+    """Run all tests"""
+    print("=" * 60)
+    print("OpenAI Refactoring Test Suite")
+    print("=" * 60)
+    print()
+    
+    results = []
+    
+    # Run sync tests
+    results.append(test_openai_client())
+    results.append(test_agent_initialization())
+    results.append(test_dataclasses())
+    
+    # Run async tests
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    results.append(loop.run_until_complete(test_async_functionality()))
+    
+    # Summary
+    print("=" * 60)
+    print("Test Summary")
+    print("=" * 60)
+    
+    passed = sum(results)
+    total = len(results)
+    
+    print(f"\nTests passed: {passed}/{total}")
+    
+    if passed == total:
+        print("\n✅ All tests passed! The refactoring maintains backward compatibility.")
+        return 0
+    else:
+        print(f"\n❌ {total - passed} tests failed. Please review the failures above.")
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

src/praisonai/tests/unit/test_openai_refactor_2.py

@@ -0,0 +1,167 @@
+#!/usr/bin/env python3
+"""
+Test script to verify the OpenAI refactoring works correctly.
+"""
+
+import asyncio
+from praisonaiagents import Agent
+from praisonaiagents.llm import (
+    get_openai_client,
+    ChatCompletionMessage,
+    Choice,
+    CompletionUsage,
+    ChatCompletion,
+    ToolCall,
+    process_stream_chunks
+)
+
+def test_data_classes():
+    """Test that data classes are properly imported and work"""
+    print("Testing data classes...")
+    
+    # Create a message
+    msg = ChatCompletionMessage(
+        content="Hello, world!",
+        role="assistant"
+    )
+    assert msg.content == "Hello, world!"
+    assert msg.role == "assistant"
+    print("✓ ChatCompletionMessage works")
+    
+    # Create a choice
+    choice = Choice(
+        finish_reason="stop",
+        index=0,
+        message=msg
+    )
+    assert choice.finish_reason == "stop"
+    assert choice.message.content == "Hello, world!"
+    print("✓ Choice works")
+    
+    # Create a tool call
+    tool_call = ToolCall(
+        id="call_123",
+        type="function",
+        function={"name": "test_tool", "arguments": "{}"}
+    )
+    assert tool_call.id == "call_123"
+    assert tool_call.function["name"] == "test_tool"
+    print("✓ ToolCall works")
+    
+    print("All data classes test passed!\n")
+
+def test_openai_client():
+    """Test that OpenAI client is properly initialized"""
+    print("Testing OpenAI client...")
+    
+    try:
+        # This might fail if OPENAI_API_KEY is not set, which is OK for testing
+        client = get_openai_client()
+        print("✓ OpenAI client created successfully")
+        
+        # Test build_messages method
+        messages, original = client.build_messages(
+            prompt="Test prompt",
+            system_prompt="You are a helpful assistant"
+        )
+        assert len(messages) == 2
+        assert messages[0]["role"] == "system"
+        assert messages[1]["role"] == "user"
+        print("✓ build_messages method works")
+        
+        # Test format_tools method (with no tools)
+        tools = client.format_tools(None)
+        assert tools is None
+        print("✓ format_tools method works")
+        
+    except ValueError as e:
+        if "OPENAI_API_KEY" in str(e):
+            print("⚠ OpenAI client requires API key (expected in test environment)")
+        else:
+            raise
+    
+    print("OpenAI client tests completed!\n")
+
+def test_agent_integration():
+    """Test that Agent class works with the refactored code"""
+    print("Testing Agent integration...")
+    
+    try:
+        # Create a simple agent
+        agent = Agent(
+            name="Test Agent",
+            role="Tester",
+            goal="Test the refactored code",
+            instructions="You are a test agent"
+        )
+        print("✓ Agent created successfully")
+        
+        # Test _build_messages
+        messages, original = agent._build_messages(
+            prompt="Test prompt",
+            temperature=0.5
+        )
+        assert len(messages) >= 1
+        assert messages[-1]["content"] == "Test prompt"
+        print("✓ Agent._build_messages works")
+        
+        # Test _format_tools_for_completion
+        def sample_tool():
+            """A sample tool for testing"""
+            pass
+        
+        formatted = agent._format_tools_for_completion([sample_tool])
+        assert isinstance(formatted, list)
+        print("✓ Agent._format_tools_for_completion works")
+        
+    except Exception as e:
+        print(f"⚠ Agent integration test failed: {e}")
+        # This is OK if dependencies are missing
+    
+    print("Agent integration tests completed!\n")
+
+async def test_async_functionality():
+    """Test async functionality"""
+    print("Testing async functionality...")
+    
+    try:
+        client = get_openai_client()
+        
+        # Test that async client can be accessed
+        async_client = client.async_client
+        print("✓ Async client accessible")
+        
+        # Test build_messages (which is sync but used in async context)
+        messages, _ = client.build_messages("Test async")
+        assert len(messages) >= 1
+        print("✓ build_messages works in async context")
+        
+    except ValueError as e:
+        if "OPENAI_API_KEY" in str(e):
+            print("⚠ Async tests require API key")
+        else:
+            raise
+    
+    print("Async functionality tests completed!\n")
+
+def main():
+    """Run all tests"""
+    print("=" * 50)
+    print("OpenAI Refactoring Test Suite")
+    print("=" * 50)
+    print()
+    
+    # Run sync tests
+    test_data_classes()
+    test_openai_client()
+    test_agent_integration()
+    
+    # Run async tests
+    asyncio.run(test_async_functionality())
+    
+    print("=" * 50)
+    print("All tests completed!")
+    print("=" * 50)
+
+if __name__ == "__main__":
+    main()

src/praisonai/tests/unit/test_xai_fix.py

@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+"""Test script to verify that xai/grok-4 works without OPENAI_API_KEY"""
+
+import os
+import sys
+
+# Ensure OPENAI_API_KEY is not set to test the fix
+if 'OPENAI_API_KEY' in os.environ:
+    del os.environ['OPENAI_API_KEY']
+
+# Test the import that was failing
+try:
+    from praisonaiagents import Agent
+    print("✅ Successfully imported praisonaiagents without OPENAI_API_KEY!")
+except ValueError as e:
+    print(f"❌ Import failed with error: {e}")
+    sys.exit(1)
+
+# Test creating an agent with xai/grok-4
+try:
+    agent = Agent(
+        instructions="You are a PhD-level mathematician.",
+        llm="xai/grok-4"
+    )
+    print("✅ Successfully created Agent with xai/grok-4!")
+except Exception as e:
+    print(f"❌ Failed to create agent: {e}")
+    sys.exit(1)
+
+print("\n✅ All tests passed! The issue has been fixed.")
+print("   You can now use litellm providers without setting OPENAI_API_KEY.")

src/praisonai/uv.lock

@@ -3931,7 +3931,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.2.38"
+version = "2.2.49"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -4073,7 +4073,7 @@ requires-dist = [
     { name = "plotly", marker = "extra == 'realtime'", specifier = ">=5.24.0" },
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.15" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.15" },
-    { name = "praisonaiagents", specifier = ">=0.0.111" },
+    { name = "praisonaiagents", specifier = ">=0.0.123" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = ">=0.2.19" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4130,7 +4130,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.111"
+version = "0.0.123"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mcp" },
@@ -4139,9 +4139,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/3c/8f/815e2074fb03d24dd0d89adb28c6150f6112ff23af36bf021f387176cc0c/praisonaiagents-0.0.111.tar.gz", hash = "sha256:17d4fec346d61a9830410a4f23a052da44b3a9978a7304b9ca9ca35cf747ceaa", size = 166895 }
+sdist = { url = "https://files.pythonhosted.org/packages/dc/d0/1d9b8a60284b206184dc0d877cc1df8d800f79d89f82c5ff88dc60202d4b/praisonaiagents-0.0.123.tar.gz", hash = "sha256:9dd2496d86de0311135f0d075a313c66979ada8104d87fdb36701b4623c47e1b", size = 174756 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/92/a6/dbbe85f62411b784a79055be924fa3d6c3481ed7659ce2ec3ab3bf315254/praisonaiagents-0.0.111-py3-none-any.whl", hash = "sha256:1a8f884c48eac03eff4ceec11fec4625f01decaaa28636a082cd61797cef984b", size = 186353 },
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