chore: bump PraisonAI to v2.2.88 and update dependencies across Dockerfiles and project files

refactor: optimise ChatOpenAI instantiation by using a dictionary for parameters and enforce temperature for gpt-5 models
chore: bump PraisonAI to v2.2.87, PraisonAI Agents to v0.0.161 and increase default temperature to 1.0
2026-11-08 08:11:07 -10:00 · 2025-08-08 19:24:43 +01:00 · 2025-08-08 19:17:45 +01:00 · 2025-08-08 13:44:29 +01:00 · 2025-08-08 12:18:30 +01:00 · 2025-08-08 12:17:18 +01:00
355 changed files with 2399 additions and 816 deletions
@@ -12,13 +12,12 @@ on:

 jobs:
  claude-response:
-    # Prevent bots from triggering this action
+    # Prevent certain bots from triggering this action (but allow github-actions[bot] for auto-comments)
    if: |
      github.actor != 'dependabot[bot]' &&
-      github.actor != 'github-actions[bot]' &&
      github.actor != 'cursor[bot]' &&
-      !contains(github.actor, '[bot]') &&
-      github.actor != 'renovate[bot]'
+      github.actor != 'renovate[bot]' &&
+      !(contains(github.actor, '[bot]') && github.actor != 'github-actions[bot]')
    runs-on: ubuntu-latest
    permissions:
      contents: write
@@ -41,7 +41,7 @@ jobs:
        run: |
          echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
          echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-          echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+          echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
          echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
          echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV

@@ -29,7 +29,7 @@ jobs:
      run: |
        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
        echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV

    - name: Install dependencies
@@ -59,7 +59,7 @@ jobs:
      run: |
        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV

    - name: Run Comprehensive Test Suite
      run: |
@@ -42,13 +42,13 @@ jobs:
      run: |
        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
        echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
        echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
        # Also export to current shell session for immediate availability
        export OPENAI_API_KEY="${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}"
        export OPENAI_API_BASE="${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}"
-        export OPENAI_MODEL_NAME="${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}"
+        export OPENAI_MODEL_NAME="${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}"
        export LOGLEVEL=DEBUG
        # Verify immediate availability
        echo "🔧 Immediate verification in same step:"
@@ -126,7 +126,7 @@ jobs:
      env:
        OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}
        OPENAI_API_BASE: ${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}
-        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}
+        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}
        LOGLEVEL: DEBUG
      run: |
        echo "🔍 Testing PraisonAI API key usage directly..."
@@ -166,7 +166,7 @@ jobs:
        
        print('\\n🧪 Testing PraisonAIModel with explicit API key (CrewAI method):')
        model_with_explicit_key = PraisonAIModel(
-            model='openai/gpt-4o-mini',
+            model='openai/gpt-5-nano',
            base_url=praisonai.config_list[0].get('base_url'),
            api_key=api_key_from_config # This will be a string from praisonai.config_list
        )
@@ -251,7 +251,7 @@ jobs:
      env:
        OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}
        OPENAI_API_BASE: ${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}
-        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}
+        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}
        LOGLEVEL: DEBUG
      run: |
        echo "🧪 Running legacy tests with real API key..."
@@ -42,7 +42,7 @@ jobs:
      run: |
        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
        echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
        echo "PRAISONAI_TELEMETRY_DISABLED=true" >> $GITHUB_ENV
        echo "PRAISONAI_DISABLE_TELEMETRY=true" >> $GITHUB_ENV
@@ -56,7 +56,7 @@ jobs:
      run: |
        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV

    - name: Test ${{ matrix.framework }} Framework
      run: |
@@ -35,7 +35,7 @@ jobs:
      run: |
        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
        echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
        echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV

@@ -316,8 +316,8 @@ jobs:
        try:
            from praisonai.inc.models import PraisonAIModel
            
-            # Test PraisonAIModel with openai/gpt-4o-mini (from YAML)
-            model = PraisonAIModel(model='openai/gpt-4o-mini')
+            # Test PraisonAIModel with openai/gpt-5-nano (from YAML)
+            model = PraisonAIModel(model='openai/gpt-5-nano')
            
            print('🤖 PraisonAIModel Configuration:')
            print(f'  model: {model.model}')
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    flask \
-    "praisonai>=2.2.79" \
+    "praisonai>=2.2.88" \
    "praisonai[api]" \
    gunicorn \
    markdown
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.2.79" \
+    "praisonai>=2.2.88" \
    "praisonai[chat]" \
    "embedchain[github,youtube]"

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.2.79" \
+    "praisonai>=2.2.88" \
    "praisonai[ui]" \
    "praisonai[chat]" \
    "praisonai[realtime]" \
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.2.79" \
+    "praisonai>=2.2.88" \
    "praisonai[ui]" \
    "praisonai[crewai]"

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions

 All Docker images use consistent, up-to-date versions:
- PraisonAI: `>=2.2.79`
+- PraisonAI: `>=2.2.88`
 - 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==2.2.79" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.88" "praisonaiagents==0.0.92"
 ```

 ## 🌐 Production Deployment
@@ -26,14 +26,14 @@
        "\n",
        "### ✨ Key Benefits:\n",
        "- 🚀 **Scalable & Customizable** – Industry-agnostic design  \n",
-        "- 💰 **Cost-Effective** – Runs on GPT-4o-mini  \n",
+        "- 💰 **Cost-Effective** – Runs on gpt-5-nano  \n",
        "- 🛠️ **Production-Ready** – Realistic automation framework  \n",
        "- 🔄 **Complete Pipeline** – End-to-end flow\n",
        "\n",
        "---\n",
        "\n",
        "### 🔧 Technologies:\n",
-        "**PraisonAI** • **OpenAI GPT-4o-mini** • **Python** • **SQLite**  \n",
+        "**PraisonAI** • **OpenAI gpt-5-nano** • **Python** • **SQLite**  \n",
        "**Multi-Agent Architecture**\n",
        "\n",
        "---\n",
@@ -100,7 +100,7 @@
          "text": [
            "📦 Dependencies installed!\n",
            "🔄 Using PraisonAI Multi-Agent Framework\n",
-            "🤖 Powered by OpenAI GPT-4o-mini\n"
+            "🤖 Powered by OpenAI gpt-5-nano\n"
          ]
        }
      ],
@@ -116,7 +116,7 @@
        "\n",
        "print(\"📦 Dependencies installed!\")\n",
        "print(\"🔄 Using PraisonAI Multi-Agent Framework\")\n",
-        "print(\"🤖 Powered by OpenAI GPT-4o-mini\")"
+        "print(\"🤖 Powered by OpenAI gpt-5-nano\")"
      ]
    },
    {
@@ -5102,7 +5102,7 @@
            "\n",
            "🎊 BLENDER PRODUCTION STUDIO COMPLETE!\n",
            "🎬 17-Agent AI System Successfully Executed\n",
-            "🤖 Powered by PraisonAI + OpenAI GPT-4o-mini\n"
+            "🤖 Powered by PraisonAI + OpenAI gpt-5-nano\n"
          ]
        }
      ],
@@ -5149,7 +5149,7 @@
        "\n",
        "print(\"\\n🎊 BLENDER PRODUCTION STUDIO COMPLETE!\")\n",
        "print(\"🎬 17-Agent AI System Successfully Executed\")\n",
-        "print(\"🤖 Powered by PraisonAI + OpenAI GPT-4o-mini\")"
+        "print(\"🤖 Powered by PraisonAI + OpenAI gpt-5-nano\")"
      ]
    }
  ],
@@ -182,7 +182,7 @@
        "        ]\n",
        "        for turn in range(max_turns):\n",
        "            response = client.chat.completions.create(\n",
-        "                model=\"gpt-4o-mini\",\n",
+        "                model=\"gpt-5-nano\",\n",
        "                messages=history,\n",
        "                max_tokens=500\n",
        "            )\n",
@@ -2642,7 +2642,7 @@
      "\n",
      "\ud83c\udf8a SEMANTIC NETWORK ANALYSIS COMPLETE!\n",
      "\ud83c\udf10 Cross-platform profile discovery and analysis\n",
-      "\ud83e\udd16 Powered by PraisonAI + OpenAI GPT-4o-mini\n"
+      "\ud83e\udd16 Powered by PraisonAI + OpenAI gpt-5-nano\n"
     ]
    }
   ],
@@ -2687,7 +2687,7 @@
    "\n",
    "print(\"\\n\ud83c\udf8a SEMANTIC NETWORK ANALYSIS COMPLETE!\")\n",
    "print(\"\ud83c\udf10 Cross-platform profile discovery and analysis\")\n",
-    "print(\"\ud83e\udd16 Powered by PraisonAI + OpenAI GPT-4o-mini\")"
+    "print(\"\ud83e\udd16 Powered by PraisonAI + OpenAI gpt-5-nano\")"
   ]
  },
  {
@@ -1262,7 +1262,7 @@
      "\n",
      "🎊 AI SHOPPING ASSISTANT ACTIVE!\n",
      "🛒 Ready to help customers with natural language shopping\n",
-      "🤖 Powered by PraisonAI + OpenAI GPT-4o-mini\n"
+      "🤖 Powered by PraisonAI + OpenAI gpt-5-nano\n"
     ]
    }
   ],
@@ -1305,7 +1305,7 @@
    "\n",
    "print(\"\\n🎊 AI SHOPPING ASSISTANT ACTIVE!\")\n",
    "print(\"🛒 Ready to help customers with natural language shopping\")\n",
-    "print(\"🤖 Powered by PraisonAI + OpenAI GPT-4o-mini\")"
+    "print(\"🤖 Powered by PraisonAI + OpenAI gpt-5-nano\")"
   ]
  },
  {
@@ -156,7 +156,7 @@
    "  - Property comparison\n",
    "  - Cost of living and taxes\n",
    "  - First-time homebuyer guidance\n",
-    "llm: gpt-4o\n",
+    "llm: gpt-5-nano\n",
    "verbosity: true\n",
    "format: markdown\n",
    "\"\"\"\n"
@@ -188,7 +188,7 @@
    "Help users evaluate neighborhoods, estimate mortgage payments, compare homes, and understand homeownership costs.\n",
    "Explain clearly, format in markdown, and be friendly but professional.\n",
    "\"\"\",\n",
-    "    llm=\"gpt-4o\",\n",
+    "    llm=\"gpt-5-nano\",\n",
    "    verbose=True,\n",
    "    markdown=True\n",
    ")\n"
@@ -311,7 +311,7 @@
     "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> You are an expert health advisor using the gpt-5-nano 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",
@@ -340,7 +340,7 @@
      ],
      "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 You are an expert health advisor using the gpt-5-nano 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",
@@ -519,7 +519,7 @@
    "    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",
+    "    You are an expert health advisor using the gpt-5-nano 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",
@@ -193,7 +193,7 @@
        "    goal=\"Evaluate hackathon projects through video demonstrations\",\n",
        "    backstory=\"\"\"You are an expert hackathon judge. You give fair scores and detailed feedback\n",
        "    on technical complexity, innovation, presentation, and UX.\"\"\",\n",
-        "    llm=\"gpt-4o-mini\",\n",
+        "    llm=\"gpt-5-nano\",\n",
        "    self_reflect=False\n",
        ")"
      ],
@@ -65,7 +65,7 @@
      "source": [
        "from llama_index.llms.openai import OpenAI\n",
        "\n",
-        "llm = OpenAI(model=\"gpt-4o-mini\", api_key=\"Enter your api key here\")  # Replace with your OpenAI API key\n"
+        "llm = OpenAI(model=\"gpt-5-nano\", api_key=\"Enter your api key here\")  # Replace with your OpenAI API key\n"
      ]
    },
    {
@@ -129,7 +129,7 @@
    "judge = Agent(\n",
    "    name=\"Judge\",\n",
    "    role=\"Preside over court proceedings\",\n",
-    "    llm=\"gpt-4o-mini\",\n",
+    "    llm=\"gpt-5-nano\",\n",
    "    instructions=[\n",
    "        \"You are an impartial judge\",\n",
    "        \"Make fair decisions based on evidence\",\n",
@@ -142,7 +142,7 @@
    "prosecutor = Agent(\n",
    "    name=\"Prosecutor\",\n",
    "    role=\"Present case against defendant\",\n",
-    "    llm=\"gpt-4o-mini\",\n",
+    "    llm=\"gpt-5-nano\",\n",
    "    instructions=[\n",
    "        \"You are a prosecutor seeking conviction\",\n",
    "        \"Present evidence methodically\",\n",
@@ -155,7 +155,7 @@
    "defense = Agent(\n",
    "    name=\"Defense\",\n",
    "    role=\"Defend the accused\",\n",
-    "    llm=\"gpt-4o-mini\",\n",
+    "    llm=\"gpt-5-nano\",\n",
    "    instructions=[\n",
    "        \"You are a defense attorney\",\n",
    "        \"Create reasonable doubt\",\n",
@@ -168,7 +168,7 @@
    "witness = Agent(\n",
    "    name=\"Witness\",\n",
    "    role=\"Provide testimony\",\n",
-    "    llm=\"gpt-4o-mini\",\n",
+    "    llm=\"gpt-5-nano\",\n",
    "    instructions=[\n",
    "        \"You are a witness testifying\",\n",
    "        \"Provide factual testimony\",\n",
@@ -76,7 +76,7 @@
     "output_type": "stream",
     "text": [
      "✅ API key configured!\n",
-      "✅ Using model: gpt-4o-mini\n"
+      "✅ Using model: gpt-5-nano\n"
     ]
    }
   ],
@@ -85,7 +85,7 @@
    "openai_key = \"sk-..\"\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = openai_key\n",
-    "model_choice = \"gpt-4o-mini\"\n",
+    "model_choice = \"gpt-5-nano\"\n",
    "\n",
    "print(\"✅ API key configured!\")\n",
    "print(f\"✅ Using model: {model_choice}\")"
@@ -336,7 +336,7 @@
    "\n",
    "temperature: 0.3\n",
    "max_tokens: 4000\n",
-    "model: \"gpt-4o-mini\"\n",
+    "model: \"gpt-5-nano\"\n",
    "\"\"\"\n",
    "\n",
    "print(\"✅ YAML Prompt configured!\")"
@@ -8,7 +8,7 @@
   "source": [
    "# 🤖 Add AI Documentation Processor: Multi-Agent Content Analysis & Code Generation System\n",
    "\n",
-    "This PR adds a comprehensive AI-powered documentation processing system using PraisonAI and OpenAI GPT-4o-mini. Features 3 specialized agents for content analysis, code generation, and example creation, perfect for automated documentation processing and code generation workflows.\n",
+    "This PR adds a comprehensive AI-powered documentation processing system using PraisonAI and OpenAI gpt-5-nano. Features 3 specialized agents for content analysis, code generation, and example creation, perfect for automated documentation processing and code generation workflows.\n",
    "\n",
    "**Key Features:** Multi-agent document analysis, automated code generation, usage example creation, content validation, and scalable processing workflows for various document formats."
   ]
@@ -152,7 +152,7 @@
    "  - Evaluate risk factors\n",
    "  - Identify scams or red flags\n",
    "  - Summarize project fundamentals\n",
-    "llm: gpt-4o\n",
+    "llm: gpt-5-nano\n",
    "verbosity: true\n",
    "format: markdown\n",
    "\"\"\"\n"
@@ -184,7 +184,7 @@
    "and market trends. Provide critical insight into legitimacy, potential growth, and associated risks.\n",
    "Use markdown formatting and structure your research clearly.\n",
    "\"\"\",\n",
-    "    llm=\"gpt-4o\",\n",
+    "    llm=\"gpt-5-nano\",\n",
    "    verbose=True,\n",
    "    markdown=True\n",
    ")\n"
@@ -491,7 +491,7 @@
    "\n",
    "temperature: 0.7\n",
    "max_tokens: 3000\n",
-    "model: \"gpt-4o-mini\"\n",
+    "model: \"gpt-5-nano\"\n",
    "\"\"\"\n",
    "\n",
    "print(\"✅ YAML Prompt configured!\")"
@@ -627,7 +627,7 @@
    "            )\n",
    "        else:  # OpenAI\n",
    "            llm = ChatOpenAI(\n",
-    "                model=\"gpt-4o\",\n",
+    "                model=\"gpt-5-nano\",\n",
    "                api_key=api_key,\n",
    "                temperature=0.0\n",
    "            )\n",
@@ -120,7 +120,7 @@
        "\n",
        "    def run(self, message: str):\n",
        "        response = client.chat.completions.create(\n",
-        "            model=\"gpt-4o-mini\",\n",
+        "            model=\"gpt-5-nano\",\n",
        "            messages=[\n",
        "                {\"role\": \"system\", \"content\": self.prompt},\n",
        "                {\"role\": \"user\", \"content\": message}\n",
@@ -153,7 +153,7 @@
    "  - Spot content patterns and trends\n",
    "  - Recommend growth strategies\n",
    "  - Detect potential monetization methods\n",
-    "llm: gpt-4o\n",
+    "llm: gpt-5-nano\n",
    "verbosity: true\n",
    "format: markdown\n",
    "\"\"\"\n"
@@ -184,7 +184,7 @@
    "You are a YouTube intelligence analyst. Your job is to research a specific channel, niche, or influencer topic.\n",
    "Provide growth recommendations, engagement analysis, trending strategies, and monetization tips. Format in markdown.\n",
    "\"\"\",\n",
-    "    llm=\"gpt-4o\",\n",
+    "    llm=\"gpt-5-nano\",\n",
    "    verbose=True,\n",
    "    markdown=True\n",
    ")\n"
@@ -136,7 +136,7 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "{'name': 'Travel Research Agent', 'description': 'A world-class travel researcher that searches for destinations, activities, and accommodations', 'instructions': ['Given a travel destination and number of days, generate 3 relevant search terms', 'Search the web for each term using web_search tool', 'Analyze results and return 10 most relevant findings', 'Focus on activities, accommodations, attractions, and local insights', 'Ensure high-quality, accurate information', 'Use travel_budget_calculator to estimate costs'], 'tools': ['web_search', 'travel_budget_calculator'], 'model': 'gpt-4o', 'temperature': 0.7}\n"
+      "{'name': 'Travel Research Agent', 'description': 'A world-class travel researcher that searches for destinations, activities, and accommodations', 'instructions': ['Given a travel destination and number of days, generate 3 relevant search terms', 'Search the web for each term using web_search tool', 'Analyze results and return 10 most relevant findings', 'Focus on activities, accommodations, attractions, and local insights', 'Ensure high-quality, accurate information', 'Use travel_budget_calculator to estimate costs'], 'tools': ['web_search', 'travel_budget_calculator'], 'model': 'gpt-5-nano', 'temperature': 0.7}\n"
     ]
    }
   ],
@@ -156,7 +156,7 @@
    "tools:\n",
    "  - web_search\n",
    "  - travel_budget_calculator\n",
-    "model: gpt-4o\n",
+    "model: gpt-5-nano\n",
    "temperature: 0.7\n",
    "\"\"\"\n",
    "\n",
@@ -17,7 +17,7 @@
    "id": "Y9FkDzt1lF47"
   },
   "source": [
-    "This notebook implements a conversational restaurant booking assistant using the Praison AI Agent framework. It handles natural interruptions such as modifying guest count or changing time mid-conversation. Built for Google Colab Free Tier, it uses OpenAI's GPT-4o model and follows a YAML-based agent design for clarity and modularity."
+    "This notebook implements a conversational restaurant booking assistant using the Praison AI Agent framework. It handles natural interruptions such as modifying guest count or changing time mid-conversation. Built for Google Colab Free Tier, it uses OpenAI's gpt-5-nano model and follows a YAML-based agent design for clarity and modularity."
   ]
  },
  {
@@ -135,7 +135,7 @@
    "  - Context management\n",
    "  - Handling mid-conversation changes\n",
    "  - Summarizing confirmed reservations\n",
-    "llm: gpt-4o\n",
+    "llm: gpt-5-nano\n",
    "verbosity: true\n",
    "format: markdown\n",
    "\"\"\"\n"
@@ -167,7 +167,7 @@
    "manage corrections like “change to 3 guests” or “move to 8 PM,”\n",
    "confirm the details once the user is satisfied. Respond in markdown.\n",
    "\"\"\",\n",
-    "    llm=\"gpt-4o\",\n",
+    "    llm=\"gpt-5-nano\",\n",
    "    verbose=True,\n",
    "    markdown=True\n",
    ")\n"
@@ -585,7 +585,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -1090,7 +1090,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -2077,7 +2077,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -151,7 +151,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -533,7 +533,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -235,7 +235,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -1250,7 +1250,7 @@
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
        "os.environ[\"TAVILY_API_KEY\"] = userdata.get('TAVILY_API_KEY') or \"ENTER TAVILY_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -1559,7 +1559,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -2159,7 +2159,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -698,7 +698,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -592,7 +592,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -572,7 +572,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -580,7 +580,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -252,7 +252,7 @@
     "text": [
      "\u001b[95m \n",
      "\n",
-      "[{'title': 'CrewAI uses third-party models to automate business tasks', 'href': 'https://techcrunch.com/2024/10/22/crewai-uses-third-party-models-to-automate-business-tasks/', 'body': 'CrewAI, a startup developing a hub where companies can build automations using third-party AI models, has raised $18 million from investors including Andrew Ng. ... 4:00 AM PDT · October 22, 2024 ...'}, {'title': 'CrewAI Launches Multi-Agentic Platform to Deliver on the Promise of ...', 'href': 'https://markets.businessinsider.com/news/stocks/crewai-launches-multi-agentic-platform-to-deliver-on-the-promise-of-generative-ai-for-enterprise-1033870246?op=1', 'body': 'SAN FRANCISCO and SÃO PAULO, Oct. 22, 2024 (GLOBE NEWSWIRE) -- CrewAI, a leading multi-agent platform, today announced $18 million in total funding that included an inception round led by ...'}, {'title': 'Agentic AI startup CrewAI closes $18M funding round', 'href': 'https://siliconangle.com/2024/10/22/agentic-ai-startup-crewai-closes-18m-funding-round/', 'body': \"An e-commerce company, for example, could create an agent that uses GPT-4o to generate shopping advice based on customers' past purchases. CrewAI has created an open-source framework of the same ...\"}, {'title': 'CrewAI Company Profile 2024: Valuation, Funding & Investors - PitchBook', 'href': 'https://pitchbook.com/profiles/company/590845-78', 'body': \"Explore institutional-grade private market research from our team of analysts. ... Artificial Intelligence & Machine Learning; Artificial Intelligence & Machine Learning Report. September 19, 2024. CrewAI Executive Team (2) Update this profile Name ... CrewAI's primary industry is Business/Productivity Software. Is CrewAI a private or public ...\"}, {'title': 'CrewAI - Autonomous AI Agents in action for a market ... - LinkedIn', 'href': 'https://www.linkedin.com/pulse/crewai-autonomous-ai-agents-action-market-research-task-kithion-ovnrc', 'body': 'The will provide a detailed market trends report, the will generate a comprehensive analysis of top competitors, and the will develop a thorough marketing plan. Conclusion. CrewAI, combined with ...'}]\n",
+      "[{'title': 'CrewAI uses third-party models to automate business tasks', 'href': 'https://techcrunch.com/2024/10/22/crewai-uses-third-party-models-to-automate-business-tasks/', 'body': 'CrewAI, a startup developing a hub where companies can build automations using third-party AI models, has raised $18 million from investors including Andrew Ng. ... 4:00 AM PDT · October 22, 2024 ...'}, {'title': 'CrewAI Launches Multi-Agentic Platform to Deliver on the Promise of ...', 'href': 'https://markets.businessinsider.com/news/stocks/crewai-launches-multi-agentic-platform-to-deliver-on-the-promise-of-generative-ai-for-enterprise-1033870246?op=1', 'body': 'SAN FRANCISCO and SÃO PAULO, Oct. 22, 2024 (GLOBE NEWSWIRE) -- CrewAI, a leading multi-agent platform, today announced $18 million in total funding that included an inception round led by ...'}, {'title': 'Agentic AI startup CrewAI closes $18M funding round', 'href': 'https://siliconangle.com/2024/10/22/agentic-ai-startup-crewai-closes-18m-funding-round/', 'body': \"An e-commerce company, for example, could create an agent that uses gpt-5-nano to generate shopping advice based on customers' past purchases. CrewAI has created an open-source framework of the same ...\"}, {'title': 'CrewAI Company Profile 2024: Valuation, Funding & Investors - PitchBook', 'href': 'https://pitchbook.com/profiles/company/590845-78', 'body': \"Explore institutional-grade private market research from our team of analysts. ... Artificial Intelligence & Machine Learning; Artificial Intelligence & Machine Learning Report. September 19, 2024. CrewAI Executive Team (2) Update this profile Name ... CrewAI's primary industry is Business/Productivity Software. Is CrewAI a private or public ...\"}, {'title': 'CrewAI - Autonomous AI Agents in action for a market ... - LinkedIn', 'href': 'https://www.linkedin.com/pulse/crewai-autonomous-ai-agents-action-market-research-task-kithion-ovnrc', 'body': 'The will provide a detailed market trends report, the will generate a comprehensive analysis of top competitors, and the will develop a thorough marketing plan. Conclusion. CrewAI, combined with ...'}]\n",
      "\u001b[00m\n",
      "\u001b[32;1m\u001b[1;3mThought: I have found more information about CrewAI's target customers and market positioning. CrewAI is a startup that has developed a hub where companies can build automations using third-party AI models, and it has recently raised $18 million in funding. Their target customers are likely businesses looking to automate complex tasks and improve efficiency using AI. They are positioned as a platform for deploying and managing AI agents to automate a variety of tasks.\n",
      "\n",
@@ -1075,7 +1075,7 @@
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
        "os.environ[\"EXA_API_KEY\"] = userdata.get('EXA_API_KEY') or \"ENTER EXA_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -667,7 +667,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -1947,7 +1947,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -73,7 +73,7 @@
    "            url=url,\n",
    "            word_count_threshold=1,\n",
    "            extraction_strategy= LLMExtractionStrategy(\n",
-    "                provider=\"openai/gpt-4o\",\n",
+    "                provider=\"openai/gpt-5-nano\",\n",
    "                api_token=os.getenv('OPENAI_API_KEY'),\n",
    "                schema=ModelFee.schema(),\n",
    "                extraction_type=\"schema\",\n",
@@ -666,7 +666,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -475,7 +475,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -2130,7 +2130,7 @@
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
    "os.environ[\"SERPER_API_KEY\"] = userdata.get('SERPER_API_KEY') or \"ENTER SERPER_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -635,7 +635,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -564,7 +564,7 @@
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
    "os.environ[\"TAVILY_API_KEY\"] = userdata.get('TAVILY_API_KEY') or \"ENTER TAVILY_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -564,7 +564,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -614,7 +614,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -66,7 +66,7 @@
    "\n",
    "# Set up OpenAI API key (add to Google Colab secrets)\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\""
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\""
   ]
  },
  {
@@ -22,7 +22,7 @@ roles:
    verbose: true
    allow_delegation: false
    llm:
-      model: "gpt-4o-mini"
+      model: "gpt-5-nano"
      temperature: 0.3
    tools:
      - "InternetSearchTool"
@@ -76,7 +76,7 @@ roles:
    verbose: true
    allow_delegation: false
    llm:
-      model: "gpt-4o-mini" 
+      model: "gpt-5-nano" 
      temperature: 0.3
    tools:
      - "InternetSearchTool"
@@ -130,7 +130,7 @@ roles:
    verbose: true
    allow_delegation: false
    llm:
-      model: "gpt-4o-mini"
+      model: "gpt-5-nano"
      temperature: 0.3
    tools:
      - "InternetSearchTool"
@@ -185,7 +185,7 @@ roles:
    verbose: true
    allow_delegation: false
    llm:
-      model: "gpt-4o-mini"
+      model: "gpt-5-nano"
      temperature: 0.4
    tools:
      - "InternetSearchTool"
@@ -239,7 +239,7 @@ roles:
    verbose: true
    allow_delegation: false
    llm:
-      model: "gpt-4o-mini"
+      model: "gpt-5-nano"
      temperature: 0.3
    tools:
      - "InternetSearchTool"
@@ -299,7 +299,7 @@ roles:
    verbose: true
    allow_delegation: false
    llm:
-      model: "gpt-4o-mini"
+      model: "gpt-5-nano"
      temperature: 0.3
    tasks:
      report_synthesis:
@@ -462,7 +462,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -380,7 +380,7 @@
        "\n",
        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
        "\n",
        "# Run PraisonAI\n",
        "result = praisonai.run()\n",
@@ -540,7 +540,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -2639,7 +2639,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -1835,7 +1835,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -623,7 +623,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -635,7 +635,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -582,7 +582,7 @@
    "\n",
    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
    "\n",
    "# Run PraisonAI\n",
    "result = praisonai.run()\n",
@@ -10,7 +10,7 @@ PraisonAI now supports intelligent multi-provider and multi-model capabilities,
 - Supports fallback mechanisms for reliability

 ### 2. **Cost Optimization**
- Routes simple tasks to cheaper models (e.g., GPT-4o-mini, Gemini Flash)
+- Routes simple tasks to cheaper models (e.g., gpt-5-nano, Gemini Flash)
 - Reserves expensive models for complex tasks
 - Tracks usage and provides cost estimates

@@ -46,7 +46,7 @@ agent = RouterAgent(
    name="Smart Assistant",
    role="Adaptive AI Assistant",
    goal="Complete tasks using the most appropriate model",
-    models=["gpt-4o-mini", "gpt-4o", "claude-3-5-sonnet-20241022"],
+    models=["gpt-5-nano", "gpt-5-nano", "claude-3-5-sonnet-20241022"],
    routing_strategy="auto"  # Automatic model selection
 )

@@ -90,7 +90,7 @@ analyzer = RouterAgent(
    goal="Analyze data efficiently",
    models={
        "gemini/gemini-1.5-flash": {},
-        "gpt-4o-mini": {},
+        "gpt-5-nano": {},
        "claude-3-haiku-20240307": {}
    },
    model_router=router,
@@ -130,10 +130,10 @@ The system includes pre-configured profiles for popular models:

 | Model | Provider | Best For | Cost/1k tokens |
 |-------|----------|----------|----------------|
-| gpt-4o-mini | OpenAI | Simple tasks, speed | $0.00075 |
+| gpt-5-nano | OpenAI | Simple tasks, speed | $0.00075 |
 | gemini-1.5-flash | Google | Cost-effective, multimodal | $0.000125 |
 | claude-3-haiku | Anthropic | Fast responses | $0.0008 |
-| gpt-4o | OpenAI | General purpose | $0.0075 |
+| gpt-5-nano | OpenAI | General purpose | $0.0075 |
 | claude-3-5-sonnet-20241022 | Anthropic | Complex reasoning | $0.009 |
 | deepseek-chat | DeepSeek | Code & math | $0.0014 |

@@ -160,8 +160,8 @@ print(report)
    'agent_name': 'Smart Assistant',
    'routing_strategy': 'auto',
    'model_usage': {
-        'gpt-4o-mini': {'calls': 5, 'tokens': 1500, 'cost': 0.0011},
-        'gpt-4o': {'calls': 2, 'tokens': 3000, 'cost': 0.0225}
+        'gpt-5-nano': {'calls': 5, 'tokens': 1500, 'cost': 0.0011},
+        'gpt-5-nano': {'calls': 2, 'tokens': 3000, 'cost': 0.0225}
    },
    'total_cost_estimate': 0.0236,
    'total_calls': 7
@@ -207,7 +207,7 @@ for i, agent in enumerate(auto.agents):
        name=agent.name,
        role=agent.role,
        goal=agent.goal,
-        models=["gpt-4o-mini", "gpt-4o", "claude-3-5-sonnet"],
+        models=["gpt-5-nano", "gpt-5-nano", "claude-3-5-sonnet"],
        routing_strategy="auto"
    )

@@ -31,7 +31,7 @@ def demonstrate_basic_context_generation():
    
    # Create a ContextAgent using the factory function
    context_agent = create_context_agent(
-        llm="gpt-4o-mini",
+        llm="gpt-5-nano",
        name="Basic Context Engineer",
        verbose=True
    )
@@ -133,7 +133,7 @@ def demonstrate_context_agent_as_tool():
    print("=" * 60)
    
    # Create ContextAgent
-    context_agent = create_context_agent(llm="gpt-4o-mini")
+    context_agent = create_context_agent(llm="gpt-5-nano")
    
    # Example scenario: Product planning workflow
    project_path = str(project_root)
@@ -186,7 +186,7 @@ def show_context_engineering_benefits():
    
    # Context Engineering approach
    print("\n✅ Context Engineering Approach:")
-    context_agent = create_context_agent(llm="gpt-4o-mini")
+    context_agent = create_context_agent(llm="gpt-5-nano")
    
    # Generate comprehensive context
    analysis = context_agent.analyze_codebase_patterns(str(project_root))
@@ -13,7 +13,7 @@ image_agent = Agent(
    goal="Analyze images and videos to extract meaningful information",
    backstory="""You are an expert in computer vision and image analysis.
    You excel at describing images, detecting objects, and understanding visual content.""",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
    self_reflect=False
 )

@@ -13,7 +13,7 @@ image_agent = Agent(
    goal="Analyze images and videos to extract meaningful information",
    backstory="""You are an expert in computer vision and image analysis.
    You excel at describing images, detecting objects, and understanding visual content.""",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
    self_reflect=False
 )

@@ -21,7 +21,7 @@ def example_auto_routing():
        role="Adaptive Research Assistant",
        goal="Research topics using the most appropriate AI model",
        backstory="I analyze task complexity and route to the best model",
-        models=["gpt-4o-mini", "gpt-4o", "claude-3-5-sonnet-20241022"],
+        models=["gpt-5-nano", "gpt-5-nano", "claude-3-5-sonnet-20241022"],
        routing_strategy="auto",  # Automatic model selection
        verbose=True
    )
@@ -81,7 +81,7 @@ def example_cost_optimized_workflow():
        goal="Analyze data efficiently while minimizing costs",
        models={
            "gemini/gemini-1.5-flash": {},
-            "gpt-4o-mini": {},
+            "gpt-5-nano": {},
            "deepseek-chat": {}
        },
        model_router=cost_router,
@@ -95,7 +95,7 @@ def example_cost_optimized_workflow():
        goal="Create high-quality content",
        models={
            "claude-3-5-sonnet-20241022": {},
-            "gpt-4o": {}
+            "gpt-5-nano": {}
        },
        routing_strategy="performance-optimized",  # Prefer better models
        verbose=True
@@ -144,7 +144,7 @@ def example_auto_agents_multi_provider():
    auto_agents = AutoAgents(
        instructions="Create a market research report on electric vehicles. Include data analysis, competitor analysis, and future projections.",
        max_agents=3,
-        llm="gpt-4o-mini",  # Default model for agent generation
+        llm="gpt-5-nano",  # Default model for agent generation
        verbose=True
    )
    
@@ -158,7 +158,7 @@ def example_auto_agents_multi_provider():
            goal=agent.goal,
            backstory=agent.backstory,
            tools=agent.tools,
-            models=["gpt-4o-mini", "gemini/gemini-1.5-flash", "claude-3-haiku-20240307", "gpt-4o"],
+            models=["gpt-5-nano", "gemini/gemini-1.5-flash", "claude-3-haiku-20240307", "gpt-5-nano"],
            routing_strategy="auto",
            verbose=True
        )
@@ -192,7 +192,7 @@ def example_custom_routing():
    # Create custom model profiles for specific use cases
    custom_models = [
        ModelProfile(
-            name="gpt-4o",
+            name="gpt-5-nano",
            provider="openai",
            complexity_range=(TaskComplexity.SIMPLE, TaskComplexity.VERY_COMPLEX),
            cost_per_1k_tokens=0.0075,
@@ -18,8 +18,8 @@ from praisonaiagents.tools import duckduckgo

 # Define model options with cost considerations
 # Using different models for different complexity levels
-cheap_model = "gpt-4o-mini"  # For simple tasks
-balanced_model = "gpt-4o"    # For moderate complexity  
+cheap_model = "gpt-5-nano"  # For simple tasks
+balanced_model = "gpt-5-nano"    # For moderate complexity  
 premium_model = "claude-3-5-sonnet-20241022"  # For complex tasks

 # Create RouterAgent with cost optimization strategy
@@ -76,7 +76,7 @@ print("\n" + "="*80)
 print("COST OPTIMIZATION ROUTER DEMONSTRATION COMPLETED")
 print("="*80)
 print("The RouterAgent automatically selected appropriate models based on task complexity:")
-print("- Simple factual questions → Cheaper model (gpt-4o-mini)")  
-print("- Moderate analysis tasks → Balanced model (gpt-4o)")
+print("- Simple factual questions → Cheaper model (gpt-5-nano)")  
+print("- Moderate analysis tasks → Balanced model (gpt-5-nano)")
 print("- Complex strategic analysis → Premium model (claude-3-5-sonnet)")
 print("- This approach optimizes costs while maintaining quality for each task type")
@@ -7,7 +7,7 @@ video_agent = Agent(
    goal="Analyze images and videos to extract meaningful information",
    backstory="""You are an expert in computer vision and image analysis.
    You excel at describing images, detecting objects, and understanding visual content.""",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
    self_reflect=False
 )

@@ -2,7 +2,7 @@ from praisonaiagents import Agent, MCP

 qa_agent = Agent(
    instructions="""You are a Question Answering Agent.""",
-    llm="openai/gpt-4o-mini",
+    llm="openai/gpt-5-nano",
    tools=MCP("http://localhost:8080/agents/sse")
 )

@@ -2,17 +2,17 @@ from praisonaiagents import Agent

 weather_agent = Agent(
    instructions="""You are a weather agent that can provide weather information for a given city.""",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )

 stock_agent = Agent(
    instructions="""You are a stock market agent that can provide information about stock prices and market trends.""",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )

 travel_agent = Agent(
    instructions="""You are a travel agent that can provide recommendations for destinations, hotels, and activities.""",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )

 weather_agent.launch(path="/weather", port=3030)
@@ -2,7 +2,7 @@ from praisonaiagents import Agent, MCP

 search_agent = Agent(
    instructions="""You are a weather agent that can provide weather information for a given city.""",
-    llm="openai/gpt-4o-mini",
+    llm="openai/gpt-5-nano",
    tools=MCP("http://localhost:8080/sse")
 )
 search_agent.launch(path="/weather", port=3030)
@@ -1,4 +1,4 @@
 from praisonaiagents import Agent

-agent = Agent(instructions="""You are a helpful assistant.""", llm="gpt-4o-mini")
+agent = Agent(instructions="""You are a helpful assistant.""", llm="gpt-5-nano")
 agent.launch(path="/ask", port=3030)
@@ -28,7 +28,7 @@ def capture_and_analyze():
        role="Camera Feed Analyzer",
        goal="Analyze camera captures in real-time",
        backstory="Expert in real-time visual analysis with computer vision skills",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    # Initialize camera (0 for default camera)
@@ -42,7 +42,7 @@ class ContinuousCameraMonitor:
            role="Security Camera Analyst",
            goal="Monitor camera feeds for activities and security events",
            backstory="Expert security analyst with computer vision skills specialized in real-time monitoring",
-            llm="gpt-4o-mini"
+            llm="gpt-5-nano"
        )
    
    def capture_frame(self, cap):
@@ -57,7 +57,7 @@ def analyze_with_multiple_agents():
        role="Security Specialist",
        goal="Identify security threats and suspicious activities",
        backstory="Expert security analyst with years of experience in surveillance and threat detection",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    object_detector = Agent(
@@ -65,7 +65,7 @@ def analyze_with_multiple_agents():
        role="Object Recognition Specialist", 
        goal="Identify and catalog all objects in the scene",
        backstory="Computer vision expert specializing in object detection and classification",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    scene_analyst = Agent(
@@ -73,7 +73,7 @@ def analyze_with_multiple_agents():
        role="Scene Understanding Expert",
        goal="Provide comprehensive scene analysis and context",
        backstory="Environmental analyst expert in understanding spatial relationships and scene context",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    # Create specialized tasks
@@ -39,7 +39,7 @@ class CameraVideoAnalyzer:
            role="Video Content Analyzer",
            goal="Analyze video content for activities, objects, and events",
            backstory="Expert in video analysis and temporal event detection with computer vision expertise",
-            llm="gpt-4o-mini"
+            llm="gpt-5-nano"
        )
    
    def record_video_segment(self, duration_seconds=10):
@@ -34,7 +34,7 @@ class ContextEngineeringWorkflow:
    to achieve higher success rates in AI-driven development tasks.
    """
    
-    def __init__(self, project_path: str, llm: str = "gpt-4o-mini"):
+    def __init__(self, project_path: str, llm: str = "gpt-5-nano"):
        self.project_path = project_path
        self.llm = llm
        self.context_data = {}
@@ -382,7 +382,7 @@ def run_example_workflow():
    project_path = str(project_root)
    workflow = ContextEngineeringWorkflow(
        project_path=project_path,
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    # Example feature request
@@ -446,7 +446,7 @@ if __name__ == "__main__":
    print("(Note: Full execution requires complete environment setup)")
    
    project_path = str(project_root)
-    workflow = ContextEngineeringWorkflow(project_path, "gpt-4o-mini")
+    workflow = ContextEngineeringWorkflow(project_path, "gpt-5-nano")
    
    print(f"\n✅ Workflow Setup Complete:")
    print(f"   • Project path: {project_path}")
@@ -68,7 +68,7 @@ def method_1_simple_loop():
        goal="Process each task from the CSV file efficiently",
        backstory="Expert at handling repetitive tasks from CSV data",
        instructions="Process each task thoroughly and provide detailed output",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    # Create a loop task that will automatically process each CSV row
@@ -127,7 +127,7 @@ def method_2_manual_csv():
        4. Create a summary of the processing results
        5. Save results to a new CSV file if needed
        """,
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    # Create a task for manual CSV processing
@@ -187,7 +187,7 @@ def method_3_url_processing():
        4. Create a detailed analysis report
        5. Save results with analysis data
        """,
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    # Create the URL processing task
@@ -264,7 +264,7 @@ def method_4_advanced_processing():
        4. Create separate files for valid and invalid data
        5. Provide detailed validation reports
        """,
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    # Create a processing agent
@@ -281,7 +281,7 @@ def method_4_advanced_processing():
        3. Generate comprehensive results
        4. Create detailed output reports
        """,
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    # Create validation task
@@ -140,7 +140,7 @@ def main():
        instructions="You are an AI research assistant. Use your knowledge base to answer questions about artificial intelligence topics.",
        knowledge=sample_documents,  # Will use default knowledge config
        knowledge_config=rerank_config,  # Use reranking config
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    print("7. Agent with reranking knowledge:")
@@ -16,7 +16,7 @@ rag_agent = Agent(
    name="RAG Agent",
    role="Information Specialist",
    goal="Retrieve knowledge efficiently",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )

 # Define a task for the agent
@@ -2,7 +2,7 @@ from praisonaiagents import Agent, Task, PraisonAIAgents

 agent = Agent(
    instructions="You are a loop agent that creating a loop of tasks.",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )

 task = Task(
@@ -20,7 +20,7 @@ reflection_agent = Agent(
    self_reflect=True,
    min_reflect=2,
    max_reflect=4,
-    reflect_llm="gpt-4o-mini",
+    reflect_llm="gpt-5-nano",
    verbose=True
 )

@@ -46,7 +46,7 @@ def main():
        goal="Analyze each URL from the CSV list",
        backstory="Expert at analyzing websites and URLs",
        instructions="Analyze each URL and provide insights about the website",
-        llm="gpt-4o-mini"  # You can change this to any supported model
+        llm="gpt-5-nano"  # You can change this to any supported model
    )
    
    # Step 3: Create a task that will loop through the CSV
@@ -27,7 +27,7 @@ def example_with_tool():
        role="Information Researcher",
        goal="Research and provide information",
        tools=[latency_tracking_tool],  # Add our custom tool
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    # Start tracking manually
@@ -60,7 +60,7 @@ def example_with_wrappers():
        name="Analyst",
        role="Data Analyst",
        goal="Analyze data and provide insights",
-        llm="gpt-4o-mini",
+        llm="gpt-5-nano",
        request_id="request_2"  # Unique request ID
    )
    
@@ -83,7 +83,7 @@ def example_with_context_manager():
        name="Calculator",
        role="Math Expert",
        goal="Solve mathematical problems",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    request_id = "request_3"
@@ -126,7 +126,7 @@ def example_mcp_tracking():
                name="Assistant",
                role="MCP Assistant",
                goal="Handle MCP requests",
-                llm="gpt-4o-mini"
+                llm="gpt-5-nano"
            )
            plan = agent.chat("Plan: Search for Python documentation")
        
@@ -166,7 +166,7 @@ def example_concurrent_tracking():
                name=f"Agent_{i}",
                role="Concurrent Worker",
                goal="Process requests concurrently",
-                llm="gpt-4o-mini"
+                llm="gpt-5-nano"
            )
            
            with tracker.track("processing", request_id):
@@ -56,7 +56,7 @@ def create_mcp_agent_with_tracking(request_id: str = "mcp_request"):
        name="MCPAssistant",
        role="MCP Request Handler",
        goal="Handle MCP requests with latency tracking",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    return agent
@@ -23,7 +23,7 @@ def handle_mcp_request(query: str, request_id: str = "mcp_1"):
        name="Assistant",
        role="MCP Handler",
        goal="Process requests",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    plan = f"I will search for information about {query}"
    time.sleep(0.1)  # Simulate planning time
@@ -29,7 +29,7 @@ qa_generator = Agent(
    role="Question Creator",
    goal="Create challenging math and logic questions",
    backstory="Expert in educational content creation",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
    tools=[write_csv, count_questions]
 )

@@ -38,7 +38,7 @@ total_questions_evaluator = Agent(
    role="Total Questions Evaluator",
    goal="Evaluate the total number of questions in qa_pairs.csv file",
    backstory="Expert in evaluating the total number of questions in a file",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
    tools=[count_questions],
    verbose=False
 )
@@ -49,7 +49,7 @@ cot_generator = Agent(
    goal="Generate and manage chain of thought solutions for Q&A pairs",
    backstory="Expert in breaking down problems and generating detailed solution steps",
    tools=[cot_save],
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
    verbose=False
 )

@@ -59,7 +59,7 @@ upload_to_huggingface = Agent(
    goal="Upload the generated chain of thought solutions to a Huggingface dataset",
    backstory="Expert in saving data to Huggingface",
    tools=[cot_upload_to_huggingface],
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
    verbose=False
 )

@@ -23,13 +23,13 @@ def code_interpreter(code: str):

 code_agent = Agent(
    name="code_agent",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
    backstory="Expert in writing Python scripts",
    self_reflect=False
 )
 execution_agent = Agent(
    name="execution_agent",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
    backstory="Expert in executing Python scripts",
    self_reflect=False,
    tools=[code_interpreter]
@@ -127,9 +127,9 @@ def main():
        verbose=True,
        allow_delegation=False,
        tools=[internet_search_tool],
-        llm="gpt-4o",
+        llm="gpt-5-nano",
        markdown=True,
-        reflect_llm="gpt-4o",
+        reflect_llm="gpt-5-nano",
        min_reflect=2,
        max_reflect=4
    )
@@ -142,7 +142,7 @@ def main():
        making complex tech topics interesting and easy to understand.""",
        verbose=True,
        allow_delegation=True,
-        llm="gpt-4o",
+        llm="gpt-5-nano",
        tools=[],
        markdown=True
    )
@@ -195,7 +195,7 @@ def main():
        tasks=[task1, task2, task3, task4],
        verbose=True,
        process="sequential",
-        manager_llm="gpt-4o"
+        manager_llm="gpt-5-nano"
    )

    agents.start()
@@ -39,9 +39,9 @@ researcher = Agent(
    verbose=True,
    allow_delegation=False,
    tools=[internet_search_tool],
-    llm="gpt-4o",
+    llm="gpt-5-nano",
    markdown=True,
-    reflect_llm="gpt-4o",
+    reflect_llm="gpt-5-nano",
    min_reflect=2,
    max_reflect=4
 )
@@ -53,7 +53,7 @@ writer = Agent(
    making complex tech topics interesting and easy to understand.""",
    verbose=True,
    allow_delegation=True,
-    llm="gpt-4o",
+    llm="gpt-5-nano",
    tools=[],
    markdown=True
 )
@@ -104,7 +104,7 @@ agents = PraisonAIAgents(
    tasks=[task1, task2, task3, task4],
    verbose=False,
    process="sequential",  # "sequential" or "hierarchical"
-    manager_llm="gpt-4o"
+    manager_llm="gpt-5-nano"
 )

 result = agents.start()
@@ -33,14 +33,14 @@ def main():
        role="Research Analyst",
        goal="Research and document key information about topics",
        backstory="Expert at analyzing and storing information in memory",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    retriever = Agent(
        role="Information Retriever",
        goal="Retrieve and verify stored information from memory",
        backstory="Specialist in searching and validating information from memory",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )

    # Task 1: Process the facts
@@ -9,19 +9,19 @@ fact3 = "The year the first Josinga was released is 2007"
 fact_agent = Agent(
    name="Fact Agent",
    instructions="You are a fact agent, you store and retrieve facts in memory",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )

 research_agent = Agent(
    name="Research Agent",
    instructions="You are a research analyst, you research and document key points about topics",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )

 blog_agent = Agent(
    name="Blog Agent",
    instructions="You are a blog writer, you write a blog post about the research",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )

 fact_task = Task(
@@ -7,7 +7,7 @@ vision_agent = Agent(
    goal="Analyze images and videos to extract meaningful information",
    backstory="""You are an expert in computer vision and image analysis.
    You excel at describing images, detecting objects, and understanding visual content.""",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
    self_reflect=False
 )

@@ -13,7 +13,7 @@ researcher = Agent(
    goal="Analyze and structure information about AI developments",
    backstory="Expert analyst specializing in AI technology trends",
    verbose=True,
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
    tools=[Tools.internet_search],
    self_reflect=False
 )
@@ -25,7 +25,7 @@ analyst = Agent(
    goal="Structure and analyze research findings",
    backstory="Senior data analyst with expertise in pattern recognition",
    verbose=True,
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
    self_reflect=False
 )

@@ -35,7 +35,7 @@ def get_structured_analysis(query: str, verbose: bool = True) -> AnalysisReport:
        verbose=verbose,
        self_reflect=True,  # Enable self-reflection for better quality
        markdown=True,
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
    )
    
    # Create the analysis prompt
@@ -16,7 +16,7 @@ class DynamicAgentSystem:
    def __init__(self):
        self.user_preferences = {}
        self.llm_config = {
-            "model": os.getenv("LLM_MODEL", "gpt-4o-mini"),
+            "model": os.getenv("LLM_MODEL", "gpt-5-nano"),
            "temperature": 0.7
        }
    
@@ -13,7 +13,7 @@ default_topic = os.getenv("RESEARCH_TOPIC", "AI trends")
 user_topic = input(f"Topic to research [{default_topic}]: ") or default_topic

 # Get other configuration from environment
-llm_model = os.getenv("LLM_MODEL", "gpt-4o-mini")
+llm_model = os.getenv("LLM_MODEL", "gpt-5-nano")
 temperature = float(os.getenv("LLM_TEMPERATURE", "0.7"))
 max_retries = int(os.getenv("MAX_RETRIES", "3"))
Author	SHA1	Message	Date
Mervin Praison	9e178b70e6	chore: bump PraisonAI to v2.2.88 and update dependencies across Dockerfiles and project files	2025-08-08 19:24:43 +01:00
Mervin Praison	ed406973cd	refactor: optimise ChatOpenAI instantiation by using a dictionary for parameters and enforce temperature for gpt-5 models	2025-08-08 19:17:45 +01:00
Mervin Praison	f91918f4f6	chore: bump PraisonAI to v2.2.87, PraisonAI Agents to v0.0.161 and increase default temperature to 1.0	2025-08-08 13:44:29 +01:00
Mervin Praison	ee21df9a6a	chore: bump praisonai to v2.2.86 and praisonaiagents to v0.0.160	2025-08-08 12:18:30 +01:00
Mervin Praison	c9159337fa	refactor: replace all instances of gpt-5-mini with gpt-5-nano across codebase	2025-08-08 12:17:18 +01:00
Mervin Praison	2258685c32	chore: bump PraisonAI to v2.2.85 and PraisonAI Agents to v0.0.159	2025-08-08 08:20:25 +01:00
Mervin Praison	7210788135	refactor: replace gpt-4o model references with gpt-5-mini across codebase	2025-08-08 08:17:23 +01:00
Mervin Praison	3f58e3b1ad	chore: bump PraisonAI to v2.2.84 and PraisonAI Agents to v0.0.158 across all files	2025-08-08 07:54:22 +01:00
Mervin Praison	561c32ddd5	refactor: update model name from gpt-4o-mini to gpt-5-nano across all files	2025-08-08 07:52:37 +01:00
Mervin Praison	79d934eec4	feat: bump praisonai to 2.2.83 and add xml-toolcall-agent implementation	2025-08-05 07:44:47 +01:00
Mervin Praison	144c7edc4a	Merge pull request #1079 from MervinPraison/claude/issue-1077-20250805-0109 feat: make XML tool call parsing dynamic and configurable	2025-08-05 02:31:08 +01:00
Mervin Praison	4425e2b743	Merge branch 'main' of https://github.com/MervinPraison/PraisonAI	2025-08-05 02:28:00 +01:00
Mervin Praison	eb8dfc02cd	feat: optimize OpenAIClient with lazy loading and tool caching	2025-08-05 02:27:59 +01:00
github-actions[bot]andMervin Praison	27b6193b90	fix: improve XML tool call parsing robustness and type handling - Fix type inconsistency in xml_tool_format handling (now supports True/''true''/''True'') - Simplify _is_qwen_provider() by removing redundant logic - Add proper XML parsing with ElementTree as primary method - Improve regex fallback pattern for nested JSON in XML tags - Add graceful error handling for malformed XML - Import xml.etree.ElementTree at module level for better performance Co-authored-by: Mervin Praison <[email protected]>	2025-08-05 01:26:48 +00:00
github-actions[bot]andMervin Praison	b0aa9dfe58	feat: make XML tool call parsing dynamic and configurable - Add xml_tool_format configuration parameter (auto/true/false) - Replace static Qwen detection with dynamic _supports_xml_tool_format() method - Add fallback XML detection for any model outputting <tool_call> tags - Maintain backward compatibility with existing Qwen auto-detection - Support manual XML format enabling for any model Fixes #1077 Co-authored-by: Mervin Praison <[email protected]>	2025-08-05 01:13:22 +00:00
Mervin Praison	89d4f52327	Merge pull request #1075 from MervinPraison/claude/issue-1074-20250731-1148 fix: Fix RAG integration tests to respect lazy loading pattern	2025-07-31 13:05:47 +01:00
github-actions[bot]andMervin Praison	da545f2e49	fix: Fix RAG integration tests to respect lazy loading pattern - Fixed test_agent_with_knowledge_config and test_ollama_rag_integration - Tests now properly check for lazy loading behavior instead of expecting immediate knowledge availability - Verifies knowledge sources are stored and manually triggers processing before checking knowledge availability - Preserves performance optimization of lazy loading while ensuring correct test behavior - All RAG integration tests now pass (11/11) 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Mervin Praison <[email protected]>	2025-07-31 11:53:01 +00:00
Mervin Praison	e794992d7b	feat: optimize LLM performance with caching and lazy loading	2025-07-31 12:39:37 +01:00
Mervin Praison	a958162704	perf: optimize agent performance with lazy loading and caching strategies	2025-07-31 12:19:17 +01:00
Mervin Praison	3b47f7be7f	chore: update MongoDB connection strings from localhost to remote cluster	2025-07-31 07:59:51 +01:00
Mervin Praison	0b8da8b4c4	Merge pull request #1072 from MervinPraison/claude/issue-1071-20250729-0039 fix: resolve MongoDB SearchIndexModel missing definition argument	2025-07-29 08:07:23 +01:00
github-actions[bot]andMervinPraison	092ea67732	fix: apply SearchIndexModel compatibility fix to memory.py and mongodb_tools.py Extends the PyMongo 4.6+ compatibility fix from knowledge.py to other files that also use create_search_index(). Applies the same backward-compatible pattern using SearchIndexModel with graceful fallback for older PyMongo versions. - memory.py: Fix _create_vector_search_indexes() method - mongodb_tools.py: Fix create_vector_index() method Maintains zero performance overhead and full backward compatibility. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: MervinPraison <[email protected]>	2025-07-29 00:54:57 +00:00
github-actions[bot]andMervin Praison	5931ad00be	fix: resolve MongoDB SearchIndexModel missing definition argument - Import SearchIndexModel from pymongo.operations for PyMongo 4.6+ compatibility - Use SearchIndexModel with definition parameter in _create_vector_index() - Add backward compatibility fallback for older PyMongo versions - Maintains zero performance overhead and preserves existing functionality Fixes #1071 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Mervin Praison <[email protected]>	2025-07-29 00:44:36 +00:00
Mervin Praison	b7676cf6c3	update github actions	2025-07-29 01:38:33 +01:00
Mervin Praison	8ec4f5efa2	Merge pull request #1070 from MervinPraison/claude/issue-1069-20250728-2326 fix: resolve MongoDB vector search index creation error (missing embedding_model_name)	2025-07-29 01:33:58 +01:00
github-actions[bot]andMervin Praison	f459c192d6	fix: initialize embedding model before MongoDB to resolve missing embedding_model_name attribute Fixed initialization order in MongoDBMemory class where _create_vector_index() was trying to access self.embedding_model_name before it was initialized. - Moved _init_embedding_model() before _init_mongodb() in __init__ - Added explanatory comment for the dependency order - Resolves AttributeError: ''MongoDBMemory'' object has no attribute ''embedding_model_name'' - Maintains full backward compatibility Fixes #1069 Co-authored-by: Mervin Praison <[email protected]>	2025-07-28 23:31:01 +00:00
Mervin Praison	18b85aada0	chore: bump package versions and reorganize test files	2025-07-28 18:43:53 +01:00
Mervin Praison	a3c77692fd	Merge pull request #1068 from MervinPraison/claude/aiohttp-session-fix-v2 fix: comprehensive aiohttp session cleanup to resolve persistent warnings	2025-07-28 18:31:47 +01:00
github-actions[bot]andMervin Praison	4a06ee3626	perf: eliminate blocking timeout in close() method to restore zero-overhead cleanup - Remove blocking future.result(timeout=2.0) that could block execution - Restore non-blocking behavior with callback-based fallback cleanup - Maintain reliability with async cleanup + force cleanup fallback - Ensure zero performance impact on normal execution paths Co-authored-by: Mervin Praison <[email protected]>	2025-07-26 20:05:52 +00:00
Mervin Praison	776e83100e	Merge pull request #1067 from MervinPraison/claude/fix-token-tracking-metrics-20250726 fix: enable token tracking by passing metrics parameter from Agent to LLM	2025-07-26 21:00:43 +01:00
github-actions[bot]andMervin Praison	f718d89ba4	test: add verification test for performance optimization - Confirms zero method calls when metrics=False - Verifies method still called when metrics=True - Demonstrates performance benefit of optimization Co-authored-by: Mervin Praison <[email protected]>	2025-07-26 19:40:17 +00:00
github-actions[bot]andMervin Praison	3469335d02	perf: optimize token tracking to eliminate overhead when metrics disabled - Move metrics check to call sites instead of inside _track_token_usage() - Eliminates function call overhead when metrics=False (default) - Add performance test to verify zero overhead implementation - Maintains full functionality when metrics=True Performance impact: - metrics=False (default): zero function call overhead - metrics=True: unchanged functionality Co-authored-by: Mervin Praison <[email protected]>	2025-07-26 19:39:46 +00:00
github-actions[bot]andMervin Praison	6caa825706	fix: comprehensive aiohttp session cleanup in MCP HTTP client - Add global client registry with weakref tracking for reliable cleanup - Implement multi-layer cleanup strategy: immediate, force, finalizer, and exit - Add atexit handler to ensure session cleanup on program termination - Implement direct connector closing for emergency cleanup scenarios - Maintain zero performance impact with non-blocking operations - Fix persistent ''Unclosed client session'' warnings in praisonaiagents Co-authored-by: Mervin Praison <[email protected]>	2025-07-26 19:38:03 +00:00
github-actions[bot]andMervin Praison	65a23df7a2	fix: enable token tracking by passing metrics parameter from Agent to LLM Fixes the persistent issue where Agent(metrics=True) showed 0 tokens despite the LLM instance access fix in PR #1064. Root Cause: - Agent.metrics=True was stored but never passed to LLM constructor - LLM class had token tracking capability but no way to enable it - Token collector received no data because LLM didn''t track tokens Solution: - Pass metrics=self.metrics to all 4 LLM constructor calls in agent.py - Add self.metrics property to LLM class from extra_settings - Only track tokens when self.metrics=True in _track_token_usage() Verified: - All LLM creation patterns (string, dict, provider/model) work - Agent(metrics=True) properly enables LLM token tracking - Zero performance impact when metrics=False (default) 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Mervin Praison <[email protected]>	2025-07-26 19:25:50 +00:00