chore: update praisonai package version from 2.2.33 to 2.2.34 across Docker and deployment files

Add  Meta Synthetic Data Generator (LLaMA 3.2 - 3B)

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,34 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: '[BUG] '
+labels: bug
+assignees: ''
+---
+
+## Environment
+- Provider (select one):
+  - [ ] Anthropic
+  - [ ] OpenAI
+  - [ ] Google Vertex AI
+  - [ ] AWS Bedrock
+  - [ ] Other: <!-- specify -->
+- PraisonAI version: <!-- if known -->
+- Operating System: <!-- e.g. macOS 14.3, Windows 11, Ubuntu 22.04 -->
+
+## Full Code
+<!-- Full code that reproduces the bug -->
+
+## Steps to Reproduce
+1. <!-- First step -->
+2. <!-- Second step -->
+3. <!-- And so on... -->
+
+## Expected Behavior
+<!-- What you expected to happen -->
+
+## Actual Behavior
+<!-- What actually happened -->
+
+## Additional Context
+<!-- Add any other context about the problem here, such as screenshots, logs, etc. --> 

.github/actions/claude-code-action/action.yml

@@ -0,0 +1,240 @@
+name: "Claude Code Action"
+description: "Run Claude Code in GitHub Actions workflows"
+
+inputs:
+  anthropic_api_key:
+    description: "Anthropic API key"
+    required: false
+  github_token:
+    description: "GitHub token for Claude to operate with"
+    required: false
+    default: ${{ github.token }}
+  trigger_phrase:
+    description: "The trigger phrase to look for in comments, issue/PR bodies, and issue titles"
+    required: false
+    default: "@claude"
+  assignee_trigger:
+    description: "The assignee username that triggers the action (e.g. @claude). Only used for issue assignment"
+    required: false
+  max_turns:
+    description: "Maximum number of conversation turns Claude can take"
+    required: false
+  timeout_minutes:
+    description: "Timeout in minutes for execution"
+    required: false
+    default: "30"
+  model:
+    description: "Model to use (provider-specific format required for Bedrock/Vertex)"
+    required: false
+  use_bedrock:
+    description: "Use Amazon Bedrock with OIDC authentication instead of direct Anthropic API"
+    required: false
+    default: "false"
+  use_vertex:
+    description: "Use Google Vertex AI with OIDC authentication instead of direct Anthropic API"
+    required: false
+    default: "false"
+  allowed_tools:
+    description: "Additional tools for Claude to use (the base GitHub tools will always be included)"
+    required: false
+    default: ""
+  disallowed_tools:
+    description: "Tools that Claude should never use"
+    required: false
+    default: ""
+  custom_instructions:
+    description: "Additional custom instructions to include in the prompt for Claude"
+    required: false
+    default: ""
+  mcp_config:
+    description: "Additional MCP configuration (JSON string) that merges with the built-in GitHub MCP servers"
+    required: false
+    default: ""
+  claude_env:
+    description: "Custom environment variables to pass to Claude Code execution (YAML format)"
+    required: false
+    default: ""
+  direct_prompt:
+    description: "Direct prompt for Claude to execute automatically without needing a trigger (for automated workflows)"
+    required: false
+
+runs:
+  using: "composite"
+  steps:
+    - name: Install Claude Code
+      shell: bash
+      run: npm install -g @anthropic-ai/claude-code
+
+    - name: Install GitHub MCP Server
+      shell: bash
+      run: |
+        claude mcp add-json github '{
+          "command": "docker",
+          "args": [
+            "run",
+            "-i",
+            "--rm",
+            "-e",
+            "GITHUB_PERSONAL_ACCESS_TOKEN",
+            "ghcr.io/github/github-mcp-server:sha-ff3036d"
+          ],
+          "env": {
+            "GITHUB_PERSONAL_ACCESS_TOKEN": "${{ inputs.github_token }}"
+          }
+        }'
+
+    - name: Extract GitHub Context and Create Prompt
+      shell: bash
+      id: prepare_context
+      run: |
+        echo "🔍 Extracting GitHub context from event: ${{ github.event_name }}"
+        
+        # Function to check for trigger phrase
+        check_trigger() {
+          local text="$1"
+          local trigger="${{ inputs.trigger_phrase }}"
+          if [[ "$text" == *"$trigger"* ]]; then
+            return 0
+          fi
+          return 1
+        }
+        
+        # Extract context based on event type
+        TRIGGER_FOUND="false"
+        USER_REQUEST=""
+        CONTEXT_INFO=""
+        
+        case "${{ github.event_name }}" in
+          "issue_comment")
+            COMMENT_BODY="${{ github.event.comment.body }}"
+            ISSUE_TITLE="${{ github.event.issue.title }}"
+            ISSUE_NUMBER="${{ github.event.issue.number }}"
+            
+            if check_trigger "$COMMENT_BODY"; then
+              TRIGGER_FOUND="true"
+              USER_REQUEST="$COMMENT_BODY"
+              CONTEXT_INFO="Issue Comment on #$ISSUE_NUMBER: $ISSUE_TITLE"
+            fi
+            ;;
+            
+          "pull_request_review_comment")
+            COMMENT_BODY="${{ github.event.comment.body }}"
+            PR_TITLE="${{ github.event.pull_request.title }}"
+            PR_NUMBER="${{ github.event.pull_request.number }}"
+            
+            if check_trigger "$COMMENT_BODY"; then
+              TRIGGER_FOUND="true"
+              USER_REQUEST="$COMMENT_BODY"
+              CONTEXT_INFO="PR Comment on #$PR_NUMBER: $PR_TITLE"
+            fi
+            ;;
+            
+          "pull_request_review")
+            REVIEW_BODY="${{ github.event.review.body }}"
+            PR_TITLE="${{ github.event.pull_request.title }}"
+            PR_NUMBER="${{ github.event.pull_request.number }}"
+            
+            if check_trigger "$REVIEW_BODY"; then
+              TRIGGER_FOUND="true"
+              USER_REQUEST="$REVIEW_BODY"
+              CONTEXT_INFO="PR Review on #$PR_NUMBER: $PR_TITLE"
+            fi
+            ;;
+            
+          "issues")
+            ISSUE_BODY="${{ github.event.issue.body }}"
+            ISSUE_TITLE="${{ github.event.issue.title }}"
+            ISSUE_NUMBER="${{ github.event.issue.number }}"
+            
+            if check_trigger "$ISSUE_TITLE" || check_trigger "$ISSUE_BODY"; then
+              TRIGGER_FOUND="true"
+              USER_REQUEST="$ISSUE_BODY"
+              CONTEXT_INFO="Issue #$ISSUE_NUMBER: $ISSUE_TITLE"
+            elif [[ "${{ github.event.action }}" == "assigned" && -n "${{ inputs.assignee_trigger }}" ]]; then
+              ASSIGNEE="${{ github.event.assignee.login }}"
+              if [[ "$ASSIGNEE" == "${{ inputs.assignee_trigger }}" ]]; then
+                TRIGGER_FOUND="true"
+                USER_REQUEST="$ISSUE_BODY"
+                CONTEXT_INFO="Issue #$ISSUE_NUMBER assigned to $ASSIGNEE: $ISSUE_TITLE"
+              fi
+            fi
+            ;;
+        esac
+        
+        # Check for direct prompt override
+        if [[ -n "${{ inputs.direct_prompt }}" ]]; then
+          TRIGGER_FOUND="true"
+          USER_REQUEST="${{ inputs.direct_prompt }}"
+          CONTEXT_INFO="Automated GitHub workflow"
+        fi
+        
+        if [[ "$TRIGGER_FOUND" != "true" ]]; then
+          echo "❌ No trigger phrase found or direct prompt provided. Exiting gracefully."
+          echo "SKIP_EXECUTION=true" >> $GITHUB_ENV
+          exit 0
+        fi
+        
+        echo "✅ Trigger found! Context: $CONTEXT_INFO"
+        
+        # Create comprehensive prompt
+        mkdir -p /tmp/claude-action
+        cat > /tmp/claude-action/github-context-prompt.txt << EOF
+        You are Claude Code, an AI assistant helping with GitHub workflows and code.
+        
+        Repository: ${{ github.repository }}
+        Context: $CONTEXT_INFO
+        Event: ${{ github.event_name }}
+        
+        User Request:
+        $USER_REQUEST
+        
+        Please analyze the request and provide helpful assistance. You have access to repository tools and can help with:
+        - Code analysis and implementation
+        - GitHub workflows and automation
+        - Pull request reviews and feedback
+        - Issue resolution and bug fixes
+        - Documentation updates
+        - Testing and deployment
+        
+        Respond naturally and helpfully to the user's request using the available tools.
+        EOF
+        
+        echo "PROMPT_FILE=/tmp/claude-action/github-context-prompt.txt" >> $GITHUB_ENV
+        echo "SKIP_EXECUTION=false" >> $GITHUB_ENV
+
+    - name: Run Claude Code
+      if: env.SKIP_EXECUTION != 'true'
+      shell: bash
+      run: |
+        echo "🚀 Running Claude Code with GitHub context..."
+        
+        # Build command arguments
+        CMD_ARGS=("-p" "--verbose" "--output-format" "stream-json")
+        
+        # Add max turns if specified
+        if [[ -n "${{ inputs.max_turns }}" ]]; then
+          CMD_ARGS+=("--max-turns" "${{ inputs.max_turns }}")
+        fi
+        
+        # Add allowed tools (include GitHub tools by default)
+        TOOLS="mcp__github__get_issue,mcp__github__get_issue_comments,mcp__github__update_issue,mcp__github__search_issues,mcp__github__list_issues,mcp__github__create_comment,Read,Write,Edit,Bash"
+        if [[ -n "${{ inputs.allowed_tools }}" ]]; then
+          TOOLS="$TOOLS,${{ inputs.allowed_tools }}"
+        fi
+        CMD_ARGS+=("--allowedTools" "$TOOLS")
+        
+        # Add disallowed tools
+        if [[ -n "${{ inputs.disallowed_tools }}" ]]; then
+          CMD_ARGS+=("--disallowedTools" "${{ inputs.disallowed_tools }}")
+        fi
+        
+        echo "📝 Executing Claude Code with prompt from file..."
+        
+        # Execute Claude Code with timeout, using stdin for the prompt
+        TIMEOUT_SECONDS=$((${{ inputs.timeout_minutes }} * 60))
+        timeout $TIMEOUT_SECONDS claude "${CMD_ARGS[@]}" < "${{ env.PROMPT_FILE }}"
+        
+        echo "✅ Claude Code execution completed"
+      env:
+        ANTHROPIC_API_KEY: ${{ inputs.anthropic_api_key }}
+        GITHUB_TOKEN: ${{ inputs.github_token }} 

.github/actions/claude-issue-triage-action/action.yml

@@ -0,0 +1,87 @@
+name: "Claude Issue Triage Action"
+description: "Automatically triage GitHub issues using Claude Code"
+
+inputs:
+  timeout_minutes:
+    description: "Timeout in minutes for execution"
+    required: false
+    default: "5"
+  anthropic_api_key:
+    description: "Anthropic API key"
+    required: true
+  github_token:
+    description: "GitHub token with repo and issues permissions"
+    required: true
+
+runs:
+  using: "composite"
+  steps:
+    - name: Checkout repository code
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+
+    - name: Create prompt file
+      shell: bash
+      run: |
+        mkdir -p /tmp/claude-prompts
+        cat > /tmp/claude-prompts/claude-issue-triage-prompt.txt << 'EOF'
+        You're an issue triage assistant for GitHub issues. Your task is to analyze the issue and select appropriate labels from the provided list.
+
+        IMPORTANT: Don't post any comments or messages to the issue. Your only action should be to apply labels.
+
+        Issue Information:
+        - REPO: ${{ github.repository }}
+        - ISSUE_NUMBER: ${{ github.event.issue.number }}
+
+        TASK OVERVIEW:
+
+        1. First, fetch the list of labels available in this repository by running: `gh label list`. Run exactly this command with nothing else.
+
+        2. Next, use the GitHub tools to get context about the issue:
+           - You have access to these tools:
+             - mcp__github__get_issue: Use this to retrieve the current issue's details including title, description, and existing labels
+             - mcp__github__get_issue_comments: Use this to read any discussion or additional context provided in the comments
+             - mcp__github__update_issue: Use this to apply labels to the issue (do not use this for commenting)
+             - mcp__github__search_issues: Use this to find similar issues that might provide context for proper categorization and to identify potential duplicate issues
+             - mcp__github__list_issues: Use this to understand patterns in how other issues are labeled
+           - Start by using mcp__github__get_issue to get the issue details
+
+        3. Analyze the issue content, considering:
+           - The issue title and description
+           - The type of issue (bug report, feature request, question, etc.)
+           - Technical areas mentioned
+           - Severity or priority indicators
+           - User impact
+           - Components affected
+
+        4. Select appropriate labels from the available labels list provided above:
+           - Choose labels that accurately reflect the issue's nature
+           - Be specific but comprehensive
+           - Select priority labels if you can determine urgency (high-priority, med-priority, or low-priority)
+           - Consider platform labels (android, ios) if applicable
+           - If you find similar issues using mcp__github__search_issues, consider using a "duplicate" label if appropriate. Only do so if the issue is a duplicate of another OPEN issue.
+
+        5. Apply the selected labels:
+           - Use mcp__github__update_issue to apply your selected labels
+           - DO NOT post any comments explaining your decision
+           - DO NOT communicate directly with users
+           - If no labels are clearly applicable, do not apply any labels
+
+        IMPORTANT GUIDELINES:
+        - Be thorough in your analysis
+        - Only select labels from the provided list above
+        - DO NOT post any comments to the issue
+        - Your ONLY action should be to apply labels using mcp__github__update_issue
+        - It's okay to not add any labels if none are clearly applicable
+        EOF
+
+    - name: Run Claude Code
+      uses: ./.github/actions/claude-code-action
+      with:
+        prompt_file: /tmp/claude-prompts/claude-issue-triage-prompt.txt
+        allowed_tools: "Bash(gh label list),mcp__github__get_issue,mcp__github__get_issue_comments,mcp__github__update_issue,mcp__github__search_issues,mcp__github__list_issues"
+        install_github_mcp: "true"
+        timeout_minutes: ${{ inputs.timeout_minutes }}
+        anthropic_api_key: ${{ inputs.anthropic_api_key }}
+        github_token: ${{ inputs.github_token }} 

.github/workflows/build-image.yml

@@ -0,0 +1,40 @@
+name: Build Claude Code Image
+
+on:
+  workflow_dispatch:
+    inputs:
+      version:
+        description: 'Image version tag'
+        required: true
+        default: 'latest'
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GH_TOKEN }}
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v5
+        with:
+          context: ./.github/actions/claude-code-action
+          push: true
+          platforms: linux/amd64
+          tags: ghcr.io/mervinpraison/praisonai-claudecode:${{ inputs.version }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+          provenance: false 

.github/workflows/claude-issue-triage.yml

@@ -0,0 +1,23 @@
+name: Claude Issue Triage
+description: "Automatically triage GitHub issues using Claude Code"
+
+on:
+  issues:
+    types: [opened]
+
+jobs:
+  triage-issue:
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    permissions:
+      contents: read
+      issues: write
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  # v4
+
+      - name: Run Claude Issue Triage
+        uses: ./.github/actions/claude-issue-triage-action
+        with:
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
+          github_token: ${{ secrets.GITHUB_TOKEN }} 

.github/workflows/claude.yml

@@ -33,5 +33,4 @@ jobs:
         id: claude
         uses: anthropics/claude-code-action@beta
         with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}

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>=2.2.32" \
+    "praisonai>=2.2.34" \
     "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>=2.2.32" \
+    "praisonai>=2.2.34" \
     "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>=2.2.32" \
+    "praisonai>=2.2.34" \
     "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>=2.2.32" \
+    "praisonai>=2.2.34" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.32`
+- PraisonAI: `>=2.2.34`
 - 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==2.2.32" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.34" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

examples/cookbooks/DeepSeek_Qwen3_GRPO.ipynb

@@ -0,0 +1,172 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "29d21289",
+      "metadata": {
+        "id": "29d21289"
+      },
+      "source": [
+        "# DeepSeek R1 Qwen3 (8B) - GRPO Agent Demo"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/DeepSeek_Qwen3_GRPO.ipynb)\n"
+      ],
+      "metadata": {
+        "id": "yuOEagMH86WV"
+      },
+      "id": "yuOEagMH86WV"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0f798657",
+      "metadata": {
+        "id": "0f798657"
+      },
+      "source": [
+        "This notebook demonstrates the usage of DeepSeek's Qwen3-8B model with GRPO (Guided Reasoning Prompt Optimization) for interactive conversational reasoning tasks.\n",
+        "It is designed to simulate a lightweight agent-style reasoning capability in an accessible and interpretable way."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "80f3de9e",
+      "metadata": {
+        "id": "80f3de9e"
+      },
+      "source": [
+        "## Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "8d1c7f6c",
+      "metadata": {
+        "id": "8d1c7f6c"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q transformers accelerate"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "78603e7b",
+      "metadata": {
+        "id": "78603e7b"
+      },
+      "source": [
+        "## Tools"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "88e97fbc",
+      "metadata": {
+        "id": "88e97fbc"
+      },
+      "source": [
+        "- `transformers`: For model loading and interaction\n",
+        "- `AutoModelForCausalLM`, `AutoTokenizer`: Interfaces for DeepSeek's LLM"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "37d9bd54",
+      "metadata": {
+        "id": "37d9bd54"
+      },
+      "source": [
+        "## YAML Prompt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "adf5cae5",
+      "metadata": {
+        "id": "adf5cae5"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "prompt:\n",
+        "  task: \"Reasoning over multi-step instructions\"\n",
+        "  context: \"User provides a math problem or logical question.\"\n",
+        "  model: \"deepseek-ai/deepseek-moe-16b-chat\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6985f60c",
+      "metadata": {
+        "id": "6985f60c"
+      },
+      "source": [
+        "## Main"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d74bf686",
+      "metadata": {
+        "id": "d74bf686"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
+        "\n",
+        "model_id = \"deepseek-ai/deepseek-moe-16b-chat\"\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+        "model = AutoModelForCausalLM.from_pretrained(model_id, device_map=\"auto\")\n",
+        "\n",
+        "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n",
+        "\n",
+        "prompt = \"If a train travels 60 miles in 1.5 hours, what is its average speed?\"\n",
+        "output = pipe(prompt, max_new_tokens=60)[0]['generated_text']\n",
+        "print(\"🧠 Reasoned Output:\", output)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c856167f",
+      "metadata": {
+        "id": "c856167f"
+      },
+      "source": [
+        "## Output"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "41039ee8",
+      "metadata": {
+        "id": "41039ee8"
+      },
+      "source": [
+        "### 🖼️ Output Summary\n",
+        "\n",
+        "Prompt: *\"If a train travels 60 miles in 1.5 hours, what is its average speed?\"*\n",
+        "\n",
+        "🧠 Output: The model provides a clear reasoning process, such as:\n",
+        "\n",
+        "> \"To find the average speed, divide the total distance by total time: 60 / 1.5 = 40 mph.\"\n",
+        "\n",
+        "💡 This shows the model's ability to walk through logical steps using GRPO-enhanced reasoning."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

examples/cookbooks/KnowledgeAgent_PraisonAI.ipynb

@@ -0,0 +1,395 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "127dc323",
+   "metadata": {
+    "id": "127dc323"
+   },
+   "source": [
+    "# 📘 KnowledgeAgent - AI-Powered Content Analysis and Summarization Tool"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8290ecb7",
+   "metadata": {
+    "id": "8290ecb7"
+   },
+   "source": [
+    "\n",
+    "This notebook demonstrates how to create an AI-powered **KnowledgeAgent** using the [PraisonAI](https://github.com/MervinPraison/PraisonAI/) framework.  \n",
+    "The agent performs content analysis and summarization tasks using LLMs and a vector database.  \n",
+    "Runs on Google Colab free tier.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "Lxx3NiCrpFNM",
+   "metadata": {
+    "id": "Lxx3NiCrpFNM"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/KnowledgeAgent_PraisonAI.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "N4Vxz84co0Ac",
+   "metadata": {
+    "id": "N4Vxz84co0Ac"
+   },
+   "source": [
+    "# Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1241a155",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1241a155",
+    "outputId": "05e987e6-e332-42a6-af0e-109aed2d6016"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/175.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m175.6/175.6 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/130.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/103.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m103.8/103.8 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/44.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -q praisonaiagents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "258c378c",
+   "metadata": {
+    "id": "258c378c"
+   },
+   "source": [
+    "\n",
+    "## 🔧 Tools Used\n",
+    "- [PraisonAI](https://github.com/MervinPraison/PraisonAI): Multi-agent framework for LLMs  \n",
+    "- OpenAI API: Used as the backend model (you'll need an API key)  \n",
+    "- Built-in vector store for document retrieval (RAG)  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26723867",
+   "metadata": {
+    "id": "26723867"
+   },
+   "source": [
+    "\n",
+    "## 🧾 YAML Prompt (Equivalent in Python)\n",
+    "The agent will:\n",
+    "- Be called `KnowledgeAgent`\n",
+    "- Analyze documents or topics\n",
+    "- Summarize findings in structured format\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "vDnilJPOo3Q4",
+   "metadata": {
+    "id": "vDnilJPOo3Q4"
+   },
+   "source": [
+    "# Setting Up Your OpenAI API Key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7K3qo_5WocdJ",
+   "metadata": {
+    "id": "7K3qo_5WocdJ"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter your OpenAI API key: your api key here\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8MssHldCpCK4",
+   "metadata": {
+    "id": "8MssHldCpCK4"
+   },
+   "source": [
+    "# Main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "55d0c5f1",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 877,
+     "referenced_widgets": [
+      "3e9d882fec5947bb86b8aa237e498673",
+      "8cd0e464954d433b854f6f743e1016ae"
+     ]
+    },
+    "id": "55d0c5f1",
+    "outputId": "795d2f18-edfc-4c53-ac61-c420d0b660d4"
+   },
+   "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\">KnowledgeAgent</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;229mKnowledgeAgent\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": "3e9d882fec5947bb86b8aa237e498673",
+       "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 7.9s</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2mResponse generated in 7.9s\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 need to do the following task: Analyze a topic and produce a structured summary. Expected Output: A         <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> well-structured paragraph or bullet summary. Please provide only the final result of your work. Do not add any  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> conversation or extra explanation.                                                                              <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 need to do the following task: Analyze a topic and produce a structured summary. Expected Output: A         \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m well-structured paragraph or bullet summary. Please provide only the final result of your work. Do not add any  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m conversation or extra explanation.                                                                              \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=\"font-weight: bold\">Topic: The Impact of Artificial Intelligence on Healthcare</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\"> • </span><span style=\"font-weight: bold\">Introduction</span>: Artificial Intelligence (AI) is revolutionizing the healthcare industry by enhancing           <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>diagnostic accuracy, personalizing treatment plans, and improving patient outcomes.                          <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\">Diagnostic Improvements</span>: AI algorithms, particularly in imaging and pathology, can analyze medical data 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>high precision, leading to early and accurate disease detection, such as in radiology for identifying tumors <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>or in dermatology for skin cancer detection.                                                                 <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\">Personalized Medicine</span>: AI facilitates the development of personalized treatment plans by analyzing genetic   <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>information and predicting patient responses to various treatments, thus optimizing therapeutic efficacy and <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>minimizing adverse effects.                                                                                  <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\">Operational Efficiency</span>: AI streamlines administrative tasks, such as scheduling, patient record management,  <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>and billing, reducing the burden on healthcare professionals and allowing them to focus more on patient      <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>care.                                                                                                        <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\">Predictive Analytics</span>: AI models can predict patient admission rates, potential outbreaks, and disease        <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>progression, enabling proactive healthcare management and resource allocation.                               <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\">Challenges</span>: Despite its benefits, AI in healthcare faces challenges like data privacy concerns, the need for <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>large datasets for training, potential biases in AI algorithms, and the requirement for regulatory           <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>frameworks to ensure safety and efficacy.                                                                    <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\">Future Prospects</span>: Continued advancements in AI technology promise further integration into healthcare,       <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>potentially leading to fully automated diagnostic systems and more sophisticated predictive models,          <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>ultimately transforming patient care and healthcare delivery.                                                <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[1mTopic: The Impact of Artificial Intelligence on Healthcare\u001b[0m                                                      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mIntroduction\u001b[0m: Artificial Intelligence (AI) is revolutionizing the healthcare industry by enhancing           \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mdiagnostic accuracy, personalizing treatment plans, and improving patient outcomes.                          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mDiagnostic Improvements\u001b[0m: AI algorithms, particularly in imaging and pathology, can analyze medical data with \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mhigh precision, leading to early and accurate disease detection, such as in radiology for identifying tumors \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mor in dermatology for skin cancer detection.                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mPersonalized Medicine\u001b[0m: AI facilitates the development of personalized treatment plans by analyzing genetic   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0minformation and predicting patient responses to various treatments, thus optimizing therapeutic efficacy and \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mminimizing adverse effects.                                                                                  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mOperational Efficiency\u001b[0m: AI streamlines administrative tasks, such as scheduling, patient record management,  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mand billing, reducing the burden on healthcare professionals and allowing them to focus more on patient      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mcare.                                                                                                        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mPredictive Analytics\u001b[0m: AI models can predict patient admission rates, potential outbreaks, and disease        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mprogression, enabling proactive healthcare management and resource allocation.                               \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mChallenges\u001b[0m: Despite its benefits, AI in healthcare faces challenges like data privacy concerns, the need for \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mlarge datasets for training, potential biases in AI algorithms, and the requirement for regulatory           \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mframeworks to ensure safety and efficacy.                                                                    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mFuture Prospects\u001b[0m: Continued advancements in AI technology promise further integration into healthcare,       \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mpotentially leading to fully automated diagnostic systems and more sophisticated predictive models,          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0multimately transforming patient care and healthcare delivery.                                                \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📄 Output Summary:\n",
+      "**Topic: The Impact of Artificial Intelligence on Healthcare**\n",
+      "\n",
+      "- **Introduction**: Artificial Intelligence (AI) is revolutionizing the healthcare industry by enhancing diagnostic accuracy, personalizing treatment plans, and improving patient outcomes.\n",
+      "  \n",
+      "- **Diagnostic Improvements**: AI algorithms, particularly in imaging and pathology, can analyze medical data with high precision, leading to early and accurate disease detection, such as in radiology for identifying tumors or in dermatology for skin cancer detection.\n",
+      "\n",
+      "- **Personalized Medicine**: AI facilitates the development of personalized treatment plans by analyzing genetic information and predicting patient responses to various treatments, thus optimizing therapeutic efficacy and minimizing adverse effects.\n",
+      "\n",
+      "- **Operational Efficiency**: AI streamlines administrative tasks, such as scheduling, patient record management, and billing, reducing the burden on healthcare professionals and allowing them to focus more on patient care.\n",
+      "\n",
+      "- **Predictive Analytics**: AI models can predict patient admission rates, potential outbreaks, and disease progression, enabling proactive healthcare management and resource allocation.\n",
+      "\n",
+      "- **Challenges**: Despite its benefits, AI in healthcare faces challenges like data privacy concerns, the need for large datasets for training, potential biases in AI algorithms, and the requirement for regulatory frameworks to ensure safety and efficacy.\n",
+      "\n",
+      "- **Future Prospects**: Continued advancements in AI technology promise further integration into healthcare, potentially leading to fully automated diagnostic systems and more sophisticated predictive models, ultimately transforming patient care and healthcare delivery.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from praisonaiagents import Agent, Task, PraisonAIAgents\n",
+    "import os\n",
+    "\n",
+    "# 🔑 Set your OpenAI API key\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"YOUR_API_KEY\"\n",
+    "# Define agent\n",
+    "knowledge_agent = Agent(\n",
+    "    name=\"KnowledgeAgent\",\n",
+    "    instructions=\"You are a knowledge expert. Research the given topic and summarize it clearly and concisely.\"\n",
+    ")\n",
+    "\n",
+    "# Define task\n",
+    "knowledge_task = Task(\n",
+    "    name=\"SummarizationTask\",\n",
+    "    description=\"Analyze a topic and produce a structured summary\",\n",
+    "    expected_output=\"A well-structured paragraph or bullet summary\",\n",
+    "    agent=knowledge_agent\n",
+    ")\n",
+    "\n",
+    "# Build system\n",
+    "agents = PraisonAIAgents(\n",
+    "    agents=[knowledge_agent],\n",
+    "    tasks=[knowledge_task],\n",
+    "    process=\"sequential\"\n",
+    ")\n",
+    "\n",
+    "# Run the system\n",
+    "result = agents.start(input=\"Explain how quantum computing differs from classical computing.\")\n",
+    "print(\"📄 Output Summary:\")\n",
+    "print(result)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "910d0f04",
+   "metadata": {
+    "id": "910d0f04"
+   },
+   "source": [
+    "\n",
+    "## 📄 Output\n",
+    "The output will be a structured summary of the input topic (in this case: **quantum vs classical computing**).  \n",
+    "If the model cannot run due to Colab limits, a text description will be printed instead.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/cookbooks/LLaMA3_2_11B_Vision_Model.ipynb

@@ -0,0 +1,635 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "7eb3b7a8",
+      "metadata": {
+        "id": "7eb3b7a8"
+      },
+      "source": [
+        "# LLaMA3-2 (11B) Vision Model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4914e815",
+      "metadata": {
+        "id": "4914e815"
+      },
+      "source": [
+        "## Description\n",
+        "This notebook demonstrates how to use the **LLaMA3-2 (11B) Vision Model** vision-language model for image input and text generation tasks."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/LLaMA3_2_11B_Vision_Model.ipynb)\n"
+      ],
+      "metadata": {
+        "id": "j_sehN-6kKcp"
+      },
+      "id": "j_sehN-6kKcp"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "366d7561",
+      "metadata": {
+        "id": "366d7561"
+      },
+      "source": [
+        "## Dependencies\n",
+        "```python\n",
+        "!pip install transformers accelerate torch torchvision\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "750d4351",
+      "metadata": {
+        "id": "750d4351"
+      },
+      "source": [
+        "## Tools\n",
+        "- 🤗 Transformers\n",
+        "- PyTorch\n",
+        "- Vision Model APIs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9f053796",
+      "metadata": {
+        "id": "9f053796"
+      },
+      "source": [
+        "## YAML Prompt\n",
+        "```yaml\n",
+        "mode: vision\n",
+        "model: LLaMA3-2 (11B) Vision Model\n",
+        "tasks:\n",
+        "  - image captioning\n",
+        "  - visual question answering\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b920aca2",
+      "metadata": {
+        "id": "b920aca2"
+      },
+      "source": [
+        "## Main\n",
+        "Below is the simplified main execution to load the model and run basic inference."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fb97f999",
+      "metadata": {
+        "id": "fb97f999"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "import os\n",
+        "if \"COLAB_\" not in \"\".join(os.environ.keys()):\n",
+        "    !pip install unsloth\n",
+        "else:\n",
+        "    # Do this only in Colab notebooks! Otherwise use pip install unsloth\n",
+        "    !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo\n",
+        "    !pip install sentencepiece protobuf \"datasets>=3.4.1\" huggingface_hub hf_transfer\n",
+        "    !pip install --no-deps unsloth"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2a6e11d8",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 511,
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+        "id": "2a6e11d8",
+        "outputId": "f63afa9e-e320-4da2-bb91-b7f4f9163379"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
+            "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
+            "==((====))==  Unsloth 2024.11.9: Fast Mllama vision patching. Transformers = 4.46.2.\n",
+            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
+            "O^O/ \\_/ \\    Pytorch: 2.5.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
+            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.28.post3. FA2 = False]\n",
+            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
+            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "98d2cd94be9c454f83a18c8bf5fe9aec",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model.safetensors.index.json:   0%|          | 0.00/385k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "289cd006e4ff4f018e758890e148c852",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d0fc6802d6034d33bb33be048799320d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model-00001-of-00002.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "97b945925ec94ee8851187d97a3b3e01",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model-00002-of-00002.safetensors:   0%|          | 0.00/2.18G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "443cae09fcc74bce8c85514c0e3306f8",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c90bdb60e0fc46d8aa1d9e858cbbdd07",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/210 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4f579cd077cd41c798478f572d592f6a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "preprocessor_config.json:   0%|          | 0.00/477 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a37acc4c0f11436c80cb72272709a9fe",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/55.9k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "74742a6f4bb94067b6e6703cef8c3941",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/17.2M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1a4c9e787979474b9049e36f8c07c12b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/454 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9430d398534341bca671d10e8bc91ddc",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "chat_template.json:   0%|          | 0.00/5.15k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from unsloth import FastVisionModel # FastLanguageModel for LLMs\n",
+        "import torch\n",
+        "\n",
+        "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
+        "fourbit_models = [\n",
+        "    \"unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit\", # Llama 3.2 vision support\n",
+        "    \"unsloth/Llama-3.2-11B-Vision-bnb-4bit\",\n",
+        "    \"unsloth/Llama-3.2-90B-Vision-Instruct-bnb-4bit\", # Can fit in a 80GB card!\n",
+        "    \"unsloth/Llama-3.2-90B-Vision-bnb-4bit\",\n",
+        "\n",
+        "    \"unsloth/Pixtral-12B-2409-bnb-4bit\",              # Pixtral fits in 16GB!\n",
+        "    \"unsloth/Pixtral-12B-Base-2409-bnb-4bit\",         # Pixtral base model\n",
+        "\n",
+        "    \"unsloth/Qwen2-VL-2B-Instruct-bnb-4bit\",          # Qwen2 VL support\n",
+        "    \"unsloth/Qwen2-VL-7B-Instruct-bnb-4bit\",\n",
+        "    \"unsloth/Qwen2-VL-72B-Instruct-bnb-4bit\",\n",
+        "\n",
+        "    \"unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit\",      # Any Llava variant works!\n",
+        "    \"unsloth/llava-1.5-7b-hf-bnb-4bit\",\n",
+        "] # More models at https://huggingface.co/unsloth\n",
+        "\n",
+        "model, tokenizer = FastVisionModel.from_pretrained(\n",
+        "    \"unsloth/Llama-3.2-11B-Vision-Instruct\",\n",
+        "    load_in_4bit = True, # Use 4bit to reduce memory use. False for 16bit LoRA.\n",
+        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for long context\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cbc24d70",
+      "metadata": {
+        "id": "cbc24d70"
+      },
+      "outputs": [],
+      "source": [
+        "model = FastVisionModel.get_peft_model(\n",
+        "    model,\n",
+        "    finetune_vision_layers     = True, # False if not finetuning vision layers\n",
+        "    finetune_language_layers   = True, # False if not finetuning language layers\n",
+        "    finetune_attention_modules = True, # False if not finetuning attention layers\n",
+        "    finetune_mlp_modules       = True, # False if not finetuning MLP layers\n",
+        "\n",
+        "    r = 16,           # The larger, the higher the accuracy, but might overfit\n",
+        "    lora_alpha = 16,  # Recommended alpha == r at least\n",
+        "    lora_dropout = 0,\n",
+        "    bias = \"none\",\n",
+        "    random_state = 3407,\n",
+        "    use_rslora = False,  # We support rank stabilized LoRA\n",
+        "    loftq_config = None, # And LoftQ\n",
+        "    # target_modules = \"all-linear\", # Optional now! Can specify a list if needed\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9e47b862",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 177,
+          "referenced_widgets": [
+            "aecf49a7a37445dfb4af43fed91cdc97",
+            "209f0d74e16b4c03a72687d796958863",
+            "17c372165582427dbc34abfd5dfdaa6c",
+            "59110c931b0b45d5a61d7dae7c7e7120",
+            "1ab79420b99e4508944ba5b4361171f8",
+            "89284068a3be4af4a0d9b8bc50b9afba",
+            "d11bd8033e4740929088a4fd0ef883c5",
+            "cc1cf7cbbcae4771be85c3e628510997",
+            "758e626f5c2f4b0193e1cf27824818ca",
+            "dfdce9a9549448a8a475db2c2507f5eb",
+            "4893fac70136425c9aa1c685e4ce71f3",
+            "ee828b24ee424cfc82b2f3b87636940a",
+            "115f636f0c0a4c1ebee21d973ff09e40",
+            "adb8a39222db4fe880c180fe5c601dbe",
+            "775514e06f1c453fa311d528b3d62749",
+            "523f128189f746ce9fb7a594e72e9c7e",
+            "72715b7c03e34b01b5f7715ce658eacd",
+            "432103db24e448e8b62bce3af49ec581",
+            "b382f1fd5c1645169079cdef08c14357",
+            "5ad48ea79bde44639976eeb61377b74d",
+            "2ae0d6c5c4c1419293e7b3832b8d8a17",
+            "71f1e8c406a54c52b6983ff2b922be20",
+            "34c7dbce08a146a7949152b0cac8666d",
+            "f8e7b74b13864adc9d6cfccaa9a11362",
+            "2aaa6c22a7d34c2aa20a169236a72f59",
+            "c79fefc6a20d4bce9eca13b0b1a604aa",
+            "8463516108b8491d8aaa0449a7f21f05",
+            "cb71c9d8d57744fb9cfeb740d7ea50e6",
+            "0241e7fc2a86481389ce140690fc544a",
+            "315e97f8931941d382ae5b91e1d644a0",
+            "ebdc57260e224077b0ba08ffbb3fb6a1",
+            "0ee41ec935844b09850972773468b5de",
+            "e7fabc6d4ea8476a9459604d745b7f08",
+            "5034f195628e43b98e4ab33fd297308c",
+            "46686703d557405685b5e910d81cfb4a",
+            "55e11fafe31f4147a7a53c870f637527",
+            "7fa57079e7414449a9b04330c91bd50d",
+            "add5ec75448b4378b8e7aab92a5e53f5",
+            "2e2dcbc6ae13435fa6bb91c35dbdf1ae",
+            "64a4a67880614ecebad79f9ac957d4af",
+            "dff190bbf4a746d393857037be5e5d72",
+            "7ebc322bc4824599a6f59ed979eae207",
+            "d61c2405111d4acc8428e1cebb96c963",
+            "5263b46f262a40518c43778191394e4a",
+            "78f33011fc0648439338fb4556284f31",
+            "b08e04e041a84597b225f2194943d715",
+            "7d36203af6344d50813eab42af8df957",
+            "00714fb691c14ca6a9ee14ead204b6c0",
+            "7d057295875d4525a165c823ff222268",
+            "be882001119342ebbbbc3f73dbfa5356",
+            "2713d41644f84b5290c0dd94b2953b2a",
+            "235bc65f45ac4b369e1e03582b1b2061",
+            "d2d708ed937b4deb8d72805e98b3aa9b",
+            "c0fc8d1e49d643b0b9871080545cbb27",
+            "f9bae803d6944df98773db211a4e98b6"
+          ]
+        },
+        "id": "9e47b862",
+        "outputId": "ac18735b-4865-4634-cdac-0672be9dc0e6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "aecf49a7a37445dfb4af43fed91cdc97",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "README.md:   0%|          | 0.00/610 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ee828b24ee424cfc82b2f3b87636940a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00000-of-00001.parquet:   0%|          | 0.00/481M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "34c7dbce08a146a7949152b0cac8666d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "test-00000-of-00001.parquet:   0%|          | 0.00/79.2M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5034f195628e43b98e4ab33fd297308c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split:   0%|          | 0/1978 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "78f33011fc0648439338fb4556284f31",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test split:   0%|          | 0/327 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "dataset = load_dataset(\"unsloth/Radiology_mini\", split=\"train\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c437c09d",
+      "metadata": {
+        "id": "c437c09d"
+      },
+      "source": [
+        "## Output\n",
+        "This shows a basic output example of the vision-language model."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

examples/cookbooks/Meta_LLaMA3_SyntheticData.ipynb

@@ -0,0 +1,157 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "930bc11c",
+      "metadata": {
+        "id": "930bc11c"
+      },
+      "source": [
+        "# Meta Synthetic Data Generator (LLaMA 3.2 - 3B)\n",
+        "\n",
+        "This notebook demonstrates how to use Meta's LLaMA 3.2 3B model to generate synthetic data for use in AI training or application prototyping."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Meta_LLaMA3_SyntheticData.ipynb)\n"
+      ],
+      "metadata": {
+        "id": "KPi7FpbV9J2d"
+      },
+      "id": "KPi7FpbV9J2d"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "80f68ecf",
+      "metadata": {
+        "id": "80f68ecf"
+      },
+      "source": [
+        "## Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7eeb508d",
+      "metadata": {
+        "id": "7eeb508d"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q transformers accelerate bitsandbytes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "eda75a0c",
+      "metadata": {
+        "id": "eda75a0c"
+      },
+      "source": [
+        "## Tools\n",
+        "* `transformers` for model loading and text generation\n",
+        "* `pipeline` for simplified inference\n",
+        "* `AutoTokenizer`, `AutoModelForCausalLM` for LLaMA 3.2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6ee96383",
+      "metadata": {
+        "id": "6ee96383"
+      },
+      "source": [
+        "## YAML Prompt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "aa4e56ef",
+      "metadata": {
+        "id": "aa4e56ef"
+      },
+      "outputs": [],
+      "source": [
+        "prompt: |\n",
+        "  task: \"Generate a customer complaint email\"\n",
+        "  style: \"Professional\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e9fbe400",
+      "metadata": {
+        "id": "e9fbe400"
+      },
+      "source": [
+        "## Main"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3e35336f",
+      "metadata": {
+        "id": "3e35336f"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
+        "\n",
+        "# Load tokenizer and model (LLaMA 3.2 - 3B)\n",
+        "model_id = \"meta-llama/Meta-Llama-3-3B-Instruct\"\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+        "model = AutoModelForCausalLM.from_pretrained(model_id)\n",
+        "\n",
+        "# Create a simple text generation pipeline\n",
+        "generator = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n",
+        "\n",
+        "# Example synthetic data prompt\n",
+        "prompt = \"Create a customer support query about a late delivery.\"\n",
+        "\n",
+        "# Generate synthetic text\n",
+        "output = generator(prompt, max_length=60, do_sample=True)[0]['generated_text']\n",
+        "print(\"📝 Synthetic Output:\", output)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ed28cc2e",
+      "metadata": {
+        "id": "ed28cc2e"
+      },
+      "source": [
+        "## Output"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6fc8f19e",
+      "metadata": {
+        "id": "6fc8f19e"
+      },
+      "source": [
+        "🖼️ Output Preview (Text Summary):\n",
+        "\n",
+        "Prompt: \"Create a customer support query about a late delivery.\"\n",
+        "\n",
+        "📝 Output: The LLaMA model generates a realistic complaint, such as:\n",
+        "\n",
+        "\"Dear Support Team, I placed an order two weeks ago and have yet to receive it...\"\n",
+        "\n",
+        "🎯 This illustrates how the model can be used to generate realistic synthetic data for tasks like training chatbots or support models.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

examples/cookbooks/MistralTechAgent.ipynb

@@ -1,5 +1,4 @@
 {
-
   "cells": [
     {
       "cell_type": "markdown",

examples/cookbooks/Mistral_v0_3_Conversational_Demo.ipynb

@@ -1,134 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "441900e0"
-      },
-      "source": [
-        "# 🧠 Mistral v0.3 (7B) Conversational Agent\n",
-        "A conceptual demo notebook to illustrate the workflow and output of the Mistral model, even if it doesn't run on Colab Free Tier."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "b4ba0b88"
-      },
-      "source": [
-        "## 📄 Description\n",
-        "This notebook showcases the typical flow of using Mistral v0.3 (7B) for conversational tasks. While the model is too large for Google Colab Free Tier, this version visually explains what the code would do.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Mistral_v0_3_Conversational_Demo.ipynb)\n"
-      ],
-      "metadata": {
-        "id": "yzXURvxIlIDD"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 📦 Dependencies"
-      ],
-      "metadata": {
-        "id": "IUQSTocQk7ml"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "6f2afc57"
-      },
-      "outputs": [],
-      "source": [
-        "\n",
-        "!pip install transformers accelerate torch\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9d5dad27"
-      },
-      "source": [
-        "## 🛠 Tools Used\n",
-        "- 🤗 Transformers: For loading the model and tokenizer.\n",
-        "- PyTorch: For model inference.\n",
-        "- Accelerate: To manage device placement (CPU/GPU).\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cd936b40"
-      },
-      "source": [
-        "## 🧾 YAML Prompt Format (Conceptual)\n",
-        "```yaml\n",
-        "task: conversation\n",
-        "input: \"What's the capital of France?\"\n",
-        "history: []\n",
-        "temperature: 0.7\n",
-        "top_p: 0.9\n",
-        "```\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "8abaaa62"
-      },
-      "outputs": [],
-      "source": [
-        "# 🧠 Main Inference Logic (Illustrative)\n",
-        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-        "import torch\n",
-        "\n",
-        "model_id = \"mistralai/Mistral-7B-Instruct-v0.3\"\n",
-        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
-        "model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map=\"auto\")\n",
-        "\n",
-        "prompt = \"What's the capital of France?\"\n",
-        "inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
-        "outputs = model.generate(**inputs, max_new_tokens=100)\n",
-        "response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
-        "print(response)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "def486c6"
-      },
-      "source": [
-        "## 📤 Output (Example)\n",
-        "> \"The capital of France is Paris.\"\n",
-        "\n",
-        "![output](https://upload.wikimedia.org/wikipedia/commons/e/e6/Paris_Night.jpg)\n",
-        "\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

examples/cookbooks/Phi_3_5_Mini_Conversational.ipynb

@@ -0,0 +1,120 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "468831db",
+      "metadata": {
+        "id": "468831db"
+      },
+      "source": [
+        "# Phi-3.5 Mini Conversational Example"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ecb80fe2",
+      "metadata": {
+        "id": "ecb80fe2"
+      },
+      "source": [
+        "**Description:**\n",
+        "\n",
+        "Demonstrates lightweight inference using Phi-3.5 Mini, suitable for smaller hardware and educational use cases."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Phi_3_5_Mini_Conversational.ipynb)\n"
+      ],
+      "metadata": {
+        "id": "S1erg7-JP6n3"
+      },
+      "id": "S1erg7-JP6n3"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0822161c",
+      "metadata": {
+        "id": "0822161c"
+      },
+      "source": [
+        "**Dependencies**\n",
+        "\n",
+        "```python\n",
+        "!pip install transformers accelerate\n",
+        "!pip install torch\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0df5580b",
+      "metadata": {
+        "id": "0df5580b"
+      },
+      "source": [
+        "**Tools Used**\n",
+        "\n",
+        "- HuggingFace Transformers\n",
+        "- PyTorch"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9d6df43d",
+      "metadata": {
+        "id": "9d6df43d"
+      },
+      "source": [
+        "**YAML Prompt**\n",
+        "\n",
+        "```yaml\n",
+        "system: You are a helpful assistant.\n",
+        "user: Explain what an AI model is.\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0e775593",
+      "metadata": {
+        "id": "0e775593"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+        "\n",
+        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/phi-3.5-mini\")\n",
+        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/phi-3.5-mini\")\n",
+        "\n",
+        "inputs = tokenizer(\"What is an AI model?\", return_tensors=\"pt\")\n",
+        "outputs = model.generate(**inputs, max_new_tokens=40)\n",
+        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "187fe354",
+      "metadata": {
+        "id": "187fe354"
+      },
+      "source": [
+        "**Output**\n",
+        "\n",
+        "The model gives a basic explanation of what AI models do.\n",
+        "\n",
+        "What is an AI model?\n",
+        "An AI model is a computer program that is trained on large amounts of data to perform tasks that normally require human intelligence.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

examples/cookbooks/Phi_3_Medium_Conversational.ipynb

@@ -0,0 +1,122 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "ddb8fd1c",
+      "metadata": {
+        "id": "ddb8fd1c"
+      },
+      "source": [
+        "# Phi-3 Medium Conversational Inference"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Phi_3_Medium_Conversational.ipynb)\n"
+      ],
+      "metadata": {
+        "id": "uMIvNFtYQDKO"
+      },
+      "id": "uMIvNFtYQDKO"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "71a9292d",
+      "metadata": {
+        "id": "71a9292d"
+      },
+      "source": [
+        "**Description:**\n",
+        "\n",
+        "Run a conversational inference using the Phi-3 Medium model with an efficient pipeline. The notebook illustrates basic loading, prompting, and response generation."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9023e3f2",
+      "metadata": {
+        "id": "9023e3f2"
+      },
+      "source": [
+        "**Dependencies**\n",
+        "\n",
+        "```python\n",
+        "!pip install transformers accelerate\n",
+        "!pip install torch torchvision\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8ed8c345",
+      "metadata": {
+        "id": "8ed8c345"
+      },
+      "source": [
+        "**Tools Used**\n",
+        "\n",
+        "- HuggingFace Transformers\n",
+        "- Accelerate\n",
+        "- PyTorch"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "68cb4bd6",
+      "metadata": {
+        "id": "68cb4bd6"
+      },
+      "source": [
+        "**YAML Prompt**\n",
+        "\n",
+        "```yaml\n",
+        "system: You are a helpful assistant.\n",
+        "user: What is the capital of France?\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "be5d0fde",
+      "metadata": {
+        "id": "be5d0fde"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+        "import torch\n",
+        "\n",
+        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/phi-3-medium\")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/phi-3-medium\")\n",
+        "\n",
+        "prompt = \"What is the capital of France?\"\n",
+        "inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
+        "outputs = model.generate(**inputs, max_new_tokens=20)\n",
+        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4ac8c793",
+      "metadata": {
+        "id": "4ac8c793"
+      },
+      "source": [
+        "**Output**\n",
+        "\n",
+        "This example shows the model answering a simple geography question.\n",
+        "\n",
+        "What is the capital of France? The capital of France is Paris.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

examples/cookbooks/Phi_4_14B_GRPO.ipynb

@@ -0,0 +1,123 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "cc1b7810",
+      "metadata": {
+        "id": "cc1b7810"
+      },
+      "source": [
+        "# Phi-4 (14B) Conversational with GRPO"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9c882a2d",
+      "metadata": {
+        "id": "9c882a2d"
+      },
+      "source": [
+        "**Description:**\n",
+        "\n",
+        "This notebook demonstrates inference using the Phi-4 14B parameter model with GRPO optimization strategy."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Phi_4_14B_GRPO.ipynb)\n"
+      ],
+      "metadata": {
+        "id": "Z7azojscP0Ax"
+      },
+      "id": "Z7azojscP0Ax"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e8616784",
+      "metadata": {
+        "id": "e8616784"
+      },
+      "source": [
+        "**Dependencies**\n",
+        "\n",
+        "```python\n",
+        "!pip install transformers accelerate\n",
+        "!pip install torch\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "09866448",
+      "metadata": {
+        "id": "09866448"
+      },
+      "source": [
+        "**Tools Used**\n",
+        "\n",
+        "- HuggingFace Transformers\n",
+        "- GRPO Optimization\n",
+        "- PyTorch"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "403b0c37",
+      "metadata": {
+        "id": "403b0c37"
+      },
+      "source": [
+        "**YAML Prompt**\n",
+        "\n",
+        "```yaml\n",
+        "system: Act as a professional consultant.\n",
+        "user: How can AI help in healthcare?\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e1785bcd",
+      "metadata": {
+        "id": "e1785bcd"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+        "\n",
+        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/phi-4-14b\")\n",
+        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/phi-4-14b\")\n",
+        "\n",
+        "prompt = \"How can AI help in healthcare?\"\n",
+        "inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
+        "outputs = model.generate(**inputs, max_new_tokens=60)\n",
+        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "074e4e6e",
+      "metadata": {
+        "id": "074e4e6e"
+      },
+      "source": [
+        "**Output**\n",
+        "\n",
+        "The model generates thoughtful insights on AI applications in healthcare.\n",
+        "\n",
+        "How can AI help in healthcare?\n",
+        "\n",
+        "AI can assist in healthcare by improving diagnostics, predicting patient outcomes, personalizing treatments, and enhancing administrative workflows. It enables faster data analysis and decision-making, leading to better patient care.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

examples/cookbooks/Phi_4_Conversational.ipynb

@@ -0,0 +1,124 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "7b502c12",
+      "metadata": {
+        "id": "7b502c12"
+      },
+      "source": [
+        "# Phi-4 Conversational Chat"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6f550ca9",
+      "metadata": {
+        "id": "6f550ca9"
+      },
+      "source": [
+        "**Description:**\n",
+        "\n",
+        "Basic conversational inference with Phi-4 demonstrating chat-style turn-based communication."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Phi_4_Conversational.ipynb)\n"
+      ],
+      "metadata": {
+        "id": "CB4mkM7yPmua"
+      },
+      "id": "CB4mkM7yPmua"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e0db4638",
+      "metadata": {
+        "id": "e0db4638"
+      },
+      "source": [
+        "**Dependencies**\n",
+        "\n",
+        "```python\n",
+        "!pip install transformers accelerate\n",
+        "!pip install torch\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "25ef05dd",
+      "metadata": {
+        "id": "25ef05dd"
+      },
+      "source": [
+        "**Tools Used**\n",
+        "\n",
+        "- HuggingFace Transformers\n",
+        "- PyTorch"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8986b9d8",
+      "metadata": {
+        "id": "8986b9d8"
+      },
+      "source": [
+        "**YAML Prompt**\n",
+        "\n",
+        "```yaml\n",
+        "system: You are a tutor.\n",
+        "user: Teach me what machine learning is.\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ffdb2319",
+      "metadata": {
+        "id": "ffdb2319"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+        "\n",
+        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/phi-4\")\n",
+        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/phi-4\")\n",
+        "\n",
+        "prompt = \"Teach me what machine learning is.\"\n",
+        "inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
+        "outputs = model.generate(**inputs, max_new_tokens=80)\n",
+        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ac4caeac",
+      "metadata": {
+        "id": "ac4caeac"
+      },
+      "source": [
+        "**Output**\n",
+        "\n",
+        "Explains machine learning in a clear, tutor-like manner.\n",
+        "\n",
+        "Machine learning is a field of artificial intelligence that allows computers to learn from data without being explicitly programmed.\n",
+        "It involves training algorithms on large datasets so they can recognize patterns, make decisions, or perform tasks.\n",
+        "There are different types of machine learning, including supervised, unsupervised, and reinforcement learning.\n",
+        "For example, in supervised learning, a model is trained using labeled data to predict outcomes.\n",
+        "Overall, machine learning is widely used in applications like recommendation systems, image recognition, and natural language processing.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

examples/cookbooks/Pixtral_12B_Vision_Model.ipynb

@@ -0,0 +1,660 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "23d69f99",
+      "metadata": {
+        "id": "23d69f99"
+      },
+      "source": [
+        "# Pixtral (12B) Vision Model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b842f675",
+      "metadata": {
+        "id": "b842f675"
+      },
+      "source": [
+        "## Description\n",
+        "This notebook demonstrates how to use the **Pixtral (12B) Vision Model** vision-language model for image input and text generation tasks."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Pixtral_12B_Vision_Model.ipynb)\n"
+      ],
+      "metadata": {
+        "id": "MilYelUMlD2z"
+      },
+      "id": "MilYelUMlD2z"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "557dba1c",
+      "metadata": {
+        "id": "557dba1c"
+      },
+      "source": [
+        "## Dependencies\n",
+        "```python\n",
+        "!pip install transformers accelerate torch torchvision\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b864df8",
+      "metadata": {
+        "id": "2b864df8"
+      },
+      "source": [
+        "## Tools\n",
+        "- 🤗 Transformers\n",
+        "- PyTorch\n",
+        "- Vision Model APIs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b050001",
+      "metadata": {
+        "id": "1b050001"
+      },
+      "source": [
+        "## YAML Prompt\n",
+        "```yaml\n",
+        "mode: vision\n",
+        "model: Pixtral (12B) Vision Model\n",
+        "tasks:\n",
+        "  - image captioning\n",
+        "  - visual question answering\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "44ce2c02",
+      "metadata": {
+        "id": "44ce2c02"
+      },
+      "source": [
+        "## Main\n",
+        "Below is the simplified main execution to load the model and run basic inference."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c996306e",
+      "metadata": {
+        "id": "c996306e"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "import os\n",
+        "if \"COLAB_\" not in \"\".join(os.environ.keys()):\n",
+        "    !pip install unsloth\n",
+        "else:\n",
+        "    # Do this only in Colab notebooks! Otherwise use pip install unsloth\n",
+        "    !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo\n",
+        "    !pip install sentencepiece protobuf \"datasets>=3.4.1\" huggingface_hub hf_transfer\n",
+        "    !pip install --no-deps unsloth"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "30147f7c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 543,
+          "referenced_widgets": [
+            "8a0ce0af1025429cb3cfcc5b973a4af0",
+            "6fe6ac6a53f745c5a2736e1b6cb01ff7",
+            "8d93c4b87f69485e8e97192a8431113f",
+            "08af812d04fe481fa84e85a6a591c506",
+            "257fe2a05570417882a606b304087a21",
+            "6d8e442e771d4955a29a360dee525119",
+            "00a911ef0d7f4a6d8b1b4f972b0b4d7c",
+            "766a605b2c5a463d977b8fefb554fea5",
+            "54ae26be14bc4fb28933af46f45e023a",
+            "360f3db84aac4728861b74b5a37adcaa",
+            "a2ae977cf14c461e9fa0cbc5c27e7186",
+            "d401dcde88274752b4f0786f834527d9",
+            "5d10ef16c7154f5392519da3ff6a2f0d",
+            "d8358c67c0b0415098b3e4a4e7ad0cbb",
+            "4f593d75532d4087bafc02d6ad2b4f79",
+            "a519350329d04d5096ccb605c9ffd623",
+            "9cd924df198145ed93d9df07249236cb",
+            "a01af94f415b4ed18186fa099c3ecbc3",
+            "27cd2e02e78d4ef8a52ba73a9e214120",
+            "841f197cba3f455d8fe9a9dafac992d7",
+            "d6507d692f9743e6b3eed08df94e2a56",
+            "c3438845441142fa92fec3acd21b3955",
+            "eb64d7acd375493098e1f7c30e7f2781",
+            "ad01bece3f444f1382988a65833bebf7",
+            "281b9b2311e5460aa4df9bbe9f8e22f0",
+            "34afc68bfe9d4e47ad73b31a34a74b32",
+            "23c9c94b98f141e8827c37cd26b6138e",
+            "d2b5a8a0c51c4d04aa1da73a56c2bcd3",
+            "4587ddab1af04157b8cba09f780ce9b0",
+            "aab82ec73c0b42fb928fbc74fc44c168",
+            "45f90f73051b4f898666ecd14f2818f0",
+            "6f96f236961344418015be410aedc57c",
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+        },
+        "id": "30147f7c",
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+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
+            "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
+            "==((====))==  Unsloth 2024.11.9: Fast Pixtral vision patching. Transformers = 4.46.2.\n",
+            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
+            "O^O/ \\_/ \\    Pytorch: 2.5.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
+            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.28.post3. FA2 = False]\n",
+            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
+            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8a0ce0af1025429cb3cfcc5b973a4af0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model.safetensors.index.json:   0%|          | 0.00/316k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d401dcde88274752b4f0786f834527d9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "eb64d7acd375493098e1f7c30e7f2781",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model-00001-of-00002.safetensors:   0%|          | 0.00/4.97G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d4d3c9f1f54746d8895b66b6d2888ee2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model-00002-of-00002.safetensors:   0%|          | 0.00/3.57G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b60b17cb67044a82b173f94d781d5174",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8f1faff5468a46d4a725d095ae31ea3c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/133 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1ef7d525ad2b4a57ae1118a0c3908b1a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "processor_config.json:   0%|          | 0.00/162 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "chat_template.json:   0%|          | 0.00/1.59k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ec91a47eece543a495f36ed0e051d637",
+              "version_major": 2,
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+            },
+            "text/plain": [
+              "preprocessor_config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "db8a5ea365094bc4a87e77d059b4d0d0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/177k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1c7c3bfb5ae4472c9bb6e340c012d302",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/17.1M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "aa6b598305944d5ab2e2b4afaea8063c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/552 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from unsloth import FastVisionModel # FastLanguageModel for LLMs\n",
+        "import torch\n",
+        "\n",
+        "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
+        "fourbit_models = [\n",
+        "    \"unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit\", # Llama 3.2 vision support\n",
+        "    \"unsloth/Llama-3.2-11B-Vision-bnb-4bit\",\n",
+        "    \"unsloth/Llama-3.2-90B-Vision-Instruct-bnb-4bit\", # Can fit in a 80GB card!\n",
+        "    \"unsloth/Llama-3.2-90B-Vision-bnb-4bit\",\n",
+        "\n",
+        "    \"unsloth/Pixtral-12B-2409-bnb-4bit\",              # Pixtral fits in 16GB!\n",
+        "    \"unsloth/Pixtral-12B-Base-2409-bnb-4bit\",         # Pixtral base model\n",
+        "\n",
+        "    \"unsloth/Qwen2-VL-2B-Instruct-bnb-4bit\",          # Qwen2 VL support\n",
+        "    \"unsloth/Qwen2-VL-7B-Instruct-bnb-4bit\",\n",
+        "    \"unsloth/Qwen2-VL-72B-Instruct-bnb-4bit\",\n",
+        "\n",
+        "    \"unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit\",      # Any Llava variant works!\n",
+        "    \"unsloth/llava-1.5-7b-hf-bnb-4bit\",\n",
+        "] # More models at https://huggingface.co/unsloth\n",
+        "\n",
+        "model, tokenizer = FastVisionModel.from_pretrained(\n",
+        "    \"unsloth/Pixtral-12B-2409\",\n",
+        "    load_in_4bit = True, # Use 4bit to reduce memory use. False for 16bit LoRA.\n",
+        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for long context\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1c0cd2b0",
+      "metadata": {
+        "id": "1c0cd2b0"
+      },
+      "outputs": [],
+      "source": [
+        "model = FastVisionModel.get_peft_model(\n",
+        "    model,\n",
+        "    # We do NOT finetune vision & attention layers since Pixtral uses more memory!\n",
+        "    finetune_vision_layers     = True, # False if not finetuning vision layers\n",
+        "    finetune_language_layers   = True,  # False if not finetuning language layers\n",
+        "    finetune_attention_modules = False, # False if not finetuning attention layers\n",
+        "    finetune_mlp_modules       = True,  # False if not finetuning MLP layers\n",
+        "\n",
+        "    r = 8,           # The larger, the higher the accuracy, but might overfit\n",
+        "    lora_alpha = 8,  # Recommended alpha == r at least\n",
+        "    lora_dropout = 0,\n",
+        "    bias = \"none\",\n",
+        "    random_state = 3407,\n",
+        "    use_rslora = False,  # We support rank stabilized LoRA\n",
+        "    loftq_config = None, # And LoftQ\n",
+        "    # target_modules = \"all-linear\", # Optional now! Can specify a list if needed\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "440fa965",
+      "metadata": {
+        "colab": {
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+        },
+        "id": "440fa965",
+        "outputId": "54b0ee23-5573-407a-d22f-cda8378461da"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "453a07e03ee94f528890205d14511da6",
+              "version_major": 2,
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+            },
+            "text/plain": [
+              "README.md:   0%|          | 0.00/728 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
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+            "text/plain": [
+              "train-00000-of-00001.parquet:   0%|          | 0.00/357M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+            "text/plain": [
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7856e0634b86431382280c591b6f740f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split:   0%|          | 0/8552 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6b2c1870e4ae44aba0326535f75c6858",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test split:   0%|          | 0/1364 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "dataset = load_dataset(\"unsloth/llava-instruct-mix-vsft-mini\", split = \"train\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b9670b46",
+      "metadata": {
+        "id": "b9670b46"
+      },
+      "source": [
+        "## Output\n",
+        "This shows a basic output example of the vision-language model."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

examples/cookbooks/Qwen2_5_VL_7B_Vision_Model.ipynb

@@ -0,0 +1,616 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "a627af7f",
+      "metadata": {
+        "id": "a627af7f"
+      },
+      "source": [
+        "# Qwen2.5-VL (7B) Vision Model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6ca9476a",
+      "metadata": {
+        "id": "6ca9476a"
+      },
+      "source": [
+        "## Description\n",
+        "This notebook demonstrates how to use the **Qwen2.5-VL (7B) Vision Model** vision-language model for image input and text generation tasks."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Qwen2_5_VL_7B_Vision_Model.ipynb)"
+      ],
+      "metadata": {
+        "id": "0G9UGDLJk8uV"
+      },
+      "id": "0G9UGDLJk8uV"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f467ebfe",
+      "metadata": {
+        "id": "f467ebfe"
+      },
+      "source": [
+        "## Dependencies\n",
+        "```python\n",
+        "!pip install transformers accelerate torch torchvision\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ea76265d",
+      "metadata": {
+        "id": "ea76265d"
+      },
+      "source": [
+        "## Tools\n",
+        "- 🤗 Transformers\n",
+        "- PyTorch\n",
+        "- Vision Model APIs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "37c57a37",
+      "metadata": {
+        "id": "37c57a37"
+      },
+      "source": [
+        "## YAML Prompt\n",
+        "```yaml\n",
+        "mode: vision\n",
+        "model: Qwen2.5-VL (7B) Vision Model\n",
+        "tasks:\n",
+        "  - image captioning\n",
+        "  - visual question answering\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7b02bc60",
+      "metadata": {
+        "id": "7b02bc60"
+      },
+      "source": [
+        "## Main\n",
+        "Below is the simplified main execution to load the model and run basic inference."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6c397c49",
+      "metadata": {
+        "id": "6c397c49"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "import os\n",
+        "if \"COLAB_\" not in \"\".join(os.environ.keys()):\n",
+        "    !pip install unsloth\n",
+        "else:\n",
+        "    # Do this only in Colab notebooks! Otherwise use pip install unsloth\n",
+        "    !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo\n",
+        "    !pip install sentencepiece protobuf \"datasets>=3.4.1\" huggingface_hub hf_transfer\n",
+        "    !pip install --no-deps unsloth"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "18b3d67c",
+      "metadata": {
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+          ]
+        },
+        "id": "18b3d67c",
+        "outputId": "f0d7f72f-8166-439b-96da-ed7e30f8bdb3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
+            "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
+            "==((====))==  Unsloth 2025.3.19: Fast Qwen2_5_Vl patching. Transformers: 4.50.0.\n",
+            "   \\\\   /|    Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n",
+            "O^O/ \\_/ \\    Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0\n",
+            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False]\n",
+            " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
+            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "73fc3ac47de840cda7a07297153404d5",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/5.97G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "78ae8aea29e04420bcfe616061934d98",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/267 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "45385885a26e42d0b1f52eda382fd6f4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "preprocessor_config.json:   0%|          | 0.00/575 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.50, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "14e519d5e0834fd9a70a111140f8429a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/7.33k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "643db314d8e94feeb9cd8747909eaabe",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "bbb5e6a0a8b64d10802fbf3e89778306",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "merges.txt:   0%|          | 0.00/1.67M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c1af10a57c0a40cf818d0974facdcc36",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c2f518986b724620bbc67f7b6ede6d96",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "added_tokens.json:   0%|          | 0.00/605 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3af59a3e9ac2430da201262fc54b5998",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a863bb17b5984981bf9bd4ff93e438f4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "chat_template.json:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from unsloth import FastVisionModel # FastLanguageModel for LLMs\n",
+        "import torch\n",
+        "\n",
+        "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
+        "fourbit_models = [\n",
+        "    \"unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit\", # Llama 3.2 vision support\n",
+        "    \"unsloth/Llama-3.2-11B-Vision-bnb-4bit\",\n",
+        "    \"unsloth/Llama-3.2-90B-Vision-Instruct-bnb-4bit\", # Can fit in a 80GB card!\n",
+        "    \"unsloth/Llama-3.2-90B-Vision-bnb-4bit\",\n",
+        "\n",
+        "    \"unsloth/Pixtral-12B-2409-bnb-4bit\",              # Pixtral fits in 16GB!\n",
+        "    \"unsloth/Pixtral-12B-Base-2409-bnb-4bit\",         # Pixtral base model\n",
+        "\n",
+        "    \"unsloth/Qwen2-VL-2B-Instruct-bnb-4bit\",          # Qwen2 VL support\n",
+        "    \"unsloth/Qwen2-VL-7B-Instruct-bnb-4bit\",\n",
+        "    \"unsloth/Qwen2-VL-72B-Instruct-bnb-4bit\",\n",
+        "\n",
+        "    \"unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit\",      # Any Llava variant works!\n",
+        "    \"unsloth/llava-1.5-7b-hf-bnb-4bit\",\n",
+        "] # More models at https://huggingface.co/unsloth\n",
+        "\n",
+        "model, tokenizer = FastVisionModel.from_pretrained(\n",
+        "    \"unsloth/Qwen2.5-VL-7B-Instruct-bnb-4bit\",\n",
+        "    load_in_4bit = True, # Use 4bit to reduce memory use. False for 16bit LoRA.\n",
+        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for long context\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "45f1a7d8",
+      "metadata": {
+        "id": "45f1a7d8"
+      },
+      "outputs": [],
+      "source": [
+        "model = FastVisionModel.get_peft_model(\n",
+        "    model,\n",
+        "    finetune_vision_layers     = True, # False if not finetuning vision layers\n",
+        "    finetune_language_layers   = True, # False if not finetuning language layers\n",
+        "    finetune_attention_modules = True, # False if not finetuning attention layers\n",
+        "    finetune_mlp_modules       = True, # False if not finetuning MLP layers\n",
+        "\n",
+        "    r = 16,           # The larger, the higher the accuracy, but might overfit\n",
+        "    lora_alpha = 16,  # Recommended alpha == r at least\n",
+        "    lora_dropout = 0,\n",
+        "    bias = \"none\",\n",
+        "    random_state = 3407,\n",
+        "    use_rslora = False,  # We support rank stabilized LoRA\n",
+        "    loftq_config = None, # And LoftQ\n",
+        "    # target_modules = \"all-linear\", # Optional now! Can specify a list if needed\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "915566b6",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 177,
+          "referenced_widgets": [
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+            "0a21bae13a3c46509db70dbe8e9de5eb"
+          ]
+        },
+        "id": "915566b6",
+        "outputId": "ece64855-55d4-4011-c5b8-33a9bb622734"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5e4f3f4910d1477b924362c095a0cedd",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "README.md:   0%|          | 0.00/519 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7365368b63d9425db5d4e5514e6e0586",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00000-of-00001.parquet:   0%|          | 0.00/344M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4ebb36e479884847bcef5736e276dfb9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "test-00000-of-00001.parquet:   0%|          | 0.00/38.2M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "639b886f72e24951909e4ffb6cfa247d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split:   0%|          | 0/68686 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f5629e8c82c848218f69810e1b21b0e4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test split:   0%|          | 0/7632 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "dataset = load_dataset(\"unsloth/LaTeX_OCR\", split = \"train\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6193435d",
+      "metadata": {
+        "id": "6193435d"
+      },
+      "source": [
+        "## Output\n",
+        "This shows a basic output example of the vision-language model."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

examples/cookbooks/Sesame_CSM_1B_TTS.ipynb

@@ -0,0 +1,146 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "a050dfc0",
+      "metadata": {
+        "id": "a050dfc0"
+      },
+      "source": [
+        "# Sesame CSM (1B) - Text to Speech"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7c2c61ec",
+      "metadata": {
+        "id": "7c2c61ec"
+      },
+      "source": [
+        "This notebook showcases how to generate speech from text using the Sesame CSM 1B model. This is ideal for converting instructional or conversational content into natural audio output."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Sesame_CSM_1B_TTS.ipynb)\n"
+      ],
+      "metadata": {
+        "id": "5SA94wTb9AH-"
+      },
+      "id": "5SA94wTb9AH-"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ab27fd8d",
+      "metadata": {
+        "id": "ab27fd8d"
+      },
+      "source": [
+        "## 🧩 Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ac3a627f",
+      "metadata": {
+        "id": "ac3a627f"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q transformers\n",
+        "!pip install -q torchaudio\n",
+        "!pip install -q soundfile"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a382e4d0",
+      "metadata": {
+        "id": "a382e4d0"
+      },
+      "source": [
+        "## 🛠️ Tools\n",
+        "- transformers for loading the model\n",
+        "- torchaudio for audio processing\n",
+        "- soundfile for playback"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7ff6eb6d",
+      "metadata": {
+        "id": "7ff6eb6d"
+      },
+      "source": [
+        "## 🧾 YAML Prompt\n",
+        "```yaml\n",
+        "task: \"Text to Speech\"\n",
+        "style: \"Clear, educational\"\n",
+        "language: \"en\"\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0ab9cda8",
+      "metadata": {
+        "id": "0ab9cda8"
+      },
+      "source": [
+        "## 🧠 Main"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6459f8bd",
+      "metadata": {
+        "id": "6459f8bd"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq\n",
+        "import torchaudio\n",
+        "import soundfile as sf\n",
+        "import torch\n",
+        "\n",
+        "processor = AutoProcessor.from_pretrained(\"m-a-p/Sesame-CM\")\n",
+        "model = AutoModelForSpeechSeq2Seq.from_pretrained(\"m-a-p/Sesame-CM\")\n",
+        "model = model.to(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "\n",
+        "text = \"Welcome to the world of voice synthesis using open models!\"\n",
+        "inputs = processor(text, return_tensors=\"pt\").to(model.device)\n",
+        "with torch.no_grad():\n",
+        "    outputs = model.generate(**inputs)\n",
+        "speech = processor.batch_decode(outputs, return_tensors=\"pt\")[0]\n",
+        "\n",
+        "sf.write(\"output.wav\", speech.numpy(), 16000)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "376fe112",
+      "metadata": {
+        "id": "376fe112"
+      },
+      "source": [
+        "## 📤 Output\n",
+        "🖼️ Output Preview (Text Summary):\n",
+        "\n",
+        "Prompt: A clear educational text is converted to a .wav file.\n",
+        "\n",
+        "🎧 The output audio will say: 'Welcome to the world of voice synthesis using open models!'\n",
+        "This demonstrates how Sesame-CM can be used for building TTS applications easily."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

examples/cookbooks/qwen_colab_agent.ipynb

@@ -6,11 +6,19 @@
         "id": "IY0bHc5lF46I"
       },
       "source": [
+
+        "# \ud83e\udd16 Qwen Model Agent\n",
+        "This notebook runs a chatbot agent using the `Qwen/Qwen1.5-1.8B` model via the `transformers` library in Google Colab.\n",
+        "\n",
+        "\u2705 Lightweight model (1.8B) so it runs on Colab Free.\n",
+        "\u2699\ufe0f No extra setup needed beyond `transformers` and `torch`.\n",
+=======
         "# 🤖 Qwen Model Agent\n",
         "This notebook runs a chatbot agent using the `Qwen/Qwen1.5-1.8B` model via the `transformers` library in Google Colab.\n",
         "\n",
         "✅ Lightweight model (1.8B) so it runs on Colab Free.\n",
         "⚙️ No extra setup needed beyond `transformers` and `torch`.\n",
+
         "---"
       ],
       "id": "IY0bHc5lF46I"
@@ -50,6 +58,18 @@
           "output_type": "stream",
           "name": "stdout",
           "text": [
+
+            "\u001b[2K   \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m36.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m31.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m23.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m81.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+=======
             "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m36.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m31.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
@@ -60,6 +80,7 @@
             "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m81.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+
             "\u001b[?25h"
           ]
         }
@@ -73,7 +94,11 @@
     {
       "cell_type": "markdown",
       "source": [
+
+        "# \u2705 Use the official BERT Large model"
+=======
         "# ✅ Use the official BERT Large model"
+
       ],
       "metadata": {
         "id": "PvTxUutEce5s"
@@ -324,6 +349,8 @@
     },
     "colab": {
       "provenance": []
+
+=======
     },
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {
@@ -2038,8 +2065,13 @@
           }
         }
       }
+
     }
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+
+}
+=======
+}
+

examples/python/api/secondary-market-research-api.py

@@ -269,7 +269,7 @@ async def generate_market_research_report(job_id: str, config: MarketResearchCon
         workflow = PraisonAIAgents(
             agents=list(agents.values()),
             tasks=tasks,
-            process="workflow",
+            process="sequential",
             verbose=False
         )
         
@@ -296,14 +296,92 @@ async def generate_market_research_report(job_id: str, config: MarketResearchCon
             "research_findings": {}
         }
         
-        # Extract results from each task
-        if "task_results" in results:
-            for task_name, result in results["task_results"].items():
-                if result:
-                    report_data["research_findings"][task_name] = {
-                        "content": result.raw,
-                        "agent": result.agent if hasattr(result, 'agent') else "Unknown"
-                    }
+        # Extract results from each task with comprehensive logging
+        print(f"DEBUG: Raw results type: {type(results)}")
+        print(f"DEBUG: Raw results content: {results}")
+        
+        if isinstance(results, str):
+            # Sequential process returns a string result directly
+            print("DEBUG: Sequential process returned string result")
+            report_data["research_findings"]["synthesis_report"] = {
+                "content": results,
+                "agent": "Research Report Synthesizer",
+                "section": "Complete Market Research Report"
+            }
+            print(f"DEBUG: Added synthesis_report with content length: {len(results)}")
+            
+        elif isinstance(results, dict):
+            print(f"DEBUG: Results is dict with keys: {list(results.keys())}")
+            
+            if "task_results" in results:
+                print(f"DEBUG: Found task_results: {results['task_results']}")
+                print(f"DEBUG: task_results type: {type(results['task_results'])}")
+                
+                for task_name, result in results["task_results"].items():
+                    print(f"DEBUG: Processing task '{task_name}', result type: {type(result)}")
+                    print(f"DEBUG: Result content: {result}")
+                    
+                    if result:
+                        # Try multiple ways to extract content
+                        content = None
+                        agent_name = "Unknown"
+                        
+                        if hasattr(result, 'raw'):
+                            content = result.raw
+                            print(f"DEBUG: Extracted content from result.raw")
+                        elif hasattr(result, 'content'):
+                            content = result.content
+                            print(f"DEBUG: Extracted content from result.content")
+                        elif isinstance(result, str):
+                            content = result
+                            print(f"DEBUG: Result is string, using directly")
+                        else:
+                            content = str(result)
+                            print(f"DEBUG: Converting result to string")
+                        
+                        if hasattr(result, 'agent'):
+                            agent_name = result.agent
+                        elif hasattr(result, 'agent_name'):
+                            agent_name = result.agent_name
+                            
+                        if content:
+                            report_data["research_findings"][task_name] = {
+                                "content": content,
+                                "agent": agent_name,
+                                "section": f"Task {task_name}"
+                            }
+                            print(f"DEBUG: Added task '{task_name}' with content length: {len(str(content))}")
+                        else:
+                            print(f"DEBUG: No content found for task '{task_name}'")
+                    else:
+                        print(f"DEBUG: Task '{task_name}' result is None or empty")
+            else:
+                print("DEBUG: No 'task_results' found in results dict")
+                # Try to use the whole results as content
+                report_data["research_findings"]["workflow_output"] = {
+                    "content": str(results),
+                    "agent": "PraisonAI Workflow",
+                    "section": "Complete Workflow Output"
+                }
+                print("DEBUG: Added entire results as workflow_output")
+        else:
+            print(f"DEBUG: Unexpected results type: {type(results)}")
+            report_data["research_findings"]["raw_output"] = {
+                "content": str(results),
+                "agent": "Unknown",
+                "section": "Raw Output"
+            }
+        
+        print(f"DEBUG: Final research_findings keys: {list(report_data['research_findings'].keys())}")
+        print(f"DEBUG: Final research_findings count: {len(report_data['research_findings'])}")
+        
+        # Generate executive summary from synthesis if available
+        if "synthesis_report" in report_data["research_findings"]:
+            content = report_data["research_findings"]["synthesis_report"]["content"]
+            # Extract first few sentences as executive summary
+            sentences = content.split('. ')[:3]
+            report_data["executive_summary"] = '. '.join(sentences) + '.' if sentences else "Market research analysis completed."
+            print("DEBUG: Generated executive summary from synthesis report")
         
         # Save report to file
         report_filename = f"market_research_{job_id}.json"

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

@@ -549,6 +549,7 @@ class LLM:
                         })
                         
                         should_continue = False
+                        tool_results = []  # Store all tool results
                         for tool_call in tool_calls:
                             # Handle both object and dict access patterns
                             if isinstance(tool_call, dict):
@@ -569,6 +570,7 @@ class LLM:
                             logging.debug(f"[TOOL_EXEC_DEBUG] About to execute tool {function_name} with args: {arguments}")
                             tool_result = execute_tool_fn(function_name, arguments)
                             logging.debug(f"[TOOL_EXEC_DEBUG] Tool execution result: {tool_result}")
+                            tool_results.append(tool_result)  # Store the result
 
                             if verbose:
                                 display_message = f"Agent {agent_name} called function '{function_name}' with arguments: {arguments}\n"
@@ -601,7 +603,8 @@ class LLM:
                         # If we reach here, no more tool calls needed - get final response
                         # Make one more call to get the final summary response
                         # Special handling for Ollama models that don't automatically process tool results
-                        if self.model and self.model.startswith("ollama/") and tool_result:
+                        ollama_handled = False
+                        if self.model and self.model.startswith("ollama/") and tool_results:
                             # For Ollama models, we need to explicitly ask the model to process the tool results
                             # First, check if the response is just a JSON tool call
                             try:
@@ -614,13 +617,30 @@ class LLM:
                                     # Create a prompt that asks the model to process the tool results based on original context
                                     # Extract the original user query from messages
                                     original_query = ""
-                                    for msg in messages:
+                                    for msg in reversed(messages):  # Look from the end to find the most recent user message
                                         if msg.get("role") == "user":
-                                            original_query = msg.get("content", "")
-                                            break
+                                            content = msg.get("content", "")
+                                            # Handle list content (multimodal)
+                                            if isinstance(content, list):
+                                                for item in content:
+                                                    if isinstance(item, dict) and item.get("type") == "text":
+                                                        original_query = item.get("text", "")
+                                                        break
+                                            else:
+                                                original_query = content
+                                            if original_query:
+                                                break
                                     
-                                    # Create a shorter follow-up prompt
-                                    follow_up_prompt = f"Results:\n{json.dumps(tool_result, indent=2)}\nProvide Answer to this Original Question based on the above results: '{original_query}'"
+                                    # Create a shorter follow-up prompt with all tool results
+                                    # If there's only one result, use it directly; otherwise combine them
+                                    if len(tool_results) == 1:
+                                        results_text = json.dumps(tool_results[0], indent=2)
+                                    else:
+                                        results_text = json.dumps(tool_results, indent=2)
+                                    
+                                    follow_up_prompt = f"Results:\n{results_text}\nProvide Answer to this Original Question based on the above results: '{original_query}'"
+                                    logging.debug(f"[OLLAMA_DEBUG] Original query extracted: {original_query}")
+                                    logging.debug(f"[OLLAMA_DEBUG] Follow-up prompt: {follow_up_prompt[:200]}...")
                                     
                                     # Make a follow-up call to process the results
                                     follow_up_messages = [
@@ -653,12 +673,33 @@ class LLM:
                                         ):
                                             if chunk and chunk.choices and chunk.choices[0].delta.content:
                                                 response_text += chunk.choices[0].delta.content
+                                    
+                                    # Set flag to indicate Ollama was handled
+                                    ollama_handled = True
+                                    final_response_text = response_text.strip()
+                                    logging.debug(f"[OLLAMA_DEBUG] Ollama follow-up response: {final_response_text[:200]}...")
+                                    
+                                    # Display the response if we got one
+                                    if final_response_text and verbose:
+                                        display_interaction(
+                                            original_prompt,
+                                            final_response_text,
+                                            markdown=markdown,
+                                            generation_time=time.time() - start_time,
+                                            console=console
+                                        )
+                                    
+                                    # Return the final response after processing Ollama's follow-up
+                                    if final_response_text:
+                                        return final_response_text
+                                    else:
+                                        logging.warning("[OLLAMA_DEBUG] Ollama follow-up returned empty response")
                             except (json.JSONDecodeError, KeyError):
                                 # Not a JSON response or not a tool call format, continue normally
                                 pass
                         
-                        # If reasoning_steps is True, do a single non-streaming call
-                        elif reasoning_steps:
+                        # If reasoning_steps is True and we haven't handled Ollama already, do a single non-streaming call
+                        if reasoning_steps and not ollama_handled:
                             resp = litellm.completion(
                                 **self._build_completion_params(
                                     messages=messages,
@@ -688,8 +729,8 @@ class LLM:
                                     console=console
                                 )
                         
-                        # Otherwise do the existing streaming approach
-                        else:
+                        # Otherwise do the existing streaming approach if not already handled
+                        elif not ollama_handled:
                             # Get response after tool calls with streaming
                             if verbose:
                                 with Live(display_generating("", current_time), console=console, refresh_per_second=4) as live:
@@ -1225,6 +1266,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         "tool_calls": serializable_tool_calls
                     })
                     
+                    tool_results = []  # Store all tool results
                     for tool_call in tool_calls:
                         # Handle both object and dict access patterns
                         if isinstance(tool_call, dict):
@@ -1243,6 +1285,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                 tool_call_id = f"tool_{id(tool_call)}"
 
                         tool_result = await execute_tool_fn(function_name, arguments)
+                        tool_results.append(tool_result)  # Store the result
 
                         if verbose:
                             display_message = f"Agent {agent_name} called function '{function_name}' with arguments: {arguments}\n"
@@ -1261,7 +1304,8 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     response_text = ""
                     
                     # Special handling for Ollama models that don't automatically process tool results
-                    if self._is_ollama_provider() and tool_result:
+                    ollama_handled = False
+                    if self._is_ollama_provider() and tool_results:
                         # For Ollama models, we need to explicitly ask the model to process the tool results
                         # First, check if the response is just a JSON tool call
                         try:
@@ -1274,13 +1318,30 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                 # Create a prompt that asks the model to process the tool results based on original context
                                 # Extract the original user query from messages
                                 original_query = ""
-                                for msg in messages:
+                                for msg in reversed(messages):  # Look from the end to find the most recent user message
                                     if msg.get("role") == "user":
-                                        original_query = msg.get("content", "")
-                                        break
+                                        content = msg.get("content", "")
+                                        # Handle list content (multimodal)
+                                        if isinstance(content, list):
+                                            for item in content:
+                                                if isinstance(item, dict) and item.get("type") == "text":
+                                                    original_query = item.get("text", "")
+                                                    break
+                                        else:
+                                            original_query = content
+                                        if original_query:
+                                            break
                                 
-                                # Create a shorter follow-up prompt
-                                follow_up_prompt = f"Results:\n{json.dumps(tool_result, indent=2)}\nProvide Answer to this Original Question based on the above results: '{original_query}'"
+                                # Create a shorter follow-up prompt with all tool results
+                                # If there's only one result, use it directly; otherwise combine them
+                                if len(tool_results) == 1:
+                                    results_text = json.dumps(tool_results[0], indent=2)
+                                else:
+                                    results_text = json.dumps(tool_results, indent=2)
+                                
+                                follow_up_prompt = f"Results:\n{results_text}\nProvide Answer to this Original Question based on the above results: '{original_query}'"
+                                logging.debug(f"[OLLAMA_DEBUG] Original query extracted: {original_query}")
+                                logging.debug(f"[OLLAMA_DEBUG] Follow-up prompt: {follow_up_prompt[:200]}...")
                                 
                                 # Make a follow-up call to process the results
                                 follow_up_messages = [
@@ -1313,12 +1374,33 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                     ):
                                         if chunk and chunk.choices and chunk.choices[0].delta.content:
                                             response_text += chunk.choices[0].delta.content
+                                
+                                # Set flag to indicate Ollama was handled
+                                ollama_handled = True
+                                final_response_text = response_text.strip()
+                                logging.debug(f"[OLLAMA_DEBUG] Ollama follow-up response: {final_response_text[:200]}...")
+                                
+                                # Display the response if we got one
+                                if final_response_text and verbose:
+                                    display_interaction(
+                                        original_prompt,
+                                        final_response_text,
+                                        markdown=markdown,
+                                        generation_time=time.time() - start_time,
+                                        console=console
+                                    )
+                                
+                                # Return the final response after processing Ollama's follow-up
+                                if final_response_text:
+                                    return final_response_text
+                                else:
+                                    logging.warning("[OLLAMA_DEBUG] Ollama follow-up returned empty response")
                         except (json.JSONDecodeError, KeyError):
                             # Not a JSON response or not a tool call format, continue normally
                             pass
                     
                     # If no special handling was needed or if it's not an Ollama model
-                    elif reasoning_steps:
+                    if reasoning_steps and not ollama_handled:
                         # Non-streaming call to capture reasoning
                         resp = await litellm.acompletion(
                             **self._build_completion_params(
@@ -1348,8 +1430,8 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                 generation_time=time.time() - start_time,
                                 console=console
                             )
-                    else:
-                        # Get response after tool calls with streaming
+                    elif not ollama_handled:
+                        # Get response after tool calls with streaming if not already handled
                         if verbose:
                             async for chunk in await litellm.acompletion(
                                 **self._build_completion_params(

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.105"
+version = "0.0.107"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [

src/praisonai-agents/test.py

@@ -1,33 +1,36 @@
 from praisonaiagents import Agent, Task, PraisonAIAgents
-import os
-from dotenv import load_dotenv
-
-load_dotenv()
-
-llm_config = {
-    "model": "openai/gpt-4o-mini",
-    "api_key": os.getenv('OPENAI_API_KEY'),
-    "temperature": 0.7,
-    "max_tokens": 2000
-}
-
-blog_agent = Agent(
-    role="Blog Writer",
-    goal="Write a blog post about AI",
-    backstory="Expert at writing blog posts",
-    llm=llm_config,
+from praisonaiagents.tools import (
+    read_file, write_file, list_files, get_file_info,
+    copy_file, move_file, delete_file
 )
 
-blog_task = Task(
-    description="Write a blog post about AI trends in 1 paragraph",
-    expected_output="Well-written blog post about AI trends",
-    agent=blog_agent
+# Get user input
+d = input("Demande: ")
+
+# Create file manager agent
+file_manager_agent = Agent(
+    name="FileManager",
+    tools=[read_file, write_file, list_files, get_file_info,
+           copy_file, move_file, delete_file],
+    llm={
+        "model": "ollama/llama3.2",
+        "base_url": "http://localhost:11434"  # Ollama default
+    }
 )
 
+# Dynamically create a task based on input
+file_task = Task(
+    name="Q",
+    description=f"faire '{d}'.",
+    expected_output=f"'{d}' bien fait.",
+    agent=file_manager_agent
+)
+
+# Run agent with the task
 agents = PraisonAIAgents(
-    agents=[blog_agent],
-    tasks=[blog_task],
-    memory=False
+    agents=[file_manager_agent],
+    tasks=[file_task],
+    process="sequential"
 )
 
-result = agents.start()
+agents.start()

src/praisonai-agents/tests/test_ollama_async_fix.py

@@ -0,0 +1,161 @@
+#!/usr/bin/env python3
+"""
+Test script to verify Ollama empty response fix for both sync and async methods.
+"""
+
+import asyncio
+import logging
+from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from typing import Dict, Any
+
+# Enable debug logging
+logging.basicConfig(level=logging.DEBUG)
+
+# Define a simple tool for testing
+def search_tool(query: str) -> Dict[str, Any]:
+    """Simulate a search tool that returns results."""
+    logging.debug(f"[TEST] Search tool called with query: {query}")
+    results = {
+        "results": [
+            {"title": "Result 1", "description": f"Information about {query}"},
+            {"title": "Result 2", "description": f"More details on {query}"}
+        ],
+        "total": 2
+    }
+    logging.debug(f"[TEST] Search tool returning: {results}")
+    return results
+
+# Test with both sync and async workflows
+async def test_model_async(model_name: str):
+    print(f"\n{'='*60}")
+    print(f"Testing ASYNC with model: {model_name}")
+    print('='*60)
+    
+    # Create agent with the search tool
+    agent = Agent(
+        name="SearchAgent",
+        role="Information Researcher",
+        goal="Search for information and provide helpful answers",
+        backstory="You are an expert at finding and summarizing information.",
+        llm=model_name,
+        tools=[search_tool],
+        verbose=True
+    )
+    
+    # Create a task that requires tool usage
+    task = Task(
+        name="search_task",
+        description="Search for information about 'Python programming' and provide a summary of what you found.",
+        expected_output="A clear summary of the search results",
+        agent=agent
+    )
+    
+    # Create and run the workflow
+    workflow = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        verbose=True
+    )
+    
+    try:
+        result = await workflow.astart()  # Use async start
+        print(f"\n{'='*60}")
+        print(f"ASYNC RESULT for {model_name}:")
+        print('='*60)
+        print(result)
+        
+        # Check if result is empty
+        if not result or (isinstance(result, dict) and not result.get('task_results', {}).get('search_task', {}).get('output')):
+            print(f"\n❌ ERROR: Empty response from {model_name} (async)")
+            return False
+        else:
+            print(f"\n✅ SUCCESS: Got valid response from {model_name} (async)")
+            return True
+            
+    except Exception as e:
+        print(f"\n❌ ERROR with {model_name} (async): {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_model_sync(model_name: str):
+    print(f"\n{'='*60}")
+    print(f"Testing SYNC with model: {model_name}")
+    print('='*60)
+    
+    # Create agent with the search tool
+    agent = Agent(
+        name="SearchAgent",
+        role="Information Researcher",
+        goal="Search for information and provide helpful answers",
+        backstory="You are an expert at finding and summarizing information.",
+        llm=model_name,
+        tools=[search_tool],
+        verbose=True
+    )
+    
+    # Create a task that requires tool usage
+    task = Task(
+        name="search_task",
+        description="Search for information about 'Python programming' and provide a summary of what you found.",
+        expected_output="A clear summary of the search results",
+        agent=agent
+    )
+    
+    # Create and run the workflow
+    workflow = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        verbose=True
+    )
+    
+    try:
+        result = workflow.start()  # Use sync start
+        print(f"\n{'='*60}")
+        print(f"SYNC RESULT for {model_name}:")
+        print('='*60)
+        print(result)
+        
+        # Check if result is empty
+        if not result or (isinstance(result, dict) and not result.get('task_results', {}).get('search_task', {}).get('output')):
+            print(f"\n❌ ERROR: Empty response from {model_name} (sync)")
+            return False
+        else:
+            print(f"\n✅ SUCCESS: Got valid response from {model_name} (sync)")
+            return True
+            
+    except Exception as e:
+        print(f"\n❌ ERROR with {model_name} (sync): {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+async def main():
+    print("Testing Ollama empty response fix for both sync and async...")
+    
+    # Test sync methods
+    print("\n1. Testing SYNC methods:")
+    openai_sync_success = test_model_sync("openai/gpt-4o-mini")
+    ollama_sync_success = test_model_sync("ollama/llama3.2")
+    
+    # Test async methods
+    print("\n2. Testing ASYNC methods:")
+    openai_async_success = await test_model_async("openai/gpt-4o-mini")
+    ollama_async_success = await test_model_async("ollama/llama3.2")
+    
+    # Summary
+    print(f"\n{'='*60}")
+    print("TEST SUMMARY:")
+    print('='*60)
+    print(f"OpenAI sync test: {'✅ PASSED' if openai_sync_success else '❌ FAILED'}")
+    print(f"Ollama sync test: {'✅ PASSED' if ollama_sync_success else '❌ FAILED'}")
+    print(f"OpenAI async test: {'✅ PASSED' if openai_async_success else '❌ FAILED'}")
+    print(f"Ollama async test: {'✅ PASSED' if ollama_async_success else '❌ FAILED'}")
+    
+    if ollama_sync_success and ollama_async_success:
+        print("\n🎉 Ollama empty response issue has been fixed for both sync and async!")
+    else:
+        print("\n⚠️  Ollama empty response issue still exists in some cases.")
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/praisonai-agents/tests/test_ollama_fix.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python3
+"""
+Test script to verify Ollama empty response fix.
+"""
+
+import logging
+from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from typing import Dict, Any
+
+# Enable debug logging
+logging.basicConfig(level=logging.DEBUG)
+
+# Define a simple tool for testing
+def search_tool(query: str) -> Dict[str, Any]:
+    """Simulate a search tool that returns results."""
+    logging.debug(f"[TEST] Search tool called with query: {query}")
+    results = {
+        "results": [
+            {"title": "Result 1", "description": f"Information about {query}"},
+            {"title": "Result 2", "description": f"More details on {query}"}
+        ],
+        "total": 2
+    }
+    logging.debug(f"[TEST] Search tool returning: {results}")
+    return results
+
+# Test with both OpenAI and Ollama models
+def test_model(model_name: str):
+    print(f"\n{'='*60}")
+    print(f"Testing with model: {model_name}")
+    print('='*60)
+    
+    # Create agent with the search tool
+    agent = Agent(
+        name="SearchAgent",
+        role="Information Researcher",
+        goal="Search for information and provide helpful answers",
+        backstory="You are an expert at finding and summarizing information.",
+        llm=model_name,
+        tools=[search_tool],
+        verbose=True
+    )
+    
+    # Create a task that requires tool usage
+    task = Task(
+        name="search_task",
+        description="Search for information about 'Python programming' and provide a summary of what you found.",
+        expected_output="A clear summary of the search results",
+        agent=agent
+    )
+    
+    # Create and run the workflow
+    workflow = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        verbose=True
+    )
+    
+    try:
+        result = workflow.start()
+        print(f"\n{'='*60}")
+        print(f"FINAL RESULT for {model_name}:")
+        print('='*60)
+        print(result)
+        
+        # Check if result is empty
+        if not result or (isinstance(result, dict) and not result.get('task_results', {}).get('search_task', {}).get('output')):
+            print(f"\n❌ ERROR: Empty response from {model_name}")
+            return False
+        else:
+            print(f"\n✅ SUCCESS: Got valid response from {model_name}")
+            return True
+            
+    except Exception as e:
+        print(f"\n❌ ERROR with {model_name}: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+if __name__ == "__main__":
+    print("Testing Ollama empty response fix...")
+    
+    # Test with OpenAI first (as baseline)
+    print("\n1. Testing with OpenAI (baseline):")
+    openai_success = test_model("openai/gpt-4o-mini")
+    
+    # Test with Ollama
+    print("\n2. Testing with Ollama:")
+    ollama_success = test_model("ollama/llama3.2")
+    
+    # Summary
+    print(f"\n{'='*60}")
+    print("TEST SUMMARY:")
+    print('='*60)
+    print(f"OpenAI test: {'✅ PASSED' if openai_success else '❌ FAILED'}")
+    print(f"Ollama test: {'✅ PASSED' if ollama_success else '❌ FAILED'}")
+    
+    if ollama_success:
+        print("\n🎉 Ollama empty response issue has been fixed!")
+    else:
+        print("\n⚠️  Ollama empty response issue still exists.")

src/praisonai-agents/weather_server.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python
+from mcp.server.fastmcp import FastMCP
+import logging
+import datetime
+from typing import Dict, List, Union
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, filename="weather_server.log")
+logger = logging.getLogger("weather-server")
+
+# Initialize FastMCP server for weather tools
+mcp = FastMCP("weather-tools")
+
+# Mock weather data for cities
+MOCK_WEATHER_DATA = {
+    "london": {
+        "temp": 15,
+        "feels_like": 13,
+        "temp_min": 12,
+        "temp_max": 17,
+        "humidity": 76,
+        "description": "Cloudy with occasional rain",
+        "wind_speed": 12,
+        "country": "GB"
+    },
+    "paris": {
+        "temp": 18,
+        "feels_like": 17,
+        "temp_min": 16,
+        "temp_max": 20,
+        "humidity": 65,
+        "description": "Partly cloudy",
+        "wind_speed": 8,
+        "country": "FR"
+    },
+    "new york": {
+        "temp": 22,
+        "feels_like": 24,
+        "temp_min": 20,
+        "temp_max": 25,
+        "humidity": 58,
+        "description": "Clear sky",
+        "wind_speed": 10,
+        "country": "US"
+    },
+    "tokyo": {
+        "temp": 26,
+        "feels_like": 28,
+        "temp_min": 24,
+        "temp_max": 29,
+        "humidity": 72,
+        "description": "Humid and warm",
+        "wind_speed": 7,
+        "country": "JP"
+    },
+    "sydney": {
+        "temp": 20,
+        "feels_like": 19,
+        "temp_min": 17,
+        "temp_max": 23,
+        "humidity": 60,
+        "description": "Sunny with light breeze",
+        "wind_speed": 15,
+        "country": "AU"
+    }
+}
+
+@mcp.tool()
+def get_weather(location: Union[str, List[str]]) -> Dict:
+    """Get current weather data for one or more locations.
+
+    Args:
+        location: City name (str) or list of city names (List[str]) to get weather for
+    
+    Returns:
+        Dict with weather data for each location or error message
+    """
+    def get_single_location_weather(loc: str) -> Dict:
+        """Helper function to get weather for a single location"""
+        loc = loc.lower().strip()
+        if loc in MOCK_WEATHER_DATA:
+            weather_data = MOCK_WEATHER_DATA[loc].copy()
+            current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            weather_data["time"] = current_time
+            weather_data["city"] = loc.title()
+            return weather_data
+        return {"city": loc.title(), "error": "Location not found in weather database"}
+    
+    logger.info(f"Getting weather data for {location}")
+    
+    # Handle list of locations
+    if isinstance(location, list):
+        results = {}
+        for loc in location:
+            if isinstance(loc, str):
+                results[loc] = get_single_location_weather(loc)
+        return {"results": results}
+    
+    # Handle single location
+    if isinstance(location, str):
+        return get_single_location_weather(location)
+    
+    return {"error": "Invalid location format. Please provide a string or list of strings."}
+
+if __name__ == "__main__":
+    # Run the MCP server
+    mcp.run()

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/v2.2.32.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.32.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.34.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.34.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/cli.py

@@ -529,6 +529,7 @@ class PraisonAI:
         parser.add_argument("--call", action="store_true", help="Start the PraisonAI Call server")
         parser.add_argument("--public", action="store_true", help="Use ngrok to expose the server publicly (only with --call)")
         parser.add_argument("--merge", action="store_true", help="Merge existing agents.yaml with auto-generated agents instead of overwriting")
+        parser.add_argument("--claudecode", action="store_true", help="Enable Claude Code integration for file modifications and coding tasks")
         
         # If we're in a test environment, parse with empty args to avoid pytest interference
         if in_test_env:
@@ -549,6 +550,10 @@ class PraisonAI:
         if args.command == 'code':
             args.ui = 'chainlit'
             args.code = True
+        
+        # Handle --claudecode flag for code command
+        if getattr(args, 'claudecode', False):
+            os.environ["PRAISONAI_CLAUDECODE_ENABLED"] = "true"
         if args.command == 'realtime':
             args.realtime = True
         if args.command == 'call':

src/praisonai/praisonai/deploy.py

@@ -56,7 +56,7 @@ 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==2.2.32 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.34 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/praisonai/ui/code.py

@@ -13,16 +13,25 @@ from PIL import Image
 from context import ContextGatherer
 from tavily import TavilyClient
 from crawl4ai import AsyncWebCrawler
+import subprocess
 
 # Local application/library imports
 import chainlit as cl
-from chainlit.input_widget import TextInput
+from chainlit.input_widget import TextInput, Switch
 from chainlit.types import ThreadDict
 import chainlit.data as cl_data
-from litellm import acompletion
-import litellm
 from db import DatabaseManager
 
+# PraisonAI Agents imports
+try:
+    from praisonaiagents import Agent
+    PRAISONAI_AGENTS_AVAILABLE = True
+except ImportError:
+    PRAISONAI_AGENTS_AVAILABLE = False
+    # Fallback to litellm for backward compatibility
+    from litellm import acompletion
+    import litellm
+
 # Load environment variables
 load_dotenv()
 
@@ -41,14 +50,131 @@ logger.addHandler(console_handler)
 # Set the logging level for the logger
 logger.setLevel(log_level)
 
-# Configure litellm same as in llm.py
-litellm.set_verbose = False
-litellm.success_callback = []
-litellm._async_success_callback = []
-litellm.callbacks = []
-litellm.drop_params = True
-litellm.modify_params = True
-litellm.suppress_debug_messages = True
+# Configure litellm for backward compatibility (only if praisonaiagents not available)
+if not PRAISONAI_AGENTS_AVAILABLE:
+    import litellm
+    litellm.set_verbose = False
+    litellm.success_callback = []
+    litellm._async_success_callback = []
+    litellm.callbacks = []
+    litellm.drop_params = True
+    litellm.modify_params = True
+    litellm.suppress_debug_messages = True
+
+# Claude Code Tool Function
+async def claude_code_tool(query: str) -> str:
+    """
+    Execute Claude Code CLI commands for file modifications and coding tasks.
+    
+    Args:
+        query: The user's request that requires file modifications or coding assistance
+        
+    Returns:
+        The output from Claude Code execution
+    """
+    try:
+        # Check if the current working directory is a git repository
+        repo_path = os.environ.get("PRAISONAI_CODE_REPO_PATH", ".")
+        
+        # Try to detect if git is available and if we're in a git repo
+        git_available = False
+        try:
+            subprocess.run(["git", "status"], cwd=repo_path, capture_output=True, check=True)
+            git_available = True
+        except (subprocess.CalledProcessError, FileNotFoundError):
+            git_available = False
+        
+        # Build Claude Code command
+        claude_cmd = ["claude", "--dangerously-skip-permissions", "-p", query]
+        
+        # Check if it's a continuation (simple heuristic)
+        user_session_context = cl.user_session.get("claude_code_context", False)
+        if user_session_context:
+            claude_cmd.insert(1, "--continue")
+        
+        # Execute Claude Code command
+        result = subprocess.run(
+            claude_cmd,
+            cwd=repo_path,
+            capture_output=True,
+            text=True,
+            timeout=300  # 5 minutes timeout
+        )
+        
+        # Set context for future requests
+        cl.user_session.set("claude_code_context", True)
+        
+        output = result.stdout
+        if result.stderr:
+            output += f"\n\nErrors:\n{result.stderr}"
+        
+        # If git is available and changes were made, try to create a branch and PR
+        if git_available and result.returncode == 0:
+            try:
+                # Check for changes
+                git_status = subprocess.run(
+                    ["git", "status", "--porcelain"],
+                    cwd=repo_path,
+                    capture_output=True,
+                    text=True,
+                    check=True
+                )
+                
+                if git_status.stdout.strip():
+                    # Create a branch for the changes
+                    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+                    branch_name = f"claude-code-{timestamp}"
+                    
+                    # Create and switch to new branch
+                    subprocess.run(["git", "checkout", "-b", branch_name], cwd=repo_path, check=True)
+                    
+                    # Add and commit changes
+                    subprocess.run(["git", "add", "."], cwd=repo_path, check=True)
+                    commit_message = f"Claude Code changes: {query[:50]}..."
+                    subprocess.run(
+                        ["git", "commit", "-m", commit_message],
+                        cwd=repo_path,
+                        check=True
+                    )
+                    
+                    # Push to remote (if configured)
+                    try:
+                        subprocess.run(
+                            ["git", "push", "-u", "origin", branch_name],
+                            cwd=repo_path,
+                            check=True
+                        )
+                        
+                        # Generate PR URL (assuming GitHub)
+                        remote_url = subprocess.run(
+                            ["git", "config", "--get", "remote.origin.url"],
+                            cwd=repo_path,
+                            capture_output=True,
+                            text=True
+                        )
+                        
+                        if remote_url.returncode == 0:
+                            repo_url = remote_url.stdout.strip()
+                            if repo_url.endswith(".git"):
+                                repo_url = repo_url[:-4]
+                            if "github.com" in repo_url:
+                                pr_url = f"{repo_url}/compare/main...{branch_name}?quick_pull=1"
+                                output += f"\n\n📋 **Pull Request Created:**\n{pr_url}"
+                                
+                    except subprocess.CalledProcessError:
+                        output += f"\n\n🌲 **Branch created:** {branch_name} (push manually if needed)"
+                        
+            except subprocess.CalledProcessError as e:
+                output += f"\n\nGit operations failed: {e}"
+        
+        return output
+        
+    except subprocess.TimeoutExpired:
+        return "Claude Code execution timed out after 5 minutes."
+    except subprocess.CalledProcessError as e:
+        return f"Claude Code execution failed: {e}\nStdout: {e.stdout}\nStderr: {e.stderr}"
+    except Exception as e:
+        return f"Error executing Claude Code: {str(e)}"
 
 CHAINLIT_AUTH_SECRET = os.getenv("CHAINLIT_AUTH_SECRET")
 
@@ -109,6 +235,12 @@ async def start():
         model_name = os.getenv("MODEL_NAME", "gpt-4o-mini")
         cl.user_session.set("model_name", model_name)
     logger.debug(f"Model name: {model_name}")
+    
+    # Load Claude Code setting (check CLI flag first, then database setting)
+    claude_code_enabled = os.getenv("PRAISONAI_CLAUDECODE_ENABLED", "false").lower() == "true"
+    if not claude_code_enabled:
+        claude_code_enabled = (load_setting("claude_code_enabled") or "false").lower() == "true"
+    
     settings = cl.ChatSettings(
         [
             TextInput(
@@ -116,6 +248,11 @@ async def start():
                 label="Enter the Model Name",
                 placeholder="e.g., gpt-4o-mini",
                 initial=model_name
+            ),
+            Switch(
+                id="claude_code_enabled",
+                label="Enable Claude Code (file modifications & coding)",
+                initial=claude_code_enabled
             )
         ]
     )
@@ -134,10 +271,13 @@ async def setup_agent(settings):
     logger.debug(settings)
     cl.user_session.set("settings", settings)
     model_name = settings["model_name"]
+    claude_code_enabled = settings.get("claude_code_enabled", False)
     cl.user_session.set("model_name", model_name)
+    cl.user_session.set("claude_code_enabled", claude_code_enabled)
     
     # Save in settings table
     save_setting("model_name", model_name)
+    save_setting("claude_code_enabled", str(claude_code_enabled).lower())
     
     # Save in thread metadata
     thread_id = cl.user_session.get("thread_id")
@@ -152,6 +292,7 @@ async def setup_agent(settings):
                     metadata = {}
             
             metadata["model_name"] = model_name
+            metadata["claude_code_enabled"] = claude_code_enabled
             
             # Always store metadata as a dictionary
             await cl_data._data_layer.update_thread(thread_id, metadata=metadata)
@@ -221,6 +362,7 @@ tools = [{
 @cl.on_message
 async def main(message: cl.Message):
     model_name = load_setting("model_name") or os.getenv("MODEL_NAME") or "gpt-4o-mini"
+    claude_code_enabled = cl.user_session.get("claude_code_enabled", False)
     message_history = cl.user_session.get("message_history", [])
     repo_path_to_use = os.environ.get("PRAISONAI_CODE_REPO_PATH", ".")
     gatherer = ContextGatherer(directory=repo_path_to_use)
@@ -258,6 +400,87 @@ Context:
     msg = cl.Message(content="")
     await msg.send()
 
+    # Use PraisonAI Agents if available, otherwise fallback to litellm
+    if PRAISONAI_AGENTS_AVAILABLE:
+        await handle_with_praisonai_agents(message, user_message, model_name, claude_code_enabled, msg, image)
+    else:
+        await handle_with_litellm(user_message, model_name, message_history, msg, image)
+
+async def handle_with_praisonai_agents(message, user_message, model_name, claude_code_enabled, msg, image):
+    """Handle message using PraisonAI Agents framework with optional Claude Code tool"""
+    try:
+        # Prepare tools list
+        available_tools = []
+        
+        # Add Tavily search tool if API key available
+        if tavily_api_key:
+            available_tools.append(tavily_web_search)
+        
+        # Add Claude Code tool if enabled
+        if claude_code_enabled:
+            available_tools.append(claude_code_tool)
+        
+        # Create agent instructions
+        instructions = """You are a helpful AI assistant. Use the available tools when needed to provide comprehensive responses.
+        
+If Claude Code tool is available and the user's request involves:
+- File modifications, code changes, or implementation tasks
+- Creating, editing, or debugging code
+- Project setup or development tasks
+- Git operations or version control
+
+Then use the Claude Code tool to handle those requests.
+
+For informational questions, explanations, or general conversations, respond normally without using Claude Code."""
+
+        # Create agent
+        agent = Agent(
+            name="PraisonAI Assistant",
+            instructions=instructions,
+            llm=model_name,
+            tools=available_tools if available_tools else None
+        )
+        
+        # Execute agent with streaming
+        full_response = ""
+        
+        # Use agent's streaming capabilities if available
+        try:
+            # For now, use synchronous execution and stream the result
+            # TODO: Implement proper streaming when PraisonAI agents support it
+            result = agent.start(user_message)
+            
+            # Stream the response character by character for better UX
+            if hasattr(result, 'raw'):
+                response_text = result.raw
+            else:
+                response_text = str(result)
+            
+            for char in response_text:
+                await msg.stream_token(char)
+                full_response += char
+                # Small delay to make streaming visible
+                await asyncio.sleep(0.01)
+            
+        except Exception as e:
+            error_response = f"Error executing agent: {str(e)}"
+            for char in error_response:
+                await msg.stream_token(char)
+                full_response += char
+                await asyncio.sleep(0.01)
+        
+        msg.content = full_response
+        await msg.update()
+        
+    except Exception as e:
+        error_msg = f"Failed to use PraisonAI Agents: {str(e)}"
+        logger.error(error_msg)
+        await msg.stream_token(error_msg)
+        msg.content = error_msg
+        await msg.update()
+
+async def handle_with_litellm(user_message, model_name, message_history, msg, image):
+    """Fallback handler using litellm for backward compatibility"""
     # Prepare the completion parameters using the helper function
     completion_params = _build_completion_params(
         model_name,
@@ -279,7 +502,7 @@ Context:
             ]
         }
         # Use a vision-capable model when an image is present
-        completion_params["model"] = "gpt-4-vision-preview"  # Adjust this to your actual vision-capable model
+        completion_params["model"] = "gpt-4-vision-preview"
 
     # Only add tools and tool_choice if Tavily API key is available and no image is uploaded
     if tavily_api_key:
@@ -412,6 +635,10 @@ async def send_count():
 async def on_chat_resume(thread: ThreadDict):
     logger.info(f"Resuming chat: {thread['id']}")
     model_name = load_setting("model_name") or os.getenv("MODEL_NAME") or "gpt-4o-mini"
+    # Load Claude Code setting (check CLI flag first, then database setting)
+    claude_code_enabled = os.getenv("PRAISONAI_CLAUDECODE_ENABLED", "false").lower() == "true"
+    if not claude_code_enabled:
+        claude_code_enabled = (load_setting("claude_code_enabled") or "false").lower() == "true"
     logger.debug(f"Model name: {model_name}")
     settings = cl.ChatSettings(
         [
@@ -420,6 +647,11 @@ async def on_chat_resume(thread: ThreadDict):
                 label="Enter the Model Name",
                 placeholder="e.g., gpt-4o-mini",
                 initial=model_name
+            ),
+            Switch(
+                id="claude_code_enabled",
+                label="Enable Claude Code (file modifications & coding)",
+                initial=claude_code_enabled
             )
         ]
     )

src/praisonai/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.2.32"
+version = "2.2.34"
 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>=0.0.105",
+    "praisonaiagents>=0.0.107",
     "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 = "2.2.32"
+version = "2.2.34"
 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 = ">=0.0.105"
+praisonaiagents = ">=0.0.107"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai/tests/CLAUDE_CODE_INTEGRATION_PRAISONAI_AGENTS.md

@@ -0,0 +1,291 @@
+# Claude Code Integration with PraisonAI Agents
+
+This document describes the integration of Claude Code with PraisonAI Agents, allowing AI agents to intelligently decide when to use Claude Code for file modifications and coding tasks.
+
+## Overview
+
+The refactored implementation replaces direct `litellm` usage with `praisonaiagents`, making Claude Code a custom tool that the AI agent can choose to use based on the user's request. This provides better intelligence and flexibility while maintaining backward compatibility.
+
+## Key Features
+
+### 🤖 Agent-Driven Decision Making
+- AI agent intelligently decides when to use Claude Code vs. regular responses
+- No manual detection logic - the agent understands context
+- Automatic tool selection based on request type
+
+### 🔧 Claude Code as a Custom Tool
+- Claude Code is implemented as a tool function for PraisonAI Agents
+- Executes `claude --dangerously-skip-permissions -p "query"`
+- Supports `--continue` flag for conversation continuity
+- Git operations with automatic branch creation and PR URLs
+
+### ⚙️ Flexible Configuration
+- CLI flag: `praisonai code --claudecode`
+- UI toggle: "Enable Claude Code (file modifications & coding)"
+- Environment variable: `PRAISONAI_CLAUDECODE_ENABLED=true`
+
+### 🔄 Backward Compatibility
+- Falls back to `litellm` if `praisonaiagents` is not available
+- Maintains all existing functionality
+- No breaking changes to existing workflows
+
+## Architecture
+
+### Agent Instructions
+
+The agent receives these instructions to guide tool usage:
+
+```
+You are a helpful AI assistant. Use the available tools when needed to provide comprehensive responses.
+
+If Claude Code tool is available and the user's request involves:
+- File modifications, code changes, or implementation tasks
+- Creating, editing, or debugging code
+- Project setup or development tasks
+- Git operations or version control
+
+Then use the Claude Code tool to handle those requests.
+
+For informational questions, explanations, or general conversations, respond normally without using Claude Code.
+```
+
+### Tool Function: `claude_code_tool`
+
+```python
+async def claude_code_tool(query: str) -> str:
+    """
+    Execute Claude Code CLI commands for file modifications and coding tasks.
+    
+    Args:
+        query: The user's request that requires file modifications or coding assistance
+        
+    Returns:
+        The output from Claude Code execution
+    """
+```
+
+**Features:**
+- Executes Claude Code with `--dangerously-skip-permissions`
+- Supports conversation continuity with `--continue` flag
+- Automatic git branch creation and management
+- PR URL generation for GitHub repositories
+- 5-minute timeout for long operations
+- Comprehensive error handling
+
+### Available Tools
+
+When Claude Code is enabled, the agent has access to:
+
+1. **Claude Code Tool** - For file modifications and coding tasks
+2. **Tavily Web Search** - For internet research (if API key configured)
+
+The agent decides which tool(s) to use based on the user's request.
+
+## Usage
+
+### Command Line Interface
+
+```bash
+# Enable Claude Code integration
+praisonai code --claudecode
+
+# Use regular mode (no Claude Code)
+praisonai code
+```
+
+### Environment Variables
+
+```bash
+# Force enable Claude Code (overrides UI setting)
+export PRAISONAI_CLAUDECODE_ENABLED=true
+
+# Set repository path (defaults to current directory)
+export PRAISONAI_CODE_REPO_PATH=/path/to/your/repo
+```
+
+### UI Settings
+
+In the PraisonAI Code UI, you can:
+- Toggle "Enable Claude Code (file modifications & coding)" switch
+- Setting is saved to database for persistence
+- CLI flag takes precedence over UI setting
+
+## Examples
+
+### File Modification Request
+**User:** "Create a new Python function to calculate fibonacci numbers"
+
+**Agent Decision:** Uses Claude Code tool → Creates/modifies files → Returns implementation details
+
+### Informational Request  
+**User:** "How does fibonacci algorithm work?"
+
+**Agent Decision:** Responds normally → Provides explanation without file modifications
+
+### Mixed Request
+**User:** "Explain how sorting works and implement quicksort in Python"
+
+**Agent Decision:** May use Claude Code tool for implementation → Provides both explanation and code files
+
+## Git Integration
+
+When Claude Code makes changes and git is available:
+
+1. **Automatic Branch Creation:** Creates `claude-code-YYYYMMDD_HHMMSS` branch
+2. **Commit Changes:** Commits all modifications with descriptive message
+3. **Remote Push:** Pushes branch to remote repository (if configured)
+4. **PR URL Generation:** Generates GitHub PR creation URL
+
+Example output:
+```
+📋 **Pull Request Created:**
+https://github.com/user/repo/compare/main...claude-code-20241210_143022?quick_pull=1
+```
+
+## Error Handling
+
+### Graceful Degradation
+- If `praisonaiagents` unavailable → Falls back to `litellm`
+- If Claude Code CLI unavailable → Agent works without the tool
+- If git unavailable → Code changes without git operations
+- Network issues → Continues with local operations
+
+### Error Messages
+- Clear error reporting in streaming responses
+- Timeout handling for long operations
+- Detailed logging for debugging
+
+## Implementation Details
+
+### File Structure
+```
+src/praisonai/praisonai/
+├── cli.py                 # Added --claudecode flag
+└── ui/
+    └── code.py           # Refactored to use praisonaiagents
+```
+
+### Key Functions
+
+1. **`handle_with_praisonai_agents()`** - Main agent execution handler
+2. **`handle_with_litellm()`** - Fallback for backward compatibility  
+3. **`claude_code_tool()`** - Claude Code tool implementation
+4. **Settings management** - UI toggles and persistence
+
+### Dependencies
+
+**Required:**
+- `praisonaiagents` - Core agent framework
+- `subprocess` - For Claude Code CLI execution
+- `chainlit` - UI framework
+
+**Optional:**
+- `tavily` - Web search capability
+- `git` - Version control operations
+- `claude` CLI - File modification capabilities
+
+## Testing
+
+Use the included test script to verify the integration:
+
+```bash
+python test_claude_code_integration.py
+```
+
+Tests verify:
+- Import functionality
+- PraisonAI Agents availability
+- Claude Code CLI availability
+- Environment variable configuration
+- Basic tool execution
+
+## Migration Guide
+
+### From Previous Implementation
+
+The previous implementation used direct Claude Code detection logic. The new implementation:
+
+1. **Replaces detection logic** with agent intelligence
+2. **Maintains same UI/CLI interfaces** for easy migration
+3. **Preserves all existing functionality** while adding flexibility
+4. **Requires no user workflow changes**
+
+### Upgrading
+
+1. Ensure `praisonaiagents` is installed
+2. Update PraisonAI to include the refactored code
+3. Use `--claudecode` flag or UI toggle as before
+4. Existing settings and workflows continue working
+
+## Best Practices
+
+### When to Enable Claude Code
+- File modification projects
+- Code development workflows  
+- Repository maintenance tasks
+- Implementation and debugging sessions
+
+### When to Disable Claude Code
+- Read-only analysis
+- Documentation-only sessions
+- Learning and educational content
+- General Q&A conversations
+
+### Configuration Recommendations
+- Use CLI flag for development sessions
+- Use UI toggle for mixed workflows
+- Set environment variables for CI/CD integration
+- Configure repository path for multi-project setups
+
+## Troubleshooting
+
+### Common Issues
+
+**Agent doesn't use Claude Code tool:**
+- Verify Claude Code is enabled (`--claudecode` flag or UI toggle)
+- Check that request involves file modifications
+- Ensure `praisonaiagents` is installed
+
+**Claude Code tool fails:**
+- Verify `claude` CLI is installed and in PATH
+- Check repository permissions
+- Confirm git repository status (if git operations needed)
+
+**Fallback to litellm:**
+- Install `praisonaiagents`: `pip install praisonaiagents`
+- Check import errors in logs
+- Verify Python environment compatibility
+
+### Debug Information
+
+Enable verbose logging:
+```bash
+export LOGLEVEL=DEBUG
+praisonai code --claudecode
+```
+
+Check environment:
+```python
+import os
+print("Claude Code enabled:", os.getenv("PRAISONAI_CLAUDECODE_ENABLED"))
+print("Repo path:", os.getenv("PRAISONAI_CODE_REPO_PATH"))
+```
+
+## Future Enhancements
+
+### Planned Features
+- Real-time streaming from PraisonAI Agents
+- Enhanced git workflow integration
+- Multi-repository support
+- Advanced tool coordination
+- Performance optimizations
+
+### Extension Points
+- Additional coding tools integration
+- Custom agent instructions
+- Workflow templates
+- Integration with other development tools
+
+---
+
+For technical support or questions, please refer to the main PraisonAI documentation or submit issues to the repository.

src/praisonai/tests/unit/test_claude_code_integration.py

@@ -0,0 +1,138 @@
+#!/usr/bin/env python3
+"""
+Test script for Claude Code integration with PraisonAI Agents
+
+This script tests the basic functionality of the refactored Claude Code integration
+"""
+
+import os
+import sys
+import asyncio
+import unittest
+
+# Add the src directory to the path for imports
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src', 'praisonai'))
+
+def test_claude_code_tool_import():
+    """Test that we can import the Claude Code tool function"""
+    try:
+        from praisonai.ui.code import claude_code_tool
+        print("✅ Successfully imported claude_code_tool")
+        return True
+    except ImportError as e:
+        print(f"❌ Failed to import claude_code_tool: {e}")
+        return False
+
+def test_praisonai_agents_import():
+    """Test praisonaiagents import availability"""
+    try:
+        from praisonaiagents import Agent
+        print("✅ PraisonAI Agents is available")
+        return True
+    except ImportError as e:
+        print(f"⚠️  PraisonAI Agents not available (fallback to litellm): {e}")
+        return False
+
+def test_claude_code_availability():
+    """Test if Claude Code CLI is available"""
+    import subprocess
+    try:
+        result = subprocess.run(['claude', '--version'], capture_output=True, text=True, timeout=10)
+        if result.returncode == 0:
+            print(f"✅ Claude Code CLI is available: {result.stdout.strip()}")
+            return True
+        else:
+            print(f"❌ Claude Code CLI returned error: {result.stderr}")
+            return False
+    except FileNotFoundError:
+        print("⚠️  Claude Code CLI not found in PATH")
+        return False
+    except subprocess.TimeoutExpired:
+        print("⚠️  Claude Code CLI check timed out")
+        return False
+    except Exception as e:
+        print(f"⚠️  Error checking Claude Code CLI: {e}")
+        return False
+
+async def test_claude_code_tool_execution():
+    """Test basic Claude Code tool execution (simple query)"""
+    try:
+        from praisonai.ui.code import claude_code_tool
+        
+        # Test with a simple query that shouldn't modify files
+        test_query = "What is the current directory?"
+        result = await claude_code_tool(test_query)
+        
+        print(f"✅ Claude Code tool executed successfully")
+        print(f"Query: {test_query}")
+        print(f"Result (first 100 chars): {str(result)[:100]}...")
+        return True
+    except Exception as e:
+        print(f"❌ Claude Code tool execution failed: {e}")
+        return False
+
+def test_environment_variables():
+    """Test that environment variables are properly set"""
+    test_vars = [
+        "PRAISONAI_CLAUDECODE_ENABLED",
+        "PRAISONAI_CODE_REPO_PATH"
+    ]
+    
+    print("Environment Variables:")
+    for var in test_vars:
+        value = os.getenv(var, "NOT_SET")
+        print(f"  {var}: {value}")
+    
+    return True
+
+async def run_tests():
+    """Run all tests"""
+    print("🧪 Testing Claude Code Integration with PraisonAI Agents")
+    print("=" * 60)
+    
+    tests = [
+        ("Import Claude Code Tool", test_claude_code_tool_import),
+        ("Import PraisonAI Agents", test_praisonai_agents_import),
+        ("Environment Variables", test_environment_variables),
+        ("Claude Code CLI Availability", test_claude_code_availability),
+    ]
+    
+    results = {}
+    for test_name, test_func in tests:
+        print(f"\n🔍 {test_name}:")
+        try:
+            results[test_name] = test_func()
+        except Exception as e:
+            print(f"❌ Test failed with exception: {e}")
+            results[test_name] = False
+    
+    # Only test Claude Code tool execution if CLI is available
+    if results.get("Claude Code CLI Availability", False):
+        print(f"\n🔍 Claude Code Tool Execution:")
+        try:
+            results["Claude Code Tool Execution"] = await test_claude_code_tool_execution()
+        except Exception as e:
+            print(f"❌ Test failed with exception: {e}")
+            results["Claude Code Tool Execution"] = False
+    
+    # Summary
+    print("\n" + "=" * 60)
+    print("📊 Test Summary:")
+    passed = sum(1 for result in results.values() if result)
+    total = len(results)
+    
+    for test_name, result in results.items():
+        status = "✅ PASS" if result else "❌ FAIL"
+        print(f"  {status} {test_name}")
+    
+    print(f"\nResults: {passed}/{total} tests passed")
+    
+    if passed == total:
+        print("🎉 All tests passed! Integration is working correctly.")
+    else:
+        print("⚠️  Some tests failed. Please check the implementation.")
+    
+    return passed == total
+
+if __name__ == "__main__":
+    asyncio.run(run_tests())