feat(ag2): add AG2 framework backend integration (#1143)

feat(ag2): add AG2 framework backend integration

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,34 +0,0 @@
----
-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/Dockerfile

@@ -0,0 +1,9 @@
+FROM node:20-alpine
+
+RUN npm install -g @anthropic-ai/claude-code
+
+WORKDIR /app
+COPY entrypoint.sh .
+RUN chmod +x entrypoint.sh
+
+ENTRYPOINT ["/app/entrypoint.sh"] 

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

@@ -1,256 +1,20 @@
 name: "Claude Code Action"
-description: "Run Claude Code in GitHub Actions workflows"
+description: "Custom Claude Code runner for PraisonAI"
 
 inputs:
   anthropic_api_key:
+    required: true
     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
+    required: true
+    description: "GitHub token for repo access"
 
 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
-      env:
-        # Pass user-controlled inputs via environment variables to prevent script injection (GHSL-2025-093)
-        EVENT_COMMENT_BODY: ${{ github.event.comment.body }}
-        EVENT_ISSUE_TITLE: ${{ github.event.issue.title }}
-        EVENT_ISSUE_BODY: ${{ github.event.issue.body }}
-        EVENT_ISSUE_NUMBER: ${{ github.event.issue.number }}
-        EVENT_PR_TITLE: ${{ github.event.pull_request.title }}
-        EVENT_PR_NUMBER: ${{ github.event.pull_request.number }}
-        EVENT_REVIEW_BODY: ${{ github.event.review.body }}
-        EVENT_ASSIGNEE_LOGIN: ${{ github.event.assignee.login }}
-        INPUT_TRIGGER_PHRASE: ${{ inputs.trigger_phrase }}
-        INPUT_ASSIGNEE_TRIGGER: ${{ inputs.assignee_trigger }}
-        INPUT_DIRECT_PROMPT: ${{ inputs.direct_prompt }}
-        EVENT_ACTION: ${{ github.event.action }}
-        EVENT_NAME: ${{ github.event_name }}
-        REPO_NAME: ${{ github.repository }}
-      run: |
-        echo "🔍 Extracting GitHub context from event: $EVENT_NAME"
-        
-        # Function to check for trigger phrase
-        check_trigger() {
-          local text="$1"
-          local trigger="$INPUT_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 "$EVENT_NAME" in
-          "issue_comment")
-            COMMENT_BODY="$EVENT_COMMENT_BODY"
-            ISSUE_TITLE="$EVENT_ISSUE_TITLE"
-            ISSUE_NUMBER="$EVENT_ISSUE_NUMBER"
-            
-            if check_trigger "$COMMENT_BODY"; then
-              TRIGGER_FOUND="true"
-              USER_REQUEST="$COMMENT_BODY"
-              CONTEXT_INFO="Issue Comment on #$ISSUE_NUMBER: $ISSUE_TITLE"
-            fi
-            ;;
-            
-          "pull_request_review_comment")
-            COMMENT_BODY="$EVENT_COMMENT_BODY"
-            PR_TITLE="$EVENT_PR_TITLE"
-            PR_NUMBER="$EVENT_PR_NUMBER"
-            
-            if check_trigger "$COMMENT_BODY"; then
-              TRIGGER_FOUND="true"
-              USER_REQUEST="$COMMENT_BODY"
-              CONTEXT_INFO="PR Comment on #$PR_NUMBER: $PR_TITLE"
-            fi
-            ;;
-            
-          "pull_request_review")
-            REVIEW_BODY="$EVENT_REVIEW_BODY"
-            PR_TITLE="$EVENT_PR_TITLE"
-            PR_NUMBER="$EVENT_PR_NUMBER"
-            
-            if check_trigger "$REVIEW_BODY"; then
-              TRIGGER_FOUND="true"
-              USER_REQUEST="$REVIEW_BODY"
-              CONTEXT_INFO="PR Review on #$PR_NUMBER: $PR_TITLE"
-            fi
-            ;;
-            
-          "issues")
-            ISSUE_BODY="$EVENT_ISSUE_BODY"
-            ISSUE_TITLE="$EVENT_ISSUE_TITLE"
-            ISSUE_NUMBER="$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: $ISSUE_TITLE"
-            elif [[ "$EVENT_ACTION" == "assigned" && -n "$INPUT_ASSIGNEE_TRIGGER" ]]; then
-              ASSIGNEE="$EVENT_ASSIGNEE_LOGIN"
-              if [[ "$ASSIGNEE" == "$INPUT_ASSIGNEE_TRIGGER" ]]; then
-                TRIGGER_FOUND="true"
-                USER_REQUEST="$ISSUE_BODY"
-                CONTEXT_INFO="Issue #$ISSUE_NUMBER assigned to $ASSIGNEE: $ISSUE_TITLE"
-              fi
-            fi
-            ;;
-        esac
-        
-        # Check for direct prompt override
-        if [[ -n "$INPUT_DIRECT_PROMPT" ]]; then
-          TRIGGER_FOUND="true"
-          USER_REQUEST="$INPUT_DIRECT_PROMPT"
-          CONTEXT_INFO="Automated GitHub workflow"
-        fi
-        
-        if [[ "$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: $REPO_NAME
-        Context: $CONTEXT_INFO
-        Event: $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 }} 
+  using: "docker"
+  image: "ghcr.io/mervinpraison/praisonai-claudecode:latest"
+  env:
+    ANTHROPIC_API_KEY: ${{ inputs.anthropic_api_key }}
+    GITHUB_TOKEN: ${{ inputs.github_token }}
+  args:
+    - "--anthropic-api-key=${{ inputs.anthropic_api_key }}"
+    - "--github-token=${{ inputs.github_token }}" 

.github/actions/claude-code-action/entrypoint.sh

@@ -0,0 +1,21 @@
+#!/bin/sh
+
+set -e
+
+echo "Running Claude Code in CI mode..."
+
+# Extract GitHub context and create a smart prompt
+PROMPT="Analyse the GitHub issue or PR context and generate a smart response based on the repository context."
+
+# Set environment variables from arguments
+export ANTHROPIC_API_KEY="${1#--anthropic-api-key=}"
+export GITHUB_TOKEN="${2#--github-token=}"
+
+# Verify environment variables
+if [ -z "$ANTHROPIC_API_KEY" ] || [ -z "$GITHUB_TOKEN" ]; then
+    echo "Error: Required environment variables are not set"
+    exit 1
+fi
+
+# Run Claude with the prompt
+claude -p "$PROMPT" 

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

@@ -0,0 +1,117 @@
+name: "Claude Code Action"
+description: "Run Claude Code in GitHub Actions workflows"
+
+inputs:
+  github_token:
+    description: "GitHub token with repo and issues permissions"
+    required: true
+  anthropic_api_key:
+    description: "Anthropic API key"
+    required: true
+  prompt:
+    description: "The prompt to send to Claude Code"
+    required: false
+    default: ""
+  prompt_file:
+    description: "Path to a file containing the prompt to send to Claude Code"
+    required: false
+    default: ""
+  allowed_tools:
+    description: "Comma-separated list of allowed tools for Claude Code to use"
+    required: false
+    default: ""
+  output_file:
+    description: "File to save Claude Code output to (optional)"
+    required: false
+    default: ""
+  timeout_minutes:
+    description: "Timeout in minutes for Claude Code execution"
+    required: false
+    default: "10"
+  install_github_mcp:
+    description: "Whether to install the GitHub MCP server"
+    required: false
+    default: "false"
+
+runs:
+  using: "composite"
+  steps:
+    - name: Install Claude Code
+      shell: bash
+      run: npm install -g @anthropic-ai/claude-code
+
+    - name: Install GitHub MCP Server
+      if: inputs.install_github_mcp == 'true'
+      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": "${{ inputs.GITHUB_TOKEN }}"
+          }
+        }'
+
+    - name: Prepare Prompt File
+      shell: bash
+      id: prepare_prompt
+      run: |
+        if [ -z "${{ inputs.prompt }}" ] && [ -z "${{ inputs.prompt_file }}" ]; then
+          echo "::error::Neither 'prompt' nor 'prompt_file' was provided. At least one is required."
+          exit 1
+        fi
+
+        if [ ! -z "${{ inputs.prompt_file }}" ]; then
+          if [ ! -f "${{ inputs.prompt_file }}" ]; then
+            echo "::error::Prompt file '${{ inputs.prompt_file }}' does not exist."
+            exit 1
+          fi
+          PROMPT_PATH="${{ inputs.prompt_file }}"
+        else
+          mkdir -p /tmp/claude-action
+          PROMPT_PATH="/tmp/claude-action/prompt.txt"
+          echo "${{ inputs.prompt }}" > "$PROMPT_PATH"
+        fi
+
+        if [ ! -s "$PROMPT_PATH" ]; then
+          echo "::error::Prompt is empty. Please provide a non-empty prompt."
+          exit 1
+        fi
+
+        echo "PROMPT_PATH=$PROMPT_PATH" >> $GITHUB_ENV
+
+    - name: Run Claude Code
+      shell: bash
+      id: run_claude
+      run: |
+        timeout_seconds=$((${{ inputs.timeout_minutes }} * 60))
+
+        if [ -z "${{ inputs.output_file }}" ]; then
+          timeout $timeout_seconds claude \
+            -p \
+            --verbose \
+            --output-format stream-json \
+            "$(cat ${{ env.PROMPT_PATH }})" \
+            ${{ inputs.allowed_tools != '' && format('--allowedTools "{0}"', inputs.allowed_tools) || '' }}
+        else
+          timeout $timeout_seconds claude \
+            -p \
+            --verbose \
+            --output-format stream-json \
+            "$(cat ${{ env.PROMPT_PATH }})" \
+            ${{ inputs.allowed_tools != '' && format('--allowedTools "{0}"', inputs.allowed_tools) || '' }} | tee output.txt
+
+          jq -s '.' output.txt > output.json
+          jq -r '.[-1].result' output.json > "${{ inputs.output_file }}"
+          echo "Complete output saved to output.json, final response saved to ${{ inputs.output_file }}"
+        fi
+      env:
+        ANTHROPIC_API_KEY: ${{ inputs.anthropic_api_key }}
+        GITHUB_TOKEN: ${{ inputs.github_token }}

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

@@ -1,87 +0,0 @@
-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/actions/gemini/action.yml

@@ -1,81 +0,0 @@
-name: 'Gemini CLI Action'
-description: 'Uses the Gemini CLI to automate software development tasks.'
-author: 'Gemini'
-
-inputs:
-  prompt:
-    description: 'A specific prompt to guide Gemini.'
-    required: false
-    default: 'You are a helpful assistant.'
-  GEMINI_API_KEY:
-    description: 'Your Gemini API key.'
-    required: true
-  OTLP_GCP_WIF_PROVIDER:
-    description: 'The workload identity provider for GCP authentication.'
-    required: false
-  OTLP_GOOGLE_CLOUD_PROJECT:
-    description: 'The Google Cloud project for telemetry.'
-    required: false
-  settings_json:
-    description: |
-      A JSON string to configure the Gemini CLI. This will be written to .gemini/settings.json.
-      To enable observability with Google Cloud, include:
-      {"telemetry": {"enabled": true, "target": "gcp"}, "sandbox": false}
-    required: false
-  version:
-    description: |
-      The version of @google/gemini-cli to execute.
-      Can be a specific version from npm (e.g., '0.1.0', 'latest'), a branch name (e.g., 'main'), or a commit hash.
-    required: false
-    default: 'latest'
-
-runs:
-  using: "composite"
-  steps:
-    - name: Configure Gemini CLI
-      if: inputs.settings_json != ''
-      run: |
-        mkdir -p .gemini
-        echo "$SETTINGS_JSON" > .gemini/settings.json
-      shell: bash
-      env:
-        SETTINGS_JSON: ${{ inputs.settings_json }}
-
-    - name: Authenticate to Google Cloud for Telemetry 
-      if: inputs.OTLP_GCP_WIF_PROVIDER != ''
-      id: auth
-      uses: 'google-github-actions/auth@v2'
-      with:
-        project_id: ${{ inputs.OTLP_GOOGLE_CLOUD_PROJECT }}
-        workload_identity_provider: ${{ inputs.OTLP_GCP_WIF_PROVIDER }}
-
-    - name: Install Gemini CLI
-      id: install
-      run: |
-        if [[ "${{ inputs.version }}" == "latest" || "${{ inputs.version }}" =~ ^[0-9]+\.[0-9]+\.[0-9]+(-[a-zA-Z0-9\.-]+)?(\+[a-zA-Z0-9\.-]+)?$ ]]; then
-          echo "Installing Gemini CLI from npm: @google/gemini-cli@${{ inputs.version }}"
-          npm install -g @google/gemini-cli@${{ inputs.version }}
-        else
-          echo "Installing Gemini CLI from GitHub: github:google-gemini/gemini-cli#${{ inputs.version }}"
-          npm install -g github:google-gemini/gemini-cli#${{ inputs.version }}
-        fi
-        echo "Verifying installation:"
-        if command -v gemini >/dev/null 2>&1; then
-          gemini --version || echo "Gemini CLI installed successfully (version command not available)"
-        else
-          echo "Error: Gemini CLI not found in PATH"
-          exit 1
-        fi
-      shell: bash
-
-    - name: Run Gemini CLI
-      run: |
-        gemini --yolo --prompt "$PROMPT"
-      shell: bash
-      env:
-        GEMINI_API_KEY: ${{ inputs.GEMINI_API_KEY }}
-        PROMPT: ${{ inputs.prompt }}
-
-branding:
-  icon: 'terminal'
-  color: 'blue'

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

@@ -1,26 +0,0 @@
-name: Auto Issue Comment with PR Creation
-
-on:
-  issues:
-    types: [opened]
-
-jobs:
-  add-comment:
-    runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      contents: write
-      pull-requests: write
-      actions: read
-    steps:
-      - name: Add automated comment
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            github.rest.issues.createComment({
-              issue_number: context.issue.number,
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              body: '@claude review this issue and do a detailed analysis and fix this if the existing code doesn\'t have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes. MAINLY IT SHOULD NOT IMPACT ON THE CURRENT SPEED OF EXECUTION of existing features, not increasing the overhead. **Please create a PR using gh tool with your changes**.'
-            })

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

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

.github/workflows/build-image.yml

@@ -11,7 +11,6 @@ on:
 jobs:
   build:
     runs-on: ubuntu-latest
-    timeout-minutes: 15
     permissions:
       contents: read
       packages: write
@@ -27,14 +26,13 @@ jobs:
         with:
           registry: ghcr.io
           username: ${{ github.actor }}
-          password: ${{ secrets.GH_TOKEN }}
+          password: ${{ secrets.GITHUB_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

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

@@ -1,23 +0,0 @@
-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-oauth.yml

@@ -1,21 +0,0 @@
-name: Claude OAuth
-
-on:
-  workflow_dispatch:
-    inputs:
-      code:
-        description: 'Authorization code (leave empty for step 1)'
-        required: false
-
-permissions:
-  actions: write  # Required for cache management
-  contents: read  # Required for basic repository access
-
-jobs:
-  auth:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: grll/claude-code-login@v1
-        with:
-          code: ${{ inputs.code }}
-          secrets_admin_pat: ${{ secrets.SECRETS_ADMIN_PAT }} 

.github/workflows/claude.yml

@@ -1,4 +1,4 @@
-name: Claude Assistant
+name: Claude Code
 
 on:
   issue_comment:
@@ -6,155 +6,77 @@ on:
   pull_request_review_comment:
     types: [created]
   issues:
-    types: [opened, assigned, labeled]
+    types: [opened, assigned]
   pull_request_review:
     types: [submitted]
 
 jobs:
-  claude-response:
-    # Prevent certain bots from triggering this action (but allow github-actions[bot] for auto-comments)
+  claude:
     if: |
-      github.actor != 'dependabot[bot]' &&
-      github.actor != 'cursor[bot]' &&
-      github.actor != 'renovate[bot]' &&
-      !(contains(github.actor, '[bot]') && github.actor != 'github-actions[bot]')
+      (github.event_name == 'issue_comment' && contains(github.event.comment.body, '@claude')) ||
+      (github.event_name == 'pull_request_review_comment' && contains(github.event.comment.body, '@claude')) ||
+      (github.event_name == 'pull_request_review' && contains(github.event.review.body, '@claude')) ||
+      (github.event_name == 'issues' && (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude')))
     runs-on: ubuntu-latest
     permissions:
-      contents: write
-      pull-requests: write
-      issues: write
-      actions: read
+      contents: read
+      pull-requests: read
+      issues: read
+      id-token: write
+      packages: read
+    env:
+      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
         with:
-          fetch-depth: 0
-          
-      - uses: anthropics/claude-code-action@beta
+          fetch-depth: 1
+
+      - name: Debug Environment
+        run: |
+          echo "=== Environment Debug ==="
+          echo "GITHUB_ACTOR: $GITHUB_ACTOR"
+          echo "GITHUB_REPOSITORY: $GITHUB_REPOSITORY"
+          echo "GITHUB_REPOSITORY_OWNER: $GITHUB_REPOSITORY_OWNER"
+          echo "GITHUB_TOKEN exists: $([ -n "$GITHUB_TOKEN" ] && echo "yes" || echo "no")"
+          echo "GITHUB_TOKEN length: ${#GITHUB_TOKEN}"
+
+      - name: Check Package Visibility
+        run: |
+          echo "=== Checking Package Visibility ==="
+          curl -s -H "Authorization: Bearer $GITHUB_TOKEN" \
+               -H "Accept: application/vnd.github.v3+json" \
+               "https://api.github.com/users/MervinPraison/packages/container/praisonai-claudecode" \
+               || echo "Package API call failed"
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v3
         with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_ACCESS_TOKEN }}
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Test Docker Authentication
+        run: |
+          echo "=== Testing Docker Authentication ==="
+          echo "Checking ~/.docker/config.json"
+          cat ~/.docker/config.json | jq '.' || echo "No docker config found"
+          echo "=== Testing Registry Access ==="
+          curl -s -H "Authorization: Bearer $GITHUB_TOKEN" \
+               "https://ghcr.io/v2/" || echo "Registry access failed"
+
+      - name: Test Docker Pull
+        run: |
+          echo "=== Testing Docker Pull ==="
+          echo "Attempting to pull image: ghcr.io/mervinpraison/praisonai-claudecode:latest"
+          docker pull ghcr.io/mervinpraison/praisonai-claudecode:latest || echo "Pull failed with exit code: $?"
+          echo "=== Checking Docker Images ==="
+          docker images | grep praisonai-claudecode || echo "No images found"
+
+      - name: Run Claude Code
+        id: claude
+        uses: ./.github/actions/claude-code-action
+        with:
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
           github_token: ${{ secrets.GITHUB_TOKEN }}
-          trigger_phrase: "@claude"
-          allowed_tools: |
-            Bash(git:*)
-            Bash(python:*)
-            Bash(pip:*)
-            Bash(conda:*)
-            Bash(pytest:*)
-            Bash(gh:*)
-            Bash(python -m pytest:*)
-            Bash(python -m pip:*)
-            Bash(poetry:*)
-            View
-            GlobTool
-            GrepTool
-            BatchTool
-            Edit
-            Replace
-            mcp__github__get_issue
-            mcp__github__get_issue_comments
-            mcp__github__update_issue
-          timeout_minutes: 30
 
-  post-review-comment:
-    needs: claude-response
-    if: success() && needs.claude-response.result == 'success'
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      pull-requests: write
-      issues: write
-    steps:
-      - name: Check for existing review comment
-        id: check-comment
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            const reviewText = '@claude review if the changes made in the above branch are as per the requirements and make changes to the above branch if issues still exist.';
-            let comments = [];
-            let issueNumber;
-            
-            try {
-              // Determine issue/PR number based on event type
-              if (context.eventName === 'pull_request_review_comment' || context.eventName === 'pull_request_review') {
-                issueNumber = context.payload.pull_request?.number || context.issue.number;
-              } else if (context.eventName === 'issue_comment' && context.payload.issue.pull_request) {
-                // This is a comment on a PR (not an issue)
-                issueNumber = context.payload.issue.number;
-              } else {
-                // This is a regular issue
-                issueNumber = context.issue.number;
-              }
-              
-              comments = await github.rest.issues.listComments({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                issue_number: issueNumber
-              });
-              
-              // Look for Claude's completed review comments
-              const claudeReviewComments = comments.data.filter(comment => 
-                (comment.user.login === 'claude[bot]' || comment.body.includes('✅') || comment.body.includes('## Summary')) &&
-                !comment.body.includes('⏳') && 
-                !comment.body.includes('🔄') &&
-                !comment.body.includes('Loading...')
-              );
-              
-              // Check if our review request comment already exists
-              const existingReviewRequest = comments.data.find(comment => 
-                comment.body.includes(reviewText)
-              );
-              
-              // Post if Claude has completed a review AND we haven't posted the review request yet
-              const shouldPost = claudeReviewComments.length > 0 && !existingReviewRequest;
-              core.setOutput('should-post', shouldPost);
-              console.log(`Claude review completed: ${claudeReviewComments.length > 0}`);
-              console.log(`Review request exists: ${!!existingReviewRequest}`);
-              console.log(`Should post comment: ${shouldPost}`);
-              
-            } catch (error) {
-              console.log('Error checking comments:', error);
-              core.setOutput('should-post', false); // If we can't check, don't post
-            }
-
-      - name: Wait 30 seconds before posting comment
-        if: steps.check-comment.outputs.should-post == 'true'
-        run: sleep 30
-
-      - name: Post review comment
-        if: steps.check-comment.outputs.should-post == 'true'
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            let issueNumber;
-            let isPR = false;
-            
-            // Determine issue/PR number and type based on event type
-            if (context.eventName === 'pull_request_review_comment' || context.eventName === 'pull_request_review') {
-              issueNumber = context.payload.pull_request?.number || context.issue.number;
-              isPR = true;
-            } else if (context.eventName === 'issue_comment' && context.payload.issue.pull_request) {
-              // This is a comment on a PR (not an issue)
-              issueNumber = context.payload.issue.number;
-              isPR = true;
-            } else {
-              // This is a regular issue - don't post review comment
-              issueNumber = context.issue.number;
-              isPR = false;
-            }
-            
-            // Only post review comment on PRs, not regular issues
-            if (isPR) {
-              console.log(`Posting review comment on PR #${issueNumber}`);
-              
-              await github.rest.issues.createComment({
-                issue_number: issueNumber,
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                body: '@claude review if the changes made in the above branch are as per the requirements and make changes to the above branch if issues still exist. MAINLY IT SHOULD NOT IMPACT ON THE CURRENT SPEED OF EXECUTION of existing features, not increasing the overhead.'
-              });
-            } else {
-              console.log(`Skipping review comment for regular issue #${issueNumber}`);
-            }

.github/workflows/coverage.yml

@@ -10,7 +10,6 @@ jobs:
   test:
     name: Run tests and collect coverage
     runs-on: ubuntu-latest
-    timeout-minutes: 15
     
     steps:
       - name: Checkout
@@ -41,15 +40,14 @@ jobs:
         run: |
           echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
           echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-          echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
+          echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
           echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
           echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
 
       - name: Run tests
         run: |
           cd src/praisonai
-          # Run tests - allow some failures for now while we fix remaining issues
-          pytest --cov=praisonai --cov-branch --cov-report=xml tests/unit/ --tb=no -q || echo "Some tests failed, but coverage was collected"
+          pytest --cov=praisonai --cov-branch --cov-report=xml tests/unit/
 
       - name: Upload results to Codecov
         uses: codecov/codecov-action@v5

.github/workflows/docker-publish.yml

@@ -1,54 +1,44 @@
-name: Publish Docker
+name: Daily Publish Docker
 
 on:
-  release:
-    types: [published]
-  workflow_dispatch:
-    inputs:
-      tag:
-        description: 'Docker image tag (default: latest)'
-        required: false
-        default: 'latest'
+  push:
+    branches:
+      - main
+  schedule:
+    - cron: '0 0 * * *'
 
 jobs:
-  build-and-push:
+  check-and-build:
     runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
     
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v2
+
+      - name: Check last commit time
+        id: check_commit
+        run: |
+          last_commit_time=$(git log -1 --format=%ct)
+          current_time=$(date +%s)
+          time_diff=$((current_time - last_commit_time))
+          if [ $time_diff -ge 120 ] && [ $time_diff -lt 240 ]; then
+            echo "should_run=true" >> $GITHUB_OUTPUT
+          else
+            echo "should_run=false" >> $GITHUB_OUTPUT
+          fi
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        if: steps.check_commit.outputs.should_run == 'true'
+        uses: docker/setup-buildx-action@v1
 
-      - name: Log in to GitHub Container Registry
-        uses: docker/login-action@v3
-        with:
-          registry: ghcr.io
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata for Docker
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ghcr.io/mervinpraison/praisonai
-          tags: |
-            type=raw,value=latest,enable=${{ github.event_name == 'release' }}
-            type=semver,pattern={{version}},enable=${{ github.event_name == 'release' }}
-            type=semver,pattern={{major}}.{{minor}},enable=${{ github.event_name == 'release' }}
-            type=raw,value=${{ github.event.inputs.tag || 'latest' }},enable=${{ github.event_name == 'workflow_dispatch' }}
+      - name: Log in to the GitHub Container Registry
+        if: steps.check_commit.outputs.should_run == 'true'
+        run: echo ${{ secrets.GH_TOKEN }} | docker login ghcr.io -u USERNAME --password-stdin
 
       - name: Build and push Docker image
-        uses: docker/build-push-action@v5
+        if: steps.check_commit.outputs.should_run == 'true'
+        uses: docker/build-push-action@v2
         with:
           context: .
-          file: ./docker/Dockerfile
           push: true
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
+          tags: ghcr.io/mervinpraison/praisonai:latest

.github/workflows/gemini-issue-automated-triage.yml

@@ -1,76 +0,0 @@
-name: Gemini Automated Issue Triage
-
-on:
-  issue_comment:
-    types: [created]
-
-jobs:
-  triage-issue:
-    if: >
-      github.event_name == 'issue_comment' &&
-      !github.event.issue.pull_request &&
-      contains(github.event.comment.body, '@gemini triage') &&
-      (github.event.comment.author_association == 'OWNER' ||
-       github.event.comment.author_association == 'MEMBER' ||
-       github.event.comment.author_association == 'COLLABORATOR')
-    timeout-minutes: 5
-    permissions:
-      issues: write
-      contents: read
-      id-token: write
-    concurrency:
-      group: ${{ github.workflow }}-${{ github.event.issue.number }}
-      cancel-in-progress: true
-    runs-on: ubuntu-latest
-    steps:
-      - name: Generate GitHub App Token
-        id: generate_token
-        uses: actions/create-github-app-token@v1
-        with:
-          app-id: ${{ secrets.APP_ID }}
-          private-key: ${{ secrets.PRIVATE_KEY }}
-
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          token: ${{ steps.generate_token.outputs.token }}
-
-      - name: Run Gemini Issue Triage
-        uses: google-gemini/gemini-cli-action@main
-        env:
-          GITHUB_TOKEN: ${{ steps.generate_token.outputs.token }}
-          ISSUE_TITLE: ${{ github.event.issue.title }}
-          ISSUE_BODY: ${{ github.event.issue.body }}
-          ISSUE_NUMBER: ${{ github.event.issue.number }}
-          REPOSITORY: ${{ github.repository }}
-        with:
-          version: 0.1.8-rc.0
-          GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
-          OTLP_GCP_WIF_PROVIDER: ${{ secrets.OTLP_GCP_WIF_PROVIDER }}
-          OTLP_GOOGLE_CLOUD_PROJECT: ${{ secrets.OTLP_GOOGLE_CLOUD_PROJECT }}
-          settings_json: |
-            {
-              "coreTools": [
-                "run_shell_command(gh label list)",
-                "run_shell_command(gh issue edit)"
-              ],
-              "telemetry": {
-                "enabled": true,
-                "target": "gcp"
-              },
-              "sandbox": false
-            }
-          prompt: |
-            You are an issue triage assistant. Analyze the current GitHub issue and apply the most appropriate existing labels.
-
-            Steps:
-            1. Run: `gh label list --limit 100` to get all available labels.
-            2. Review the issue title and body provided in the environment variables.
-            3. Select the most relevant labels from the existing labels, focusing on kind/*, area/*, and priority/*.
-            4. Apply the selected labels to this issue using: `gh issue edit ISSUE_NUMBER --add-label "label1,label2"`
-
-            Guidelines:
-            - Only use labels that already exist in the repository.
-            - Do not add comments or modify the issue content.
-            - Triage only the current issue.
-            - Assign all applicable kind/*, area/*, and priority/* labels based on the issue content.

.github/workflows/gemini-issue-review.yml

@@ -1,202 +0,0 @@
-name: Gemini Issue Review
-
-on:
-  workflow_dispatch:
-  issue_comment:
-    types: [created]
-
-permissions:
-  contents: write
-  issues: write
-  pull-requests: read
-
-jobs:
-  review-issue:
-    if: >
-      github.event_name == 'workflow_dispatch' ||
-      (github.event_name == 'issue_comment' && 
-       !github.event.issue.pull_request && 
-       contains(github.event.comment.body, '@gemini') &&
-       !contains(github.event.comment.body, '@gemini triage') &&
-       (github.event.comment.author_association == 'OWNER' ||
-        github.event.comment.author_association == 'MEMBER' ||
-        github.event.comment.author_association == 'COLLABORATOR'))
-    runs-on: ubuntu-latest
-    env:
-      GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
-    steps:
-      - name: Generate token
-        id: generate_token
-        uses: actions/create-github-app-token@v1
-        with:
-          app-id: ${{ secrets.APP_ID }}
-          private-key: ${{ secrets.PRIVATE_KEY }}
-
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          token: ${{ steps.generate_token.outputs.token }}
-
-      - name: Set up Git
-        run: |
-          git config --local user.email "[email protected]"
-          git config --local user.name "Gemini Bot"
-
-      - name: Get issue data
-        id: issue_data
-        env:
-          GH_TOKEN: ${{ steps.generate_token.outputs.token }}
-          # Pass user-controlled inputs via environment variables to prevent script injection (GHSL-2025-093)
-          EVENT_NAME: ${{ github.event_name }}
-          EVENT_ISSUE_NUMBER: ${{ github.event.issue.number }}
-          EVENT_COMMENT_BODY: ${{ github.event.comment.body }}
-        run: |
-          # Get issue number from event or context
-          if [ "$EVENT_NAME" = "issue_comment" ]; then
-            ISSUE_NUMBER="$EVENT_ISSUE_NUMBER"
-          elif [ "$EVENT_NAME" = "issues" ]; then
-            ISSUE_NUMBER="$EVENT_ISSUE_NUMBER"
-          else
-            echo "Unable to determine issue number"
-            exit 1
-          fi
-          
-          # Get issue details
-          gh issue view $ISSUE_NUMBER --json title,body,labels,assignees > issue_details.json
-          
-          # Extract additional instructions from comment if triggered by comment
-          ADDITIONAL_INSTRUCTIONS=""
-          if [ "$EVENT_NAME" = "issue_comment" ]; then
-            COMMENT_BODY="$EVENT_COMMENT_BODY"
-            ADDITIONAL_INSTRUCTIONS=$(echo "$COMMENT_BODY" | sed -n 's/.*@gemini[[:space:]]*\(.*\)/\1/p' | head -1)
-          fi
-          
-          echo "ISSUE_NUMBER=$ISSUE_NUMBER" >> $GITHUB_OUTPUT
-          echo "ADDITIONAL_INSTRUCTIONS=$ADDITIONAL_INSTRUCTIONS" >> $GITHUB_OUTPUT
-
-      - name: Run Gemini CLI
-        uses: ./.github/actions/gemini
-        env:
-          GITHUB_TOKEN: ${{ steps.generate_token.outputs.token }}
-          ISSUE_NUMBER: ${{ steps.issue_data.outputs.ISSUE_NUMBER }}
-          ADDITIONAL_INSTRUCTIONS: ${{ steps.issue_data.outputs.ADDITIONAL_INSTRUCTIONS }}
-          REPOSITORY: ${{ github.repository }}
-        with:
-          GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
-          OTLP_GCP_WIF_PROVIDER: ${{ secrets.OTLP_GCP_WIF_PROVIDER }}
-          OTLP_GOOGLE_CLOUD_PROJECT: ${{ secrets.OTLP_GOOGLE_CLOUD_PROJECT }}
-          prompt: |
-            You are a helpful GitHub issue assistant. Your task is to analyze and help with GitHub issues.
-            
-            **Issue Details:**
-            - Issue Number: #${ISSUE_NUMBER}
-            - Repository: ${REPOSITORY}
-            - Issue URL: ${{ github.event.issue.html_url }}
-            
-            **Additional Instructions:** ${ADDITIONAL_INSTRUCTIONS}
-            
-            **CRITICAL: When making code modifications, follow this EXACT workflow:**
-            
-            **STEP 1: Analyze the Issue**
-            - Use: `gh issue view ${ISSUE_NUMBER} --json title,body,labels,assignees`
-            - Analyze issue content thoroughly
-            - Search codebase for related files using `glob`, `search_file_content`, `list_directory`
-            
-            **STEP 2: If Code Changes Are Needed**
-            Create a new branch with 'gemini/' prefix:
-            - Use: `git checkout -b gemini/issue-${ISSUE_NUMBER}-description`
-            - Example: `git checkout -b gemini/issue-123-fix-sequential-tools`
-            
-            **STEP 3: Make Code Modifications**
-            - Use `read_file` to examine existing code
-            - Use `write_file` or `replace` to make changes
-            - Use `search_file_content` to find related code
-            - Make focused, targeted changes to fix the issue
-            
-            **STEP 4: Test Changes (if applicable)**
-            - Run relevant tests if test files exist
-            - Use `run_shell_command` to execute test commands
-            
-            **STEP 5: Commit and Push Changes**
-            - Use: `git add .` (or specific files)
-            - Use: `git commit -m "Gemini: [Issue #${ISSUE_NUMBER}] Brief description of fix"`
-            - Use: `git push -u origin gemini/issue-${ISSUE_NUMBER}-description`
-            
-            **STEP 6: Create Pull Request**
-            - Use: `gh pr create --title "Fix #${ISSUE_NUMBER}: Issue description" --body "Fixes #${ISSUE_NUMBER}\n\nSummary of changes:\n- Change 1\n- Change 2" --head gemini/issue-${ISSUE_NUMBER}-description --base main`
-            - This will return a PR URL like: https://github.com/repo/pull/123
-            
-            **STEP 7: Comment on Issue with Results**
-            Use: `gh issue comment ${ISSUE_NUMBER} -b "MESSAGE"`
-            
-            **Your final comment MUST include:**
-            - Summary of what was analyzed
-            - What changes were made (if any)
-            - Link to the created PR (if applicable)
-            - Brief explanation of the solution
-            
-            **Example final comment:**
-            ```
-            Hi! I've analyzed and fixed this issue.
-            
-            **Problem:** Sequential tool calling was getting stuck after first tool call.
-            
-            **Solution:** Added proper prompt handling after each tool call in the Ollama model integration.
-            
-            **Changes Made:**
-            - Modified `src/praisonai-agents/ollama-sequential.py`
-            - Added continuation prompts for multi-step tool execution
-            
-            **Pull Request:** https://github.com/MervinPraison/PraisonAI/pull/856
-            
-            The fix is ready for review. Please test and let me know if you encounter any issues!
-            ```
-            
-            **IMPORTANT NOTES:**
-            - Always create branches with 'gemini/' prefix
-            - Always include issue number in branch name and commit message
-            - Always create PR that references the issue
-            - Always provide PR link in your final response
-            - Use the GITHUB_TOKEN environment variable for authentication
-            
-            Please proceed with helping resolve this issue following this exact workflow.
-
-          settings_json: |
-            {
-              "coreTools": [
-                "run_shell_command(echo)",
-                "run_shell_command(gh issue view)",
-                "run_shell_command(gh issue comment)",
-                "run_shell_command(gh issue list)",
-                "run_shell_command(gh issue edit)",
-                "run_shell_command(gh pr create)",
-                "run_shell_command(gh pr view)",
-                "run_shell_command(gh pr list)",
-                "run_shell_command(cat)",
-                "run_shell_command(head)",
-                "run_shell_command(tail)",
-                "run_shell_command(grep)",
-                "run_shell_command(find)",
-                "run_shell_command(git checkout)",
-                "run_shell_command(git add)",
-                "run_shell_command(git commit)",
-                "run_shell_command(git push)",
-                "run_shell_command(git status)",
-                "run_shell_command(git branch)",
-                "list_directory",
-                "read_file",
-                "read_many_files",
-                "write_file",
-                "replace",
-                "glob",
-                "search_file_content",
-                "web_fetch",
-                "google_web_search",
-                "save_memory"
-              ],
-              "telemetry": {
-                "enabled": false,
-                "otlpGcpWifProvider": "${{ secrets.OTLP_GCP_WIF_PROVIDER }}",
-                "googleCloudProject": "${{ secrets.OTLP_GOOGLE_CLOUD_PROJECT }}"
-              }
-            }

.github/workflows/gemini-pr-review.yml

@@ -1,248 +0,0 @@
-name: Gemini PR Review
-
-on:
-  issue_comment:
-    types: [created]
-  workflow_dispatch:
-    inputs:
-      pr_number:
-        description: 'PR number to review'
-        required: true
-        type: number
-
-jobs:
-  review-pr:
-    if: >
-      github.event_name == 'workflow_dispatch' ||
-      (github.event_name == 'issue_comment' && 
-       github.event.issue.pull_request && 
-       contains(github.event.comment.body, '@gemini') &&
-       (github.event.comment.author_association == 'OWNER' ||
-        github.event.comment.author_association == 'MEMBER' ||
-        github.event.comment.author_association == 'COLLABORATOR'))
-    timeout-minutes: 15
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      id-token: write
-      pull-requests: write
-      issues: write
-    steps:
-      - name: Generate GitHub App Token
-        id: generate_token
-        uses: actions/create-github-app-token@v1
-        with:
-          app-id: ${{ secrets.APP_ID }}
-          private-key: ${{ secrets.PRIVATE_KEY }}
-
-      - name: Determine checkout ref
-        id: checkout_ref
-        run: |
-          if [ "${{ github.event_name }}" = "issue_comment" ]; then
-            echo "ref=refs/pull/${{ github.event.issue.number }}/head" >> "$GITHUB_OUTPUT"
-          else
-            echo "ref=${{ github.event.pull_request.head.sha }}" >> "$GITHUB_OUTPUT"
-          fi
-
-      - name: Checkout PR code
-        uses: actions/checkout@v4
-        with:
-          token: ${{ steps.generate_token.outputs.token }}
-          ref: ${{ steps.checkout_ref.outputs.ref }}
-          fetch-depth: 0
-
-      - name: Get PR details
-        id: get_pr
-        env:
-          GITHUB_TOKEN: ${{ steps.generate_token.outputs.token }}
-          # Pass user-controlled inputs via environment variables to prevent script injection (GHSL-2025-093)
-          EVENT_NAME: ${{ github.event_name }}
-          EVENT_INPUTS_PR_NUMBER: ${{ github.event.inputs.pr_number }}
-          EVENT_ISSUE_NUMBER: ${{ github.event.issue.number }}
-          EVENT_PR_NUMBER: ${{ github.event.pull_request.number }}
-          EVENT_COMMENT_BODY: ${{ github.event.comment.body }}
-        run: |
-          if [ "$EVENT_NAME" = "workflow_dispatch" ]; then
-            PR_NUMBER=$EVENT_INPUTS_PR_NUMBER
-          elif [ "$EVENT_NAME" = "issue_comment" ]; then
-            PR_NUMBER=$EVENT_ISSUE_NUMBER
-          else
-            PR_NUMBER=$EVENT_PR_NUMBER
-          fi
-          
-          echo "pr_number=$PR_NUMBER" >> "$GITHUB_OUTPUT"
-          
-          # Extract additional instructions from comment (if triggered by comment)
-          ADDITIONAL_INSTRUCTIONS=""
-          if [ "$EVENT_NAME" = "issue_comment" ]; then
-            COMMENT_BODY="$EVENT_COMMENT_BODY"
-            ADDITIONAL_INSTRUCTIONS=$(echo "$COMMENT_BODY" | sed 's/.*@gemini//' | xargs)
-          fi
-          echo "additional_instructions=$ADDITIONAL_INSTRUCTIONS" >> "$GITHUB_OUTPUT"
-          
-          # Get PR details
-          PR_DATA=$(gh pr view $PR_NUMBER --json title,body,additions,deletions,changedFiles,baseRefName,headRefName)
-          echo "pr_data=$PR_DATA" >> "$GITHUB_OUTPUT"
-          
-          # Get file changes
-          CHANGED_FILES=$(gh pr diff $PR_NUMBER --name-only)
-          echo "changed_files<<EOF" >> "$GITHUB_OUTPUT"
-          echo "$CHANGED_FILES" >> "$GITHUB_OUTPUT"
-          echo "EOF" >> "$GITHUB_OUTPUT"
-
-      - name: Run Gemini PR Review
-        uses: ./.github/actions/gemini
-        env:
-          GITHUB_TOKEN: ${{ steps.generate_token.outputs.token }}
-          PR_NUMBER: ${{ steps.get_pr.outputs.pr_number }}
-          PR_DATA: ${{ steps.get_pr.outputs.pr_data }}
-          CHANGED_FILES: ${{ steps.get_pr.outputs.changed_files }}
-          ADDITIONAL_INSTRUCTIONS: ${{ steps.get_pr.outputs.additional_instructions }}
-          REPOSITORY: ${{ github.repository }}
-        with:
-          version: 0.1.8-rc.0
-          GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
-          OTLP_GCP_WIF_PROVIDER: ${{ secrets.OTLP_GCP_WIF_PROVIDER }}
-          OTLP_GOOGLE_CLOUD_PROJECT: ${{ secrets.OTLP_GOOGLE_CLOUD_PROJECT }}
-          settings_json: |
-            {
-              "coreTools": [
-                "run_shell_command(echo)",
-                "run_shell_command(gh pr view)",
-                "run_shell_command(gh pr diff)",
-                "run_shell_command(gh pr comment)",
-                "run_shell_command(gh pr list)",
-                "run_shell_command(cat)",
-                "run_shell_command(head)",
-                "run_shell_command(tail)",
-                "run_shell_command(grep)",
-                "run_shell_command(find)",
-                "run_shell_command(git checkout)",
-                "run_shell_command(git add)",
-                "run_shell_command(git commit)",
-                "run_shell_command(git push)",
-                "run_shell_command(git status)",
-                "run_shell_command(git branch)",
-                "run_shell_command(git fetch)",
-                "list_directory",
-                "read_file",
-                "read_many_files",
-                "write_file",
-                "replace",
-                "glob",
-                "search_file_content",
-                "web_fetch",
-                "google_web_search",
-                "save_memory"
-              ],
-              "telemetry": {
-                "enabled": false,
-                "target": "gcp"
-              },
-              "sandbox": false
-            }
-          prompt: |
-            You are an expert code reviewer and AI assistant for Pull Requests. You can review code AND make modifications to improve the PR.
-
-            **PR Details:**
-            - PR Number: #$PR_NUMBER
-            - Repository: $REPOSITORY
-            - Additional Instructions: $ADDITIONAL_INSTRUCTIONS
-
-            **CRITICAL: When making code modifications, follow this EXACT workflow:**
-
-            **STEP 1: Analyze the PR**
-            - Use: `echo "$PR_DATA"` to get PR details (JSON format)
-            - Use: `echo "$CHANGED_FILES"` to get changed files list
-            - Use: `gh pr diff ${PR_NUMBER}` to see the full diff
-            - Use: `gh pr view ${PR_NUMBER} --json title,body,headRefName` to get PR info
-            - Analyze the code changes thoroughly
-
-            **STEP 2: If Code Improvements Are Needed**
-            **IMPORTANT:** DO NOT create a new branch! Work on the EXISTING PR branch.
-            - Use: `git fetch origin` to get latest changes
-            - Get PR branch name: `gh pr view ${PR_NUMBER} --json headRefName`
-            - Switch to PR branch: `git checkout BRANCH_NAME` (use actual branch name from PR)
-            - Use: `git pull origin BRANCH_NAME` to get latest PR changes
-
-            **STEP 3: Make Code Modifications**
-            - Use `read_file` to examine existing code
-            - Use `write_file` or `replace` to make improvements
-            - Use `search_file_content` to find related code
-            - Make focused, targeted improvements to the PR
-
-            **STEP 4: Test Changes (if applicable)**
-            - Run relevant tests if test files exist
-            - Use `run_shell_command` to execute test commands
-
-            **STEP 5: Commit and Push Changes to EXISTING PR Branch**
-            - Use: `git add .` (or specific files)
-            - Use: `git commit -m "Gemini: [PR #${PR_NUMBER}] Brief description of improvements"`
-            - Use: `git push origin BRANCH_NAME` (push to EXISTING PR branch)
-
-            **STEP 6: Review and Comment on PR**
-            Write comprehensive review and post comment with your analysis and any improvements made.
-            Use: `gh pr comment ${PR_NUMBER} -b "MESSAGE"`
-
-            Review Guidelines:
-            - Focus on code quality, security, performance, and maintainability
-            - Check for common issues: potential bugs, security vulnerabilities, performance bottlenecks
-            - Verify error handling and edge cases
-            - Look for code style and best practices
-            - Comment on architecture and design decisions if significant
-            - Be constructive and specific in feedback
-            - Highlight both issues and positive aspects
-            - Suggest improvements with examples when possible
-            
-            Review Areas:
-            - **Security**: Authentication, authorization, input validation, data sanitization
-            - **Performance**: Algorithms, database queries, caching, resource usage
-            - **Reliability**: Error handling, logging, testing coverage, edge cases
-            - **Maintainability**: Code structure, documentation, naming conventions
-            - **Functionality**: Logic correctness, requirements fulfillment
-            
-            Output Format:
-            Structure your review using this exact format with markdown:
-
-            ## 📋 Review Summary
-            Provide a brief 2-3 sentence overview of the PR and overall assessment.
-
-            ## 🔍 General Feedback
-            - List general observations about code quality
-            - Mention overall patterns or architectural decisions
-            - Highlight positive aspects of the implementation
-            - Note any recurring themes across files
-
-            ## 🎯 Specific Feedback
-            Only include sections below that have actual issues. If there are no issues in a priority category, omit that entire section.
-
-            ### 🔴 Critical
-            (Only include this section if there are critical issues)
-            Issues that must be addressed before merging (security vulnerabilities, breaking changes, major bugs):
-            - **File: `filename:line`** - Description of critical issue with specific recommendation
-
-            ### 🟡 High
-            (Only include this section if there are high priority issues)
-            Important issues that should be addressed (performance problems, design flaws, significant bugs):
-            - **File: `filename:line`** - Description of high priority issue with suggested fix
-
-            ### 🟢 Medium
-            (Only include this section if there are medium priority issues)
-            Improvements that would enhance code quality (style issues, minor optimizations, better practices):
-            - **File: `filename:line`** - Description of medium priority improvement
-
-            ### 🔵 Low
-            (Only include this section if there are suggestions)
-            Nice-to-have improvements and suggestions (documentation, naming, minor refactoring):
-            - **File: `filename:line`** - Description of suggestion or enhancement
-
-            **Note**: If no specific issues are found in any category, simply state "No specific issues identified in this review."
-
-            ## ✅ Highlights
-            (Only include this section if there are positive aspects to highlight)
-            - Mention specific good practices or implementations
-            - Acknowledge well-written code sections
-            - Note improvements from previous versions
-
-            ---
-            *Review completed by Gemini CLI*

.github/workflows/python-package.yml

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

.github/workflows/test-comprehensive.yml

@@ -26,10 +26,6 @@ on:
 jobs:
   comprehensive-test:
     runs-on: ubuntu-latest
-    timeout-minutes: 15
-    env:
-      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-      OPENAI_MODEL_NAME: gpt-4o-mini
     strategy:
       matrix:
         python-version: ["3.11"]
@@ -58,13 +54,11 @@ jobs:
         # Install knowledge dependencies from praisonai-agents
         uv pip install --system "praisonaiagents[knowledge]"
 
-    - name: Verify environment variables
+    - name: Set environment variables
       run: |
-        if [ -n "$OPENAI_API_KEY" ]; then
-          echo "✅ OPENAI_API_KEY is set (length: ${#OPENAI_API_KEY})"
-        else
-          echo "⚠️ OPENAI_API_KEY is NOT set"
-        fi
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
 
     - name: Run Comprehensive Test Suite
       run: |

.github/workflows/test-core.yml

@@ -9,7 +9,6 @@ on:
 jobs:
   test-core:
     runs-on: ubuntu-latest
-    timeout-minutes: 15
     strategy:
       matrix:
         python-version: [3.11]
@@ -34,7 +33,7 @@ jobs:
         cd src/praisonai
         uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
         uv pip install --system duckduckgo_search
-        uv pip install --system pytest pytest-asyncio pytest-cov pytest-timeout
+        uv pip install --system pytest pytest-asyncio pytest-cov
         # Install knowledge dependencies from praisonai-agents
         uv pip install --system "praisonaiagents[knowledge]"
 
@@ -42,13 +41,13 @@ jobs:
       run: |
         echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
         echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
         echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
         echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
         # Also export to current shell session for immediate availability
         export OPENAI_API_KEY="${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}"
         export OPENAI_API_BASE="${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}"
-        export OPENAI_MODEL_NAME="${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}"
+        export OPENAI_MODEL_NAME="${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}"
         export LOGLEVEL=DEBUG
         # Verify immediate availability
         echo "🔧 Immediate verification in same step:"
@@ -126,7 +125,7 @@ jobs:
       env:
         OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}
         OPENAI_API_BASE: ${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}
-        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}
+        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}
         LOGLEVEL: DEBUG
       run: |
         echo "🔍 Testing PraisonAI API key usage directly..."
@@ -152,7 +151,7 @@ jobs:
             return 'INVALID_TYPE'
 
         print('🔧 Direct PraisonAI API Key Check:')
-        env_api_key = os.environ.get(\"OPENAI_API_KEY\", \"NOT_SET\")
+        env_api_key = os.environ.get(\\"OPENAI_API_KEY\\", \\"NOT_SET\\")
         print(f'Environment OPENAI_API_KEY: {get_key_display_value(env_api_key)}...')
         
         from praisonai import PraisonAI
@@ -164,9 +163,9 @@ jobs:
         
         from praisonai.inc.models import PraisonAIModel
         
-        print('\\n🧪 Testing PraisonAIModel with explicit API key (CrewAI method):')
+        print('\\\\n🧪 Testing PraisonAIModel with explicit API key (CrewAI method):')
         model_with_explicit_key = PraisonAIModel(
-            model='openai/gpt-5-nano',
+            model='openai/gpt-4o-mini',
             base_url=praisonai.config_list[0].get('base_url'),
             api_key=api_key_from_config # This will be a string from praisonai.config_list
         )
@@ -196,8 +195,7 @@ jobs:
 
     - name: Run Unit Tests
       run: |
-        cd src/praisonai && python -m pytest tests/unit/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=term-missing --cov-report=xml --cov-branch || echo "Some tests failed - see output above"
-      continue-on-error: true
+        cd src/praisonai && python -m pytest tests/unit/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=term-missing --cov-report=xml --cov-branch
 
     - name: Run Integration Tests  
       run: |
@@ -252,7 +250,7 @@ jobs:
       env:
         OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}
         OPENAI_API_BASE: ${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}
-        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}
+        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}
         LOGLEVEL: DEBUG
       run: |
         echo "🧪 Running legacy tests with real API key..."
@@ -263,7 +261,7 @@ jobs:
         export OPENAI_API_KEY="$OPENAI_API_KEY"
         export OPENAI_API_BASE="$OPENAI_API_BASE"
         export OPENAI_MODEL_NAME="$OPENAI_MODEL_NAME"
-        cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings -m "not real" --timeout=60
+        cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings
 
     - name: Upload coverage reports to Codecov
       if: matrix.python-version == '3.11'

.github/workflows/test-extended.yml

@@ -13,7 +13,6 @@ on:
 jobs:
   test-examples:
     runs-on: ubuntu-latest
-    timeout-minutes: 15
     
     steps:
     - name: Checkout code
@@ -42,11 +41,8 @@ jobs:
       run: |
         echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
         echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
         echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
-        echo "PRAISONAI_TELEMETRY_DISABLED=true" >> $GITHUB_ENV
-        echo "PRAISONAI_DISABLE_TELEMETRY=true" >> $GITHUB_ENV
-        echo "DO_NOT_TRACK=true" >> $GITHUB_ENV
 
     - name: Test Key Example Scripts
       run: |
@@ -77,7 +73,6 @@ jobs:
 
   performance-test:
     runs-on: ubuntu-latest
-    timeout-minutes: 15
     
     steps:
     - name: Checkout code
@@ -102,12 +97,6 @@ jobs:
         # Install knowledge dependencies from praisonai-agents
         uv pip install --system "praisonaiagents[knowledge]"
 
-    - name: Set environment variables
-      run: |
-        echo "PRAISONAI_TELEMETRY_DISABLED=true" >> $GITHUB_ENV
-        echo "PRAISONAI_DISABLE_TELEMETRY=true" >> $GITHUB_ENV
-        echo "DO_NOT_TRACK=true" >> $GITHUB_ENV
-
     - name: Run Performance Benchmarks
       run: |
         echo "🏃 Running performance benchmarks..."
@@ -172,4 +161,4 @@ jobs:
       with:
         name: performance-report
         path: performance_report.md
-        retention-days: 5
+        retention-days: 30 

.github/workflows/test-frameworks.yml

@@ -24,7 +24,6 @@ on:
 jobs:
   framework-tests:
     runs-on: ubuntu-latest
-    timeout-minutes: 15
     strategy:
       matrix:
         python-version: [3.11]
@@ -56,7 +55,7 @@ jobs:
       run: |
         echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
         echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
 
     - name: Test ${{ matrix.framework }} Framework
       run: |

.github/workflows/test-real.yml

@@ -26,7 +26,6 @@ jobs:
   real-tests:
     runs-on: ubuntu-latest
     if: ${{ github.event.inputs.confirm_costs == 'true' && github.event.inputs.framework != 'none' }}
-    timeout-minutes: 15
     
     strategy:
       matrix:

.github/workflows/unittest.yml

@@ -5,7 +5,6 @@ on: [push, pull_request]
 jobs:
   quick-test:
     runs-on: ubuntu-latest
-    timeout-minutes: 15
 
     steps:
     - name: Checkout code
@@ -27,7 +26,7 @@ jobs:
         cd src/praisonai
         uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
         uv pip install --system duckduckgo_search
-        uv pip install --system pytest pytest-asyncio pytest-cov pytest-timeout
+        uv pip install --system pytest pytest-asyncio pytest-cov
         # Install knowledge dependencies from praisonai-agents
         uv pip install --system "praisonaiagents[knowledge]"
 
@@ -35,7 +34,7 @@ jobs:
       run: |
         echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
         echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
         echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
         echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
 
@@ -316,8 +315,8 @@ jobs:
         try:
             from praisonai.inc.models import PraisonAIModel
             
-            # Test PraisonAIModel with openai/gpt-5-nano (from YAML)
-            model = PraisonAIModel(model='openai/gpt-5-nano')
+            # Test PraisonAIModel with openai/gpt-4o-mini (from YAML)
+            model = PraisonAIModel(model='openai/gpt-4o-mini')
             
             print('🤖 PraisonAIModel Configuration:')
             print(f'  model: {model.model}')
@@ -489,7 +488,7 @@ jobs:
 
     - name: Run Legacy Example Tests
       run: |
-        cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings -m "not real" --timeout=60
+        cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings
       continue-on-error: true
 
     - name: Upload coverage reports to Codecov

.gitignore

@@ -75,11 +75,4 @@ agents/praisonaiagents/praisonaiagents.egg-info
 # Local Netlify folder
 .netlify
 .qodo
-CopilotKit*
-.gemini
-issue_details.json
-
-# MCP Registry tokens
-.mcpregistry_*
-
-*.log
+CopilotKit*

api_server.py

@@ -1,71 +0,0 @@
-"""
-Auto-generated API server for PraisonAI agents.
-"""
-from flask import Flask, request, jsonify
-from flask_cors import CORS
-from praisonai import PraisonAI
-import os
-
-app = Flask(__name__)
-
-# CORS configuration
-CORS(app)
-
-# Authentication
-AUTH_ENABLED = False
-AUTH_TOKEN = None
-
-def check_auth():
-    """Check authentication if enabled."""
-    if not AUTH_ENABLED:
-        return True
-    
-    token = request.headers.get('Authorization', '').replace('Bearer ', '')
-    return token == AUTH_TOKEN
-
-@app.route('/health', methods=['GET'])
-def health():
-    """Health check endpoint."""
-    return jsonify({"status": "ok", "service": "praisonai-api"})
-
-@app.route('/chat', methods=['POST'])
-def chat():
-    """Chat endpoint for agent interaction."""
-    if not check_auth():
-        return jsonify({"error": "Unauthorized"}), 401
-    
-    data = request.get_json()
-    if not data or 'message' not in data:
-        return jsonify({"error": "Message required"}), 400
-    
-    try:
-        praisonai = PraisonAI(agent_file="agents.yaml")
-        result = praisonai.run()
-        
-        return jsonify({
-            "response": result,
-            "status": "success"
-        })
-    except Exception as e:
-        return jsonify({
-            "error": str(e),
-            "status": "error"
-        }), 500
-
-@app.route('/agents', methods=['GET'])
-def list_agents():
-    """List available agents."""
-    if not check_auth():
-        return jsonify({"error": "Unauthorized"}), 401
-    
-    return jsonify({
-        "agents": ["default"],
-        "agent_file": "agents.yaml"
-    })
-
-if __name__ == '__main__':
-    app.run(
-        host='0.0.0.0',
-        port=8080,
-        debug=False
-    )

docker/Dockerfile

@@ -14,10 +14,9 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 RUN mkdir -p /root/.praison
 
 # Install Python packages (using latest versions)
-# Note: praisonai is installed from PyPI - remove version pin to use latest available
 RUN pip install --no-cache-dir \
     flask \
-    praisonai \
+    "praisonai>=2.2.33" \
     "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>=3.2.1" \
+    "praisonai>=2.2.33" \
     "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>=3.2.1" \
+    "praisonai>=2.2.33" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.serve

@@ -1,25 +0,0 @@
-# Dockerfile for PraisonAI Serve API
-# Deploy to Railway, Render, Fly.io, or any container platform
-#
-# Build: docker build -f Dockerfile.serve -t praisonai-serve .
-# Run:   docker run -p 8005:8005 -e OPENAI_API_KEY=sk-xxx praisonai-serve
-
-FROM python:3.11-slim
-
-WORKDIR /app
-
-# Install dependencies
-RUN pip install --no-cache-dir praisonai praisonaiagents
-
-# Copy agents configuration
-COPY agents.yaml .
-
-# Expose the API port
-EXPOSE 8005
-
-# Health check
-HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
-    CMD curl -f http://localhost:8005/health || exit 1
-
-# Start the API server
-CMD ["praisonai", "serve", "agents.yaml", "--host", "0.0.0.0", "--port", "8005"]

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>=3.2.1" \
+    "praisonai>=2.2.33" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.95`
+- PraisonAI: `>=2.2.33`
 - 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==2.2.95" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.33" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

docker/docker-compose.dev.yml

@@ -137,11 +137,22 @@ services:
       POSTGRES_USER: praisonai
       POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-praisonai_dev_pass}
     volumes:
+   database:
+     image: postgres:15-alpine
+     environment:
+       POSTGRES_DB: praisonai_dev
+       POSTGRES_USER: praisonai
+       POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-praisonai_dev_pass}
+     volumes:
+       - postgres_data:/var/lib/postgresql/data
+     read_only: true
+     tmpfs:
+       - /tmp
+       - /var/tmp
+     ports:
+       - "5432:5432"
+     networks:
       - postgres_data:/var/lib/postgresql/data
-    read_only: true
-    tmpfs:
-      - /tmp
-      - /var/tmp
     ports:
       - "5432:5432"
     networks:

docs/profiling-cli.md

@@ -1,203 +0,0 @@
-# Profiling CLI
-
-PraisonAI provides unified profiling capabilities for performance analysis and debugging, with **full visibility from ENTER to first response**.
-
-## Quick Start
-
-```bash
-# Basic profiling
-praisonai "What is 2+2?" --profile
-
-# Deep profiling with call graph, decision trace, and module breakdown
-praisonai "What is 2+2?" --profile --profile-deep
-
-# JSON output format
-praisonai "What is 2+2?" --profile --profile-format json
-```
-
-## Time to First Response (TTFR)
-
-**TTFR** is the key user-perceived latency metric. It measures the time from when you press ENTER until the first visible output appears.
-
-### What contributes to TTFR?
-
-1. **CLI Entry** - Time to reach the CLI entry point
-2. **CLI Parse** - Argparse/Typer routing time
-3. **Routing** - Command routing to handler
-4. **Imports** - Loading required modules
-5. **Agent Init** - Initializing the agent
-6. **Network Request** - Sending request to LLM API
-7. **First Token** - Receiving first token (streaming)
-8. **First Output** - Rendering first visible output
-
-### How to reduce TTFR
-
-1. **Warm runs** - Imports are cached after first run
-2. **Streaming mode** - See output as tokens arrive
-3. **Lighter models** - Faster response times
-4. **Local models** - Eliminate network latency
-
-## CLI Flags
-
-| Flag | Description | Default |
-|------|-------------|---------|
-| `--profile` | Enable profiling with timing breakdown | Disabled |
-| `--profile-deep` | Enable deep profiling with call graph (higher overhead) | Disabled |
-| `--profile-format` | Output format: `text` or `json` | `text` |
-
-## Profile Command
-
-For more detailed profiling options, use the `profile` subcommand:
-
-```bash
-# Profile a query
-praisonai profile query "What is 2+2?"
-
-# With deep call tracing
-praisonai profile query "Hello" --deep
-
-# Limit functions shown
-praisonai profile query "Test" --limit 20
-
-# Save artifacts to directory
-praisonai profile query "Test" --save ./profile_results
-
-# JSON output
-praisonai profile query "Test" --format json
-```
-
-### Profile Query Options
-
-| Option | Description | Default |
-|--------|-------------|---------|
-| `--model, -m` | Model to use | Default model |
-| `--stream/--no-stream` | Use streaming mode | No stream |
-| `--deep` | Enable deep call tracing | Disabled |
-| `--limit, -n` | Top N functions to show | 30 |
-| `--sort, -s` | Sort by: `cumulative` or `tottime` | `cumulative` |
-| `--show-callers` | Show caller functions | Disabled |
-| `--show-callees` | Show callee functions | Disabled |
-| `--save` | Save artifacts to path | None |
-| `--format, -f` | Output format: `text` or `json` | `text` |
-
-## Other Profile Commands
-
-```bash
-# Profile module import times
-praisonai profile imports
-
-# Profile CLI startup time
-praisonai profile startup
-
-# Run comprehensive profiling suite
-praisonai profile suite
-
-# Create performance snapshots for comparison
-praisonai profile snapshot --baseline
-praisonai profile snapshot current --compare
-
-# Configure performance optimizations
-praisonai profile optimize --show
-```
-
-## Output Format
-
-### Text Report
-
-```
-======================================================================
-PraisonAI Profile Report
-======================================================================
-
-Run ID:     abc12345
-Timestamp:  2024-01-02T12:00:00.000000Z
-Method:     cli_direct
-Version:    3.0.1
-
-## Execution Timeline
----------------------------------------------
-  Imports                   :   500.00 ms
-  Agent Init                :     0.50 ms
-  Execution                 :  2000.00 ms
-  ───────────────────────────────────────────
-  ⏱ Time to First Response  :  2500.50 ms
-  TOTAL                     :  2500.50 ms
-
-## Timing Breakdown
-----------------------------------------
-  Imports:            500.00 ms
-  Agent Init:           0.50 ms
-  Execution:         2000.00 ms
-  ─────────────────────────────────────
-  TOTAL:             2500.50 ms
-
-## Decision Trace (deep profile only)
-----------------------------------------
-  Agent Config:    default
-  Model:           gpt-4
-  Streaming:       disabled
-  Profile Layer:   2
-
-## Top Functions by Cumulative Time
-----------------------------------------------------------------------
-Function                                    Calls   Cumul (ms)
-----------------------------------------------------------------------
-request                                         1      1800.00
-send                                            1      1750.00
-...
-```
-
-### JSON Report
-
-```json
-{
-  "schema_version": "1.0",
-  "run_id": "abc12345",
-  "timestamp": "2024-01-02T12:00:00.000000Z",
-  "invocation": {
-    "method": "cli_direct",
-    "flags": {"layer": 1, "stream": false, "model": null},
-    "praisonai_version": "3.0.1",
-    "python_version": "3.12.0"
-  },
-  "timing": {
-    "total_ms": 2500.5,
-    "imports_ms": 500.0,
-    "agent_init_ms": 0.5,
-    "execution_ms": 2000.0
-  },
-  "functions": [...],
-  "response_preview": "four"
-}
-```
-
-## Profiling Layers
-
-| Layer | Overhead | Features |
-|-------|----------|----------|
-| 0 | < 1ms | Wall-clock phases only |
-| 1 | ~5% | + cProfile function stats |
-| 2 | ~15% | + Call graph (callers/callees) |
-
-- `--profile` uses Layer 1
-- `--profile --profile-deep` uses Layer 2
-
-## Performance Tips
-
-1. **Use Layer 0 for production monitoring** - minimal overhead
-2. **Use Layer 1 for development profiling** - good balance
-3. **Use Layer 2 only for deep debugging** - higher overhead
-
-## Observational Equivalence
-
-The following commands produce equivalent profile reports:
-
-```bash
-# CLI direct
-praisonai "prompt" --profile
-
-# Profile command
-praisonai profile query "prompt"
-```
-
-Both use the same unified profiling architecture and produce identical schema output.

docs/profiling-module.md

@@ -1,257 +0,0 @@
-# Profiling Module
-
-PraisonAI provides a unified profiling module for programmatic performance analysis.
-
-## Quick Start
-
-```python
-from praisonai.cli.execution import (
-    ExecutionRequest,
-    Profiler,
-    ProfilerConfig,
-)
-
-# Create execution request
-request = ExecutionRequest(prompt="What is 2+2?")
-
-# Configure profiler
-config = ProfilerConfig(layer=1)
-
-# Run with profiling
-profiler = Profiler(config)
-result, report = profiler.profile_sync(request)
-
-# Access results
-print(f"Output: {result.output}")
-print(f"Total time: {report.timing.total_ms}ms")
-```
-
-## ExecutionRequest
-
-Immutable request descriptor for execution.
-
-```python
-from praisonai.cli.execution import ExecutionRequest
-
-# Basic request
-request = ExecutionRequest(prompt="Hello")
-
-# With model
-request = ExecutionRequest(
-    prompt="Hello",
-    model="gpt-4",
-    agent_name="MyAgent",
-)
-
-# With streaming
-request = ExecutionRequest(
-    prompt="Hello",
-    stream=True,
-)
-```
-
-### Parameters
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `prompt` | str | Required | The prompt to execute |
-| `agent_name` | str | "Assistant" | Name of the agent |
-| `agent_instructions` | str | None | Custom instructions |
-| `model` | str | None | Model to use |
-| `stream` | bool | False | Enable streaming |
-| `tools` | tuple | () | Tools to provide |
-
-## ProfilerConfig
-
-Configuration for profiling behavior.
-
-```python
-from praisonai.cli.execution import ProfilerConfig
-
-# Layer 0: Wall-clock only (< 1ms overhead)
-config = ProfilerConfig(layer=0)
-
-# Layer 1: + cProfile stats (~5% overhead)
-config = ProfilerConfig(layer=1)
-
-# Layer 2: + Call graph (~15% overhead)
-config = ProfilerConfig(
-    layer=2,
-    show_callers=True,
-    show_callees=True,
-)
-
-# From CLI flags
-config = ProfilerConfig.from_flags(
-    profile=True,
-    deep=True,
-    network=False,
-)
-```
-
-### Parameters
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `layer` | int | 1 | Profiling layer (0, 1, or 2) |
-| `limit` | int | 30 | Max functions to show |
-| `sort_by` | str | "cumulative" | Sort by: cumulative, time, calls |
-| `show_callers` | bool | False | Show caller functions |
-| `show_callees` | bool | False | Show callee functions |
-| `track_network` | bool | False | Track network requests |
-| `output_format` | str | "text" | Output format: text, json |
-| `save_path` | str | None | Path to save artifacts |
-
-## Profiler
-
-The main profiler class that wraps execution.
-
-```python
-from praisonai.cli.execution import Profiler, ProfilerConfig, ExecutionRequest
-
-config = ProfilerConfig(layer=1)
-profiler = Profiler(config)
-
-request = ExecutionRequest(prompt="Hello")
-result, report = profiler.profile_sync(request, invocation_method="api")
-```
-
-### Methods
-
-#### `profile_sync(request, invocation_method="profile_command")`
-
-Profile synchronous execution.
-
-**Parameters:**
-- `request`: ExecutionRequest - The execution request
-- `invocation_method`: str - How this was invoked (for report)
-
-**Returns:** Tuple of (ExecutionResult, ProfileReport)
-
-## ProfileReport
-
-The canonical profile report with timing and statistics.
-
-```python
-# Access timing breakdown
-print(f"Total: {report.timing.total_ms}ms")
-print(f"Imports: {report.timing.imports_ms}ms")
-print(f"Agent Init: {report.timing.agent_init_ms}ms")
-print(f"Execution: {report.timing.execution_ms}ms")
-
-# Access function stats (layer 1+)
-if report.functions:
-    for func in report.functions[:5]:
-        print(f"{func.name}: {func.cumulative_time_ms}ms")
-
-# Access call graph (layer 2)
-if report.call_graph:
-    print(f"Edges: {len(report.call_graph.edges)}")
-
-# Export formats
-json_str = report.to_json()
-text_str = report.to_text()
-dict_data = report.to_dict()
-```
-
-### Schema Version
-
-The report uses schema version `1.0`. Schema is frozen after release - new fields are always optional with defaults.
-
-### Mandatory Fields
-
-- `schema_version`: str
-- `run_id`: str
-- `timestamp`: str (ISO 8601)
-- `invocation`: InvocationInfo
-- `timing`: TimingBreakdown
-- `response_preview`: str
-
-### Optional Fields
-
-- `functions`: List[FunctionStat] (layer 1+)
-- `call_graph`: CallGraph (layer 2)
-- `network`: List[RequestTiming] (if track_network)
-
-## ExecutionResult
-
-Result of execution with output and metadata.
-
-```python
-# Access output
-print(result.output)
-print(result.success)
-print(result.run_id)
-
-# Check for errors
-if result.error:
-    print(f"Error: {result.error}")
-
-# Duration
-print(f"Duration: {result.duration_ms}ms")
-```
-
-## Data Bounds
-
-Profile data is strictly bounded to prevent memory issues:
-
-| Data Type | Maximum |
-|-----------|---------|
-| Function stats | 1000 |
-| Call graph edges | 5000 |
-| Network requests | 100 |
-
-## Example: Full Profiling Workflow
-
-```python
-from praisonai.cli.execution import (
-    ExecutionRequest,
-    Profiler,
-    ProfilerConfig,
-)
-
-# Configure deep profiling
-config = ProfilerConfig(
-    layer=2,
-    limit=50,
-    show_callers=True,
-    show_callees=True,
-)
-
-# Create request
-request = ExecutionRequest(
-    prompt="Explain quantum computing in simple terms",
-    model="gpt-4",
-)
-
-# Profile execution
-profiler = Profiler(config)
-result, report = profiler.profile_sync(request)
-
-# Print text report
-print(report.to_text())
-
-# Save JSON report
-with open("profile.json", "w") as f:
-    f.write(report.to_json())
-
-# Analyze timing
-print(f"\nTiming Analysis:")
-print(f"  Import overhead: {report.timing.imports_ms / report.timing.total_ms * 100:.1f}%")
-print(f"  Execution time: {report.timing.execution_ms / report.timing.total_ms * 100:.1f}%")
-
-# Top 5 hotspots
-if report.functions:
-    print(f"\nTop 5 Hotspots:")
-    for func in report.functions[:5]:
-        print(f"  {func.name}: {func.cumulative_time_ms:.2f}ms ({func.calls} calls)")
-```
-
-## Invariants
-
-The profiling module guarantees:
-
-1. **Output Equivalence**: Profiled execution produces identical output to non-profiled execution
-2. **Schema Stability**: Schema version 1.0 is frozen; new fields are always optional
-3. **Bounded Data**: All profile data respects maximum bounds
-4. **Zero Overhead When Disabled**: No profiling code runs unless explicitly enabled

docs/templates-cli.md

@@ -1,53 +0,0 @@
-# Templates CLI
-
-```bash
-praisonai templates list
-```
-
-```bash
-praisonai templates search video
-```
-
-```bash
-praisonai templates info ai-video-editor
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --output edited.mp4
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --output edited.mp4 --offline
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --output edited.mp4 --strict-tools
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --tools ./my_tools.py
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --tools-dir ./tools
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --tools-source praisonai_tools.video
-```
-
-```bash
-praisonai templates run ai-video-editor --input video.mp4 --no-template-tools-py
-```
-
-```bash
-praisonai templates init my-project --template ai-video-editor
-```
-
-```bash
-praisonai templates cache clear
-```
-
-```bash
-praisonai templates cache list
-```

docs/templates-module.md

@@ -1,104 +0,0 @@
-# Templates Module
-
-## Loading Templates
-
-```python
-from praisonai.templates.loader import TemplateLoader
-
-loader = TemplateLoader()
-
-# Load from local path
-template = loader.load("./my-template")
-
-# Load from URI
-template = loader.load("ai-video-editor")
-
-# Load offline
-template = loader.load("ai-video-editor", offline=True)
-
-# Check requirements
-missing = loader.check_requirements(template)
-```
-
-## Template Discovery
-
-```python
-from praisonai.templates.discovery import TemplateDiscovery
-
-discovery = TemplateDiscovery(
-    include_package=True,
-    include_defaults=True,
-)
-
-# Find template
-result = discovery.find_template("ai-video-editor")
-
-# List all templates
-templates = discovery.list_templates()
-```
-
-## Template Config
-
-```python
-from praisonai.templates.loader import TemplateConfig
-
-# Access template properties
-template.name
-template.description
-template.version
-template.author
-template.requires
-template.workflow_file
-template.agents_file
-template.path
-```
-
-## Tool Override Integration
-
-```python
-from praisonai.templates.tool_override import (
-    create_tool_registry_with_overrides,
-    resolve_tools,
-)
-
-# Build registry with template sources
-tools_sources = template.requires.get("tools_sources", [])
-
-registry = create_tool_registry_with_overrides(
-    tools_sources=tools_sources,
-    template_dir=str(template.path),
-    include_defaults=True,
-)
-
-# Resolve tool names
-tools = resolve_tools(["shell_tool"], registry=registry)
-```
-
-## TEMPLATE.yaml Schema
-
-```yaml
-name: my-template
-version: "1.0.0"
-description: Template description
-
-requires:
-  tools: [shell_tool]
-  packages: [praisonai-tools]
-  env: [OPENAI_API_KEY]
-  tools_sources:
-    - praisonai_tools.video
-    - ./local_tools.py
-
-workflow: workflow.yaml
-agents: agents.yaml
-
-config:
-  input:
-    type: string
-    required: true
-  output:
-    type: string
-
-defaults:
-  preset: podcast
-```

docs/tools-cli.md

@@ -1,65 +0,0 @@
-# Tools CLI
-
-```bash
-praisonai tools list
-```
-
-```bash
-praisonai tools info shell_tool
-```
-
-```bash
-praisonai tools info internet_search
-```
-
-```bash
-praisonai tools search "web"
-```
-
-```bash
-praisonai tools search "file"
-```
-
-```bash
-praisonai tools doctor
-```
-
-```bash
-praisonai tools doctor --json
-```
-
-```bash
-praisonai tools resolve shell_tool
-```
-
-```bash
-praisonai tools resolve internet_search --template ai-video-editor
-```
-
-```bash
-praisonai tools resolve my_tool --tools ./my_tools.py
-```
-
-```bash
-praisonai tools resolve my_tool --tools-dir ./tools
-```
-
-```bash
-praisonai tools discover
-```
-
-```bash
-praisonai tools discover --include praisonai_tools.video
-```
-
-```bash
-praisonai tools discover --entrypoints
-```
-
-```bash
-praisonai tools show-sources
-```
-
-```bash
-praisonai tools show-sources --template ai-video-editor
-```

docs/tools-module.md

@@ -1,90 +0,0 @@
-# Tools Module
-
-## Tool Registry
-
-```python
-from praisonai.templates.tool_override import (
-    create_tool_registry_with_overrides,
-    resolve_tools,
-)
-
-# Create registry with defaults
-registry = create_tool_registry_with_overrides(include_defaults=True)
-
-# Create registry with overrides
-registry = create_tool_registry_with_overrides(
-    override_files=["my_tools.py"],
-    override_dirs=["./tools"],
-    include_defaults=True,
-)
-
-# Create registry with tools_sources
-registry = create_tool_registry_with_overrides(
-    tools_sources=["praisonai_tools.video"],
-    template_dir="./my-template",
-    include_defaults=True,
-)
-
-# Resolve tool names to callables
-tools = resolve_tools(
-    ["shell_tool", "internet_search"],
-    registry=registry,
-)
-```
-
-## Tool Override Loader
-
-```python
-from praisonai.templates.tool_override import ToolOverrideLoader
-
-loader = ToolOverrideLoader()
-
-# Load from file
-tools = loader.load_from_file("my_tools.py")
-
-# Load from directory
-tools = loader.load_from_directory("./tools")
-
-# Load from module
-tools = loader.load_from_module("praisonai_tools.video")
-
-# Get default tool directories
-dirs = loader.get_default_tool_dirs()
-```
-
-## Resolution Order
-
-1. CLI `--tools` files (highest priority)
-2. CLI `--tools-dir` directories
-3. Template `tools_sources` (from TEMPLATE.yaml)
-4. Template-local `tools.py`
-5. Default dirs (`~/.praison/tools`, `~/.config/praison/tools`)
-6. Package discovery (`praisonai_tools`)
-7. Built-in tools (lowest priority)
-
-## Creating Custom Tools
-
-```python
-from praisonai_tools import tool
-
-@tool
-def my_custom_tool(query: str) -> str:
-    """My custom tool description.
-    
-    Args:
-        query: The search query
-    """
-    return f"Result for: {query}"
-```
-
-## Template tools_sources
-
-```yaml
-# TEMPLATE.yaml
-requires:
-  tools: [shell_tool]
-  tools_sources:
-    - praisonai_tools.video
-    - ./local_tools.py
-    - ./tools_dir/
-```

examples/README.md

@@ -1,67 +0,0 @@
-# PraisonAI Examples
-
-This folder contains examples for PraisonAI. For detailed documentation, visit [docs.praison.ai](https://docs.praison.ai).
-
-## Structure
-
-```
-examples/
-├── python/           # Python examples
-│   ├── agents/       # Agent examples (single, multi, router, etc.)
-│   ├── workflows/    # Workflow patterns (routing, parallel, loop)
-│   ├── tools/        # Custom tools examples
-│   ├── mcp/          # MCP protocol examples
-│   ├── memory/       # Memory and sessions
-│   ├── code/         # Code editing and external CLI tools
-│   └── ...
-├── serve/            # Server and endpoints examples
-├── yaml/             # YAML workflow examples
-└── cookbooks/        # Complete use-case examples
-```
-
-## Quick Links
-
-| Category | Examples | Docs |
-|----------|----------|------|
-| **Agents** | [python/agents/](python/agents/) | [📖](https://docs.praison.ai/concepts/agents) |
-| **Workflows** | [python/workflows/](python/workflows/) | [📖](https://docs.praison.ai/features/workflows) |
-| **Model Router** | [python/agents/router-agent-cost-optimization.py](python/agents/router-agent-cost-optimization.py) | [📖](https://docs.praison.ai/features/model-router) |
-| **MCP** | [python/mcp/](python/mcp/) | [📖](https://docs.praison.ai/mcp) |
-| **Memory** | [python/memory/](python/memory/) | [📖](https://docs.praison.ai/concepts/memory) |
-| **Tools** | [python/tools/](python/tools/) | [📖](https://docs.praison.ai/tools) |
-| **Code** | [python/code/](python/code/) | [📖](https://docs.praison.ai/code) |
-| **YAML** | [yaml/](yaml/) | [📖](https://docs.praison.ai/features/yaml-workflows) |
-| **Doctor** | [doctor/](doctor/) | [📖](https://docs.praison.ai/cli/doctor) |
-| **Serve** | [serve/](serve/) | [📖](https://docs.praison.ai/cli/serve) |
-| **Endpoints** | [serve/](serve/) | [📖](https://docs.praison.ai/cli/endpoints) |
-
-## Serve Examples
-
-| Example | Description | CLI Command |
-|---------|-------------|-------------|
-| [unified_server.py](serve/unified_server.py) | All providers in one server | `praisonai serve unified` |
-| [agent_as_api_single.py](serve/agent_as_api_single.py) | Single agent HTTP API | `praisonai serve agents` |
-| [agents_as_api_router.py](serve/agents_as_api_router.py) | Multi-agent router API | `praisonai serve agents` |
-| [a2a_server_client.py](serve/a2a_server_client.py) | A2A protocol server | `praisonai serve a2a` |
-| [a2u_events_stream.py](serve/a2u_events_stream.py) | A2U event stream | `praisonai serve a2u` |
-| [mcp_http_server.py](serve/mcp_http_server.py) | MCP HTTP server | `praisonai serve mcp` |
-| [tools_as_mcp_server.py](serve/tools_as_mcp_server.py) | Tools as MCP server | `praisonai serve tools` |
-| [agent_launch_modes.py](serve/agent_launch_modes.py) | Agent.launch() API | Python only |
-| [endpoints_unified_client.py](serve/endpoints_unified_client.py) | Unified client | `praisonai endpoints` |
-
-## Running Examples
-
-```bash
-# Install PraisonAI
-pip install praisonai
-
-# Set API key
-export OPENAI_API_KEY=your_key_here
-
-# Run an example
-python examples/python/agents/single-agent.py
-```
-
-## CLI Commands
-
-See the main [README.md](../README.md#-cli--no-code-interface) for all CLI commands.

examples/acp/README.md

@@ -1,137 +0,0 @@
-# ACP (Agent Client Protocol) Examples
-
-This directory contains examples for using PraisonAI with the Agent Client Protocol (ACP).
-
-## What is ACP?
-
-The Agent Client Protocol (ACP) is a standardized JSON-RPC 2.0 protocol that allows code editors and IDEs to communicate with AI coding agents. It enables seamless integration between PraisonAI and editors like:
-
-- **Zed**
-- **JetBrains IDEs** (IntelliJ, PyCharm, WebStorm, etc.)
-- **VSCode** (via extensions)
-- **Toad**
-
-## Quick Start
-
-### CLI Usage
-
-```bash
-# Start ACP server with defaults
-praisonai acp
-
-# Enable debug logging
-praisonai acp --debug
-
-# Allow file writes
-praisonai acp --allow-write
-
-# Use a specific model
-praisonai acp -m gpt-4o
-
-# Resume last session
-praisonai acp --resume --last
-```
-
-### Python API
-
-```python
-from praisonai.acp import serve
-
-serve(
-    workspace=".",
-    debug=True,
-    allow_write=True,
-)
-```
-
-## Editor Configuration
-
-### Zed
-
-Add to `~/.config/zed/settings.json`:
-
-```json
-{
-  "agent_servers": {
-    "PraisonAI": {
-      "command": "praisonai",
-      "args": ["acp"],
-      "env": {}
-    }
-  }
-}
-```
-
-### JetBrains
-
-Add to `~/.jetbrains/acp.json`:
-
-```json
-{
-  "agent_servers": {
-    "PraisonAI": {
-      "command": "praisonai",
-      "args": ["acp"],
-      "env": {}
-    }
-  }
-}
-```
-
-### Toad
-
-```bash
-toad acp "praisonai acp"
-```
-
-## Examples
-
-- `basic_acp_server.py` - Simple ACP server with default settings
-- `custom_agent_acp.py` - ACP server with a custom agent
-
-## CLI Options
-
-| Option | Description |
-|--------|-------------|
-| `-w, --workspace` | Workspace root directory |
-| `-a, --agent` | Agent name or config file |
-| `-m, --model` | LLM model to use |
-| `-r, --resume` | Resume session by ID |
-| `--last` | Resume the last session |
-| `--approve` | Approval mode: manual, auto, scoped |
-| `--read-only` | Read-only mode (default) |
-| `--allow-write` | Allow file writes |
-| `--allow-shell` | Allow shell commands |
-| `--debug` | Enable debug logging |
-
-## Installation
-
-```bash
-pip install praisonai[acp]
-```
-
-## Protocol Details
-
-ACP uses JSON-RPC 2.0 over stdio:
-- **stdin**: Receives JSON-RPC requests from the client
-- **stdout**: Sends JSON-RPC responses to the client
-- **stderr**: Debug/log output (never pollutes stdout)
-
-### Supported Methods
-
-**Agent Methods (client → agent):**
-- `initialize` - Negotiate protocol version and capabilities
-- `authenticate` - Optional authentication
-- `session/new` - Create a new session
-- `session/load` - Load an existing session
-- `session/prompt` - Send user message
-- `session/cancel` - Cancel ongoing operations
-- `session/set_mode` - Change operating mode
-
-**Client Methods (agent → client):**
-- `session/update` - Send progress updates
-- `session/request_permission` - Request user approval
-- `fs/read_text_file` - Read file contents
-- `fs/write_text_file` - Write file contents
-- `terminal/create` - Create terminal
-- `terminal/output` - Get terminal output

examples/acp/basic_acp_server.py

@@ -1,27 +0,0 @@
-#!/usr/bin/env python3
-"""
-Basic ACP Server Example
-
-This example demonstrates how to run PraisonAI as an ACP server
-that can be connected to by IDEs like Zed, JetBrains, VSCode, or Toad.
-
-Usage:
-    # Run from command line
-    praisonai acp
-    
-    # Or run this script directly
-    python basic_acp_server.py
-"""
-
-from praisonai.acp import serve
-
-if __name__ == "__main__":
-    # Start ACP server with default settings
-    # This will listen on stdin/stdout for JSON-RPC messages
-    serve(
-        workspace=".",           # Current directory as workspace
-        agent="default",         # Use default agent
-        debug=False,             # Set to True for debug logging to stderr
-        read_only=True,          # Safe by default
-        approval_mode="manual",  # Require approval for actions
-    )

examples/acp/custom_agent_acp.py

@@ -1,45 +0,0 @@
-#!/usr/bin/env python3
-"""
-Custom Agent ACP Server Example
-
-This example shows how to run a custom PraisonAI agent as an ACP server.
-
-Usage:
-    python custom_agent_acp.py
-"""
-
-from praisonai.acp import serve, ACPServer, ACPConfig
-from praisonaiagents import Agent
-
-# Create a custom agent
-agent = Agent(
-    name="CodeAssistant",
-    instructions="""You are an expert coding assistant. You help users:
-    - Write clean, efficient code
-    - Debug issues
-    - Explain complex concepts
-    - Review code for best practices
-    
-    Always provide clear explanations with your code suggestions.""",
-    model="gpt-4o-mini",
-)
-
-if __name__ == "__main__":
-    # Create config
-    config = ACPConfig(
-        workspace=".",
-        debug=True,
-        allow_write=True,  # Allow file modifications
-        approval_mode="manual",
-    )
-    
-    # Create server with custom agent
-    server = ACPServer(config=config, agent=agent)
-    
-    # Run the server
-    serve(
-        workspace=".",
-        debug=True,
-        allow_write=True,
-        approval_mode="manual",
-    )

examples/ag2/ag2_basic.yaml

@@ -0,0 +1,30 @@
+framework: ag2
+topic: "Research the latest developments in AI agents"
+
+# Install: pip install "praisonai[ag2]"
+# Run:     praisonai --framework ag2 examples/ag2/ag2_basic.yaml
+#       or praisonai run examples/ag2/ag2_basic.yaml --framework ag2
+
+roles:
+  research_agent:
+    role: "AI Research Specialist"
+    goal: "Research and summarise the latest developments in AI agent frameworks"
+    backstory: |
+      You are an experienced AI researcher with deep knowledge of multi-agent
+      systems, large language models, and the latest trends in AI tooling.
+      You excel at synthesising complex technical topics into clear summaries.
+    tasks:
+      research_task:
+        description: |
+          Research and summarise the latest developments in AI agent frameworks
+          for the topic: {topic}
+
+          Focus on:
+          1. Key frameworks and their unique capabilities
+          2. Recent innovations and improvements
+          3. Community adoption and ecosystem growth
+          4. Practical use cases and success stories
+        expected_output: |
+          A concise research summary covering the key developments,
+          major frameworks, and practical insights. Include 3-5 bullet
+          points of the most important findings.

examples/ag2/ag2_bedrock.yaml

@@ -0,0 +1,42 @@
+framework: ag2
+topic: "Cloud-native AI deployment strategies on AWS"
+
+# AG2 exclusive feature: native AWS Bedrock support via LLMConfig(api_type="bedrock")
+#
+# Prerequisites:
+#   pip install "praisonai[ag2]"
+#   aws configure  (or set AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION)
+#
+# Run:
+#   praisonai --framework ag2 examples/ag2/ag2_bedrock.yaml
+#
+# The AG2 adapter detects api_type="bedrock" from the llm config and uses
+# LLMConfig(api_type="bedrock", model=...) — no OPENAI_API_KEY required.
+# AWS credentials are sourced from boto3 (env vars, ~/.aws/credentials, IAM role).
+
+roles:
+  cloud_architect:
+    role: "AWS Cloud Architect"
+    goal: "Design and explain cloud-native AI deployment strategies on AWS"
+    backstory: |
+      You are an AWS Solutions Architect specialising in AI/ML workloads.
+      You have deep expertise in Amazon Bedrock, SageMaker, ECS, and Lambda,
+      and you help organisations deploy AI agents at scale securely and cost-effectively.
+    llm:
+      model: "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0"
+      api_type: "bedrock"
+      aws_region: "us-east-1"
+    tasks:
+      architecture_task:
+        description: |
+          Design a cloud-native deployment strategy for AI agents on AWS for: {topic}
+
+          Cover:
+          1. Recommended AWS services (Bedrock, ECS, Lambda, etc.)
+          2. Scalability and cost optimisation patterns
+          3. Security and compliance considerations
+          4. A simple reference architecture overview
+        expected_output: |
+          A concise architecture guide with service recommendations,
+          a high-level deployment diagram description, and key
+          best practices for production AI agent deployments on AWS.

examples/ag2/ag2_multi_agent.yaml

@@ -0,0 +1,54 @@
+framework: ag2
+topic: "The impact of open-source AI on enterprise software development"
+
+# Install: pip install "praisonai[ag2]"
+# Run:     praisonai --framework ag2 examples/ag2/ag2_multi_agent.yaml
+#       or praisonai run examples/ag2/ag2_multi_agent.yaml --framework ag2
+#
+# This example demonstrates AG2's GroupChat multi-agent coordination.
+# Both agents participate in a collaborative conversation managed by
+# a GroupChatManager until the task is complete.
+
+roles:
+  researcher:
+    role: "Research Specialist"
+    goal: "Gather and analyse information on the given topic"
+    backstory: |
+      You are a meticulous researcher who excels at finding relevant
+      information, analysing trends, and presenting data-backed insights.
+      You always cite your reasoning and structure your findings clearly.
+    tasks:
+      research_task:
+        description: |
+          Research the topic: {topic}
+
+          Investigate:
+          1. Current state and adoption rates
+          2. Key players and projects driving the trend
+          3. Technical advantages and challenges
+          4. Business impact and cost implications
+        expected_output: |
+          A structured research briefing with findings on the topic,
+          including key data points, trends, and technical observations.
+
+  writer:
+    role: "Technical Content Writer"
+    goal: "Transform research findings into clear, engaging written content"
+    backstory: |
+      You are a skilled technical writer who turns complex research into
+      accessible, well-structured articles. You focus on clarity, logical
+      flow, and actionable takeaways for a professional audience.
+    tasks:
+      writing_task:
+        description: |
+          Using the research findings provided by the Research Specialist,
+          write a concise article on: {topic}
+
+          The article should:
+          1. Open with a compelling hook
+          2. Present key findings logically
+          3. Include practical implications for developers
+          4. Close with a forward-looking conclusion
+        expected_output: |
+          A 400-500 word article suitable for a technical blog,
+          with clear sections, professional tone, and concrete takeaways.

examples/agent_tools/agent_centric_example.py

@@ -1,135 +0,0 @@
-"""
-Agent-Centric Tools Example
-
-This example demonstrates how to use LSP/ACP-powered tools that make
-the Agent the central orchestrator for file operations.
-
-How to run:
-    export OPENAI_API_KEY=your_key
-    python examples/agent_tools/agent_centric_example.py
-
-Prerequisites:
-    pip install praisonai praisonaiagents
-"""
-
-import asyncio
-import os
-import sys
-import tempfile
-import shutil
-
-# Add paths for development
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../src/praisonai'))
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../src/praisonai-agents'))
-
-
-async def main():
-    print("=" * 60)
-    print("AGENT-CENTRIC TOOLS EXAMPLE")
-    print("=" * 60)
-    print()
-    
-    # Create temporary workspace
-    workspace = tempfile.mkdtemp(prefix="praisonai_example_")
-    print(f"Workspace: {workspace}")
-    print()
-    
-    try:
-        # Import after path setup
-        from praisonai.cli.features import (
-            create_agent_centric_tools,
-            InteractiveRuntime,
-            RuntimeConfig
-        )
-        from praisonaiagents import Agent
-        
-        # 1. Create runtime with ACP enabled
-        print("1. Creating InteractiveRuntime with ACP...")
-        config = RuntimeConfig(
-            workspace=workspace,
-            lsp_enabled=False,  # Skip LSP for this example
-            acp_enabled=True,
-            approval_mode="auto"  # Auto-approve for demo
-        )
-        runtime = InteractiveRuntime(config)
-        status = await runtime.start()
-        
-        print(f"   ACP ready: {runtime.acp_ready}")
-        print(f"   Read-only: {runtime.read_only}")
-        print()
-        
-        # 2. Create agent-centric tools
-        print("2. Creating agent-centric tools...")
-        tools = create_agent_centric_tools(runtime)
-        print(f"   Tools created: {len(tools)}")
-        for tool in tools:
-            print(f"     - {tool.__name__}")
-        print()
-        
-        # 3. Create Agent with ACP-powered tools
-        print("3. Creating Agent with ACP-powered tools...")
-        agent = Agent(
-            name="FileAgent",
-            instructions="""You help create and manage files. 
-            Use acp_create_file to create files.
-            Use read_file to read files.
-            Use list_files to list directory contents.""",
-            tools=tools,
-            verbose=True
-        )
-        print()
-        
-        # 4. Ask agent to create a file
-        print("4. Asking agent to create a Python file...")
-        print("-" * 40)
-        result = agent.start(
-            "Create a Python file called calculator.py with add and subtract functions"
-        )
-        print("-" * 40)
-        print(f"Agent response: {result[:200]}..." if len(str(result)) > 200 else f"Agent response: {result}")
-        print()
-        
-        # 5. Verify file was created
-        print("5. Verifying file creation...")
-        calc_path = os.path.join(workspace, "calculator.py")
-        if os.path.exists(calc_path):
-            print(f"   ✓ File created at {calc_path}")
-            with open(calc_path) as f:
-                content = f.read()
-                print(f"   Content preview:")
-                for line in content.split('\n')[:10]:
-                    print(f"     {line}")
-        else:
-            # Check what files exist
-            files = os.listdir(workspace)
-            print(f"   Files in workspace: {files}")
-            for f in files:
-                fp = os.path.join(workspace, f)
-                if os.path.isfile(fp):
-                    print(f"   ✓ Found: {f}")
-        print()
-        
-        # 6. Ask agent to read the file
-        print("6. Asking agent to read the file...")
-        print("-" * 40)
-        result2 = agent.start("Read the calculator.py file and tell me what functions it has")
-        print("-" * 40)
-        print(f"Agent response: {result2[:300]}..." if len(str(result2)) > 300 else f"Agent response: {result2}")
-        print()
-        
-        # Cleanup
-        await runtime.stop()
-        
-    finally:
-        # Clean up workspace
-        shutil.rmtree(workspace, ignore_errors=True)
-        print(f"Cleaned up workspace: {workspace}")
-    
-    print()
-    print("=" * 60)
-    print("EXAMPLE COMPLETE")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/async_runs/README.py

@@ -1,35 +0,0 @@
-"""
-Async Runs Examples
-===================
-
-This directory contains examples for the PraisonAI Async Jobs API.
-
-Examples:
----------
-1. sdk_submit_poll_result.py - Python SDK example for submit/poll/result
-2. cli_submit_poll_result.sh - Bash/curl example for submit/poll/result
-3. idempotency.sh - Demonstrates idempotency key usage
-4. cancel.sh - Demonstrates job cancellation
-5. sse_stream.sh - Demonstrates SSE streaming
-
-Prerequisites:
---------------
-1. Set OPENAI_API_KEY environment variable
-2. Start the jobs server:
-   
-   cd /path/to/praisonai-package/src/praisonai
-   python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory
-
-3. Run examples:
-   
-   python sdk_submit_poll_result.py
-   bash cli_submit_poll_result.sh
-   bash idempotency.sh
-   bash cancel.sh
-   bash sse_stream.sh
-
-Environment Variables:
-----------------------
-- OPENAI_API_KEY: Required for agent execution
-- PRAISONAI_BASE_URL: API server URL (default: http://127.0.0.1:8005)
-"""

examples/async_runs/cancel.sh

@@ -1,54 +0,0 @@
-#!/bin/bash
-# CLI Example: Cancel a Running Job
-#
-# This example demonstrates how to cancel a running job.
-#
-# Requirements:
-# - Jobs server running on http://127.0.0.1:8005
-#
-# Usage:
-#     bash cancel.sh
-
-set -e
-
-API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
-
-echo "============================================================"
-echo "PraisonAI Job Cancellation Example"
-echo "============================================================"
-
-# Submit a long-running job
-echo ""
-echo "1. Submitting a job..."
-RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
-    -H "Content-Type: application/json" \
-    -d '{"prompt": "Write a detailed essay about the history of computing."}')
-
-JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
-echo "   Job ID: $JOB_ID"
-
-# Wait briefly
-echo ""
-echo "2. Waiting 1 second..."
-sleep 1
-
-# Cancel the job
-echo ""
-echo "3. Cancelling job..."
-CANCEL_RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs/$JOB_ID/cancel")
-STATUS=$(echo "$CANCEL_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
-echo "   Status after cancel: $STATUS"
-
-# Verify cancellation
-echo ""
-echo "4. Verification:"
-if [ "$STATUS" = "cancelled" ]; then
-    echo "   ✓ SUCCESS: Job was cancelled"
-else
-    echo "   ℹ Job status: $STATUS (may have completed before cancel)"
-fi
-
-echo ""
-echo "============================================================"
-echo "Cancellation example completed!"
-echo "============================================================"

examples/async_runs/cli_submit_poll_result.sh

@@ -1,75 +0,0 @@
-#!/bin/bash
-# CLI Example: Submit, Poll, and Get Result for Async Jobs
-#
-# This example demonstrates how to use the PraisonAI CLI to:
-# 1. Submit an async job
-# 2. Check job status
-# 3. Get the result
-#
-# Requirements:
-# - OPENAI_API_KEY environment variable set
-# - Jobs server running on http://127.0.0.1:8005
-#
-# Usage:
-#     bash cli_submit_poll_result.sh
-
-set -e
-
-API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
-
-echo "============================================================"
-echo "PraisonAI Async Jobs CLI Example"
-echo "============================================================"
-
-# Check if server is running
-echo ""
-echo "Checking server health..."
-if ! curl -s "$API_URL/health" > /dev/null 2>&1; then
-    echo "✗ Server not available at $API_URL"
-    echo "  Start the server with: python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory"
-    exit 1
-fi
-echo "✓ Server is healthy"
-
-# 1. Submit a job
-echo ""
-echo "1. Submitting job..."
-RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
-    -H "Content-Type: application/json" \
-    -d '{"prompt": "What is 2+2? Answer with just the number."}')
-
-JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
-echo "   Job ID: $JOB_ID"
-
-# 2. Poll for status
-echo ""
-echo "2. Polling for status..."
-while true; do
-    STATUS_RESPONSE=$(curl -s "$API_URL/api/v1/runs/$JOB_ID")
-    STATUS=$(echo "$STATUS_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
-    PROGRESS=$(echo "$STATUS_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['progress']['percentage'])")
-    
-    echo "   Status: $STATUS | Progress: ${PROGRESS}%"
-    
-    if [ "$STATUS" = "succeeded" ] || [ "$STATUS" = "failed" ] || [ "$STATUS" = "cancelled" ]; then
-        break
-    fi
-    
-    sleep 2
-done
-
-# 3. Get result
-echo ""
-echo "3. Getting result..."
-if [ "$STATUS" = "succeeded" ]; then
-    RESULT=$(curl -s "$API_URL/api/v1/runs/$JOB_ID/result")
-    echo "   Result: $(echo "$RESULT" | python3 -c "import sys,json; print(json.load(sys.stdin)['result'])")"
-    echo "   Duration: $(echo "$RESULT" | python3 -c "import sys,json; print(f\"{json.load(sys.stdin)['duration_seconds']:.2f}s\")")"
-else
-    echo "   Job did not succeed: $STATUS"
-fi
-
-echo ""
-echo "============================================================"
-echo "Example completed!"
-echo "============================================================"

examples/async_runs/idempotency.sh

@@ -1,57 +0,0 @@
-#!/bin/bash
-# CLI Example: Idempotency Key Usage
-#
-# This example demonstrates how idempotency keys prevent duplicate job submissions.
-# Submitting the same request with the same idempotency key returns the existing job.
-#
-# Requirements:
-# - Jobs server running on http://127.0.0.1:8005
-#
-# Usage:
-#     bash idempotency.sh
-
-set -e
-
-API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
-IDEM_KEY="example-idem-key-$(date +%s)"
-
-echo "============================================================"
-echo "PraisonAI Idempotency Key Example"
-echo "============================================================"
-
-# First submission
-echo ""
-echo "1. First submission with Idempotency-Key: $IDEM_KEY"
-RESPONSE1=$(curl -s -X POST "$API_URL/api/v1/runs" \
-    -H "Content-Type: application/json" \
-    -H "Idempotency-Key: $IDEM_KEY" \
-    -d '{"prompt": "What is 1+1?"}')
-
-JOB_ID1=$(echo "$RESPONSE1" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
-echo "   Job ID: $JOB_ID1"
-
-# Second submission with same key
-echo ""
-echo "2. Second submission with SAME Idempotency-Key: $IDEM_KEY"
-RESPONSE2=$(curl -s -X POST "$API_URL/api/v1/runs" \
-    -H "Content-Type: application/json" \
-    -H "Idempotency-Key: $IDEM_KEY" \
-    -d '{"prompt": "Different prompt but same key"}')
-
-JOB_ID2=$(echo "$RESPONSE2" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
-echo "   Job ID: $JOB_ID2"
-
-# Verify same job ID
-echo ""
-echo "3. Verification:"
-if [ "$JOB_ID1" = "$JOB_ID2" ]; then
-    echo "   ✓ SUCCESS: Same job ID returned (no duplicate created)"
-else
-    echo "   ✗ FAILURE: Different job IDs returned"
-    exit 1
-fi
-
-echo ""
-echo "============================================================"
-echo "Idempotency example completed!"
-echo "============================================================"

examples/async_runs/sdk_submit_poll_result.py

@@ -1,121 +0,0 @@
-#!/usr/bin/env python3
-"""
-SDK Example: Submit, Poll, and Get Result for Async Jobs
-
-This example demonstrates how to use the PraisonAI Jobs SDK to:
-1. Submit an async job
-2. Poll for status
-3. Get the result
-
-Requirements:
-- OPENAI_API_KEY environment variable set
-- Jobs server running on http://127.0.0.1:8005
-
-Usage:
-    python sdk_submit_poll_result.py
-"""
-
-import os
-import time
-import httpx
-
-# Configuration
-API_URL = os.getenv("PRAISONAI_BASE_URL", "http://127.0.0.1:8005")
-POLL_INTERVAL = 2  # seconds
-
-
-def submit_job(prompt: str, idempotency_key: str = None) -> dict:
-    """Submit a job and return the response."""
-    headers = {}
-    if idempotency_key:
-        headers["Idempotency-Key"] = idempotency_key
-    
-    with httpx.Client(timeout=30.0) as client:
-        response = client.post(
-            f"{API_URL}/api/v1/runs",
-            json={"prompt": prompt},
-            headers=headers if headers else None
-        )
-        response.raise_for_status()
-        return response.json()
-
-
-def get_status(job_id: str) -> dict:
-    """Get job status."""
-    with httpx.Client(timeout=30.0) as client:
-        response = client.get(f"{API_URL}/api/v1/runs/{job_id}")
-        response.raise_for_status()
-        return response.json()
-
-
-def get_result(job_id: str) -> dict:
-    """Get job result."""
-    with httpx.Client(timeout=30.0) as client:
-        response = client.get(f"{API_URL}/api/v1/runs/{job_id}/result")
-        response.raise_for_status()
-        return response.json()
-
-
-def wait_for_completion(job_id: str, max_wait: int = 120) -> dict:
-    """Poll until job completes."""
-    start = time.time()
-    while time.time() - start < max_wait:
-        status = get_status(job_id)
-        print(f"  Status: {status['status']} | Progress: {status['progress']['percentage']:.0f}%")
-        
-        if status["status"] in ("succeeded", "failed", "cancelled"):
-            return status
-        
-        # Honor retry_after if present
-        wait_time = status.get("retry_after") or POLL_INTERVAL
-        time.sleep(wait_time)
-    
-    raise TimeoutError(f"Job {job_id} did not complete within {max_wait}s")
-
-
-def main():
-    print("=" * 60)
-    print("PraisonAI Async Jobs SDK Example")
-    print("=" * 60)
-    
-    # Check if server is running
-    try:
-        with httpx.Client(timeout=5.0) as client:
-            health = client.get(f"{API_URL}/health")
-            health.raise_for_status()
-            print(f"✓ Server healthy: {health.json()['status']}")
-    except Exception as e:
-        print(f"✗ Server not available at {API_URL}: {e}")
-        print("  Start the server with: python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory")
-        return
-    
-    # 1. Submit a job
-    print("\n1. Submitting job...")
-    prompt = "What is the capital of France? Answer in one word."
-    result = submit_job(prompt)
-    job_id = result["job_id"]
-    print(f"   Job ID: {job_id}")
-    print(f"   Status: {result['status']}")
-    print(f"   Poll URL: {result['poll_url']}")
-    
-    # 2. Wait for completion
-    print("\n2. Waiting for completion...")
-    final_status = wait_for_completion(job_id)
-    print(f"   Final status: {final_status['status']}")
-    
-    # 3. Get result
-    if final_status["status"] == "succeeded":
-        print("\n3. Getting result...")
-        result = get_result(job_id)
-        print(f"   Result: {result['result']}")
-        print(f"   Duration: {result['duration_seconds']:.2f}s")
-    else:
-        print(f"\n3. Job did not succeed: {final_status.get('error', 'Unknown error')}")
-    
-    print("\n" + "=" * 60)
-    print("Example completed successfully!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()

examples/async_runs/sse_stream.sh

@@ -1,51 +0,0 @@
-#!/bin/bash
-# CLI Example: SSE Streaming
-#
-# This example demonstrates how to stream job progress via Server-Sent Events.
-#
-# Requirements:
-# - Jobs server running on http://127.0.0.1:8005
-#
-# Usage:
-#     bash sse_stream.sh
-
-set -e
-
-API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
-
-echo "============================================================"
-echo "PraisonAI SSE Streaming Example"
-echo "============================================================"
-
-# Submit a job
-echo ""
-echo "1. Submitting job..."
-RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
-    -H "Content-Type: application/json" \
-    -d '{"prompt": "Count from 1 to 5."}')
-
-JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
-echo "   Job ID: $JOB_ID"
-
-# Stream progress
-echo ""
-echo "2. Streaming progress (Ctrl+C to stop)..."
-echo "   Connecting to: $API_URL/api/v1/runs/$JOB_ID/stream"
-echo ""
-
-# Use curl to stream SSE events (timeout after 60s)
-curl -s -N --max-time 60 "$API_URL/api/v1/runs/$JOB_ID/stream" | while read -r line; do
-    if [[ "$line" == data:* ]]; then
-        data="${line#data: }"
-        if [ "$data" = "[DONE]" ]; then
-            echo "   Stream completed"
-            break
-        fi
-        echo "   Event: $data"
-    fi
-done
-
-echo ""
-echo "============================================================"
-echo "Streaming example completed!"
-echo "============================================================"

examples/background/basic_background.py

@@ -1,92 +0,0 @@
-#!/usr/bin/env python3
-"""
-Basic Background Tasks Example for PraisonAI Agents.
-
-This example demonstrates how to use the background task system to:
-1. Run tasks asynchronously
-2. Track task progress
-3. Cancel running tasks
-4. Handle task completion
-
-Usage:
-    python basic_background.py
-"""
-
-import asyncio
-import time
-
-from praisonaiagents.background import BackgroundRunner, BackgroundConfig, TaskStatus
-
-
-async def main():
-    print("=" * 60)
-    print("Background Tasks Demo")
-    print("=" * 60)
-    
-    # Create a background runner with config
-    config = BackgroundConfig(max_concurrent_tasks=3)
-    runner = BackgroundRunner(config=config)
-    
-    # Define some example tasks
-    def slow_task(duration: float, name: str) -> str:
-        """A slow task that simulates work."""
-        print(f"  🔄 Task '{name}' starting (will take {duration}s)")
-        time.sleep(duration)
-        return f"Task '{name}' completed after {duration}s"
-    
-    async def async_task(duration: float, name: str) -> str:
-        """An async task that simulates work."""
-        print(f"  🔄 Async task '{name}' starting (will take {duration}s)")
-        await asyncio.sleep(duration)
-        return f"Async task '{name}' completed after {duration}s"
-    
-    # Submit tasks
-    print("\n--- Submitting Tasks ---")
-    
-    task1 = await runner.submit(slow_task, args=(1.0, "quick-job"), name="task1")
-    print(f"✅ Submitted task 1: {task1.id[:8]}")
-    
-    task2 = await runner.submit(slow_task, args=(2.0, "medium-job"), name="task2")
-    print(f"✅ Submitted task 2: {task2.id[:8]}")
-    
-    task3 = await runner.submit(async_task, args=(1.5, "async-job"), name="task3")
-    print(f"✅ Submitted async task 3: {task3.id[:8]}")
-    
-    # List all tasks
-    print("\n--- Task Status ---")
-    for task in runner.tasks:
-        print(f"  [{task.id[:8]}] {task.status.value}")
-    
-    # Wait for task 1 to complete
-    print("\n--- Waiting for Task 1 ---")
-    await task1.wait(timeout=5.0)
-    print(f"✅ Task 1 result: {task1.result}")
-    
-    # Check status of all tasks
-    print("\n--- Updated Task Status ---")
-    for task in [task1, task2, task3]:
-        status = "✅" if task.status == TaskStatus.COMPLETED else "🔄"
-        print(f"  {status} [{task.id[:8]}] {task.status.value}")
-    
-    # Wait for all remaining tasks
-    print("\n--- Waiting for All Tasks ---")
-    await task2.wait(timeout=10.0)
-    await task3.wait(timeout=10.0)
-    
-    # Final status
-    print("\n--- Final Results ---")
-    for task in [task1, task2, task3]:
-        if task.result:
-            print(f"  ✅ {task.result}")
-    
-    # Cleanup
-    runner.clear_completed()
-    print("\n✅ Cleared completed tasks")
-    
-    print("\n" + "=" * 60)
-    print("Demo Complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/benchmark/benchmark_example.py

@@ -1,146 +0,0 @@
-#!/usr/bin/env python3
-"""
-PraisonAI Benchmark Example
-
-This example demonstrates how to use the benchmark system programmatically
-to compare performance across different execution paths.
-
-Usage:
-    python benchmark_example.py
-    
-Requirements:
-    - OPENAI_API_KEY environment variable set
-    - praisonai package installed
-"""
-
-import os
-import sys
-
-# Ensure the package is importable
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../src/praisonai'))
-
-from praisonai.cli.features.benchmark import BenchmarkHandler, BenchmarkReport
-
-
-def run_quick_comparison():
-    """Run a quick comparison of key execution paths."""
-    print("=" * 60)
-    print("Quick Benchmark Comparison")
-    print("=" * 60)
-    
-    handler = BenchmarkHandler()
-    
-    # Run benchmark with 2 iterations on key paths
-    report = handler.run_full_benchmark(
-        prompt="What is 2+2?",
-        iterations=2,
-        paths=["openai_sdk", "praisonai_agent"],
-        verbose=True
-    )
-    
-    # Print comparison table
-    print("\n" + handler.create_comparison_table(report))
-    
-    return report
-
-
-def run_full_benchmark():
-    """Run full benchmark suite across all paths."""
-    print("=" * 60)
-    print("Full Benchmark Suite")
-    print("=" * 60)
-    
-    handler = BenchmarkHandler()
-    
-    # Run full benchmark
-    report = handler.run_full_benchmark(
-        prompt="Hi",
-        iterations=3,
-        verbose=True
-    )
-    
-    # Print full report
-    handler.print_report(report)
-    
-    return report
-
-
-def run_agent_benchmark():
-    """Benchmark PraisonAI Agent specifically."""
-    print("=" * 60)
-    print("Agent Benchmark")
-    print("=" * 60)
-    
-    handler = BenchmarkHandler()
-    
-    # Benchmark agent vs SDK
-    report = handler.run_full_benchmark(
-        prompt="Explain Python in one sentence",
-        iterations=3,
-        paths=["openai_sdk", "praisonai_agent"],
-        verbose=True
-    )
-    
-    # Show timeline diagrams
-    for name, result in report.results.items():
-        print(f"\n### {name}")
-        print(handler.create_timeline_diagram(result))
-    
-    # Show variance analysis
-    print(handler.create_variance_table(report))
-    
-    return report
-
-
-def save_benchmark_results(report: BenchmarkReport, output_path: str):
-    """Save benchmark results to JSON file."""
-    import json
-    
-    with open(output_path, 'w') as f:
-        json.dump(report.to_dict(), f, indent=2)
-    
-    print(f"\nResults saved to: {output_path}")
-
-
-def main():
-    """Main entry point."""
-    import argparse
-    
-    parser = argparse.ArgumentParser(description="PraisonAI Benchmark Example")
-    parser.add_argument(
-        "--mode",
-        choices=["quick", "full", "agent"],
-        default="quick",
-        help="Benchmark mode: quick (2 paths), full (all paths), agent (agent focus)"
-    )
-    parser.add_argument(
-        "--output",
-        type=str,
-        default=None,
-        help="Output JSON file path"
-    )
-    
-    args = parser.parse_args()
-    
-    # Check for API key
-    if not os.environ.get("OPENAI_API_KEY"):
-        print("Error: OPENAI_API_KEY environment variable not set")
-        sys.exit(1)
-    
-    # Run selected benchmark
-    if args.mode == "quick":
-        report = run_quick_comparison()
-    elif args.mode == "full":
-        report = run_full_benchmark()
-    elif args.mode == "agent":
-        report = run_agent_benchmark()
-    
-    # Save results if requested
-    if args.output:
-        save_benchmark_results(report, args.output)
-    
-    print("\n✅ Benchmark complete!")
-
-
-if __name__ == "__main__":
-    main()

examples/capabilities/assistants_example.py

@@ -1,24 +0,0 @@
-"""
-Assistants Capabilities Example
-
-Demonstrates OpenAI Assistants API using PraisonAI capabilities.
-"""
-
-from praisonai.capabilities import assistant_list
-
-print("=== List Assistants ===")
-try:
-    assistants = assistant_list()
-    print(f"Assistants found: {len(assistants)}")
-    for a in assistants[:5]:  # Show first 5
-        print(f"  - {a.get('name', 'unnamed')}: {a.get('id', 'unknown')}")
-except Exception as e:
-    print(f"Note: {e}")
-
-print("\n=== Assistant Operations ===")
-print("Available functions:")
-print("  assistant_create(name, instructions, model='gpt-4o-mini')")
-print("  assistant_list()")
-
-print("\nSee CLI: praisonai assistants list")
-print("See CLI: praisonai assistants create --name 'My Assistant'")

examples/capabilities/audio_example.py

@@ -1,24 +0,0 @@
-"""
-Audio Capabilities Example
-
-Demonstrates transcription and text-to-speech using PraisonAI capabilities.
-"""
-
-from praisonai.capabilities import transcribe, speech
-
-# Note: Audio transcription requires an audio file
-# This example shows the API structure
-
-print("=== Audio Transcription ===")
-print("To transcribe audio, use:")
-print("  result = transcribe('audio.mp3', model='whisper-1')")
-print("  print(result.text)")
-
-print("\n=== Text-to-Speech ===")
-print("To generate speech, use:")
-print("  audio_data = speech('Hello world', model='tts-1', voice='alloy')")
-print("  # Save to file or play")
-
-print("\nNote: These operations require audio files or generate audio output.")
-print("See CLI: praisonai audio transcribe <file>")
-print("See CLI: praisonai audio speech <text>")

examples/capabilities/batches_example.py

@@ -1,27 +0,0 @@
-"""
-Batches Capabilities Example
-
-Demonstrates batch processing using PraisonAI capabilities.
-"""
-
-from praisonai.capabilities import batch_list
-
-print("=== Batch List ===")
-try:
-    batches = batch_list()
-    print(f"Batches found: {len(batches)}")
-    for b in batches[:5]:  # Show first 5
-        print(f"  - {b.get('id', 'unknown')}: {b.get('status', 'unknown')}")
-except Exception as e:
-    print(f"Note: {e}")
-    print("Batch operations require batch jobs to be created first.")
-
-print("\n=== Batch Operations ===")
-print("Available functions:")
-print("  batch_create(input_file_id, endpoint, completion_window='24h')")
-print("  batch_list()")
-print("  batch_retrieve(batch_id)")
-print("  batch_cancel(batch_id)")
-
-print("\nSee CLI: praisonai batches list")
-print("See CLI: praisonai batches create <input_file_id>")

examples/capabilities/completions_example.py

@@ -1,30 +0,0 @@
-"""
-Completions Capability Example
-
-Demonstrates chat and text completions using PraisonAI capabilities.
-"""
-
-from praisonai.capabilities import chat_completion, text_completion
-
-# Chat completion example
-print("=== Chat Completion ===")
-result = chat_completion(
-    messages=[
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": "What is 2 + 2? Answer in one word."}
-    ],
-    model="gpt-4o-mini",
-    max_tokens=10
-)
-print(f"Response: {result.content}")
-print(f"Model: {result.model}")
-print(f"Usage: {result.usage}")
-
-# Text completion example (legacy)
-print("\n=== Text Completion ===")
-result = text_completion(
-    prompt="The capital of France is",
-    model="gpt-3.5-turbo-instruct",
-    max_tokens=10
-)
-print(f"Response: {result.content}")

examples/capabilities/embeddings_example.py

@@ -1,27 +0,0 @@
-"""
-Embeddings Capability Example
-
-Demonstrates text embedding generation using PraisonAI capabilities.
-"""
-
-from praisonai.capabilities import embed
-
-# Single text embedding
-print("=== Single Text Embedding ===")
-result = embed(
-    input="Hello, world!",
-    model="text-embedding-3-small"
-)
-print(f"Embedding dimensions: {len(result.embeddings[0])}")
-print(f"First 5 values: {result.embeddings[0][:5]}")
-print(f"Usage: {result.usage}")
-
-# Multiple text embeddings
-print("\n=== Multiple Text Embeddings ===")
-result = embed(
-    input=["Hello", "World", "AI"],
-    model="text-embedding-3-small"
-)
-print(f"Number of embeddings: {len(result.embeddings)}")
-for i, emb in enumerate(result.embeddings):
-    print(f"  Text {i+1}: {len(emb)} dimensions")

examples/capabilities/files_example.py

@@ -1,28 +0,0 @@
-"""
-Files Capabilities Example
-
-Demonstrates file management using PraisonAI capabilities.
-"""
-
-from praisonai.capabilities import file_list
-
-print("=== File List ===")
-try:
-    files = file_list()
-    print(f"Files found: {len(files)}")
-    for f in files[:5]:  # Show first 5
-        print(f"  - {f.get('filename', 'unknown')}: {f.get('id', 'unknown')}")
-except Exception as e:
-    print(f"Note: {e}")
-    print("File operations require files to be uploaded first.")
-
-print("\n=== File Operations ===")
-print("Available functions:")
-print("  file_create(file_path, purpose='assistants')")
-print("  file_list(purpose=None)")
-print("  file_retrieve(file_id)")
-print("  file_delete(file_id)")
-print("  file_content(file_id)")
-
-print("\nSee CLI: praisonai files list")
-print("See CLI: praisonai files upload <file>")

examples/capabilities/images_example.py

@@ -1,31 +0,0 @@
-"""
-Images Capability Example
-
-Demonstrates image generation using PraisonAI capabilities.
-"""
-
-from praisonai.capabilities import image_generate
-
-# Generate an image with DALL-E 2
-print("=== Image Generation (DALL-E 2) ===")
-result = image_generate(
-    prompt="A simple blue circle on a white background",
-    model="dall-e-2",
-    size="256x256",
-    n=1
-)
-print(f"Images generated: {len(result)}")
-if result:
-    print(f"URL: {result[0].url[:80]}...")
-
-# Generate with DALL-E 3 (higher quality)
-print("\n=== Image Generation (DALL-E 3) ===")
-result = image_generate(
-    prompt="A serene mountain landscape at sunset",
-    model="dall-e-3",
-    size="1024x1024",
-    quality="standard"
-)
-print(f"Images generated: {len(result)}")
-if result:
-    print(f"Revised prompt: {result[0].revised_prompt[:80]}..." if result[0].revised_prompt else "No revised prompt")

examples/capabilities/messages_example.py

@@ -1,33 +0,0 @@
-"""
-Messages Capability Example
-
-Demonstrates Anthropic-style messages API and token counting.
-"""
-
-from praisonai.capabilities import messages_create, count_tokens
-
-# Create a message (Anthropic-style)
-print("=== Messages Create ===")
-result = messages_create(
-    messages=[{"role": "user", "content": "What is the meaning of life? Answer briefly."}],
-    model="gpt-4o-mini",  # Works with any model via LiteLLM
-    max_tokens=100,
-    system="You are a philosophical assistant."
-)
-print(f"Message ID: {result.id}")
-if result.content:
-    for block in result.content:
-        if block.get("type") == "text":
-            print(f"Response: {block.get('text')}")
-print(f"Usage: {result.usage}")
-
-# Count tokens
-print("\n=== Token Counting ===")
-result = count_tokens(
-    messages=[
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": "Hello, how are you today?"}
-    ],
-    model="gpt-4o-mini"
-)
-print(f"Token count: {result.input_tokens}")

examples/capabilities/moderations_example.py

@@ -1,24 +0,0 @@
-"""
-Moderations Capability Example
-
-Demonstrates content moderation using PraisonAI capabilities.
-"""
-
-from praisonai.capabilities import moderate
-
-# Check safe content
-print("=== Safe Content Check ===")
-result = moderate(
-    input="Hello, how are you today?"
-)
-print(f"Flagged: {result[0].flagged}")
-print(f"Categories: {list(result[0].categories.keys())[:5]}...")
-
-# Check multiple texts
-print("\n=== Multiple Text Moderation ===")
-result = moderate(
-    input=["Hello world", "Have a nice day", "This is a test"]
-)
-print(f"Number of results: {len(result)}")
-for i, r in enumerate(result):
-    print(f"  Text {i+1}: Flagged = {r.flagged}")

examples/capabilities/realtime_example.py

@@ -1,23 +0,0 @@
-"""
-Realtime Capability Example
-
-Demonstrates realtime session creation for audio/video streaming.
-"""
-
-from praisonai.capabilities import realtime_connect
-
-# Create a realtime session
-print("=== Realtime Session ===")
-session = realtime_connect(
-    model="gpt-4o-realtime-preview",
-    modalities=["text", "audio"],
-    voice="alloy"
-)
-print(f"Session ID: {session.id}")
-print(f"Status: {session.status}")
-print(f"URL: {session.url}")
-print(f"Modalities: {session.metadata.get('modalities')}")
-print(f"Voice: {session.metadata.get('voice')}")
-
-print("\nNote: To use this session, connect via WebSocket to the URL above.")
-print("See OpenAI Realtime API documentation for WebSocket protocol details.")

examples/capabilities/rerank_example.py

@@ -1,32 +0,0 @@
-"""
-Rerank Capabilities Example
-
-Demonstrates document reranking using PraisonAI capabilities.
-"""
-
-from praisonai.capabilities import rerank
-
-print("=== Document Reranking ===")
-try:
-    documents = [
-        "Python is a programming language",
-        "Machine learning uses algorithms",
-        "Python is great for data science",
-        "JavaScript runs in browsers"
-    ]
-    
-    results = rerank(
-        query="Python programming",
-        documents=documents,
-        model="cohere/rerank-english-v3.0"
-    )
-    
-    print(f"Query: 'Python programming'")
-    print(f"Reranked results:")
-    for r in results:
-        print(f"  Score {r.get('relevance_score', 0):.4f}: {r.get('document', {}).get('text', '')[:50]}")
-except Exception as e:
-    print(f"Note: Reranking requires Cohere API key")
-    print(f"Error: {e}")
-
-print("\nSee CLI: praisonai rerank <query> --documents 'doc1' 'doc2'")

examples/capabilities/vector_stores_example.py

@@ -1,22 +0,0 @@
-"""
-Vector Stores Capabilities Example
-
-Demonstrates vector store management using PraisonAI capabilities.
-"""
-
-from praisonai.capabilities import vector_store_create, vector_store_search
-
-print("=== Vector Store Operations ===")
-print("Available functions:")
-print("  vector_store_create(name)")
-print("  vector_store_search(vector_store_id, query)")
-
-print("\n=== Create Vector Store ===")
-try:
-    store = vector_store_create(name="test-store")
-    print(f"Created: {store}")
-except Exception as e:
-    print(f"Note: {e}")
-
-print("\nSee CLI: praisonai vector-stores create --name 'my-store'")
-print("See CLI: praisonai vector-stores search <store_id> <query>")

examples/catalog/fetch_templates.py

@@ -1,148 +0,0 @@
-#!/usr/bin/env python3
-"""
-Example: Fetch and search PraisonAI templates from the catalog.
-
-This example demonstrates how to:
-- Fetch templates.json from the deployed catalog
-- Search templates by keyword
-- Filter by tags and requirements
-- Generate CLI commands
-"""
-
-import json
-import urllib.request
-from typing import List, Dict, Any, Optional
-
-CATALOG_URL = "https://mervinpraison.github.io/praisonai-template-catalog/data/templates.json"
-
-
-def fetch_catalog(url: str = CATALOG_URL) -> Dict[str, Any]:
-    """Fetch the template catalog from URL."""
-    try:
-        with urllib.request.urlopen(url, timeout=10) as response:
-            return json.loads(response.read().decode())
-    except Exception as e:
-        print(f"Failed to fetch catalog: {e}")
-        # Return empty catalog on failure
-        return {"version": "0.0.0", "count": 0, "templates": []}
-
-
-def search_templates(templates: List[Dict], query: str) -> List[Dict]:
-    """Search templates by name, description, or tags."""
-    query = query.lower()
-    results = []
-    for t in templates:
-        if (query in t.get("name", "").lower() or 
-            query in t.get("description", "").lower() or
-            any(query in tag.lower() for tag in t.get("tags", []))):
-            results.append(t)
-    return results
-
-
-def filter_by_tags(templates: List[Dict], tags: List[str]) -> List[Dict]:
-    """Filter templates that have all specified tags."""
-    return [
-        t for t in templates
-        if all(tag in t.get("tags", []) for tag in tags)
-    ]
-
-
-def filter_by_tool(templates: List[Dict], tool_name: str) -> List[Dict]:
-    """Filter templates that require a specific tool."""
-    return [
-        t for t in templates
-        if tool_name in t.get("requires", {}).get("tools", [])
-    ]
-
-
-def get_template(templates: List[Dict], name: str) -> Optional[Dict]:
-    """Get a template by name."""
-    for t in templates:
-        if t.get("name") == name:
-            return t
-    return None
-
-
-def generate_cli_command(template: Dict, action: str = "run") -> str:
-    """Generate CLI command for a template."""
-    name = template.get("name", "unknown")
-    if action == "run":
-        return f"praisonai templates run {name}"
-    elif action == "info":
-        return f"praisonai templates info {name}"
-    elif action == "init":
-        return f"praisonai templates init my-project --template {name}"
-    return f"praisonai templates {action} {name}"
-
-
-def print_template_summary(template: Dict):
-    """Print a summary of a template."""
-    print(f"\n{'='*60}")
-    print(f"Name: {template.get('name')}")
-    print(f"Version: {template.get('version')}")
-    print(f"Description: {template.get('description', '')[:100]}...")
-    print(f"Tags: {', '.join(template.get('tags', []))}")
-    
-    requires = template.get("requires", {})
-    if requires.get("tools"):
-        print(f"Tools: {', '.join(requires['tools'])}")
-    if requires.get("packages"):
-        print(f"Packages: {', '.join(requires['packages'])}")
-    if requires.get("env"):
-        print(f"Env vars: {', '.join(requires['env'])}")
-    
-    print(f"\nRun: {generate_cli_command(template, 'run')}")
-    print(f"{'='*60}")
-
-
-def main():
-    print("PraisonAI Template Catalog - Python Example")
-    print("-" * 45)
-    
-    # Fetch catalog
-    print("\n1. Fetching template catalog...")
-    catalog = fetch_catalog()
-    templates = catalog.get("templates", [])
-    
-    print(f"   Catalog version: {catalog.get('version', 'unknown')}")
-    print(f"   Total templates: {len(templates)}")
-    
-    if not templates:
-        print("   No templates found. The catalog may not be deployed yet.")
-        print("   Try building locally: praisonai templates catalog build")
-        return
-    
-    # List all templates
-    print("\n2. Available templates:")
-    for t in templates:
-        print(f"   - {t.get('name')} (v{t.get('version')})")
-    
-    # Search for video templates
-    print("\n3. Searching for 'video' templates...")
-    video_templates = search_templates(templates, "video")
-    print(f"   Found {len(video_templates)} video templates:")
-    for t in video_templates:
-        print(f"   - {t.get('name')}: {t.get('description', '')[:50]}...")
-    
-    # Filter by tool
-    print("\n4. Templates using 'shell_tool':")
-    shell_templates = filter_by_tool(templates, "shell_tool")
-    for t in shell_templates:
-        print(f"   - {t.get('name')}")
-    
-    # Get specific template details
-    if templates:
-        first_template = templates[0]
-        print(f"\n5. Template details for '{first_template.get('name')}':")
-        print_template_summary(first_template)
-    
-    # Generate CLI commands
-    print("\n6. CLI commands for all templates:")
-    for t in templates[:5]:  # First 5 only
-        print(f"   {generate_cli_command(t, 'run')}")
-    
-    print("\nDone!")
-
-
-if __name__ == "__main__":
-    main()

examples/catalog/fetch_templates.ts

@@ -1,167 +0,0 @@
-#!/usr/bin/env npx ts-node
-/**
- * Example: Fetch and search PraisonAI templates from the catalog.
- * 
- * This TypeScript example demonstrates how to:
- * - Fetch templates.json from the deployed catalog
- * - Search templates by keyword
- * - Filter by tags and requirements
- * - Generate CLI commands
- */
-
-const CATALOG_URL = "https://mervinpraison.github.io/praisonai-template-catalog/data/templates.json";
-
-interface Template {
-  name: string;
-  version: string;
-  description: string;
-  author?: string;
-  license?: string;
-  tags?: string[];
-  category?: string;
-  difficulty?: "beginner" | "intermediate" | "advanced";
-  requires?: {
-    tools?: string[];
-    packages?: string[];
-    env?: string[];
-  };
-}
-
-interface Catalog {
-  version: string;
-  generated_at: string;
-  count: number;
-  templates: Template[];
-}
-
-async function fetchCatalog(url: string = CATALOG_URL): Promise<Catalog> {
-  const response = await fetch(url);
-  if (!response.ok) {
-    throw new Error(`Failed to fetch catalog: ${response.statusText}`);
-  }
-  return response.json();
-}
-
-function searchTemplates(templates: Template[], query: string): Template[] {
-  const q = query.toLowerCase();
-  return templates.filter(t =>
-    t.name.toLowerCase().includes(q) ||
-    t.description?.toLowerCase().includes(q) ||
-    t.tags?.some(tag => tag.toLowerCase().includes(q))
-  );
-}
-
-function filterByTags(templates: Template[], tags: string[]): Template[] {
-  return templates.filter(t =>
-    tags.every(tag => t.tags?.includes(tag))
-  );
-}
-
-function filterByTool(templates: Template[], toolName: string): Template[] {
-  return templates.filter(t =>
-    t.requires?.tools?.includes(toolName)
-  );
-}
-
-function getTemplate(templates: Template[], name: string): Template | undefined {
-  return templates.find(t => t.name === name);
-}
-
-function generateCLICommand(template: Template, action: string = "run"): string {
-  const name = template.name;
-  switch (action) {
-    case "run":
-      return `praisonai templates run ${name}`;
-    case "info":
-      return `praisonai templates info ${name}`;
-    case "init":
-      return `praisonai templates init my-project --template ${name}`;
-    default:
-      return `praisonai templates ${action} ${name}`;
-  }
-}
-
-function printTemplateSummary(template: Template): void {
-  console.log("\n" + "=".repeat(60));
-  console.log(`Name: ${template.name}`);
-  console.log(`Version: ${template.version}`);
-  console.log(`Description: ${template.description?.slice(0, 100)}...`);
-  console.log(`Tags: ${template.tags?.join(", ") || "none"}`);
-  
-  const requires = template.requires;
-  if (requires?.tools?.length) {
-    console.log(`Tools: ${requires.tools.join(", ")}`);
-  }
-  if (requires?.packages?.length) {
-    console.log(`Packages: ${requires.packages.join(", ")}`);
-  }
-  if (requires?.env?.length) {
-    console.log(`Env vars: ${requires.env.join(", ")}`);
-  }
-  
-  console.log(`\nRun: ${generateCLICommand(template, "run")}`);
-  console.log("=".repeat(60));
-}
-
-async function main(): Promise<void> {
-  console.log("PraisonAI Template Catalog - TypeScript Example");
-  console.log("-".repeat(45));
-  
-  // Fetch catalog
-  console.log("\n1. Fetching template catalog...");
-  let catalog: Catalog;
-  try {
-    catalog = await fetchCatalog();
-  } catch (error) {
-    console.log(`   Failed to fetch: ${error}`);
-    console.log("   The catalog may not be deployed yet.");
-    return;
-  }
-  
-  const templates = catalog.templates;
-  console.log(`   Catalog version: ${catalog.version}`);
-  console.log(`   Total templates: ${templates.length}`);
-  
-  if (templates.length === 0) {
-    console.log("   No templates found.");
-    return;
-  }
-  
-  // List all templates
-  console.log("\n2. Available templates:");
-  templates.forEach(t => {
-    console.log(`   - ${t.name} (v${t.version})`);
-  });
-  
-  // Search for video templates
-  console.log("\n3. Searching for 'video' templates...");
-  const videoTemplates = searchTemplates(templates, "video");
-  console.log(`   Found ${videoTemplates.length} video templates:`);
-  videoTemplates.forEach(t => {
-    console.log(`   - ${t.name}: ${t.description?.slice(0, 50)}...`);
-  });
-  
-  // Filter by tool
-  console.log("\n4. Templates using 'shell_tool':");
-  const shellTemplates = filterByTool(templates, "shell_tool");
-  shellTemplates.forEach(t => {
-    console.log(`   - ${t.name}`);
-  });
-  
-  // Get specific template details
-  if (templates.length > 0) {
-    const firstTemplate = templates[0];
-    console.log(`\n5. Template details for '${firstTemplate.name}':`);
-    printTemplateSummary(firstTemplate);
-  }
-  
-  // Generate CLI commands
-  console.log("\n6. CLI commands for all templates:");
-  templates.slice(0, 5).forEach(t => {
-    console.log(`   ${generateCLICommand(t, "run")}`);
-  });
-  
-  console.log("\nDone!");
-}
-
-main().catch(console.error);

examples/catalog/run_all_catalog_examples.py

@@ -1,121 +0,0 @@
-#!/usr/bin/env python3
-"""
-Run all catalog examples and report pass/fail status.
-
-Usage:
-    python run_all_catalog_examples.py
-    
-Environment variables:
-    OPENAI_API_KEY or PRAISONAI_OPENAI_API_KEY - Required for agent example
-    PRAISONAI_RUN_INTEGRATION - Set to "1" to run integration tests
-"""
-
-import os
-import subprocess
-import sys
-from pathlib import Path
-
-SCRIPT_DIR = Path(__file__).parent
-
-
-def run_example(name: str, script: str, requires_api_key: bool = False) -> bool:
-    """Run an example script and return success status."""
-    print(f"\n{'='*60}")
-    print(f"Running: {name}")
-    print(f"Script: {script}")
-    print("="*60)
-    
-    # Check API key requirement
-    if requires_api_key:
-        api_key = os.environ.get("OPENAI_API_KEY") or os.environ.get("PRAISONAI_OPENAI_API_KEY")
-        run_integration = os.environ.get("PRAISONAI_RUN_INTEGRATION") == "1"
-        
-        if not api_key:
-            print(f"⏭️  SKIPPED: {name} (no API key)")
-            print("   Set OPENAI_API_KEY to run this example")
-            return True  # Not a failure, just skipped
-        
-        if not run_integration:
-            print(f"⏭️  SKIPPED: {name} (integration tests disabled)")
-            print("   Set PRAISONAI_RUN_INTEGRATION=1 to run")
-            return True
-    
-    script_path = SCRIPT_DIR / script
-    if not script_path.exists():
-        print(f"❌ FAILED: Script not found: {script_path}")
-        return False
-    
-    try:
-        result = subprocess.run(
-            [sys.executable, str(script_path)],
-            capture_output=True,
-            text=True,
-            timeout=60,
-            cwd=str(SCRIPT_DIR)
-        )
-        
-        print(result.stdout)
-        if result.stderr:
-            print(f"STDERR: {result.stderr}")
-        
-        if result.returncode == 0:
-            print(f"✅ PASSED: {name}")
-            return True
-        else:
-            print(f"❌ FAILED: {name} (exit code {result.returncode})")
-            return False
-            
-    except subprocess.TimeoutExpired:
-        print(f"❌ FAILED: {name} (timeout)")
-        return False
-    except Exception as e:
-        print(f"❌ FAILED: {name} ({e})")
-        return False
-
-
-def main():
-    print("PraisonAI Template Catalog - Example Runner")
-    print("=" * 50)
-    
-    # Check environment
-    api_key = os.environ.get("OPENAI_API_KEY") or os.environ.get("PRAISONAI_OPENAI_API_KEY")
-    if api_key:
-        print(f"API key: present (...{api_key[-4:]})")
-    else:
-        print("API key: not set")
-    
-    run_integration = os.environ.get("PRAISONAI_RUN_INTEGRATION") == "1"
-    print(f"Integration tests: {'enabled' if run_integration else 'disabled'}")
-    
-    # Define examples
-    examples = [
-        ("Fetch Templates (Python)", "fetch_templates.py", False),
-        ("Template Finder Agent", "template_finder_agent.py", True),
-    ]
-    
-    # Run examples
-    results = []
-    for name, script, requires_key in examples:
-        success = run_example(name, script, requires_key)
-        results.append((name, success))
-    
-    # Summary
-    print("\n" + "=" * 60)
-    print("SUMMARY")
-    print("=" * 60)
-    
-    passed = sum(1 for _, s in results if s)
-    failed = sum(1 for _, s in results if not s)
-    
-    for name, success in results:
-        status = "✅ PASS" if success else "❌ FAIL"
-        print(f"  {status}: {name}")
-    
-    print(f"\nTotal: {passed} passed, {failed} failed")
-    
-    # Exit with appropriate code
-    sys.exit(0 if failed == 0 else 1)
-
-
-if __name__ == "__main__":
-    main()

examples/catalog/template_finder_agent.py

@@ -1,193 +0,0 @@
-#!/usr/bin/env python3
-"""
-Example: Template Finder Agent
-
-An Agent()-centric example that uses an AI agent to find templates
-based on natural language queries.
-
-Requires: OPENAI_API_KEY environment variable
-"""
-
-import json
-import os
-import urllib.request
-
-CATALOG_URL = "https://mervinpraison.github.io/praisonai-template-catalog/data/templates.json"
-
-
-def fetch_templates() -> list:
-    """Fetch templates from catalog."""
-    try:
-        with urllib.request.urlopen(CATALOG_URL, timeout=10) as response:
-            data = json.loads(response.read().decode())
-            return data.get("templates", [])
-    except Exception:
-        return []
-
-
-def search_templates_tool(query: str) -> str:
-    """
-    Search templates by keyword.
-    
-    Args:
-        query: Search query (e.g., "video editing", "transcript", "data")
-    
-    Returns:
-        JSON string with matching templates
-    """
-    templates = fetch_templates()
-    query_lower = query.lower()
-    
-    results = []
-    for t in templates:
-        if (query_lower in t.get("name", "").lower() or
-            query_lower in t.get("description", "").lower() or
-            any(query_lower in tag.lower() for tag in t.get("tags", []))):
-            results.append({
-                "name": t.get("name"),
-                "description": t.get("description", "")[:100],
-                "tags": t.get("tags", []),
-                "cli_command": f"praisonai templates run {t.get('name')}"
-            })
-    
-    if not results:
-        return json.dumps({"message": "No templates found matching your query", "templates": []})
-    
-    return json.dumps({"count": len(results), "templates": results})
-
-
-def list_all_templates_tool() -> str:
-    """
-    List all available templates.
-    
-    Returns:
-        JSON string with all template names and descriptions
-    """
-    templates = fetch_templates()
-    
-    if not templates:
-        return json.dumps({"message": "No templates available", "templates": []})
-    
-    results = [
-        {"name": t.get("name"), "description": t.get("description", "")[:80]}
-        for t in templates
-    ]
-    
-    return json.dumps({"count": len(results), "templates": results})
-
-
-def get_template_details_tool(name: str) -> str:
-    """
-    Get detailed information about a specific template.
-    
-    Args:
-        name: Template name (e.g., "ai-video-editor")
-    
-    Returns:
-        JSON string with template details
-    """
-    templates = fetch_templates()
-    
-    for t in templates:
-        if t.get("name") == name:
-            return json.dumps({
-                "name": t.get("name"),
-                "version": t.get("version"),
-                "description": t.get("description"),
-                "author": t.get("author"),
-                "tags": t.get("tags", []),
-                "requires": t.get("requires", {}),
-                "cli_commands": {
-                    "run": f"praisonai templates run {name}",
-                    "info": f"praisonai templates info {name}",
-                    "init": f"praisonai templates init my-project --template {name}"
-                }
-            })
-    
-    return json.dumps({"error": f"Template '{name}' not found"})
-
-
-def check_api_key() -> bool:
-    """Check if API key is available."""
-    key = os.environ.get("OPENAI_API_KEY") or os.environ.get("PRAISONAI_OPENAI_API_KEY")
-    if key:
-        print(f"API key present: ...{key[-4:]}")
-        return True
-    return False
-
-
-def main():
-    print("PraisonAI Template Finder Agent")
-    print("=" * 40)
-    
-    # Check for API key
-    if not check_api_key():
-        print("\n⚠️  No API key found.")
-        print("Set OPENAI_API_KEY or PRAISONAI_OPENAI_API_KEY to use the agent.")
-        print("\nFalling back to direct tool usage:\n")
-        
-        # Demo the tools directly
-        print("1. Listing all templates:")
-        result = list_all_templates_tool()
-        data = json.loads(result)
-        for t in data.get("templates", [])[:5]:
-            print(f"   - {t['name']}: {t['description'][:50]}...")
-        
-        print("\n2. Searching for 'video' templates:")
-        result = search_templates_tool("video")
-        data = json.loads(result)
-        for t in data.get("templates", []):
-            print(f"   - {t['name']}")
-            print(f"     Run: {t['cli_command']}")
-        
-        return
-    
-    # Import Agent only if API key is available
-    try:
-        from praisonaiagents import Agent
-    except ImportError:
-        print("praisonaiagents not installed. Install with: pip install praisonaiagents")
-        return
-    
-    # Create the Template Finder Agent
-    agent = Agent(
-        name="Template Finder",
-        role="PraisonAI Template Expert",
-        goal="Help users find the right template for their needs",
-        backstory="""You are an expert on PraisonAI templates. You help users 
-        discover templates that match their requirements. You can search templates,
-        list all available templates, and provide detailed information about specific
-        templates. Always provide the CLI command to run the template.""",
-        tools=[
-            search_templates_tool,
-            list_all_templates_tool,
-            get_template_details_tool
-        ],
-        verbose=True
-    )
-    
-    # Example queries
-    queries = [
-        "Find me a template for editing videos",
-        "What templates are available for working with transcripts?",
-        "Show me details about the ai-video-editor template"
-    ]
-    
-    print("\nRunning Template Finder Agent...")
-    print("-" * 40)
-    
-    for query in queries:
-        print(f"\n📝 Query: {query}")
-        print("-" * 40)
-        
-        try:
-            result = agent.start(query)
-            print(f"\n🤖 Response:\n{result}")
-        except Exception as e:
-            print(f"Error: {e}")
-        
-        print("\n" + "=" * 40)
-
-
-if __name__ == "__main__":
-    main()

examples/checkpoints/basic_checkpoints.py

@@ -1,110 +0,0 @@
-#!/usr/bin/env python3
-"""
-Basic Checkpoints Example for PraisonAI Agents.
-
-This example demonstrates how to use the shadow git checkpointing system to:
-1. Save checkpoints before making changes
-2. List available checkpoints
-3. View diffs between checkpoints
-4. Restore to a previous checkpoint
-
-Usage:
-    python basic_checkpoints.py
-"""
-
-import asyncio
-import tempfile
-from pathlib import Path
-
-from praisonaiagents.checkpoints import CheckpointService
-
-
-async def main():
-    # Create a temporary workspace for demonstration
-    with tempfile.TemporaryDirectory() as workspace:
-        print("=" * 60)
-        print("Shadow Git Checkpointing Demo")
-        print("=" * 60)
-        print(f"\nWorkspace: {workspace}")
-        
-        # Create some initial files
-        test_file = Path(workspace) / "example.py"
-        test_file.write_text("# Initial content\nprint('Hello, World!')\n")
-        print(f"\n✅ Created initial file: {test_file.name}")
-        
-        # Initialize the checkpoint service
-        service = CheckpointService(workspace_dir=workspace)
-        
-        try:
-            # Initialize the shadow git repository
-            print("\n--- Initializing Checkpoint Service ---")
-            await service.initialize()
-            print("✅ Shadow git repository initialized")
-            
-            # Save first checkpoint
-            print("\n--- Saving Checkpoint 1: Initial State ---")
-            result1 = await service.save("Initial state")
-            print(f"✅ Checkpoint saved: {result1.checkpoint.id[:8]}")
-            print(f"   Message: {result1.checkpoint.message}")
-            
-            # Make some changes
-            print("\n--- Making Changes ---")
-            test_file.write_text("# Modified content\nprint('Hello, PraisonAI!')\nprint('Checkpoints are awesome!')\n")
-            print("✅ Modified example.py")
-            
-            # Create a new file
-            new_file = Path(workspace) / "utils.py"
-            new_file.write_text("def helper():\n    return 'I help!'\n")
-            print("✅ Created utils.py")
-            
-            # Save second checkpoint
-            print("\n--- Saving Checkpoint 2: After Changes ---")
-            result2 = await service.save("Added features")
-            print(f"✅ Checkpoint saved: {result2.checkpoint.id[:8]}")
-            
-            # List all checkpoints
-            print("\n--- Listing All Checkpoints ---")
-            checkpoints = await service.list_checkpoints()
-            for i, cp in enumerate(checkpoints, 1):
-                print(f"  {i}. [{cp.id[:8]}] {cp.message} ({cp.timestamp})")
-            
-            # Show diff
-            print("\n--- Current Diff (uncommitted changes) ---")
-            diff = await service.diff()
-            if diff.files:
-                for f in diff.files:
-                    print(f"  {f.status}: {f.path}")
-            else:
-                print("  No uncommitted changes")
-            
-            # Make more changes to show diff
-            test_file.write_text("# Even more changes\nprint('This will be reverted')\n")
-            print("\n✅ Made more changes to example.py")
-            
-            diff = await service.diff()
-            print("\n--- New Diff ---")
-            if diff.files:
-                for f in diff.files:
-                    print(f"  {f.status}: {f.path}")
-            
-            # Restore to checkpoint 2
-            print(f"\n--- Restoring to Checkpoint: {result2.checkpoint.id[:8]} ---")
-            await service.restore(result2.checkpoint.id)
-            print("✅ Restored successfully!")
-            
-            # Verify restoration
-            content = test_file.read_text()
-            print(f"\n--- Verified Content After Restore ---")
-            print(content)
-            
-        except Exception as e:
-            print(f"\n❌ Error: {e}")
-            raise
-        
-        print("\n" + "=" * 60)
-        print("Demo Complete!")
-        print("=" * 60)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/cli/persistence_commands.md

@@ -1,93 +0,0 @@
-# Persistence CLI Commands
-
-## Quick Reference
-
-```bash
-# Check database connectivity
-praisonai persistence doctor \
-  --conversation-url postgresql://localhost:5432/praisonai \
-  --state-url redis://localhost:6379
-
-# Run agent with persistence
-praisonai persistence run \
-  --conversation-url postgresql://localhost:5432/praisonai \
-  --session-id my-session
-
-# Resume a session
-praisonai persistence resume \
-  --conversation-url postgresql://localhost:5432/praisonai \
-  --session-id my-session
-
-# Export session to file
-praisonai persistence export \
-  --conversation-url postgresql://localhost:5432/praisonai \
-  --session-id my-session \
-  --output session_backup.jsonl
-
-# Import session from file
-praisonai persistence import \
-  --conversation-url postgresql://localhost:5432/praisonai \
-  --file session_backup.jsonl
-
-# Check schema status
-praisonai persistence status \
-  --conversation-url postgresql://localhost:5432/praisonai \
-  --state-url redis://localhost:6379
-
-# Apply migrations
-praisonai persistence migrate \
-  --conversation-url postgresql://localhost:5432/praisonai \
-  --state-url redis://localhost:6379
-```
-
-## Environment Variables
-
-Instead of CLI flags, you can use environment variables:
-
-```bash
-export PRAISON_CONVERSATION_URL=postgresql://localhost:5432/praisonai
-export PRAISON_STATE_URL=redis://localhost:6379
-export PRAISON_KNOWLEDGE_URL=http://localhost:6333
-
-# Then run without URL flags
-praisonai persistence doctor
-praisonai persistence status
-```
-
-## Docker Setup
-
-```bash
-# Start all services
-docker run -d --name praison-postgres \
-  -e POSTGRES_PASSWORD=praison123 \
-  -e POSTGRES_DB=praisonai \
-  -p 5432:5432 postgres:16
-
-docker run -d --name praison-redis \
-  -p 6379:6379 redis:7
-
-docker run -d --name praison-qdrant \
-  -p 6333:6333 -p 6334:6334 qdrant/qdrant
-
-# Verify
-docker ps
-```
-
-## Example Workflow
-
-```bash
-# 1. Start services
-docker-compose up -d
-
-# 2. Check connectivity
-praisonai persistence doctor
-
-# 3. Run agent
-praisonai persistence run --session-id demo-session
-
-# 4. Export for backup
-praisonai persistence export --session-id demo-session --output backup.jsonl
-
-# 5. Import to another environment
-praisonai persistence import --file backup.jsonl
-```

examples/cli/persistence_end_to_end.md

@@ -1,124 +0,0 @@
-# PraisonAI Persistence CLI - End-to-End Guide
-
-This guide demonstrates the CLI commands for database persistence.
-
-## Prerequisites
-
-```bash
-pip install "praisonai[tools]"
-
-# Start Docker containers
-docker run -d --name praison-postgres -p 5432:5432 \
-    -e POSTGRES_PASSWORD=praison123 \
-    -e POSTGRES_DB=praisonai \
-    postgres:16
-
-docker run -d --name praison-qdrant -p 6333:6333 qdrant/qdrant
-docker run -d --name praison-redis -p 6379:6379 redis:7
-```
-
-## 1. Doctor Command - Validate Connectivity
-
-```bash
-# Check all stores
-praisonai persistence doctor \
-    --conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
-    --knowledge-url "http://localhost:6333" \
-    --state-url "redis://localhost:6379"
-```
-
-**Expected Output:**
-```
-==================================================
-PraisonAI Persistence Doctor
-==================================================
-
-[Conversation Store] Testing: postgresql://postgres:****@localhost:5432/praisonai
-  ✅ Connected (postgres)
-
-[Knowledge Store] Testing: http://localhost:6333
-  ✅ Connected (qdrant)
-
-[State Store] Testing: redis://localhost:6379
-  ✅ Connected (redis)
-
-==================================================
-Results: 3/3 stores connected successfully
-==================================================
-```
-
-## 2. Run Command - Execute Agent with Persistence
-
-```bash
-# Run agent with persistence (dry-run first)
-praisonai persistence run \
-    --session-id "cli-demo-001" \
-    --conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
-    --dry-run \
-    "Hello, my name is Bob"
-
-# Actually run it
-praisonai persistence run \
-    --session-id "cli-demo-001" \
-    --conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
-    "Hello, my name is Bob"
-```
-
-**Expected Output:**
-```
-[Session: cli-demo-001]
-Agent: Hello Bob! Nice to meet you. How can I help you today?
-```
-
-## 3. Resume Command - Continue a Session
-
-```bash
-# Show history
-praisonai persistence resume \
-    --session-id "cli-demo-001" \
-    --conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
-    --show-history
-
-# Continue conversation
-praisonai persistence resume \
-    --session-id "cli-demo-001" \
-    --conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
-    --continue "What's my name?"
-```
-
-**Expected Output:**
-```
-[Session: cli-demo-001]
-Messages in history: 2
-
---- Conversation History ---
-[USER] Hello, my name is Bob
-[ASSISTANT] Hello Bob! Nice to meet you...
---- End History ---
-
-Agent: Your name is Bob!
-```
-
-## Environment Variables
-
-Instead of passing URLs on command line, use environment variables:
-
-```bash
-export PRAISON_CONVERSATION_URL="postgresql://postgres:praison123@localhost:5432/praisonai"
-export PRAISON_KNOWLEDGE_URL="http://localhost:6333"
-export PRAISON_STATE_URL="redis://localhost:6379"
-export OPENAI_API_KEY="your-key"
-
-# Now commands are simpler
-praisonai persistence doctor --all
-praisonai persistence run --session-id "my-session" "Hello!"
-```
-
-## Full CLI Reference
-
-```
-praisonai persistence --help
-praisonai persistence doctor --help
-praisonai persistence run --help
-praisonai persistence resume --help
-```

examples/compaction/basic_compaction.py

@@ -1,146 +0,0 @@
-#!/usr/bin/env python3
-"""
-Basic Context Compaction Example for PraisonAI Agents.
-
-This example demonstrates how to use context compaction to:
-1. Manage conversation context length
-2. Use different compaction strategies
-3. Track compaction results
-4. Preserve important messages
-
-Usage:
-    python basic_compaction.py
-"""
-
-from praisonaiagents.compaction import (
-    ContextCompactor, CompactionConfig, CompactionStrategy, CompactionResult
-)
-
-
-def main():
-    print("=" * 60)
-    print("Context Compaction Demo")
-    print("=" * 60)
-    
-    # ==========================================================================
-    # Sample Messages
-    # ==========================================================================
-    messages = [
-        {"role": "system", "content": "You are a helpful AI assistant."},
-        {"role": "user", "content": "Hello! Can you help me with Python?"},
-        {"role": "assistant", "content": "Of course! I'd be happy to help you with Python. What would you like to know?"},
-        {"role": "user", "content": "How do I read a file in Python?"},
-        {"role": "assistant", "content": "To read a file in Python, you can use the built-in open() function. Here's an example:\n\nwith open('filename.txt', 'r') as f:\n    content = f.read()\n\nThis opens the file, reads its content, and automatically closes it."},
-        {"role": "user", "content": "What about writing to a file?"},
-        {"role": "assistant", "content": "To write to a file, use the 'w' mode:\n\nwith open('filename.txt', 'w') as f:\n    f.write('Hello, World!')\n\nUse 'a' mode to append instead of overwriting."},
-        {"role": "user", "content": "Can you explain list comprehensions?"},
-        {"role": "assistant", "content": "List comprehensions are a concise way to create lists. Instead of:\n\nresult = []\nfor x in range(10):\n    result.append(x * 2)\n\nYou can write:\n\nresult = [x * 2 for x in range(10)]"},
-    ]
-    
-    print(f"\n--- Original Messages ---")
-    print(f"  Message count: {len(messages)}")
-    
-    # ==========================================================================
-    # Basic Compaction
-    # ==========================================================================
-    print("\n--- Basic Compaction (Truncate Strategy) ---")
-    
-    compactor = ContextCompactor(
-        max_tokens=50,  # Low limit to trigger compaction
-        strategy=CompactionStrategy.TRUNCATE,
-        preserve_system=True,
-        preserve_recent=2
-    )
-    
-    # Check stats before
-    stats = compactor.get_stats(messages)
-    print(f"  Total tokens: {stats['total_tokens']}")
-    print(f"  Max tokens: {stats['max_tokens']}")
-    print(f"  Needs compaction: {stats['needs_compaction']}")
-    
-    # Compact
-    compacted, result = compactor.compact(messages)
-    
-    print(f"\n  After compaction:")
-    print(f"    Messages: {len(messages)} -> {len(compacted)}")
-    print(f"    Tokens: {result.original_tokens} -> {result.compacted_tokens}")
-    print(f"    Saved: {result.tokens_saved} tokens")
-    print(f"    Compression: {result.compression_ratio:.1%}")
-    
-    # ==========================================================================
-    # Sliding Window Strategy
-    # ==========================================================================
-    print("\n--- Sliding Window Strategy ---")
-    
-    compactor = ContextCompactor(
-        max_tokens=50,
-        strategy=CompactionStrategy.SLIDING,
-        preserve_system=True,
-        preserve_recent=3
-    )
-    
-    compacted, result = compactor.compact(messages)
-    
-    print(f"  Messages kept: {result.messages_kept}")
-    print(f"  Messages removed: {result.messages_removed}")
-    print(f"  Strategy: {result.strategy_used.value}")
-    
-    # ==========================================================================
-    # Summarize Strategy
-    # ==========================================================================
-    print("\n--- Summarize Strategy ---")
-    
-    compactor = ContextCompactor(
-        max_tokens=50,
-        strategy=CompactionStrategy.SUMMARIZE,
-        preserve_system=True,
-        preserve_recent=2
-    )
-    
-    compacted, result = compactor.compact(messages)
-    
-    print(f"  Messages kept: {result.messages_kept}")
-    print(f"  Strategy: {result.strategy_used.value}")
-    
-    # Show what messages remain
-    print(f"\n  Remaining message roles:")
-    for msg in compacted:
-        role = msg.get("role", "unknown")
-        content_preview = msg.get("content", "")[:50]
-        print(f"    [{role}] {content_preview}...")
-    
-    # ==========================================================================
-    # No Compaction Needed
-    # ==========================================================================
-    print("\n--- No Compaction Needed ---")
-    
-    compactor = ContextCompactor(
-        max_tokens=10000,  # High limit
-        strategy=CompactionStrategy.TRUNCATE
-    )
-    
-    compacted, result = compactor.compact(messages)
-    
-    print(f"  Was compacted: {result.was_compacted}")
-    print(f"  Messages unchanged: {len(compacted) == len(messages)}")
-    
-    # ==========================================================================
-    # Result Serialization
-    # ==========================================================================
-    print("\n--- Result Serialization ---")
-    
-    compactor = ContextCompactor(max_tokens=50)
-    _, result = compactor.compact(messages)
-    
-    data = result.to_dict()
-    print(f"  Original tokens: {data['original_tokens']}")
-    print(f"  Compacted tokens: {data['compacted_tokens']}")
-    print(f"  Compression ratio: {data['compression_ratio']:.2f}")
-    
-    print("\n" + "=" * 60)
-    print("Demo Complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()

examples/compare/cli_compare_advanced.py

@@ -1,85 +0,0 @@
-"""
-CLI Compare Example - Advanced Usage
-
-This example demonstrates advanced comparison features including
-custom mode configurations and result analysis.
-"""
-
-from praisonai.cli.features.compare import (
-    CompareHandler,
-    CompareResult,
-    ModeResult,
-    get_mode_config,
-    save_compare_result,
-    format_comparison_table,
-)
-
-# Create custom mode configurations
-custom_modes = {
-    "fast": {"llm": "gpt-4o-mini"},
-    "accurate": {"llm": "gpt-4o", "planning": True},
-    "research": {"research": True, "web_search": True},
-}
-
-# Create handler with verbose output
-handler = CompareHandler(verbose=True)
-
-# Example: Manually create comparison results for analysis
-# (useful for testing without API calls)
-mock_result = CompareResult(
-    query="What are the latest AI trends?",
-    comparisons=[
-        ModeResult(
-            mode="basic",
-            output="AI trends include LLMs, multimodal models, and agents.",
-            execution_time_ms=1234.5,
-            model_used="gpt-4o-mini",
-            tools_used=[]
-        ),
-        ModeResult(
-            mode="tools",
-            output="Based on recent search results, AI trends in 2024 include...",
-            execution_time_ms=2567.8,
-            model_used="gpt-4o-mini",
-            tools_used=["internet_search"]
-        ),
-        ModeResult(
-            mode="research",
-            output="Comprehensive research shows that AI trends...",
-            execution_time_ms=5432.1,
-            model_used="gpt-4o-mini",
-            tools_used=["internet_search", "web_scraper"]
-        ),
-    ]
-)
-
-# Get summary statistics
-summary = mock_result.get_summary()
-print("Comparison Summary:")
-print(f"  Fastest mode: {summary['fastest']} ({summary['fastest_time_ms']:.1f}ms)")
-print(f"  Slowest mode: {summary['slowest']} ({summary['slowest_time_ms']:.1f}ms)")
-
-# Format as table
-print("\n" + format_comparison_table(mock_result))
-
-# Convert to JSON for storage
-print("\nJSON Output:")
-print(mock_result.to_json())
-
-# Save to file
-# save_compare_result(mock_result, "advanced_comparison.json")
-
-print("\n--- CLI Usage Examples ---")
-print("""
-# Basic comparison
-praisonai "What is AI?" --compare "basic,tools"
-
-# Compare with specific model
-praisonai "Explain quantum computing" --compare "basic,planning,research" --model gpt-4o
-
-# Save results to file
-praisonai "Latest tech news" --compare "basic,tools,web_search" --compare-output results.json
-
-# Verbose output
-praisonai "Write a poem" --compare "basic,planning" --verbose
-""")

examples/compare/cli_compare_basic.py

@@ -1,45 +0,0 @@
-"""
-CLI Compare Example - Basic Usage
-
-This example demonstrates how to compare different CLI modes programmatically.
-"""
-
-from praisonai.cli.features.compare import (
-    CompareHandler,
-    get_mode_config,
-    list_available_modes,
-    parse_modes,
-)
-
-# List all available comparison modes
-print("Available modes:")
-for mode in list_available_modes():
-    config = get_mode_config(mode)
-    print(f"  - {mode}: {config}")
-
-# Parse modes from comma-separated string
-modes = parse_modes("basic,tools,planning")
-print(f"\nParsed modes: {modes}")
-
-# Create compare handler
-handler = CompareHandler(verbose=True)
-
-# Compare modes (this will run actual agent calls)
-# Uncomment to run:
-# result = handler.compare(
-#     query="What is artificial intelligence?",
-#     modes=["basic", "tools"],
-#     model="gpt-4o-mini"
-# )
-# handler.print_result(result)
-
-# Save results to file
-# result = handler.execute(
-#     query="Explain machine learning",
-#     modes_str="basic,planning",
-#     output_path="comparison_results.json"
-# )
-
-print("\nTo run comparison via CLI:")
-print('  praisonai "What is AI?" --compare "basic,tools,planning"')
-print('  praisonai "What is AI?" --compare "basic,research" --compare-output results.json')

examples/context/00_agent_fast_context_basic.py

@@ -1,89 +0,0 @@
-"""
-Fast Context with Agent - Basic Example
-
-This is the simplest way to use Fast Context with an Agent.
-Just set fast_context=True and the agent will automatically
-have access to rapid code search capabilities.
-
-Fast Context provides:
-- 10-20x faster code search than traditional methods
-- Parallel execution (8 concurrent searches)
-- Automatic caching for repeated queries
-- Seamless integration with Agent workflows
-"""
-
-from praisonaiagents import Agent
-
-def main():
-    print("=" * 60)
-    print("Agent with Fast Context - Basic Example")
-    print("=" * 60)
-    
-    # Create an agent with Fast Context enabled
-    # This is the simplest configuration
-    agent = Agent(
-        name="CodeAssistant",
-        instructions="You are a helpful code assistant.",
-        fast_context=True,  # Enable Fast Context
-        fast_context_path=".",  # Search in current directory
-        verbose=False
-    )
-    
-    print("\n✓ Agent created with Fast Context enabled")
-    print(f"  - fast_context_enabled: {agent.fast_context_enabled}")
-    print(f"  - fast_context_path: {agent.fast_context_path}")
-    print(f"  - fast_context_model: {agent.fast_context_model}")
-    
-    # Use delegate_to_fast_context to search code
-    print("\n" + "-" * 60)
-    print("Searching for 'class Agent' in the codebase...")
-    print("-" * 60)
-    
-    context = agent.delegate_to_fast_context("class Agent")
-    
-    if context:
-        print(f"\n✓ Found relevant code!")
-        print(f"  Context length: {len(context)} characters")
-        print("\nPreview of found context:")
-        print("-" * 40)
-        # Show first 500 characters
-        preview = context[:500]
-        print(preview)
-        if len(context) > 500:
-            print("...")
-    else:
-        print("\n✗ No relevant code found")
-    
-    # Another search example
-    print("\n" + "-" * 60)
-    print("Searching for 'def chat' in the codebase...")
-    print("-" * 60)
-    
-    context = agent.delegate_to_fast_context("def chat")
-    
-    if context:
-        print(f"\n✓ Found relevant code!")
-        print(f"  Context length: {len(context)} characters")
-    else:
-        print("\n✗ No relevant code found")
-    
-    # Access FastContext directly for more control
-    print("\n" + "-" * 60)
-    print("Direct FastContext access for advanced usage...")
-    print("-" * 60)
-    
-    fc = agent.fast_context
-    if fc:
-        result = fc.search("import")
-        print(f"\n✓ Direct search for 'import':")
-        print(f"  Files found: {result.total_files}")
-        print(f"  Search time: {result.search_time_ms}ms")
-        print(f"  From cache: {result.from_cache}")
-    
-    print("\n" + "=" * 60)
-    print("Fast Context makes code search instant for your agents!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()

examples/context/01_basic_fast_context.py

@@ -1,84 +0,0 @@
-"""
-Example 1: Basic Fast Context Usage
-
-This example demonstrates the basic usage of Fast Context for rapid code search.
-Fast Context is 10-20x faster than traditional sequential search because it:
-- Executes up to 8 tool calls in parallel
-- Limits search to 4 turns maximum
-- Uses a lightweight model optimized for search
-"""
-
-from praisonaiagents.context.fast import FastContext
-import time
-
-def main():
-    print("=" * 70)
-    print("Fast Context - Basic Usage Example")
-    print("=" * 70)
-    
-    # Create FastContext instance pointing to the praisonai-agents codebase
-    fc = FastContext(
-        workspace_path="/Users/praison/praisonai-package/src/praisonai-agents"
-    )
-    
-    # Example 1: Simple pattern search
-    print("\n1. Simple Pattern Search")
-    print("-" * 40)
-    
-    start = time.perf_counter()
-    result = fc.search("def chat")
-    elapsed = (time.perf_counter() - start) * 1000
-    
-    print(f"   Query: 'def chat'")
-    print(f"   Files found: {result.total_files}")
-    print(f"   Search time: {result.search_time_ms}ms (total: {elapsed:.0f}ms)")
-    print(f"   Turns used: {result.turns_used}")
-    print(f"   Tool calls: {result.total_tool_calls}")
-    
-    if result.files:
-        print("\n   Top matches:")
-        for f in result.files[:3]:
-            print(f"   - {f.path}")
-            for lr in f.line_ranges[:2]:
-                print(f"     Lines {lr.start}-{lr.end}")
-    
-    # Example 2: Search for class definitions
-    print("\n2. Search for Class Definitions")
-    print("-" * 40)
-    
-    result = fc.search("class Agent")
-    print(f"   Query: 'class Agent'")
-    print(f"   Files found: {result.total_files}")
-    print(f"   Search time: {result.search_time_ms}ms")
-    
-    if result.files:
-        print("\n   Files containing 'class Agent':")
-        for f in result.files[:5]:
-            print(f"   - {f.path} (relevance: {f.relevance_score:.2f})")
-    
-    # Example 3: Search for imports
-    print("\n3. Search for Specific Imports")
-    print("-" * 40)
-    
-    result = fc.search("from typing import")
-    print(f"   Query: 'from typing import'")
-    print(f"   Files found: {result.total_files}")
-    print(f"   Search time: {result.search_time_ms}ms")
-    
-    # Example 4: Get formatted context string
-    print("\n4. Get Formatted Context for Agent")
-    print("-" * 40)
-    
-    context = fc.get_context_for_agent("authentication", max_files=3)
-    print(f"   Context length: {len(context)} characters")
-    print(f"   Preview:")
-    preview = context[:300].replace('\n', '\n   ')
-    print(f"   {preview}...")
-    
-    print("\n" + "=" * 70)
-    print("Fast Context provides rapid, parallel code search!")
-    print("=" * 70)
-
-
-if __name__ == "__main__":
-    main()

examples/context/02_parallel_vs_sequential.py

@@ -1,113 +0,0 @@
-"""
-Example 2: Parallel vs Sequential Search Comparison
-
-This example demonstrates the speed advantage of Fast Context's parallel
-execution compared to traditional sequential search.
-
-Key insight: Fast Context executes up to 8 searches simultaneously,
-making it 5-10x faster for complex queries.
-"""
-
-import time
-import os
-from praisonaiagents.context.fast import FastContext
-from praisonaiagents.context.fast.search_tools import grep_search, glob_search
-
-WORKSPACE = "/Users/praison/praisonai-package/src/praisonai-agents"
-
-
-def sequential_search(queries: list) -> dict:
-    """Traditional sequential search - one query at a time."""
-    results = {}
-    for query in queries:
-        results[query] = grep_search(
-            search_path=WORKSPACE,
-            pattern=query,
-            max_results=20
-        )
-    return results
-
-
-def parallel_search_with_fast_context(queries: list) -> dict:
-    """Fast Context parallel search - multiple queries at once."""
-    fc = FastContext(workspace_path=WORKSPACE)
-    results = {}
-    for query in queries:
-        result = fc.search(query)
-        results[query] = result
-    return results
-
-
-def main():
-    print("=" * 70)
-    print("Parallel vs Sequential Search Comparison")
-    print("=" * 70)
-    
-    # Define multiple search queries
-    queries = [
-        "def __init__",
-        "class Agent",
-        "async def",
-        "import logging",
-        "from typing",
-    ]
-    
-    print(f"\nSearching for {len(queries)} patterns in the codebase...")
-    print(f"Workspace: {WORKSPACE}")
-    
-    # Sequential search
-    print("\n1. Sequential Search (Traditional)")
-    print("-" * 40)
-    
-    start = time.perf_counter()
-    seq_results = sequential_search(queries)
-    seq_time = (time.perf_counter() - start) * 1000
-    
-    total_seq_matches = sum(len(r) for r in seq_results.values())
-    print(f"   Total matches: {total_seq_matches}")
-    print(f"   Time: {seq_time:.0f}ms")
-    
-    # Fast Context search (with caching disabled for fair comparison)
-    print("\n2. Fast Context Search (Parallel)")
-    print("-" * 40)
-    
-    fc = FastContext(workspace_path=WORKSPACE, cache_enabled=False)
-    
-    start = time.perf_counter()
-    fc_results = {}
-    for query in queries:
-        fc_results[query] = fc.search(query)
-    fc_time = (time.perf_counter() - start) * 1000
-    
-    total_fc_matches = sum(r.total_files for r in fc_results.values())
-    print(f"   Total files found: {total_fc_matches}")
-    print(f"   Time: {fc_time:.0f}ms")
-    
-    # Comparison
-    print("\n3. Comparison")
-    print("-" * 40)
-    
-    speedup = seq_time / fc_time if fc_time > 0 else float('inf')
-    print(f"   Sequential: {seq_time:.0f}ms")
-    print(f"   Fast Context: {fc_time:.0f}ms")
-    print(f"   Speedup: {speedup:.1f}x faster")
-    
-    # Detailed results
-    print("\n4. Results by Query")
-    print("-" * 40)
-    
-    for query in queries:
-        seq_count = len(seq_results[query])
-        fc_count = fc_results[query].total_files
-        fc_ms = fc_results[query].search_time_ms
-        print(f"   '{query}':")
-        print(f"      Sequential: {seq_count} matches")
-        print(f"      Fast Context: {fc_count} files in {fc_ms}ms")
-    
-    print("\n" + "=" * 70)
-    print(f"Fast Context is {speedup:.1f}x faster due to parallel execution!")
-    print("=" * 70)
-
-
-if __name__ == "__main__":
-    main()

examples/context/03_caching_performance.py

@@ -1,132 +0,0 @@
-"""
-Example 3: Caching Performance Demonstration
-
-This example shows how Fast Context's caching dramatically improves
-performance for repeated queries.
-
-Key benefits:
-- First query: Full search execution
-- Subsequent queries: Instant cache hit (0ms search time)
-- Configurable TTL (time-to-live)
-"""
-
-import time
-from praisonaiagents.context.fast import FastContext
-
-WORKSPACE = "/Users/praison/praisonai-package/src/praisonai-agents"
-
-
-def main():
-    print("=" * 70)
-    print("Fast Context Caching Performance")
-    print("=" * 70)
-    
-    # Create FastContext with caching enabled
-    fc = FastContext(
-        workspace_path=WORKSPACE,
-        cache_enabled=True,
-        cache_ttl=300  # 5 minutes
-    )
-    
-    query = "def execute"
-    
-    print(f"\nQuery: '{query}'")
-    print(f"Cache TTL: 300 seconds")
-    
-    # First search - cache miss
-    print("\n1. First Search (Cache Miss)")
-    print("-" * 40)
-    
-    start = time.perf_counter()
-    result1 = fc.search(query)
-    elapsed1 = (time.perf_counter() - start) * 1000
-    
-    print(f"   Files found: {result1.total_files}")
-    print(f"   From cache: {result1.from_cache}")
-    print(f"   Search time: {result1.search_time_ms}ms")
-    print(f"   Total time: {elapsed1:.1f}ms")
-    
-    # Second search - cache hit
-    print("\n2. Second Search (Cache Hit)")
-    print("-" * 40)
-    
-    start = time.perf_counter()
-    result2 = fc.search(query)
-    elapsed2 = (time.perf_counter() - start) * 1000
-    
-    print(f"   Files found: {result2.total_files}")
-    print(f"   From cache: {result2.from_cache}")
-    print(f"   Search time: {result2.search_time_ms}ms")
-    print(f"   Total time: {elapsed2:.1f}ms")
-    
-    # Third search - still cached
-    print("\n3. Third Search (Still Cached)")
-    print("-" * 40)
-    
-    start = time.perf_counter()
-    result3 = fc.search(query)
-    elapsed3 = (time.perf_counter() - start) * 1000
-    
-    print(f"   Files found: {result3.total_files}")
-    print(f"   From cache: {result3.from_cache}")
-    print(f"   Total time: {elapsed3:.1f}ms")
-    
-    # Clear cache and search again
-    print("\n4. After Cache Clear")
-    print("-" * 40)
-    
-    fc.clear_cache()
-    print("   Cache cleared!")
-    
-    start = time.perf_counter()
-    result4 = fc.search(query)
-    elapsed4 = (time.perf_counter() - start) * 1000
-    
-    print(f"   Files found: {result4.total_files}")
-    print(f"   From cache: {result4.from_cache}")
-    print(f"   Search time: {result4.search_time_ms}ms")
-    print(f"   Total time: {elapsed4:.1f}ms")
-    
-    # Performance comparison
-    print("\n5. Performance Summary")
-    print("-" * 40)
-    
-    cache_speedup = elapsed1 / elapsed2 if elapsed2 > 0 else float('inf')
-    
-    print(f"   First search (no cache): {elapsed1:.1f}ms")
-    print(f"   Cached searches: ~{elapsed2:.1f}ms")
-    print(f"   Cache speedup: {cache_speedup:.0f}x faster")
-    
-    # Multiple different queries
-    print("\n6. Multiple Query Caching")
-    print("-" * 40)
-    
-    queries = ["class Agent", "async def", "import os", "def __init__"]
-    
-    # First pass - populate cache
-    print("   First pass (populating cache):")
-    start = time.perf_counter()
-    for q in queries:
-        fc.search(q)
-    first_pass = (time.perf_counter() - start) * 1000
-    print(f"   Time: {first_pass:.0f}ms")
-    
-    # Second pass - all cached
-    print("   Second pass (all cached):")
-    start = time.perf_counter()
-    for q in queries:
-        r = fc.search(q)
-        assert r.from_cache, f"Expected cache hit for '{q}'"
-    second_pass = (time.perf_counter() - start) * 1000
-    print(f"   Time: {second_pass:.1f}ms")
-    
-    multi_speedup = first_pass / second_pass if second_pass > 0 else float('inf')
-    print(f"   Speedup: {multi_speedup:.0f}x faster")
-    
-    print("\n" + "=" * 70)
-    print("Caching provides instant results for repeated queries!")
-    print("=" * 70)
-
-
-if __name__ == "__main__":
-    main()

examples/context/04_agent_integration.py

@@ -1,112 +0,0 @@
-"""
-Example 4: Agent Integration with Fast Context
-
-This example shows how to use Fast Context integrated with the Agent class
-for automatic code search delegation.
-
-Benefits:
-- Seamless integration with existing Agent workflows
-- Automatic context injection for code-related queries
-- Configurable via Agent parameters or environment variables
-"""
-
-from praisonaiagents import Agent
-
-WORKSPACE = "/Users/praison/praisonai-package/src/praisonai-agents"
-
-
-def main():
-    print("=" * 70)
-    print("Agent Integration with Fast Context")
-    print("=" * 70)
-    
-    # Create an agent with Fast Context enabled
-    print("\n1. Creating Agent with Fast Context")
-    print("-" * 40)
-    
-    agent = Agent(
-        name="CodeAssistant",
-        instructions="You are a helpful code assistant that can search and analyze code.",
-        fast_context=True,
-        fast_context_path=WORKSPACE,
-        fast_context_model="gpt-4o-mini",
-        fast_context_max_turns=4,
-        fast_context_parallelism=8,
-        fast_context_timeout=30.0,
-        verbose=False
-    )
-    
-    print(f"   Agent name: {agent.name}")
-    print(f"   Fast Context enabled: {agent.fast_context_enabled}")
-    print(f"   Fast Context path: {agent.fast_context_path}")
-    print(f"   Fast Context model: {agent.fast_context_model}")
-    print(f"   Max turns: {agent.fast_context_max_turns}")
-    print(f"   Parallelism: {agent.fast_context_parallelism}")
-    
-    # Use delegate_to_fast_context for code search
-    print("\n2. Delegating Code Search to Fast Context")
-    print("-" * 40)
-    
-    queries = [
-        "find the Agent class definition",
-        "search for authentication handlers",
-        "locate memory management code",
-    ]
-    
-    for query in queries:
-        print(f"\n   Query: '{query}'")
-        context = agent.delegate_to_fast_context(query)
-        
-        if context:
-            # Show preview of context
-            lines = context.split('\n')
-            preview_lines = lines[:5]
-            print(f"   Found context ({len(context)} chars):")
-            for line in preview_lines:
-                print(f"      {line[:60]}{'...' if len(line) > 60 else ''}")
-            if len(lines) > 5:
-                print(f"      ... and {len(lines) - 5} more lines")
-        else:
-            print("   No relevant code found")
-    
-    # Access the FastContext instance directly
-    print("\n3. Direct FastContext Access")
-    print("-" * 40)
-    
-    fc = agent.fast_context
-    if fc:
-        result = fc.search("def chat")
-        print(f"   Direct search for 'def chat':")
-        print(f"   Files found: {result.total_files}")
-        print(f"   Search time: {result.search_time_ms}ms")
-        
-        for f in result.files[:3]:
-            print(f"   - {f.path}")
-    
-    # Show configuration options
-    print("\n4. Configuration Options")
-    print("-" * 40)
-    
-    print("   Agent parameters:")
-    print("   - fast_context=True              # Enable Fast Context")
-    print("   - fast_context_path='.'          # Workspace path")
-    print("   - fast_context_model='gpt-4o-mini'  # Search model")
-    print("   - fast_context_max_turns=4       # Max search turns")
-    print("   - fast_context_parallelism=8     # Parallel calls")
-    print("   - fast_context_timeout=30.0      # Timeout (seconds)")
-    
-    print("\n   Environment variables:")
-    print("   - FAST_CONTEXT_MODEL")
-    print("   - FAST_CONTEXT_MAX_TURNS")
-    print("   - FAST_CONTEXT_PARALLELISM")
-    print("   - FAST_CONTEXT_TIMEOUT")
-    print("   - FAST_CONTEXT_CACHE")
-    print("   - FAST_CONTEXT_CACHE_TTL")
-    
-    print("\n" + "=" * 70)
-    print("Fast Context integrates seamlessly with Agent for code search!")
-    print("=" * 70)
-
-
-if __name__ == "__main__":
-    main()

examples/context/05_file_and_symbol_indexing.py

@@ -1,121 +0,0 @@
-"""
-Example 5: File and Symbol Indexing
-
-This example demonstrates the file and symbol indexing capabilities
-that power Fast Context's rapid search.
-
-Features:
-- FileIndexer: Index file metadata for fast lookups
-- SymbolIndexer: Extract code symbols (functions, classes, imports)
-- Support for Python, JavaScript, TypeScript, Go, Rust, Java
-"""
-
-import time
-from praisonaiagents.context.fast.indexer import (
-    FileIndexer,
-    SymbolIndexer,
-    SymbolType
-)
-
-WORKSPACE = "/Users/praison/praisonai-package/src/praisonai-agents"
-
-
-def main():
-    print("=" * 70)
-    print("File and Symbol Indexing")
-    print("=" * 70)
-    
-    # File Indexer
-    print("\n1. File Indexer")
-    print("-" * 40)
-    
-    start = time.perf_counter()
-    file_indexer = FileIndexer(workspace_path=WORKSPACE)
-    file_count = file_indexer.index()
-    file_time = (time.perf_counter() - start) * 1000
-    
-    print(f"   Indexed {file_count} files in {file_time:.0f}ms")
-    
-    stats = file_indexer.get_stats()
-    print(f"   Total size: {stats['total_size_mb']} MB")
-    print(f"   Extensions: {list(stats['extensions'].keys())[:5]}")
-    
-    # Find files by pattern
-    print("\n   Finding files by pattern:")
-    py_files = file_indexer.find_by_pattern("**/*.py")
-    print(f"   - Python files (*.py): {len(py_files)}")
-    
-    agent_files = file_indexer.find_by_pattern("**/agent*.py")
-    print(f"   - Agent files (agent*.py): {len(agent_files)}")
-    
-    # Find files by name
-    print("\n   Finding files by name:")
-    init_files = file_indexer.find_by_name("__init__.py", exact=True)
-    print(f"   - __init__.py files: {len(init_files)}")
-    
-    agent_matches = file_indexer.find_by_name("agent")
-    print(f"   - Files containing 'agent': {len(agent_matches)}")
-    
-    # Symbol Indexer
-    print("\n2. Symbol Indexer")
-    print("-" * 40)
-    
-    start = time.perf_counter()
-    symbol_indexer = SymbolIndexer(workspace_path=WORKSPACE)
-    symbol_count = symbol_indexer.index()
-    symbol_time = (time.perf_counter() - start) * 1000
-    
-    print(f"   Indexed {symbol_count} symbols in {symbol_time:.0f}ms")
-    
-    stats = symbol_indexer.get_stats()
-    print(f"   Files indexed: {stats['total_files']}")
-    print(f"   Symbols by type:")
-    for sym_type, count in stats['by_type'].items():
-        print(f"      - {sym_type}: {count}")
-    
-    # Find symbols by name
-    print("\n   Finding symbols by name:")
-    
-    agent_symbols = symbol_indexer.find_by_name("Agent", exact=True)
-    print(f"   - 'Agent' (exact): {len(agent_symbols)} symbols")
-    for sym in agent_symbols[:3]:
-        print(f"      {sym.symbol_type.value}: {sym.name} in {sym.file_path}:{sym.line_number}")
-    
-    chat_symbols = symbol_indexer.find_by_name("chat")
-    print(f"   - 'chat' (partial): {len(chat_symbols)} symbols")
-    for sym in chat_symbols[:3]:
-        print(f"      {sym.symbol_type.value}: {sym.name} in {sym.file_path}:{sym.line_number}")
-    
-    # Find symbols by type
-    print("\n   Finding symbols by type:")
-    
-    classes = symbol_indexer.find_by_type(SymbolType.CLASS)
-    print(f"   - Classes: {len(classes)}")
-    for cls in classes[:5]:
-        print(f"      class {cls.name} in {cls.file_path}:{cls.line_number}")
-    
-    functions = symbol_indexer.find_by_type(SymbolType.FUNCTION)
-    print(f"   - Functions: {len(functions)}")
-    
-    methods = symbol_indexer.find_by_type(SymbolType.METHOD)
-    print(f"   - Methods: {len(methods)}")
-    
-    imports = symbol_indexer.find_by_type(SymbolType.IMPORT)
-    print(f"   - Imports: {len(imports)}")
-    
-    # Performance comparison
-    print("\n3. Performance Summary")
-    print("-" * 40)
-    
-    print(f"   File indexing: {file_time:.0f}ms for {file_count} files")
-    print(f"   Symbol indexing: {symbol_time:.0f}ms for {symbol_count} symbols")
-    print(f"   Files per second: {file_count / (file_time / 1000):.0f}")
-    print(f"   Symbols per second: {symbol_count / (symbol_time / 1000):.0f}")
-    
-    print("\n" + "=" * 70)
-    print("Indexing enables instant lookups for files and symbols!")
-    print("=" * 70)
-
-
-if __name__ == "__main__":
-    main()

examples/context/06_context_injection.py

@@ -1,163 +0,0 @@
-"""
-Example 6: Context Injection for Agents
-
-This example demonstrates how Fast Context results can be formatted
-and injected into agent prompts for better code understanding.
-
-Features:
-- Multiple format styles (markdown, xml, plain)
-- Token budget management
-- Precision over recall (avoid context pollution)
-"""
-
-from praisonaiagents.context.fast import FastContext
-from praisonaiagents.context.fast.context_injector import (
-    ContextInjector,
-    InjectionConfig,
-    inject_fast_context
-)
-
-WORKSPACE = "/Users/praison/praisonai-package/src/praisonai-agents"
-
-
-def main():
-    print("=" * 70)
-    print("Context Injection for Agents")
-    print("=" * 70)
-    
-    # Search for code
-    fc = FastContext(workspace_path=WORKSPACE)
-    result = fc.search("class Agent")
-    
-    print(f"\nSearch results: {result.total_files} files found")
-    
-    # Markdown format (default)
-    print("\n1. Markdown Format")
-    print("-" * 40)
-    
-    config_md = InjectionConfig(
-        max_tokens=2000,
-        max_files=3,
-        max_lines_per_file=20,
-        format_style="markdown",
-        include_line_numbers=True,
-        include_file_content=True,
-        prioritize_precision=True
-    )
-    
-    injector_md = ContextInjector(config_md)
-    md_context = injector_md.format_context(result)
-    
-    print(f"   Format: Markdown")
-    print(f"   Length: {len(md_context)} chars (~{len(md_context)//4} tokens)")
-    print(f"   Preview:")
-    for line in md_context.split('\n')[:10]:
-        print(f"   {line[:70]}")
-    print("   ...")
-    
-    # XML format
-    print("\n2. XML Format")
-    print("-" * 40)
-    
-    config_xml = InjectionConfig(
-        max_tokens=2000,
-        max_files=3,
-        format_style="xml",
-        prioritize_precision=True
-    )
-    
-    injector_xml = ContextInjector(config_xml)
-    xml_context = injector_xml.format_context(result)
-    
-    print(f"   Format: XML")
-    print(f"   Length: {len(xml_context)} chars")
-    print(f"   Preview:")
-    for line in xml_context.split('\n')[:10]:
-        print(f"   {line[:70]}")
-    print("   ...")
-    
-    # Plain text format
-    print("\n3. Plain Text Format")
-    print("-" * 40)
-    
-    config_plain = InjectionConfig(
-        max_tokens=2000,
-        max_files=3,
-        format_style="plain"
-    )
-    
-    injector_plain = ContextInjector(config_plain)
-    plain_context = injector_plain.format_context(result)
-    
-    print(f"   Format: Plain")
-    print(f"   Length: {len(plain_context)} chars")
-    print(f"   Preview:")
-    for line in plain_context.split('\n')[:8]:
-        print(f"   {line[:70]}")
-    print("   ...")
-    
-    # Inject into prompts
-    print("\n4. Injecting into Prompts")
-    print("-" * 40)
-    
-    system_prompt = "You are a helpful code assistant."
-    user_message = "Explain how the Agent class works."
-    
-    injected = inject_fast_context(
-        result=result,
-        system_prompt=system_prompt,
-        user_message=user_message,
-        max_tokens=1500,
-        format_style="markdown"
-    )
-    
-    print(f"   Original system prompt: {len(system_prompt)} chars")
-    print(f"   Injected system prompt: {len(injected['system_prompt'])} chars")
-    print(f"   User message unchanged: {injected['user_message'] == user_message}")
-    
-    # Token budget management
-    print("\n5. Token Budget Management")
-    print("-" * 40)
-    
-    budgets = [500, 1000, 2000, 4000]
-    
-    for budget in budgets:
-        config = InjectionConfig(max_tokens=budget, max_files=10)
-        injector = ContextInjector(config)
-        context = injector.format_context(result)
-        estimated_tokens = len(context) // 4
-        print(f"   Budget: {budget} tokens -> {len(context)} chars (~{estimated_tokens} tokens)")
-    
-    # Precision mode
-    print("\n6. Precision vs Recall")
-    print("-" * 40)
-    
-    # High precision (fewer, more relevant results)
-    config_precision = InjectionConfig(
-        max_tokens=2000,
-        max_files=5,
-        prioritize_precision=True
-    )
-    injector_precision = ContextInjector(config_precision)
-    precision_context = injector_precision.format_context(result)
-    
-    # Lower precision (more results)
-    config_recall = InjectionConfig(
-        max_tokens=2000,
-        max_files=10,
-        prioritize_precision=False
-    )
-    injector_recall = ContextInjector(config_recall)
-    recall_context = injector_recall.format_context(result)
-    
-    print(f"   High precision mode: {len(precision_context)} chars")
-    print(f"   High recall mode: {len(recall_context)} chars")
-    print(f"   Precision mode is more focused for better agent understanding")
-    
-    print("\n" + "=" * 70)
-    print("Context injection formats search results for optimal agent use!")
-    print("=" * 70)
-
-
-if __name__ == "__main__":
-    main()

examples/context/07_search_tools_demo.py

@@ -1,213 +0,0 @@
-"""
-Example 7: Search Tools Deep Dive
-
-This example demonstrates the individual search tools that power Fast Context:
-- grep_search: Pattern-based search with regex support
-- glob_search: File pattern matching
-- read_file: Read file contents with line ranges
-- list_directory: Directory listing with filtering
-"""
-
-import time
-from praisonaiagents.context.fast.search_tools import (
-    grep_search,
-    glob_search,
-    read_file,
-    list_directory
-)
-
-WORKSPACE = "/Users/praison/praisonai-package/src/praisonai-agents"
-
-
-def main():
-    print("=" * 70)
-    print("Search Tools Deep Dive")
-    print("=" * 70)
-    
-    # grep_search
-    print("\n1. grep_search - Pattern Search")
-    print("-" * 40)
-    
-    # Simple pattern
-    start = time.perf_counter()
-    results = grep_search(
-        search_path=WORKSPACE,
-        pattern="def __init__",
-        max_results=10
-    )
-    elapsed = (time.perf_counter() - start) * 1000
-    
-    print(f"   Pattern: 'def __init__'")
-    print(f"   Matches: {len(results)}")
-    print(f"   Time: {elapsed:.0f}ms")
-    if results:
-        print(f"   First match: {results[0]['path']}:{results[0]['line_number']}")
-    
-    # Regex pattern
-    results = grep_search(
-        search_path=WORKSPACE,
-        pattern=r"class \w+Agent",
-        is_regex=True,
-        max_results=10
-    )
-    print(f"\n   Regex: 'class \\w+Agent'")
-    print(f"   Matches: {len(results)}")
-    for r in results[:3]:
-        print(f"   - {r['path']}:{r['line_number']}: {r['content'][:50]}...")
-    
-    # Case sensitive
-    results_sensitive = grep_search(
-        search_path=WORKSPACE,
-        pattern="Agent",
-        case_sensitive=True,
-        max_results=50
-    )
-    results_insensitive = grep_search(
-        search_path=WORKSPACE,
-        pattern="agent",
-        case_sensitive=False,
-        max_results=50
-    )
-    print(f"\n   Case sensitive 'Agent': {len(results_sensitive)} matches")
-    print(f"   Case insensitive 'agent': {len(results_insensitive)} matches")
-    
-    # With context lines
-    results = grep_search(
-        search_path=WORKSPACE,
-        pattern="class Agent:",
-        context_lines=2,
-        max_results=3
-    )
-    print(f"\n   With 2 context lines:")
-    if results:
-        print(f"   {results[0]['path']}:")
-        if results[0].get('context'):
-            for line in results[0]['context'].split('\n')[:5]:
-                print(f"      {line[:60]}")
-    
-    # glob_search
-    print("\n2. glob_search - File Pattern Matching")
-    print("-" * 40)
-    
-    # Find Python files
-    start = time.perf_counter()
-    results = glob_search(
-        search_path=WORKSPACE,
-        pattern="**/*.py",
-        max_results=100
-    )
-    elapsed = (time.perf_counter() - start) * 1000
-    
-    print(f"   Pattern: '**/*.py'")
-    print(f"   Files found: {len(results)}")
-    print(f"   Time: {elapsed:.0f}ms")
-    
-    # Find specific files
-    results = glob_search(
-        search_path=WORKSPACE,
-        pattern="**/agent*.py",
-        max_results=20
-    )
-    print(f"\n   Pattern: '**/agent*.py'")
-    print(f"   Files found: {len(results)}")
-    for r in results[:5]:
-        print(f"   - {r['path']} ({r['size']} bytes)")
-    
-    # Find test files
-    results = glob_search(
-        search_path=WORKSPACE,
-        pattern="**/test_*.py",
-        max_results=50
-    )
-    print(f"\n   Pattern: '**/test_*.py'")
-    print(f"   Test files: {len(results)}")
-    
-    # read_file
-    print("\n3. read_file - Read File Contents")
-    print("-" * 40)
-    
-    # Read entire file
-    result = read_file(
-        filepath=f"{WORKSPACE}/praisonaiagents/__init__.py",
-        max_lines=20
-    )
-    
-    print(f"   File: praisonaiagents/__init__.py")
-    print(f"   Success: {result['success']}")
-    print(f"   Lines: {result.get('total_lines', 'N/A')}")
-    if result['success']:
-        lines = result['content'].split('\n')[:5]
-        print(f"   Preview:")
-        for line in lines:
-            print(f"      {line[:60]}")
-    
-    # Read specific line range
-    result = read_file(
-        filepath=f"{WORKSPACE}/praisonaiagents/agent/agent.py",
-        start_line=1,
-        end_line=20
-    )
-    
-    print(f"\n   File: agent/agent.py (lines 1-20)")
-    print(f"   Success: {result['success']}")
-    if result['success']:
-        lines = result['content'].split('\n')[:5]
-        print(f"   Preview:")
-        for line in lines:
-            print(f"      {line[:60]}")
-    
-    # Read with context
-    result = read_file(
-        filepath=f"{WORKSPACE}/praisonaiagents/agent/agent.py",
-        start_line=100,
-        end_line=105,
-        context_lines=3
-    )
-    
-    print(f"\n   File: agent/agent.py (lines 100-105 with 3 context lines)")
-    print(f"   Success: {result['success']}")
-    
-    # list_directory
-    print("\n4. list_directory - Directory Listing")
-    print("-" * 40)
-    
-    # List root
-    result = list_directory(
-        dir_path=WORKSPACE,
-        recursive=False,
-        max_entries=20
-    )
-    
-    print(f"   Directory: {WORKSPACE}")
-    print(f"   Success: {result['success']}")
-    print(f"   Entries: {len(result.get('entries', []))}")
-    
-    dirs = [e for e in result.get('entries', []) if e.get('is_dir')]
-    files = [e for e in result.get('entries', []) if not e.get('is_dir')]
-    print(f"   Directories: {len(dirs)}")
-    print(f"   Files: {len(files)}")
-    
-    # List recursive with depth limit
-    result = list_directory(
-        dir_path=f"{WORKSPACE}/praisonaiagents",
-        recursive=True,
-        max_depth=2,
-        max_entries=50
-    )
-    
-    print(f"\n   Directory: praisonaiagents (recursive, depth=2)")
-    print(f"   Entries: {len(result.get('entries', []))}")
-    
-    # Show directory structure
-    print(f"   Structure:")
-    for entry in result.get('entries', [])[:10]:
-        prefix = "📁" if entry.get('is_dir') else "📄"
-        print(f"      {prefix} {entry['name']}")
-    
-    print("\n" + "=" * 70)
-    print("These search tools power Fast Context's rapid code search!")
-    print("=" * 70)
-
-
-if __name__ == "__main__":
-    main()

examples/context/08_comprehensive_benchmark.py

@@ -1,227 +0,0 @@
-"""
-Example 8: Comprehensive Benchmark
-
-This example provides a comprehensive benchmark comparing Fast Context
-performance against traditional search approaches.
-
-Metrics measured:
-- Search latency
-- Throughput (queries per second)
-- Cache effectiveness
-- Parallel execution speedup
-"""
-
-import time
-import statistics
-from praisonaiagents.context.fast import FastContext
-from praisonaiagents.context.fast.search_tools import grep_search
-from praisonaiagents.context.fast.indexer import FileIndexer, SymbolIndexer
-
-WORKSPACE = "/Users/praison/praisonai-package/src/praisonai-agents"
-
-
-def benchmark_search_latency():
-    """Benchmark search latency."""
-    print("\n1. Search Latency Benchmark")
-    print("-" * 50)
-    
-    fc = FastContext(workspace_path=WORKSPACE, cache_enabled=False)
-    
-    queries = [
-        "def __init__",
-        "class Agent",
-        "import logging",
-        "async def",
-        "from typing import",
-    ]
-    
-    # Warm up
-    fc.search("warmup")
-    
-    # Benchmark each query
-    latencies = []
-    for query in queries:
-        times = []
-        for _ in range(3):  # 3 runs per query
-            start = time.perf_counter()
-            fc.search(query)
-            elapsed = (time.perf_counter() - start) * 1000
-            times.append(elapsed)
-        
-        avg = statistics.mean(times)
-        latencies.append(avg)
-        print(f"   '{query}': {avg:.1f}ms (min: {min(times):.1f}, max: {max(times):.1f})")
-    
-    print(f"\n   Average latency: {statistics.mean(latencies):.1f}ms")
-    print(f"   Median latency: {statistics.median(latencies):.1f}ms")
-    print(f"   P95 latency: {sorted(latencies)[int(len(latencies)*0.95)]:.1f}ms")
-
-
-def benchmark_throughput():
-    """Benchmark queries per second."""
-    print("\n2. Throughput Benchmark")
-    print("-" * 50)
-    
-    fc = FastContext(workspace_path=WORKSPACE, cache_enabled=True)
-    
-    queries = ["def", "class", "import", "return", "if", "for", "while", "try"]
-    
-    # Cold run (no cache)
-    fc.clear_cache()
-    start = time.perf_counter()
-    for query in queries:
-        fc.search(query)
-    cold_time = time.perf_counter() - start
-    cold_qps = len(queries) / cold_time
-    
-    print(f"   Cold (no cache):")
-    print(f"      Queries: {len(queries)}")
-    print(f"      Time: {cold_time*1000:.0f}ms")
-    print(f"      Throughput: {cold_qps:.1f} queries/second")
-    
-    # Warm run (cached)
-    start = time.perf_counter()
-    for _ in range(10):  # 10 iterations
-        for query in queries:
-            fc.search(query)
-    warm_time = time.perf_counter() - start
-    warm_qps = (len(queries) * 10) / warm_time
-    
-    print(f"\n   Warm (cached):")
-    print(f"      Queries: {len(queries) * 10}")
-    print(f"      Time: {warm_time*1000:.0f}ms")
-    print(f"      Throughput: {warm_qps:.0f} queries/second")
-    
-    print(f"\n   Cache speedup: {warm_qps/cold_qps:.0f}x")
-
-
-def benchmark_cache_effectiveness():
-    """Benchmark cache hit rate and speedup."""
-    print("\n3. Cache Effectiveness Benchmark")
-    print("-" * 50)
-    
-    fc = FastContext(workspace_path=WORKSPACE, cache_enabled=True)
-    fc.clear_cache()
-    
-    queries = ["Agent", "chat", "memory", "tool", "execute"]
-    
-    # First pass - cache misses
-    miss_times = []
-    for query in queries:
-        start = time.perf_counter()
-        result = fc.search(query)
-        elapsed = (time.perf_counter() - start) * 1000
-        miss_times.append(elapsed)
-        assert not result.from_cache
-    
-    # Second pass - cache hits
-    hit_times = []
-    for query in queries:
-        start = time.perf_counter()
-        result = fc.search(query)
-        elapsed = (time.perf_counter() - start) * 1000
-        hit_times.append(elapsed)
-        assert result.from_cache
-    
-    avg_miss = statistics.mean(miss_times)
-    avg_hit = statistics.mean(hit_times)
-    
-    print(f"   Cache miss (first query): {avg_miss:.1f}ms avg")
-    print(f"   Cache hit (repeat query): {avg_hit:.2f}ms avg")
-    print(f"   Cache speedup: {avg_miss/avg_hit:.0f}x faster")
-    print(f"   Time saved per cached query: {avg_miss - avg_hit:.1f}ms")
-
-
-def benchmark_parallel_execution():
-    """Benchmark parallel vs sequential execution."""
-    print("\n4. Parallel Execution Benchmark")
-    print("-" * 50)
-    
-    queries = ["def", "class", "import", "return", "async"]
-    
-    # Sequential (one at a time)
-    start = time.perf_counter()
-    for query in queries:
-        grep_search(WORKSPACE, query, max_results=20)
-    seq_time = (time.perf_counter() - start) * 1000
-    
-    # Parallel (Fast Context)
-    fc = FastContext(workspace_path=WORKSPACE, cache_enabled=False)
-    start = time.perf_counter()
-    for query in queries:
-        fc.search(query)
-    par_time = (time.perf_counter() - start) * 1000
-    
-    print(f"   Sequential: {seq_time:.0f}ms")
-    print(f"   Parallel (Fast Context): {par_time:.0f}ms")
-    print(f"   Speedup: {seq_time/par_time:.1f}x")
-
-
-def benchmark_indexing():
-    """Benchmark indexing performance."""
-    print("\n5. Indexing Performance Benchmark")
-    print("-" * 50)
-    
-    # File indexing
-    start = time.perf_counter()
-    file_indexer = FileIndexer(workspace_path=WORKSPACE)
-    file_count = file_indexer.index()
-    file_time = (time.perf_counter() - start) * 1000
-    
-    print(f"   File Indexer:")
-    print(f"      Files: {file_count}")
-    print(f"      Time: {file_time:.0f}ms")
-    print(f"      Rate: {file_count/(file_time/1000):.0f} files/second")
-    
-    # Symbol indexing
-    start = time.perf_counter()
-    symbol_indexer = SymbolIndexer(workspace_path=WORKSPACE)
-    symbol_count = symbol_indexer.index()
-    symbol_time = (time.perf_counter() - start) * 1000
-    
-    print(f"\n   Symbol Indexer:")
-    print(f"      Symbols: {symbol_count}")
-    print(f"      Time: {symbol_time:.0f}ms")
-    print(f"      Rate: {symbol_count/(symbol_time/1000):.0f} symbols/second")
-    
-    # Lookup performance
-    start = time.perf_counter()
-    for _ in range(100):
-        file_indexer.find_by_pattern("**/*.py")
-    lookup_time = (time.perf_counter() - start) * 1000
-    
-    print(f"\n   Lookup Performance:")
-    print(f"      100 pattern lookups: {lookup_time:.1f}ms")
-    print(f"      Per lookup: {lookup_time/100:.2f}ms")
-
-
-def main():
-    print("=" * 70)
-    print("Fast Context Comprehensive Benchmark")
-    print("=" * 70)
-    print(f"\nWorkspace: {WORKSPACE}")
-    
-    benchmark_search_latency()
-    benchmark_throughput()
-    benchmark_cache_effectiveness()
-    benchmark_parallel_execution()
-    benchmark_indexing()
-    
-    print("\n" + "=" * 70)
-    print("Summary")
-    print("=" * 70)
-    print("""
-   Fast Context provides:
-   ✓ Sub-100ms search latency for most queries
-   ✓ 100+ queries/second with caching
-   ✓ 100x+ speedup for cached queries
-   ✓ 2-5x speedup from parallel execution
-   ✓ 1000+ files/second indexing rate
-   
-   These benchmarks demonstrate why Fast Context is ideal for
-   rapid code search in AI agent workflows.
-""")
-
-
-if __name__ == "__main__":
-    main()

examples/context/09_context_budgeting.py

@@ -1,101 +0,0 @@
-#!/usr/bin/env python3
-"""
-Context Budgeting Example
-
-Demonstrates how to use the ContextBudgeter to allocate token budgets
-across different context segments for various models.
-"""
-
-from praisonaiagents.context import (
-    ContextBudgeter,
-    BudgetAllocation,
-    get_model_limit,
-    get_output_reserve,
-)
-
-
-def main():
-    print("=" * 60)
-    print("Context Budgeting Example")
-    print("=" * 60)
-    
-    # Example 1: Basic budget allocation for GPT-4o-mini
-    print("\n1. Basic Budget Allocation (gpt-4o-mini)")
-    print("-" * 40)
-    
-    budgeter = ContextBudgeter(model="gpt-4o-mini")
-    budget = budgeter.allocate()
-    
-    print(f"Model: gpt-4o-mini")
-    print(f"Model Limit: {budget.model_limit:,} tokens")
-    print(f"Output Reserve: {budget.output_reserve:,} tokens")
-    print(f"Usable Context: {budget.usable:,} tokens")
-    
-    # Example 2: Different models have different limits
-    print("\n2. Model Comparison")
-    print("-" * 40)
-    
-    models = ["gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "claude-3-opus", "gemini-1.5-pro"]
-    
-    for model in models:
-        limit = get_model_limit(model)
-        reserve = get_output_reserve(model)
-        print(f"{model:20s}: {limit:>10,} tokens (reserve: {reserve:,})")
-    
-    # Example 3: Custom segment budgets
-    print("\n3. Custom Segment Budgets")
-    print("-" * 40)
-    
-    budgeter = ContextBudgeter(
-        model="gpt-4o",
-        system_prompt_budget=2000,
-        rules_budget=1000,
-        skills_budget=500,
-        memory_budget=5000,
-        tools_schema_budget=3000,
-    )
-    budget = budgeter.allocate()
-    
-    print(f"System prompt budget: 2,000 tokens")
-    print(f"Rules budget: 1,000 tokens")
-    print(f"Skills budget: 500 tokens")
-    print(f"Memory budget: 5,000 tokens")
-    print(f"Tools schema budget: 3,000 tokens")
-    print(f"Total usable: {budget.usable:,} tokens")
-    
-    # Example 4: Check for overflow
-    print("\n4. Overflow Detection")
-    print("-" * 40)
-    
-    budgeter = ContextBudgeter(model="gpt-4o-mini")
-    
-    # Simulate different usage levels
-    test_cases = [
-        ("Low usage", 10000),
-        ("Medium usage", 80000),
-        ("High usage", 100000),
-        ("Near limit", 110000),
-        ("Over limit", 130000),
-    ]
-    
-    for name, tokens in test_cases:
-        overflow = budgeter.check_overflow(tokens)
-        utilization = budgeter.get_utilization(tokens)
-        remaining = budgeter.get_remaining(tokens)
-        status = "⚠️ OVERFLOW" if overflow else "✓ OK"
-        print(f"{name:15s}: {tokens:>7,} tokens | {utilization:>5.1%} | {status}")
-    
-    # Example 5: Budget to dict for serialization
-    print("\n5. Budget Serialization")
-    print("-" * 40)
-    
-    budget_dict = budgeter.to_dict()
-    print(f"Budget as dict: {budget_dict}")
-    
-    print("\n" + "=" * 60)
-    print("✓ Context budgeting examples complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()

examples/context/10_context_monitoring.py

@@ -1,184 +0,0 @@
-#!/usr/bin/env python3
-"""
-Context Monitoring Example
-
-Demonstrates how to use the ContextMonitor to write runtime context
-snapshots to disk for debugging and analysis.
-"""
-
-import os
-import tempfile
-from praisonaiagents.context import (
-    ContextMonitor,
-    ContextLedger,
-    BudgetAllocation,
-    format_human_snapshot,
-    format_json_snapshot,
-    redact_sensitive,
-    ContextSnapshot,
-)
-
-
-def main():
-    print("=" * 60)
-    print("Context Monitoring Example")
-    print("=" * 60)
-    
-    # Sample conversation for demonstration
-    messages = [
-        {"role": "system", "content": "You are a helpful AI assistant."},
-        {"role": "user", "content": "What is the weather like today?"},
-        {"role": "assistant", "content": "I don't have access to real-time weather data."},
-        {"role": "user", "content": "Can you help me write Python code?"},
-        {"role": "assistant", "content": "Of course! I'd be happy to help with Python."},
-    ]
-    
-    # Example 1: Basic monitoring setup
-    print("\n1. Basic Monitor Setup")
-    print("-" * 40)
-    
-    with tempfile.TemporaryDirectory() as tmpdir:
-        context_path = os.path.join(tmpdir, "context.txt")
-        
-        monitor = ContextMonitor(
-            enabled=True,
-            path=context_path,
-            format="human",
-            frequency="turn",
-        )
-        
-        print(f"Monitor enabled: {monitor.enabled}")
-        print(f"Output path: {context_path}")
-        print(f"Format: {monitor.format}")
-        
-        # Create sample ledger and budget
-        ledger = ContextLedger(
-            system_prompt=500,
-            history=2500,
-            tools_schema=300,
-            tool_outputs=1200,
-        )
-        
-        budget = BudgetAllocation(
-            model_limit=128000,
-            output_reserve=8000,
-        )
-        
-        # Write snapshot
-        result_path = monitor.snapshot(
-            ledger=ledger,
-            budget=budget,
-            messages=messages,
-            trigger="turn",
-        )
-        
-        print(f"\n✓ Snapshot written to: {result_path}")
-        
-        # Read and display
-        with open(context_path) as f:
-            content = f.read()
-        print(f"\nSnapshot preview ({len(content)} chars):")
-        print("-" * 40)
-        print(content[:500] + "..." if len(content) > 500 else content)
-    
-    # Example 2: JSON format
-    print("\n\n2. JSON Format Output")
-    print("-" * 40)
-    
-    with tempfile.TemporaryDirectory() as tmpdir:
-        json_path = os.path.join(tmpdir, "context.json")
-        
-        monitor = ContextMonitor(
-            enabled=True,
-            path=json_path,
-            format="json",
-        )
-        
-        monitor.snapshot(
-            ledger=ledger,
-            budget=budget,
-            messages=messages,
-            trigger="manual",
-        )
-        
-        with open(json_path) as f:
-            content = f.read()
-        
-        print(f"JSON snapshot ({len(content)} chars):")
-        print(content[:300] + "..." if len(content) > 300 else content)
-    
-    # Example 3: Sensitive data redaction
-    print("\n\n3. Sensitive Data Redaction")
-    print("-" * 40)
-    
-    sensitive_text = """
-    Here's my API key: sk-proj-abc123def456ghi789
-    And my password is: supersecret123
-    Database connection: postgresql://user:pass@localhost/db
-    """
-    
-    redacted = redact_sensitive(sensitive_text)
-    print("Original text contains sensitive data:")
-    print(f"  - API key pattern found: {'sk-proj-' in sensitive_text}")
-    print(f"  - Password pattern found: {'password' in sensitive_text.lower()}")
-    print("\nAfter redaction:")
-    print(redacted)
-    
-    # Example 4: Monitor frequency options
-    print("\n4. Monitor Frequency Options")
-    print("-" * 40)
-    
-    frequencies = ["turn", "tool_call", "manual", "overflow"]
-    
-    for freq in frequencies:
-        monitor = ContextMonitor(enabled=True, frequency=freq)
-        
-        # Check what triggers writing
-        triggers = {
-            "turn": monitor.should_write("turn"),
-            "tool_call": monitor.should_write("tool_call"),
-            "manual": monitor.should_write("manual"),
-            "overflow": monitor.should_write("overflow"),
-        }
-        
-        active = [k for k, v in triggers.items() if v]
-        print(f"Frequency '{freq}': writes on {active}")
-    
-    # Example 5: Snapshot data structure
-    print("\n5. Snapshot Data Structure")
-    print("-" * 40)
-    
-    snapshot = ContextSnapshot(
-        timestamp="2025-01-07T19:30:00Z",
-        session_id="example-session-123",
-        agent_name="ExampleAgent",
-        model_name="gpt-4o-mini",
-        budget=budget,
-        ledger=ledger,
-        utilization=0.45,
-        warnings=["Context usage at 45%"],
-    )
-    
-    print(f"Snapshot fields:")
-    print(f"  - timestamp: {snapshot.timestamp}")
-    print(f"  - session_id: {snapshot.session_id}")
-    print(f"  - agent_name: {snapshot.agent_name}")
-    print(f"  - model_name: {snapshot.model_name}")
-    print(f"  - utilization: {snapshot.utilization:.1%}")
-    print(f"  - warnings: {snapshot.warnings}")
-    
-    # Format as human-readable
-    human_output = format_human_snapshot(snapshot)
-    print(f"\nHuman-readable format ({len(human_output)} chars)")
-    
-    # Format as JSON
-    json_output = format_json_snapshot(snapshot)
-    print(f"JSON format ({len(json_output)} chars)")
-    
-    print("\n" + "=" * 60)
-    print("✓ Context monitoring examples complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()

examples/context/11_context_optimization.py

@@ -1,142 +0,0 @@
-#!/usr/bin/env python3
-"""
-Context Optimization Example
-
-Demonstrates how to use various optimization strategies to reduce
-context size when approaching model limits.
-"""
-
-from praisonaiagents.context import (
-    get_optimizer,
-    OptimizerStrategy,
-    TruncateOptimizer,
-    SlidingWindowOptimizer,
-    PruneToolsOptimizer,
-    SmartOptimizer,
-    estimate_messages_tokens,
-)
-
-
-def main():
-    print("=" * 60)
-    print("Context Optimization Example")
-    print("=" * 60)
-    
-    # Create sample conversation with tool calls
-    messages = [
-        {"role": "system", "content": "You are a helpful AI assistant with access to tools."},
-        {"role": "user", "content": "What's the weather in Paris?"},
-        {"role": "assistant", "content": "", "tool_calls": [{"id": "call_1", "function": {"name": "get_weather", "arguments": '{"city": "Paris"}'}}]},
-        {"role": "tool", "tool_call_id": "call_1", "content": "Weather in Paris: 15°C, partly cloudy, humidity 65%, wind 10 km/h from the west. Extended forecast shows temperatures ranging from 12-18°C over the next week with occasional rain expected on Thursday and Friday."},
-        {"role": "assistant", "content": "The weather in Paris is currently 15°C and partly cloudy."},
-        {"role": "user", "content": "What about London?"},
-        {"role": "assistant", "content": "", "tool_calls": [{"id": "call_2", "function": {"name": "get_weather", "arguments": '{"city": "London"}'}}]},
-        {"role": "tool", "tool_call_id": "call_2", "content": "Weather in London: 12°C, overcast with light drizzle, humidity 80%, wind 15 km/h from the southwest. Rain expected to continue throughout the day with temperatures dropping to 8°C overnight."},
-        {"role": "assistant", "content": "London is 12°C with light drizzle."},
-    ]
-    
-    # Add more history to simulate a long conversation
-    for i in range(10):
-        messages.append({"role": "user", "content": f"Question {i}: Can you explain concept number {i} in detail? " * 20})
-        messages.append({"role": "assistant", "content": f"Answer {i}: Here's a detailed explanation of concept {i}. " * 30})
-    
-    original_tokens = estimate_messages_tokens(messages)
-    print(f"\nOriginal conversation: {len(messages)} messages, ~{original_tokens} tokens")
-    
-    # Example 1: Truncate Optimizer
-    print("\n1. Truncate Optimizer")
-    print("-" * 40)
-    
-    optimizer = TruncateOptimizer()
-    result, stats = optimizer.optimize(messages, target_tokens=2000)
-    result_tokens = estimate_messages_tokens(result)
-    
-    print(f"Strategy: Remove oldest messages first")
-    print(f"Result: {len(messages)} -> {len(result)} messages")
-    print(f"Tokens: {original_tokens} -> {result_tokens}")
-    
-    # Example 2: Sliding Window Optimizer
-    print("\n2. Sliding Window Optimizer")
-    print("-" * 40)
-    
-    optimizer = SlidingWindowOptimizer()
-    result, stats = optimizer.optimize(messages, target_tokens=2000)
-    result_tokens = estimate_messages_tokens(result)
-    
-    print(f"Strategy: Keep most recent messages within window")
-    print(f"Result: {len(messages)} -> {len(result)} messages")
-    print(f"Tokens: {original_tokens} -> {result_tokens}")
-    
-    # Example 3: Prune Tools Optimizer
-    print("\n3. Prune Tools Optimizer")
-    print("-" * 40)
-    
-    optimizer = PruneToolsOptimizer()
-    result, stats = optimizer.optimize(messages, target_tokens=2000)
-    result_tokens = estimate_messages_tokens(result)
-    
-    print(f"Strategy: Truncate old tool outputs, preserve recent")
-    print(f"Result: {len(messages)} -> {len(result)} messages")
-    print(f"Tokens: {original_tokens} -> {result_tokens}")
-    
-    # Example 4: Smart Optimizer (combines strategies)
-    print("\n4. Smart Optimizer (Recommended)")
-    print("-" * 40)
-    
-    optimizer = SmartOptimizer()
-    result, stats = optimizer.optimize(messages, target_tokens=2000)
-    result_tokens = estimate_messages_tokens(result)
-    
-    print(f"Strategy: Intelligent combination of all strategies")
-    print(f"Result: {len(messages)} -> {len(result)} messages")
-    print(f"Tokens: {original_tokens} -> {result_tokens}")
-    print(f"Stats: {stats}")
-    
-    # Example 5: Using the factory function
-    print("\n5. Optimizer Factory")
-    print("-" * 40)
-    
-    strategies = [
-        OptimizerStrategy.TRUNCATE,
-        OptimizerStrategy.SLIDING_WINDOW,
-        OptimizerStrategy.PRUNE_TOOLS,
-        OptimizerStrategy.SMART,
-    ]
-    
-    for strategy in strategies:
-        optimizer = get_optimizer(strategy)
-        result, _ = optimizer.optimize(messages, target_tokens=3000)
-        result_tokens = estimate_messages_tokens(result)
-        print(f"{strategy.value:15s}: {len(messages)} -> {len(result)} msgs, ~{result_tokens} tokens")
-    
-    # Example 6: Tool call/result pair preservation
-    print("\n6. Tool Call/Result Pair Preservation")
-    print("-" * 40)
-    
-    tool_messages = [
-        {"role": "system", "content": "You are helpful."},
-        {"role": "user", "content": "Get data"},
-        {"role": "assistant", "content": "", "tool_calls": [{"id": "tc1"}]},
-        {"role": "tool", "tool_call_id": "tc1", "content": "Data result"},
-        {"role": "assistant", "content": "Here's the data."},
-    ]
-    
-    optimizer = SmartOptimizer()
-    result, _ = optimizer.optimize(tool_messages, target_tokens=100)
-    
-    # Check if tool pairs are preserved
-    tool_calls = [m for m in result if m.get("tool_calls")]
-    tool_results = [m for m in result if m.get("role") == "tool"]
-    
-    print(f"Original: {len(tool_messages)} messages")
-    print(f"Optimized: {len(result)} messages")
-    print(f"Tool calls preserved: {len(tool_calls)}")
-    print(f"Tool results preserved: {len(tool_results)}")
-    
-    print("\n" + "=" * 60)
-    print("✓ Context optimization examples complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()

examples/context/12_multi_agent_context.py

@@ -1,158 +0,0 @@
-#!/usr/bin/env python3
-"""
-Multi-Agent Context Isolation Example
-
-Demonstrates how to use MultiAgentLedger and MultiAgentMonitor
-for managing context across multiple agents with isolation.
-"""
-
-import os
-import tempfile
-from praisonaiagents.context import (
-    MultiAgentLedger,
-    MultiAgentMonitor,
-    ContextLedgerManager,
-    ContextMonitor,
-    ContextBudgeter,
-)
-
-
-def main():
-    print("=" * 60)
-    print("Multi-Agent Context Isolation Example")
-    print("=" * 60)
-    
-    # Example 1: Multi-Agent Ledger
-    print("\n1. Multi-Agent Ledger")
-    print("-" * 40)
-    
-    multi_ledger = MultiAgentLedger()
-    
-    # Get ledgers for different agents
-    researcher_ledger = multi_ledger.get_agent_ledger("researcher")
-    writer_ledger = multi_ledger.get_agent_ledger("writer")
-    reviewer_ledger = multi_ledger.get_agent_ledger("reviewer")
-    
-    # Track different context for each agent
-    researcher_ledger.track_system_prompt("You are a research specialist.")
-    researcher_ledger.track_history([
-        {"role": "user", "content": "Research AI trends"},
-        {"role": "assistant", "content": "Here are the latest AI trends..."},
-    ])
-    
-    writer_ledger.track_system_prompt("You are a technical writer.")
-    writer_ledger.track_history([
-        {"role": "user", "content": "Write an article about AI"},
-        {"role": "assistant", "content": "# AI in 2025\n\nArtificial Intelligence..."},
-    ])
-    
-    reviewer_ledger.track_system_prompt("You are a content reviewer.")
-    reviewer_ledger.track_history([
-        {"role": "user", "content": "Review this article"},
-        {"role": "assistant", "content": "The article is well-written..."},
-    ])
-    
-    # Check token usage per agent
-    print("Token usage per agent:")
-    for agent_id in multi_ledger.get_agent_ids():
-        ledger = multi_ledger.get_agent_ledger(agent_id)
-        print(f"  {agent_id}: {ledger.get_total()} tokens")
-    
-    # Get combined total
-    total = multi_ledger.get_combined_total()
-    print(f"\nTotal across all agents: {total} tokens")
-    
-    # Example 2: Multi-Agent Monitor
-    print("\n2. Multi-Agent Monitor")
-    print("-" * 40)
-    
-    with tempfile.TemporaryDirectory() as tmpdir:
-        multi_monitor = MultiAgentMonitor(base_path=tmpdir)
-        
-        # Get monitors for each agent
-        for agent_id in ["researcher", "writer", "reviewer"]:
-            monitor = multi_monitor.get_agent_monitor(agent_id)
-            print(f"Monitor for {agent_id}: {monitor.path}")
-        
-        # Enable all monitors
-        multi_monitor.enable_all()
-        print(f"\nAll monitors enabled: {multi_monitor.enabled}")
-        
-        # List all agent IDs
-        agent_ids = multi_monitor.get_agent_ids()
-        print(f"Monitored agents: {agent_ids}")
-    
-    # Example 3: Per-Agent Budgeting
-    print("\n3. Per-Agent Budgeting")
-    print("-" * 40)
-    
-    agents_config = {
-        "researcher": {"model": "gpt-4o", "role": "Research"},
-        "writer": {"model": "gpt-4o-mini", "role": "Writing"},
-        "reviewer": {"model": "gpt-4o-mini", "role": "Review"},
-    }
-    
-    for agent_id, config in agents_config.items():
-        budgeter = ContextBudgeter(model=config["model"])
-        budget = budgeter.allocate()
-        print(f"{agent_id} ({config['model']}): {budget.usable:,} usable tokens")
-    
-    # Example 4: Context Isolation Verification
-    print("\n4. Context Isolation Verification")
-    print("-" * 40)
-    
-    # Create fresh multi-ledger
-    isolated_ledger = MultiAgentLedger()
-    
-    # Agent A tracks some context
-    agent_a = isolated_ledger.get_agent_ledger("agent_a")
-    agent_a.track_system_prompt("Agent A system prompt")
-    agent_a.track_history([{"role": "user", "content": "Message for A"}])
-    
-    # Agent B tracks different context
-    agent_b = isolated_ledger.get_agent_ledger("agent_b")
-    agent_b.track_system_prompt("Agent B system prompt")
-    agent_b.track_history([{"role": "user", "content": "Message for B"}])
-    
-    # Verify isolation
-    print(f"Agent A tokens: {agent_a.get_total()}")
-    print(f"Agent B tokens: {agent_b.get_total()}")
-    print(f"Agents are isolated: {agent_a.get_total() != agent_b.get_total() or True}")
-    
-    # Verify they don't share state
-    agent_a_ledger = agent_a.get_ledger()
-    agent_b_ledger = agent_b.get_ledger()
-    print(f"Agent A system_prompt tokens: {agent_a_ledger.system_prompt}")
-    print(f"Agent B system_prompt tokens: {agent_b_ledger.system_prompt}")
-    
-    # Example 5: Shared Team Memory (Optional)
-    print("\n5. Shared Team Memory Pattern")
-    print("-" * 40)
-    
-    # For shared context, you can use a dedicated "team" ledger
-    team_ledger = MultiAgentLedger()
-    
-    # Shared team memory
-    shared = team_ledger.get_agent_ledger("shared_memory")
-    shared.track_system_prompt("Team goal: Complete the research project by Friday")
-    
-    # Individual agents can reference shared memory
-    agent1 = team_ledger.get_agent_ledger("agent1")
-    agent2 = team_ledger.get_agent_ledger("agent2")
-    
-    # Each agent has their own context plus can access shared
-    agent1.track_system_prompt("You are agent 1")
-    agent2.track_system_prompt("You are agent 2")
-    
-    print(f"Shared memory tokens: {shared.get_total()}")
-    print(f"Agent 1 tokens: {agent1.get_total()}")
-    print(f"Agent 2 tokens: {agent2.get_total()}")
-    print(f"Total team tokens: {team_ledger.get_combined_total()}")
-    
-    print("\n" + "=" * 60)
-    print("✓ Multi-agent context isolation examples complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()

examples/context/13_agent_with_context_management.py

@@ -1,127 +0,0 @@
-#!/usr/bin/env python3
-"""
-Agent with Context Management Example
-
-Demonstrates how to use context management features with a real Agent
-making actual API calls.
-
-Requires: OPENAI_API_KEY environment variable
-"""
-
-import os
-from praisonaiagents import Agent
-from praisonaiagents.context import (
-    ContextBudgeter,
-    ContextLedgerManager,
-    ContextMonitor,
-    get_optimizer,
-    OptimizerStrategy,
-    estimate_messages_tokens,
-)
-
-
-def main():
-    # Check for API key
-    if not os.environ.get("OPENAI_API_KEY"):
-        print("Error: OPENAI_API_KEY environment variable not set")
-        return
-    
-    print("=" * 60)
-    print("Agent with Context Management Example")
-    print("=" * 60)
-    
-    # Initialize context management components
-    budgeter = ContextBudgeter(model="gpt-4o-mini")
-    ledger = ContextLedgerManager()
-    optimizer = get_optimizer(OptimizerStrategy.SMART)
-    
-    # Get budget info
-    budget = budgeter.allocate()
-    print(f"\nModel: gpt-4o-mini")
-    print(f"Context budget: {budget.usable:,} tokens")
-    print(f"Output reserve: {budget.output_reserve:,} tokens")
-    
-    # Create an agent
-    agent = Agent(
-        instructions="You are a helpful AI assistant. Keep responses concise.",
-        llm="gpt-4o-mini",
-    )
-    
-    # Track the system prompt
-    ledger.track_system_prompt(agent.instructions)
-    print(f"\nSystem prompt tracked: {ledger.get_total()} tokens")
-    
-    # Simulate a conversation
-    print("\n" + "-" * 40)
-    print("Starting conversation...")
-    print("-" * 40)
-    
-    conversation = []
-    questions = [
-        "What is Python?",
-        "What are its main features?",
-        "Give me a simple example.",
-    ]
-    
-    for question in questions:
-        print(f"\nUser: {question}")
-        
-        # Add user message to conversation
-        conversation.append({"role": "user", "content": question})
-        
-        # Track in ledger
-        ledger.track_history(conversation[-1:])
-        
-        # Check context usage before API call
-        current_tokens = ledger.get_total()
-        utilization = budgeter.get_utilization(current_tokens)
-        
-        print(f"[Context: {current_tokens} tokens, {utilization:.1%} used]")
-        
-        # Check if optimization needed
-        if utilization > 0.8:
-            print("[Warning: High context usage, optimizing...]")
-            conversation, _ = optimizer.optimize(conversation, target_tokens=int(budget.usable * 0.7))
-        
-        # Make API call
-        response = agent.chat(question)
-        
-        # Add assistant response to conversation
-        conversation.append({"role": "assistant", "content": response})
-        ledger.track_history(conversation[-1:])
-        
-        print(f"Assistant: {response[:200]}..." if len(response) > 200 else f"Assistant: {response}")
-    
-    # Final stats
-    print("\n" + "-" * 40)
-    print("Final Context Statistics")
-    print("-" * 40)
-    
-    final_tokens = ledger.get_total()
-    final_utilization = budgeter.get_utilization(final_tokens)
-    
-    print(f"Total tokens used: {final_tokens:,}")
-    print(f"Context utilization: {final_utilization:.1%}")
-    print(f"Messages in conversation: {len(conversation)}")
-    print(f"Remaining capacity: {budgeter.get_remaining(final_tokens):,} tokens")
-    
-    # Demonstrate optimization on the final conversation
-    print("\n" + "-" * 40)
-    print("Optimization Demo")
-    print("-" * 40)
-    
-    original_tokens = estimate_messages_tokens(conversation)
-    optimized, stats = optimizer.optimize(conversation, target_tokens=500)
-    optimized_tokens = estimate_messages_tokens(optimized)
-    
-    print(f"Original: {len(conversation)} messages, {original_tokens} tokens")
-    print(f"Optimized: {len(optimized)} messages, {optimized_tokens} tokens")
-    print(f"Reduction: {((original_tokens - optimized_tokens) / original_tokens * 100):.1f}%")
-    
-    print("\n" + "=" * 60)
-    print("✓ Agent with context management example complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()

examples/cookbooks/Code_Analysis_Agent.ipynb

@@ -0,0 +1,467 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bLeMxwWimvzX"
+      },
+      "source": [
+        "# Code Analysis Agent\n",
+        "Learn how to create AI agents for comprehensive code analysis and quality assessment."
+      ],
+      "id": "bLeMxwWimvzX"
+    },
+    {
+      "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/Code_Analysis_Agent.ipynb)\n"
+=======
+        "[![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/code_analysis_agent.ipynb)\n"
+
+      ],
+      "metadata": {
+        "id": "P3X8PXPyojnL"
+      },
+      "id": "P3X8PXPyojnL"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xEs8bwWfmvza"
+      },
+      "source": [
+        "## Dependencies\n",
+        "Install the required packages."
+      ],
+      "id": "xEs8bwWfmvza"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_RPiU3j6mvzb"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install praisonaiagents gitingest"
+      ],
+      "id": "_RPiU3j6mvzb"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "81-orTkzmvzc"
+      },
+      "source": [
+        "## Set API Key\n",
+        "Set your OpenAI API key as an environment variable in your terminal:"
+      ],
+      "id": "81-orTkzmvzc"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "84iL7rGimvzc"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ['OPENAI_API_KEY'] = 'your_api_key_here'"
+      ],
+      "id": "84iL7rGimvzc"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TV15GFsRmvzc"
+      },
+      "source": [
+        "## Tools and Data Models\n",
+        "Define the agent, task, and output schema."
+      ],
+      "id": "TV15GFsRmvzc"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0Wq_g0pNmvzd"
+      },
+      "outputs": [],
+      "source": [
+        "from praisonaiagents import Agent, Task, PraisonAIAgents\n",
+        "from pydantic import BaseModel\n",
+        "from typing import List, Dict\n",
+        "from gitingest import ingest\n",
+        "\n",
+        "class CodeMetrics(BaseModel):\n",
+        "    category: str\n",
+        "    score: int\n",
+        "    findings: List[str]\n",
+        "\n",
+        "class CodeAnalysisReport(BaseModel):\n",
+        "    overall_quality: int\n",
+        "    code_metrics: List[CodeMetrics]\n",
+        "    architecture_score: int\n",
+        "    maintainability_score: int\n",
+        "    performance_score: int\n",
+        "    security_score: int\n",
+        "    test_coverage: int\n",
+        "    key_strengths: List[str]\n",
+        "    improvement_areas: List[str]\n",
+        "    tech_stack: List[str]\n",
+        "    recommendations: List[str]\n",
+        "    complexity_metrics: Dict[str, int]\n",
+        "    best_practices: List[Dict[str, str]]\n",
+        "    potential_risks: List[str]\n",
+        "    documentation_quality: int"
+      ],
+      "id": "0Wq_g0pNmvzd"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EatrK7-wmvzd"
+      },
+      "source": [
+        "## YAML Prompt Setup\n",
+        "Define the agent and task with expectations."
+      ],
+      "id": "EatrK7-wmvzd"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EdZNSOy-mvze"
+      },
+      "outputs": [],
+      "source": [
+        "code_analyzer = Agent(\n",
+        "    role=\"Code Analysis Expert\",\n",
+        "    goal=\"Provide comprehensive code evaluation and recommendations\",\n",
+        "    backstory=\"\"\"Expert code analyst specializing in architecture review,\n",
+        "    best practices, and technical debt assessment.\"\"\",\n",
+        "    verbose=True\n",
+        ")\n",
+        "\n",
+        "code_analysis_task = Task(\n",
+        "    description=\"\"\"Analyze code repository and provide structured evaluation:\n",
+        "\n",
+        "    1. Overall Quality (0-100)\n",
+        "    2. Core Metrics Analysis:\n",
+        "       - Architecture and Design\n",
+        "       - Code Maintainability\n",
+        "       - Performance Optimization\n",
+        "       - Security Practices\n",
+        "       - Test Coverage\n",
+        "    3. Technical Assessment:\n",
+        "       - Technology Stack Review\n",
+        "       - Code Complexity Analysis\n",
+        "       - Best Practices Adherence\n",
+        "       - Risk Assessment\n",
+        "    4. Recommendations:\n",
+        "       - Key Improvements\n",
+        "       - Architecture Suggestions\n",
+        "       - Security Enhancements\"\"\",\n",
+        "    expected_output=\"Detailed code analysis report with metrics and recommendations\",\n",
+        "    agent=code_analyzer,\n",
+        "    output_pydantic=CodeAnalysisReport\n",
+        ")"
+      ],
+      "id": "EdZNSOy-mvze"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CeLS55ROmvzf"
+      },
+      "source": [
+        "## Main Function\n",
+        "Function to analyze code from GitHub URL or local path."
+      ],
+      "id": "CeLS55ROmvzf"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3Eo6LEOxmvzf"
+      },
+      "outputs": [],
+      "source": [
+        "def analyze_code(code_source: str) -> CodeAnalysisReport:\n",
+        "    \"\"\"\n",
+        "    Analyze code from directory path or GitHub URL\n",
+        "    \"\"\"\n",
+        "    # Ingest code content\n",
+        "    summary, tree, content = ingest(code_source)\n",
+        "\n",
+        "    # Concatenate context into structured format\n",
+        "    context_text = f\"\"\"\n",
+        "    CODE REPOSITORY ANALYSIS\n",
+        "    =======================\n",
+        "\n",
+        "    SUMMARY\n",
+        "    -------\n",
+        "    {summary}\n",
+        "\n",
+        "    REPOSITORY STRUCTURE\n",
+        "    -------------------\n",
+        "    {tree}\n",
+        "\n",
+        "    SOURCE CODE\n",
+        "    -----------\n",
+        "    {content}\n",
+        "    \"\"\"\n",
+        "\n",
+        "    # Initialize and run analysis\n",
+        "    agents = PraisonAIAgents(\n",
+        "        agents=[code_analyzer],\n",
+        "        tasks=[code_analysis_task]\n",
+        "    )\n",
+        "\n",
+        "    return agents.start(context_text)"
+      ],
+      "id": "3Eo6LEOxmvzf"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v25nLA0pmvzf"
+      },
+      "source": [
+        "## Output\n",
+        "Example usage to run the analysis and view results."
+      ],
+      "id": "v25nLA0pmvzf"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "5BzccaeFmvzg",
+        "outputId": "8b4cddae-b6b3-4a92-8a13-30694cd09c52"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "\n### 👤 Agent: Code Analysis Expert\n\n**Role**: Provides comprehensive code evaluation and recommendations  \n**Backstory**: Expert in architecture, best practices, and technical assessment  \n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "─── 📊 AGENT CODE ANALYSIS REPORT ───\n",
+            "{\n",
+            "    \"overall_quality\": 85,\n",
+            "    \"code_metrics\": [\n",
+            "        {\n",
+            "            \"category\": \"Architecture and Design\",\n",
+            "            \"score\": 80,\n",
+            "            \"findings\": [\n",
+            "                \"Modular structure with clear separation of concerns.\",\n",
+            "                \"Use of type annotations improves code readability and maintainability.\"\n",
+            "            ]\n",
+            "        },\n",
+            "        {\n",
+            "            \"category\": \"Code Maintainability\",\n",
+            "            \"score\": 85,\n",
+            "            \"findings\": [\n",
+            "                \"Consistent use of type hints and NamedTuple for structured data.\",\n",
+            "                \"Logical organization of functions and classes.\"\n",
+            "            ]\n",
+            "        },\n",
+            "        {\n",
+            "            \"category\": \"Performance Optimization\",\n",
+            "            \"score\": 75,\n",
+            "            \"findings\": [\n",
+            "                \"Potential performance overhead due to repeated sys.stdout.write calls.\",\n",
+            "                \"Efficient use of optional parameters to control execution flow.\"\n",
+            "            ]\n",
+            "        },\n",
+            "        {\n",
+            "            \"category\": \"Security Practices\",\n",
+            "            \"score\": 80,\n",
+            "            \"findings\": [\n",
+            "                \"No obvious security vulnerabilities in the code.\",\n",
+            "                \"Proper encapsulation of functionality.\"\n",
+            "            ]\n",
+            "        },\n",
+            "        {\n",
+            "            \"category\": \"Test Coverage\",\n",
+            "            \"score\": 70,\n",
+            "            \"findings\": [\n",
+            "                \"Lack of explicit test cases in the provided code.\",\n",
+            "                \"Use of type checking suggests some level of validation.\"\n",
+            "            ]\n",
+            "        }\n",
+            "    ],\n",
+            "    \"architecture_score\": 80,\n",
+            "    \"maintainability_score\": 85,\n",
+            "    \"performance_score\": 75,\n",
+            "    \"security_score\": 80,\n",
+            "    \"test_coverage\": 70,\n",
+            "    \"key_strengths\": [\n",
+            "        \"Strong use of type annotations and typing extensions.\",\n",
+            "        \"Clear separation of CLI argument parsing and business logic.\"\n",
+            "    ],\n",
+            "    \"improvement_areas\": [\n",
+            "        \"Increase test coverage to ensure robustness.\",\n",
+            "        \"Optimize I/O operations to improve performance.\"\n",
+            "    ],\n",
+            "    \"tech_stack\": [\n",
+            "        \"Python\",\n",
+            "        \"argparse\",\n",
+            "        \"typing_extensions\"\n",
+            "    ],\n",
+            "    \"recommendations\": [\n",
+            "        \"Add unit tests to improve reliability.\",\n",
+            "        \"Consider async I/O for improved performance in CLI tools.\"\n",
+            "    ]\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "import json\n",
+        "from IPython.display import display, Markdown\n",
+        "\n",
+        "# Optional: Define agent info\n",
+        "agent_info = \"\"\"\n",
+        "### 👤 Agent: Code Analysis Expert\n",
+        "\n",
+        "**Role**: Provides comprehensive code evaluation and recommendations\n",
+        "**Backstory**: Expert in architecture, best practices, and technical assessment\n",
+        "\"\"\"\n",
+        "\n",
+        "# Analysis Result Data\n",
+        "analysis_result = {\n",
+        "    \"overall_quality\": 85,\n",
+        "    \"code_metrics\": [\n",
+        "        {\n",
+        "            \"category\": \"Architecture and Design\",\n",
+        "            \"score\": 80,\n",
+        "            \"findings\": [\n",
+        "                \"Modular structure with clear separation of concerns.\",\n",
+        "                \"Use of type annotations improves code readability and maintainability.\"\n",
+        "            ]\n",
+        "        },\n",
+        "        {\n",
+        "            \"category\": \"Code Maintainability\",\n",
+        "            \"score\": 85,\n",
+        "            \"findings\": [\n",
+        "                \"Consistent use of type hints and NamedTuple for structured data.\",\n",
+        "                \"Logical organization of functions and classes.\"\n",
+        "            ]\n",
+        "        },\n",
+        "        {\n",
+        "            \"category\": \"Performance Optimization\",\n",
+        "            \"score\": 75,\n",
+        "            \"findings\": [\n",
+        "                \"Potential performance overhead due to repeated sys.stdout.write calls.\",\n",
+        "                \"Efficient use of optional parameters to control execution flow.\"\n",
+        "            ]\n",
+        "        },\n",
+        "        {\n",
+        "            \"category\": \"Security Practices\",\n",
+        "            \"score\": 80,\n",
+        "            \"findings\": [\n",
+        "                \"No obvious security vulnerabilities in the code.\",\n",
+        "                \"Proper encapsulation of functionality.\"\n",
+        "            ]\n",
+        "        },\n",
+        "        {\n",
+        "            \"category\": \"Test Coverage\",\n",
+        "            \"score\": 70,\n",
+        "            \"findings\": [\n",
+        "                \"Lack of explicit test cases in the provided code.\",\n",
+        "                \"Use of type checking suggests some level of validation.\"\n",
+        "            ]\n",
+        "        }\n",
+        "    ],\n",
+        "    \"architecture_score\": 80,\n",
+        "    \"maintainability_score\": 85,\n",
+        "    \"performance_score\": 75,\n",
+        "    \"security_score\": 80,\n",
+        "    \"test_coverage\": 70,\n",
+        "    \"key_strengths\": [\n",
+        "        \"Strong use of type annotations and typing extensions.\",\n",
+        "        \"Clear separation of CLI argument parsing and business logic.\"\n",
+        "    ],\n",
+        "    \"improvement_areas\": [\n",
+        "        \"Increase test coverage to ensure robustness.\",\n",
+        "        \"Optimize I/O operations to improve performance.\"\n",
+        "    ],\n",
+        "    \"tech_stack\": [\"Python\", \"argparse\", \"typing_extensions\"],\n",
+        "    \"recommendations\": [\n",
+        "        \"Add unit tests to improve reliability.\",\n",
+        "        \"Consider async I/O for improved performance in CLI tools.\"\n",
+        "    ]\n",
+        "}\n",
+        "\n",
+        "# Display Agent Info and Analysis Report\n",
+        "display(Markdown(agent_info))\n",
+        "print(\"─── 📊 AGENT CODE ANALYSIS REPORT ───\")\n",
+        "print(json.dumps(analysis_result, indent=4))\n"
+      ],
+      "id": "5BzccaeFmvzg"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%cd PraisonAI"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mNZjZLedorlu",
+        "outputId": "82c96cdf-e2e5-4a9c-e633-f0fc225ad973"
+      },
+      "id": "mNZjZLedorlu",
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/PraisonAI\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+
+}
+=======
+}
+

examples/cookbooks/Conversational_Chat_Agents/Phi_3_5_Mini_Conversational.ipynb

@@ -1,120 +0,0 @@
-{
-  "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/Conversational_Chat_Agents/Phi_3_Medium_Conversational.ipynb

@@ -1,122 +0,0 @@
-{
-  "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/Conversational_Chat_Agents/Phi_4_Conversational.ipynb

@@ -1,124 +0,0 @@
-{
-  "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/Conversational_Chat_Agents/Qwen_Colab_Agent.ipynb

@@ -1,331 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "IY0bHc5lF46I",
-   "metadata": {
-    "id": "IY0bHc5lF46I"
-   },
-   "source": [
-    "# 🤖 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",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "k37CX_LeeY_1",
-   "metadata": {
-    "id": "k37CX_LeeY_1"
-   },
-   "source": [
-    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/qwen_colab_agent.ipynb)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "r8_iKyw_cbIx",
-   "metadata": {
-    "id": "r8_iKyw_cbIx"
-   },
-   "source": [
-    "# Install required libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "qOiX2hqlF46K",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "qOiX2hqlF46K",
-    "outputId": "c9781e74-cb79-4512-87ab-4805a14b240e"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\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",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\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"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "!pip install -q transformers accelerate torch\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "PvTxUutEce5s",
-   "metadata": {
-    "id": "PvTxUutEce5s"
-   },
-   "source": [
-    "# ✅ Use the official BERT Large model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "q898nxgyF46L",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 177,
-     "referenced_widgets": [
-      "7dfb60f8868d49d08a38cafd31f9801b",
-      "d59ec5856d294b1aa35e543b4e5cb542",
-      "1457a5a7e1774b8a9b857d5612d17dab",
-      "deb60e57aa994fc9b35f3cfdc1ccc773",
-      "52a33ecf50024111b7cd38aec15e7a24",
-      "a187d524446c4d2384e2bf1f3e95e7f4",
-      "3c294b94c6494727a202771ced455cc5",
-      "6265eaa200c74ad1a71cf0d9687d96b5",
-      "f95ef373d5bf47659081f7dd5451da10",
-      "8a7e9defb4564e3e8bd81fa8c64a511b",
-      "0ec0f49489e042bb9979d1908ddd88bc",
-      "d264ba22bcd247fdbfcfc920d525769b",
-      "63246722cf6f4ff2a86077dd81cf6fe1",
-      "300a704a527f40e4ac1aa42b3cfe73de",
-      "697ad21a2440459ba2fe58970018ab36",
-      "f473043a0c174c72a7e370bef9446d64",
-      "85e681d7a00344df96dbbae0f6959822",
-      "e4ae9e23561642a7a9b637a573e15638",
-      "6acca0b962a745cb8801005757dd5814",
-      "a785e8ee00ba47c3b267cbf9af23c308",
-      "cf28774ceb87469687299e358fbcd458",
-      "ee1fff0b490d40dd859dbced838983b1",
-      "c40fd1a2e60a42b686e33a4476dc9100",
-      "a798a3704a234a65a124050666b6b990",
-      "d16fa1087f6747e0a1618bb269a2680b",
-      "cca65a59559d4440a210d0b402a70656",
-      "d0a59c8d2bc048d4bbec5c9ab76b387b",
-      "a45c0d091c334f60b81d95ecee27ec7d",
-      "70680996ad7743c8bcf732f6fad29f11",
-      "b5ca16637f4e41ecbdbceb7cb7efa9dd",
-      "9ce4a0d49355444c927cea148e095581",
-      "5c0cb655347246548e97187512083ef6",
-      "2cf395b2a0ab4fcf9679c5a2063b138e",
-      "d41037046f8f43ccb008e6796a107a18",
-      "304646ee1ed74c03b4962628a80618b9",
-      "812518655bc04923972b18a6963e4c07",
-      "f814bc25369e46d6b5a1ee0717f95bdb",
-      "4e6e66ce7ca54447871899683f80cf05",
-      "eccca4ade2404c84b787ffe45e625e99",
-      "a1c746f57c9b4dacba349257bd3598ad",
-      "acbe0aff4b6249c28636cdcf97445a28",
-      "fdee9e7c52de42278364b228cb1fdda2",
-      "f59346d4b3ce4e5896f4ec2b5600fd9a",
-      "177feb0a5c9340ce8d8b8ca7f1539bf0",
-      "d33ea2cd6c964b0da990c11fcc811359",
-      "0a60b58528a8444fab2a1551f430a9ac",
-      "dc11385d525445bd972c4de432de0d6a",
-      "7bd7dc33413f4f3b86214a52d68db591",
-      "fe3b7d8f9f514666b851771f77cfb1e7",
-      "7d4d2c277a8746c7aebfbaaf21b59734",
-      "33f354ddd9f54e02975dde2d0631ae04",
-      "85ae7ced25884771a0a3f0b9871190e5",
-      "a6db2526ea5a4c519ad2e4276b6f2e80",
-      "fc771570af3644cc86a5305642cdb42b",
-      "0964039f89604e7bbdc168fbddbfe620"
-     ]
-    },
-    "id": "q898nxgyF46L",
-    "outputId": "144b0e69-680e-4633-8875-a3e23cccbcc1"
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7dfb60f8868d49d08a38cafd31f9801b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "config.json:   0%|          | 0.00/762 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d264ba22bcd247fdbfcfc920d525769b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "model.safetensors:   0%|          | 0.00/1.34G [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c40fd1a2e60a42b686e33a4476dc9100",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d41037046f8f43ccb008e6796a107a18",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d33ea2cd6c964b0da990c11fcc811359",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "import torch\n",
-    "\n",
-    "bert_model = AutoModel.from_pretrained(\"bert-large-cased\")\n",
-    "bert_tokenizer = AutoTokenizer.from_pretrained(\"bert-large-cased\")\n",
-    "\n",
-    "def classify_text(text):\n",
-    "    inputs = bert_tokenizer(text, return_tensors=\"pt\")\n",
-    "    with torch.no_grad():\n",
-    "        outputs = bert_model(**inputs)\n",
-    "    return outputs.last_hidden_state.mean(dim=1)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "jCvfJT7pcg7P",
-   "metadata": {
-    "id": "jCvfJT7pcg7P"
-   },
-   "source": [
-    "# Agent Function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "RGVb7FBdF46L",
-   "metadata": {
-    "id": "RGVb7FBdF46L"
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "def chat_with_qwen(prompt: str, max_length: int = 256):\n",
-    "    inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
-    "    with torch.no_grad():\n",
-    "        outputs = model.generate(**inputs, max_new_tokens=max_length)\n",
-    "    return tokenizer.decode(outputs[0], skip_special_tokens=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "JRx9L-oYcixp",
-   "metadata": {
-    "id": "JRx9L-oYcixp"
-   },
-   "source": [
-    "# Example prompt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0atGDbNAF46M",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "0atGDbNAF46M",
-    "outputId": "8878d6fe-aff7-4e9d-d359-e1f219c4dc47"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Setting `pad_token_id` to `eos_token_id`:151643 for open-end generation.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "What are the benefits of using transformers library? The transformers library is a Python library that provides a set of tools for working with data in a more efficient and organized way. Here are some of the benefits of using the transformers library:\n",
-      "\n",
-      "1. Improved data processing: The transformers library provides a set of tools for processing data in a more efficient and organized way. This can help to reduce the amount of time and resources required to process data, making it easier to work with large datasets.\n",
-      "2. Better data organization: The transformers library provides a set of tools for organizing data in a more efficient and organized way. This can help to reduce the amount of time and resources required to work with large datasets, making it easier to analyze and visualize data.\n",
-      "3. Improved data analysis: The transformers library provides a set of tools for analyzing data in a more efficient and organized way. This can help to reduce the amount of time and resources required to analyze data, making it easier to identify patterns and trends.\n",
-      "4. Better data visualization: The transformers library provides a set of tools for creating more efficient and organized data visualizations. This can help to make it easier to understand and communicate complex data sets.\n",
-      "5. Improved data security: The transformers library provides a set of tools for improving data security. This can help to reduce the risk of data\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "response = chat_with_qwen(\"What are the benefits of using transformers library?\")\n",
-    "print(response)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}