v0.0.9: Update project configuration and dependencies

add llamaindex tool

.env.example

@@ -1,12 +0,0 @@
-# PraisonAI-Tools environment variables (examples)
-#
-# Copy to `.env` and fill in the values for the tools you use.
-# Never commit real secrets.
-
-# ── Stripe (StripeTool) ─────────────────────────────────────────────
-# Stripe secret key. Prefer least-privilege *restricted* keys (rk_...).
-# Use test keys (sk_test_/rk_test_) for development and live keys
-# (sk_live_/rk_live_) only in production. STRIPE_API_KEY is accepted as a
-# fallback if STRIPE_SECRET_KEY is unset.
-STRIPE_SECRET_KEY=sk_test_your_key_here
-# STRIPE_API_KEY=sk_test_your_key_here

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

@@ -1,290 +0,0 @@
-name: "Claude Code Action"
-description: "Run Claude Code in GitHub Actions workflows"
-
-inputs:
-  anthropic_api_key:
-    description: "Anthropic API key"
-    required: false
-  claude_code_oauth_token:
-    description: "Claude Code OAuth Token for Claude Pro"
-    required: false
-  github_token:
-    description: "GitHub token for Claude to operate with"
-    required: false
-    default: ${{ github.token }}
-  trigger_phrase:
-    description: "The trigger phrase to look for in comments, issue/PR bodies, and issue titles"
-    required: false
-    default: "@claude"
-  assignee_trigger:
-    description: "The assignee username that triggers the action (e.g. @claude). Only used for issue assignment"
-    required: false
-  max_turns:
-    description: "Maximum number of conversation turns Claude can take"
-    required: false
-  timeout_minutes:
-    description: "Timeout in minutes for execution"
-    required: false
-    default: "30"
-  model:
-    description: "Model to use (provider-specific format required for Bedrock/Vertex)"
-    required: false
-  use_bedrock:
-    description: "Use Amazon Bedrock with OIDC authentication instead of direct Anthropic API"
-    required: false
-    default: "false"
-  use_vertex:
-    description: "Use Google Vertex AI with OIDC authentication instead of direct Anthropic API"
-    required: false
-    default: "false"
-  allowed_tools:
-    description: "Additional tools for Claude to use (the base GitHub tools will always be included)"
-    required: false
-    default: ""
-  disallowed_tools:
-    description: "Tools that Claude should never use"
-    required: false
-    default: ""
-  custom_instructions:
-    description: "Additional custom instructions to include in the prompt for Claude"
-    required: false
-    default: ""
-  mcp_config:
-    description: "Additional MCP configuration (JSON string) that merges with the built-in GitHub MCP servers"
-    required: false
-    default: ""
-  claude_env:
-    description: "Custom environment variables to pass to Claude Code execution (YAML format)"
-    required: false
-    default: ""
-  direct_prompt:
-    description: "Direct prompt for Claude to execute automatically without needing a trigger (for automated workflows)"
-    required: false
-
-runs:
-  using: "composite"
-  steps:
-    - name: Install Claude Code
-      shell: bash
-      run: npm install -g @anthropic-ai/claude-code
-
-    - name: Install GitHub MCP Server
-      shell: bash
-      run: |
-        claude mcp add-json github '{
-          "command": "docker",
-          "args": [
-            "run",
-            "-i",
-            "--rm",
-            "-e",
-            "GITHUB_PERSONAL_ACCESS_TOKEN",
-            "ghcr.io/github/github-mcp-server:sha-ff3036d"
-          ],
-          "env": {
-            "GITHUB_PERSONAL_ACCESS_TOKEN": "${{ inputs.github_token }}"
-          }
-        }'
-
-    - name: Extract GitHub Context and Create Prompt
-      shell: bash
-      id: prepare_context
-      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.
-
-        CRITICAL INSTRUCTION: If your task involves modifying codebase files to add a feature or fix a bug:
-
-        STEP 0 — SETUP GIT IDENTITY:
-        git config user.name "MervinPraison"
-        git config user.email "[email protected]"
-
-        STEP 1 — READ GUIDELINES:
-        MANDATORY: Before writing any code, you MUST carefully read "src/praisonai-agents/AGENTS.md" to understand this repository's extremely specific Architecture, Protocol-First design rules, and Agent-Centric Philosophy.
-
-        STEP 2 — ARCHITECTURE VALIDATION & ROUTING (MANDATORY before writing code):
-        Before implementing anything, answer these questions:
-        - CORE vs WRAPPER vs TOOLS ROUTING:
-          1. Core SDK (praisonaiagents/): Only core protocols, base classes, decorators. No heavy implementations.
-          2. Wrapper (praisonai/): CLI, heavy implementations, optional dependencies.
-          3. Tools (PraisonAI-Tools): Specific integrations, external tools (e.g. SurrealDB, Slack), and community extensions MUST go to the `MervinPraison/PraisonAI-Tools` repository. If the feature is a tool, clone `https://github.com/MervinPraison/PraisonAI-Tools`, implement it there, and create the PR using `gh pr create -R MervinPraison/PraisonAI-Tools`.
-        - Does it duplicate existing functionality? Check if Agent already supports this via existing params (reflection, planning, tools, hooks, memory).
-        - Does it inherit from Agent properly? New agent types MUST inherit Agent, not wrap it with composition.
-        - Does it add new dependencies? Only optional deps allowed, must be lazy-imported.
-        - Will agent.py grow larger? If the change adds >50 lines to agent.py, find a way to extract instead.
-        - Is there a name collision with existing exports in __init__.py?
-        If ANY of these conceptual checks fail (excluding routing), add a comment to the issue explaining why and close it. Do NOT create a PR.
-
-        STEP 3 — IMPLEMENT:
-        - Create a new git branch appropriately named (e.g. \`feature/issue-<number>\`)
-        - Follow protocol-driven design: protocols in core SDK, heavy implementations in wrapper
-        - Keep changes small and backward-compatible
-
-        STEP 4 — TEST:
-        - Run: cd src/praisonai-agents && PYTHONPATH=. python -m pytest tests/ -x -q --timeout=30
-        - Ensure no regressions
-
-        STEP 5 — CREATE PR:
-        - Commit with descriptive message, push the branch
-        - Use the GitHub CLI (\`gh pr create --title "..." --body "Fixes #<number>"\`) to open a Pull Request
-        CRITICAL: You MUST create the PR automatically using \`gh pr create\`. Do NOT just provide a link.
-
-        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 }}
-        CLAUDE_CODE_OAUTH_TOKEN: ${{ inputs.claude_code_oauth_token }}
-        GITHUB_TOKEN: ${{ inputs.github_token }} 

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

@@ -1,91 +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"
-  claude_code_oauth_token:
-    description: "Claude Code OAuth token for Pro subscription access"
-    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
-      id: generate_prompt
-      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
-        
-        echo "PROMPT<<DELIMITER" >> $GITHUB_OUTPUT
-        cat /tmp/claude-prompts/claude-issue-triage-prompt.txt >> $GITHUB_OUTPUT
-        echo "DELIMITER" >> $GITHUB_OUTPUT
-
-    - name: Run Claude Code
-      uses: ./.github/actions/claude-code-action
-      with:
-        direct_prompt: ${{ steps.generate_prompt.outputs.PROMPT }}
-        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"
-        timeout_minutes: ${{ inputs.timeout_minutes }}
-        claude_code_oauth_token: ${{ inputs.claude_code_oauth_token }}
-        github_token: ${{ inputs.github_token }} 

.github/automation.meta.json

@@ -1,9 +0,0 @@
-{
-  "templateRepo": "MervinPraison/github-automation-template",
-  "stack": "claude",
-  "profile": "tools",
-  "mode": "hub",
-  "hubRef": "v1.1.15",
-  "templateVersion": "1.1.6",
-  "installedAt": "2026-09-07T12:54:25Z"
-}

.github/prompts/final-review.md

@@ -1,4 +0,0 @@
-# Final architecture review prompt (referenced by merge-gate.js via gate-config fields).
-# Most FINAL review text is assembled dynamically from gate-config.js at runtime.
-
-See `finalClaudeScope`, `finalClaudeProductValue` in `.github/scripts/gate-config.js`.

.github/scripts/bot-pr-review-chain-selftest.js

@@ -1,34 +0,0 @@
-#!/usr/bin/env node
-const chain = require('./bot-pr-review-chain.js');
-
-let failed = 0;
-function assert(name, cond) {
-  if (!cond) {
-    console.error('FAIL:', name);
-    failed++;
-  } else {
-    console.log('ok:', name);
-  }
-}
-
-assert('chainKickPosted requires both markers', chain.chainKickPosted([
-  { user: { login: 'MervinPraison' }, body: '@coderabbitai review' },
-  { user: { login: 'MervinPraison' }, body: '/review' },
-]));
-assert('chainKickPosted false with only coderabbit', !chain.chainKickPosted([
-  { user: { login: 'MervinPraison' }, body: '@coderabbitai review' },
-]));
-assert('coderabbitKickPosted detects coderabbit', chain.coderabbitKickPosted([
-  { user: { login: 'MervinPraison' }, body: '@coderabbitai review' },
-]));
-assert('qodoKickPosted detects qodo', chain.qodoKickPosted([
-  { user: { login: 'github-actions[bot]' }, body: '/review' },
-]));
-assert('ignores coderabbit bot comment', !chain.coderabbitKickPosted([
-  { user: { login: 'coderabbitai[bot]' }, body: '@coderabbitai review' },
-]));
-assert('bot PR author match', chain.isBotOpenedPr({ user: { login: 'praisonai-triage-agent[bot]' } }));
-assert('generic bot type match', chain.isBotOpenedPr({ user: { login: 'my-bot', type: 'Bot' } }));
-assert('human author excluded', !chain.isBotOpenedPr({ user: { login: 'MervinPraison', type: 'User' } }));
-
-process.exit(failed ? 1 : 0);

.github/scripts/bot-pr-review-chain.js

@@ -1,153 +0,0 @@
-/**
- * Idempotent CodeRabbit/Qodo kick for bot-opened PRs.
- * @see .github/workflows/auto-pr-comment.yml, claude.yml, bot-pr-recovery.yml
- */
-
-const KICK_AUTHORS = new Set(['MervinPraison', 'github-actions[bot]']);
-const BOT_PR_AUTHORS = new Set(['praisonai-triage-agent[bot]', 'github-actions[bot]']);
-
-async function listAllComments(github, owner, repo, issueNumber) {
-  if (typeof github.paginate === 'function') {
-    return github.paginate(github.rest.issues.listComments, {
-      owner,
-      repo,
-      issue_number: issueNumber,
-      per_page: 100,
-    });
-  }
-  const { data } = await github.rest.issues.listComments({
-    owner,
-    repo,
-    issue_number: issueNumber,
-    per_page: 100,
-  });
-  return data;
-}
-
-function kickAuthored(comment, marker) {
-  return (
-    KICK_AUTHORS.has(comment.user?.login) &&
-    (comment.body || '').includes(marker)
-  );
-}
-
-function coderabbitKickPosted(comments) {
-  return comments.some((c) => kickAuthored(c, '@coderabbitai review'));
-}
-
-function qodoKickPosted(comments) {
-  return comments.some((c) => kickAuthored(c, '/review'));
-}
-
-function chainKickPosted(comments) {
-  return coderabbitKickPosted(comments) && qodoKickPosted(comments);
-}
-
-function isBotOpenedPr(pr) {
-  if (BOT_PR_AUTHORS.has(pr.user?.login)) return true;
-  return pr.user?.type === 'Bot';
-}
-
-async function kickReviewChain(github, owner, repo, prNumber, core, preFetchedComments = null) {
-  const comments = preFetchedComments || await listAllComments(github, owner, repo, prNumber);
-  const needCoderabbit = !coderabbitKickPosted(comments);
-  const needQodo = !qodoKickPosted(comments);
-
-  if (!needCoderabbit && !needQodo) {
-    core?.info?.(`Review chain already kicked on PR #${prNumber}`);
-    return { kicked: false, reason: 'already_kicked' };
-  }
-
-  if (needCoderabbit) {
-    await github.rest.issues.createComment({
-      owner,
-      repo,
-      issue_number: prNumber,
-      body: '@coderabbitai review',
-    });
-  }
-  if (needQodo) {
-    await github.rest.issues.createComment({
-      owner,
-      repo,
-      issue_number: prNumber,
-      body: '/review',
-    });
-  }
-  core?.info?.(`Kicked review chain for PR #${prNumber} (coderabbit=${needCoderabbit}, qodo=${needQodo})`);
-  return { kicked: true, coderabbit: needCoderabbit, qodo: needQodo };
-}
-
-async function findOpenPrForIssue(github, owner, repo, issueNumber) {
-  const prefix = `claude/issue-${issueNumber}-`;
-  const { data: prs } = await github.rest.pulls.list({
-    owner,
-    repo,
-    state: 'open',
-    sort: 'created',
-    direction: 'desc',
-    per_page: 30,
-  });
-  return (
-    prs.find(
-      (p) => (p.head?.ref || '').startsWith(prefix) && isBotOpenedPr(p)
-    ) || null
-  );
-}
-
-async function kickReviewChainForIssue(github, owner, repo, issueNumber, core) {
-  const pr = await findOpenPrForIssue(github, owner, repo, issueNumber);
-  if (!pr) {
-    core?.info?.(`No open PR for issue #${issueNumber}, skipping review kick`);
-    return { kicked: false, reason: 'no_pr' };
-  }
-  const result = await kickReviewChain(github, owner, repo, pr.number, core);
-  return { ...result, prNumber: pr.number };
-}
-
-async function recoverStalledBotPrs(github, owner, repo, options, core) {
-  const { prNumber = null, minAgeMs = 10 * 60 * 1000, maxRecover = 10 } = options || {};
-  let prs;
-  if (prNumber) {
-    const { data } = await github.rest.pulls.get({ owner, repo, pull_number: prNumber });
-    prs = [data];
-  } else if (typeof github.paginate === 'function') {
-    prs = await github.paginate(github.rest.pulls.list, {
-      owner,
-      repo,
-      state: 'open',
-      per_page: 100,
-    });
-  } else {
-    const { data } = await github.rest.pulls.list({ owner, repo, state: 'open', per_page: 100 });
-    prs = data;
-  }
-
-  const cutoff = Date.now() - minAgeMs;
-  let recovered = 0;
-  for (const pr of prs) {
-    if (recovered >= maxRecover) break;
-    if (!isBotOpenedPr(pr)) continue;
-    if (!prNumber && new Date(pr.created_at).getTime() > cutoff) continue;
-    const comments = await listAllComments(github, owner, repo, pr.number);
-    if (chainKickPosted(comments)) continue;
-    await kickReviewChain(github, owner, repo, pr.number, core, comments);
-    recovered += 1;
-  }
-  core?.info?.(`Recovery complete (${recovered} PR(s) kicked)`);
-  return recovered;
-}
-
-module.exports = {
-  KICK_AUTHORS,
-  BOT_PR_AUTHORS,
-  listAllComments,
-  chainKickPosted,
-  coderabbitKickPosted,
-  qodoKickPosted,
-  isBotOpenedPr,
-  kickReviewChain,
-  findOpenPrForIssue,
-  kickReviewChainForIssue,
-  recoverStalledBotPrs,
-};

.github/scripts/ci-failure-claude-selftest.js

@@ -1,35 +0,0 @@
-#!/usr/bin/env node
-/**
- * Run: node .github/scripts/ci-failure-claude-selftest.js
- */
-const ciFix = require('./ci-failure-claude.js');
-const mergeGate = require('./merge-gate.js');
-const config = require('./gate-config.js');
-
-let failed = 0;
-function assert(name, cond) {
-  if (!cond) {
-    console.error('FAIL:', name);
-    failed += 1;
-  } else {
-    console.log('ok:', name);
-  }
-}
-
-const LOG = `
-python	UNKNOWN STEP	FAILED (0.0100s) tests/unit/test_example.py::test_foo - AssertionError: bar
-python	UNKNOWN STEP	##[error]Process completed with exit code 1.
-`;
-
-const parsed = ciFix.parsePytestFailures(LOG);
-assert('parses pytest failure', parsed.length === 1);
-assert('uses CI workflow list', config.ciFailureWorkflowRuns.includes('CI'));
-
-const comment = ciFix.buildCiFixComment({
-  headSha: 'abc1234567890abcdef1234567890abcdef12',
-  failedChecks: [{ name: 'python', workflow: 'CI', html_url: 'https://example.com/job/1' }],
-  failureSummaries: [{ jobName: 'python', failures: parsed }],
-});
-assert('comment mentions product guardrails', comment.includes('Product guardrails'));
-
-process.exit(failed ? 1 : 0);

.github/scripts/ci-failure-claude.js

@@ -1,294 +0,0 @@
-/**
- * Post @claude comments with CI failure details for internal PRs.
- * @see .github/workflows/ci-failure-claude.yml
- */
-
-const mergeGate = require('./merge-gate.js');
-const config = require('./gate-config.js');
-
-const CI_FIX_LABEL = 'claude-ci-fix-pending';
-const COOLDOWN_MS = 12 * 60 * 60 * 1000;
-const AUTO_ACTORS = mergeGate.AUTO_ACTORS;
-const MAX_FAILURES = 15;
-const MAX_JOBS_TO_FETCH = 5;
-
-function shortSha(headSha) {
-  return (headSha || '').slice(0, 8);
-}
-
-function ciFixShaMarker(headSha) {
-  return `ci failed on head \`${shortSha(headSha)}`.toLowerCase();
-}
-
-function isCiFixComment(comment) {
-  const body = (comment.body || '').toLowerCase();
-  return body.includes('@claude') && body.includes('ci failed on head');
-}
-
-function hasCiFixCommentForSha(comments, headSha) {
-  const marker = shortSha(headSha).toLowerCase();
-  return comments.some((c) => {
-    if (!AUTO_ACTORS.includes(c.user.login)) return false;
-    if (!isCiFixComment(c)) return false;
-    return (c.body || '').toLowerCase().includes(marker);
-  });
-}
-
-function hasRecentCiFixComment(comments, headSha) {
-  const cutoff = Date.now() - COOLDOWN_MS;
-  const marker = shortSha(headSha).toLowerCase();
-  return comments.some((c) => {
-    if (!AUTO_ACTORS.includes(c.user.login)) return false;
-    if (!isCiFixComment(c)) return false;
-    const body = (c.body || '').toLowerCase();
-    if (body.includes(marker)) return true;
-    return new Date(c.created_at).getTime() > cutoff;
-  });
-}
-
-function shouldSkipCiFix({ comments, headSha, labels, hasFinal, claudeInProgress, failedChecks }) {
-  if (!failedChecks.length) return { skip: true, reason: 'no failed checks' };
-  if (labels.includes(CI_FIX_LABEL)) return { skip: true, reason: 'ci fix pending' };
-  if (!hasFinal) return { skip: true, reason: 'awaiting final claude review trigger' };
-  if (claudeInProgress) return { skip: true, reason: 'claude in progress' };
-  if (hasCiFixCommentForSha(comments, headSha)) {
-    return { skip: true, reason: 'already commented for this sha' };
-  }
-  if (hasRecentCiFixComment(comments, headSha)) {
-    return { skip: true, reason: 'recent ci fix comment cooldown' };
-  }
-  return { skip: false };
-}
-
-function parsePytestFailures(logText) {
-  if (!logText) return [];
-  const lines = logText.split('\n');
-  const failures = [];
-  const seen = new Set();
-
-  for (let i = lines.length - 1; i >= 0 && failures.length < MAX_FAILURES; i -= 1) {
-    const line = lines[i].trim();
-    if (!line) continue;
-
-    const pytestMatch = line.match(
-      /FAILED\s*(?:\([^)]+\))?\s+(tests\/[^\s]+(?:::[^\s-]+)*)\s*-\s*(.+)/i
-    );
-    if (pytestMatch) {
-      const testId = pytestMatch[1];
-      if (!seen.has(testId)) {
-        seen.add(testId);
-        failures.push({ testId, error: pytestMatch[2].trim() });
-      }
-      continue;
-    }
-
-    const errorMatch = line.match(/##\[error\](.+)/);
-    if (errorMatch && /tests\//.test(errorMatch[1])) {
-      const msg = errorMatch[1].trim();
-      if (!seen.has(msg)) {
-        seen.add(msg);
-        failures.push({ testId: msg, error: msg });
-      }
-    }
-  }
-
-  return failures;
-}
-
-async function fetchJobFailureSummary(github, owner, repo, jobId, jobName) {
-  try {
-    const response = await github.rest.actions.downloadJobLogsForWorkflowRun({
-      owner,
-      repo,
-      job_id: jobId,
-    });
-    let logText = typeof response.data === 'string' ? response.data : String(response.data || '');
-    if (logText.length > 100000) {
-      logText = logText.slice(-100000);
-    }
-    return { jobName, jobId, failures: parsePytestFailures(logText) };
-  } catch (err) {
-    return { jobName, jobId, failures: [], error: err.message };
-  }
-}
-
-function mapFailedChecks(runs) {
-  return runs.map((run) => ({
-    name: run.name,
-    id: run.id,
-    html_url: run.html_url || run.details_url,
-    workflow: run.app?.slug || run.check_suite?.app?.slug || run.name,
-  }));
-}
-
-function buildCiFixComment({ headSha, failedChecks, failureSummaries }) {
-  const parts = [
-    `@claude CI failed on HEAD \`${shortSha(headSha)}\`. Please fix the failures below and push to this branch.`,
-    '',
-    '## Failed checks',
-  ];
-
-  for (const check of failedChecks) {
-    parts.push(`- **${check.workflow || check.name}** / \`${check.name}\` — ${check.html_url}`);
-  }
-
-  parts.push('', '## Failures (extracted)');
-  let idx = 0;
-  for (const summary of failureSummaries) {
-    for (const failure of summary.failures) {
-      idx += 1;
-      if (idx > MAX_FAILURES) break;
-      parts.push(`${idx}. \`${failure.testId}\` — \`${failure.error}\``);
-      parts.push(`   - Job: \`${summary.jobName}\``);
-    }
-    if (idx >= MAX_FAILURES) break;
-  }
-
-  if (idx === 0) {
-    parts.push('_(Could not extract pytest details — see job logs above.)_');
-  }
-
-  const exampleTest = failureSummaries.find((s) => s.failures.length)?.failures[0]?.testId;
-  parts.push(
-    '',
-    '## Critical review first',
-    'Before changing code or tests, decide **which side is wrong**:',
-    '1. **Legitimate feature change** — the PR intent is correct but implementation or tests need updating. Preserve product guarantees; update tests only when behaviour intentionally changed and document why.',
-    '2. **Regression / bug in this PR** — the failure exposes a real breakage introduced here. Fix the implementation; **do not weaken, skip, or delete tests** just to go green.',
-    '3. **Pre-existing flake or unrelated failure** — say so explicitly; prefer fixing the root cause over masking it.',
-    '',
-    '**Product guardrails (AGENTS.md):**',
-    `- ${config.finalClaudeScope}`,
-    '- Tests must continue to guard backward compatibility — passing CI by lowering test standards is not acceptable.',
-    '- If the feature does not genuinely add product value, recommend reverting or narrowing scope instead of patching around failures.',
-    '',
-    '## What to do',
-    '1. State your verdict: **legitimate fix**, **regression fix**, or **needs human review** — and why (1–3 sentences).',
-    '2. Fix root cause with **minimal changes**; never bloat the Agent class with extra params.',
-    exampleTest
-      ? `3. Run failing tests locally, e.g. \`pytest ${exampleTest} -q\`, plus any related tests touched by the PR.`
-      : '3. Run failing tests locally with targeted pytest, plus any related tests touched by the PR.',
-    '4. Push to this branch and comment: files changed, review verdict, and why tests still protect product behaviour.',
-  );
-
-  return parts.join('\n');
-}
-
-async function maybeClearCiFixLabel(github, owner, repo, prNumber, labels, headSha, core) {
-  if (!labels.includes(CI_FIX_LABEL)) return false;
-  const ciGreen = await mergeGate.allChecksGreenOnSha(github, owner, repo, headSha, core);
-  if (!ciGreen) return false;
-  try {
-    await github.rest.issues.removeLabel({
-      owner,
-      repo,
-      issue_number: prNumber,
-      name: CI_FIX_LABEL,
-    });
-    core?.info?.(`Removed ${CI_FIX_LABEL} from PR #${prNumber}`);
-  } catch (err) {
-    if (err.status !== 404) throw err;
-  }
-  return true;
-}
-
-async function maybeTriggerCiFixClaude(github, owner, repo, prNumber, core, opts = {}) {
-  const baseRepo = `${owner}/${repo}`;
-  const ctx = await mergeGate.loadPrContext(github, owner, repo, prNumber);
-  const pr = ctx.pr;
-
-  if (pr.state !== 'open') return { skipped: true, reason: 'not open' };
-  if (pr.draft) return { skipped: true, reason: 'draft' };
-
-  const headRepo = pr.head.repo?.full_name;
-  if (headRepo && headRepo !== baseRepo && !pr.maintainer_can_modify) {
-    return { skipped: true, reason: 'fork without maintainer edits' };
-  }
-
-  const headSha = pr.head.sha;
-  const labels = ctx.labels;
-
-  if (await maybeClearCiFixLabel(github, owner, repo, prNumber, labels, headSha, core)) {
-    return { skipped: true, reason: 'ci green, label cleared' };
-  }
-
-  const ciGreen = await mergeGate.allChecksGreenOnSha(github, owner, repo, headSha, core);
-  if (ciGreen) return { skipped: true, reason: 'ci green' };
-
-  const runs = await mergeGate.listChecksOnSha(github, owner, repo, headSha);
-  const failedRuns = mergeGate.listFailedChecksOnSha(runs);
-  if (failedRuns.length === 0) {
-    return { skipped: true, reason: 'no failed checks yet (may be pending)' };
-  }
-
-  const hasFinal = mergeGate.hasFinalClaudeReviewTrigger(ctx.comments);
-  const claudeInProgress = await mergeGate.hasInProgressClaudeAssistant(
-    github, owner, repo, prNumber
-  );
-  const skip = shouldSkipCiFix({
-    comments: ctx.comments,
-    headSha,
-    labels,
-    hasFinal,
-    claudeInProgress,
-    failedChecks: failedRuns,
-  });
-  if (skip.skip) return { skipped: true, reason: skip.reason };
-
-  const failedChecks = mapFailedChecks(failedRuns);
-  const failureSummaries = [];
-  for (const check of failedChecks.slice(0, MAX_JOBS_TO_FETCH)) {
-    failureSummaries.push(
-      await fetchJobFailureSummary(github, owner, repo, check.id, check.name)
-    );
-  }
-
-  const body = buildCiFixComment({ headSha, failedChecks, failureSummaries });
-
-  await github.rest.issues.addLabels({
-    owner,
-    repo,
-    issue_number: prNumber,
-    labels: [CI_FIX_LABEL],
-  });
-  await github.rest.issues.createComment({
-    owner,
-    repo,
-    issue_number: prNumber,
-    body,
-  });
-
-  core?.info?.(`Posted CI fix @claude on PR #${prNumber} (${shortSha(headSha)})`);
-  return { triggered: true, headSha: shortSha(headSha) };
-}
-
-async function processWorkflowRunFailure(github, owner, repo, workflowRun, core) {
-  if (workflowRun.conclusion !== 'failure') {
-    return { skipped: true, reason: `conclusion=${workflowRun.conclusion}` };
-  }
-  const allowed = config.ciFailureWorkflowRuns;
-  if (!allowed.includes(workflowRun.name)) {
-    return { skipped: true, reason: `workflow ${workflowRun.name}` };
-  }
-  const prNumber = await mergeGate.resolvePrNumberFromWorkflowRun(
-    github, owner, repo, workflowRun
-  );
-  if (!prNumber) return { skipped: true, reason: 'no linked PR' };
-  return maybeTriggerCiFixClaude(github, owner, repo, prNumber, core);
-}
-
-module.exports = {
-  CI_FIX_LABEL,
-  COOLDOWN_MS,
-  ciFixShaMarker,
-  isCiFixComment,
-  hasCiFixCommentForSha,
-  hasRecentCiFixComment,
-  shouldSkipCiFix,
-  parsePytestFailures,
-  fetchJobFailureSummary,
-  buildCiFixComment,
-  maybeClearCiFixLabel,
-  maybeTriggerCiFixClaude,
-  processWorkflowRunFailure,
-};

.github/scripts/gate-config.js

@@ -1,35 +0,0 @@
-/**
- * PraisonAI-Tools gate configuration.
- */
-
-module.exports = {
-  repoFullName: 'MervinPraison/PraisonAI-Tools',
-  triggerLogins: ['MervinPraison', 'github-actions[bot]'],
-  productPathPrefixes: ['praisonai_tools/', 'tests/'],
-  // Workflows stay gated; pyproject.toml is normal for optional tool deps.
-  sensitivePathPatterns: [
-    /^\.github\/workflows\//,
-  ],
-  // Tool + tests + docs routinely exceed the UI/SDK 800-line default.
-  maxAutoAdditions: 2000,
-  maxAutoFiles: 30,
-  requiredCheckPatterns: [/^ci$/i, /python/i, /test/i, /lint/i, /ruff/i],
-  ciWorkflowFile: 'ci.yml',
-  ciWorkflowName: 'CI',
-  mergeGateWorkflowRuns: ['CI', 'Claude Assistant'],
-  ciFailureWorkflowRuns: ['CI'],
-  pypiPackageName: 'praisonai-tools',
-  packagePaths: ['praisonai_tools', 'pyproject.toml'],
-  finalClaudeScope:
-    'SCOPE: Focus ONLY on PraisonAI-Tools (praisonai_tools/, tests). '
-    + 'Agent-callable integrations belong here, not praisonaiagents core or PraisonAI-Plugins.',
-  finalClaudeProductValue:
-    '4. Product value: tools must be agent-invokable via parameters; lazy imports; '
-    + 'no lifecycle hooks or sandbox backends in this repo.',
-  agentPyChecks: false,
-  reviewBotLogins: [
-    'coderabbitai[bot]',
-    'qodo-code-review[bot]',
-    'greptile-apps[bot]',
-  ],
-};

.github/scripts/load-gate-config.js

@@ -1,15 +0,0 @@
-/**
- * Load per-repo gate configuration (installed as .github/scripts/gate-config.js).
- */
-function loadGateConfig() {
-  try {
-    return require('./gate-config.js');
-  } catch (err) {
-    if (err.code !== 'MODULE_NOT_FOUND') throw err;
-    throw new Error(
-      'Missing .github/scripts/gate-config.js — run install.sh from github-automation-template'
-    );
-  }
-}
-
-module.exports = { loadGateConfig };

.github/scripts/merge-gate-selftest.js

@@ -1,300 +0,0 @@
-#!/usr/bin/env node
-/**
- * Local self-test for merge-gate.js heuristics (no GitHub API).
- * Run: node .github/scripts/merge-gate-selftest.js
- */
-const mg = require('./merge-gate.js');
-const config = require('./gate-config.js');
-const productSample = `${config.productPathPrefixes[0]}a/b.py`;
-const repoShort = config.repoFullName.split('/')[1];
-
-let failed = 0;
-function assert(name, cond) {
-  if (!cond) {
-    console.error('FAIL:', name);
-    failed++;
-  } else {
-    console.log('ok:', name);
-  }
-}
-
-// Stale FINAL: push after FINAL, no @claude since head
-const finals = [
-  { user: { login: 'github-actions[bot]' }, body: '@claude FINAL architecture reviewer', created_at: '2026-06-12T08:00:00Z' },
-];
-assert('stale when head after final', mg.isStaleFinalAfterPush(finals, '2026-06-12T09:00:00Z'));
-
-const withRecovery = [
-  ...finals,
-  { user: { login: 'github-actions[bot]' }, body: '@claude FINAL architecture reviewer', created_at: '2026-06-12T09:30:00Z' },
-];
-assert('not stale when @claude after head', !mg.isStaleFinalAfterPush(withRecovery, '2026-06-12T09:00:00Z'));
-
-const withClaudeReply = [
-  ...finals,
-  {
-    user: { login: 'praisonai-triage-agent[bot]' },
-    body: "**Claude finished @MervinPraison's task** —— [View job](https://github.com/)",
-    created_at: '2026-06-12T08:30:00Z',
-  },
-];
-assert('not stale when Claude replied after FINAL', !mg.isStaleFinalAfterPush(withClaudeReply, '2026-06-12T09:00:00Z'));
-assert('claude final reply detected', mg.isClaudeFinalReplyComment(withClaudeReply[1]));
-
-const mervinReply = {
-  user: { login: 'MervinPraison' },
-  body: "**Claude finished @MervinPraison's task** —— [View job](https://github.com/)",
-  created_at: '2026-06-12T08:30:00Z',
-};
-assert('mervin claude final reply detected', mg.isClaudeFinalReplyComment(mervinReply));
-assert(
-  'not stale when MervinPraison posted Claude finished',
-  !mg.isStaleFinalAfterPush([...finals, mervinReply], '2026-06-12T09:00:00Z')
-);
-
-const conflictRebaseComments = [
-  {
-    user: { login: 'MervinPraison' },
-    body: '@claude You are the FINAL architecture reviewer.',
-    created_at: '2026-06-12T08:00:00Z',
-  },
-  {
-    user: { login: 'MervinPraison' },
-    body: '@claude this PR has merge conflicts with `main`.',
-    created_at: '2026-06-12T08:10:00Z',
-  },
-  {
-    user: { login: 'MervinPraison' },
-    body: 'Rebase onto main complete. Force-pushed to branch.',
-    created_at: '2026-06-12T08:20:00Z',
-  },
-];
-assert(
-  'allow stale-FINAL recovery after conflict rebase push',
-  !mg.shouldSkipStaleFinalRecovery(conflictRebaseComments, '2026-06-12T08:25:00Z').skip
-);
-
-assert('cancelled detect-and-trigger does not block', mg.OPTIONAL_CANCELLED_CHECKS.has('detect-and-trigger'));
-
-// Stale-FINAL recovery guards (PR #2560 push loop)
-const nowMs = Date.now();
-const iso = (ms) => new Date(ms).toISOString();
-const firstFinal = {
-  user: { login: 'MervinPraison' },
-  body: '@claude You are the FINAL architecture reviewer.',
-  created_at: iso(nowMs - 20 * 60 * 1000),
-};
-const pushSoonAfter = mg.shouldSkipStaleFinalRecovery(
-  [firstFinal],
-  iso(nowMs - 16 * 60 * 1000)
-);
-assert('debounce push soon after FINAL', pushSoonAfter.skip && pushSoonAfter.reason.includes('soon after'));
-
-const secondFinal = {
-  user: { login: 'MervinPraison' },
-  body: '@claude You are the FINAL architecture reviewer.',
-  created_at: iso(nowMs - 20 * 60 * 1000),
-};
-const capped = mg.shouldSkipStaleFinalRecovery(
-  [firstFinal, secondFinal],
-  iso(nowMs - 2 * 60 * 1000)
-);
-assert('hourly cap blocks third FINAL in window', capped.skip && capped.reason.includes('capped'));
-
-const botPush = mg.shouldSkipStaleFinalRecovery(
-  [firstFinal],
-  iso(nowMs - 16 * 60 * 1000),
-  'praisonai-triage-agent[bot]'
-);
-assert('skip when automation pushed head', botPush.skip && botPush.reason.includes('automation'));
-
-assert('claude automation login', mg.isClaudeAutomationLogin('praisonai-triage-agent[bot]'));
-
-// Bot CHANGES_REQUESTED then APPROVE
-const reviews = [
-  { user: { login: 'coderabbit[bot]', type: 'Bot' }, state: 'CHANGES_REQUESTED', submitted_at: '2026-06-12T08:00:00Z' },
-  { user: { login: 'coderabbit[bot]', type: 'Bot' }, state: 'APPROVED', submitted_at: '2026-06-12T09:00:00Z' },
-];
-assert('bot approve clears CR', !mg.hasAnyChangesRequested(reviews));
-assert('human CR blocks', mg.hasAnyChangesRequested([
-  { user: { login: 'MervinPraison', type: 'User' }, state: 'CHANGES_REQUESTED', submitted_at: '2026-06-12T09:00:00Z' },
-]));
-
-// Verdict after HEAD
-const verdictComments = [
-  { body: 'MERGE_GATE_VERDICT: APPROVE', created_at: '2026-06-12T08:00:00Z' },
-];
-assert('verdict before head rejected', mg.findMergeGateVerdict(verdictComments, null, '2026-06-12T09:00:00Z') === null);
-assert('verdict after head accepted', mg.findMergeGateVerdict(
-  [{ body: 'MERGE_GATE_VERDICT: APPROVE', created_at: '2026-06-12T10:00:00Z' }],
-  null,
-  '2026-06-12T09:00:00Z'
-) === 'APPROVE');
-
-const fallbackApprove = [
-  {
-    body: 'MERGE_GATE_VERDICT: APPROVE\n\nAutomated fallback — Claude assess did not post a verdict comment.',
-    created_at: '2026-06-12T10:00:00Z',
-  },
-];
-assert(
-  'automated fallback APPROVE ignored when Opus required',
-  mg.findMergeGateVerdict(fallbackApprove, null, '2026-06-12T09:00:00Z', { excludeAutomatedFallback: true }) === null
-);
-assert(
-  'automated fallback APPROVE still visible without Opus-only filter',
-  mg.findMergeGateVerdict(fallbackApprove, null, '2026-06-12T09:00:00Z') === 'APPROVE'
-);
-
-const fallbackBlockNewFormat = [
-  {
-    body: 'MERGE_GATE_VERDICT: BLOCK\n\nAutomated fallback — Opus merge gate assessment did not complete.',
-    created_at: '2026-06-12T10:00:00Z',
-  },
-];
-assert(
-  'new-format fallback BLOCK ignored when Opus required',
-  mg.findMergeGateVerdict(fallbackBlockNewFormat, null, '2026-06-12T09:00:00Z', { excludeAutomatedFallback: true }) === null
-);
-assert(
-  'new-format fallback BLOCK detected by marker helper',
-  mg.isAutomatedFallbackVerdict(fallbackBlockNewFormat[0].body)
-);
-assert(
-  'findMergeGateVerdict null-safe with explicit null options',
-  mg.findMergeGateVerdict(fallbackApprove, null, '2026-06-12T09:00:00Z', null) === 'APPROVE'
-);
-
-const noise = [{ user: { login: 'MervinPraison' }, body: '**Merge gate scan** — wait for `@claude`', created_at: new Date().toISOString() }];
-assert('diagnostic comment not a trigger', !mg.hasRecentClaudeTrigger(noise, 35));
-
-// Cooldown skip requires an actual verdict on HEAD, not just a FINAL trigger comment
-const recentFinalOnHead = [
-  {
-    user: { login: 'MervinPraison' },
-    body: '@claude You are the FINAL architecture reviewer.',
-    created_at: '2026-06-27T10:00:00Z',
-  },
-];
-assert(
-  'final trigger alone does not count as verdict on head',
-  mg.findMergeGateVerdict(recentFinalOnHead, null, '2026-06-27T09:55:00Z') === null
-);
-const recentVerdictOnHead = [
-  ...recentFinalOnHead,
-  {
-    user: { login: 'github-actions[bot]' },
-    body: 'MERGE_GATE_VERDICT: APPROVE',
-    created_at: '2026-06-27T10:05:00Z',
-  },
-];
-assert(
-  'verdict on head skips cooldown gate',
-  mg.findMergeGateVerdict(recentVerdictOnHead, null, '2026-06-27T09:55:00Z') === 'APPROVE'
-);
-
-// Sensitive + secrets
-assert('workflow path sensitive', mg.sensitivePathReasons([{ filename: '.github/workflows/foo.yml' }]).length === 1);
-assert('ci-only label exempts workflows', mg.sensitivePathReasons(
-  [{ filename: '.github/workflows/foo.yml' }],
-  [mg.WORKFLOW_ONLY_LABEL]
-).length === 0);
-assert('ci-only label not exempt mixed changes', mg.sensitivePathReasons(
-  [{ filename: '.github/workflows/foo.yml' }, { filename: productSample }],
-  [mg.WORKFLOW_ONLY_LABEL]
-).length === 1);
-assert('secret in patch', mg.secretScanReasons([{ filename: 'x.py', patch: '+key = "ghp_abcdefghijklmnopqrstuvwxyz1234567890"' }]).length === 1);
-
-// Claude run scoping — other PR branches must not block
-assert('other branch claude does not block', !mg.hasBlockingClaudeRunForPr(
-  [{ event: 'issue_comment', head_branch: 'other-branch' }],
-  'my-branch'
-));
-assert('same branch claude blocks', mg.hasBlockingClaudeRunForPr(
-  [{ event: 'issue_comment', head_branch: 'my-branch' }],
-  'my-branch'
-));
-assert('issues event never blocks', !mg.hasBlockingClaudeRunForPr(
-  [{ event: 'issues', head_branch: 'my-branch' }],
-  'my-branch'
-));
-
-// Conflict rebase clears after bot completion + FINAL on HEAD (within 12h cooldown)
-const conflictNowMs = Date.now();
-const conflictIso = (offsetMs) => new Date(conflictNowMs + offsetMs).toISOString();
-const conflictTrigger = {
-  user: { login: 'MervinPraison' },
-  body: '@claude this PR has merge conflicts with main. Please rebase',
-  created_at: conflictIso(-30 * 60 * 1000),
-};
-const rebaseDone = {
-  user: { login: 'praisonai-triage-agent[bot]' },
-  body: 'Rebase complete — PR #2308 onto latest main',
-  created_at: conflictIso(-29 * 60 * 1000),
-};
-const finalAfterRebase = {
-  user: { login: 'MervinPraison' },
-  body: '@claude You are the FINAL architecture reviewer.',
-  created_at: conflictIso(-20 * 60 * 1000),
-};
-const headAfterRebase = conflictIso(-21 * 60 * 1000);
-const rebaseComments = [conflictTrigger, rebaseDone, finalAfterRebase];
-assert('conflict blocks before rebase done', mg.hasRecentConflictComment([conflictTrigger], headAfterRebase));
-assert('conflict clears after rebase + FINAL on HEAD', !mg.hasRecentConflictComment(rebaseComments, headAfterRebase));
-assert('conflict still blocks without FINAL on HEAD', mg.hasRecentConflictComment(
-  [conflictTrigger, rebaseDone],
-  headAfterRebase
-));
-assert(
-  'CLEAN + head after conflict clears without completion comment',
-  !mg.hasRecentConflictComment([conflictTrigger], headAfterRebase, 'CLEAN')
-);
-assert(
-  'UNSTABLE + head after conflict clears without completion comment',
-  !mg.hasRecentConflictComment([conflictTrigger], headAfterRebase, 'UNSTABLE')
-);
-assert(
-  'DIRTY still blocks even with head after conflict',
-  mg.hasRecentConflictComment([conflictTrigger], headAfterRebase, 'DIRTY')
-);
-assert(
-  'owner rebase-complete comment clears conflict',
-  !mg.hasRecentConflictComment(
-    [
-      conflictTrigger,
-      {
-        user: { login: 'MervinPraison' },
-        body: '**Rebase complete** — conflicts resolved.',
-        created_at: conflictIso(-29 * 60 * 1000),
-      },
-      finalAfterRebase,
-    ],
-    headAfterRebase
-  )
-);
-
-// Tests heuristic
-assert('product code without tests', mg.missingTestsReason([{ filename: productSample, additions: 3 }]) !== null);
-assert('product code with tests ok', mg.missingTestsReason([
-  { filename: productSample, additions: 3 },
-  { filename: 'tests/test_x.py', additions: 10 },
-]) === null);
-
-// PR size (threshold from gate-config; Tools allows larger tool+tests+docs PRs)
-const sizeLimit = config.maxAutoAdditions ?? 800;
-assert('under size limit ok', mg.prSizeReasons([{ additions: sizeLimit }]).length === 0);
-assert('large PR blocked', mg.prSizeReasons([{ additions: sizeLimit + 1 }]).length > 0);
-assert('typical tool PR under Tools limit', mg.prSizeReasons([{ additions: 1510 }]).length === 0);
-
-assert('internal PR link accepted', mg.isInternalPullRequestLink(
-  { base: { repo: { full_name: config.repoFullName } } },
-  'MervinPraison',
-  repoShort
-));
-assert('fork sync PR link rejected', !mg.isInternalPullRequestLink(
-  { number: 21, base: { repo: { full_name: `Milkmange/${repoShort}` } } },
-  'MervinPraison',
-  repoShort
-));
-
-process.exit(failed ? 1 : 0);

.github/scripts/merge-gate.js

@@ -1,957 +0,0 @@
-/**
- * Shared merge-gate helpers for claude-merge-gate.yml
- * @see .github/workflows/claude-merge-gate.yml
- */
-
-const config = require('./gate-config.js');
-
-const CLAUDE_TRIGGER_LOGINS = ['MervinPraison', 'github-actions[bot]'];
-const AUTO_ACTORS = CLAUDE_TRIGGER_LOGINS;
-const CONFLICT_COOLDOWN_MS = 12 * 60 * 60 * 1000;
-const CLAUDE_ACTIVE_MS = 35 * 60 * 1000;
-const STALE_FINAL_RECOVERY_WINDOW_MS = 60 * 60 * 1000;
-const STALE_FINAL_MAX_PER_WINDOW = 2;
-const PUSH_AFTER_FINAL_DEBOUNCE_MS = 15 * 60 * 1000;
-const ALLOWED_MERGE_STATES = new Set(['CLEAN', 'UNSTABLE']);
-const AGENT_PY_MAX_AUTO_LINES = 100;
-const PR_MAX_AUTO_ADDITIONS = config.maxAutoAdditions ?? 800;
-const PR_MAX_AUTO_FILES = config.maxAutoFiles ?? 30;
-const MANUAL_ONLY_LABELS = new Set(['security', 'breaking-change', 'needs-manual-review', 'release']);
-const WORKFLOW_ONLY_LABEL = 'merge-gate-ci-only';
-const CI_ONLY_PATH_PREFIXES = ['.github/workflows/', '.github/actions/', '.github/scripts/merge-gate'];
-const SDK_PATH_PREFIXES = config.productPathPrefixes;
-const SENSITIVE_PATH_PATTERNS = config.sensitivePathPatterns;
-const REQUIRED_SDK_CHECK_PATTERNS = config.requiredCheckPatterns;
-const SECRET_PATTERNS = [
-  /sk-[a-zA-Z0-9]{20,}/,
-  /AKIA[0-9A-Z]{16}/,
-  /ghp_[a-zA-Z0-9]{36,}/,
-  /github_pat_/,
-  /Bearer eyJ[a-zA-Z0-9_-]+\.[a-zA-Z0-9_-]+/,
-  /-----BEGIN (RSA |EC |OPENSSH )?PRIVATE KEY-----/,
-];
-const BLOCK_LABELS = new Set([
-  'claude-conflict-pending',
-  'claude-merge-gate-active',
-  'no-auto-merge',
-  'auto-merged-by-gate',
-]);
-const MERGE_READY_LABEL = 'pipeline/merge-ready';
-/** Superseded concurrency runs; must not block merge when real tests passed. */
-const OPTIONAL_CANCELLED_CHECKS = new Set(['detect-and-trigger']);
-const BOT_REVIEWER_PATTERNS = [
-  'coderabbit',
-  'qodo',
-  'gemini',
-  'copilot',
-  'greptile',
-];
-
-const sleep = (ms) => new Promise((r) => setTimeout(r, ms));
-
-function isFinalClaudeTriggerComment(c) {
-  const body = (c.body || '').toLowerCase();
-  if (!AUTO_ACTORS.includes(c.user.login)) return false;
-  if (!body.includes('@claude')) return false;
-  if (body.includes('merge conflict')) return false;
-  return body.includes('final architecture reviewer') || body.includes('lead engineer');
-}
-
-function isClaudeTriggerNoise(c) {
-  const body = c.body || '';
-  if (body.includes('Merge gate scan')) return true;
-  if (body.includes('MERGE_GATE_VERDICT')) return true;
-  if (body.includes('Merged by **Claude PR merge gate**')) return true;
-  return false;
-}
-
-function isClaudeFinalReplyComment(c) {
-  const login = (c.user?.login || '').toLowerCase();
-  const fromAutomation =
-    login.includes('praisonai-triage') ||
-    login.includes('github-actions') ||
-    AUTO_ACTORS.some((a) => a.toLowerCase() === login);
-  if (!fromAutomation) return false;
-  return (c.body || '').includes('Claude finished');
-}
-
-function hasRecentClaudeTrigger(comments, minutes = 35) {
-  const cutoff = Date.now() - minutes * 60 * 1000;
-  return comments.some((c) => {
-    if (!CLAUDE_TRIGGER_LOGINS.includes(c.user.login)) return false;
-    if (isClaudeTriggerNoise(c)) return false;
-    if (!(c.body || '').includes('@claude')) return false;
-    return new Date(c.created_at).getTime() > cutoff;
-  });
-}
-
-function isConflictRebaseTriggerComment(c) {
-  if (!AUTO_ACTORS.includes(c.user.login)) return false;
-  const body = (c.body || '').toLowerCase();
-  return body.includes('@claude') && body.includes('merge conflict');
-}
-
-function isConflictRebaseCompletionComment(c) {
-  const login = (c.user?.login || '').toLowerCase();
-  const allowed =
-    login.includes('praisonai-triage') ||
-    login.includes('github-actions') ||
-    AUTO_ACTORS.some((a) => a.toLowerCase() === login);
-  if (!allowed) return false;
-  const body = (c.body || '').toLowerCase();
-  return (
-    body.includes('rebase complete') ||
-    body.includes('rebase onto') ||
-    body.includes('force-pushed') ||
-    (body.includes('conflict') && body.includes('resolved'))
-  );
-}
-
-/** HEAD landed after conflict rebase but no FINAL @claude since that push. */
-function needsFinalReReviewAfterConflictRebase(comments, headPushedAt) {
-  if (!headPushedAt) return false;
-  const headTime = new Date(headPushedAt).getTime();
-  const conflictTriggers = comments.filter(isConflictRebaseTriggerComment);
-  if (conflictTriggers.length === 0) return false;
-
-  const latestConflict = conflictTriggers.reduce((a, b) =>
-    new Date(a.created_at) > new Date(b.created_at) ? a : b
-  );
-  const conflictTime = new Date(latestConflict.created_at).getTime();
-  if (headTime <= conflictTime) return false;
-
-  const rebaseDone = comments.some(
-    (c) =>
-      new Date(c.created_at).getTime() > conflictTime && isConflictRebaseCompletionComment(c)
-  );
-  if (!rebaseDone) return false;
-
-  const finalsAfterHead = comments.filter(
-    (c) =>
-      isFinalClaudeTriggerComment(c) &&
-      new Date(c.created_at).getTime() >= headTime - 60000
-  );
-  return finalsAfterHead.length === 0;
-}
-
-function conflictRebaseQuiescent(comments, headPushedAt) {
-  const conflictTriggers = comments.filter(isConflictRebaseTriggerComment);
-  if (conflictTriggers.length === 0) return true;
-
-  const latestConflict = conflictTriggers.reduce((a, b) =>
-    new Date(a.created_at) > new Date(b.created_at) ? a : b
-  );
-  const conflictTime = new Date(latestConflict.created_at).getTime();
-
-  const rebaseDone = comments.some(
-    (c) =>
-      new Date(c.created_at).getTime() > conflictTime && isConflictRebaseCompletionComment(c)
-  );
-  if (!rebaseDone) return false;
-
-  return finalClaudeCompletedOnSha(comments, headPushedAt);
-}
-
-function hasRecentConflictComment(comments, headPushedAt = null, mergeStateStatus = null) {
-  const cutoff = Date.now() - CONFLICT_COOLDOWN_MS;
-  const hasRecentTrigger = comments.some((c) => {
-    if (!isConflictRebaseTriggerComment(c)) return false;
-    return new Date(c.created_at).getTime() > cutoff;
-  });
-  if (!hasRecentTrigger) return false;
-
-  if (headPushedAt && conflictRebaseQuiescent(comments, headPushedAt)) {
-    return false;
-  }
-
-  // GitHub mergeable (CLEAN/UNSTABLE) and HEAD pushed after the conflict trigger → resolved
-  const status = String(mergeStateStatus || '').toUpperCase();
-  if (headPushedAt && ALLOWED_MERGE_STATES.has(status)) {
-    // conflictTriggers is guaranteed non-empty here (hasRecentTrigger is true above);
-    // the null seed keeps reduce() safe if that invariant ever changes.
-    const conflictTriggers = comments.filter(isConflictRebaseTriggerComment);
-    const latestConflict = conflictTriggers.reduce(
-      (a, b) => (a && new Date(a.created_at) > new Date(b.created_at) ? a : b),
-      null
-    );
-    if (
-      latestConflict &&
-      new Date(headPushedAt).getTime() > new Date(latestConflict.created_at).getTime()
-    ) {
-      return false;
-    }
-  }
-  return true;
-}
-
-function isBotReviewer(login, userType) {
-  const lower = (login || '').toLowerCase();
-  if (userType === 'Bot') return true;
-  if (lower.endsWith('[bot]')) return true;
-  return ['coderabbit', 'qodo', 'gemini', 'copilot', 'greptile'].some((p) => lower.includes(p));
-}
-
-function latestReviewsByUser(reviews) {
-  const latestByUser = new Map();
-  for (const r of reviews) {
-    const login = r.user?.login;
-    if (!login) continue;
-    const prev = latestByUser.get(login);
-    if (!prev || new Date(r.submitted_at) > new Date(prev.submitted_at)) {
-      latestByUser.set(login, r);
-    }
-  }
-  return latestByUser;
-}
-
-function hasHumanChangesRequested(reviews) {
-  for (const [login, review] of latestReviewsByUser(reviews)) {
-    if (review.state !== 'CHANGES_REQUESTED') continue;
-    if (!isBotReviewer(login, review.user?.type)) return true;
-  }
-  return false;
-}
-
-function hasAnyChangesRequested(reviews) {
-  for (const [, review] of latestReviewsByUser(reviews)) {
-    if (review.state === 'CHANGES_REQUESTED') return true;
-  }
-  return false;
-}
-
-function hasFinalClaudeReviewTrigger(comments) {
-  return comments.some(isFinalClaudeTriggerComment);
-}
-
-function isStaleFinalAfterPush(comments, headPushedAt) {
-  if (!headPushedAt) return false;
-  const headTime = new Date(headPushedAt).getTime();
-  const finals = comments.filter(isFinalClaudeTriggerComment);
-  if (finals.length === 0) return true;
-  const latestFinal = finals.reduce((a, b) =>
-    new Date(a.created_at) > new Date(b.created_at) ? a : b
-  );
-  const finalTime = new Date(latestFinal.created_at).getTime();
-  if (headTime <= finalTime + 60000) return false;
-  const claudeRepliedAfterFinal = comments.some((c) => {
-    if (!isClaudeFinalReplyComment(c)) return false;
-    return new Date(c.created_at).getTime() >= finalTime - 60000;
-  });
-  if (claudeRepliedAfterFinal) return false;
-  const claudeSinceHead = comments.some((c) => {
-    if (!CLAUDE_TRIGGER_LOGINS.includes(c.user.login)) return false;
-    if (isClaudeTriggerNoise(c)) return false;
-    if (!(c.body || '').includes('@claude')) return false;
-    return new Date(c.created_at).getTime() >= headTime - 60000;
-  });
-  return !claudeSinceHead;
-}
-
-function needsStaleFinalRecovery(comments, headPushedAt) {
-  return (
-    hasFinalClaudeReviewTrigger(comments) &&
-    isStaleFinalAfterPush(comments, headPushedAt)
-  );
-}
-
-function shouldSkipFinalRecovery(comments, headPushedAt) {
-  const isStale = isStaleFinalAfterPush(comments, headPushedAt);
-  if (isStale) return false;
-  return hasRecentClaudeTrigger(comments, 35);
-}
-
-function countFinalTriggersSince(comments, sinceMs) {
-  return comments.filter(
-    (c) => isFinalClaudeTriggerComment(c) && new Date(c.created_at).getTime() > sinceMs
-  ).length;
-}
-
-function isClaudeAutomationLogin(login) {
-  const lower = (login || '').toLowerCase();
-  return lower.includes('praisonai-triage') || lower === 'github-actions[bot]';
-}
-
-function isPushSoonAfterLatestFinal(comments, headPushedAt) {
-  if (!headPushedAt) return false;
-  const finals = comments.filter(isFinalClaudeTriggerComment);
-  if (finals.length === 0) return false;
-  const latestFinal = finals.reduce((a, b) =>
-    new Date(a.created_at) > new Date(b.created_at) ? a : b
-  );
-  const finalTime = new Date(latestFinal.created_at).getTime();
-  const headTime = new Date(headPushedAt).getTime();
-  if (headTime <= finalTime) return false;
-  return headTime - finalTime < PUSH_AFTER_FINAL_DEBOUNCE_MS;
-}
-
-/** Returns { skip: true, reason } when stale-FINAL recovery should not post. */
-function shouldSkipStaleFinalRecovery(comments, headPushedAt, headPusherLogin = null) {
-  if (!isStaleFinalAfterPush(comments, headPushedAt)) {
-    return { skip: true, reason: 'not stale' };
-  }
-  if (headPusherLogin && isClaudeAutomationLogin(headPusherLogin)) {
-    return { skip: true, reason: 'head pushed by Claude automation' };
-  }
-  if (needsFinalReReviewAfterConflictRebase(comments, headPushedAt)) {
-    return { skip: false, reason: '' };
-  }
-  if (isPushSoonAfterLatestFinal(comments, headPushedAt)) {
-    return {
-      skip: true,
-      reason: 'head pushed soon after FINAL (wait for CI / batched fixes)',
-    };
-  }
-  const windowStart = Date.now() - STALE_FINAL_RECOVERY_WINDOW_MS;
-  const finalsInWindow = countFinalTriggersSince(comments, windowStart);
-  if (finalsInWindow >= STALE_FINAL_MAX_PER_WINDOW) {
-    return {
-      skip: true,
-      reason: `stale-FINAL capped (${finalsInWindow} FINAL triggers in last hour)`,
-    };
-  }
-  return { skip: false, reason: '' };
-}
-
-function finalClaudeCompletedOnSha(comments, headPushedAt) {
-  if (!hasFinalClaudeReviewTrigger(comments)) return false;
-  if (isStaleFinalAfterPush(comments, headPushedAt)) return false;
-  return true;
-}
-
-const FINAL_CLAUDE_REVIEW_BODY =
-  `@claude You are the FINAL architecture reviewer. If the branch is under ${config.repoFullName} (not a fork), you are able to make modifications to this branch and push directly. ${config.finalClaudeScope} Read ALL comments above from Gemini, Qodo, CodeRabbit, and Copilot carefully before responding.\n\n**Phase 1: Review per AGENTS.md**\n1. Protocol-driven: check heavy implementations vs core SDK\n2. Backward compatible: ensure zero feature regressions\n3. Performance: no hot-path regressions (lazy imports, import-time <200ms)\n${config.finalClaudeProductValue}\n\n**Phase 2: FIX Valid Issues**\n5. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n6. Push all code fixes directly to THIS branch (do NOT create a new PR)\n7. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n8. If all issues are resolved, approve the PR / close the Issue\n9. If blocking issues remain, request changes / leave clear action items`;
-
-async function getMergeState(github, owner, repo, prNumber) {
-  const query = `
-    query($owner: String!, $repo: String!, $number: Int!) {
-      repository(owner: $owner, name: $repo) {
-        pullRequest(number: $number) {
-          mergeStateStatus
-          maintainerCanModify
-          isDraft
-          headRefOid
-          headRef { repository { nameWithOwner } }
-        }
-      }
-    }
-  `;
-  for (let attempt = 0; attempt < 3; attempt++) {
-    const result = await github.graphql(query, { owner, repo, number: prNumber });
-    const prGql = result.repository.pullRequest;
-    const status = (prGql?.mergeStateStatus || '').toUpperCase();
-    if (status && status !== 'UNKNOWN') {
-      return {
-        status,
-        isDraft: prGql.isDraft,
-        headRepo: prGql.headRef?.repository?.nameWithOwner,
-        headSha: prGql.headRefOid,
-        maintainerCanModify: prGql.maintainerCanModify === true,
-      };
-    }
-    if (attempt < 2) await sleep(10000);
-  }
-  const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: prNumber });
-  return {
-    status: (pr.mergeable_state || '').toUpperCase(),
-    isDraft: pr.draft,
-    headRepo: pr.head.repo?.full_name,
-    headSha: pr.head.sha,
-    maintainerCanModify: pr.maintainer_can_modify === true,
-  };
-}
-
-async function listChecksOnSha(github, owner, repo, sha) {
-  const { data } = await github.rest.checks.listForRef({
-    owner,
-    repo,
-    ref: sha,
-    per_page: 100,
-  });
-  return (data.check_runs || []).filter((r) => r.head_sha === sha);
-}
-
-function listFailedChecksOnSha(runs) {
-  return (runs || []).filter((run) => {
-    if (run.status !== 'completed') return false;
-    return run.conclusion === 'failure';
-  });
-}
-
-async function allChecksGreenOnSha(github, owner, repo, sha, core) {
-  const runs = await listChecksOnSha(github, owner, repo, sha);
-  if (runs.length === 0) {
-    core?.info?.(`No check runs on ${sha.slice(0, 7)} — allowing (e.g. docs-only PR)`);
-    return true;
-  }
-  for (const run of runs) {
-    if (run.status !== 'completed') {
-      core?.info?.(`Check pending: ${run.name} (${run.status})`);
-      return false;
-    }
-    const ok =
-      ['success', 'neutral', 'skipped'].includes(run.conclusion) ||
-      (run.conclusion === 'cancelled' && OPTIONAL_CANCELLED_CHECKS.has(run.name));
-    if (!ok) {
-      core?.info?.(`Check failed: ${run.name} (${run.conclusion})`);
-      return false;
-    }
-  }
-  return true;
-}
-
-function claudeRunBlocksPr(run, headRef) {
-  if (!run || run.event === 'issues') return false;
-  if (!headRef) return true;
-  return run.head_branch === headRef;
-}
-
-function hasBlockingClaudeRunForPr(runs, headRef) {
-  return (runs || []).some((r) => claudeRunBlocksPr(r, headRef));
-}
-
-async function hasInProgressClaudeAssistant(github, owner, repo, prNumber = null) {
-  try {
-    const { data } = await github.rest.actions.listWorkflowRuns({
-      owner,
-      repo,
-      workflow_id: 'claude.yml',
-      status: 'in_progress',
-      per_page: 20,
-    });
-    const runs = (data.workflow_runs || []).filter((r) => r.event !== 'issues');
-    if (runs.length === 0) return false;
-    if (prNumber == null) return runs.length > 0;
-
-    const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: prNumber });
-    const headRef = pr.head.ref;
-    return hasBlockingClaudeRunForPr(runs, headRef);
-  } catch {
-    return false;
-  }
-}
-
-function isCiOnlyChange(files) {
-  if (!files.length) return false;
-  return files.every((f) => CI_ONLY_PATH_PREFIXES.some((p) => f.filename.startsWith(p)));
-}
-
-function isInternalPullRequestLink(link, owner, repo) {
-  if (link?.base?.repo?.full_name === `${owner}/${repo}`) return true;
-  const baseUrl = link?.base?.repo?.url || '';
-  return baseUrl.endsWith(`/repos/${owner}/${repo}`);
-}
-
-async function resolvePrNumberFromLinkedPullRequests(github, owner, repo, linked) {
-  const repoFull = `${owner}/${repo}`;
-  const internal = (linked || []).filter((l) => isInternalPullRequestLink(l, owner, repo));
-  for (const link of internal) {
-    if (!link.number) continue;
-    try {
-      const { data } = await github.rest.pulls.get({
-        owner,
-        repo,
-        pull_number: link.number,
-      });
-      if (data.state === 'open' && data.base?.repo?.full_name === repoFull) {
-        return data.number;
-      }
-    } catch (err) {
-      if (err.status !== 404) throw err;
-    }
-  }
-  return null;
-}
-
-async function resolvePrNumberFromHeadBranch(github, owner, repo, branch) {
-  if (!branch || branch === 'main' || branch === 'master') return null;
-  const { data } = await github.rest.pulls.list({
-    owner,
-    repo,
-    state: 'open',
-    head: `${owner}:${branch}`,
-    per_page: 1,
-  });
-  return data[0]?.number || null;
-}
-
-async function resolvePrNumberFromWorkflowRun(github, owner, repo, workflowRun) {
-  const fromLinked = await resolvePrNumberFromLinkedPullRequests(
-    github, owner, repo, workflowRun.pull_requests
-  );
-  if (fromLinked) return fromLinked;
-
-  const fromBranch = await resolvePrNumberFromHeadBranch(
-    github, owner, repo, workflowRun.head_branch
-  );
-  if (fromBranch) return fromBranch;
-
-  return null;
-}
-
-async function listPullFiles(github, owner, repo, prNumber) {
-  if (typeof github.paginate === 'function') {
-    return github.paginate(github.rest.pulls.listFiles, {
-      owner,
-      repo,
-      pull_number: prNumber,
-      per_page: 100,
-    });
-  }
-  const { data } = await github.rest.pulls.listFiles({
-    owner,
-    repo,
-    pull_number: prNumber,
-    per_page: 100,
-  });
-  return data;
-}
-
-function countNewAgentParams(patch) {
-  if (!patch) return 0;
-  return patch
-    .split('\n')
-    .filter((l) => l.startsWith('+') && !l.startsWith('+++'))
-    .filter((l) => {
-      const t = l.slice(1).trim();
-      if (!t || t.startsWith('#') || t.startsWith('"""') || t.startsWith("'''")) return false;
-      if (/^(def|class|@|return\b|if\b|elif\b|else\b|for\b|while\b)/.test(t)) return false;
-      if (/^\w+\s*[:=]/.test(t) && !t.startsWith('self.')) return true;
-      return false;
-    }).length;
-}
-
-function getAgentPyChangeFromFiles(files) {
-  const agentFile = files.find((f) => f.filename.endsWith('praisonaiagents/agent/agent.py'));
-  if (!agentFile) {
-    return { touched: false, additions: 0, newParams: 0 };
-  }
-  return {
-    touched: true,
-    additions: agentFile.additions || 0,
-    newParams: countNewAgentParams(agentFile.patch || ''),
-  };
-}
-
-async function getAgentPyChange(github, owner, repo, prNumber) {
-  const files = await listPullFiles(github, owner, repo, prNumber);
-  return getAgentPyChangeFromFiles(files);
-}
-
-function touchesSdk(files) {
-  return files.some((f) => SDK_PATH_PREFIXES.some((p) => f.filename.startsWith(p)));
-}
-
-function hasManualOnlyLabel(labels) {
-  return labels.some((l) => MANUAL_ONLY_LABELS.has(l));
-}
-
-function sensitivePathReasons(files, labels = []) {
-  if (labels.includes(WORKFLOW_ONLY_LABEL) && isCiOnlyChange(files)) {
-    return [];
-  }
-  const reasons = [];
-  for (const f of files) {
-    if (SENSITIVE_PATH_PATTERNS.some((p) => p.test(f.filename))) {
-      reasons.push(`sensitive path: ${f.filename}`);
-      break;
-    }
-  }
-  return reasons;
-}
-
-function prSizeReasons(files) {
-  const reasons = [];
-  const totalAdditions = files.reduce((sum, f) => sum + (f.additions || 0), 0);
-  if (totalAdditions > PR_MAX_AUTO_ADDITIONS) {
-    reasons.push(`PR +${totalAdditions} lines (>${PR_MAX_AUTO_ADDITIONS}) requires manual review`);
-  }
-  if (files.length > PR_MAX_AUTO_FILES) {
-    reasons.push(`${files.length} files changed (>${PR_MAX_AUTO_FILES}) requires manual review`);
-  }
-  return reasons;
-}
-
-function missingTestsReason(files) {
-  const productAdds = files.filter(
-    (f) =>
-      SDK_PATH_PREFIXES.some((p) => f.filename.startsWith(p)) &&
-      f.filename.endsWith('.py') &&
-      !f.filename.endsWith('__init__.py') &&
-      (f.additions || 0) > 0
-  );
-  if (productAdds.length === 0) return null;
-  const hasTestChange = files.some(
-    (f) => /\/tests?\//.test(f.filename) || /test_.*\.py$/.test(f.filename) || /_test\.py$/.test(f.filename)
-  );
-  if (!hasTestChange) return 'Product code added without test file changes — requires manual review';
-  return null;
-}
-
-function secretScanReasons(files) {
-  for (const f of files) {
-    if (/\/tests?\//.test(f.filename) || /test_.*\.py$/.test(f.filename)) continue;
-    const patch = f.patch || '';
-    if (!patch) continue;
-    for (const pattern of SECRET_PATTERNS) {
-      if (pattern.test(patch)) {
-        return [`possible secret in diff (${f.filename}) — requires manual review`];
-      }
-    }
-  }
-  return [];
-}
-
-async function sdkTestChecksReason(github, owner, repo, sha, files, core) {
-  if (!touchesSdk(files)) return null;
-  const { data } = await github.rest.checks.listForRef({
-    owner,
-    repo,
-    ref: sha,
-    per_page: 100,
-  });
-  const runs = (data.check_runs || []).filter((r) => r.head_sha === sha);
-  if (runs.length === 0) {
-    return 'SDK code changed but no CI checks on HEAD';
-  }
-  const testRuns = runs.filter((r) => REQUIRED_SDK_CHECK_PATTERNS.some((p) => p.test(r.name || '')));
-  if (testRuns.length === 0) {
-    return 'SDK code changed but no test check runs on HEAD';
-  }
-  core?.info?.(`SDK test checks on HEAD: ${testRuns.map((r) => r.name).join(', ')}`);
-  return null;
-}
-
-function manualReviewReasonForAgentPy(agentChange) {
-  if (!agentChange.touched) return null;
-  if (agentChange.additions > AGENT_PY_MAX_AUTO_LINES) {
-    return `agent.py +${agentChange.additions} lines (>${AGENT_PY_MAX_AUTO_LINES}) requires manual review`;
-  }
-  if (agentChange.newParams > 0) {
-    return `agent.py adds ${agentChange.newParams} new Agent param(s) — requires manual review`;
-  }
-  return null;
-}
-
-async function listAllComments(github, owner, repo, issueNumber) {
-  if (typeof github.paginate === 'function') {
-    return github.paginate(github.rest.issues.listComments, {
-      owner,
-      repo,
-      issue_number: issueNumber,
-      per_page: 100,
-    });
-  }
-  const { data } = await github.rest.issues.listComments({
-    owner,
-    repo,
-    issue_number: issueNumber,
-    per_page: 100,
-  });
-  return data;
-}
-
-async function getHeadCommitDate(github, owner, repo, prNumber) {
-  try {
-    let commits;
-    if (typeof github.paginate === 'function') {
-      commits = await github.paginate(github.rest.pulls.listCommits, {
-        owner,
-        repo,
-        pull_number: prNumber,
-        per_page: 100,
-      });
-    } else {
-      const { data } = await github.rest.pulls.listCommits({
-        owner,
-        repo,
-        pull_number: prNumber,
-        per_page: 100,
-      });
-      commits = data;
-    }
-    const last = commits[commits.length - 1];
-    return last?.commit?.committer?.date || last?.commit?.author?.date || null;
-  } catch {
-    return null;
-  }
-}
-
-async function loadPrContext(github, owner, repo, prNumber) {
-  const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: prNumber });
-  const { data: issue } = await github.rest.issues.get({ owner, repo, issue_number: prNumber });
-  const comments = await listAllComments(github, owner, repo, prNumber);
-  const { data: reviews } = await github.rest.pulls.listReviews({
-    owner,
-    repo,
-    pull_number: prNumber,
-    per_page: 100,
-  });
-  const mergeState = await getMergeState(github, owner, repo, prNumber);
-  const headSha = mergeState.headSha || pr.head.sha;
-  const headCommitDate = await getHeadCommitDate(github, owner, repo, prNumber);
-  const headPushedAt = headCommitDate || pr.updated_at;
-
-  return {
-    pr,
-    issue,
-    comments,
-    reviews,
-    mergeState,
-    headSha,
-    headPushedAt,
-    labels: issue.labels.map((l) => l.name),
-    baseRepo: `${owner}/${repo}`,
-  };
-}
-
-async function evaluatePipelineQuiescent(github, owner, repo, prNumber, core, options = {}) {
-  const {
-    forMergeStep = false,
-    skipGlobalClaudeRunCheck = false,
-    skipRecentClaudeCooldown = false,
-  } = options;
-  const ctx = await loadPrContext(github, owner, repo, prNumber);
-  const reasons = [];
-
-  if (ctx.pr.draft) reasons.push('draft');
-  if (ctx.pr.state !== 'open') reasons.push('not open');
-  if (ctx.labels.includes('auto-merged-by-gate')) reasons.push('already merged by gate');
-  if (ctx.labels.includes('no-auto-merge')) reasons.push('no-auto-merge label');
-  if (ctx.labels.includes('claude-conflict-pending')) reasons.push('claude-conflict-pending');
-  if (!forMergeStep && ctx.labels.includes('claude-merge-gate-active')) {
-    reasons.push('claude-merge-gate-active');
-  }
-
-  const { status, headRepo, maintainerCanModify } = ctx.mergeState;
-  if (!ALLOWED_MERGE_STATES.has(status)) reasons.push(`mergeState=${status}`);
-
-  if (headRepo && headRepo !== ctx.baseRepo) {
-    reasons.push('fork PR');
-  }
-
-  if (hasRecentConflictComment(ctx.comments, ctx.headPushedAt, status)) {
-    reasons.push('recent merge-conflict @claude');
-  }
-  if (!skipRecentClaudeCooldown && hasRecentClaudeTrigger(ctx.comments, 35)) {
-    const verdictOnHead = findMergeGateVerdict(ctx.comments, null, ctx.headPushedAt) !== null;
-    if (!verdictOnHead) reasons.push('recent @claude within 35min');
-  }
-
-  if (!skipGlobalClaudeRunCheck && (await hasInProgressClaudeAssistant(github, owner, repo, prNumber))) {
-    reasons.push('claude.yml in progress');
-  }
-
-  const checksOk = await allChecksGreenOnSha(github, owner, repo, ctx.headSha, core);
-  if (!checksOk) reasons.push('CI not green on HEAD');
-
-  if (!finalClaudeCompletedOnSha(ctx.comments, ctx.headPushedAt)) {
-    if (!hasFinalClaudeReviewTrigger(ctx.comments)) {
-      reasons.push('no FINAL Claude review trigger');
-    } else if (isStaleFinalAfterPush(ctx.comments, ctx.headPushedAt)) {
-      reasons.push('stale FINAL after new commits (needs @claude re-review)');
-    } else {
-      reasons.push('FINAL Claude not complete on HEAD');
-    }
-  }
-
-  if (hasAnyChangesRequested(ctx.reviews)) reasons.push('CHANGES_REQUESTED review');
-
-  if (hasManualOnlyLabel(ctx.labels)) {
-    const manualLabel = ctx.labels.find((l) => MANUAL_ONLY_LABELS.has(l));
-    reasons.push(`manual-only label: ${manualLabel}`);
-  }
-
-  const pullFiles = await listPullFiles(github, owner, repo, prNumber);
-
-  if (config.agentPyChecks) {
-    const agentChange = getAgentPyChangeFromFiles(pullFiles);
-    const agentManual = manualReviewReasonForAgentPy(agentChange);
-    if (agentManual) reasons.push(agentManual);
-  }
-
-  reasons.push(...sensitivePathReasons(pullFiles, ctx.labels));
-  reasons.push(...prSizeReasons(pullFiles));
-  reasons.push(...secretScanReasons(pullFiles));
-
-  const testsReason = missingTestsReason(pullFiles);
-  if (testsReason) reasons.push(testsReason);
-
-  const sdkCheckReason = await sdkTestChecksReason(github, owner, repo, ctx.headSha, pullFiles, core);
-  if (sdkCheckReason) reasons.push(sdkCheckReason);
-
-  if (ctx.pr.mergeable === false) reasons.push('not mergeable');
-
-  const ready = reasons.length === 0;
-  return {
-    ready,
-    reasons,
-    headSha: ctx.headSha,
-    prNumber,
-  };
-}
-
-async function listPrNumbersForMergeGateScan(github, owner, repo, core) {
-  let mergeReadyIssues;
-  if (typeof github.paginate === 'function') {
-    mergeReadyIssues = await github.paginate(github.rest.issues.listForRepo, {
-      owner,
-      repo,
-      state: 'open',
-      labels: MERGE_READY_LABEL,
-      per_page: 100,
-    });
-  } else {
-    ({ data: mergeReadyIssues } = await github.rest.issues.listForRepo({
-      owner,
-      repo,
-      state: 'open',
-      labels: MERGE_READY_LABEL,
-      per_page: 100,
-    }));
-  }
-  const mergeReady = mergeReadyIssues
-    .filter((issue) => issue.pull_request)
-    .map((issue) => issue.number)
-    .sort((a, b) => a - b);
-
-  if (mergeReady.length > 0) {
-    core?.info?.(
-      `Merge gate scan: ${mergeReady.length} ${MERGE_READY_LABEL} PR(s) (skipping unlabelled open PRs)`
-    );
-    return mergeReady;
-  }
-
-  core?.info?.(`No ${MERGE_READY_LABEL} PRs — scanning oldest open PRs`);
-  const prs =
-    typeof github.paginate === 'function'
-      ? await github.paginate(github.rest.pulls.list, {
-          owner,
-          repo,
-          state: 'open',
-          per_page: 100,
-          sort: 'created',
-          direction: 'asc',
-        })
-      : (await github.rest.pulls.list({
-          owner,
-          repo,
-          state: 'open',
-          per_page: 100,
-          sort: 'created',
-          direction: 'asc',
-        })).data;
-  return prs.map((pr) => pr.number);
-}
-
-async function selectMergeGateCandidates(github, owner, repo, prNumbers, maxCandidates, core) {
-  const readyList = [];
-  const skipped = [];
-  for (const num of prNumbers) {
-    if (readyList.length >= maxCandidates) {
-      core?.info?.(`Found ${maxCandidates} candidate(s) — skipping remaining PR scans`);
-      break;
-    }
-    const result = await evaluatePipelineQuiescent(github, owner, repo, num, core);
-    if (result.ready) {
-      readyList.push({ pr_number: num, head_sha: result.headSha });
-    } else {
-      skipped.push({ pr: num, reasons: result.reasons });
-    }
-  }
-  readyList.sort((a, b) => a.pr_number - b.pr_number);
-  return {
-    candidates: readyList.slice(0, maxCandidates),
-    skipped,
-  };
-}
-
-const AUTOMATED_FALLBACK_MARKER = 'Automated fallback —';
-
-function isAutomatedFallbackVerdict(body) {
-  return (body || '').includes(AUTOMATED_FALLBACK_MARKER);
-}
-
-function findMergeGateVerdict(comments, minCreatedAt = null, headPushedAt = null, options = {}) {
-  const { excludeAutomatedFallback = false } = options || {};
-  const minTime = minCreatedAt ? new Date(minCreatedAt).getTime() : 0;
-  const headTime = headPushedAt ? new Date(headPushedAt).getTime() - 60000 : 0;
-  const gateComments = comments
-    .filter((c) => {
-      if (!(c.body || '').includes('MERGE_GATE_VERDICT:')) return false;
-      if (excludeAutomatedFallback && isAutomatedFallbackVerdict(c.body)) return false;
-      const created = new Date(c.created_at).getTime();
-      if (minTime && created < minTime) return false;
-      if (headTime && created < headTime) return false;
-      return true;
-    })
-    .sort((a, b) => new Date(b.created_at) - new Date(a.created_at));
-  if (gateComments.length === 0) return null;
-  const body = gateComments[0].body || '';
-  if (body.includes('MERGE_GATE_VERDICT: APPROVE')) return 'APPROVE';
-  if (body.includes('MERGE_GATE_VERDICT: BLOCK')) return 'BLOCK';
-  return null;
-}
-
-module.exports = {
-  CLAUDE_TRIGGER_LOGINS,
-  AUTO_ACTORS,
-  isFinalClaudeTriggerComment,
-  isClaudeTriggerNoise,
-  isClaudeFinalReplyComment,
-  hasRecentClaudeTrigger,
-  hasRecentConflictComment,
-  isConflictRebaseTriggerComment,
-  isConflictRebaseCompletionComment,
-  needsFinalReReviewAfterConflictRebase,
-  conflictRebaseQuiescent,
-  hasHumanChangesRequested,
-  hasAnyChangesRequested,
-  hasFinalClaudeReviewTrigger,
-  finalClaudeCompletedOnSha,
-  getMergeState,
-  OPTIONAL_CANCELLED_CHECKS,
-  listChecksOnSha,
-  listFailedChecksOnSha,
-  allChecksGreenOnSha,
-  hasInProgressClaudeAssistant,
-  claudeRunBlocksPr,
-  hasBlockingClaudeRunForPr,
-  isCiOnlyChange,
-  isInternalPullRequestLink,
-  resolvePrNumberFromLinkedPullRequests,
-  resolvePrNumberFromHeadBranch,
-  resolvePrNumberFromWorkflowRun,
-  WORKFLOW_ONLY_LABEL,
-  getAgentPyChange,
-  getAgentPyChangeFromFiles,
-  manualReviewReasonForAgentPy,
-  countNewAgentParams,
-  listPullFiles,
-  touchesSdk,
-  hasManualOnlyLabel,
-  sensitivePathReasons,
-  prSizeReasons,
-  missingTestsReason,
-  secretScanReasons,
-  sdkTestChecksReason,
-  listAllComments,
-  getHeadCommitDate,
-  isStaleFinalAfterPush,
-  needsStaleFinalRecovery,
-  shouldSkipFinalRecovery,
-  shouldSkipStaleFinalRecovery,
-  isClaudeAutomationLogin,
-  isPushSoonAfterLatestFinal,
-  countFinalTriggersSince,
-  STALE_FINAL_RECOVERY_WINDOW_MS,
-  STALE_FINAL_MAX_PER_WINDOW,
-  FINAL_CLAUDE_REVIEW_BODY,
-  loadPrContext,
-  evaluatePipelineQuiescent,
-  MERGE_READY_LABEL,
-  listPrNumbersForMergeGateScan,
-  selectMergeGateCandidates,
-  isAutomatedFallbackVerdict,
-  AUTOMATED_FALLBACK_MARKER,
-  findMergeGateVerdict,
-};

.github/scripts/pipeline-status-selftest.js

@@ -1,82 +0,0 @@
-#!/usr/bin/env node
-/**
- * Run: node .github/scripts/pipeline-status-selftest.js
- */
-const ps = require('./pipeline-status.js');
-const mg = require('./merge-gate.js');
-const config = require('./gate-config.js');
-
-let failed = 0;
-function assert(name, cond) {
-  if (!cond) {
-    console.error('FAIL:', name);
-    failed++;
-  } else {
-    console.log('ok:', name);
-  }
-}
-
-const kickComments = [
-  { user: { login: 'MervinPraison' }, body: '@coderabbitai review' },
-  { user: { login: 'MervinPraison' }, body: '/review' },
-];
-
-const finalComment = {
-  user: { login: 'MervinPraison' },
-  body: '@claude You are the FINAL architecture reviewer.',
-  created_at: '2026-06-26T10:00:00Z',
-};
-
-assert(
-  'reviews pending when not kicked',
-  ps.deriveStage([], { ready: false, reasons: ['no FINAL Claude review trigger'] })
-    === 'pipeline/reviews-pending'
-);
-assert(
-  'final pending after kick',
-  ps.deriveStage(kickComments, { ready: false, reasons: ['no FINAL Claude review trigger'] })
-    === 'pipeline/final-claude-pending'
-);
-assert(
-  'awaiting merge gate after bot review comment',
-  ps.deriveStage(
-    [
-      { user: { login: 'coderabbitai[bot]' }, body: 'review summary' },
-      finalComment,
-    ],
-    { ready: false, reasons: ['recent @claude within 35min'] }
-  ) === 'pipeline/awaiting-merge-gate'
-);
-
-assert('review bot logins configured', config.reviewBotLogins.includes('coderabbitai[bot]'));
-assert(
-  'merge ready',
-  ps.deriveStage([...kickComments, finalComment], { ready: true, reasons: [] })
-    === 'pipeline/merge-ready'
-);
-
-const blockers = ps.deriveBlockerLabels({
-  ready: false,
-  reasons: ['CI not green on HEAD', 'SDK code added without test file changes — requires manual review'],
-});
-assert('maps ci blocker', blockers.includes('pipeline/blocked:ci'));
-assert('maps manual blocker', blockers.includes('pipeline/blocked:manual-review'));
-
-assert(
-  'internal link matches upstream base',
-  mg.isInternalPullRequestLink(
-    { base: { repo: { full_name: 'MervinPraison/PraisonAI' } } },
-    'MervinPraison',
-    'PraisonAI'
-  )
-);
-assert(
-  'fork sync link rejected',
-  !mg.isInternalPullRequestLink(
-    { number: 21, base: { repo: { full_name: 'Milkmange/PraisonAI' } } },
-    'MervinPraison',
-    'PraisonAI'
-  )
-);
-
-process.exit(failed ? 1 : 0);

.github/scripts/pipeline-status.js

@@ -1,264 +0,0 @@
-/**
- * Sync pipeline stage/blocker labels on open PRs.
- * @see .github/workflows/pipeline-status-sync.yml
- */
-
-const mergeGate = require('./merge-gate.js');
-const chain = require('./bot-pr-review-chain.js');
-const ciFix = require('./ci-failure-claude.js');
-const config = require('./gate-config.js');
-
-const PIPELINE_PREFIX = 'pipeline/';
-const STAGE_LABELS = [
-  'pipeline/reviews-pending',
-  'pipeline/final-claude-pending',
-  'pipeline/awaiting-merge-gate',
-  'pipeline/merge-ready',
-  'pipeline/merged',
-];
-const BLOCKER_LABELS = [
-  'pipeline/blocked:ci',
-  'pipeline/blocked:conflict',
-  'pipeline/blocked:manual-review',
-  'pipeline/blocked:cooldown',
-  'pipeline/blocked:stale-final',
-  'pipeline/blocked:no-final',
-];
-const ALL_PIPELINE_LABELS = [...STAGE_LABELS, ...BLOCKER_LABELS];
-
-const LABEL_SPECS = [
-  { name: 'pipeline/reviews-pending', color: 'fbca04', description: 'Waiting for CodeRabbit/Qodo/Copilot reviews' },
-  { name: 'pipeline/final-claude-pending', color: 'd4c5f9', description: 'Reviews done; waiting for FINAL @claude' },
-  { name: 'pipeline/awaiting-merge-gate', color: 'c5def5', description: 'FINAL done; waiting for merge gate / CI' },
-  { name: 'pipeline/merge-ready', color: '0e8a16', description: 'Eligible for merge gate auto-merge' },
-  { name: 'pipeline/merged', color: '6f42c1', description: 'Merged via pipeline' },
-  { name: 'pipeline/blocked:ci', color: 'd93f0b', description: 'Blocked: CI not green on HEAD' },
-  { name: 'pipeline/blocked:conflict', color: 'b60205', description: 'Blocked: merge conflict or rebase pending' },
-  { name: 'pipeline/blocked:manual-review', color: 'e99695', description: 'Blocked: requires manual review' },
-  { name: 'pipeline/blocked:cooldown', color: 'fef2c0', description: 'Blocked: post-push or @claude cooldown' },
-  { name: 'pipeline/blocked:stale-final', color: 'f9d0c4', description: 'Blocked: FINAL stale after new commits' },
-  { name: 'pipeline/blocked:no-final', color: 'ededed', description: 'Blocked: no FINAL @claude trigger yet' },
-];
-
-function reviewChainStarted(comments) {
-  if (chain.chainKickPosted(comments)) return true;
-  return comments.some((c) => config.reviewBotLogins.includes(c.user?.login));
-}
-
-function deriveStage(comments, evalResult) {
-  if (evalResult.ready) return 'pipeline/merge-ready';
-  if (!reviewChainStarted(comments)) return 'pipeline/reviews-pending';
-  if (!mergeGate.hasFinalClaudeReviewTrigger(comments)) return 'pipeline/final-claude-pending';
-  return 'pipeline/awaiting-merge-gate';
-}
-
-function reasonToBlockerLabel(reason) {
-  const r = (reason || '').toLowerCase();
-  if (r.includes('ci not green') || r.includes('sdk code changed but no ci')) return 'pipeline/blocked:ci';
-  if (
-    r.includes('conflict') ||
-    r.includes('mergestate=dirty') ||
-    r.includes('not mergeable')
-  ) return 'pipeline/blocked:conflict';
-  if (
-    r.includes('manual') ||
-    r.includes('requires manual') ||
-    r.includes('sensitive path') ||
-    r.includes('no-auto-merge') ||
-    r.includes('manual-only label') ||
-    r.includes('without test') ||
-    r.includes('product code') ||
-    r.includes('files changed') ||
-    r.includes('possible secret')
-  ) return 'pipeline/blocked:manual-review';
-  if (r.includes('recent @claude') || r.includes('post-push buffer')) return 'pipeline/blocked:cooldown';
-  if (r.includes('stale final')) return 'pipeline/blocked:stale-final';
-  if (r.includes('no final claude')) return 'pipeline/blocked:no-final';
-  if (r.includes('final claude not complete')) return 'pipeline/blocked:stale-final';
-  if (r.includes('claude.yml in progress') || r.includes('claude-merge-gate-active')) {
-    return null;
-  }
-  return null;
-}
-
-function deriveBlockerLabels(evalResult) {
-  if (evalResult.ready) return [];
-  const labels = new Set();
-  for (const reason of evalResult.reasons || []) {
-    const mapped = reasonToBlockerLabel(reason);
-    if (mapped && mapped.startsWith('pipeline/blocked:')) labels.add(mapped);
-  }
-  return [...labels];
-}
-
-function computePipelineLabels(comments, evalResult) {
-  const stage = deriveStage(comments, evalResult);
-  const blockers = stage === 'pipeline/merge-ready' ? [] : deriveBlockerLabels(evalResult);
-  return { stage, blockers, all: [stage, ...blockers] };
-}
-
-async function ensurePipelineLabels(github, owner, repo, core) {
-  let existing;
-  if (typeof github.paginate === 'function') {
-    existing = await github.paginate(github.rest.issues.listLabelsForRepo, {
-      owner,
-      repo,
-      per_page: 100,
-    });
-  } else {
-    ({ data: existing } = await github.rest.issues.listLabelsForRepo({
-      owner,
-      repo,
-      per_page: 100,
-    }));
-  }
-  const names = new Set(existing.map((l) => l.name));
-  for (const spec of LABEL_SPECS) {
-    if (names.has(spec.name)) continue;
-    try {
-      await github.rest.issues.createLabel({
-        owner,
-        repo,
-        name: spec.name,
-        color: spec.color,
-        description: spec.description,
-      });
-      core?.info?.(`Created label ${spec.name}`);
-    } catch (err) {
-      if (err.status !== 422) throw err;
-      core?.info?.(`Label ${spec.name} already exists`);
-    }
-  }
-}
-
-async function syncPipelineLabels(github, owner, repo, prNumber, core) {
-  const ctx = await mergeGate.loadPrContext(github, owner, repo, prNumber);
-  if (ctx.pr.state !== 'open') return { synced: false, reason: 'not_open' };
-
-  const evalResult = await mergeGate.evaluatePipelineQuiescent(
-    github, owner, repo, prNumber, core
-  );
-  const { stage, blockers, all } = computePipelineLabels(ctx.comments, evalResult);
-
-  const current = ctx.labels.filter((l) => l.startsWith(PIPELINE_PREFIX));
-  const desired = new Set(all);
-  const toRemove = current.filter((l) => !desired.has(l) && l !== 'pipeline/merged');
-  const toAdd = all.filter((l) => !current.includes(l));
-
-  for (const name of toRemove) {
-    try {
-      await github.rest.issues.removeLabel({ owner, repo, issue_number: prNumber, name });
-    } catch (err) {
-      if (err.status !== 404) throw err;
-    }
-  }
-  if (toAdd.length) {
-    await github.rest.issues.addLabels({
-      owner,
-      repo,
-      issue_number: prNumber,
-      labels: toAdd,
-    });
-  }
-
-  core?.info?.(`PR #${prNumber}: stage=${stage} blockers=[${blockers.join(', ')}]`);
-
-  const ciNotGreen = (evalResult.reasons || []).some((r) =>
-    r.toLowerCase().includes('ci not green')
-  );
-  if (ciNotGreen || blockers.includes('pipeline/blocked:ci')) {
-    await ciFix.maybeTriggerCiFixClaude(github, owner, repo, prNumber, core);
-  } else {
-    await ciFix.maybeClearCiFixLabel(
-      github, owner, repo, prNumber, ctx.labels, ctx.headSha, core
-    );
-  }
-
-  return {
-    synced: true,
-    stage,
-    blockers,
-    ready: evalResult.ready,
-    reasons: evalResult.reasons,
-    labels: ctx.labels,
-    createdAt: new Date(ctx.pr.created_at).getTime(),
-  };
-}
-
-async function dispatchMergeGateForOldestReady(github, owner, repo, readyCandidates, core) {
-  if (!readyCandidates.length) return 0;
-  readyCandidates.sort((a, b) => a.createdAt - b.createdAt);
-  for (const cand of readyCandidates) {
-    if ((cand.labels || []).includes('claude-merge-gate-active')) {
-      core?.info?.(`Skip dispatch PR #${cand.prNumber}: merge gate already active`);
-      continue;
-    }
-    await github.rest.repos.createDispatchEvent({
-      owner,
-      repo,
-      event_type: 'claude-merge-gate',
-      client_payload: { pr_number: cand.prNumber },
-    });
-    core?.info?.(`Dispatched merge gate for ready PR #${cand.prNumber}`);
-    return cand.prNumber;
-  }
-  return 0;
-}
-
-async function syncOpenPullRequests(github, owner, repo, options, core) {
-  const { maxPrs = 20, dispatchMergeGate = true } = options || {};
-  await ensurePipelineLabels(github, owner, repo, core);
-  let prs;
-  if (maxPrs <= 100) {
-    ({ data: prs } = await github.rest.pulls.list({
-      owner,
-      repo,
-      state: 'open',
-      per_page: 100,
-    }));
-  } else {
-    prs = await github.paginate(github.rest.pulls.list, {
-      owner,
-      repo,
-      state: 'open',
-      per_page: 100,
-    });
-  }
-  let synced = 0;
-  const readyCandidates = [];
-  for (const pr of prs) {
-    if (synced >= maxPrs) break;
-    if (pr.draft) continue;
-    if (pr.head?.repo?.full_name && pr.head.repo.full_name !== `${owner}/${repo}`) continue;
-    const result = await syncPipelineLabels(github, owner, repo, pr.number, core);
-    if (result.ready) {
-      readyCandidates.push({
-        prNumber: pr.number,
-        createdAt: result.createdAt || new Date(pr.created_at).getTime(),
-        labels: result.labels || [],
-      });
-    }
-    synced += 1;
-  }
-  let dispatched = 0;
-  if (dispatchMergeGate && readyCandidates.length) {
-    dispatched = await dispatchMergeGateForOldestReady(
-      github, owner, repo, readyCandidates, core
-    );
-  }
-  core?.info?.(`Pipeline label sync complete (${synced} PR(s), dispatched=${dispatched || 'none'})`);
-  return synced;
-}
-
-module.exports = {
-  STAGE_LABELS,
-  BLOCKER_LABELS,
-  ALL_PIPELINE_LABELS,
-  deriveStage,
-  deriveBlockerLabels,
-  computePipelineLabels,
-  ensurePipelineLabels,
-  syncPipelineLabels,
-  syncOpenPullRequests,
-  dispatchMergeGateForOldestReady,
-};

.github/scripts/pr-review-chain-selftest.js

@@ -1,111 +0,0 @@
-#!/usr/bin/env node
-const chain = require('./pr-review-chain.js');
-
-let failed = 0;
-function assert(name, cond) {
-  if (!cond) {
-    console.error('FAIL:', name);
-    failed++;
-  } else {
-    console.log('ok:', name);
-  }
-}
-
-const coderabbit = {
-  user: { login: 'coderabbitai[bot]' },
-  body: '<!-- summarize by coderabbit.ai --> summary',
-  created_at: '2026-07-02T07:16:00Z',
-};
-const greptile = {
-  user: { login: 'greptile-apps[bot]' },
-  body: '<h3>Greptile Summary</h3><p>Looks good</p>',
-  created_at: '2026-07-02T07:19:00Z',
-};
-const copilotKick = {
-  user: { login: 'MervinPraison' },
-  body: '@copilot Do a thorough review',
-  created_at: '2026-07-02T07:20:00Z',
-};
-const copilotReply = {
-  user: { login: 'copilot-swe-agent' },
-  body: 'Copilot review here',
-  created_at: '2026-07-02T07:22:00Z',
-};
-
-assert('prior not ready without greptile', !chain.priorReviewersReady([coderabbit]).ready);
-assert('prior ready with coderabbit + greptile', chain.priorReviewersReady([coderabbit, greptile]).ready);
-assert('copilot not ready before trigger', !chain.copilotReviewReady([coderabbit, greptile]).ready);
-assert(
-  'copilot ready after reply',
-  chain.copilotReviewReady([coderabbit, greptile, copilotKick, copilotReply]).ready
-);
-assert(
-  'claude not ready before copilot',
-  !chain.claudeFinalReady([coderabbit, greptile]).ready
-);
-assert(
-  'claude ready after full chain',
-  chain.claudeFinalReady([coderabbit, greptile, copilotKick, copilotReply]).ready
-);
-assert(
-  'claude timeout fallback when copilot triggered but silent',
-  chain.claudeFinalReady([coderabbit, greptile, copilotKick], [], { allowCopilotTimeout: true }).ready
-);
-assert(
-  'no timeout fallback without copilot trigger',
-  !chain.claudeFinalReady([coderabbit, greptile], [], { allowCopilotTimeout: true }).ready
-);
-assert(
-  'skipCopilot proceeds after prior reviewers',
-  chain.claudeFinalReady([coderabbit, greptile], [], { skipCopilot: true }).ready
-);
-const prev = process.env.REVIEW_CHAIN_SKIP_COPILOT;
-process.env.REVIEW_CHAIN_SKIP_COPILOT = '1';
-assert(
-  'REVIEW_CHAIN_SKIP_COPILOT env skips copilot',
-  chain.claudeFinalReady([coderabbit, greptile], [], {}).ready
-);
-process.env.REVIEW_CHAIN_SKIP_COPILOT = prev;
-
-const geminiReview = {
-  user: { login: 'gemini-code-assist[bot]' },
-  body: '## Code Review\n\nThis pull request decomposes the large main.py file',
-  submitted_at: '2026-07-03T08:08:00Z',
-};
-assert(
-  'gemini pull review counts as prior reviewer',
-  chain.hasGeminiReview([], [geminiReview])
-);
-assert(
-  'prior ready with gemini review only (no gemini comment)',
-  chain.priorReviewersReady([coderabbit, greptile], [geminiReview], { optionalWaitMs: 0 }).ready
-);
-
-const oldPr = new Date(Date.now() - 31 * 60 * 1000).toISOString();
-assert(
-  'prior not ready without coderabbit before timeout',
-  !chain.priorReviewersReady([greptile], [], { prCreatedAt: new Date().toISOString(), requiredWaitMs: 30 * 60 * 1000 }).ready
-);
-assert(
-  'prior timeout when coderabbit silent after 30m',
-  chain.priorReviewersReady([greptile], [], { prCreatedAt: oldPr, requiredWaitMs: 30 * 60 * 1000 }).ready
-);
-assert(
-  'claude FINAL timeout when required reviewer silent and no prior FINAL',
-  chain.claudeFinalReady([greptile], [], { prCreatedAt: oldPr, requiredWaitMs: 30 * 60 * 1000 }).ready
-);
-assert(
-  'claude FINAL timeout auto-skips copilot without explicit skipCopilot',
-  chain.claudeFinalReady([greptile], [], { prCreatedAt: oldPr, requiredWaitMs: 30 * 60 * 1000 }).copilotSkipped
-);
-const finalClaude = {
-  user: { login: 'github-actions[bot]' },
-  body: '@claude You are the FINAL architecture reviewer for this PR.',
-  created_at: '2026-07-02T07:25:00Z',
-};
-assert(
-  'claude FINAL not re-posted when already triggered',
-  !chain.claudeFinalReady([greptile, finalClaude], [], { prCreatedAt: oldPr, requiredWaitMs: 0 }).ready
-);
-
-process.exit(failed ? 1 : 0);

.github/scripts/pr-review-chain.js

@@ -1,300 +0,0 @@
-/**
- * PR review pipeline: CodeRabbit / Greptile (+ optional Qodo/Gemini) → Claude (FINAL).
- * Copilot step optional — set REVIEW_CHAIN_SKIP_COPILOT=1 or skipCopilot: true to bypass.
- * @see .github/workflows/auto-pr-comment.yml
- */
-
-function isSkipCopilot(options = {}) {
-  if (options.skipCopilot === true) return true;
-  if (options.skipCopilot === false) return false;
-  return process.env.REVIEW_CHAIN_SKIP_COPILOT === '1';
-}
-
-const COPILOT_TRIGGER_LOGINS = new Set(['MervinPraison', 'github-actions[bot]']);
-const CLAUDE_TRIGGER_LOGINS = new Set(['MervinPraison', 'github-actions[bot]']);
-
-const REQUIRED_PRIOR = ['coderabbit', 'greptile'];
-const OPTIONAL_PRIOR = ['qodo', 'gemini'];
-const OPTIONAL_PRIOR_WAIT_MS = 20 * 60 * 1000;
-
-function loginOf(item) {
-  return (item?.user?.login || '').toLowerCase();
-}
-
-function bodyOf(item) {
-  return item?.body || '';
-}
-
-function hasCoderabbitSummary(comments) {
-  return comments.some(
-    (c) =>
-      loginOf(c).includes('coderabbit') &&
-      bodyOf(c).toLowerCase().includes('summarize by coderabbit')
-  );
-}
-
-function hasGreptileReview(comments) {
-  return comments.some((c) => {
-    if (!loginOf(c).includes('greptile')) return false;
-    const body = bodyOf(c);
-    return body.includes('Greptile Summary') || body.includes('<h3>Greptile Summary</h3>') || body.length > 150;
-  });
-}
-
-function hasQodoReview(comments, reviews = []) {
-  if (comments.some((c) => loginOf(c).includes('qodo'))) return true;
-  return reviews.some((r) => loginOf(r).includes('qodo'));
-}
-
-function hasGeminiReview(comments, reviews = []) {
-  if (reviews.some((r) => loginOf(r).includes('gemini'))) return true;
-  return comments.some((c) => {
-    if (loginOf(c).includes('gemini')) return true;
-    const body = bodyOf(c);
-    return (
-      body.includes('Review completed by Gemini CLI') ||
-      body.includes('The fix is ready for review. Please test')
-    );
-  });
-}
-
-const PRIOR_CHECKS = {
-  coderabbit: (comments) => hasCoderabbitSummary(comments),
-  greptile: (comments) => hasGreptileReview(comments),
-  qodo: (comments, reviews) => hasQodoReview(comments, reviews),
-  gemini: (comments, reviews) => hasGeminiReview(comments, reviews),
-};
-
-function priorReviewerStatus(comments, reviews = []) {
-  const status = {};
-  for (const [id, check] of Object.entries(PRIOR_CHECKS)) {
-    status[id] = check(comments, reviews);
-  }
-  return status;
-}
-
-function priorReviewersReady(comments, reviews = [], options = {}) {
-  const waitMs = options.optionalWaitMs ?? OPTIONAL_PRIOR_WAIT_MS;
-  const prCreatedAt = options.prCreatedAt || null;
-  const status = priorReviewerStatus(comments, reviews);
-
-  const missingRequired = REQUIRED_PRIOR.filter((id) => !status[id]);
-  if (missingRequired.length) {
-    return { ready: false, reason: `waiting for ${missingRequired.join(', ')}`, status };
-  }
-
-  const missingOptional = OPTIONAL_PRIOR.filter((id) => !status[id]);
-  if (missingOptional.length && prCreatedAt) {
-    const age = Date.now() - new Date(prCreatedAt).getTime();
-    if (age < waitMs) {
-      return {
-        ready: false,
-        reason: `waiting for optional ${missingOptional.join(', ')} (${Math.round((waitMs - age) / 60000)}m left)`,
-        status,
-      };
-    }
-  }
-
-  return { ready: true, reason: '', status };
-}
-
-function copilotTriggerComment(comments) {
-  return comments.find(
-    (c) =>
-      bodyOf(c).includes('@copilot') &&
-      COPILOT_TRIGGER_LOGINS.has(c.user?.login) &&
-      !bodyOf(c).toLowerCase().includes('@claude')
-  );
-}
-
-function copilotTriggered(comments) {
-  return Boolean(copilotTriggerComment(comments));
-}
-
-function copilotReviewReady(comments, reviews = []) {
-  const trigger = copilotTriggerComment(comments);
-  if (!trigger) {
-    return { ready: false, reason: 'copilot not triggered yet' };
-  }
-  const triggerTime = new Date(trigger.created_at).getTime();
-  const copilotComment = comments.some((c) => {
-    if (c === trigger) return false;
-    if (new Date(c.created_at).getTime() < triggerTime) return false;
-    const login = loginOf(c);
-    return (
-      login.includes('copilot') ||
-      c.user?.login === 'Copilot' ||
-      c.user?.login === 'copilot-swe-agent'
-    );
-  });
-  const copilotReview = reviews.some((r) => {
-    if (new Date(r.submitted_at).getTime() < triggerTime) return false;
-    return loginOf(r).includes('copilot');
-  });
-  if (copilotComment || copilotReview) {
-    return { ready: true, reason: '' };
-  }
-  return { ready: false, reason: 'awaiting copilot review' };
-}
-
-function isFinalClaudeTriggerComment(c) {
-  const body = bodyOf(c).toLowerCase();
-  if (!CLAUDE_TRIGGER_LOGINS.has(c.user?.login)) return false;
-  if (!body.includes('@claude')) return false;
-  if (body.includes('merge conflict')) return false;
-  return body.includes('final architecture reviewer') || body.includes('lead engineer');
-}
-
-function claudeFinalAlreadyTriggered(comments) {
-  return comments.some(isFinalClaudeTriggerComment);
-}
-
-function claudeFinalReady(comments, reviews = [], options = {}) {
-  if (!options.allowStaleRepost && claudeFinalAlreadyTriggered(comments)) {
-    return { ready: false, reason: 'claude FINAL already triggered', already: true };
-  }
-  const prior = priorReviewersReady(comments, reviews, options);
-  if (!prior.ready) {
-    return { ready: false, reason: prior.reason };
-  }
-  if (isSkipCopilot(options)) {
-    return { ready: true, reason: 'copilot skipped', copilotSkipped: true };
-  }
-  const copilot = copilotReviewReady(comments, reviews);
-  if (copilot.ready) {
-    return { ready: true, reason: '' };
-  }
-  if (options.allowCopilotTimeout && copilotTriggered(comments)) {
-    return { ready: true, reason: 'copilot timeout fallback', copilotSkipped: true };
-  }
-  return { ready: false, reason: copilot.reason };
-}
-
-/** Post @copilot when prior reviewers are ready, then @claude FINAL when Copilot done (or timeout). */
-async function advanceReviewChain(github, owner, repo, prNumber, finalBody, core, options = {}) {
-  const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: prNumber });
-  const chainOpts = {
-    prCreatedAt: pr.created_at,
-    optionalWaitMs: options.optionalWaitMs ?? 0,
-  };
-  const copilot = options.skipCopilot
-    ? { triggered: false, reason: 'skipped' }
-    : await maybeTriggerCopilot(github, owner, repo, prNumber, core, chainOpts);
-  const claude = await maybeTriggerClaudeFinal(
-    github, owner, repo, prNumber, finalBody, core,
-    { ...options, ...chainOpts }
-  );
-  return { copilot, claude };
-}
-
-async function maybeTriggerClaudeFinal(github, owner, repo, prNumber, finalBody, core, options = {}) {
-  const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: prNumber });
-  const { comments, reviews } = await listCommentsAndReviews(github, owner, repo, prNumber);
-  const gate = claudeFinalReady(comments, reviews, {
-    prCreatedAt: pr.created_at,
-    optionalWaitMs: options.optionalWaitMs,
-    allowCopilotTimeout: options.allowCopilotTimeout !== false,
-    allowStaleRepost: options.allowStaleRepost === true,
-    skipCopilot: options.skipCopilot,
-  });
-  if (!gate.ready) {
-    core?.info?.(`PR #${prNumber}: skip Claude FINAL — ${gate.reason}`);
-    return { posted: false, reason: gate.reason };
-  }
-  await github.rest.issues.createComment({
-    owner,
-    repo,
-    issue_number: prNumber,
-    body: finalBody,
-  });
-  const note = gate.copilotSkipped ? ' (Copilot timeout fallback)' : '';
-  core?.info?.(`Posted Claude FINAL on PR #${prNumber}${note}`);
-  return { posted: true, reason: '' };
-}
-
-const COPILOT_REVIEW_BODY =
-  '@copilot Do a thorough review of this PR. Read ALL existing reviewer comments above from Qodo, CodeRabbit, Gemini, and Greptile first — incorporate their findings.\n\nReview areas:\n1. **Bloat check**: Are changes minimal and focused? Any unnecessary code or scope creep?\n2. **Security**: Any hardcoded secrets, unsafe eval/exec, missing input validation?\n3. **Performance**: Any module-level heavy imports? Hot-path regressions?\n4. **Tests**: Are tests included? Do they cover the changes adequately?\n5. **Backward compat**: Any public API changes without deprecation?\n6. **Code quality**: DRY violations, naming conventions, error handling?\n7. **Address reviewer feedback**: If Qodo, CodeRabbit, Gemini, or Greptile flagged valid issues, include them in your review\n8. Suggest specific improvements with code examples where possible';
-
-async function listCommentsAndReviews(github, owner, repo, prNumber) {
-  let comments;
-  if (typeof github.paginate === 'function') {
-    comments = await github.paginate(github.rest.issues.listComments, {
-      owner,
-      repo,
-      issue_number: prNumber,
-      per_page: 100,
-    });
-  } else {
-    const { data } = await github.rest.issues.listComments({
-      owner,
-      repo,
-      issue_number: prNumber,
-      per_page: 100,
-    });
-    comments = data;
-  }
-  const { data: reviews } = await github.rest.pulls.listReviews({
-    owner,
-    repo,
-    pull_number: prNumber,
-    per_page: 100,
-  });
-  return { comments, reviews };
-}
-
-async function maybeTriggerCopilot(github, owner, repo, prNumber, core, options = {}) {
-  const { comments, reviews } = await listCommentsAndReviews(github, owner, repo, prNumber);
-  if (copilotTriggered(comments)) {
-    core?.info?.(`Copilot already triggered on PR #${prNumber}`);
-    return { triggered: false, reason: 'already_triggered' };
-  }
-  const prior = priorReviewersReady(comments, reviews, options);
-  if (!prior.ready) {
-    core?.info?.(`PR #${prNumber}: not ready for Copilot — ${prior.reason}`);
-    return { triggered: false, reason: prior.reason };
-  }
-  await github.rest.issues.createComment({
-    owner,
-    repo,
-    issue_number: prNumber,
-    body: COPILOT_REVIEW_BODY,
-  });
-  core?.info?.(`Posted @copilot on PR #${prNumber} after prior reviewers`);
-  return { triggered: true };
-}
-
-async function pollCopilotResponse(github, owner, repo, prNumber, options = {}) {
-  const maxAttempts = options.maxAttempts ?? 20;
-  const delayMs = options.delayMs ?? 30000;
-  for (let i = 0; i < maxAttempts; i += 1) {
-    const { comments, reviews } = await listCommentsAndReviews(github, owner, repo, prNumber);
-    const copilot = copilotReviewReady(comments, reviews);
-    if (copilot.ready) {
-      return { ready: true, reason: '' };
-    }
-    if (i < maxAttempts - 1) {
-      await new Promise((resolve) => setTimeout(resolve, delayMs));
-    }
-  }
-  return { ready: false, reason: 'copilot timeout' };
-}
-
-module.exports = {
-  isSkipCopilot,
-  REQUIRED_PRIOR,
-  OPTIONAL_PRIOR,
-  COPILOT_REVIEW_BODY,
-  priorReviewerStatus,
-  priorReviewersReady,
-  copilotTriggered,
-  copilotReviewReady,
-  claudeFinalReady,
-  claudeFinalAlreadyTriggered,
-  isFinalClaudeTriggerComment,
-  maybeTriggerCopilot,
-  maybeTriggerClaudeFinal,
-  advanceReviewChain,
-  pollCopilotResponse,
-  hasGeminiReview,
-  listCommentsAndReviews,
-};

.github/scripts/release-gate-selftest.js

@@ -1,81 +0,0 @@
-#!/usr/bin/env node
-/**
- * Run: node .github/scripts/release-gate-selftest.js
- */
-const rg = require('./release-gate.js');
-const config = require('./gate-config.js');
-
-let failed = 0;
-function assert(name, cond) {
-  if (!cond) {
-    console.error('FAIL:', name);
-    failed += 1;
-  } else {
-    console.log('ok:', name);
-  }
-}
-
-assert(`package name is ${config.pypiPackageName}`, config.pypiPackageName === 'praisonai-tools');
-assert('bumpPatch works', rg.bumpPatch('0.3.127') === '0.3.128');
-
-try {
-  const versions = rg.readVersionsFromTree(process.cwd());
-  assert('reads current version', /^\d+\.\d+\.\d+$/.test(versions.current));
-  assert('computes target patch', versions.target === rg.bumpPatch(versions.current));
-} catch (err) {
-  console.error('FAIL: readVersionsFromTree', err.message);
-  failed += 1;
-}
-
-assert('PACKAGE_PATHS includes pyproject', rg.PACKAGE_PATHS.includes('pyproject.toml'));
-
-(async () => {
-  const noonUtc = new Date('2026-07-08T12:00:00Z');
-  const mockGithub = {
-    rest: {
-      actions: {
-        listWorkflowRuns: async () => ({
-          data: {
-            workflow_runs: [
-              {
-                conclusion: 'success',
-                created_at: '2026-07-08T09:00:00Z',
-                status: 'completed',
-              },
-            ],
-          },
-        }),
-      },
-    },
-  };
-
-  assert('PATCH_RELEASE_INTERVAL_DAYS is 3', rg.PATCH_RELEASE_INTERVAL_DAYS === 3);
-  assert(
-    'hasSuccessfulReleaseWithinDays blocks recent release',
-    await rg.hasSuccessfulReleaseWithinDays(mockGithub, 'o', 'r', noonUtc)
-  );
-
-  const oldReleaseGithub = {
-    rest: {
-      actions: {
-        listWorkflowRuns: async () => ({
-          data: {
-            workflow_runs: [
-              {
-                conclusion: 'success',
-                created_at: '2026-07-04T09:00:00Z',
-                status: 'completed',
-              },
-            ],
-          },
-        }),
-      },
-    },
-  };
-  assert(
-    'hasSuccessfulReleaseWithinDays allows after 3 days',
-    !(await rg.hasSuccessfulReleaseWithinDays(oldReleaseGithub, 'o', 'r', noonUtc))
-  );
-
-  process.exit(failed ? 1 : 0);
-})();

.github/scripts/release-gate.js

@@ -1,227 +0,0 @@
-/**
- * Release gate preflight — path changes, CI SHA, dedupe, PyPI version checks (PraisonAIUI).
- */
-
-const https = require('https');
-const config = require('./gate-config.js');
-
-const PACKAGE_PATHS = config.packagePaths;
-/** Minimum days between successful patch auto-releases. */
-const PATCH_RELEASE_INTERVAL_DAYS = 3;
-const ACTIVE_RELEASE_STATUSES = new Set([
-  'queued', 'in_progress', 'waiting', 'pending', 'requested',
-]);
-
-function bumpPatch(version) {
-  const parts = version.split('.');
-  if (parts.length !== 3) throw new Error(`Invalid version: ${version}`);
-  return `${parts[0]}.${parts[1]}.${Number(parts[2]) + 1}`;
-}
-
-function readVersionsFromTree(root = '.') {
-  const fs = require('fs');
-  const path = require('path');
-  const toml = fs.readFileSync(path.join(root, 'pyproject.toml'), 'utf8');
-  const match = toml.match(/^version\s*=\s*"([^"]+)"/m);
-  if (!match) throw new Error('Could not read aiui version from pyproject.toml');
-  const current = match[1];
-  const target = bumpPatch(current);
-  return { current, target, packageName: config.pypiPackageName };
-}
-
-function pypiVersionExists(packageName, version) {
-  return new Promise((resolve) => {
-    const req = https.get(
-      `https://pypi.org/pypi/${packageName}/${version}/json`,
-      { timeout: 15000 },
-      (res) => {
-        res.resume();
-        resolve(res.statusCode === 200);
-      }
-    );
-    req.on('error', () => resolve(false));
-    req.on('timeout', () => {
-      req.destroy();
-      resolve(false);
-    });
-  });
-}
-
-async function hasActiveReleaseRun(github, owner, repo) {
-  const runs = await github.rest.actions.listWorkflowRuns({
-    owner,
-    repo,
-    workflow_id: 'pypi-release.yml',
-    per_page: 20,
-  });
-  return runs.data.workflow_runs.some(
-    (r) => ACTIVE_RELEASE_STATUSES.has(r.status) && !r.conclusion
-  );
-}
-
-function utcDayStart(now = new Date()) {
-  return new Date(Date.UTC(now.getUTCFullYear(), now.getUTCMonth(), now.getUTCDate()));
-}
-
-function releaseIntervalStart(now = new Date(), intervalDays = PATCH_RELEASE_INTERVAL_DAYS) {
-  return new Date(now.getTime() - intervalDays * 24 * 60 * 60 * 1000);
-}
-
-async function hasSuccessfulReleaseWithinDays(
-  github,
-  owner,
-  repo,
-  now = new Date(),
-  intervalDays = PATCH_RELEASE_INTERVAL_DAYS,
-) {
-  const windowStart = releaseIntervalStart(now, intervalDays);
-  const runs = await github.rest.actions.listWorkflowRuns({
-    owner,
-    repo,
-    workflow_id: 'pypi-release.yml',
-    status: 'completed',
-    per_page: 30,
-  });
-  return runs.data.workflow_runs.some(
-    (r) => r.conclusion === 'success' && new Date(r.created_at) >= windowStart
-  );
-}
-
-/** @deprecated Use hasSuccessfulReleaseWithinDays — kept for selftests. */
-async function hasSuccessfulReleaseToday(github, owner, repo, now = new Date()) {
-  return hasSuccessfulReleaseWithinDays(github, owner, repo, now, 1);
-}
-
-async function lastGreenCiSha(github, owner, repo) {
-  const runs = await github.rest.actions.listWorkflowRuns({
-    owner,
-    repo,
-    workflow_id: config.ciWorkflowFile,
-    branch: 'main',
-    status: 'completed',
-    per_page: 30,
-  });
-  const hit = runs.data.workflow_runs.find((r) => r.conclusion === 'success');
-  return hit ? hit.head_sha : '';
-}
-
-async function evaluateReleasePreflight(github, owner, repo, options, core) {
-  const {
-    headSha,
-    isCiTrigger = false,
-    bump = 'patch',
-  } = options;
-
-  const reasons = [];
-  const out = {
-    ready: false,
-    reasons,
-    headSha: headSha || '',
-    lastTag: '',
-    targetVersion: '',
-    packageName: config.pypiPackageName,
-  };
-
-  if (bump !== 'patch') {
-    reasons.push('only patch auto-release supported');
-    return out;
-  }
-
-  if (await hasActiveReleaseRun(github, owner, repo)) {
-    reasons.push('PyPI Release already in progress or awaiting approval');
-    return out;
-  }
-
-  const referenceTime = options.now instanceof Date ? options.now : new Date();
-  if (await hasSuccessfulReleaseWithinDays(github, owner, repo, referenceTime)) {
-    reasons.push(
-      `already released within last ${PATCH_RELEASE_INTERVAL_DAYS} days; `
-      + `max one patch release every ${PATCH_RELEASE_INTERVAL_DAYS} days`
-    );
-    return out;
-  }
-
-  let versions;
-  try {
-    versions = readVersionsFromTree();
-  } catch (err) {
-    reasons.push(err.message);
-    return out;
-  }
-  out.targetVersion = versions.target;
-
-  const onPypi = await pypiVersionExists(config.pypiPackageName, versions.target);
-  if (onPypi) {
-    reasons.push(`already published: ${config.pypiPackageName}==${versions.target}`);
-    return out;
-  }
-
-  const { execSync } = require('child_process');
-  let lastTag = '';
-  try {
-    lastTag = execSync('git describe --tags --match "v*" --abbrev=0', { encoding: 'utf8' }).trim();
-  } catch {
-    reasons.push('no v* tag found');
-    return out;
-  }
-  out.lastTag = lastTag;
-
-  let changed = '';
-  try {
-    changed = execSync(
-      `git diff --name-only ${lastTag} HEAD -- ${PACKAGE_PATHS.join(' ')}`,
-      { encoding: 'utf8' }
-    ).trim();
-  } catch {
-    reasons.push('git diff failed');
-    return out;
-  }
-
-  if (!changed) {
-    reasons.push(`no changes in ${PACKAGE_PATHS.join(' or ')} since ${lastTag}`);
-    return out;
-  }
-
-  if (isCiTrigger) {
-    let mainSha = '';
-    try {
-      mainSha = execSync('git rev-parse origin/main', { encoding: 'utf8' }).trim();
-    } catch {
-      reasons.push('could not resolve origin/main');
-      return out;
-    }
-    if (headSha !== mainSha) {
-      reasons.push(`superseded: green SHA ${headSha.slice(0, 7)} != main ${mainSha.slice(0, 7)}`);
-      return out;
-    }
-    out.headSha = headSha;
-  } else {
-    const evalSha = execSync('git rev-parse HEAD', { encoding: 'utf8' }).trim();
-    out.headSha = evalSha;
-    const greenSha = await lastGreenCiSha(github, owner, repo);
-    if (greenSha !== evalSha) {
-      reasons.push(`CI not green on HEAD (last green: ${greenSha ? greenSha.slice(0, 7) : 'none'})`);
-      return out;
-    }
-  }
-
-  out.ready = true;
-  out.reasons = ['ready'];
-  if (core) core.info(`Release preflight passed for ${out.headSha || headSha}`);
-  return out;
-}
-
-module.exports = {
-  bumpPatch,
-  readVersionsFromTree,
-  pypiVersionExists,
-  PATCH_RELEASE_INTERVAL_DAYS,
-  releaseIntervalStart,
-  hasSuccessfulReleaseWithinDays,
-  hasSuccessfulReleaseToday,
-  utcDayStart,
-  evaluateReleasePreflight,
-  lastGreenCiSha,
-  ACTIVE_RELEASE_STATUSES,
-  PACKAGE_PATHS,
-};

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

@@ -1,33 +0,0 @@
-name: Auto PR Comment
-
-on:
-  issue_comment:
-    types: [created]
-  pull_request_review:
-    types: [submitted]
-  pull_request:
-    types: [opened, synchronize]
-  pull_request_target:
-    types: [opened, synchronize]
-  schedule:
-    - cron: '30 * * * *'
-  workflow_dispatch:
-
-env:
-  REVIEW_CHAIN_SKIP_COPILOT: '1'
-
-permissions:
-  issues: write
-  pull-requests: write
-
-jobs:
-  review-chain:
-    # Grant union of callee job permissions (repos with default_workflow_permissions: read
-    # reject reusable calls when the caller ceiling is lower than any callee job).
-    permissions:
-      issues: write
-      pull-requests: write
-    uses: MervinPraison/github-automation-template/.github/workflows/[email protected]
-    secrets: inherit
-    with:
-      hub_ref: v1.1.20

.github/workflows/bot-pr-recovery.yml

@@ -1,23 +0,0 @@
-name: Bot PR Recovery
-
-on:
-  schedule:
-    - cron: '*/15 * * * *'
-  workflow_dispatch:
-    inputs:
-      pr_number:
-        description: 'Specific PR number to kick (optional)'
-        required: false
-        type: string
-
-permissions:
-  pull-requests: write
-  issues: write
-
-jobs:
-  recover:
-    uses: MervinPraison/github-automation-template/.github/workflows/[email protected]
-    secrets: inherit
-    with:
-      hub_ref: v1.1.20
-      pr_number: ${{ github.event.inputs.pr_number || '' }}

.github/workflows/chain-claude-after-copilot.yml

@@ -1,37 +0,0 @@
-name: Chain Claude After Reviewers
-
-on:
-  pull_request_review:
-    types: [submitted]
-
-jobs:
-  claude-final-review:
-    if: |
-      github.event_name == 'pull_request_review' &&
-      github.event.review.user.login == 'copilot-pull-request-reviewer[bot]'
-    runs-on: ubuntu-latest
-    permissions:
-      pull-requests: write
-    steps:
-      - name: Post Claude final review + fix request
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            const issue_number = context.payload.pull_request?.number ?? context.issue?.number;
-            const comments = await github.rest.issues.listComments({
-              issue_number,
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              per_page: 100
-            });
-            if (comments.data.some(c => c.body && /@claude\b/i.test(c.body))) {
-              console.log('@claude already requested, skipping');
-              return;
-            }
-            await github.rest.issues.createComment({
-              issue_number,
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              body: '@claude You are the FINAL reviewer. Read ALL comments above from Qodo, Coderabbit, and Copilot.\n\n**Phase 1: Review**\n1. Code follows existing tool patterns (see lumalabs_tool.py)?\n2. Lazy imports for optional deps?\n3. All options as function parameters?\n4. Proper error handling?\n\n**Phase 2: Fix valid issues**\n5. Implement fixes from other reviewers\n6. Push to THIS branch\n\n**Phase 3: Verdict**\n7. Approve or request changes'
-            });

.github/workflows/ci-failure-claude.yml

@@ -1,32 +0,0 @@
-name: Auto CI Failure → Claude
-
-on:
-  workflow_run:
-    workflows: [CI, Optimized Test Suite]
-    types: [completed]
-  pull_request:
-    types: [opened, synchronize, reopened]
-  workflow_dispatch:
-    inputs:
-      pr_number:
-        required: false
-        type: string
-  schedule:
-    - cron: '15 */6 * * *'
-
-concurrency:
-  group: ci-failure-scan-${{ github.repository }}-${{ github.event.pull_request.number || github.event.workflow_run.id || github.run_id }}
-  cancel-in-progress: true
-
-permissions:
-  pull-requests: read
-  issues: write
-  actions: read
-
-jobs:
-  detect-and-trigger:
-    uses: MervinPraison/github-automation-template/.github/workflows/[email protected]
-    secrets: inherit
-    with:
-      hub_ref: v1.1.20
-      pr_number: ${{ github.event.inputs.pr_number || '' }}

.github/workflows/ci.yml

@@ -1,34 +0,0 @@
-name: CI
-
-on:
-  push:
-    branches: [main]
-  pull_request:
-    branches: [main]
-
-jobs:
-  python:
-    name: python
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ["3.10", "3.11", "3.12"]
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: Install dependencies
-        run: |
-          pip install -e ".[dev]"
-          pip install ruff pytest-timeout
-
-      - name: Lint with ruff
-        run: ruff check praisonai_tools tests --ignore E501,E701,E722,F401,F402
-
-      - name: Test with pytest
-        run: pytest tests/unit --ignore=tests/unit/video -v --timeout=120 -x -q

.github/workflows/claude-merge-gate.yml

@@ -1,96 +0,0 @@
-name: Claude PR Merge Gate
-
-on:
-  schedule:
-    - cron: '*/10 * * * *'
-  workflow_dispatch:
-    inputs:
-      pr_number:
-        required: false
-        type: string
-      merge_method:
-        required: false
-        type: choice
-        default: merge
-        options: [merge, squash, rebase]
-  workflow_run:
-    workflows:
-      - Claude Assistant
-      - CI
-    types: [completed]
-  repository_dispatch:
-    types: [claude-merge-gate]
-
-concurrency:
-  group: claude-merge-gate-${{ github.repository }}-${{ github.event.inputs.pr_number || github.event.client_payload.pr_number || github.event.workflow_run.id || 'scan' }}
-  cancel-in-progress: false
-
-permissions:
-  contents: read
-  pull-requests: read
-  issues: read
-  actions: read
-
-jobs:
-  auto-dispatch:
-    if: github.event_name == 'workflow_run'
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      pull-requests: read
-      issues: read
-      actions: write
-      id-token: write
-    steps:
-      - uses: actions/checkout@v4
-      - uses: MervinPraison/github-automation-template/.github/actions/[email protected]
-        with:
-          hub_ref: v1.1.20
-      - name: Generate GitHub App token
-        id: app-token
-        uses: actions/create-github-app-token@v1
-        with:
-          app-id: ${{ secrets.CLAUDE_APP_ID }}
-          private-key: ${{ secrets.CLAUDE_APP_PRIVATE_KEY }}
-          owner: ${{ github.repository_owner }}
-      - name: Dispatch merge gate when PR pipeline is ready
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ steps.app-token.outputs.token }}
-          script: |
-            const path = require('path');
-            const mergeGate = require(path.join(process.env.HUB_SCRIPTS, 'merge-gate.js'));
-            const wr = context.payload.workflow_run;
-            const owner = context.repo.owner;
-            const repo = context.repo.repo;
-            if (wr.conclusion !== 'success') return;
-            const allowed = ['Claude Assistant', 'CI'];
-            if (!allowed.includes(wr.name)) return;
-            const prNumber = await mergeGate.resolvePrNumberFromWorkflowRun(github, owner, repo, wr);
-            if (!prNumber) return;
-            const fromClaude = wr.name === 'Claude Assistant';
-            const check = await mergeGate.evaluatePipelineQuiescent(github, owner, repo, prNumber, core, {
-              skipGlobalClaudeRunCheck: fromClaude,
-              skipRecentClaudeCooldown: fromClaude,
-            });
-            if (!check.ready) return;
-            await github.rest.repos.createDispatchEvent({
-              owner, repo, event_type: 'claude-merge-gate',
-              client_payload: { pr_number: prNumber },
-            });
-
-  merge-gate:
-    if: github.event_name != 'workflow_run'
-    permissions:
-      contents: write
-      pull-requests: write
-      issues: write
-      actions: read
-      id-token: write
-      statuses: write   # the assess job records security/outsider-review
-    uses: MervinPraison/github-automation-template/.github/workflows/[email protected]
-    secrets: inherit
-    with:
-      hub_ref: v1.1.20
-      pr_number: ${{ github.event.inputs.pr_number || github.event.client_payload.pr_number || '' }}
-      merge_method: ${{ github.event.inputs.merge_method || 'merge' }}

.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

@@ -1,24 +0,0 @@
-name: Claude Assistant
-
-on:
-  issue_comment:
-    types: [created]
-  pull_request_review_comment:
-    types: [created]
-  issues:
-    types: [assigned, labeled, opened]
-  pull_request_review:
-    types: [submitted]
-
-jobs:
-  claude:
-    permissions:
-      contents: write
-      pull-requests: write
-      issues: write
-      actions: read
-      id-token: write
-    uses: MervinPraison/github-automation-template/.github/workflows/[email protected]
-    secrets: inherit
-    with:
-      hub_ref: v1.1.20

.github/workflows/generate-docs.yml

@@ -1,99 +0,0 @@
-name: Generate SDK Reference Docs
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - 'praisonai_tools/**'
-      - 'pyproject.toml'
-      - 'scripts/generate_reference_docs.py'
-  workflow_run:
-    workflows:
-      - CI
-    types:
-      - completed
-    branches:
-      - main
-  repository_dispatch:
-    types: [trigger-docs-generation]
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-concurrency:
-  group: generate-docs-praisonaidocs
-  cancel-in-progress: false
-
-jobs:
-  generate-docs:
-    if: |
-      github.event_name != 'workflow_run' ||
-      (
-        github.event.workflow_run.conclusion == 'success' &&
-        github.event.workflow_run.head_branch == 'main' &&
-        github.event.workflow_run.event == 'push' &&
-        !startsWith(github.event.workflow_run.head_commit.message, 'docs: auto-generate')
-      )
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-    env:
-      DOCS_PUSH_TOKEN: ${{ secrets.DOCS_GITHUB_TOKEN || secrets.GH_TOKEN }}
-    steps:
-      - name: Checkout PraisonAI Tools
-        uses: actions/checkout@v4
-        with:
-          path: praisonai-tools
-
-      - name: Checkout PraisonAI Source Repo
-        uses: actions/checkout@v4
-        with:
-          repository: MervinPraison/PraisonAI
-          path: praisonai-source
-
-      - name: Checkout PraisonAI Docs
-        uses: actions/checkout@v4
-        with:
-          repository: MervinPraison/PraisonAIDocs
-          path: praisonai-docs
-          token: ${{ env.DOCS_PUSH_TOKEN }}
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-
-      - name: Install PraisonAI Tools
-        run: |
-          cd praisonai-tools
-          pip install -e .
-
-      - name: Run Docs Generation
-        run: |
-          # Use the new unified CLI
-          praisonai-tools docs-generate \
-            --package all \
-            --docs-root $(pwd)/praisonai-docs \
-            --source-root $(pwd)/praisonai-source
-
-      - name: Sync SDK reference navigation
-        run: python3 praisonai-docs/scripts/sync_sdk_reference_nav.py
-        env:
-          PYTHONPATH: $(pwd)/praisonai-tools
-
-      - name: Commit and Push Docs
-        if: env.DOCS_PUSH_TOKEN != ''
-        run: |
-          cd praisonai-docs
-          git config --global user.name "github-actions[bot]"
-          git config --global user.email "github-actions[bot]@users.noreply.github.com"
-          git add .
-          if git diff --staged --quiet; then
-            echo "No changes to commit"
-          else
-            git commit -m "docs: auto-generate SDK reference documentation"
-            git pull --rebase origin main
-            git push origin main
-          fi

.github/workflows/merge-conflict-claude.yml

@@ -1,52 +0,0 @@
-name: Auto Merge Conflict → Claude
-
-on:
-  push:
-    branches: [main]
-  pull_request:
-    types: [opened, synchronize, reopened]
-  workflow_dispatch:
-  schedule:
-    - cron: '0 */6 * * *'
-
-concurrency:
-  group: merge-conflict-scan-${{ github.repository }}-${{ github.event.pull_request.number || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  GH_TOKEN: ${{ secrets.GH_TOKEN || github.token }}
-
-permissions:
-  pull-requests: write
-  issues: write
-
-jobs:
-  detect-and-trigger:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: MervinPraison/github-automation-template/.github/actions/[email protected]
-        with:
-          hub_ref: v1.1.20
-      - name: Detect conflicting PRs and trigger Claude
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ env.GH_TOKEN }}
-          script: |
-            const path = require('path');
-            const mg = require(path.join(process.env.HUB_SCRIPTS, 'merge-gate.js'));
-            const owner = context.repo.owner;
-            const repo = context.repo.repo;
-            const baseRepo = `${owner}/${repo}`;
-            const prs = await github.paginate(github.rest.pulls.list, { owner, repo, state: 'open', per_page: 50 });
-            for (const pr of prs) {
-              if (pr.draft) continue;
-              if (pr.head?.repo?.full_name !== baseRepo) continue;
-              const state = await mg.getMergeState(github, owner, repo, pr.number);
-              if (state.status !== 'DIRTY') continue;
-              const { data: comments } = await github.rest.issues.listComments({ owner, repo, issue_number: pr.number, per_page: 100 });
-              if (mg.hasRecentConflictComment(comments)) continue;
-              const body = '@claude this PR has merge conflicts with `main`. Rebase onto latest main, resolve conflicts, run tests, force-push with --force-with-lease.';
-              await github.rest.issues.createComment({ owner, repo, issue_number: pr.number, body });
-              await github.rest.issues.addLabels({ owner, repo, issue_number: pr.number, labels: ['claude-conflict-pending'] });
-            }

.github/workflows/nightly-release-gate.yml

@@ -1,132 +0,0 @@
-name: Nightly Release Gate
-
-# Preflight before PyPI Release (patch bump).
-# Primary: CI success on main (workflow_run).
-# Backstop: cron every 3 days at 00:00 UTC.
-# Dedupe: max one successful patch release every 3 days (see release-gate.js).
-
-on:
-  schedule:
-    - cron: '0 0 */3 * *'
-  workflow_dispatch:
-    inputs:
-      dry_run:
-        description: 'Run preflight only; do not dispatch pypi-release.yml'
-        required: false
-        default: false
-        type: boolean
-  workflow_run:
-    workflows:
-      - CI
-    types:
-      - completed
-    branches:
-      - main
-
-permissions:
-  contents: read
-  actions: write
-
-concurrency:
-  group: nightly-release-gate
-  cancel-in-progress: false
-
-jobs:
-  preflight:
-    if: |
-      github.event_name == 'workflow_dispatch' ||
-      github.event_name == 'schedule' ||
-      (
-        github.event_name == 'workflow_run' &&
-        github.event.workflow_run.conclusion == 'success' &&
-        github.event.workflow_run.head_branch == 'main' &&
-        github.event.workflow_run.event == 'push' &&
-        !startsWith(github.event.workflow_run.head_commit.message, 'Release v') &&
-        !startsWith(github.event.workflow_run.head_commit.message, 'Bump ')
-      )
-    runs-on: ubuntu-latest
-    env:
-      GH_TOKEN: ${{ secrets.GH_TOKEN }}
-    steps:
-      - name: Resolve trigger context
-        id: ctx
-        run: |
-          if [ "${{ github.event_name }}" = "workflow_run" ]; then
-            echo "checkout_sha=${{ github.event.workflow_run.head_sha }}" >> "$GITHUB_OUTPUT"
-            echo "source=ci" >> "$GITHUB_OUTPUT"
-            echo "is_ci=true" >> "$GITHUB_OUTPUT"
-          else
-            echo "checkout_sha=main" >> "$GITHUB_OUTPUT"
-            echo "source=nightly" >> "$GITHUB_OUTPUT"
-            echo "is_ci=false" >> "$GITHUB_OUTPUT"
-          fi
-
-      - name: Checkout
-        uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4
-        with:
-          ref: ${{ steps.ctx.outputs.checkout_sha }}
-          fetch-depth: 0
-
-      - name: Fetch main and tags
-        run: git fetch --tags origin main
-
-      - name: Preflight — path changes, CI SHA, dedupe
-        id: preflight
-        uses: actions/github-script@v7
-        env:
-          HEAD_SHA: ${{ steps.ctx.outputs.checkout_sha }}
-          IS_CI: ${{ steps.ctx.outputs.is_ci }}
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            const path = require('path');
-            const rg = require(path.join(process.env.GITHUB_WORKSPACE, '.github/scripts/release-gate.js'));
-            const owner = context.repo.owner;
-            const repo = context.repo.repo;
-            const headSha = process.env.HEAD_SHA;
-            const isCi = process.env.IS_CI === 'true';
-
-            const result = await rg.evaluateReleasePreflight(
-              github, owner, repo,
-              { headSha, isCiTrigger: isCi, bump: 'patch' },
-              core
-            );
-
-            core.setOutput('should_release', result.ready ? 'true' : 'false');
-            core.setOutput('reason', result.reasons.join('; '));
-            core.setOutput('head_sha', result.headSha || headSha);
-            core.setOutput('last_tag', result.lastTag || '');
-            core.setOutput('target_version', result.targetVersion || '');
-
-            core.summary.addRaw('## Release gate preflight\n');
-            core.summary.addRaw(`Ready: ${result.ready}\n`);
-            core.summary.addRaw(`Reason: ${result.reasons.join('; ')}\n`);
-            if (result.targetVersion) {
-              core.summary.addRaw(`Target: package==${result.targetVersion}\n`);
-            }
-            await core.summary.write();
-
-            if (!result.ready) {
-              core.info(`Skip release: ${result.reasons.join('; ')}`);
-            }
-
-      - name: Dry run summary
-        if: inputs.dry_run == true
-        run: |
-          echo "Dry run — preflight only, no pypi-release dispatch."
-          echo "should_release=${{ steps.preflight.outputs.should_release }}"
-          echo "reason=${{ steps.preflight.outputs.reason }}"
-
-      - name: Dispatch PyPI Release (patch)
-        if: steps.preflight.outputs.should_release == 'true' && inputs.dry_run != true
-        run: |
-          set -euo pipefail
-          gh workflow run pypi-release.yml \
-            --repo "${GITHUB_REPOSITORY}" \
-            --ref main \
-            -f bump=patch \
-            -f source=${{ steps.ctx.outputs.source }} \
-            -f trigger_sha=${{ steps.preflight.outputs.head_sha }} \
-            -f dry_run=false
-          echo "Dispatched pypi-release.yml (patch, source=${{ steps.ctx.outputs.source }})"
-          echo "Uses pypi-auto environment for ci/nightly patch releases."

.github/workflows/pipeline-status-sync.yml

@@ -1,19 +0,0 @@
-name: Pipeline Status Sync
-
-on:
-  schedule:
-    - cron: '*/10 * * * *'
-  workflow_dispatch:
-
-permissions:
-  pull-requests: read
-  issues: write
-  actions: write
-  id-token: write
-
-jobs:
-  sync:
-    uses: MervinPraison/github-automation-template/.github/workflows/[email protected]
-    secrets: inherit
-    with:
-      hub_ref: v1.1.20

.github/workflows/pypi-release.yml

@@ -1,148 +0,0 @@
-name: PyPI Release
-
-on:
-  workflow_dispatch:
-    inputs:
-      bump:
-        description: 'Version bump type'
-        required: true
-        default: patch
-        type: choice
-        options: [patch, minor, major]
-      dry_run:
-        description: 'Preview only; do not publish'
-        required: false
-        default: false
-        type: boolean
-      version:
-        description: 'Override version (empty = auto bump)'
-        required: false
-        default: ''
-        type: string
-      source:
-        description: 'Trigger source (audit trail)'
-        required: false
-        default: manual
-        type: string
-      trigger_sha:
-        description: 'SHA that triggered this release'
-        required: false
-        default: ''
-        type: string
-
-permissions:
-  contents: write
-
-concurrency:
-  group: pypi-release
-  cancel-in-progress: false
-
-jobs:
-  release:
-    if: github.ref == 'refs/heads/main'
-    runs-on: ubuntu-latest
-    environment: >-
-      ${{
-        (inputs.source == 'ci' || inputs.source == 'nightly') &&
-        inputs.bump == 'patch' &&
-        'pypi-auto' || 'pypi'
-      }}
-    env:
-      PACKAGE: praisonai-tools
-      GH_TOKEN: ${{ secrets.GH_TOKEN }}
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: main
-          fetch-depth: 0
-          token: ${{ secrets.GH_TOKEN }}
-
-      - uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-
-      - uses: astral-sh/setup-uv@v4
-
-      - name: Configure git
-        run: |
-          git config user.name "MervinPraison"
-          git config user.email "[email protected]"
-          gh auth setup-git
-
-      - name: Compute release version
-        id: versions
-        env:
-          BUMP: ${{ inputs.bump }}
-          VERSION_OVERRIDE: ${{ inputs.version }}
-        run: |
-          python <<'PY'
-          import os, re, sys
-          from pathlib import Path
-          bump = os.environ["BUMP"]
-          override = os.environ.get("VERSION_OVERRIDE", "").strip()
-          content = Path("pyproject.toml").read_text()
-          match = re.search(r'^version\s*=\s*"([^"]+)"', content, re.MULTILINE)
-          if not match:
-              sys.exit("Could not read version from pyproject.toml")
-          current = match.group(1)
-          maj, min_, patch = map(int, current.split("."))
-          if override:
-              new_version = override
-          elif bump == "major":
-              new_version = f"{maj + 1}.0.0"
-          elif bump == "minor":
-              new_version = f"{maj}.{min_ + 1}.0"
-          else:
-              new_version = f"{maj}.{min_}.{patch + 1}"
-          with open(os.environ["GITHUB_OUTPUT"], "a") as fh:
-              fh.write(f"current_version={current}\nnew_version={new_version}\n")
-          PY
-
-      - name: Bump pyproject.toml
-        if: inputs.dry_run != true
-        env:
-          CURRENT: ${{ steps.versions.outputs.current_version }}
-          NEW_VERSION: ${{ steps.versions.outputs.new_version }}
-        run: |
-          python -c "
-          from pathlib import Path
-          import os
-          p = Path('pyproject.toml')
-          p.write_text(p.read_text().replace(f'version = \"{os.environ[\"CURRENT\"]}\"', f'version = \"{os.environ[\"NEW_VERSION\"]}\"', 1))
-          "
-
-      - name: Build distribution
-        if: inputs.dry_run != true
-        run: |
-          uv lock
-          uv build
-
-      - name: Commit, tag, and push
-        if: inputs.dry_run != true
-        env:
-          VERSION: ${{ steps.versions.outputs.new_version }}
-        run: |
-          TAG="v${VERSION}"
-          git add pyproject.toml
-          git commit -m "Release ${TAG}" || exit 0
-          git tag -f "${TAG}"
-          git pull --rebase origin main
-          git push origin main
-          git push origin "${TAG}" --force
-
-      - name: Publish to PyPI
-        if: inputs.dry_run != true
-        env:
-          # PyPI API token auth: username __token__, password = pypi-… token (see pypi.org/help/#invalid-auth)
-          UV_PUBLISH_USERNAME: __token__
-          UV_PUBLISH_PASSWORD: ${{ secrets.PYPI_TOKEN || secrets.PYPI_API_TOKEN }}
-        run: |
-          uv publish
-
-      - name: Create GitHub release
-        if: inputs.dry_run != true
-        env:
-          VERSION: ${{ steps.versions.outputs.new_version }}
-        run: |
-          TAG="v${VERSION}"
-          gh release create "${TAG}" --title "praisonai-tools ${TAG}" --notes "Release ${TAG}" --latest

.github/workflows/python-package.yml

@@ -0,0 +1,31 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python package
+
+on:
+  push:
+    branches: [ "develop" ]
+  pull_request:
+    branches: [ "develop" ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11"]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install flake8 pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi

.github/workflows/sync-claude-secrets.yml

@@ -1,36 +0,0 @@
-name: Sync Claude Secrets
-
-on:
-  workflow_dispatch:
-    inputs:
-      target_repo:
-        description: 'Destination repo (owner/name)'
-        required: true
-        type: string
-
-permissions:
-  contents: read
-
-jobs:
-  sync:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Sync Claude secrets to target repo
-        env:
-          GH_TOKEN: ${{ secrets.SECRETS_ADMIN_PAT }}
-          TARGET: ${{ inputs.target_repo }}
-          CLAUDE_APP_ID: ${{ secrets.CLAUDE_APP_ID }}
-          CLAUDE_APP_PRIVATE_KEY: ${{ secrets.CLAUDE_APP_PRIVATE_KEY }}
-          CLAUDE_CODE_OAUTH_TOKEN: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
-          GH_TOKEN_VALUE: ${{ secrets.GH_TOKEN }}
-        run: |
-          set -euo pipefail
-          if [ -z "${GH_TOKEN:-}" ]; then
-            echo "SECRETS_ADMIN_PAT is not configured in this repository"
-            exit 1
-          fi
-          echo -n "$CLAUDE_APP_ID" | gh secret set CLAUDE_APP_ID --repo "$TARGET"
-          echo -n "$CLAUDE_APP_PRIVATE_KEY" | gh secret set CLAUDE_APP_PRIVATE_KEY --repo "$TARGET"
-          echo -n "$CLAUDE_CODE_OAUTH_TOKEN" | gh secret set CLAUDE_CODE_OAUTH_TOKEN --repo "$TARGET"
-          echo -n "$GH_TOKEN_VALUE" | gh secret set GH_TOKEN --repo "$TARGET"
-          echo "Synced CLAUDE_APP_ID, CLAUDE_APP_PRIVATE_KEY, CLAUDE_CODE_OAUTH_TOKEN, GH_TOKEN → $TARGET"

.github/workflows/sync-secrets-from-praisonai.yml

@@ -1,21 +0,0 @@
-name: Sync Claude Secrets from PraisonAI
-
-on:
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  trigger-sync:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Trigger PraisonAI secret sync
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          gh workflow run sync-secrets-to-aiui.yml \
-            --repo MervinPraison/PraisonAI \
-            --ref main \
-            -f target_repo=MervinPraison/PraisonAI-Tools
-          echo "Triggered secret sync → MervinPraison/PraisonAI-Tools"

.gitignore

@@ -1,46 +1,16 @@
-venv
 .DS_Store
 .pytest_cache
-__pycache__/
-*.py[cod]
-*$py.class
-
-# Distribution / packaging
-.Python
-build/
+__pycache__
 dist/
-*.egg-info/
-
-# Virtual environments
-.venv/
-venv/
-
-# IDE
-.vscode/
-.idea/
-
-# Testing
-.pytest_cache/
-.coverage
-htmlcov/
-
-# Tool auto-discovery cache (generated at import time)
-._discovery_cache_v*.json
-
-# Lock files
-poetry.lock
-uv.lock
-
-# Database
-*.db
-db/
+.env
+.idea
+test.py
 chroma.sqlite3
-
-# OS
-.DS_Store
+.vscode
 praisonai
 .cache
 __pycache__
+chroma.sqlite3
 test/
 .env
 assets/*

.pre-commit-config.yaml

@@ -0,0 +1,21 @@
+repos:
+
+  - repo: https://github.com/psf/black-pre-commit-mirror
+    rev: 23.12.1
+    hooks:
+      - id: black
+        language_version: python3.11
+        files: \.(py)$
+
+  - repo: https://github.com/pycqa/isort
+    rev: 5.13.2
+    hooks:
+      - id: isort
+        name: isort (python)
+        args: ["--profile", "black", "--filter-files"]
+
+  - repo: https://github.com/PyCQA/autoflake
+    rev: v2.2.1
+    hooks:
+      - id: autoflake
+        args: ['--in-place', '--remove-all-unused-imports', '--remove-unused-variables', '--ignore-init-module-imports']

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2024 Mervin Praison
+Copyright (c) 2024 João Moura
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -1,784 +1 @@
-# PraisonAI Tools
-
-Base classes for creating **custom tools** for [PraisonAI Agents](https://github.com/MervinPraison/PraisonAI).
-
-[![PyPI version](https://badge.fury.io/py/praisonai-tools.svg)](https://badge.fury.io/py/praisonai-tools)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
-
-## What is this package?
-
-This package provides **base classes** (`BaseTool`, `@tool` decorator) for creating custom tools that work with PraisonAI Agents.
-
-> **Note:** Common tools like Tavily, Exa, You.com, DuckDuckGo, Wikipedia, arXiv, and many more are **already built into `praisonaiagents`**. You don't need this package for those - just use them directly from `praisonaiagents.tools`.
-
-## Installation
-
-```bash
-pip install praisonai-tools
-```
-
----
-
-# All Ways to Add Tools to PraisonAI Agents
-
-PraisonAI Agents supports **8 different ways** to add tools. Choose the method that best fits your use case:
-
-| Method | Best For | Complexity |
-|--------|----------|------------|
-| [1. Plain Python Functions](#1-plain-python-functions) | Quick custom tools | ⭐ Easy |
-| [2. Built-in Tools](#2-built-in-tools) | Common operations | ⭐ Easy |
-| [3. @tool Decorator](#3-tool-decorator) | Custom tools with metadata | ⭐ Easy |
-| [4. BaseTool Class](#4-basetool-class) | Complex tools with state | ⭐⭐ Medium |
-| [5. Pydantic Class with run()](#5-pydantic-class-with-run) | Validated tools | ⭐⭐ Medium |
-| [6. LangChain Tools](#6-langchain-tools) | LangChain ecosystem | ⭐⭐ Medium |
-| [7. CrewAI Tools](#7-crewai-tools) | CrewAI ecosystem | ⭐⭐ Medium |
-| [8. MCP Tools](#8-mcp-model-context-protocol-tools) | External services | ⭐⭐⭐ Advanced |
-
----
-
-## 1. Plain Python Functions
-
-The simplest way - just write a function with type hints and docstring:
-
-```python
-from praisonaiagents import Agent
-
-def search_web(query: str, max_results: int = 5) -> list:
-    """Search the web for information.
-    
-    Args:
-        query: The search query
-        max_results: Maximum number of results to return
-    """
-    # Your implementation
-    return [{"title": "Result 1", "url": "https://..."}]
-
-def calculate(expression: str) -> float:
-    """Evaluate a mathematical expression."""
-    return eval(expression)
-
-# Just pass the functions!
-agent = Agent(
-    instructions="You are a helpful assistant",
-    tools=[search_web, calculate]
-)
-
-agent.start("Search for Python tutorials and calculate 15 * 7")
-```
-
-**How it works:** PraisonAI automatically:
-- Extracts function name as tool name
-- Uses docstring as description
-- Generates JSON schema from type hints
-- Parses Args section for parameter descriptions
-
----
-
-## 2. Built-in Tools
-
-Use pre-built tools from `praisonaiagents.tools`:
-
-```python
-from praisonaiagents import Agent
-from praisonaiagents.tools import (
-    # Search
-    tavily_search,
-    exa_search,
-    internet_search,  # DuckDuckGo
-    
-    # Wikipedia
-    wiki_search,
-    wiki_summary,
-    
-    # News
-    get_article,
-    get_trending_topics,
-    
-    # Files
-    read_file,
-    write_file,
-    
-    # Code
-    execute_code,
-    
-    # And many more...
-)
-
-agent = Agent(
-    instructions="You are a research assistant",
-    tools=[tavily_search, wiki_search, read_file]
-)
-
-agent.start("Search for AI news and save a summary to a file")
-```
-
-### Available Built-in Tools
-
-| Category | Tools |
-|----------|-------|
-| **Search** | `tavily_search`, `exa_search`, `ydc_search`, `internet_search`, `searxng_search` |
-| **Wikipedia** | `wiki_search`, `wiki_summary`, `wiki_page`, `wiki_random` |
-| **arXiv** | `search_arxiv`, `get_arxiv_paper`, `get_papers_by_author`, `get_papers_by_category` |
-| **News** | `get_article`, `get_news_sources`, `get_articles_from_source`, `get_trending_topics` |
-| **Web Crawling** | `crawl4ai`, `scrape_page`, `extract_links`, `extract_text` |
-| **Files** | `read_file`, `write_file`, `list_files`, `copy_file`, `move_file`, `delete_file` |
-| **Data** | `read_csv`, `write_csv`, `read_json`, `write_json`, `read_excel`, `read_yaml` |
-| **Code** | `execute_code`, `analyze_code`, `format_code`, `lint_code` |
-| **Shell** | `execute_command`, `list_processes`, `kill_process`, `get_system_info` |
-| **Calculator** | `evaluate`, `solve_equation`, `convert_units`, `calculate_statistics` |
-| **Finance** | `get_stock_price`, `get_stock_info`, `get_historical_data` |
-| **Database** | `query` (DuckDB), `insert_document`, `find_documents` (MongoDB) |
-
----
-
-## 3. @tool Decorator
-
-Use the `@tool` decorator for custom tools with metadata:
-
-```python
-from praisonaiagents import Agent
-from praisonai_tools import tool
-
-@tool
-def get_weather(location: str, units: str = "celsius") -> dict:
-    """Get current weather for a location.
-    
-    Args:
-        location: City name or coordinates
-        units: Temperature units (celsius/fahrenheit)
-    """
-    # Your implementation
-    return {"temp": 22, "condition": "sunny", "location": location}
-
-@tool(name="custom_search", description="Search with custom parameters")
-def my_search(query: str, limit: int = 10) -> list:
-    """Custom search implementation."""
-    return [{"result": query}]
-
-agent = Agent(
-    instructions="You are a weather assistant",
-    tools=[get_weather, my_search]
-)
-
-agent.start("What's the weather in London?")
-```
-
----
-
-## 4. BaseTool Class
-
-For complex tools with state, validation, or multiple methods:
-
-```python
-from praisonaiagents import Agent
-from praisonai_tools import BaseTool
-
-class DatabaseTool(BaseTool):
-    name = "database_query"
-    description = "Query a database and return results"
-    
-    def __init__(self, connection_string: str):
-        self.connection_string = connection_string
-        self._connection = None
-        super().__init__()
-    
-    def run(self, query: str, limit: int = 100) -> list:
-        """Execute a database query.
-        
-        Args:
-            query: SQL query to execute
-            limit: Maximum rows to return
-        """
-        # Your implementation
-        return [{"id": 1, "name": "Example"}]
-
-# Create instance with configuration
-db_tool = DatabaseTool(connection_string="postgresql://...")
-
-agent = Agent(
-    instructions="You are a data analyst",
-    tools=[db_tool]
-)
-
-agent.start("Query the users table for active users")
-```
-
----
-
-## 5. Pydantic Class with run()
-
-Use Pydantic for validated tools with type checking:
-
-```python
-from praisonaiagents import Agent
-from pydantic import BaseModel, Field
-from typing import Optional
-import requests
-
-class APISearchTool(BaseModel):
-    """Search tool using an external API."""
-    
-    api_url: str = "https://api.example.com/search"
-    api_key: Optional[str] = None
-    max_results: int = Field(default=10, ge=1, le=100)
-    
-    def run(self, query: str) -> dict:
-        """Execute search query.
-        
-        Args:
-            query: Search query string
-        """
-        headers = {"Authorization": f"Bearer {self.api_key}"} if self.api_key else {}
-        response = requests.get(
-            self.api_url,
-            params={"q": query, "limit": self.max_results},
-            headers=headers
-        )
-        return response.json()
-
-# Pass the class (not instance) - PraisonAI will instantiate it
-agent = Agent(
-    instructions="You are a search assistant",
-    tools=[APISearchTool]
-)
-
-agent.start("Search for machine learning tutorials")
-```
-
----
-
-## 6. LangChain Tools
-
-Use any LangChain tool directly:
-
-```python
-from praisonaiagents import Agent
-from langchain_community.tools import YouTubeSearchTool, DuckDuckGoSearchRun
-from langchain_community.utilities import WikipediaAPIWrapper
-
-# Method 1: Pass LangChain tool classes directly
-agent = Agent(
-    instructions="You are a research assistant",
-    tools=[YouTubeSearchTool, WikipediaAPIWrapper]
-)
-
-agent.start("Find YouTube videos about Python and search Wikipedia for its history")
-```
-
-### Wrapping LangChain Tools
-
-For more control, wrap LangChain tools in functions:
-
-```python
-from praisonaiagents import Agent
-from langchain_community.tools import YouTubeSearchTool
-from langchain_community.utilities import WikipediaAPIWrapper
-
-def youtube_search(query: str, max_results: int = 5) -> str:
-    """Search YouTube for videos.
-    
-    Args:
-        query: Search query
-        max_results: Number of results
-    """
-    yt = YouTubeSearchTool()
-    return yt.run(f"{query}, {max_results}")
-
-def wikipedia_search(query: str) -> str:
-    """Search Wikipedia for information.
-    
-    Args:
-        query: Search query
-    """
-    wiki = WikipediaAPIWrapper()
-    return wiki.run(query)
-
-agent = Agent(
-    instructions="You are a research assistant",
-    tools=[youtube_search, wikipedia_search]
-)
-
-agent.start("Find videos about AI and get Wikipedia info on machine learning")
-```
-
-### Using LangChain Toolkits
-
-```python
-from praisonaiagents import Agent
-from langchain_agentql.tools import ExtractWebDataTool
-
-def extract_web_data(url: str, query: str) -> dict:
-    """Extract structured data from a webpage.
-    
-    Args:
-        url: URL to extract from
-        query: What data to extract
-    """
-    tool = ExtractWebDataTool()
-    return tool.invoke({"url": url, "prompt": query})
-
-agent = Agent(
-    instructions="You are a web scraping assistant",
-    tools=[extract_web_data]
-)
-
-agent.start("Extract product names and prices from https://example.com/products")
-```
-
----
-
-## 7. CrewAI Tools
-
-Use CrewAI tools (classes with `_run` method):
-
-```python
-from praisonaiagents import Agent
-
-# CrewAI-style tool class
-class CrewAISearchTool:
-    """A CrewAI-compatible search tool."""
-    
-    name = "web_search"
-    description = "Search the web for information"
-    
-    def _run(self, query: str) -> str:
-        """Execute the search.
-        
-        Args:
-            query: Search query
-        """
-        # Your implementation
-        return f"Results for: {query}"
-
-# Pass the class - PraisonAI detects _run method
-agent = Agent(
-    instructions="You are a search assistant",
-    tools=[CrewAISearchTool]
-)
-
-agent.start("Search for latest tech news")
-```
-
----
-
-## 8. MCP (Model Context Protocol) Tools
-
-Use external tools via MCP servers:
-
-### Filesystem MCP
-
-```python
-from praisonaiagents import Agent, MCP
-
-agent = Agent(
-    instructions="You are a file manager assistant",
-    tools=MCP("npx -y @modelcontextprotocol/server-filesystem", 
-              args=["/Users/username/Documents"])
-)
-
-agent.start("List all Python files in the Documents folder")
-```
-
-### Time MCP
-
-```python
-from praisonaiagents import Agent, MCP
-
-agent = Agent(
-    instructions="You are a time assistant",
-    tools=MCP("python -m mcp_server_time --local-timezone=America/New_York")
-)
-
-agent.start("What time is it in New York? Convert to UTC.")
-```
-
-### GitHub MCP
-
-```python
-from praisonaiagents import Agent, MCP
-import os
-
-agent = Agent(
-    instructions="You are a GitHub assistant",
-    tools=MCP("npx -y @modelcontextprotocol/server-github",
-              env={"GITHUB_TOKEN": os.environ["GITHUB_TOKEN"]})
-)
-
-agent.start("List my recent repositories")
-```
-
-### Multiple MCP Servers
-
-```python
-from praisonaiagents import Agent, MCP
-
-agent = Agent(
-    instructions="You are a multi-capable assistant",
-    tools=[
-        MCP("npx -y @modelcontextprotocol/server-filesystem", args=["/tmp"]),
-        MCP("python -m mcp_server_time"),
-        my_custom_function,  # Can mix with other tool types!
-    ]
-)
-
-agent.start("Create a file with the current timestamp")
-```
-
----
-
-## Combining Multiple Tool Types
-
-You can mix and match all tool types:
-
-```python
-from praisonaiagents import Agent, MCP
-from praisonaiagents.tools import tavily_search, wiki_search
-from praisonai_tools import tool, BaseTool
-from langchain_community.tools import YouTubeSearchTool
-
-# Plain function
-def calculate(expression: str) -> float:
-    """Calculate a math expression."""
-    return eval(expression)
-
-# @tool decorator
-@tool
-def format_output(data: dict) -> str:
-    """Format data as a nice string."""
-    return str(data)
-
-# BaseTool class
-class CustomTool(BaseTool):
-    name = "custom"
-    description = "A custom tool"
-    def run(self, input: str) -> str:
-        return f"Processed: {input}"
-
-# Combine everything!
-agent = Agent(
-    instructions="You are a super assistant with many capabilities",
-    tools=[
-        # Built-in tools
-        tavily_search,
-        wiki_search,
-        
-        # Plain function
-        calculate,
-        
-        # Decorated function
-        format_output,
-        
-        # BaseTool instance
-        CustomTool(),
-        
-        # LangChain tool
-        YouTubeSearchTool,
-        
-        # MCP server
-        MCP("python -m mcp_server_time"),
-    ]
-)
-
-agent.start("Search for AI news, find related YouTube videos, and calculate 2^10")
-```
-
----
-
-## Creating Distributable Tool Packages
-
-To create a pip-installable tool package:
-
-### 1. Create your tool module
-
-```python
-# my_tools/weather.py
-from praisonai_tools import BaseTool
-
-class WeatherTool(BaseTool):
-    name = "get_weather"
-    description = "Get weather for a location"
-    
-    def run(self, location: str) -> dict:
-        # Implementation
-        return {"temp": 22, "location": location}
-
-# Convenience instance
-weather_tool = WeatherTool()
-```
-
-### 2. Create pyproject.toml
-
-```toml
-[project]
-name = "my-weather-tools"
-version = "0.1.0"
-dependencies = ["praisonai-tools"]
-
-[project.entry-points."praisonaiagents.tools"]
-weather = "my_tools.weather:WeatherTool"
-```
-
-### 3. Users can then:
-
-```python
-from praisonaiagents import Agent
-from my_tools.weather import weather_tool
-
-agent = Agent(
-    instructions="Weather assistant",
-    tools=[weather_tool]
-)
-```
-
-### How `praisonai-tools` registers its own tools
-
-The bundled tools in this package are **discovered automatically**. Every
-module under `praisonai_tools/tools/` is scanned (at import time, without
-executing the module) and its public top-level classes and functions become
-importable directly:
-
-```python
-from praisonai_tools.tools import WeatherTool, get_weather   # discovered
-from praisonai_tools import WeatherTool                       # same symbol
-```
-
-Adding a new tool therefore means **adding one file** to
-`praisonai_tools/tools/` — there is no import map or `__all__` to edit, and
-imports stay lazy (a tool module and its optional dependencies are only loaded
-the first time one of its symbols is accessed).
-
-By default the discovered public surface is the module's top-level classes and
-functions. If a module exposes tools through module-level aliases
-(e.g. `read_yaml = _yaml_tools.read_yaml`), declare a standard `__all__` in that
-module and the listed names are exported verbatim. Modules and names starting
-with `_` are treated as private and never exported.
-
-The scan result is memoised in a small on-disk cache next to the package, so
-import stays fast; the cache is rebuilt automatically whenever a tool file is
-added, removed, or edited.
-
-External packages that want their tools discovered by `praisonaiagents` should
-use the `[project.entry-points."praisonaiagents.tools"]` group shown above,
-which the core `praisonaiagents` registry resolves.
-
----
-
-## 9. Video Editing Tools
-
-```bash
-python -m praisonai_tools.video probe input.mp4
-```
-
-```bash
-python -m praisonai_tools.video transcribe input.mp4 --output transcript.srt
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --preset podcast
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --preset podcast -v
-```
-
-```bash
-python -m praisonai_tools.video render input.mp4 --timeline plan.json --output output.mp4
-```
-
-| Feature | Code Docs | CLI Docs |
-|---------|-----------|----------|
-| Video Module | [docs/video-module.md](docs/video-module.md) | [docs/video-cli.md](docs/video-cli.md) |
-
----
-
-## 10. Ready-to-Use Tools from praisonai-tools
-
-This package also includes ready-to-use tools for common integrations:
-
-### Communication Tools
-
-```python
-from praisonai_tools import EmailTool, SlackTool, DiscordTool
-
-# Email (Gmail, Outlook, Yahoo)
-email = EmailTool(provider="gmail")
-email.send(to="[email protected]", subject="Hello", body="Message")
-emails = email.read(limit=10)
-
-# Slack
-slack = SlackTool()  # Uses SLACK_TOKEN env var
-slack.send_message(channel="#general", text="Hello from AI!")
-channels = slack.list_channels()
-
-# Discord (webhook or bot)
-discord = DiscordTool(webhook_url="https://discord.com/api/webhooks/...")
-discord.send_webhook(content="Hello!", embed=DiscordTool.create_embed(title="Alert"))
-```
-
-### Developer Tools
-
-```python
-from praisonai_tools import GitHubTool
-
-github = GitHubTool()  # Uses GITHUB_TOKEN env var
-repos = github.search_repos("machine learning python", limit=10)
-github.create_issue(repo="owner/repo", title="Bug", body="Description")
-prs = github.list_pull_requests(repo="owner/repo")
-```
-
-### AI/Media Tools
-
-```python
-from praisonai_tools import ImageTool, TTSTool, YouTubeTool
-
-# Image Generation (DALL-E)
-img = ImageTool(model="dall-e-3", quality="hd")
-result = img.generate("A sunset over mountains")
-
-# Text-to-Speech (OpenAI or ElevenLabs)
-tts = TTSTool(provider="openai", voice="nova")
-tts.speak("Hello world!", output_path="output.mp3")
-
-# YouTube
-yt = YouTubeTool()  # Uses YOUTUBE_API_KEY env var
-videos = yt.search("python tutorial", limit=5)
-transcript = yt.get_transcript("dQw4w9WgXcQ")
-```
-
-### Meeting Intelligence Tools
-
-```python
-from praisonai_tools import FirefliesTool
-
-# Fireflies.ai meeting intelligence (curated GraphQL operations)
-fireflies = FirefliesTool()  # Uses FIREFLIES_API_KEY env var
-meetings = fireflies.list_recent_meetings(limit=5)
-meetings = fireflies.list_recent_meetings(limit=5, after="5")  # cursor pagination
-transcript = fireflies.get_transcript(meeting_id="abc123")
-results = fireflies.search(keywords="roadmap")
-```
-
-`FirefliesTool` distinguishes a missing key (`{"error": "FIREFLIES_API_KEY required"}`)
-from a rejected key (HTTP 401/403), and surfaces `retry_after` on rate limits.
-Set `FIREFLIES_API_URL` to override the endpoint (previews/proxies).
-
-### Data Tools
-
-```python
-from praisonai_tools import WeatherTool
-
-weather = WeatherTool(units="metric")  # Uses OPENWEATHER_API_KEY env var
-current = weather.get_current("London")
-forecast = weather.get_forecast("New York", days=5)
-air = weather.get_air_quality("Tokyo")
-```
-
-### Environment Variables
-
-| Tool | Required Environment Variable |
-|------|------------------------------|
-| EmailTool | `EMAIL_USERNAME`, `EMAIL_PASSWORD` |
-| SlackTool | `SLACK_TOKEN` |
-| DiscordTool | `DISCORD_BOT_TOKEN` or `DISCORD_WEBHOOK_URL` |
-| GitHubTool | `GITHUB_TOKEN` |
-| ImageTool | `OPENAI_API_KEY` |
-| TTSTool | `OPENAI_API_KEY` or `ELEVENLABS_API_KEY` |
-| YouTubeTool | `YOUTUBE_API_KEY` |
-| WeatherTool | `OPENWEATHER_API_KEY` |
-| FirefliesTool | `FIREFLIES_API_KEY` (optional `FIREFLIES_API_URL`) |
-| SwarmScoreTool | None (uses public API) |
-
-### Using with PraisonAI Agents
-
-```python
-from praisonaiagents import Agent
-from praisonai_tools import EmailTool, SlackTool, GitHubTool, WeatherTool, SwarmScoreTool
-
-agent = Agent(
-    instructions="You are a helpful assistant with access to email, slack, github, weather, and trust ratings",
-    tools=[
-        EmailTool(provider="gmail"),
-        SlackTool(),
-        GitHubTool(),
-        WeatherTool(),
-        SwarmScoreTool(),
-    ]
-)
-
-agent.start("Check the weather in London and send a Slack message about it")
-```
-
----
-
-## Composio Integration
-
-Load 250+ managed app integrations (GitHub, Slack, Gmail, Jira, etc.) as agent-callable tools:
-
-```bash
-pip install praisonai-tools[composio]
-export COMPOSIO_API_KEY=your_api_key
-```
-
-```python
-from praisonaiagents import Agent
-from praisonai_tools import composio_tools, composio_list_apps
-
-print(composio_list_apps()[:5])  # ["github", "slack", ...]
-
-agent = Agent(name="dev", tools=composio_tools(apps=["github"]))
-agent.start("Star the praisonai/PraisonAI repository")
-```
-
-For explicit action execution, use `ComposioTool` or `composio_execute()`.
-
----
-
-## Contributing
-
-We welcome contributions! To add a new tool:
-
-1. Fork this repository
-2. Create your tool using `BaseTool` or `@tool` decorator
-3. Add tests in `tests/`
-4. Submit a pull request
-
----
-
-## Testing
-
-```bash
-pip install praisonai-tools[dev]
-pytest tests/ -v
-```
-
-The `dev` extra installs the video/motion-graphics render deps
-(`playwright`, `imageio-ffmpeg`) so the full suite runs. To exercise the
-HTML render backend you also need the Chromium browser:
-
-```bash
-pip install -e ".[dev]"
-playwright install chromium
-pytest tests/ -v
-```
-
-If you use a minimal install without those deps, the video tests under
-`tests/unit/video/` are skipped automatically rather than failing.
-
----
-
-## License
-
-MIT License - see [LICENSE](LICENSE) for details.
-
-## Author
-
-**Mervin Praison** - [GitHub](https://github.com/MervinPraison)
-
-## Links
-
-- [PraisonAI Documentation](https://docs.praison.ai)
-- [PraisonAI Agents](https://github.com/MervinPraison/PraisonAI)
-- [PyPI](https://pypi.org/project/praisonai-tools/)
-- [GitHub](https://github.com/MervinPraison/PraisonAI-Tools)
-- [Issues](https://github.com/MervinPraison/PraisonAI-Tools/issues)
+# Praison AI Tools

docs/inkog_security_integration.md

@@ -1,378 +0,0 @@
-# Inkog Security Analysis Integration
-
-This document describes the integration of [Inkog](https://github.com/inkog-io/inkog) security analysis capabilities into PraisonAI-Tools.
-
-## Overview
-
-Inkog is a specialized security analysis tool for AI agent code that detects vulnerabilities unique to agentic systems. It performs static analysis to catch bugs that only break in agent code, such as:
-
-- **Token bombing** - loops where the LLM controls termination, draining API budgets
-- **Recursive tool calling** - one user request fans out into thousands of LLM invocations  
-- **Prompt injection sinks** - RAG output flowing into system prompts without review
-- **Missing oversight** - destructive tools firing without human approval
-- **Cross-tenant leakage** - global state shared between agent invocations
-- **MCP tool poisoning** - malicious tool descriptions hijacking agents
-
-## Features
-
-### Framework Support
-- **21+ frameworks**: LangChain, CrewAI, AutoGen, OpenAI Agents, LangGraph, DSPy, Phidata, and more
-- **Multiple languages**: Python, TypeScript, JavaScript, and no-code platforms
-- **MCP servers**: Audit Model Context Protocol servers for security issues
-
-### Compliance Mapping
-- **EU AI Act** Articles 14/15 (human oversight requirements)
-- **NIST AI RMF** (risk management framework)
-- **ISO 42001** (AI management systems)
-- **OWASP LLM Top 10** (security vulnerabilities)
-
-### Scan Types
-- **Core scan**: Fast static analysis with vulnerability detection
-- **Deep scan**: AI orchestrator-driven analysis with enhanced context
-- **Skill scan**: Security analysis of SKILL.md packages and agent tools
-- **MCP scan**: Audit MCP servers for tool poisoning and privilege escalation
-
-## Installation
-
-### Prerequisites
-
-1. **Install Inkog CLI**:
-   ```bash
-   # macOS (Homebrew)
-   brew tap inkog-io/inkog && brew install inkog
-   
-   # Or install via Go
-   go install github.com/inkog-io/inkog/cmd/inkog@latest
-   
-   # Or download binary from releases
-   # https://github.com/inkog-io/inkog/releases
-   ```
-
-2. **Get API Key**:
-   - Visit [app.inkog.io](https://app.inkog.io) to get a free API key
-   - Set environment variable: `export INKOG_API_KEY=your_key_here`
-
-3. **Verify Installation**:
-   ```bash
-   inkog -version
-   ```
-
-## Usage
-
-### Basic Usage
-
-```python
-from praisonai_tools import InkogTool
-
-# Initialize the tool
-inkog = InkogTool()
-
-# Scan current directory for vulnerabilities
-result = inkog.scan_directory(
-    path=".",
-    output_format="json", 
-    policy="balanced"
-)
-
-# Analyze results
-analysis = inkog.analyze_findings(result)
-print(analysis)
-```
-
-### Standalone Functions
-
-```python
-from praisonai_tools.tools.inkog_tool import scan_agent_code, scan_skill_package
-
-# Quick scan using standalone function
-result = scan_agent_code("./my-agent", policy="low-noise")
-
-# Scan a skill package
-skill_result = scan_skill_package("./my-skill", deep=True)
-```
-
-### Advanced Scanning
-
-```python
-# Deep scan with AI orchestrator (requires Inkog Deep role)
-deep_result = inkog.scan_directory(
-    path="./agent-code",
-    output_format="json",
-    policy="comprehensive", 
-    deep=True,
-    severity="high"
-)
-
-# Generate HTML report
-html_report = inkog.scan_directory(
-    path="./agent-code",
-    output_format="html",
-    policy="balanced"
-)
-
-# Save report to file
-with open("security_report.html", "w") as f:
-    f.write(html_report)
-```
-
-### MCP Server Auditing
-
-```python
-# Scan MCP server by name
-mcp_result = inkog.mcp_scan("github")
-
-# Scan MCP server with custom repository
-mcp_result = inkog.mcp_scan(
-    "my-server", 
-    repo="https://github.com/org/my-mcp-server",
-    deep=True
-)
-```
-
-## Configuration
-
-### Environment Variables
-
-- **`INKOG_API_KEY`** (required): API key from app.inkog.io
-- **`INKOG_SERVER_URL`** (optional): Custom server URL for enterprise deployments
-
-### Security Policies
-
-Choose the appropriate policy based on your use case:
-
-- **`low-noise`**: Only exploitable vulnerabilities (good for CI/CD)
-- **`balanced`**: Vulnerabilities + risk patterns (default, good for code review)  
-- **`comprehensive`**: All findings including hardening recommendations (good for security audits)
-
-### Output Formats
-
-- **`json`**: Structured data for programmatic processing
-- **`text`**: Human-readable terminal output  
-- **`html`**: Interactive report with visualizations
-- **`sarif`**: SARIF format for GitHub Security tab integration
-
-### Severity Levels
-
-- **`critical`**: Immediate security threats
-- **`high`**: Serious vulnerabilities that should be addressed soon
-- **`medium`**: Moderate issues worth reviewing
-- **`low`**: Minor issues and best practice recommendations
-
-## Integration Examples
-
-### PraisonAI Agent Security Audit
-
-```python
-from praisonaiagents import Agent
-from praisonai_tools import InkogTool
-
-# Create an agent with security analysis capability
-security_agent = Agent(
-    name="security_auditor",
-    instructions="You analyze AI agent code for security vulnerabilities",
-    tools=[InkogTool()]
-)
-
-# Audit agent code
-audit_result = security_agent.start("""
-Scan the ./my-agent directory for security vulnerabilities.
-Focus on critical and high severity issues.
-Provide a summary of findings and remediation recommendations.
-""")
-
-print(audit_result)
-```
-
-### CI/CD Pipeline Integration
-
-```python
-import sys
-from praisonai_tools.tools.inkog_tool import scan_agent_code
-
-def security_check():
-    """Security check for CI/CD pipeline."""
-    result = scan_agent_code(
-        path=".",
-        policy="low-noise",  # Only critical vulnerabilities
-        severity="critical"
-    )
-    
-    if "error" in result:
-        print(f"Security scan failed: {result['error']}")
-        return 1
-    
-    critical_count = result.get("summary", {}).get("critical", 0)
-    if critical_count > 0:
-        print(f"❌ Found {critical_count} critical security issues")
-        return 1
-    
-    print("✅ No critical security vulnerabilities found")
-    return 0
-
-if __name__ == "__main__":
-    sys.exit(security_check())
-```
-
-### Multi-Agent Security Review
-
-```python
-from praisonaiagents import Agent, AgentTeam
-from praisonai_tools import InkogTool
-
-# Security analysis agent
-security_analyzer = Agent(
-    name="security_analyzer",
-    instructions="Analyze code for security vulnerabilities using Inkog",
-    tools=[InkogTool()]
-)
-
-# Code reviewer agent  
-code_reviewer = Agent(
-    name="code_reviewer",
-    instructions="Review security findings and provide remediation advice"
-)
-
-# Create security review team
-security_team = AgentTeam(
-    name="security_review_team",
-    agents=[security_analyzer, code_reviewer],
-    tasks=[
-        {"agent": "security_analyzer", "task": "Scan the codebase for vulnerabilities"},
-        {"agent": "code_reviewer", "task": "Review findings and create remediation plan"}
-    ]
-)
-
-# Run security review
-review_result = security_team.start("Perform comprehensive security review of ./agent-code")
-```
-
-## Error Handling
-
-The tool provides detailed error messages for common issues:
-
-```python
-result = inkog.scan_directory("./nonexistent")
-
-if "error" in result:
-    error_msg = result["error"]
-    
-    if "not installed" in error_msg:
-        print("Install inkog CLI first")
-    elif "API_KEY" in error_msg:
-        print("Set INKOG_API_KEY environment variable")
-    elif "timeout" in error_msg:
-        print("Scan timed out - try smaller directory or increase timeout")
-    else:
-        print(f"Scan error: {error_msg}")
-```
-
-## Best Practices
-
-### 1. Development Workflow
-```bash
-# Add to your development script
-inkog . -policy balanced -output json > security_report.json
-```
-
-### 2. Pre-commit Hooks
-```yaml
-# .pre-commit-config.yaml
-repos:
-  - repo: local
-    hooks:
-      - id: inkog-security-scan
-        name: Inkog Security Scan
-        entry: inkog
-        args: [".", "-policy", "low-noise", "-severity", "critical"]
-        language: system
-        always_run: true
-```
-
-### 3. GitHub Actions
-```yaml
-name: Security Scan
-on: [push, pull_request]
-
-jobs:
-  security:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      
-      - name: Install Inkog
-        run: |
-          curl -fsSL https://inkog.io/install.sh | sh
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-      
-      - name: Run Security Scan
-        env:
-          INKOG_API_KEY: ${{ secrets.INKOG_API_KEY }}
-        run: inkog . -output sarif > results.sarif
-      
-      - name: Upload SARIF
-        uses: github/codeql-action/upload-sarif@v2
-        with:
-          sarif_file: results.sarif
-```
-
-## Privacy & Security
-
-Inkog uses "surgical redaction" to protect sensitive data:
-
-### What Gets Redacted
-- AWS access keys, GitHub tokens, Stripe keys
-- Database connection strings with passwords
-- Private keys and JWT tokens  
-- High-entropy strings (API keys)
-
-### What is NOT Redacted
-- Prompts and templates (needed for injection detection)
-- Business logic (needed for flow analysis)
-- Configuration values (model names, etc.)
-- Normal strings that don't match credential patterns
-
-## Troubleshooting
-
-### Common Issues
-
-1. **"inkog CLI not installed"**
-   - Install via Homebrew: `brew tap inkog-io/inkog && brew install inkog`
-   - Or download from releases: https://github.com/inkog-io/inkog/releases
-
-2. **"INKOG_API_KEY required"**
-   - Get free key at https://app.inkog.io
-   - Set environment variable: `export INKOG_API_KEY=your_key`
-
-3. **"Deep scan requires Inkog Deep role"**
-   - Deep scans need special account permissions
-   - Use standard scan instead: `deep=False`
-
-4. **"Scan timed out"**
-   - Large codebases may timeout
-   - Scan specific directories instead of entire project
-   - Use policy="low-noise" for faster scans
-
-### Debug Mode
-
-Enable verbose output for troubleshooting:
-
-```python
-result = inkog.scan_directory(
-    path=".",
-    verbose=True  # Shows detailed analysis steps
-)
-```
-
-## Support
-
-- **Documentation**: https://docs.inkog.io
-- **GitHub Issues**: https://github.com/inkog-io/inkog/issues  
-- **Community**: https://discord.gg/NuG4SSGRH
-- **Security**: security@inkog.io
-
-## References
-
-- [Inkog Repository](https://github.com/inkog-io/inkog)
-- [State of AI Agent Security 2026 Report](https://inkog.io/report)
-- [EU AI Act Compliance Guide](https://docs.inkog.io/compliance/eu-ai-act)
-- [OWASP LLM Top 10](https://owasp.org/www-project-top-10-for-large-language-model-applications/)

docs/joy_trust_integration.md

@@ -1,302 +0,0 @@
-# Joy Trust Integration for PraisonAI
-
-Native Joy Trust Network integration for secure agent handoffs in PraisonAI.
-
-## Overview
-
-The Joy Trust integration provides automatic trust verification for agent handoffs, ensuring that tasks are only delegated to verified, trustworthy agents. This integration builds on the existing Joy Trust Network documentation and provides seamless, zero-configuration security for multi-agent workflows.
-
-## Features
-
-- **Automatic Trust Verification**: Environment variable configuration for seamless integration
-- **Handoff Safety Checks**: Verify agent trustworthiness before delegation
-- **Decorator-based Security**: `@trust_verified_handoff` decorator for secure functions
-- **Configurable Thresholds**: Set minimum trust scores for different use cases
-- **Caching**: Built-in result caching to reduce API calls
-- **Fallback Handling**: Graceful degradation when trust verification fails
-
-## Quick Start
-
-### 1. Environment Variable Configuration
-
-```bash
-# Enable trust verification for all agents
-export PRAISONAI_TRUST_PROVIDER=joy
-
-# Set minimum trust score threshold (default: 3.0)
-export PRAISONAI_TRUST_MIN_SCORE=3.5
-
-# Enable automatic handoff verification (default: true)
-export PRAISONAI_TRUST_AUTO_VERIFY=true
-
-# Optional: Set Joy Trust API key for enhanced features
-export JOY_TRUST_API_KEY=your_api_key_here
-```
-
-### 2. Basic Usage
-
-```python
-from praisonai_tools import check_trust_score, verify_handoff_safety
-
-# Check an agent's trust score
-result = check_trust_score("researcher_agent", min_score=3.0)
-print(f"Trust Score: {result['trust_score']}")
-print(f"Safe to use: {result['meets_threshold']}")
-
-# Verify handoff safety with recommendations
-verification = verify_handoff_safety("data_analyst", min_score=3.5)
-print(f"Handoff Safe: {verification['handoff_safe']}")
-print(f"Recommendation: {verification['recommendation']}")
-```
-
-### 3. Decorator-based Security
-
-```python
-from praisonai_tools import trust_verified_handoff
-
-@trust_verified_handoff(min_score=4.0)
-def delegate_analysis_task(agent_name: str, data: dict):
-    # This function only executes if the agent meets trust requirements
-    return perform_delegation(agent_name, data)
-
-# The decorator automatically checks trust before execution
-result = delegate_analysis_task("expert_analyst", {"data": "..."})
-```
-
-## API Reference
-
-### Functions
-
-#### `check_trust_score(agent_name: str, min_score: float = 3.0) -> dict`
-
-Check an agent's trust score on the Joy Trust Network.
-
-**Parameters:**
-- `agent_name`: Name/identifier of the agent to check
-- `min_score`: Minimum acceptable trust score
-
-**Returns:**
-```python
-{
-    "agent_name": str,
-    "trust_score": float,
-    "verified": bool,
-    "meets_threshold": bool,
-    "threshold_used": float,
-    "reputation": dict,
-    "recommendations": int,
-    "error": str | None
-}
-```
-
-#### `verify_handoff_safety(agent_name: str, min_score: float = 3.0) -> dict`
-
-Verify if it's safe to hand off to the specified agent.
-
-**Parameters:**
-- `agent_name`: Target agent for handoff
-- `min_score`: Minimum acceptable trust score
-
-**Returns:**
-```python
-{
-    "agent_name": str,
-    "trust_score": float,
-    "verified": bool,
-    "handoff_safe": bool,
-    "recommendation": str,
-    "threshold_used": float,
-    "verification_time": float,
-    "error": str | None
-}
-```
-
-#### `trust_verified_handoff(min_score: float = 3.0)`
-
-Decorator for automatic trust verification before function execution.
-
-**Parameters:**
-- `min_score`: Minimum trust score required
-
-**Usage:**
-```python
-@trust_verified_handoff(min_score=3.5)
-def my_delegation_function(agent_name, task):
-    return delegate_task(agent_name, task)
-```
-
-#### `is_trust_verification_enabled() -> bool`
-
-Check if trust verification is enabled via environment variables.
-
-#### `get_trust_config() -> TrustConfig`
-
-Get current trust configuration from environment variables.
-
-### Classes
-
-#### `JoyTrustTool`
-
-Enhanced tool class for Joy Trust Network verification.
-
-```python
-from praisonai_tools import JoyTrustTool
-
-tool = JoyTrustTool(api_key="optional_key")
-result = tool.check_trust("agent_name")
-```
-
-#### `TrustConfig`
-
-Configuration dataclass for Joy Trust integration.
-
-```python
-from praisonai_tools.tools.joy_trust_tool import TrustConfig
-
-config = TrustConfig.from_env()
-print(f"Enabled: {config.enabled}")
-print(f"Min Score: {config.min_score}")
-```
-
-## Environment Variables
-
-| Variable | Description | Default |
-|----------|-------------|---------|
-| `PRAISONAI_TRUST_PROVIDER` | Set to 'joy' to enable trust verification | None |
-| `PRAISONAI_TRUST_MIN_SCORE` | Minimum trust score threshold | 3.0 |
-| `PRAISONAI_TRUST_AUTO_VERIFY` | Enable automatic handoff verification | true |
-| `PRAISONAI_TRUST_TIMEOUT` | Request timeout in seconds | 10.0 |
-| `PRAISONAI_TRUST_CACHE_DURATION` | Cache duration in seconds | 300 |
-| `PRAISONAI_TRUST_FALLBACK` | Allow fallback on errors | true |
-| `JOY_TRUST_API_KEY` | Joy Trust API key (optional) | None |
-
-## Trust Score Ranges
-
-The Joy Trust Network uses a 0-5 scale for trust scores:
-
-- **4.5-5.0**: Excellent - Highly trusted, verified agents
-- **3.5-4.4**: Good - Reliable agents with good reputation
-- **2.5-3.4**: Moderate - Acceptable with caution
-- **1.0-2.4**: Low - Not recommended for important tasks
-- **0.0-0.9**: Very Low - Strongly discouraged
-
-## Integration Patterns
-
-### 1. Workflow-level Integration
-
-```python
-import os
-from praisonai_tools import verify_handoff_safety
-
-# Enable trust verification
-os.environ['PRAISONAI_TRUST_PROVIDER'] = 'joy'
-
-def secure_workflow(agents: list, min_score: float = 3.0):
-    trusted_agents = []
-    
-    for agent in agents:
-        verification = verify_handoff_safety(agent['name'], min_score)
-        if verification['handoff_safe']:
-            trusted_agents.append(agent)
-        else:
-            print(f"⚠️ Agent {agent['name']} failed trust check")
-    
-    return trusted_agents
-```
-
-### 2. Conditional Trust Checking
-
-```python
-from praisonai_tools import is_trust_verification_enabled, check_trust_score
-
-def delegate_with_optional_trust(agent_name: str, task: str):
-    if is_trust_verification_enabled():
-        trust_result = check_trust_score(agent_name)
-        if not trust_result['meets_threshold']:
-            return {"error": f"Agent {agent_name} doesn't meet trust requirements"}
-    
-    return perform_delegation(agent_name, task)
-```
-
-### 3. Custom Trust Policies
-
-```python
-from praisonai_tools import JoyTrustTool
-from praisonai_tools.tools.joy_trust_tool import TrustConfig
-
-# Create custom configuration
-custom_config = TrustConfig(
-    enabled=True,
-    min_score=4.0,  # High security requirement
-    timeout_seconds=5.0,  # Fast timeout
-    fallback_on_error=False  # Strict mode
-)
-
-tool = JoyTrustTool(config=custom_config)
-result = tool.verify_handoff_safety("critical_agent")
-```
-
-## Error Handling
-
-The integration provides robust error handling:
-
-```python
-from praisonai_tools import check_trust_score
-
-result = check_trust_score("unknown_agent")
-
-if result['error']:
-    print(f"Trust check failed: {result['error']}")
-    
-    if result.get('fallback_used'):
-        print("Using fallback behavior")
-    else:
-        print("Strict mode - blocking delegation")
-else:
-    print(f"Trust verification successful: {result['trust_score']}")
-```
-
-## Best Practices
-
-1. **Start with moderate thresholds** (3.0-3.5) and adjust based on your security needs
-2. **Use environment variables** for configuration to avoid hardcoding values
-3. **Enable caching** in production to reduce API calls and improve performance
-4. **Set appropriate timeouts** based on your application's requirements
-5. **Use fallback mode** for non-critical operations to maintain availability
-6. **Monitor trust scores** and update agent permissions accordingly
-
-## Troubleshooting
-
-### Common Issues
-
-1. **"httpx is required" error**: Install httpx with `pip install httpx` or `pip install praisonai-tools[marketplace]`
-2. **Connection timeouts**: Increase `PRAISONAI_TRUST_TIMEOUT` value
-3. **False negatives**: Check if agent names match Joy Trust Network entries exactly
-4. **API rate limits**: Enable caching with `PRAISONAI_TRUST_CACHE_DURATION`
-
-### Debug Mode
-
-Enable debug logging to troubleshoot issues:
-
-```python
-import logging
-logging.basicConfig(level=logging.DEBUG)
-
-from praisonai_tools import check_trust_score
-result = check_trust_score("debug_agent")
-```
-
-## Contributing
-
-To contribute to the Joy Trust integration:
-
-1. Fork the PraisonAI-Tools repository
-2. Create a feature branch
-3. Add tests for new functionality
-4. Submit a pull request
-
-## Support
-
-- **Documentation**: [Joy Trust Network Docs](https://docs.praison.ai/joy-trust-network)
-- **Issues**: [PraisonAI-Tools Issues](https://github.com/MervinPraison/PraisonAI-Tools/issues)
-- **Community**: [PraisonAI Discord](https://discord.gg/praisonai)

docs/n8n_integration.md

@@ -1,399 +0,0 @@
-# n8n Integration for PraisonAI Agents
-
-Enable bidirectional integration between PraisonAI agents and n8n workflow automation platform, providing access to **400+ integrations** including Slack, Gmail, Notion, databases, APIs, and more.
-
-## Overview
-
-The n8n integration allows:
-
-- **PraisonAI → n8n**: Agents can execute n8n workflows to access 400+ integrations
-- **n8n → PraisonAI**: n8n workflows can invoke PraisonAI agents (see API endpoint section)
-
-## Installation
-
-```bash
-# Install with n8n support
-pip install "praisonai-tools[n8n]"
-```
-
-## Setup
-
-### 1. n8n Instance
-
-**Local Development:**
-```bash
-# Run n8n locally with Docker
-docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n
-```
-
-**Production:**
-- Use [n8n Cloud](https://n8n.cloud) (recommended)
-- Or self-host n8n on your infrastructure
-
-### 2. Environment Variables
-
-```bash
-export N8N_URL="http://localhost:5678"  # Your n8n instance URL
-export N8N_API_KEY="your-api-key"       # Optional for local, required for production
-```
-
-### 3. Create API Key (Production)
-
-1. Open n8n instance
-2. Go to Settings → API
-3. Generate new API key
-4. Set `N8N_API_KEY` environment variable
-
-## Quick Start
-
-### Basic Usage
-
-```python
-from praisonai_tools.n8n import n8n_workflow, n8n_list_workflows
-
-# List available workflows
-workflows = n8n_list_workflows()
-print(f"Available workflows: {len(workflows.get('data', []))}")
-
-# Execute a workflow
-result = n8n_workflow(
-    workflow_id="your-workflow-id",
-    input_data={"message": "Hello from PraisonAI!"}
-)
-```
-
-### With PraisonAI Agents
-
-```python
-from praisonaiagents import Agent
-from praisonai_tools.n8n import n8n_workflow, n8n_list_workflows
-
-# Create agent with n8n tools
-agent = Agent(
-    name="automation-agent",
-    instructions="You help automate tasks using n8n workflows with 400+ integrations",
-    tools=[n8n_workflow, n8n_list_workflows],
-)
-
-# Agent can now use n8n workflows
-response = agent.start("Send a Slack message to #general saying 'Hello team!'")
-```
-
-## Available Tools
-
-### `n8n_workflow`
-
-Execute an n8n workflow and return the result.
-
-**Parameters:**
-- `workflow_id` (str): The n8n workflow ID to execute
-- `input_data` (dict, optional): Input data to pass to the workflow
-- `n8n_url` (str, optional): n8n instance URL (defaults to `N8N_URL` env var)
-- `api_key` (str, optional): n8n API key (defaults to `N8N_API_KEY` env var)
-- `timeout` (float, optional): Request timeout in seconds (default: 60)
-- `wait_for_completion` (bool, optional): Wait for workflow completion (default: True)
-
-**Returns:** Dict with execution result or error information
-
-**Example:**
-```python
-# Send Slack message via n8n workflow
-result = n8n_workflow(
-    workflow_id="slack-notify",
-    input_data={
-        "channel": "#general",
-        "message": "Hello from PraisonAI!"
-    }
-)
-
-# Create Notion page via n8n workflow
-result = n8n_workflow(
-    workflow_id="notion-create-page",
-    input_data={
-        "title": "Meeting Notes",
-        "content": "Discussion about project timeline"
-    }
-)
-```
-
-### `n8n_list_workflows`
-
-List available n8n workflows.
-
-**Parameters:**
-- `n8n_url` (str, optional): n8n instance URL
-- `api_key` (str, optional): n8n API key
-
-**Returns:** Dict containing list of workflows or error information
-
-**Example:**
-```python
-workflows = n8n_list_workflows()
-for workflow in workflows.get("data", []):
-    print(f"- {workflow['name']} (ID: {workflow['id']})")
-```
-
-## Common Use Cases
-
-### 1. Communication Automation
-
-```python
-# Slack notifications
-n8n_workflow("slack-notify", {
-    "channel": "#alerts",
-    "message": "Task completed successfully!",
-    "emoji": ":white_check_mark:"
-})
-
-# Email with Gmail
-n8n_workflow("gmail-send", {
-    "to": "[email protected]",
-    "subject": "Report Ready",
-    "body": "Your daily report is attached."
-})
-```
-
-### 2. Data Management
-
-```python
-# Add to Google Sheets
-n8n_workflow("sheets-append", {
-    "spreadsheet_id": "1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms",
-    "range": "Sheet1!A1:E1",
-    "values": [["Date", "Name", "Status", "Notes"]]
-})
-
-# Create Notion page
-n8n_workflow("notion-create", {
-    "database_id": "your-database-id",
-    "properties": {
-        "Title": {"title": [{"text": {"content": "New Task"}}]},
-        "Status": {"select": {"name": "To Do"}}
-    }
-})
-```
-
-### 3. Task Management
-
-```python
-# Create Jira ticket
-n8n_workflow("jira-create-issue", {
-    "project": "PROJ",
-    "summary": "Bug report from agent",
-    "description": "Issue details...",
-    "issue_type": "Bug"
-})
-
-# Add Trello card
-n8n_workflow("trello-add-card", {
-    "list_id": "your-list-id",
-    "name": "New Task",
-    "desc": "Task description"
-})
-```
-
-## Agent Examples
-
-### Multi-Platform Notification Agent
-
-```python
-agent = Agent(
-    name="notifier",
-    instructions="""
-    You send notifications across multiple platforms using n8n workflows.
-    Available workflows:
-    - slack-notify: Send Slack messages
-    - discord-webhook: Send Discord messages  
-    - email-send: Send emails
-    - teams-notify: Send Microsoft Teams messages
-    
-    Always ask which platform and customize the message appropriately.
-    """,
-    tools=[n8n_workflow, n8n_list_workflows]
-)
-```
-
-### Data Sync Agent
-
-```python
-agent = Agent(
-    name="data-sync",
-    instructions="""
-    You help synchronize data between different systems using n8n.
-    You can:
-    - Sync data between databases
-    - Update spreadsheets
-    - Create/update CRM records
-    - Generate reports
-    
-    Always confirm the source and destination before proceeding.
-    """,
-    tools=[n8n_workflow]
-)
-```
-
-## Error Handling
-
-The n8n tools provide detailed error information:
-
-```python
-result = n8n_workflow("invalid-workflow")
-
-if "error" in result:
-    print(f"Error: {result['error']}")
-    # Common errors:
-    # - "workflow_id is required"
-    # - "httpx not installed. Install with: pip install 'praisonai-tools[n8n]'"
-    # - "HTTP 404: Workflow not found"
-    # - "Workflow execution timed out after 60 seconds"
-```
-
-## Configuration
-
-### Timeout Settings
-
-```python
-# Short timeout for quick workflows
-result = n8n_workflow(
-    workflow_id="quick-task",
-    timeout=10.0
-)
-
-# Long timeout for complex workflows
-result = n8n_workflow(
-    workflow_id="data-processing",
-    timeout=300.0  # 5 minutes
-)
-```
-
-### Async vs Sync Execution
-
-```python
-# Wait for completion (default)
-result = n8n_workflow("workflow-id", wait_for_completion=True)
-
-# Fire-and-forget
-result = n8n_workflow("workflow-id", wait_for_completion=False)
-# Returns immediately with executionId
-```
-
-## Integration with n8n → PraisonAI
-
-To enable n8n workflows to invoke PraisonAI agents, add this endpoint to your PraisonAI application:
-
-```python
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from praisonaiagents import Agent
-
-app = FastAPI()
-
-class InvokeRequest(BaseModel):
-    agent_name: str
-    message: str
-    session_id: str = None
-
-@app.post("/api/v1/agents/{agent_id}/invoke")
-async def invoke_agent(agent_id: str, request: InvokeRequest):
-    """Endpoint for n8n to invoke PraisonAI agents."""
-    # Load your agent
-    agent = get_agent(agent_id)  # Your agent loading logic
-    if not agent:
-        raise HTTPException(404, f"Agent {agent_id} not found")
-    
-    result = await agent.astart(request.message)
-    return {"result": result, "session_id": request.session_id or "default"}
-```
-
-Then use n8n's HTTP Request node to call this endpoint.
-
-## Best Practices
-
-### 1. Workflow Design
-- Create focused workflows for specific tasks
-- Use descriptive workflow names and IDs
-- Add error handling in your n8n workflows
-- Test workflows manually before using with agents
-
-### 2. Security
-- Use API keys for production environments
-- Implement input validation in n8n workflows
-- Consider rate limiting for agent-triggered workflows
-- Audit workflow executions regularly
-
-### 3. Performance
-- Use appropriate timeouts based on workflow complexity
-- Consider async execution for long-running workflows
-- Monitor n8n instance performance
-- Cache workflow lists when possible
-
-### 4. Error Handling
-- Always check for error responses
-- Implement retry logic for transient failures
-- Log workflow execution results
-- Provide fallback options when workflows fail
-
-## Troubleshooting
-
-### Common Issues
-
-**Connection Errors:**
-```python
-# Check n8n connectivity
-result = n8n_list_workflows()
-if "error" in result:
-    print("n8n connection issue:", result["error"])
-```
-
-**Workflow Not Found:**
-```python
-# Verify workflow exists
-workflows = n8n_list_workflows()
-workflow_ids = [w["id"] for w in workflows.get("data", [])]
-print("Available workflows:", workflow_ids)
-```
-
-**Authentication Issues:**
-```python
-# Check API key configuration
-import os
-api_key = os.getenv("N8N_API_KEY")
-if not api_key:
-    print("N8N_API_KEY not set - OK for local testing")
-else:
-    print("API key configured")
-```
-
-### Debug Mode
-
-Enable detailed logging:
-
-```python
-import logging
-logging.basicConfig(level=logging.DEBUG)
-
-# Now n8n tool calls will show detailed logs
-result = n8n_workflow("test-workflow")
-```
-
-## Resources
-
-- [n8n Documentation](https://docs.n8n.io/)
-- [n8n API Reference](https://docs.n8n.io/api/)
-- [n8n Workflow Templates](https://n8n.io/workflows)
-- [n8n Integrations](https://n8n.io/integrations)
-- [PraisonAI Documentation](https://docs.praison.ai)
-
-## Contributing
-
-To contribute to the n8n integration:
-
-1. Fork the [PraisonAI-Tools repository](https://github.com/MervinPraison/PraisonAI-Tools)
-2. Create your feature branch
-3. Add tests for your changes
-4. Update documentation
-5. Submit a pull request
-
-## License
-
-This integration is released under the MIT License, same as PraisonAI Tools.

docs/nexus_prediction_market_integration.md

@@ -1,382 +0,0 @@
-# NEXUS Prediction Market Integration
-
-This document provides comprehensive information about integrating the NEXUS prediction market API with PraisonAI agents.
-
-## Overview
-
-NEXUS provides live Kalshi/Polymarket prediction market data and arbitrage opportunities through a validated API. The integration allows PraisonAI agents to:
-
-- Access real-time prediction market data
-- Identify arbitrage opportunities between markets
-- Analyze market sentiment and trends
-- Build sophisticated trading and analysis workflows
-
-## API Information
-
-### Endpoints
-
-- **Free Market Data**: `/kalshi?market={market_name}`
-- **Paid Arbitrage Analysis**: `/arb/check?markets={market1,market2}` ($0.02 USDC on eip155:8453)
-- **Agent Metadata**: `/.well-known/agent.json`
-
-### Validation
-
-The API is fully validated on Agentic Market:
-- **Validation URL**: https://agentic.market/validate?url=https://nexus-agent-xa12.onrender.com/arb/check
-- **API Card**: https://nexus-agent-xa12.onrender.com/.well-known/agent.json
-
-### Usage Tracking
-
-Include the `X-NEXUS-Source` header to track per-source call volume:
-```
-X-NEXUS-Source: your-bot-name
-```
-
-## Installation
-
-```bash
-pip install praisonai-tools
-```
-
-## Quick Start
-
-### Basic Usage
-
-```python
-from praisonai_tools.tools import NexusPredictionMarketTool
-
-# Initialize the tool
-nexus = NexusPredictionMarketTool(source_name="my-agent")
-
-# Get market data
-fed_data = nexus.get_market_data("Fed")
-print(fed_data)
-
-# Check arbitrage opportunities (paid)
-arb_data = nexus.check_arbitrage_opportunities(["Fed", "BTC"])
-print(arb_data)
-```
-
-### Agent Integration
-
-```python
-from praisonaiagents import Agent, tool
-from praisonai_tools.tools import nexus_prediction_market_tool
-
-@tool
-def get_market_data(market: str) -> str:
-    """Get prediction market data."""
-    return nexus_prediction_market_tool("get_market_data", market=market)
-
-agent = Agent(
-    name="MarketAnalyst",
-    tools=[get_market_data],
-    instructions="Analyze prediction markets using NEXUS data."
-)
-
-response = agent.start("What do Fed rate predictions show?")
-```
-
-## Features
-
-### Free Features
-
-1. **Market Data Queries**
-   - Real-time price data
-   - Volume and liquidity information
-   - Market sentiment indicators
-   - Historical trends
-
-2. **Agent Metadata**
-   - API capabilities and endpoints
-   - Service status and version info
-   - Usage guidelines
-
-### Paid Features
-
-1. **Arbitrage Analysis** ($0.02 USDC on eip155:8453)
-   - Cross-market opportunity detection
-   - Profit potential calculations
-   - Risk assessment
-   - Execution recommendations
-
-## Supported Markets
-
-Common market identifiers include:
-
-- `Fed` - Federal Reserve interest rate predictions
-- `BTC` - Bitcoin price predictions
-- `Election` - Political election outcomes
-- `ETH` - Ethereum price predictions
-- `Inflation` - Inflation rate predictions
-
-*Note: Market availability may vary. Check current markets via the API.*
-
-## API Reference
-
-### NexusPredictionMarketTool Class
-
-```python
-class NexusPredictionMarketTool:
-    def __init__(self, source_name: Optional[str] = None, api_key: Optional[str] = None)
-    def get_market_data(self, market: str) -> Dict[str, Any]
-    def check_arbitrage_opportunities(self, markets: List[str]) -> Dict[str, Any]
-    def get_agent_info(self) -> Dict[str, Any]
-    def run(self, action: str, market: Optional[str] = None, markets: Optional[List[str]] = None) -> str
-```
-
-### Function Interface
-
-```python
-def nexus_prediction_market_tool(
-    action: str,
-    market: Optional[str] = None,
-    markets: Optional[List[str]] = None,
-    source_name: Optional[str] = None
-) -> str
-```
-
-## Examples
-
-### Market Analysis Agent
-
-```python
-from praisonaiagents import Agent
-from praisonai_tools.tools import NexusPredictionMarketTool
-
-class MarketAnalysisAgent:
-    def __init__(self):
-        self.nexus = NexusPredictionMarketTool(source_name="analysis-bot")
-        
-    def analyze_market_sentiment(self, market):
-        """Analyze market sentiment for a specific prediction market."""
-        data = self.nexus.get_market_data(market)
-        
-        if data["success"]:
-            market_data = data["data"]
-            # Analyze price, volume, trends etc.
-            return f"Market sentiment for {market}: {self.interpret_data(market_data)}"
-        else:
-            return f"Error getting data: {data['error']}"
-    
-    def interpret_data(self, data):
-        """Custom logic to interpret market data."""
-        # Implementation specific to your analysis needs
-        pass
-```
-
-### Multi-Agent Workflow
-
-```python
-from praisonaiagents import Agent, AgentTeam, Task
-
-# Data collector agent
-collector = Agent(
-    name="DataCollector",
-    tools=[get_market_data],
-    instructions="Collect prediction market data from NEXUS."
-)
-
-# Analyst agent
-analyst = Agent(
-    name="Analyst", 
-    instructions="Analyze market data and provide insights."
-)
-
-# Create workflow
-team = AgentTeam(agents=[collector, analyst])
-
-# Define tasks
-collect_task = Task(
-    name="collect_data",
-    description="Collect data for Fed and BTC markets",
-    agent=collector
-)
-
-analyze_task = Task(
-    name="analyze_trends",
-    description="Analyze the collected market data",
-    agent=analyst,
-    dependencies=[collect_task]
-)
-
-# Execute workflow
-results = team.run(tasks=[collect_task, analyze_task])
-```
-
-## Error Handling
-
-The tool provides comprehensive error handling for common scenarios:
-
-### Network Errors
-```python
-{
-    "success": False,
-    "error": "Failed to fetch market data: Connection timeout",
-    "market": "Fed"
-}
-```
-
-### Payment Required (Arbitrage Features)
-```python
-{
-    "success": False,
-    "error": "Payment required for arbitrage analysis. Cost: $0.02 USDC on eip155:8453",
-    "markets": ["Fed", "BTC"],
-    "payment_info": "This feature requires payment on eip155:8453 blockchain"
-}
-```
-
-### Invalid Parameters
-```python
-{
-    "success": False,
-    "error": "Market parameter required for get_market_data action"
-}
-```
-
-## Best Practices
-
-### 1. Source Identification
-Always set a descriptive source name for tracking:
-```python
-nexus = NexusPredictionMarketTool(source_name="trading-bot-v1")
-```
-
-### 2. Error Handling
-Always check the `success` field before processing results:
-```python
-result = nexus.get_market_data("Fed")
-if result["success"]:
-    data = result["data"]
-    # Process data
-else:
-    print(f"Error: {result['error']}")
-```
-
-### 3. Rate Limiting
-Implement appropriate delays between requests to avoid overwhelming the API:
-```python
-import time
-import asyncio
-
-async def batch_collect_data(markets):
-    data = {}
-    for market in markets:
-        result = nexus.get_market_data(market)
-        data[market] = result
-        await asyncio.sleep(1)  # 1 second delay between requests
-    return data
-```
-
-### 4. Caching
-Cache results for frequently accessed data:
-```python
-from functools import lru_cache
-import time
-
-@lru_cache(maxsize=100)
-def cached_market_data(market, timestamp):
-    return nexus.get_market_data(market)
-
-def get_cached_data(market, cache_duration=300):  # 5 minutes
-    timestamp = int(time.time() // cache_duration)
-    return cached_market_data(market, timestamp)
-```
-
-## Security Considerations
-
-### 1. API Key Management
-If using paid features, securely manage your API keys:
-```python
-import os
-api_key = os.getenv("NEXUS_API_KEY")
-nexus = NexusPredictionMarketTool(api_key=api_key)
-```
-
-### 2. Input Validation
-Validate market identifiers before making requests:
-```python
-VALID_MARKETS = ["Fed", "BTC", "ETH", "Election", "Inflation"]
-
-def validate_market(market):
-    if market not in VALID_MARKETS:
-        raise ValueError(f"Invalid market: {market}")
-    return market
-```
-
-### 3. Rate Limiting
-Implement client-side rate limiting to prevent abuse:
-```python
-from time import time, sleep
-
-class RateLimiter:
-    def __init__(self, max_calls_per_minute=60):
-        self.max_calls = max_calls_per_minute
-        self.calls = []
-    
-    def wait_if_needed(self):
-        now = time()
-        self.calls = [call_time for call_time in self.calls if now - call_time < 60]
-        
-        if len(self.calls) >= self.max_calls:
-            sleep(1)
-        
-        self.calls.append(now)
-```
-
-## Troubleshooting
-
-### Common Issues
-
-1. **Connection Timeout**
-   - Check internet connectivity
-   - Verify the API endpoint is accessible
-   - Implement retry logic with exponential backoff
-
-2. **Invalid Market Identifier**
-   - Verify the market name is correct
-   - Check available markets via agent info endpoint
-   - Use exact case-sensitive market names
-
-3. **Payment Required Errors**
-   - Ensure you have sufficient USDC balance on eip155:8453
-   - Verify payment setup for arbitrage features
-   - Contact NEXUS support if payment issues persist
-
-4. **JSON Decode Errors**
-   - Check API response format
-   - Verify the endpoint returned valid JSON
-   - Implement fallback error handling
-
-### Debug Mode
-
-Enable debug logging to troubleshoot issues:
-```python
-import logging
-logging.basicConfig(level=logging.DEBUG)
-
-nexus = NexusPredictionMarketTool(source_name="debug-agent")
-# Requests will now be logged with full details
-```
-
-## Support and Resources
-
-- **API Documentation**: https://nexus-agent-xa12.onrender.com/.well-known/agent.json
-- **Validation Check**: https://agentic.market/validate?url=https://nexus-agent-xa12.onrender.com/arb/check
-- **PraisonAI Documentation**: https://docs.praison.ai
-- **Issue Tracking**: Report issues via GitHub issues in the PraisonAI-Tools repository
-
-## Contributing
-
-To contribute improvements or report bugs:
-
-1. Fork the PraisonAI-Tools repository
-2. Create a feature branch
-3. Make your changes
-4. Add tests for new functionality
-5. Submit a pull request
-
-## License
-
-This tool is part of the PraisonAI-Tools package and follows the same licensing terms.

docs/registry_proxy_integration.md

@@ -1,141 +0,0 @@
-# Tool-Registry Proxy Connector
-
-An **optional, bring-your-own-account** connector that lets any PraisonAI agent
-discover and call a large catalogue of third-party API endpoints (SEO,
-enrichment, social data, scraping, ads) through a single proxy token — with
-**credentials injected server-side by the registry, never held by the agent**.
-
-PraisonAI ships **no registry code, no bundled binary and no default endpoint**.
-You supply your own account token or a self-hosted base URL. The connector is
-disabled unless `TOOL_PROXY_URL` is configured.
-
-## Overview
-
-The connector mirrors PraisonAI's deferred tool-search bridge
-(`tool_search` / `tool_describe` / `tool_call`) with three functions:
-
-| Function | Cost | Purpose |
-| --- | --- | --- |
-| `registry_search(query)` | free | capability search over the catalogue |
-| `registry_describe(tool_id)` | free | params, price-per-call, example response |
-| `registry_call(tool_id, params)` | paid | invoke via proxy; credential injected server-side |
-
-## Installation
-
-```bash
-pip install "praisonai-tools[registry-proxy]"
-```
-
-## Setup (3 lines)
-
-```bash
-export TOOL_PROXY_URL="https://your-registry.example.com"   # or a self-hosted instance
-export TOOL_PROXY_TOKEN="your-proxy-token"
-# Optional: override the auth header for registries that expect a custom header.
-# Default is "Authorization" (sent as "Bearer <token>"); any other name (e.g.
-# "X-Treg-Token") sends the raw token in that header.
-export TOOL_PROXY_AUTH_HEADER="Authorization"
-# Optional: override the describe route for registries with a different layout.
-# Must contain "{tool_id}"; defaults to the live registry route below.
-export TOOL_PROXY_DESCRIBE_PATH="/catalog/endpoints/{tool_id}"
-```
-
-```python
-from praisonaiagents import Agent
-from praisonai_tools.registry_proxy import (
-    registry_search, registry_describe, registry_call,
-)
-
-agent = Agent(
-    instructions="SEO analyst",
-    tools=[registry_search, registry_describe, registry_call],
-)
-agent.start("Find the top backlink sources for example.com and summarise")
-# → registry_search("backlinks for a domain") → describe → call
-# one token, no vendor keys in the agent
-```
-
-## Spend safety
-
-`registry_call` accepts optional budget guards. When a configured budget would
-be exceeded, the call is **denied and reported** as a tool-result error (never
-raised as an exception):
-
-```python
-registry_call(
-    "seo.backlinks",
-    {"domain": "example.com"},
-    max_cost_per_call=0.10,     # deny if price-per-call exceeds this
-    max_session_spend=5.00,     # deny if cumulative spend would exceed this
-)
-```
-
-Per-call price is read from `registry_describe`, so guards work without any
-vendor-specific configuration. Guards **fail closed**: if a budget is set but
-the price cannot be validated, the call is denied. Cumulative `max_session_spend`
-persists across separate `registry_call` invocations within the same process,
-scoped per `(proxy_url, token)` so distinct accounts never share a budget.
-
-## HTTP methods
-
-The registry enforces each endpoint's declared HTTP method on `/call/{id}`.
-`registry_call` derives the method automatically from `registry_describe`
-(the endpoint doc carries `method`): `GET` endpoints send `params` as the query
-string, `POST`/`PUT` send them as a JSON body. Pass `method="GET"` explicitly to
-skip the describe lookup when you already know the method. Only the standard
-`GET`/`POST`/`PUT`/`PATCH`/`DELETE` verbs are accepted; any other value (e.g. a
-mislabelled endpoint doc) is rejected as a tool-result error before any request.
-`tool_id` is URL-encoded before being placed in the describe/call route, so it
-cannot alter the requested path.
-
-## Passthrough-URL calls
-
-Passthrough-URL calls require the upstream to be **registered on the registry
-first**. Loopback/private upstreams (e.g. `127.0.0.1`, RFC-1918 addresses) are
-**refused by the registry's SSRF guard by design** — the base URL must be a
-public `http(s)` address. This is registry-side policy, not a connector setting.
-
-## Credential safety
-
-The proxy token is only sent to the endpoint it was configured for. If a caller
-(or a prompt-injected agent) supplies an explicit `proxy_url` without a matching
-`token`, the ambient `TOOL_PROXY_TOKEN` is **not** attached — preventing the
-token from being exfiltrated to an untrusted endpoint. For normal use, leave
-`proxy_url`/`token` unset and configure `TOOL_PROXY_URL`/`TOOL_PROXY_TOKEN` via
-the environment.
-
-## Error taxonomy
-
-All failures are returned as `{"error": ...}` tool results:
-
-- Authentication failed (HTTP 401/403)
-- Insufficient balance (HTTP 402)
-- Upstream 4xx/5xx passed through
-- Request timeout
-- `httpx` not installed → install hint
-
-## Tier 0 — Skills directory compatibility (zero code)
-
-Registries that expose `skill install <name>` install skills into
-`./.claude/skills/`. PraisonAI **already scans** this directory, so
-registry-installed skills are discoverable by PraisonAI agents today with no
-additional code:
-
-- `praisonaiagents/skills/discovery.py` — `.claude/skills` compatibility scan
-  plus an ancestor walk for monorepos.
-
-## Tier 2 — Environment / CLI passthrough (config-only)
-
-A registry's `shell` / `run <vendor> -- args` inject vendor-CLI credentials at
-execution time. Using a PraisonAI environment definition, install the registry
-CLI and log in via a token env var in `setup:`; the agent's `execute_command`
-then has authenticated vendor CLIs (e.g. `run gh -- pr list`) without any key in
-the container. Sandbox / board workers inherit the same via the environment
-definition.
-
-## Licensing note
-
-Some registries are Apache-2.0 **with additional terms** prohibiting embedding
-in commercially distributed products without permission. This connector is
-therefore **optional and bring-your-own-account only**: it ships no registry
-code and no default endpoint — the same posture as other BYO-account connectors.

docs/swarmscore_integration.md

@@ -1,357 +0,0 @@
-# SwarmScore Integration for PraisonAI
-
-SwarmScore is a portable trust rating system for AI agents that provides cryptographically signed reputation scores based on verified execution history. This integration allows PraisonAI agents to leverage trust ratings for enhanced security and decision-making.
-
-## Overview
-
-SwarmScore provides:
-- **Portable Trust Ratings**: Cryptographically signed scores that travel with your agent across platforms
-- **Verified Execution History**: Trust based on actual performance, not self-reported metrics
-- **Consistency Tracking**: 80 jobs at 95% success rate beats 1 job at 100%
-- **Agent-to-Agent Discovery**: Machine-readable trust data for autonomous agent interactions
-
-## Installation
-
-```bash
-pip install praisonai-tools[swarmscore]
-# or
-pip install praisonai-tools requests
-```
-
-## Quick Start
-
-### Basic Usage
-
-```python
-from praisonai_tools.tools import SwarmScoreTool
-
-# Initialize the tool
-swarmscore = SwarmScoreTool()
-
-# Load your agent's trust score
-result = swarmscore.load_swarmscore("your-agent-slug")
-
-if result.success:
-    score_data = result.output
-    print(f"Trust Score: {score_data['score']}")
-    print(f"Trust Tier: {score_data['tier']}")
-    print(f"Success Rate: {score_data['success_rate']}%")
-else:
-    print(f"Error: {result.error}")
-```
-
-### Standalone Functions
-
-```python
-from praisonai_tools.tools import (
-    load_swarmscore_by_slug,
-    verify_swarmscore_freshness,
-    get_agent_discovery_manifest
-)
-
-# Load score data
-try:
-    score_data = load_swarmscore_by_slug("your-agent-slug")
-    print(f"Score: {score_data['score']}")
-except Exception as e:
-    print(f"Failed to load score: {e}")
-
-# Verify score freshness
-try:
-    verification = verify_swarmscore_freshness(score_data['verify_payload'])
-    print(f"Verification: {verification}")
-except Exception as e:
-    print(f"Verification failed: {e}")
-
-# Get discovery manifest
-try:
-    manifest = get_agent_discovery_manifest()
-    print(f"Discovery data: {manifest}")
-except Exception as e:
-    print(f"Failed to get manifest: {e}")
-```
-
-## Integration Patterns
-
-### 1. Trust-Based Decision Making
-
-```python
-from praisonai_tools.tools import SwarmScoreTool
-
-def execute_with_trust_level(agent_slug, task):
-    """Execute task based on agent's trust level."""
-    swarmscore = SwarmScoreTool()
-    result = swarmscore.load_swarmscore(agent_slug)
-    
-    if not result.success:
-        # Default to restricted mode if score unavailable
-        return execute_restricted(task)
-    
-    trust_score = result.output.get('score', 0)
-    
-    if trust_score >= 80:
-        # High trust - full autonomy
-        return execute_autonomous(task)
-    elif trust_score >= 60:
-        # Medium trust - human oversight
-        return execute_supervised(task)
-    else:
-        # Low trust - restricted operations
-        return execute_restricted(task)
-```
-
-### 2. Agent Authentication
-
-```python
-def authenticate_agent(agent_slug):
-    """Verify agent identity and trust level."""
-    swarmscore = SwarmScoreTool()
-    
-    # Load and verify score
-    load_result = swarmscore.load_swarmscore(agent_slug)
-    if not load_result.success:
-        return False, "Failed to load trust score"
-    
-    # Verify freshness
-    verify_payload = load_result.output.get('verify_payload')
-    if verify_payload:
-        verify_result = swarmscore.verify_swarmscore(verify_payload)
-        if not verify_result.success:
-            return False, "Trust score verification failed"
-    
-    trust_score = load_result.output.get('score', 0)
-    return trust_score >= 50, f"Trust score: {trust_score}"
-```
-
-### 3. Multi-Agent Coordination
-
-```python
-def find_trusted_collaborators(task_requirements):
-    """Find agents suitable for collaboration based on trust scores."""
-    swarmscore = SwarmScoreTool()
-    
-    # Get discovery manifest for agent lookup
-    manifest_result = swarmscore.get_discovery_manifest()
-    if not manifest_result.success:
-        return []
-    
-    trusted_agents = []
-    discovery_data = manifest_result.output
-    
-    # Example: Filter agents by trust score and capabilities
-    for agent_id in discovery_data.get('agents', []):
-        score_result = swarmscore.load_swarmscore(agent_id)
-        if score_result.success:
-            score_data = score_result.output
-            if score_data.get('score', 0) >= 70:
-                trusted_agents.append({
-                    'id': agent_id,
-                    'score': score_data['score'],
-                    'capabilities': score_data.get('capabilities', [])
-                })
-    
-    return trusted_agents
-```
-
-## PraisonAI Agent Integration
-
-### Using with PraisonAI Agent Tools
-
-```python
-from praisonaiagents import Agent, tool
-from praisonai_tools.tools import SwarmScoreTool
-
-@tool
-def check_agent_trust_score(agent_slug: str) -> str:
-    """Check the trust score for an agent."""
-    swarmscore = SwarmScoreTool()
-    result = swarmscore.load_swarmscore(agent_slug)
-    
-    if result.success:
-        score_data = result.output
-        return f"Agent {agent_slug} has trust score {score_data.get('score', 'unknown')} with {score_data.get('success_rate', 'unknown')}% success rate"
-    else:
-        return f"Failed to load trust score for {agent_slug}: {result.error}"
-
-# Create agent with SwarmScore capability
-agent = Agent(
-    name="trust-aware-agent",
-    instructions="You can check agent trust scores using the check_agent_trust_score tool.",
-    tools=[check_agent_trust_score]
-)
-
-# Use the agent
-response = agent.start("What is the trust score for agent 'trading-bot-v2'?")
-print(response)
-```
-
-### Agent with Built-in Trust Management
-
-```python
-from praisonaiagents import Agent
-from praisonai_tools.tools import SwarmScoreTool
-
-class TrustAwareAgent(Agent):
-    """PraisonAI Agent with built-in SwarmScore integration."""
-    
-    def __init__(self, agent_slug: str, **kwargs):
-        super().__init__(**kwargs)
-        self.agent_slug = agent_slug
-        self.swarmscore = SwarmScoreTool()
-        self._trust_score = None
-    
-    async def check_trust_level(self):
-        """Check and cache current trust level."""
-        result = self.swarmscore.load_swarmscore(self.agent_slug)
-        if result.success:
-            self._trust_score = result.output.get('score', 0)
-        return self._trust_score
-    
-    def can_execute_action(self, action_type: str) -> bool:
-        """Determine if action can be executed based on trust level."""
-        if self._trust_score is None:
-            return False  # Default to safe
-        
-        # Define trust requirements for different actions
-        trust_requirements = {
-            'read': 30,
-            'write': 60,
-            'execute': 80,
-            'admin': 95
-        }
-        
-        required_trust = trust_requirements.get(action_type, 100)
-        return self._trust_score >= required_trust
-
-# Usage
-agent = TrustAwareAgent(
-    agent_slug="my-agent-123",
-    name="secure-agent",
-    instructions="Execute tasks based on trust level"
-)
-
-# Check trust before executing
-trust_score = await agent.check_trust_level()
-if agent.can_execute_action('execute'):
-    print("Proceeding with high-trust execution")
-else:
-    print("Restricted to low-trust operations")
-```
-
-## API Reference
-
-### SwarmScoreTool Class
-
-#### Methods
-
-**`__init__(api_base_url: str = "https://api.swarmsync.ai/v1/swarmscore/")`**
-- Initialize SwarmScore tool
-- `api_base_url`: Base URL for SwarmScore API
-
-**`load_swarmscore(slug: str) -> ToolResult`**
-- Load SwarmScore data by agent slug
-- Returns: ToolResult with passport, certificate, and verification data
-
-**`verify_swarmscore(verify_payload: Dict[str, Any]) -> ToolResult`**
-- Verify SwarmScore freshness using verification payload
-- Returns: ToolResult with verification status
-
-**`get_discovery_manifest() -> ToolResult`**
-- Get machine-readable agent discovery manifest
-- Returns: ToolResult with discovery data
-
-**`run(action: str, **kwargs) -> ToolResult`**
-- Execute SwarmScore action
-- Actions: 'load', 'verify', 'discover'
-
-### Standalone Functions
-
-**`load_swarmscore_by_slug(slug: str) -> Dict[str, Any]`**
-- Load SwarmScore data by agent slug
-- Raises: Exception if loading fails
-
-**`verify_swarmscore_freshness(verify_payload: Dict[str, Any]) -> Dict[str, Any]`**
-- Verify SwarmScore freshness
-- Raises: Exception if verification fails
-
-**`get_agent_discovery_manifest() -> Dict[str, Any]`**
-- Get agent discovery manifest
-- Raises: Exception if retrieval fails
-
-## SwarmScore Data Structure
-
-```json
-{
-  "score": 85,
-  "tier": "gold",
-  "jobs_completed": 150,
-  "success_rate": 94.5,
-  "consistency_score": 88,
-  "signed_certificate": "...",
-  "verify_payload": {
-    "signature": "...",
-    "timestamp": "...",
-    "agent_id": "..."
-  },
-  "capabilities": ["trading", "analysis", "reporting"],
-  "last_updated": "2024-01-15T10:30:00Z"
-}
-```
-
-## Error Handling
-
-```python
-from praisonai_tools.tools import SwarmScoreTool
-
-swarmscore = SwarmScoreTool()
-result = swarmscore.load_swarmscore("agent-123")
-
-if not result.success:
-    error_msg = result.error
-    
-    if "network" in error_msg.lower():
-        # Handle network issues
-        print("Network connectivity problem")
-    elif "not found" in error_msg.lower():
-        # Handle missing agent
-        print("Agent not registered in SwarmScore")
-    else:
-        # Handle other errors
-        print(f"Unexpected error: {error_msg}")
-```
-
-## Best Practices
-
-1. **Cache Trust Scores**: Cache scores temporarily to avoid excessive API calls
-2. **Graceful Degradation**: Have fallback behavior when SwarmScore is unavailable
-3. **Verify Freshness**: Always verify score freshness for critical decisions
-4. **Trust Thresholds**: Define clear trust score requirements for different operations
-5. **Error Handling**: Handle network and API errors gracefully
-
-## Getting Started with SwarmScore
-
-1. **Register**: Create an account at [SwarmSync.AI](https://swarmsync.ai)
-2. **Get Your Slug**: Obtain your agent identifier from the dashboard
-3. **Start Building**: Begin tracking execution history to build trust
-4. **Integrate**: Use this tool to incorporate trust ratings into your workflows
-
-## Resources
-
-- [SwarmScore Documentation](https://swarmsync.ai/docs/protocol-specs/swarmscore)
-- [SwarmScore GitHub Spec](https://github.com/swarmsync-ai/swarmscore-spec)
-- [SwarmSync.AI Platform](https://swarmsync.ai)
-- [Example Implementation](../examples/swarmscore_example.py)
-
-## Contributing
-
-To contribute improvements to the SwarmScore integration:
-
-1. Fork the PraisonAI-Tools repository
-2. Create a feature branch
-3. Make your changes
-4. Add tests for new functionality
-5. Submit a pull request
-
-## License
-
-This integration is part of PraisonAI-Tools and follows the same license terms.

docs/video-cli.md

@@ -1,153 +0,0 @@
-# Video CLI
-
-```bash
-python -m praisonai_tools.video probe input.mp4
-```
-
-```bash
-python -m praisonai_tools.video probe input.mp4 --output probe.json
-```
-
-```bash
-python -m praisonai_tools.video probe input.mp4 --json
-```
-
-```bash
-python -m praisonai_tools.video transcribe input.mp4
-```
-
-```bash
-python -m praisonai_tools.video transcribe input.mp4 --output transcript.srt
-```
-
-```bash
-python -m praisonai_tools.video transcribe input.mp4 --format txt --output transcript.txt
-```
-
-```bash
-python -m praisonai_tools.video transcribe input.mp4 --format json --output transcript.json
-```
-
-```bash
-python -m praisonai_tools.video transcribe input.mp4 --local
-```
-
-```bash
-python -m praisonai_tools.video plan input.mp4
-```
-
-```bash
-python -m praisonai_tools.video plan input.mp4 --output plan.json
-```
-
-```bash
-python -m praisonai_tools.video plan input.mp4 --preset podcast
-```
-
-```bash
-python -m praisonai_tools.video plan input.mp4 --no-fillers --no-repetitions
-```
-
-```bash
-python -m praisonai_tools.video render input.mp4 --timeline plan.json --output output.mp4
-```
-
-```bash
-python -m praisonai_tools.video render input.mp4 --timeline plan.json --output output.mp4 --reencode
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --preset podcast
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --preset meeting
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --preset course
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --preset clean
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --no-fillers
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --no-repetitions
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --no-silence
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --tangents
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --target-length 6m
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --captions srt
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --captions burn
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --captions off
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --force
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --provider openai
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --provider local
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --no-llm
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --model gpt-4o
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --whisper-model large-v3
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --local
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --reencode
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --verbose
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --workdir ./artifacts
-```
-
-```bash
-python -m praisonai_tools.video edit input.mp4 --output edited.mp4 --no-artifacts
-```

docs/video-module.md

@@ -1,126 +0,0 @@
-# Video Module
-
-## Installation
-
-```python
-pip install praisonai-tools
-```
-
-## Code Usage
-
-```python
-from praisonai_tools.video import (
-    probe_video,
-    transcribe_video,
-    create_edit_plan,
-    render_video,
-    edit_video,
-)
-
-# Probe video metadata
-result = probe_video("input.mp4")
-print(f"Duration: {result.duration}s")
-print(f"Resolution: {result.width}x{result.height}")
-
-# Transcribe audio
-transcript = transcribe_video("input.mp4")
-print(transcript.text)
-print(transcript.to_srt())
-
-# Create edit plan
-plan = create_edit_plan(
-    transcript=transcript,
-    duration=result.duration,
-    remove_fillers=True,
-    remove_repetitions=True,
-    remove_silence=True,
-)
-
-# Render video
-render_video(
-    input_path="input.mp4",
-    output_path="output.mp4",
-    plan=plan,
-)
-
-# Full pipeline
-result = edit_video(
-    input_path="input.mp4",
-    output_path="edited.mp4",
-    preset="podcast",
-    remove_fillers=True,
-    remove_repetitions=True,
-    remove_silence=True,
-    verbose=True,
-)
-```
-
-## Classes
-
-### VideoProbeResult
-
-```python
-@dataclass
-class VideoProbeResult:
-    path: str
-    duration: float
-    width: int
-    height: int
-    fps: float
-    codec: str
-    bitrate: int
-    size_bytes: int
-    audio_codec: Optional[str]
-    audio_sample_rate: Optional[int]
-    audio_channels: Optional[int]
-```
-
-### TranscriptResult
-
-```python
-@dataclass
-class TranscriptResult:
-    text: str
-    words: List[Word]
-    language: str
-    duration: float
-    
-    def to_srt(self) -> str: ...
-    def to_dict(self) -> dict: ...
-```
-
-### EditPlan
-
-```python
-@dataclass
-class EditPlan:
-    segments: List[Segment]
-    original_duration: float
-    edited_duration: float
-    removed_duration: float
-    removal_summary: Dict[str, float]
-```
-
-### VideoEditResult
-
-```python
-@dataclass
-class VideoEditResult:
-    input_path: str
-    output_path: str
-    probe: VideoProbeResult
-    transcript: TranscriptResult
-    plan: EditPlan
-    success: bool
-    error: Optional[str]
-    artifacts: Dict[str, str]
-```
-
-## Presets
-
-| Preset | Fillers | Repetitions | Silence | Tangents |
-|--------|---------|-------------|---------|----------|
-| podcast | ✓ | ✓ | ✓ | ✗ |
-| meeting | ✓ | ✓ | ✓ | ✗ |
-| course | ✓ | ✓ | ✓ | ✗ |
-| clean | ✓ | ✓ | ✓ | ✓ |

examples/inkog_security_example.py

@@ -1,261 +0,0 @@
-"""
-Inkog Security Analysis Tool Example
-
-This example demonstrates how to use the Inkog tool to analyze AI agent code
-for security vulnerabilities like token bombing, prompt injection, and more.
-
-Prerequisites:
-1. Install inkog CLI: brew tap inkog-io/inkog && brew install inkog
-2. Get free API key from https://app.inkog.io  
-3. Set environment variable: export INKOG_API_KEY=your_key_here
-
-The Inkog tool can detect:
-- Token bombing attacks (loops where LLM controls termination)
-- Prompt injection vulnerabilities
-- Recursive tool calling without cycle detection  
-- Missing human oversight for destructive operations
-- Cross-tenant data leakage
-- MCP tool poisoning
-- EU AI Act / NIST / OWASP compliance issues
-"""
-
-import os
-from praisonai_tools import InkogTool, scan_agent_code
-
-
-def basic_security_scan():
-    """Perform a basic security scan of the current directory."""
-    print("🔍 Basic Security Scan Example")
-    print("=" * 50)
-    
-    # Method 1: Using the class
-    inkog = InkogTool()
-    result = inkog.scan_directory(
-        path=".",
-        output_format="json",
-        policy="balanced"  # balanced, low-noise, comprehensive
-    )
-    
-    if "error" in result:
-        print(f"❌ Scan failed: {result['error']}")
-        return
-    
-    # Analyze the results
-    analysis = inkog.analyze_findings(result)
-    print(analysis)
-    
-    print("\n" + "=" * 50)
-
-
-def advanced_security_scan():
-    """Perform an advanced security scan with deep analysis."""
-    print("🔬 Advanced Security Scan Example (Deep Analysis)")
-    print("=" * 60)
-    
-    inkog = InkogTool()
-    
-    # Deep scan provides enhanced analysis with AI orchestrator
-    result = inkog.scan_directory(
-        path=".",
-        output_format="json",
-        policy="comprehensive",  # Show all findings including best practices
-        deep=True,  # Enable AI orchestrator analysis (requires Inkog Deep role)
-        verbose=True
-    )
-    
-    if "error" in result:
-        print(f"❌ Deep scan failed: {result['error']}")
-        print("Note: Deep scans require the Inkog Deep role on your account")
-        return
-        
-    # Show detailed analysis
-    analysis = inkog.analyze_findings(result)
-    print(analysis)
-    
-    # Show raw findings for debugging
-    if "server_findings" in result:
-        print(f"\n📊 Raw Findings ({len(result['server_findings'])} total):")
-        for i, finding in enumerate(result["server_findings"][:3]):  # Show first 3
-            print(f"{i+1}. {finding.get('pattern', 'Unknown')} - {finding.get('severity', 'N/A')}")
-            print(f"   File: {finding.get('file', 'N/A')}:{finding.get('line', 'N/A')}")
-            print(f"   Message: {finding.get('message', 'N/A')}")
-            print()
-    
-    print("=" * 60)
-
-
-def skill_package_scan():
-    """Scan a SKILL.md package or MCP server."""
-    print("📦 Skill Package Security Scan Example")
-    print("=" * 50)
-    
-    inkog = InkogTool()
-    
-    # Scan current directory as a skill package
-    result = inkog.skill_scan(
-        path=".",
-        output_format="json",
-        deep=False
-    )
-    
-    if "error" in result:
-        print(f"❌ Skill scan failed: {result['error']}")
-        return
-        
-    analysis = inkog.analyze_findings(result)
-    print(analysis)
-    
-    print("\n" + "=" * 50)
-
-
-def mcp_server_scan():
-    """Scan an MCP server from the registry."""
-    print("🔌 MCP Server Security Scan Example")
-    print("=" * 50)
-    
-    inkog = InkogTool()
-    
-    # Scan a popular MCP server (GitHub)
-    result = inkog.mcp_scan(
-        server_name="github",
-        output_format="json"
-    )
-    
-    if "error" in result:
-        print(f"❌ MCP scan failed: {result['error']}")
-        return
-        
-    analysis = inkog.analyze_findings(result)
-    print(analysis)
-    
-    print("\n" + "=" * 50)
-
-
-def standalone_function_example():
-    """Example using standalone functions for quick scans."""
-    print("⚡ Standalone Function Examples")
-    print("=" * 40)
-    
-    # Quick scan using standalone function
-    print("Running quick security scan...")
-    result = scan_agent_code(
-        path=".", 
-        output_format="json",
-        policy="low-noise",  # Only critical vulnerabilities
-        deep=False
-    )
-    
-    if isinstance(result, dict) and "error" not in result:
-        tool = InkogTool()
-        analysis = tool.analyze_findings(result)
-        print(analysis)
-    else:
-        print(f"Scan result: {result}")
-    
-    print("\n" + "=" * 40)
-
-
-def html_report_example():
-    """Generate an HTML security report."""
-    print("📄 HTML Report Generation Example")
-    print("=" * 45)
-    
-    inkog = InkogTool()
-    
-    # Generate HTML report
-    html_report = inkog.scan_directory(
-        path=".",
-        output_format="html",
-        policy="comprehensive",
-        deep=False
-    )
-    
-    if isinstance(html_report, str) and not html_report.startswith("Error"):
-        # Save report to file
-        report_path = "inkog_security_report.html"
-        with open(report_path, "w", encoding="utf-8") as f:
-            f.write(html_report)
-        print(f"✅ HTML report generated: {report_path}")
-        print(f"📄 Report size: {len(html_report)} characters")
-    else:
-        print(f"❌ HTML report failed: {html_report}")
-    
-    print("=" * 45)
-
-
-def compliance_scan_example():
-    """Example focused on compliance mapping."""
-    print("🏛️ Compliance Mapping Example")
-    print("=" * 40)
-    
-    # Use comprehensive policy to see compliance mappings
-    inkog = InkogTool()
-    result = inkog.scan_directory(
-        path=".",
-        output_format="json", 
-        policy="comprehensive",  # Shows compliance mappings
-        deep=False
-    )
-    
-    if "error" in result:
-        print(f"❌ Compliance scan failed: {result['error']}")
-        return
-        
-    # Look for compliance information
-    findings = result.get("server_findings", [])
-    compliance_findings = []
-    
-    for finding in findings:
-        if any(term in finding.get("message", "").lower() 
-               for term in ["eu ai act", "nist", "owasp", "iso", "article"]):
-            compliance_findings.append(finding)
-    
-    if compliance_findings:
-        print(f"📋 Found {len(compliance_findings)} compliance-related findings:")
-        for finding in compliance_findings[:3]:  # Show first 3
-            print(f"• {finding.get('pattern', 'Unknown')}")
-            print(f"  {finding.get('message', 'N/A')[:100]}...")
-    else:
-        print("✅ No specific compliance violations found")
-        
-    print("=" * 40)
-
-
-def main():
-    """Run all examples."""
-    print("Inkog Security Analysis Tool Examples")
-    print("=" * 60)
-    
-    # Check if inkog is available
-    api_key = os.environ.get("INKOG_API_KEY")
-    if not api_key:
-        print("⚠️  WARNING: INKOG_API_KEY environment variable not set")
-        print("   Get your free API key at https://app.inkog.io")
-        print("   Set it with: export INKOG_API_KEY=your_key_here")
-        print()
-    
-    try:
-        # Run examples in order of complexity
-        basic_security_scan()
-        standalone_function_example()
-        skill_package_scan()
-        html_report_example()
-        compliance_scan_example()
-        
-        # Advanced features (may require special access)
-        print("\n🎓 Advanced Examples (may require Inkog Deep role):")
-        print("-" * 50)
-        advanced_security_scan()
-        mcp_server_scan()
-        
-    except Exception as e:
-        print(f"❌ Example failed: {e}")
-        print("This may be due to:")
-        print("1. Inkog CLI not installed")
-        print("2. Missing INKOG_API_KEY environment variable")
-        print("3. Network connectivity issues")
-        print("4. Insufficient permissions for deep scans")
-
-
-if __name__ == "__main__":
-    main()

examples/jira_watch_example.py

@@ -1,55 +0,0 @@
-#!/usr/bin/env python3
-"""JIRA Agent watch example using praisonai-tools.
-
-Setup:
-    pip install praisonai-tools jira
-    export JIRA_URL=https://yourcompany.atlassian.net
-    export [email protected]
-    export JIRA_API_TOKEN=your_api_token
-"""
-
-import os
-
-from praisonaiagents import Agent
-from praisonai_tools import jira_tools
-
-
-def main():
-    jira_url = os.getenv("JIRA_URL")
-    jira_token = os.getenv("JIRA_API_TOKEN")
-    jira_email = os.getenv("JIRA_EMAIL")
-    jira_username = os.getenv("JIRA_USERNAME")
-
-    if not jira_url or not jira_token or not (jira_email or jira_username):
-        print("Set JIRA_URL, JIRA_API_TOKEN, and JIRA_EMAIL (or JIRA_USERNAME)")
-        return
-
-    agent = Agent(
-        name="JIRA Monitor Agent",
-        instructions=(
-            "You monitor JIRA issues and projects. Use jira_watch_issue, "
-            "jira_watch_project, jira_get_issue_info, and jira_search_issues."
-        ),
-        tools=jira_tools(),
-        llm="gpt-4o-mini",
-    )
-
-    print("JIRA watch tools ready. Example prompts:")
-    print("  Get info for PROJ-123")
-    print("  Search open issues: project = PROJ AND status = Open")
-    print("  Watch PROJ-123 for changes since 2024-01-01T00:00:00")
-
-    while True:
-        try:
-            user_input = input("\nYou: ").strip()
-            if user_input.lower() in {"quit", "exit", "q"}:
-                break
-            if not user_input:
-                continue
-            print("Agent:", agent.start(user_input))
-        except KeyboardInterrupt:
-            break
-
-
-if __name__ == "__main__":
-    main()

examples/joy_trust_integration.py

@@ -1,198 +0,0 @@
-#!/usr/bin/env python3
-"""
-Joy Trust Integration Examples for PraisonAI
-
-This example demonstrates the native Joy Trust integration for secure agent handoffs.
-"""
-
-import os
-from praisonai_tools import (
-    check_trust_score, 
-    verify_handoff_safety,
-    trust_verified_handoff,
-    is_trust_verification_enabled,
-    get_trust_config,
-    JoyTrustTool
-)
-
-def example_basic_trust_check():
-    """Example 1: Basic trust score verification"""
-    print("=== Basic Trust Score Check ===")
-    
-    # Check trust for a specific agent
-    result = check_trust_score("researcher_agent", min_score=3.0)
-    
-    print(f"Agent: {result['agent_name']}")
-    print(f"Trust Score: {result['trust_score']}")
-    print(f"Verified: {result['verified']}")
-    print(f"Meets Threshold: {result['meets_threshold']}")
-    print(f"Recommendation: {result.get('recommendation', 'N/A')}")
-    
-    if result['error']:
-        print(f"Error: {result['error']}")
-    
-    print()
-
-
-def example_handoff_verification():
-    """Example 2: Handoff safety verification"""
-    print("=== Handoff Safety Verification ===")
-    
-    target_agent = "data_analyst"
-    verification = verify_handoff_safety(target_agent, min_score=3.5)
-    
-    print(f"Target Agent: {verification['agent_name']}")
-    print(f"Trust Score: {verification['trust_score']}")
-    print(f"Handoff Safe: {verification['handoff_safe']}")
-    print(f"Recommendation: {verification['recommendation']}")
-    print(f"Threshold Used: {verification['threshold_used']}")
-    
-    if verification['handoff_safe']:
-        print("✅ Safe to proceed with handoff")
-    else:
-        print("❌ Handoff not recommended")
-    
-    print()
-
-
-@trust_verified_handoff(min_score=4.0)
-def example_secure_delegation(agent_name: str, task: str):
-    """Example 3: Decorator-based secure delegation"""
-    print(f"Delegating task '{task}' to {agent_name}")
-    
-    # Simulate task delegation
-    # In a real scenario, this would delegate to the actual agent
-    return {
-        "success": True,
-        "result": f"Task completed by {agent_name}",
-        "agent": agent_name,
-        "task": task
-    }
-
-
-def example_environment_configuration():
-    """Example 4: Environment-based configuration"""
-    print("=== Environment Configuration ===")
-    
-    # Set environment variables for automatic trust verification
-    os.environ['PRAISONAI_TRUST_PROVIDER'] = 'joy'
-    os.environ['PRAISONAI_TRUST_MIN_SCORE'] = '3.5'
-    os.environ['PRAISONAI_TRUST_AUTO_VERIFY'] = 'true'
-    
-    # Check if trust verification is enabled
-    enabled = is_trust_verification_enabled()
-    print(f"Trust Verification Enabled: {enabled}")
-    
-    # Get current configuration
-    config = get_trust_config()
-    print(f"Provider: {config.provider}")
-    print(f"Min Score: {config.min_score}")
-    print(f"Auto Verify: {config.auto_verify_handoffs}")
-    print(f"Timeout: {config.timeout_seconds}s")
-    
-    print()
-
-
-def example_advanced_configuration():
-    """Example 5: Advanced tool configuration"""
-    print("=== Advanced Configuration ===")
-    
-    # Create a custom configured tool
-    trust_tool = JoyTrustTool(
-        api_key=os.getenv("JOY_TRUST_API_KEY"),
-        config=None  # Will use environment config
-    )
-    
-    # Configure the tool
-    config_result = trust_tool.configure(
-        min_score=4.5,
-        timeout_seconds=15.0,
-        auto_verify_handoffs=True
-    )
-    
-    print(f"Configuration Status: {config_result['status']}")
-    print(f"Current Config: {config_result['config']}")
-    
-    # Use the configured tool
-    result = trust_tool.check_trust("expert_agent")
-    print(f"\nExpert Agent Trust: {result['trust_score']}")
-    print(f"Meets Threshold: {result['meets_threshold']}")
-    
-    print()
-
-
-def example_workflow_integration():
-    """Example 6: Workflow integration pattern"""
-    print("=== Workflow Integration ===")
-    
-    # Enable trust verification
-    os.environ['PRAISONAI_TRUST_PROVIDER'] = 'joy'
-    
-    # Define a multi-agent workflow
-    workflow_agents = [
-        {"name": "data_collector", "role": "collect"},
-        {"name": "data_processor", "role": "process"}, 
-        {"name": "report_generator", "role": "generate"}
-    ]
-    
-    trusted_agents = []
-    
-    for agent in workflow_agents:
-        verification = verify_handoff_safety(agent["name"], min_score=3.0)
-        
-        print(f"Agent: {agent['name']}")
-        print(f"  Role: {agent['role']}")
-        print(f"  Trust Score: {verification['trust_score']}")
-        print(f"  Safe: {verification['handoff_safe']}")
-        print(f"  Recommendation: {verification['recommendation']}")
-        
-        if verification['handoff_safe']:
-            trusted_agents.append(agent)
-        
-        print()
-    
-    print(f"Trusted agents for workflow: {len(trusted_agents)}/{len(workflow_agents)}")
-    
-    # Proceed with trusted agents only
-    if len(trusted_agents) == len(workflow_agents):
-        print("✅ All agents trusted - workflow can proceed safely")
-    else:
-        print("⚠️ Some agents not trusted - review workflow security")
-
-
-def example_decorator_usage():
-    """Example 7: Using the decorator in practice"""
-    print("=== Decorator Usage ===")
-    
-    # Enable automatic trust verification
-    os.environ['PRAISONAI_TRUST_PROVIDER'] = 'joy'
-    os.environ['PRAISONAI_TRUST_AUTO_VERIFY'] = 'true'
-    
-    # Example with high trust requirement
-    try:
-        result = example_secure_delegation("trusted_expert", "analyze_data")
-        print(f"Delegation Result: {result}")
-    except Exception as e:
-        print(f"Delegation Failed: {e}")
-    
-    print()
-
-
-if __name__ == "__main__":
-    print("Joy Trust Integration Examples\n")
-    
-    # Note: These examples will make actual API calls if JOY_TRUST_API_KEY is set
-    # and the agents exist in the Joy Trust Network
-    
-    try:
-        example_basic_trust_check()
-        example_handoff_verification()
-        example_environment_configuration()
-        example_advanced_configuration()
-        example_workflow_integration()
-        example_decorator_usage()
-        
-    except Exception as e:
-        print(f"Example execution error: {e}")
-        print("\nNote: Some examples may fail if Joy Trust Network is not accessible")
-        print("or if the example agent names don't exist in the network.")

examples/langextract_example.py

@@ -1,275 +0,0 @@
-"""
-Langextract Tool Example
-
-This example demonstrates how to use the langextract tools for 
-interactive text visualization and analysis with PraisonAI agents.
-
-Prerequisites:
-    pip install praisonai-tools[langextract]
-    # or 
-    pip install praisonai-tools langextract
-
-Features demonstrated:
-- Text analysis with highlighted extractions
-- File-based document analysis  
-- Integration with PraisonAI agents
-- Interactive HTML generation
-"""
-
-import os
-import tempfile
-from pathlib import Path
-
-from praisonai_tools import langextract_extract, langextract_render_file
-
-
-def basic_text_analysis():
-    """Demonstrate basic text analysis with highlighted extractions."""
-    print("🔍 Basic Text Analysis with Langextract")
-    print("=" * 50)
-    
-    # Sample contract text
-    contract_text = """
-    CONSULTING AGREEMENT
-    
-    This Agreement is entered into on March 15, 2024, between TechCorp Inc. 
-    (the "Client") and Jane Smith (the "Consultant"). The Consultant will 
-    provide AI development services for a period of 6 months starting 
-    April 1, 2024. 
-    
-    Payment terms: $5,000 per month, payable within 15 days of invoice date.
-    Confidentiality obligations remain in effect for 2 years after termination.
-    """
-    
-    # Key terms to highlight
-    key_terms = [
-        "TechCorp Inc.",
-        "Jane Smith", 
-        "March 15, 2024",
-        "April 1, 2024",
-        "$5,000 per month",
-        "15 days",
-        "2 years",
-        "AI development services"
-    ]
-    
-    # Extract and visualize
-    result = langextract_extract(
-        text=contract_text,
-        extractions=key_terms,
-        document_id="consulting-agreement",
-        output_path="contract_analysis.html",
-        auto_open=False  # Set to True to open in browser automatically
-    )
-    
-    if result.get("success"):
-        print(f"✅ Analysis complete!")
-        print(f"   Document ID: {result['document_id']}")
-        print(f"   Output file: {result['output_path']}")
-        print(f"   Extractions: {result['extractions_count']} terms highlighted")
-        print(f"   Text length: {result['text_length']} characters")
-        print()
-        print(f"💡 Open {result['output_path']} in your browser to view the interactive visualization!")
-    else:
-        print(f"❌ Error: {result.get('error')}")
-        if "langextract not installed" in result.get("error", ""):
-            print("💡 Install with: pip install langextract")
-
-
-def file_analysis_example():
-    """Demonstrate file-based document analysis."""
-    print("📄 File-based Document Analysis")
-    print("=" * 50)
-    
-    # Create a sample document
-    document_content = """
-    TECHNICAL SPECIFICATION DOCUMENT
-    
-    Project: AI-Powered Analytics Dashboard
-    Version: 2.1.0
-    Date: April 17, 2026
-    
-    REQUIREMENTS:
-    - Python 3.10+ runtime environment
-    - PostgreSQL 14+ database
-    - Redis for caching layer
-    - Docker for containerization
-    - Kubernetes for orchestration
-    
-    SECURITY CONSIDERATIONS:
-    - JWT authentication with 24-hour expiry
-    - Rate limiting: 1000 requests per hour per API key
-    - HTTPS-only communication
-    - Data encryption at rest using AES-256
-    
-    PERFORMANCE TARGETS:
-    - API response time: < 200ms for 95th percentile
-    - Database query optimization required
-    - Concurrent user support: 10,000+ users
-    """
-    
-    # Create temporary file
-    with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.txt') as f:
-        f.write(document_content)
-        temp_file_path = f.name
-    
-    try:
-        # Technical terms to highlight
-        tech_terms = [
-            "Python 3.10+",
-            "PostgreSQL 14+", 
-            "Redis",
-            "Docker",
-            "Kubernetes",
-            "JWT authentication",
-            "24-hour expiry",
-            "1000 requests per hour",
-            "AES-256",
-            "< 200ms",
-            "10,000+ users"
-        ]
-        
-        # Analyze the file
-        result = langextract_render_file(
-            file_path=temp_file_path,
-            extractions=tech_terms,
-            output_path="tech_spec_analysis.html",
-            auto_open=False
-        )
-        
-        if result.get("success"):
-            print(f"✅ File analysis complete!")
-            print(f"   Source file: {temp_file_path}")
-            print(f"   Document ID: {result['document_id']}")
-            print(f"   Output file: {result['output_path']}")
-            print(f"   Extractions: {result['extractions_count']} terms highlighted")
-            print(f"   Text length: {result['text_length']} characters")
-            print()
-            print(f"💡 Open {result['output_path']} in your browser to view the interactive visualization!")
-        else:
-            print(f"❌ Error: {result.get('error')}")
-    
-    finally:
-        # Clean up temporary file
-        try:
-            os.unlink(temp_file_path)
-        except OSError:
-            pass
-
-
-def agent_integration_example():
-    """Demonstrate integration with PraisonAI agents."""
-    print("🤖 PraisonAI Agent Integration")
-    print("=" * 50)
-    
-    # This is how you would integrate with agents
-    agent_code = '''
-from praisonaiagents import Agent
-from praisonai_tools import langextract_extract, langextract_render_file
-
-# Create an agent that analyzes documents
-document_analyzer = Agent(
-    name="DocumentAnalyzer",
-    instructions="""
-    You are a document analysis expert. When given text or documents,
-    identify key entities, dates, financial figures, and important terms.
-    Use langextract tools to create interactive visualizations highlighting
-    your findings.
-    """,
-    tools=[langextract_extract, langextract_render_file]
-)
-
-# Agent workflow example
-def analyze_document(text_or_file_path):
-    """Agent analyzes document and creates visualization."""
-    
-    if text_or_file_path.endswith('.txt'):
-        # File-based analysis
-        response = document_analyzer.start(
-            f"Analyze this document file and highlight key terms: {text_or_file_path}"
-        )
-    else:
-        # Text-based analysis
-        response = document_analyzer.start(
-            f"Analyze this text and highlight important information: {text_or_file_path}"
-        )
-    
-    return response
-
-# Example usage:
-# result = analyze_document("contract.txt")
-# result = analyze_document("The quarterly report shows revenue of $1.2M...")
-'''
-    
-    print("💻 Example agent integration code:")
-    print(agent_code)
-    print()
-    print("🔧 Key integration points:")
-    print("   - Import tools: langextract_extract, langextract_render_file")
-    print("   - Add to agent tools list")
-    print("   - Agent can automatically use tools based on instructions")
-    print("   - Interactive HTML files created for human review")
-
-
-def error_handling_example():
-    """Demonstrate graceful error handling."""
-    print("⚠️  Error Handling and Graceful Degradation")
-    print("=" * 50)
-    
-    # Test without langextract installed (simulated)
-    print("Testing graceful degradation when langextract is not installed:")
-    
-    result = langextract_extract(
-        text="Sample text for analysis",
-        extractions=["Sample"],
-        document_id="test"
-    )
-    
-    if "error" in result:
-        print(f"✅ Graceful error handling: {result['error']}")
-        print("💡 Users get clear installation instructions")
-    else:
-        print("✅ Langextract is available and working!")
-    
-    print()
-    print("Common error scenarios handled:")
-    print("   - ❌ langextract not installed → Clear installation message")
-    print("   - ❌ Invalid file path → File not found error") 
-    print("   - ❌ Empty text input → Parameter validation")
-    print("   - ❌ Browser auto-open fails → Graceful fallback")
-
-
-def main():
-    """Run all examples."""
-    print("🚀 Langextract Tool Examples for PraisonAI")
-    print("=" * 70)
-    print()
-    
-    try:
-        basic_text_analysis()
-        print()
-        
-        file_analysis_example()  
-        print()
-        
-        agent_integration_example()
-        print()
-        
-        error_handling_example()
-        print()
-        
-        print("🎉 All examples completed!")
-        print()
-        print("📚 Next Steps:")
-        print("   1. Install langextract: pip install langextract")
-        print("   2. Run your own text analysis")
-        print("   3. Integrate with your PraisonAI agents")
-        print("   4. Open generated HTML files to see interactive visualizations")
-        
-    except Exception as e:
-        print(f"❌ Example error: {e}")
-        print("💡 This is expected if langextract is not installed")
-
-
-if __name__ == "__main__":
-    main()

examples/motion_graphics_example.py

@@ -1,238 +0,0 @@
-#!/usr/bin/env python3
-"""Example usage of motion graphics video pipeline.
-
-This example demonstrates how to use the motion graphics pipeline to create
-videos from natural language prompts using HTML/GSAP animations.
-
-Requirements:
-    pip install praisonai-tools[video-motion]
-    playwright install chromium
-"""
-
-import asyncio
-import tempfile
-from pathlib import Path
-
-try:
-    from praisonai_tools.video.motion_graphics import (
-        create_motion_graphics_agent,
-        motion_graphics_team,
-        HtmlRenderBackend,
-        RenderOpts
-    )
-    from praisonai_tools.tools.git_tools import GitTools
-except ImportError as e:
-    print(f"Import error: {e}")
-    print("Please install with: pip install praisonai-tools[video-motion]")
-    exit(1)
-
-
-async def example_basic_animation():
-    """Example 1: Basic motion graphics with simple animations."""
-    print("Example 1: Creating basic motion graphics animation...")
-    
-    with tempfile.TemporaryDirectory() as tmpdir:
-        workspace = Path(tmpdir)
-        
-        # Create a simple HTML composition
-        html_content = """
-        <!DOCTYPE html>
-        <html>
-        <head>
-            <script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.2/gsap.min.js"></script>
-            <style>
-                body { margin: 0; padding: 0; background: #1a1a1a; overflow: hidden; font-family: Arial, sans-serif; }
-                #stage { width: 1920px; height: 1080px; position: relative; }
-                .title { font-size: 72px; color: #ffffff; position: absolute; top: 50%; left: 50%; 
-                        transform: translate(-50%, -50%); opacity: 0; }
-                .subtitle { font-size: 36px; color: #888; position: absolute; top: 60%; left: 50%; 
-                           transform: translate(-50%, -50%); opacity: 0; }
-            </style>
-        </head>
-        <body>
-            <div id="stage" data-duration="3.0">
-                <div class="title">Motion Graphics</div>
-                <div class="subtitle">Powered by PraisonAI</div>
-            </div>
-            
-            <script>
-                const tl = gsap.timeline({ paused: true });
-                
-                // Animate title in
-                tl.to(".title", { 
-                    duration: 1, 
-                    opacity: 1, 
-                    y: -20, 
-                    ease: "power2.out" 
-                })
-                // Animate subtitle in
-                .to(".subtitle", { 
-                    duration: 0.8, 
-                    opacity: 1, 
-                    y: -10, 
-                    ease: "power2.out" 
-                }, "-=0.3")
-                // Hold for a moment
-                .to({}, { duration: 1 })
-                // Fade out
-                .to([".title", ".subtitle"], { 
-                    duration: 0.5, 
-                    opacity: 0, 
-                    ease: "power2.in" 
-                });
-                
-                // Required: Export timeline
-                window.__timelines = [tl];
-            </script>
-        </body>
-        </html>
-        """
-        
-        # Save HTML file
-        (workspace / "index.html").write_text(html_content)
-        
-        # Create backend and render
-        backend = HtmlRenderBackend()
-        
-        # Lint first
-        lint_result = await backend.lint(workspace)
-        print(f"Lint result: {'✓ Passed' if lint_result.ok else '✗ Failed'}")
-        if not lint_result.ok:
-            print(f"  Errors: {', '.join(lint_result.messages)}")
-        
-        # Render video
-        if lint_result.ok:
-            render_opts = RenderOpts(
-                output_name="basic_animation.mp4",
-                fps=30,
-                quality="standard"
-            )
-            
-            print("Rendering video... (this may take a moment)")
-            render_result = await backend.render(workspace, render_opts)
-            
-            if render_result.ok:
-                print(f"✓ Video rendered successfully!")
-                print(f"  Output: {render_result.output_path}")
-                print(f"  Size: {render_result.size_kb}KB")
-            else:
-                print(f"✗ Render failed: {render_result.stderr}")
-
-
-async def example_git_tools():
-    """Example 2: Using GitTools for code exploration."""
-    print("\nExample 2: Using GitTools for safe git operations...")
-    
-    with tempfile.TemporaryDirectory() as tmpdir:
-        git_tools = GitTools(base_dir=tmpdir)
-        
-        print("Testing repository URL parsing...")
-        
-        # Test different repository formats
-        test_repos = [
-            "octocat/Hello-World",  # owner/repo format
-            "https://github.com/octocat/Hello-World.git",  # HTTPS URL
-        ]
-        
-        for repo_input in test_repos:
-            try:
-                url, name = git_tools._parse_repo_input(repo_input)
-                print(f"  {repo_input} -> URL: {url}, Name: {name}")
-            except Exception as e:
-                print(f"  {repo_input} -> Error: {e}")
-        
-        print("\nTesting file path safety...")
-        
-        # Test file path validation
-        test_paths = [
-            "README.md",  # Safe
-            "src/main.py",  # Safe
-            "../etc/passwd",  # Unsafe
-            "../../secret.txt",  # Unsafe
-        ]
-        
-        for path in test_paths:
-            try:
-                safe_path = git_tools._validate_file_path(path)
-                print(f"  {path} -> ✓ Safe: {safe_path}")
-            except ValueError as e:
-                print(f"  {path} -> ✗ Unsafe: {e}")
-
-
-def example_agent_factory():
-    """Example 3: Using motion graphics agent factory."""
-    print("\nExample 3: Creating motion graphics agent...")
-    
-    try:
-        # This would normally require praisonaiagents to be installed
-        agent = create_motion_graphics_agent(
-            backend="html",
-            max_retries=3,
-            llm="claude-sonnet-4"
-        )
-        
-        print("✓ Motion graphics agent created successfully!")
-        print(f"  Backend: {agent._motion_graphics_backend.__class__.__name__}")
-        print(f"  Workspace: {agent._motion_graphics_workspace}")
-        print(f"  Max retries: {agent._motion_graphics_max_retries}")
-        
-    except ImportError as e:
-        print(f"⚠ Agent creation skipped: {e}")
-        print("  Install praisonaiagents to use agent features")
-
-
-def example_team_preset():
-    """Example 4: Using motion graphics team preset."""
-    print("\nExample 4: Creating motion graphics team...")
-    
-    try:
-        team = motion_graphics_team(
-            research=True,
-            code_exploration=True,
-            backend="html"
-        )
-        
-        print("✓ Motion graphics team created successfully!")
-        print(f"  Agents: {[agent.name for agent in team.agents]}")
-        print(f"  Coordinator: {team.agents[0].name}")
-        print(f"  Workspace: {team._motion_graphics_workspace}")
-        
-    except ImportError as e:
-        print(f"⚠ Team creation skipped: {e}")
-        print("  Install praisonaiagents to use team features")
-
-
-async def main():
-    """Run all examples."""
-    print("Motion Graphics Pipeline Examples")
-    print("=" * 50)
-    
-    # Example 1: Basic animation (requires Playwright)
-    try:
-        await example_basic_animation()
-    except ImportError as e:
-        print(f"Example 1 skipped: {e}")
-        print("Install with: pip install playwright && playwright install chromium")
-    except Exception as e:
-        print(f"Example 1 failed: {e}")
-    
-    # Example 2: GitTools (no extra dependencies)
-    await example_git_tools()
-    
-    # Example 3: Agent factory (requires praisonaiagents)
-    example_agent_factory()
-    
-    # Example 4: Team preset (requires praisonaiagents)
-    example_team_preset()
-    
-    print("\n" + "=" * 50)
-    print("Examples complete!")
-    print("\nNext steps:")
-    print("1. Install optional dependencies: pip install praisonai-tools[video-motion]")
-    print("2. Install Playwright: playwright install chromium")
-    print("3. Install praisonaiagents for agent features")
-    print("4. Try creating your own motion graphics!")
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/motion_graphics_team.yaml

@@ -1,118 +0,0 @@
-# Motion Graphics Team Configuration
-# This file shows how to configure a motion graphics team using YAML
-
-framework: praisonai
-project_name: "Motion Graphics Pipeline"
-
-agents:
-  - name: "Coordinator"
-    role: "Team Leader"
-    backstory: |
-      You are the motion graphics team coordinator. Your role is to analyze incoming 
-      requests, route them to appropriate specialists, coordinate between team members, 
-      and validate final outputs with strict rules.
-    goal: |
-      Route requests effectively, coordinate team work, and ensure high-quality video 
-      output with concrete file paths and proper error handling.
-    instructions: |
-      CRITICAL OUTPUT VALIDATION RULES:
-      - A render succeeded ONLY IF the reply contains a concrete file path AND no error indicators
-      - Never fabricate file paths or claim success without concrete evidence
-      - Surface all errors from the Animator
-      - Stop work after maximum retry budget is exceeded
-
-  - name: "Researcher" 
-    role: "Research Specialist"
-    backstory: |
-      You are a research specialist who gathers information about topics in user requests.
-      You search the web for relevant facts, concepts, and examples, then summarize findings
-      in a brief that the Animator can use for on-screen content.
-    goal: |
-      Provide focused research that can be turned into visual motion graphics elements.
-    tools:
-      - search_web
-
-  - name: "CodeExplorer"
-    role: "Code Analysis Specialist" 
-    backstory: |
-      You are a code exploration specialist who can clone and explore git repositories
-      on-demand. You read source code, understand implementations, and extract key 
-      algorithms, data structures, and concepts for visualization.
-    goal: |
-      Extract essential programming concepts that can be animated visually.
-    tools:
-      - GitTools
-    instructions: |
-      IMPORTANT: You are READ-ONLY. Never write or modify code.
-      Always validate file paths to prevent directory traversal.
-
-  - name: "Animator"
-    role: "Motion Graphics Creator"
-    backstory: |
-      You are a motion graphics specialist who creates HTML/CSS/GSAP compositions 
-      that render to high-quality MP4 videos. You follow strict authoring guidelines
-      and iterate on render failures.
-    goal: |
-      Create deterministic, frame-perfect HTML/GSAP compositions for video export.
-    tools:
-      - FileTools
-      - RenderTools
-    instructions: |
-      Follow the motion graphics authoring skill guide precisely:
-      - Use deterministic animations only (no Math.random, no infinite loops)
-      - Export timelines as window.__timelines = [tl]
-      - Add data-duration attributes
-      - Use 1920x1080 viewport
-      - Test with lint and render tools
-      - Iterate on failures with maximum retry limit
-
-tasks:
-  - description: |
-      Create a 30-second motion graphics video explaining the concept requested by the user.
-      
-      Process:
-      1. Coordinator routes to Researcher if topic research is needed
-      2. Coordinator routes to CodeExplorer if code analysis is needed  
-      3. Coordinator always routes to Animator for final composition and rendering
-      4. Animator creates HTML/GSAP composition following authoring guidelines
-      5. Animator tests with lint and render tools
-      6. Animator iterates on failures up to maximum retries
-      7. Coordinator validates final output has concrete file path
-      
-    expected_output: |
-      A rendered MP4 file with:
-      - Concrete file path (e.g., /renders/video_123.mp4)
-      - Video duration matching user request
-      - High-quality motion graphics explaining the concept
-      - No render errors or fabricated paths
-      
-    agent: "Coordinator"
-
-# Team configuration
-team:
-  type: "hierarchical"
-  manager: "Coordinator"
-  verbose: true
-  
-# Tool configuration  
-tools:
-  - name: "GitTools"
-    config:
-      base_dir: "./repos"
-      
-  - name: "RenderTools" 
-    config:
-      backend: "html"
-      workspace: "./renders"
-      max_retries: 3
-      
-  - name: "FileTools"
-    config:
-      base_dir: "./workspace"
-
-# Output configuration
-output:
-  format: "mp4"
-  quality: "standard" 
-  fps: 30
-  workspace: "./motion_graphics_output"

examples/n8n_integration_example.py

@@ -1,182 +0,0 @@
-"""n8n Integration Example for PraisonAI Agents.
-
-This example demonstrates how to use the n8n integration tools to enable
-PraisonAI agents to access n8n's 400+ integrations (Slack, Gmail, Notion, etc.).
-
-Prerequisites:
-1. Install PraisonAI Tools with n8n support:
-   pip install "praisonai-tools[n8n]"
-
-2. Set up n8n (local or cloud):
-   - Local: docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n
-   - Cloud: https://n8n.cloud
-
-3. Configure environment variables:
-   export N8N_URL="http://localhost:5678"  # or your n8n instance URL
-   export N8N_API_KEY="your-api-key"       # optional for local testing
-
-4. Create some workflows in n8n that your agents can use
-
-Usage:
-    python examples/n8n_integration_example.py
-"""
-
-import os
-from praisonaiagents import Agent
-from praisonai_tools.n8n import n8n_workflow, n8n_list_workflows
-
-
-def example_list_workflows():
-    """Example: List available n8n workflows."""
-    print("🔍 Listing available n8n workflows...")
-    
-    try:
-        result = n8n_list_workflows()
-        
-        if "error" in result:
-            print(f"❌ Error: {result['error']}")
-            return
-        
-        workflows = result.get("data", [])
-        if workflows:
-            print(f"✅ Found {len(workflows)} workflows:")
-            for workflow in workflows[:5]:  # Show first 5
-                print(f"  - {workflow.get('name', 'Unnamed')} (ID: {workflow.get('id')})")
-        else:
-            print("📝 No workflows found. Create some workflows in n8n first!")
-        
-        return workflows
-        
-    except Exception as e:
-        print(f"❌ Exception: {e}")
-        return []
-
-
-def example_execute_workflow():
-    """Example: Execute a simple n8n workflow."""
-    print("\n🚀 Executing n8n workflow...")
-    
-    # This would be the ID of a workflow you created in n8n
-    # For demo purposes, we'll use a placeholder
-    workflow_id = os.getenv("N8N_TEST_WORKFLOW_ID", "demo-workflow-123")
-    
-    try:
-        result = n8n_workflow(
-            workflow_id=workflow_id,
-            input_data={
-                "message": "Hello from PraisonAI!",
-                "timestamp": "2026-04-16T12:00:00Z",
-                "source": "praisonai-agent"
-            },
-            wait_for_completion=True
-        )
-        
-        if "error" in result:
-            print(f"❌ Error: {result['error']}")
-            if "workflow" in result["error"].lower():
-                print("💡 Tip: Create a test workflow in n8n and set N8N_TEST_WORKFLOW_ID")
-            return
-        
-        print("✅ Workflow executed successfully!")
-        print(f"📊 Result: {result}")
-        
-    except Exception as e:
-        print(f"❌ Exception: {e}")
-
-
-def example_agent_with_n8n_tools():
-    """Example: PraisonAI agent using n8n tools."""
-    print("\n🤖 Creating PraisonAI agent with n8n tools...")
-    
-    # Create an agent with n8n tools
-    agent = Agent(
-        name="automation-agent",
-        instructions="""
-        You are an automation assistant with access to n8n workflows.
-        You can help users automate tasks using n8n's 400+ integrations.
-        
-        Available capabilities through n8n:
-        - Send messages to Slack, Discord, Telegram
-        - Create pages in Notion, Google Docs
-        - Add rows to Google Sheets, Airtable
-        - Send emails via Gmail, Outlook
-        - Create tasks in Trello, Jira, Linear
-        - Store data in databases (PostgreSQL, MongoDB, etc.)
-        - Call external APIs and webhooks
-        
-        Always explain what you're doing and ask for workflow IDs when needed.
-        """,
-        tools=[n8n_workflow, n8n_list_workflows],
-        llm="gpt-4o-mini",  # Use a lightweight model for demo
-    )
-    
-    print("✅ Agent created with n8n tools!")
-    return agent
-
-
-def example_agent_conversation():
-    """Example conversation with the n8n-enabled agent."""
-    agent = example_agent_with_n8n_tools()
-    
-    if not agent:
-        print("❌ Failed to create agent")
-        return
-    
-    print("\n💬 Starting conversation with agent...")
-    
-    # Example conversation
-    conversations = [
-        "Can you list the available n8n workflows?",
-        "What can you help me automate using n8n?",
-        # "Execute the Slack notification workflow with the message 'Test from PraisonAI'"
-    ]
-    
-    for i, message in enumerate(conversations, 1):
-        print(f"\n👤 User #{i}: {message}")
-        try:
-            response = agent.start(message)
-            print(f"🤖 Agent: {response}")
-        except Exception as e:
-            print(f"❌ Error in conversation: {e}")
-
-
-def main():
-    """Main function to run all examples."""
-    print("🔗 n8n Integration Example for PraisonAI Agents")
-    print("=" * 50)
-    
-    # Check configuration
-    n8n_url = os.getenv("N8N_URL", "http://localhost:5678")
-    n8n_api_key = os.getenv("N8N_API_KEY")
-    
-    print(f"🔧 Configuration:")
-    print(f"   N8N_URL: {n8n_url}")
-    print(f"   N8N_API_KEY: {'Set' if n8n_api_key else 'Not set (OK for local testing)'}")
-    
-    # Run examples
-    workflows = example_list_workflows()
-    
-    if workflows:
-        example_execute_workflow()
-    else:
-        print("\n💡 To test workflow execution:")
-        print("   1. Open your n8n instance and create a simple workflow")
-        print("   2. Set the workflow ID: export N8N_TEST_WORKFLOW_ID='your-workflow-id'")
-        print("   3. Run this example again")
-    
-    # Agent examples (these work even without actual workflows)
-    try:
-        example_agent_conversation()
-    except Exception as e:
-        print(f"❌ Agent example failed: {e}")
-        print("💡 This might be due to missing OpenAI API key or n8n connectivity")
-    
-    print("\n✨ Example completed!")
-    print("\n📚 Next steps:")
-    print("   - Create workflows in your n8n instance")
-    print("   - Use the agent in your own applications")
-    print("   - Explore n8n's 400+ integrations at https://n8n.io/integrations")
-
-
-if __name__ == "__main__":
-    main()

examples/nexus_prediction_market_example.py

@@ -1,198 +0,0 @@
-"""Example: Using NEXUS Prediction Market Tool with PraisonAI
-
-This example demonstrates how to use the NEXUS prediction market tool to:
-1. Get free market data for prediction markets
-2. Check arbitrage opportunities between markets (paid feature)
-3. Build an intelligent prediction market analysis agent
-
-NEXUS API Information:
-- Free endpoint: /kalshi?market={market_name}
-- Paid endpoint: /arb/check?markets={market1,market2} ($0.02 USDC on eip155:8453)
-- Validation: https://agentic.market/validate?url=https://nexus-agent-xa12.onrender.com/arb/check
-"""
-
-from praisonaiagents import Agent, tool
-from praisonai_tools.tools.nexus_prediction_market_tool import NexusPredictionMarketTool
-import json
-
-
-# Example 1: Direct tool usage
-def example_direct_usage():
-    """Example of using the NEXUS tool directly."""
-    print("=== Direct NEXUS Tool Usage ===\n")
-    
-    # Initialize the tool
-    nexus_tool = NexusPredictionMarketTool(source_name="praisonai-example")
-    
-    # Get market data for Fed predictions
-    print("1. Getting Fed market data...")
-    fed_data = nexus_tool.get_market_data("Fed")
-    print(json.dumps(fed_data, indent=2))
-    print()
-    
-    # Get BTC market data  
-    print("2. Getting BTC market data...")
-    btc_data = nexus_tool.get_market_data("BTC")
-    print(json.dumps(btc_data, indent=2))
-    print()
-    
-    # Check arbitrage opportunities (paid feature)
-    print("3. Checking arbitrage opportunities between Fed and BTC markets...")
-    arb_data = nexus_tool.check_arbitrage_opportunities(["Fed", "BTC"])
-    print(json.dumps(arb_data, indent=2))
-    print()
-    
-    # Get agent information
-    print("4. Getting NEXUS agent information...")
-    agent_info = nexus_tool.get_agent_info()
-    print(json.dumps(agent_info, indent=2))
-
-
-# Example 2: Create a custom tool wrapper for agents
-@tool
-def get_prediction_market_data(market: str) -> str:
-    """Get prediction market data for a specific market.
-    
-    Args:
-        market: Market identifier (e.g., 'Fed', 'BTC', 'Election')
-        
-    Returns:
-        JSON string with market data including prices and trends
-    """
-    nexus_tool = NexusPredictionMarketTool(source_name="praisonai-agent")
-    return nexus_tool.run(action="get_market_data", market=market)
-
-
-@tool  
-def check_prediction_market_arbitrage(markets: str) -> str:
-    """Check for arbitrage opportunities between prediction markets.
-    
-    Args:
-        markets: Comma-separated list of markets to analyze (e.g., 'Fed,BTC')
-        
-    Returns:
-        JSON string with arbitrage opportunities and analysis
-        
-    Note:
-        This is a paid feature requiring $0.02 USDC on eip155:8453
-    """
-    markets_list = [m.strip() for m in markets.split(',')]
-    nexus_tool = NexusPredictionMarketTool(source_name="praisonai-agent")
-    return nexus_tool.run(action="check_arbitrage", markets=markets_list)
-
-
-# Example 3: Prediction market analysis agent
-def example_prediction_market_agent():
-    """Example of a prediction market analysis agent."""
-    print("\n=== Prediction Market Analysis Agent ===\n")
-    
-    # Create an agent with NEXUS prediction market tools
-    market_agent = Agent(
-        name="PredictionMarketAnalyst",
-        instructions="""You are an expert prediction market analyst. 
-        
-        Your capabilities:
-        - Analyze prediction market data from Kalshi and Polymarket via NEXUS API
-        - Identify market trends and sentiment
-        - Spot potential arbitrage opportunities
-        - Provide insights on market dynamics
-        
-        When analyzing markets:
-        1. First get current market data
-        2. Analyze the data for trends and insights
-        3. Look for arbitrage opportunities if requested
-        4. Provide clear explanations of your findings
-        
-        Be thorough in your analysis and cite specific data points.""",
-        
-        tools=[get_prediction_market_data, check_prediction_market_arbitrage],
-        llm="gpt-4o-mini"
-    )
-    
-    # Example queries
-    queries = [
-        "Get the current Fed interest rate prediction market data and analyze what it shows about market sentiment.",
-        "Compare Fed and BTC prediction markets. What insights can you derive?",
-        "Check if there are any arbitrage opportunities between Fed and BTC markets."
-    ]
-    
-    for i, query in enumerate(queries, 1):
-        print(f"Query {i}: {query}")
-        response = market_agent.start(query)
-        print(f"Response: {response}\n")
-        print("-" * 80)
-
-
-# Example 4: Multi-agent prediction market workflow
-def example_multi_agent_workflow():
-    """Example of a multi-agent workflow for comprehensive market analysis."""
-    print("\n=== Multi-Agent Prediction Market Workflow ===\n")
-    
-    # Data gathering agent
-    data_agent = Agent(
-        name="MarketDataCollector",
-        instructions="""You collect prediction market data from NEXUS API.
-        Focus on gathering comprehensive data for analysis.""",
-        tools=[get_prediction_market_data]
-    )
-    
-    # Analysis agent  
-    analysis_agent = Agent(
-        name="MarketAnalyst", 
-        instructions="""You analyze prediction market data to identify trends, 
-        sentiment, and trading opportunities. Provide detailed insights.""",
-        tools=[]
-    )
-    
-    # Arbitrage agent
-    arbitrage_agent = Agent(
-        name="ArbitrageSpecialist",
-        instructions="""You specialize in finding arbitrage opportunities 
-        between prediction markets. Assess risk and profitability.""",
-        tools=[check_prediction_market_arbitrage]
-    )
-    
-    # Example workflow
-    markets = ["Fed", "BTC", "Election"]
-    
-    print("Step 1: Collecting market data...")
-    market_data = {}
-    for market in markets:
-        print(f"Collecting data for {market} market...")
-        data = data_agent.start(f"Get current market data for {market}")
-        market_data[market] = data
-        print(f"Data collected for {market}: {len(data)} characters")
-    
-    print("\nStep 2: Analyzing market trends...")
-    combined_data = "\n".join([f"{market}: {data}" for market, data in market_data.items()])
-    analysis = analysis_agent.start(f"Analyze this prediction market data and identify key trends:\n{combined_data}")
-    print(f"Analysis: {analysis[:200]}...")
-    
-    print("\nStep 3: Checking arbitrage opportunities...")
-    arb_analysis = arbitrage_agent.start("Check for arbitrage opportunities between Fed,BTC markets")
-    print(f"Arbitrage analysis: {arb_analysis[:200]}...")
-
-
-if __name__ == "__main__":
-    print("NEXUS Prediction Market Tool Examples")
-    print("=" * 50)
-    
-    # Run examples
-    try:
-        example_direct_usage()
-        example_prediction_market_agent()
-        example_multi_agent_workflow()
-        
-    except Exception as e:
-        print(f"Error running examples: {e}")
-        print("\nNote: Some features require API access or payment.")
-        print("Free features: Market data queries")
-        print("Paid features: Arbitrage analysis ($0.02 USDC on eip155:8453)")
-    
-    print("\n" + "=" * 50)
-    print("Examples completed!")
-    print("\nTo use in your own projects:")
-    print("1. Install: pip install praisonai-tools")
-    print("2. Import: from praisonai_tools.tools import NexusPredictionMarketTool")
-    print("3. Use: tool = NexusPredictionMarketTool()")
-    print("\nFor arbitrage features, ensure you have the required payment setup.")

examples/swarmscore_example.py

@@ -1,141 +0,0 @@
-#!/usr/bin/env python3
-"""
-SwarmScore Integration Example for PraisonAI
-
-This example demonstrates how to integrate SwarmScore trust ratings 
-with PraisonAI agents for portable reputation management.
-
-SwarmScore provides cryptographically signed trust ratings based on
-verified execution history, success rates, and consistency.
-
-Requirements:
-    pip install requests praisonai-tools
-
-Usage:
-    python swarmscore_example.py
-"""
-
-import json
-from praisonai_tools.tools import SwarmScoreTool, load_swarmscore_by_slug
-
-
-def main():
-    """Demonstrate SwarmScore integration with PraisonAI."""
-    print("🔍 SwarmScore Integration Example")
-    print("=" * 50)
-    
-    # Initialize SwarmScore tool
-    swarmscore = SwarmScoreTool()
-    
-    # Example agent slug (replace with actual agent identifier)
-    agent_slug = "example-agent-123"
-    
-    print(f"\n📊 Loading SwarmScore for agent: {agent_slug}")
-    
-    # Load SwarmScore data
-    result = swarmscore.load_swarmscore(agent_slug)
-    
-    if result.success:
-        print("✅ SwarmScore loaded successfully!")
-        
-        # Display basic score information
-        score_data = result.output
-        if 'score' in score_data:
-            print(f"   Trust Score: {score_data['score']}")
-        if 'tier' in score_data:
-            print(f"   Trust Tier: {score_data['tier']}")
-        if 'jobs_completed' in score_data:
-            print(f"   Jobs Completed: {score_data['jobs_completed']}")
-        if 'success_rate' in score_data:
-            print(f"   Success Rate: {score_data['success_rate']}%")
-        
-        print("\n🔍 Full SwarmScore data:")
-        print(json.dumps(score_data, indent=2))
-        
-        # Verify score freshness
-        if 'verify_payload' in score_data:
-            print("\n🔐 Verifying score freshness...")
-            verify_result = swarmscore.verify_swarmscore(score_data['verify_payload'])
-            
-            if verify_result.success:
-                print("✅ Score verification successful!")
-                print(f"   Verification data: {verify_result.output}")
-            else:
-                print(f"❌ Score verification failed: {verify_result.error}")
-    else:
-        print(f"❌ Failed to load SwarmScore: {result.error}")
-        print("\n💡 This might happen if:")
-        print("   • The agent slug doesn't exist in SwarmScore")
-        print("   • The SwarmScore API is temporarily unavailable")
-        print("   • Network connectivity issues")
-    
-    # Get discovery manifest
-    print("\n🌐 Loading agent discovery manifest...")
-    manifest_result = swarmscore.get_discovery_manifest()
-    
-    if manifest_result.success:
-        print("✅ Discovery manifest loaded!")
-        print("   This contains machine-readable data for agent-to-agent discovery")
-        print(f"   Manifest keys: {list(manifest_result.output.keys())}")
-    else:
-        print(f"❌ Failed to load manifest: {manifest_result.error}")
-    
-    # Demonstrate standalone function usage
-    print("\n🛠️  Using standalone functions:")
-    try:
-        # This will likely fail with the example slug, but shows the API
-        score_data = load_swarmscore_by_slug(agent_slug)
-        print(f"✅ Standalone function succeeded: {score_data}")
-    except Exception as e:
-        print(f"❌ Standalone function failed (expected): {e}")
-    
-    print("\n📚 Next Steps:")
-    print("   1. Register your agent at https://swarmsync.ai")
-    print("   2. Get your agent slug from the SwarmSync dashboard")
-    print("   3. Replace 'example-agent-123' with your actual slug")
-    print("   4. Integrate SwarmScore checks into your agent workflows")
-    print("\n   📖 Documentation: https://swarmsync.ai/docs/protocol-specs/swarmscore")
-
-
-def agent_workflow_example():
-    """Example of how to integrate SwarmScore into an agent workflow."""
-    print("\n🤖 Agent Workflow Integration Example")
-    print("=" * 45)
-    
-    # Simulated agent execution flow
-    agent_id = "my-trading-agent"
-    
-    # 1. Load current trust score before executing task
-    swarmscore = SwarmScoreTool()
-    score_result = swarmscore.load_swarmscore(agent_id)
-    
-    if score_result.success:
-        trust_score = score_result.output.get('score', 0)
-        print(f"🔍 Agent {agent_id} current trust score: {trust_score}")
-        
-        # 2. Make decisions based on trust level
-        if trust_score >= 80:
-            print("✅ High trust - proceeding with full autonomy")
-            task_approach = "autonomous"
-        elif trust_score >= 60:
-            print("⚠️  Medium trust - requesting human approval for critical actions")
-            task_approach = "supervised"
-        else:
-            print("❌ Low trust - operating in restricted mode")
-            task_approach = "restricted"
-        
-        # 3. Execute task based on trust level
-        print(f"🚀 Executing task with {task_approach} approach...")
-        
-        # 4. After task completion, the score would be updated by SwarmSync
-        # (This happens externally based on verified execution results)
-        print("📝 Task completed - SwarmScore will be updated based on results")
-        
-    else:
-        print(f"⚠️  Could not load trust score: {score_result.error}")
-        print("🔄 Falling back to default security settings")
-
-
-if __name__ == "__main__":
-    main()
-    agent_workflow_example()

examples/video/example_edit.py

@@ -1,21 +0,0 @@
-"""Example: Full video editing pipeline."""
-import sys
-sys.path.insert(0, "/Users/praison/PraisonAI-tools/praisonai_tools/video")
-
-from pipeline import edit_video
-
-result = edit_video(
-    input_path="/Users/praison/Agent-Recipes/agent_recipes/input.mov",
-    output_path="/tmp/edited_example.mp4",
-    preset="podcast",
-    remove_fillers=True,
-    remove_repetitions=True,
-    remove_silence=True,
-    verbose=True,
-)
-
-if result.success:
-    print(f"Success! Output: {result.output_path}")
-    print(f"Artifacts: {result.artifacts}")
-else:
-    print(f"Failed: {result.error}")

examples/video/example_probe.py

@@ -1,12 +0,0 @@
-"""Example: Probe video metadata."""
-import sys
-sys.path.insert(0, "/Users/praison/PraisonAI-tools/praisonai_tools/video")
-
-from probe import probe_video
-
-result = probe_video("/Users/praison/Agent-Recipes/agent_recipes/input.mov")
-
-print(f"Duration: {result.duration}s")
-print(f"Resolution: {result.width}x{result.height}")
-print(f"FPS: {result.fps}")
-print(f"Codec: {result.codec}")

praisonai_tools/__init__.py

@@ -1,81 +1,27 @@
-"""PraisonAI Tools - Tools and base classes for AI agents.
-
-This package provides:
-1. Base classes for creating custom tools (BaseTool, @tool decorator)
-2. Ready-to-use tools for common tasks (Email, Slack, GitHub, etc.)
-
-Usage:
-    from praisonai_tools import BaseTool, tool
-    
-    # Method 1: Using @tool decorator
-    @tool
-    def my_search(query: str) -> str:
-        '''Search for something.'''
-        return f"Results for {query}"
-    
-    # Method 2: Subclassing BaseTool
-    class MyTool(BaseTool):
-        name = "my_tool"
-        description = "Does something useful"
-        
-        def run(self, query: str) -> str:
-            return f"Result for {query}"
-    
-    # Method 3: Use built-in tools
-    from praisonai_tools import EmailTool, SlackTool, GitHubTool
-    
-    email = EmailTool(provider="gmail")
-    slack = SlackTool()
-    github = GitHubTool()
-    
-    # Use with PraisonAI Agents
-    from praisonaiagents import Agent
-    agent = Agent(tools=[my_search, MyTool(), email, slack])
-"""
-
-from importlib.metadata import version, PackageNotFoundError
-
-from praisonai_tools.tools.base import BaseTool, ToolResult, ToolValidationError, validate_tool
-from praisonai_tools.tools.decorator import tool, FunctionTool, is_tool, get_tool_schema
-from praisonai_tools.catalogue import (
-    ToolCatalogueEntry,
-    list_tools,
-    get_tool_names,
-)
-
-# Imported at top (not below ``__getattr__``) to satisfy ruff E402. This only
-# reads the auto-discovered manifest - no tool module is eagerly imported.
-from praisonai_tools.tools import __all__ as _tools_all
-
-try:
-    __version__ = version("praisonai-tools")
-except PackageNotFoundError:
-    __version__ = "0.0.0"
-__author__ = "Mervin Praison"
-
-# Lazy imports for tool classes
-def __getattr__(name):
-    """Lazy load tool classes to avoid loading dependencies until needed."""
-    from praisonai_tools.tools import __getattr__ as tools_getattr
-    try:
-        return tools_getattr(name)
-    except AttributeError:
-        raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
-
-# Catalogue/discovery helpers exported eagerly above (not tool symbols, so they
-# are not part of the auto-discovered manifest).
-_CATALOGUE_EXPORTS = [
-    "ToolCatalogueEntry",
-    "list_tools",
-    "get_tool_names",
-]
-
-# The remainder of ``__all__`` is derived from the tools package's
-# automatically-discovered manifest so the two levels cannot drift apart. Only
-# source files are parsed (via AST) to build it - no tool module is eagerly
-# imported here.
-__all__ = sorted(set(_tools_all) | set(_CATALOGUE_EXPORTS))
-
-
-def __dir__():
-    return sorted(set(globals()) | set(__all__))
+from crewai_tools import BaseTool, Tool, tool
+from crewai_tools import (
+	BrowserbaseLoadTool,
+	CodeDocsSearchTool,
+	CSVSearchTool,
+	DirectorySearchTool,
+	DOCXSearchTool,
+	DirectoryReadTool,
+ 	EXASearchTool,
+	FileReadTool,
+	GithubSearchTool,
+	SerperDevTool,
+	TXTSearchTool,
+	JSONSearchTool,
+	MDXSearchTool,
+	PDFSearchTool,
+	PGSearchTool,
+	RagTool,
+	ScrapeElementFromWebsiteTool,
+	ScrapeWebsiteTool,
+	SeleniumScrapingTool,
+	WebsiteSearchTool,
+	XMLSearchTool,
+	YoutubeChannelSearchTool,
+	YoutubeVideoSearchTool,
+	LlamaIndexTool
+)

praisonai_tools/adapters/embedchain_adapter.py

@@ -0,0 +1,25 @@
+from typing import Any
+
+from embedchain import App
+
+from praisonai_tools.tools.rag.rag_tool import Adapter
+
+
+class EmbedchainAdapter(Adapter):
+    embedchain_app: App
+    summarize: bool = False
+
+    def query(self, question: str) -> str:
+        result, sources = self.embedchain_app.query(
+            question, citations=True, dry_run=(not self.summarize)
+        )
+        if self.summarize:
+            return result
+        return "\n\n".join([source[0] for source in sources])
+
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        self.embedchain_app.add(*args, **kwargs)

praisonai_tools/adapters/lancedb_adapter.py

@@ -0,0 +1,56 @@
+from pathlib import Path
+from typing import Any, Callable
+
+from lancedb import DBConnection as LanceDBConnection
+from lancedb import connect as lancedb_connect
+from lancedb.table import Table as LanceDBTable
+from openai import Client as OpenAIClient
+from pydantic import Field, PrivateAttr
+
+from praisonai_tools.tools.rag.rag_tool import Adapter
+
+
+def _default_embedding_function():
+    client = OpenAIClient()
+
+    def _embedding_function(input):
+        rs = client.embeddings.create(input=input, model="text-embedding-ada-002")
+        return [record.embedding for record in rs.data]
+
+    return _embedding_function
+
+
+class LanceDBAdapter(Adapter):
+    uri: str | Path
+    table_name: str
+    embedding_function: Callable = Field(default_factory=_default_embedding_function)
+    top_k: int = 3
+    vector_column_name: str = "vector"
+    text_column_name: str = "text"
+
+    _db: LanceDBConnection = PrivateAttr()
+    _table: LanceDBTable = PrivateAttr()
+
+    def model_post_init(self, __context: Any) -> None:
+        self._db = lancedb_connect(self.uri)
+        self._table = self._db.open_table(self.table_name)
+
+        super().model_post_init(__context)
+
+    def query(self, question: str) -> str:
+        query = self.embedding_function([question])[0]
+        results = (
+            self._table.search(query, vector_column_name=self.vector_column_name)
+            .limit(self.top_k)
+            .select([self.text_column_name])
+            .to_list()
+        )
+        values = [result[self.text_column_name] for result in results]
+        return "\n".join(values)
+
+    def add(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        self._table.add(*args, **kwargs)

praisonai_tools/catalogue.py

@@ -1,274 +0,0 @@
-"""Tool discovery / catalogue contract for PraisonAI Tools.
-
-This module provides a single, programmatic way to enumerate every tool that
-``praisonai-tools`` ships, plus any third-party tool registered via the
-``praisonai.tools`` entry-point group. It exists so that the wider SDK (CLI,
-docs, YAML validators, third-party packages) can answer *"what tools can I
-actually use here?"* against a live catalogue derived from the real registry
-rather than a hand-maintained allowlist that drifts.
-
-Usage:
-    from praisonai_tools import list_tools
-
-    for entry in list_tools():
-        print(entry.name, entry.module, entry.extras, entry.summary)
-
-Third-party packages can register their own tools by declaring an entry point:
-
-    # pyproject.toml (third-party tool package)
-    [project.entry-points."praisonai.tools"]
-    my_tool = "my_pkg:MyTool"
-
-Nothing here imports optional/heavy dependencies: the catalogue is built from
-the module map alone, so ``list_tools()`` is safe to call without any extras
-installed.
-"""
-
-from __future__ import annotations
-
-import ast
-import os
-from dataclasses import dataclass, field
-from functools import lru_cache
-from typing import Dict, Optional, Tuple
-
-ENTRY_POINT_GROUP = "praisonai.tools"
-
-# Directory that holds the first-party ``*_tool.py`` files. The catalogue is
-# derived by scanning this directory, so dropping a new ``*_tool.py`` that
-# defines a ``*Tool`` class makes the tool discoverable with no edits to any
-# hand-maintained list.
-_TOOLS_DIR = os.path.join(os.path.dirname(__file__), "tools")
-
-# Map from the module (as recorded in ``_TOOL_MAP``) to the optional-dependency
-# extras required to use tools defined in it. Modules absent from this mapping
-# have no extra requirements (they rely only on the standard library or on the
-# base dependencies). Keep this aligned with ``[project.optional-dependencies]``
-# in ``pyproject.toml``.
-_MODULE_EXTRAS = {
-    "wordpress_tool": ("wordpress",),
-    "pinchwork_tool": ("pinchwork", "marketplace"),
-    "agentid_tool": ("agentid", "marketplace"),
-    "joy_trust_tool": ("joy-trust", "marketplace"),
-    "agentfolio_tool": ("agentfolio", "marketplace"),
-    "praisonai_tools.n8n.n8n_workflow": ("n8n",),
-    "langextract_tool": ("langextract",),
-    "swarmscore_tool": ("swarmscore",),
-    "composio_tool": ("composio",),
-    "stripe_tool": ("stripe",),
-}
-
-
-@dataclass(frozen=True)
-class ToolCatalogueEntry:
-    """A single discoverable tool.
-
-    Attributes:
-        name: Public name used to import/reference the tool, e.g. ``"QdrantTool"``.
-        module: Dotted or relative module path that defines the tool.
-        extras: Optional-dependency extras required to use the tool, e.g.
-            ``("swarmscore",)``. Empty when no extra install is needed.
-        summary: A one-line human-readable description of the tool.
-    """
-
-    name: str
-    module: str
-    extras: Tuple[str, ...] = field(default_factory=tuple)
-    summary: str = ""
-
-
-def _summary_for(name: str) -> str:
-    """Derive a concise one-line summary for a tool class name.
-
-    Fallback used only when a tool class does not declare a literal
-    ``description``. We avoid importing the tool (which could pull heavy/optional
-    deps) and instead produce a stable, human-friendly summary from the name.
-    """
-    if name.endswith("Tool"):
-        base = name[:-4]
-    else:
-        base = name
-    return f"{base} integration tool".strip()
-
-
-def _first_line(text: str) -> str:
-    """Return the first non-empty line of ``text`` (a tool ``description``)."""
-    for line in text.splitlines():
-        line = line.strip()
-        if line:
-            return line
-    return ""
-
-
-def _scan_module(path: str) -> "list[tuple[str, str]]":
-    """AST-scan a ``*_tool.py`` file for ``*Tool`` classes and descriptions.
-
-    Returns a list of ``(class_name, summary)`` tuples. The file is parsed, not
-    imported, so this stays cheap and never triggers optional dependencies.
-    Classes without a literal ``description`` fall back to a name-synthesised
-    summary.
-    """
-    try:
-        with open(path, "r", encoding="utf-8") as fh:
-            tree = ast.parse(fh.read(), filename=path)
-    except (OSError, SyntaxError):  # pragma: no cover - defensive
-        return []
-
-    found = []
-    for node in tree.body:
-        if not isinstance(node, ast.ClassDef) or not node.name.endswith("Tool"):
-            continue
-        description: Optional[str] = None
-        for stmt in node.body:
-            # Accept both plain ``description = "..."`` and annotated
-            # ``description: str = "..."`` class attributes.
-            if isinstance(stmt, ast.Assign):
-                targets = [t.id for t in stmt.targets if isinstance(t, ast.Name)]
-                value = stmt.value
-            elif isinstance(stmt, ast.AnnAssign) and isinstance(stmt.target, ast.Name):
-                targets = [stmt.target.id]
-                value = stmt.value
-            else:
-                continue
-            if (
-                "description" in targets
-                and isinstance(value, ast.Constant)
-                and isinstance(value.value, str)
-            ):
-                description = value.value
-        summary = _first_line(description) if description else _summary_for(node.name)
-        found.append((node.name, summary or _summary_for(node.name)))
-    return found
-
-
-@lru_cache(maxsize=1)
-def _scan_source_tree() -> "Dict[str, Tuple[str, str]]":
-    """Derive the first-party tool catalogue from the source tree.
-
-    Scans every ``praisonai_tools/tools/*_tool.py`` file for ``*Tool`` classes
-    and returns ``{class_name: (module, summary)}``. Because the mapping is
-    derived from the files that actually exist, phantom names (advertised tools
-    with no file) are impossible by construction, and adding a tool is a single
-    new-file change.
-    """
-    derived: "Dict[str, Tuple[str, str]]" = {}
-    try:
-        filenames = sorted(os.listdir(_TOOLS_DIR))
-    except OSError:  # pragma: no cover - defensive
-        return derived
-
-    for filename in filenames:
-        if not filename.endswith("_tool.py"):
-            continue
-        module = filename[:-3]  # strip ".py"; relative module name within tools
-        path = os.path.join(_TOOLS_DIR, filename)
-        for class_name, summary in _scan_module(path):
-            derived.setdefault(class_name, (module, summary))
-    return derived
-
-
-def _first_party_entries() -> "list[ToolCatalogueEntry]":
-    """Build catalogue entries for every first-party tool class.
-
-    The catalogue is source-derived: it is built by scanning the actual
-    ``*_tool.py`` files on disk (see ``_scan_source_tree``) rather than a
-    hand-maintained dictionary, so it can never advertise a tool whose file does
-    not exist. Summaries come from each tool's own ``description``.
-
-    A few tools live in nested packages (e.g. the n8n workflow tool under
-    ``praisonai_tools.n8n``) rather than flat ``*_tool.py`` files; those are
-    still surfaced via ``_TOOL_MAP`` so their public names remain stable.
-    """
-    entries: "Dict[str, ToolCatalogueEntry]" = {}
-
-    for name, (module, summary) in _scan_source_tree().items():
-        entries[name] = ToolCatalogueEntry(
-            name=name,
-            module=module,
-            extras=_MODULE_EXTRAS.get(module, ()),
-            summary=summary,
-        )
-
-    # Include tool classes defined outside the flat ``*_tool.py`` layout (e.g.
-    # nested subpackages such as ``praisonai_tools.n8n``) that the scan cannot
-    # reach, keeping their names stable. Only *nested* (absolute-module) tools
-    # are merged here: flat ``*_tool.py`` classes are already source-derived by
-    # the scan above, so restoring them from ``_TOOL_MAP`` would reintroduce the
-    # hand-maintained bookkeeping this catalogue exists to eliminate.
-    try:
-        from praisonai_tools.tools import _TOOL_MAP
-    except Exception:  # pragma: no cover - defensive
-        _TOOL_MAP = {}
-    for name, module in _TOOL_MAP.items():
-        if not name.endswith("Tool") or name in entries:
-            continue
-        if not module.startswith("praisonai_tools."):
-            continue
-        entries[name] = ToolCatalogueEntry(
-            name=name,
-            module=module,
-            extras=_MODULE_EXTRAS.get(module, ()),
-            summary=_summary_for(name),
-        )
-
-    return list(entries.values())
-
-
-def _entry_point_entries() -> "list[ToolCatalogueEntry]":
-    """Build catalogue entries for third-party tools registered via entry points.
-
-    Third-party packages register under the ``praisonai.tools`` entry-point
-    group. We do not import the target object (to keep discovery cheap and
-    dependency-free); we record the declared name and module value instead.
-    """
-    try:
-        from importlib.metadata import entry_points
-    except ImportError:  # pragma: no cover - Python < 3.8
-        return []
-
-    entries = []
-    try:
-        eps = entry_points()
-        # importlib.metadata API differs across versions.
-        if hasattr(eps, "select"):
-            selected = eps.select(group=ENTRY_POINT_GROUP)
-        else:  # pragma: no cover - Python < 3.10 dict interface
-            selected = eps.get(ENTRY_POINT_GROUP, [])
-    except Exception:  # pragma: no cover - defensive
-        return []
-
-    for ep in selected:
-        entries.append(
-            ToolCatalogueEntry(
-                name=ep.name,
-                module=getattr(ep, "value", "") or "",
-                extras=(),
-                summary=f"Third-party tool registered via {ENTRY_POINT_GROUP}",
-            )
-        )
-    return entries
-
-
-def list_tools() -> "list[ToolCatalogueEntry]":
-    """Return the full catalogue of available tools.
-
-    Includes every first-party tool shipped with ``praisonai-tools`` plus any
-    third-party tool registered via the ``praisonai.tools`` entry-point group.
-    Entries are de-duplicated by name (first-party wins) and returned sorted by
-    name.
-    """
-    seen = {}
-    for entry in _first_party_entries():
-        seen[entry.name] = entry
-    for entry in _entry_point_entries():
-        seen.setdefault(entry.name, entry)
-    return sorted(seen.values(), key=lambda e: e.name)
-
-
-def get_tool_names() -> "set[str]":
-    """Return the set of every available tool name.
-
-    Convenience helper for validators that only need membership checks (e.g. to
-    replace a hand-maintained allowlist).
-    """
-    return {entry.name for entry in list_tools()}

praisonai_tools/cli.py

@@ -1,81 +0,0 @@
-"""
-Unified CLI for PraisonAI Tools.
-"""
-
-import argparse
-import sys
-from importlib import import_module
-
-def main():
-    if len(sys.argv) < 2:
-        print("PraisonAI Developer Tools CLI")
-        print("\nUsage: praisonai-tools [tool] [args]")
-        print("\nAvailable tools:")
-        print("  list             List the available tool catalogue")
-        print("  docs-generate    Generate SDK reference documentation")
-        print("  video            AI-powered video editing tools")
-        print("  fcp              Final Cut Pro automation tools")
-        return 1
-    
-    tool = sys.argv[1]
-    tool_args = sys.argv[2:]
-    
-    if tool == "list":
-        parser = argparse.ArgumentParser(prog="praisonai-tools list")
-        parser.add_argument("--extras", action="store_true",
-                            help="Only show tools that require optional-dependency extras")
-        args = parser.parse_args(tool_args)
-        from .catalogue import list_tools
-        entries = list_tools()
-        if args.extras:
-            entries = [e for e in entries if e.extras]
-        width = max((len(e.name) for e in entries), default=0)
-        for entry in entries:
-            extras = f"  [extras: {', '.join(entry.extras)}]" if entry.extras else ""
-            print(f"{entry.name.ljust(width)}  {entry.summary}{extras}")
-        print(f"\n{len(entries)} tools")
-        return 0
-
-    elif tool == "docs-generate":
-        # Handle docs-generate with its own parser for better help
-        parser = argparse.ArgumentParser(prog="praisonai-tools docs-generate")
-        parser.add_argument("--package", choices=["praisonaiagents", "praisonai", "typescript", "all"], 
-                            default="all", help="Package to generate docs for")
-        parser.add_argument("--dry-run", action="store_true", help="Don't write files")
-        parser.add_argument("--docs-root", default="/Users/praison/PraisonAIDocs", 
-                            help="Root directory of the documentation repository")
-        parser.add_argument("--source-root", help="Root directory of the source code")
-        
-        args = parser.parse_args(tool_args)
-        from .docs_generator.generator import ReferenceDocsGenerator
-        generator = ReferenceDocsGenerator(docs_root=args.docs_root, source_root=args.source_root)
-        
-        if args.package == "all":
-            generator.generate_all(dry_run=args.dry_run)
-        else:
-            generator.generate_package(args.package, dry_run=args.dry_run)
-        return 0
-        
-    elif tool == "video":
-        try:
-            video_main = import_module(".video.__main__", package="praisonai_tools").main
-            sys.argv = [f"{sys.argv[0]} video"] + tool_args
-            return video_main()
-        except ImportError as e:
-            print(f"Error: Video tool not available. {e}")
-            return 1
-            
-    elif tool == "fcp":
-        try:
-            fcp_main = import_module(".fcp_tool.cli", package="praisonai_tools").main
-            return fcp_main(tool_args)
-        except ImportError as e:
-            print(f"Error: FCP tool not available. {e}")
-            return 1
-    else:
-        print(f"Unknown tool: {tool}")
-        return 1
-
-
-if __name__ == "__main__":
-    sys.exit(main())

praisonai_tools/docs_generator/README.md

@@ -1,109 +0,0 @@
-# PraisonAI Docs Generator
-
-A powerful documentation generator for PraisonAI SDK reference documentation.
-
-## Installation
-
-```bash
-pip install praisonai-tools
-```
-
-## Quick Start
-
-### Generate Rust SDK Documentation (Standalone)
-
-The Rust documentation generator works without dependencies:
-
-```bash
-# Direct execution (recommended for Rust)
-cd praisonai_tools/docs_generator
-python3 __main__.py --package rust
-
-# Or use the standalone script
-python3 generate_rust_docs.py --rust-path /path/to/praisonai-rust
-
-# Dry run
-python3 __main__.py --package rust --dry-run
-```
-
-### Generate All SDK Documentation
-
-For Python/TypeScript docs, install the full package first:
-
-```bash
-pip install praisonaiagents
-python -m praisonai_tools.docs_generator --package all
-```
-
-### Using CLI
-
-```bash
-# Generate documentation with default settings
-praisonai-tools docs-generate
-
-# Generate with granular layout (separate pages for classes/functions)
-praisonai-tools docs-generate --layout granular
-
-# Specify custom output directory
-praisonai-tools docs-generate --output /path/to/docs
-```
-
-### Direct Script Execution
-
-```bash
-python generator.py --layout granular
-```
-
-## Options
-
-| Option | Description | Default |
-|--------|-------------|---------|
-| `--layout` | Documentation layout: `compact` or `granular` | `granular` |
-| `--package` | Package to generate: `praisonaiagents`, `praisonai`, `typescript`, `rust`, `all` | `all` |
-| `--docs-root` | Root directory of the documentation repository | Auto-detected |
-| `--dry-run` | Show what would be generated without writing files | `false` |
-
-## Layouts
-
-### Compact Layout
-- Single page per module
-- Classes and functions inline
-
-### Granular Layout (Recommended)
-- Separate pages for each class and function
-- Module hub pages with links to components
-- Better navigation and searchability
-
-## Features
-
-- **Dynamic lazy-loading support**: Captures `_LAZY_IMPORTS`, `_LAZY_GROUPS`, `TOOL_MAPPINGS`
-- **Recursive scanning**: Scans all `__init__.py` files in package tree
-- **MDX output**: Mintlify-compatible MDX format
-- **Navigation generation**: Auto-updates `docs.json` navigation
-- **Icon support**: Maps modules to Font Awesome icons
-- **Multi-language support**: Python, TypeScript, and Rust
-
-## Supported Packages
-
-- `praisonaiagents` (Python Core SDK)
-- `praisonai` (Python Wrapper)
-- TypeScript SDK (`praisonai-ts`)
-- **Rust SDK** (`praisonai-rust`) - Agent, Tools, Workflows, Memory, LLM
-
-## Output Structure
-
-```
-docs/sdk/reference/
-├── praisonaiagents/
-│   ├── modules/      # Module hub pages
-│   ├── classes/      # Class pages
-│   └── functions/    # Function pages
-├── praisonai/
-│   └── ...
-├── typescript/
-│   └── ...
-└── rust/
-    ├── modules/      # praisonai.agent, praisonai.tools, etc.
-    ├── classes/      # Agent, ToolRegistry, AgentTeam, etc.
-    └── functions/    # Standalone functions
-```

praisonai_tools/docs_generator/__init__.py

@@ -1,14 +0,0 @@
-"""
-PraisonAI Documentation Generator.
-
-This module can be run standalone without requiring the praisonaiagents dependency.
-
-Usage:
-    python -m praisonai_tools.docs_generator --package rust
-    python -m praisonai_tools.docs_generator --package all
-"""
-
-# This file is intentionally minimal to allow standalone execution
-# without importing the parent package dependencies.
-
-__version__ = "0.1.0"

praisonai_tools/docs_generator/__main__.py

@@ -1,66 +0,0 @@
-"""
-CLI entry point for praisonai_tools.docs_generator.
-
-This can be run in two ways:
-1. Directly: cd praisonai_tools/docs_generator && python __main__.py --package rust
-2. As module (requires praisonaiagents): python -m praisonai_tools.docs_generator --package rust
-"""
-
-import argparse
-import sys
-
-def main():
-    parser = argparse.ArgumentParser(description="PraisonAI SDK Documentation Generator")
-    parser.add_argument("--package", choices=["praisonaiagents", "praisonai", "typescript", "rust", "all"], 
-                        default="all", help="Package to generate docs for")
-    parser.add_argument("--dry-run", action="store_true", help="Don't write files")
-    parser.add_argument("--docs-root", default="/Users/praison/PraisonAIDocs", 
-                        help="Root directory of the documentation repository")
-    parser.add_argument("--layout", choices=["legacy", "granular"], default="granular",
-                        help="Documentation layout type")
-    parser.add_argument("--rust-path", default="/Users/praison/praisonai-package/src/praisonai-rust",
-                        help="Path to Rust SDK workspace")
-    
-    args = parser.parse_args()
-    
-    # For Rust-only generation, use the standalone script to avoid praisonaiagents dependency
-    if args.package == "rust":
-        # Use the standalone Rust generator
-        from pathlib import Path
-        script_dir = Path(__file__).parent
-        sys.path.insert(0, str(script_dir))
-        
-        from generate_rust_docs import main as rust_main
-        # Patch sys.argv for the rust script
-        sys.argv = [
-            'generate_rust_docs.py',
-            '--rust-path', args.rust_path,
-            '--docs-root', args.docs_root,
-        ]
-        if args.dry_run:
-            sys.argv.append('--dry-run')
-        rust_main()
-        return 0
-    
-    # For other packages, use the full generator (requires praisonaiagents)
-    try:
-        from .generator import ReferenceDocsGenerator, LayoutType
-    except ImportError as e:
-        print(f"Error: {e}")
-        print("\nFor generating Python/TypeScript docs, install praisonaiagents:")
-        print("  pip install praisonaiagents")
-        print("\nFor Rust docs only, use: --package rust")
-        return 1
-    
-    layout = LayoutType.LEGACY if args.layout == "legacy" else LayoutType.GRANULAR
-    generator = ReferenceDocsGenerator(docs_root=args.docs_root, layout=layout)
-    
-    if args.package == "all":
-        generator.generate_all(dry_run=args.dry_run)
-    else:
-        generator.generate_package(args.package, dry_run=args.dry_run)
-    
-    return 0
-
-if __name__ == "__main__":
-    sys.exit(main())

praisonai_tools/docs_generator/generate_rust_docs.py

@@ -1,573 +0,0 @@
-#!/usr/bin/env python3
-"""
-Standalone Rust SDK documentation generator.
-This can be run without the praisonaiagents dependency.
-"""
-
-import sys
-import json
-import argparse
-from pathlib import Path
-
-# Add docs_generator to path
-sys.path.insert(0, str(Path(__file__).parent))
-
-from rust_parser import RustWorkspaceParser
-
-
-def escape_mdx(text: str) -> str:
-    """Escape special MDX characters."""
-    if not text:
-        return ""
-    return text.replace('<', '&lt;').replace('>', '&gt;').replace('{', '&#123;').replace('}', '&#125;')
-
-
-def sanitize_description(text: str, max_len: int = 150) -> str:
-    """Sanitize description for frontmatter. Truncates at word boundary."""
-    if not text:
-        return ""
-    clean = text.replace('"', "'").replace('\n', ' ').strip()
-    if len(clean) <= max_len:
-        return clean
-    # Truncate at word boundary
-    truncated = clean[:max_len].rsplit(' ', 1)[0]
-    return truncated + "..."
-
-
-# Icon map
-ICON_MAP = {
-    "agent": "robot", "tools": "wrench", "workflows": "route",
-    "memory": "brain", "llm": "microchip", "config": "gear",
-    "error": "circle-exclamation", "builder": "hammer",
-    "derive": "wand-magic-sparkles", "cli": "terminal",
-    "commands": "terminal", "default": "file-code"
-}
-
-# Friendly title mappings for modules
-MODULE_TITLES = {
-    "agent": "Agent",
-    "builder": "Agent Builder",
-    "config": "Configuration",
-    "error": "Error Handling",
-    "llm": "LLM Providers",
-    "memory": "Memory",
-    "tools": "Tools",
-    "workflows": "Workflows",
-    "praisonai_derive": "Derive Macros",
-    "chat": "Chat Command",
-    "prompt": "Prompt Command",
-    "run": "Run Command",
-}
-
-# Rich descriptions for modules (used when docstring is empty or as fallback)
-MODULE_DESCRIPTIONS = {
-    "agent": "Core AI Agent implementation for building intelligent agents in Rust",
-    "builder": "Fluent builder pattern for constructing Rust AI agents",
-    "config": "Configuration types for PraisonAI Rust AI agents",
-    "error": "Error handling utilities for Rust AI agent operations",
-    "llm": "LLM provider abstractions for Rust AI agents (OpenAI, Anthropic, Ollama)",
-    "memory": "Memory and conversation history for Rust AI agents",
-    "tools": "Tool system for extending Rust AI agent capabilities",
-    "workflows": "Multi-agent workflow patterns for Rust AI orchestration",
-    "praisonai_derive": "Procedural macros for defining Rust AI agent tools",
-    "chat": "Interactive chat command for Rust AI agents",
-    "prompt": "Single-shot prompt execution for Rust AI agents",
-    "run": "Workflow execution command for Rust AI agents",
-}
-
-# Abbreviations to preserve in titles
-ABBREVIATIONS = {"llm", "api", "cli", "id", "url", "http", "ai", "io"}
-
-# Related documentation mapping - Rust SDK only links to Rust docs
-RELATED_DOCS = {
-    "agent": [
-        ("Rust Overview", "/docs/rust/overview", "book-open"),
-        ("Rust Quickstart", "/docs/rust/quickstart", "rocket"),
-        ("Rust Agent Guide", "/docs/rust/agent", "robot"),
-        ("Rust Installation", "/docs/rust/installation", "download"),
-    ],
-    "builder": [
-        ("Rust Agent Guide", "/docs/rust/agent", "robot"),
-        ("Rust Quickstart", "/docs/rust/quickstart", "rocket"),
-    ],
-    "tool": [
-        ("Rust Tools Guide", "/docs/rust/tools", "wrench"),
-        ("Rust Agent Guide", "/docs/rust/agent", "robot"),
-    ],
-    "workflow": [
-        ("Rust Agent Flow", "/docs/rust/agent-flow", "diagram-project"),
-        ("Rust Agent Team", "/docs/rust/agent-team", "users"),
-    ],
-    "memory": [
-        ("Rust Memory Guide", "/docs/rust/memory", "brain"),
-        ("Rust Agent Guide", "/docs/rust/agent", "robot"),
-    ],
-    "llm": [
-        ("Rust Overview", "/docs/rust/overview", "book-open"),
-        ("Rust Agent Guide", "/docs/rust/agent", "robot"),
-    ],
-    "config": [
-        ("Rust Installation", "/docs/rust/installation", "gear"),
-        ("Rust Overview", "/docs/rust/overview", "book-open"),
-    ],
-    "error": [
-        ("Rust Overview", "/docs/rust/overview", "book-open"),
-    ],
-    "derive": [
-        ("Rust Tools Guide", "/docs/rust/tools", "wrench"),
-    ],
-    "cli": [
-        ("Rust CLI Guide", "/docs/rust/cli", "terminal"),
-        ("Rust Quickstart", "/docs/rust/quickstart", "rocket"),
-    ],
-    "chat": [
-        ("Rust CLI Guide", "/docs/rust/cli", "terminal"),
-    ],
-    "prompt": [
-        ("Rust CLI Guide", "/docs/rust/cli", "terminal"),
-    ],
-    "run": [
-        ("Rust CLI Guide", "/docs/rust/cli", "terminal"),
-        ("Rust Agent Flow", "/docs/rust/agent-flow", "diagram-project"),
-    ],
-    "task": [
-        ("Rust Agent Team", "/docs/rust/agent-team", "users"),
-        ("Rust Agent Flow", "/docs/rust/agent-flow", "diagram-project"),
-    ],
-    "team": [
-        ("Rust Agent Team", "/docs/rust/agent-team", "users"),
-        ("Rust Agent Flow", "/docs/rust/agent-flow", "diagram-project"),
-    ],
-    "flow": [
-        ("Rust Agent Flow", "/docs/rust/agent-flow", "diagram-project"),
-        ("Rust Agent Team", "/docs/rust/agent-team", "users"),
-    ],
-}
-
-
-def get_related_docs(name: str, max_items: int = 5) -> list:
-    """Find related documentation based on keywords in the name."""
-    name_lower = name.lower()
-    related = []
-    seen_paths = set()
-    
-    for keyword, docs in RELATED_DOCS.items():
-        if keyword in name_lower:
-            for doc in docs:
-                if doc[1] not in seen_paths:
-                    related.append(doc)
-                    seen_paths.add(doc[1])
-    
-    return related[:max_items]
-
-
-def render_related_section(name: str, max_items: int = 5) -> str:
-    """Render a CardGroup section with related documentation links."""
-    related = get_related_docs(name, max_items)
-    if not related:
-        return ""
-    
-    cards = []
-    for title, path, icon in related:
-        cards.append(f'  <Card title="{title}" icon="{icon}" href="{path}" />')
-    
-    return f"""
-
----
-
-## Related Documentation
-
-<CardGroup cols={{2}}>
-{chr(10).join(cards)}
-</CardGroup>
-"""
-
-
-def friendly_title(name: str, page_type: str = "class") -> str:
-    """Convert a name to a friendly, human-readable title.
-    
-    Args:
-        name: The raw name (e.g., "AgentBuilder", "praisonai_derive")
-        page_type: One of "module", "class", "function"
-    """
-    # Check for explicit module title mapping
-    if page_type == "module" and name.lower() in MODULE_TITLES:
-        return MODULE_TITLES[name.lower()]
-    
-    # For functions, add parentheses
-    if page_type == "function":
-        # Special case for macros
-        if name == "tool":
-            return "#[tool] Macro"
-        return f"{name}()"
-    
-    # Convert snake_case to Title Case
-    if "_" in name:
-        parts = name.split("_")
-        titled_parts = []
-        for part in parts:
-            if part.lower() in ABBREVIATIONS:
-                titled_parts.append(part.upper())
-            else:
-                titled_parts.append(part.capitalize())
-        return " ".join(titled_parts)
-    
-    # Convert PascalCase to Title Case with spaces
-    # e.g., "AgentBuilder" -> "Agent Builder", "LlmConfig" -> "LLM Config"
-    result = []
-    current_word = []
-    
-    for i, char in enumerate(name):
-        if char.isupper():
-            # Check if this is part of an abbreviation
-            if current_word:
-                word = "".join(current_word)
-                # If previous chars form an abbreviation, keep them together
-                if word.lower() in ABBREVIATIONS:
-                    result.append(word.upper())
-                else:
-                    result.append(word)
-                current_word = []
-            current_word.append(char)
-        else:
-            current_word.append(char)
-    
-    # Don't forget the last word
-    if current_word:
-        word = "".join(current_word)
-        if word.lower() in ABBREVIATIONS:
-            result.append(word.upper())
-        else:
-            result.append(word)
-    
-    # Join and fix common patterns
-    title = " ".join(result)
-    
-    # Fix cases like "L L M" -> "LLM"
-    for abbr in ABBREVIATIONS:
-        spaced = " ".join(abbr.upper())
-        if spaced in title:
-            title = title.replace(spaced, abbr.upper())
-    
-    return title
-
-
-def get_icon(name: str) -> str:
-    """Get icon for module name."""
-    return ICON_MAP.get(name.lower(), ICON_MAP["default"])
-
-
-def generate_module_page(info, output_dir: Path, dry_run: bool = False) -> str:
-    """Generate a module hub page."""
-    short_name = info.short_name or info.name.split('.')[-1]
-    title = friendly_title(short_name, "module")
-    title_suffix = " • Rust AI Agent SDK"
-    # Use docstring if available, otherwise use our rich descriptions
-    desc = sanitize_description(info.docstring) or MODULE_DESCRIPTIONS.get(short_name.lower(), f"Rust AI Agent SDK - {title}")
-    
-    content = f'''---
-title: "{title}{title_suffix}"
-sidebarTitle: "{title}"
-description: "{desc}"
-icon: "{get_icon(short_name)}"
----
-
-# {short_name}
-
-<Badge color="orange">Rust AI Agent SDK</Badge>
-
-{escape_mdx(info.docstring) if info.docstring else ""}
-
-## Import
-
-```rust
-use {info.name.replace('.', '::')}::*;
-```
-
-'''
-    
-    if info.classes:
-        content += "## Types\n\n<CardGroup cols={2}>\n"
-        for cls in info.classes:
-            cls_title = friendly_title(cls.name, "class")
-            cls_desc = sanitize_description(cls.docstring) or "Type definition."
-            content += f'  <Card title="{cls_title}" icon="brackets-curly" href="../classes/{cls.name}">\n'
-            content += f"    {cls_desc}\n"
-            content += "  </Card>\n"
-        content += "</CardGroup>\n\n"
-    
-    if info.functions:
-        content += "## Functions\n\n<CardGroup cols={2}>\n"
-        for func in info.functions:
-            func_title = friendly_title(func.name, "function")
-            func_desc = sanitize_description(func.docstring) or "Function definition."
-            content += f'  <Card title="{func_title}" icon="function" href="../functions/{func.name}">\n'
-            content += f"    {func_desc}\n"
-            content += "  </Card>\n"
-        content += "</CardGroup>\n\n"
-    
-    # Add related documentation section
-    related = render_related_section(short_name, max_items=5)
-    if related:
-        content += related
-    
-    module_file = output_dir / 'modules' / f'{short_name}.mdx'
-    if not dry_run:
-        module_file.parent.mkdir(parents=True, exist_ok=True)
-        module_file.write_text(content)
-    
-    return f"docs/sdk/reference/rust/modules/{short_name}"
-
-
-def generate_class_page(cls, module_info, output_dir: Path, dry_run: bool = False) -> str:
-    """Generate a class/struct page."""
-    short_name = module_info.short_name or module_info.name.split('.')[-1]
-    module_title = friendly_title(short_name, "module")
-    title = friendly_title(cls.name, "class")
-    title_suffix = " • Rust AI Agent SDK"
-    # Include original name in description for searchability
-    base_desc = sanitize_description(cls.docstring) if cls.docstring else f"{cls.name} struct for Rust AI agents"
-    desc = f"{base_desc}" if cls.name in base_desc else f"{cls.name}: {base_desc}"
-    
-    content = f'''---
-title: "{title}{title_suffix}"
-sidebarTitle: "{title}"
-description: "{desc}"
-icon: "brackets-curly"
----
-
-# {cls.name}
-
-> Defined in the [**{module_title}**](../modules/{short_name}) module.
-
-<Badge color="orange">Rust AI Agent SDK</Badge>
-
-{escape_mdx(cls.docstring) if cls.docstring else ""}
-
-'''
-    
-    # Properties/Fields
-    if cls.properties:
-        content += "## Fields\n\n"
-        content += "| Name | Type | Description |\n"
-        content += "|------|------|-------------|\n"
-        for p in cls.properties:
-            p_type = escape_mdx(p.type) if hasattr(p, 'type') and p.type else "-"
-            p_desc = escape_mdx(p.description)[:80] if hasattr(p, 'description') and p.description else "-"
-            content += f"| `{p.name}` | `{p_type}` | {p_desc} |\n"
-        content += "\n"
-    
-    # Methods
-    if cls.methods:
-        content += "## Methods\n\n"
-        for m in cls.methods:
-            async_prefix = "async " if hasattr(m, 'is_async') and m.is_async else ""
-            ret_type = m.return_type if hasattr(m, 'return_type') and m.return_type else "()"
-            content += f"### `{m.name}`\n\n"
-            content += f"```rust\n{async_prefix}fn {m.name}({m.signature}) -> {ret_type}\n```\n\n"
-            if hasattr(m, 'docstring') and m.docstring:
-                content += f"{escape_mdx(m.docstring)}\n\n"
-            if hasattr(m, 'params') and m.params:
-                content += "**Parameters:**\n\n"
-                content += "| Name | Type |\n|------|------|\n"
-                for p in m.params:
-                    p_type = escape_mdx(p.type) if hasattr(p, 'type') and p.type else "-"
-                    content += f"| `{p.name}` | `{p_type}` |\n"
-                content += "\n"
-    
-    # Add related documentation section
-    related = render_related_section(cls.name, max_items=5)
-    if related:
-        content += related
-    
-    class_file = output_dir / 'classes' / f'{cls.name}.mdx'
-    if not dry_run:
-        class_file.parent.mkdir(parents=True, exist_ok=True)
-        class_file.write_text(content)
-    
-    return f"docs/sdk/reference/rust/classes/{cls.name}"
-
-
-def generate_function_page(func, module_info, output_dir: Path, dry_run: bool = False) -> str:
-    """Generate a function page."""
-    short_name = module_info.short_name or module_info.name.split('.')[-1]
-    module_title = friendly_title(short_name, "module")
-    title = friendly_title(func.name, "function")
-    title_suffix = " • Rust AI Agent SDK"
-    # Include original name in description for searchability
-    base_desc = sanitize_description(func.docstring) if func.docstring else f"{func.name} function for Rust AI agents"
-    desc = f"{base_desc}" if func.name in base_desc else f"{func.name}: {base_desc}"
-    async_prefix = "async " if hasattr(func, 'is_async') and func.is_async else ""
-    ret_type = func.return_type if hasattr(func, 'return_type') and func.return_type else "()"
-    
-    content = f'''---
-title: "{title}{title_suffix}"
-sidebarTitle: "{title}"
-description: "{desc}"
-icon: "function"
----
-
-# {func.name}
-
-> Defined in the [**{module_title}**](../modules/{short_name}) module.
-
-<Badge color="orange">Rust AI Agent SDK</Badge>
-
-```rust
-{async_prefix}fn {func.name}({func.signature}) -> {ret_type}
-```
-
-{escape_mdx(func.docstring) if func.docstring else ""}
-
-'''
-    
-    if hasattr(func, 'params') and func.params:
-        content += "## Parameters\n\n"
-        content += "| Name | Type | Description |\n"
-        content += "|------|------|-------------|\n"
-        for p in func.params:
-            p_type = escape_mdx(p.type) if hasattr(p, 'type') and p.type else "-"
-            p_desc = escape_mdx(p.description) if hasattr(p, 'description') and p.description else "-"
-            content += f"| `{p.name}` | `{p_type}` | {p_desc} |\n"
-        content += "\n"
-    
-    # Add related documentation section
-    related = render_related_section(func.name, max_items=5)
-    if related:
-        content += related
-    
-    func_file = output_dir / 'functions' / f'{func.name}.mdx'
-    if not dry_run:
-        func_file.parent.mkdir(parents=True, exist_ok=True)
-        func_file.write_text(content)
-    
-    return f"docs/sdk/reference/rust/functions/{func.name}"
-
-
-def update_docs_json(docs_json_path: Path, generated_files: list):
-    """Update docs.json with Rust SDK navigation."""
-    if not docs_json_path.exists():
-        print(f"docs.json not found at {docs_json_path}")
-        return
-    
-    with open(docs_json_path, 'r') as f:
-        docs_config = json.load(f)
-    
-    # Find or create SDK tab
-    sdk_tab = None
-    for tab in docs_config.get('navigation', {}).get('tabs', []):
-        if tab.get('tab') == 'SDK':
-            sdk_tab = tab
-            break
-    
-    if not sdk_tab:
-        print("SDK tab not found in docs.json")
-        return
-    
-    # Find Reference group
-    ref_group = None
-    for g in sdk_tab.get('groups', []):
-        if isinstance(g, dict) and g.get('group') == 'Reference':
-            ref_group = g
-            break
-    
-    if not ref_group:
-        print("Reference group not found in docs.json")
-        return
-    
-    # Find or create rust group
-    rust_group = None
-    for pg in ref_group.get('pages', []):
-        if isinstance(pg, dict) and pg.get('group') == 'rust':
-            rust_group = pg
-            break
-    
-    if not rust_group:
-        rust_group = {"group": "rust", "pages": []}
-        ref_group['pages'].append(rust_group)
-    
-    # Organize by type
-    modules = sorted([p for p in generated_files if '/modules/' in p])
-    classes = sorted([p for p in generated_files if '/classes/' in p])
-    functions = sorted([p for p in generated_files if '/functions/' in p])
-    
-    new_pages = []
-    if modules:
-        new_pages.append({"group": "Modules", "icon": "box", "pages": modules})
-    if classes:
-        new_pages.append({"group": "Classes", "icon": "brackets-curly", "pages": classes})
-    if functions:
-        new_pages.append({"group": "Functions", "icon": "function", "pages": functions})
-    
-    rust_group['pages'] = new_pages
-    
-    with open(docs_json_path, 'w') as f:
-        json.dump(docs_config, f, indent=2)
-    
-    print("Updated docs.json with Rust SDK navigation")
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Generate Rust SDK documentation")
-    parser.add_argument("--rust-path", default="/Users/praison/praisonai-package/src/praisonai-rust",
-                        help="Path to Rust SDK workspace")
-    parser.add_argument("--docs-root", default="/Users/praison/PraisonAIDocs",
-                        help="Root directory of documentation")
-    parser.add_argument("--dry-run", action="store_true", help="Don't write files")
-    args = parser.parse_args()
-    
-    rust_path = Path(args.rust_path)
-    docs_root = Path(args.docs_root)
-    output_dir = docs_root / 'docs/sdk/reference/rust'
-    
-    # Ensure directories exist
-    if not args.dry_run:
-        (output_dir / 'modules').mkdir(parents=True, exist_ok=True)
-        (output_dir / 'classes').mkdir(parents=True, exist_ok=True)
-        (output_dir / 'functions').mkdir(parents=True, exist_ok=True)
-    
-    # Parse all modules
-    workspace_parser = RustWorkspaceParser(rust_path)
-    all_modules = workspace_parser.parse_all()
-    
-    generated_files = []
-    
-    print("=" * 60)
-    print("Generating Rust SDK Documentation")
-    print("=" * 60)
-    
-    for crate_name, modules in all_modules.items():
-        print(f"\nCrate: {crate_name}")
-        
-        for info in modules:
-            # Skip empty modules
-            if not info.classes and not info.functions:
-                print(f"  Skipping empty: {info.name}")
-                continue
-            
-            # Generate module page
-            path = generate_module_page(info, output_dir, args.dry_run)
-            generated_files.append(path)
-            print(f"  Generated: modules/{info.short_name}.mdx")
-            
-            # Generate class pages
-            for cls in info.classes:
-                path = generate_class_page(cls, info, output_dir, args.dry_run)
-                generated_files.append(path)
-                print(f"    Class: classes/{cls.name}.mdx")
-            
-            # Generate function pages
-            for func in info.functions:
-                path = generate_function_page(func, info, output_dir, args.dry_run)
-                generated_files.append(path)
-                print(f"    Function: functions/{func.name}.mdx")
-    
-    print(f"\n=== Generated {len(generated_files)} files ===")
-    
-    # Update docs.json
-    if not args.dry_run:
-        update_docs_json(docs_root / 'docs.json', generated_files)
-
-
-if __name__ == "__main__":
-    main()

praisonai_tools/docs_generator/rust_parser.py

@@ -1,826 +0,0 @@
-"""
-Rust Documentation Parser for PraisonAI.
-
-Parses Rust source code to extract documentation for MDX generation.
-Uses tree-sitter for robust Rust syntax parsing.
-"""
-
-from __future__ import annotations
-
-import re
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple
-
-# Import shared data classes from generator
-try:
-    from .generator import (
-        ParamInfo,
-        MethodInfo,
-        ClassInfo,
-        FunctionInfo,
-        ModuleInfo,
-        SKIP_MODULES,
-        SKIP_METHODS,
-        extract_rust_function_calls,
-    )
-except ImportError:
-    # Define minimal fallback classes when run standalone
-    from dataclasses import dataclass, field
-    from typing import List, Optional, Tuple
-    
-    @dataclass
-    class ParamInfo:
-        name: str
-        type: str = ""
-        default: Optional[str] = None
-        description: str = ""
-        required: bool = True
-    
-    @dataclass
-    class MethodInfo:
-        name: str
-        signature: str = ""
-        return_type: str = ""
-        docstring: str = ""
-        params: List[ParamInfo] = field(default_factory=list)
-        is_async: bool = False
-        is_static: bool = False
-        examples: List[str] = field(default_factory=list)
-    
-    @dataclass
-    class ClassInfo:
-        name: str
-        docstring: str = ""
-        bases: List[str] = field(default_factory=list)
-        methods: List[MethodInfo] = field(default_factory=list)
-        static_methods: List[MethodInfo] = field(default_factory=list)
-        properties: List[ParamInfo] = field(default_factory=list)
-        init_params: List[ParamInfo] = field(default_factory=list)
-        examples: List[str] = field(default_factory=list)
-        source_file: str = ""
-        source_line: int = 0
-    
-    @dataclass
-    class FunctionInfo:
-        name: str
-        signature: str = ""
-        return_type: str = ""
-        docstring: str = ""
-        params: List[ParamInfo] = field(default_factory=list)
-        is_async: bool = False
-        examples: List[str] = field(default_factory=list)
-        source_file: str = ""
-        source_line: int = 0
-        calls: List[str] = field(default_factory=list)
-    
-    @dataclass 
-    class ModuleInfo:
-        name: str
-        short_name: str = ""
-        display_name: str = ""
-        docstring: str = ""
-        is_init: bool = False
-        package: str = ""
-        classes: List[ClassInfo] = field(default_factory=list)
-        functions: List[FunctionInfo] = field(default_factory=list)
-        constants: List[Tuple[str, str]] = field(default_factory=list)
-        source_file: str = ""
-    
-    def extract_rust_function_calls(source_code: str) -> List[str]:
-        """Fallback when run standalone - return empty list."""
-        return []
-    
-    SKIP_MODULES = {"tests", "__pycache__", "conftest"}
-    SKIP_METHODS = {"__init__", "__repr__", "__str__"}
-
-# Try to import tree-sitter, provide fallback
-try:
-    import tree_sitter_rust as ts_rust
-    from tree_sitter import Language, Parser
-    TREE_SITTER_AVAILABLE = True
-except ImportError:
-    TREE_SITTER_AVAILABLE = False
-
-
-# =============================================================================
-# RUST-SPECIFIC DATA CLASSES
-# =============================================================================
-
-@dataclass
-class TraitInfo:
-    """Information about a Rust trait."""
-    name: str
-    docstring: str = ""
-    methods: List[MethodInfo] = field(default_factory=list)
-    supertraits: List[str] = field(default_factory=list)
-    source_file: str = ""
-    source_line: int = 0
-
-
-@dataclass
-class EnumInfo:
-    """Information about a Rust enum."""
-    name: str
-    docstring: str = ""
-    variants: List[Tuple[str, str]] = field(default_factory=list)  # (name, docstring)
-    source_file: str = ""
-    source_line: int = 0
-
-
-@dataclass
-class MacroInfo:
-    """Information about a Rust macro."""
-    name: str
-    docstring: str = ""
-    attributes: List[str] = field(default_factory=list)
-    examples: List[str] = field(default_factory=list)
-
-
-# =============================================================================
-# RUST DOC PARSER (REGEX-BASED FALLBACK)
-# =============================================================================
-
-class RustDocParser:
-    """Parse Rust source code for documentation.
-    
-    This parser extracts documentation from Rust source files using regex patterns.
-    It handles:
-    - Module doc comments (//!)
-    - Item doc comments (///)
-    - Structs, traits, enums, and functions
-    - Impl blocks and methods
-    - Proc-macro definitions
-    """
-    
-    # Skip these module names
-    SKIP_MODULES = {"target", "tests", "examples", "benches", ".git"}
-    
-    def __init__(self, crate_path: Path, crate_name: str = "praisonai"):
-        """Initialize the parser.
-        
-        Args:
-            crate_path: Path to the crate root (containing Cargo.toml)
-            crate_name: Name of the crate for module prefixes
-        """
-        self.crate_path = crate_path
-        self.crate_name = crate_name
-        self.src_path = crate_path / "src"
-        self.workspace_root = crate_path.parent  # Parent of crate for relative paths
-        
-        # Cache for parsed files
-        self._file_cache: Dict[Path, str] = {}
-    
-    def get_modules(self) -> List[str]:
-        """Get list of modules to document.
-        
-        Returns:
-            List of module paths (e.g., ['praisonai.agent', 'praisonai.tools'])
-        """
-        modules = set()
-        
-        if not self.src_path.exists():
-            return []
-        
-        # Find all .rs files
-        for rs_file in self.src_path.rglob("*.rs"):
-            # Skip test files
-            if "tests" in rs_file.parts or "test_" in rs_file.name:
-                continue
-            
-            # Convert file path to module path
-            rel_path = rs_file.relative_to(self.src_path)
-            parts = list(rel_path.parts)
-            
-            # Handle mod.rs and lib.rs
-            if parts[-1] == "mod.rs":
-                parts = parts[:-1]
-            elif parts[-1] == "lib.rs":
-                parts = []  # lib.rs is the crate root
-            else:
-                parts[-1] = parts[-1].replace(".rs", "")
-            
-            if parts:
-                module_name = f"{self.crate_name}.{'.'.join(parts)}"
-            else:
-                module_name = self.crate_name
-            
-            modules.add(module_name)
-        
-        return sorted(modules)
-    
-    def parse_module(self, module_path: str) -> Optional[ModuleInfo]:
-        """Parse a module and extract documentation.
-        
-        Args:
-            module_path: Fully qualified module path (e.g., 'praisonai.agent')
-            
-        Returns:
-            ModuleInfo or None if module not found
-        """
-        # Convert module path to file path
-        file_path = self._module_to_file(module_path)
-        if not file_path or not file_path.exists():
-            return None
-        
-        try:
-            content = self._read_file(file_path)
-            
-            # Extract module-level doc comments
-            module_doc = self._extract_module_docs(content)
-            
-            # Get module short name
-            parts = module_path.split(".")
-            short_name = parts[-1] if len(parts) > 1 else module_path
-            
-            # Get relative source path for GitHub links
-            try:
-                source_file = str(file_path.relative_to(self.workspace_root))
-            except ValueError:
-                source_file = str(file_path)
-            
-            info = ModuleInfo(
-                name=module_path,
-                short_name=short_name,
-                docstring=module_doc,
-                is_init=file_path.name in ("lib.rs", "mod.rs"),
-                package="rust",
-                source_file=source_file,
-            )
-            
-            # Parse structs as classes
-            info.classes = self._parse_structs(content, source_file)
-            
-            # Parse traits (also as classes with special handling)
-            traits = self._parse_traits(content)
-            for trait in traits:
-                info.classes.append(ClassInfo(
-                    name=trait.name,
-                    docstring=trait.docstring,
-                    bases=trait.supertraits,
-                    methods=trait.methods,
-                    source_file=source_file,
-                ))
-            
-            # Parse enums
-            enums = self._parse_enums(content)
-            for enum in enums:
-                info.classes.append(ClassInfo(
-                    name=enum.name,
-                    docstring=enum.docstring,
-                    properties=[
-                        ParamInfo(name=v[0], description=v[1], type="variant")
-                        for v in enum.variants
-                    ],
-                    source_file=source_file,
-                ))
-            
-            # Parse standalone functions
-            info.functions = self._parse_functions(content, source_file)
-            
-            # Parse impl blocks and attach methods to structs
-            self._parse_impl_blocks(content, info)
-            
-            return info
-            
-        except Exception as e:
-            print(f"Error parsing {file_path}: {e}")
-            return None
-    
-    def _module_to_file(self, module_path: str) -> Optional[Path]:
-        """Convert module path to file path."""
-        parts = module_path.split(".")
-        
-        # Remove crate name prefix
-        if parts[0] == self.crate_name:
-            parts = parts[1:]
-        
-        if not parts:
-            # Crate root - try lib.rs
-            lib_rs = self.src_path / "lib.rs"
-            if lib_rs.exists():
-                return lib_rs
-            return None
-        
-        # Try mod.rs first
-        mod_path = self.src_path / "/".join(parts) / "mod.rs"
-        if mod_path.exists():
-            return mod_path
-        
-        # Try direct file
-        file_path = self.src_path / "/".join(parts[:-1]) / f"{parts[-1]}.rs"
-        if file_path.exists():
-            return file_path
-        
-        # Try as single file in src/
-        single_file = self.src_path / f"{parts[-1]}.rs"
-        if single_file.exists():
-            return single_file
-        
-        return None
-    
-    def _read_file(self, path: Path) -> str:
-        """Read file with caching."""
-        if path not in self._file_cache:
-            self._file_cache[path] = path.read_text(encoding="utf-8")
-        return self._file_cache[path]
-    
-    def _extract_module_docs(self, content: str) -> str:
-        """Extract module-level doc comments (//!)."""
-        lines = []
-        for line in content.split("\n"):
-            stripped = line.strip()
-            if stripped.startswith("//!"):
-                # Remove //! prefix
-                doc_line = stripped[3:].strip()
-                lines.append(doc_line)
-            elif stripped.startswith("//") or stripped.startswith("#"):
-                continue  # Skip other comments and attributes
-            elif stripped:
-                break  # Stop at first non-comment/non-attribute line
-        
-        return "\n".join(lines)
-    
-    def _extract_doc_comments(self, content: str, start_pos: int) -> str:
-        """Extract doc comments (///) before an item.
-        
-        Args:
-            content: Full file content
-            start_pos: Position where the item definition starts
-            
-        Returns:
-            Extracted documentation string
-        """
-        # Find the start of line containing start_pos
-        line_start = content.rfind("\n", 0, start_pos) + 1
-        
-        # Work backwards to find doc comments
-        lines = []
-        pos = line_start - 1
-        
-        while pos > 0:
-            # Find previous line
-            prev_line_end = pos
-            prev_line_start = content.rfind("\n", 0, prev_line_end) + 1
-            line = content[prev_line_start:prev_line_end].strip()
-            
-            if line.startswith("///"):
-                lines.insert(0, line[3:].strip())
-                pos = prev_line_start - 1
-            elif line.startswith("#["):
-                # Skip attributes
-                pos = prev_line_start - 1
-            elif not line:
-                # Skip empty lines
-                pos = prev_line_start - 1
-            else:
-                break
-        
-        return "\n".join(lines)
-    
-    def _parse_structs(self, content: str, source_file: str = "") -> List[ClassInfo]:
-        """Parse struct definitions."""
-        structs = []
-        
-        # Match: pub struct Name { ... } or pub struct Name;
-        pattern = r'pub\s+struct\s+(\w+)(?:<[^>]*>)?\s*(?:\{([^}]*)\}|;)'
-        
-        for match in re.finditer(pattern, content, re.DOTALL):
-            name = match.group(1)
-            fields_str = match.group(2) or ""
-            
-            # Calculate line number
-            line_no = content[:match.start()].count('\n') + 1
-            
-            # Extract doc comments
-            doc = self._extract_doc_comments(content, match.start())
-            
-            # Parse fields
-            properties = []
-            if fields_str:
-                field_pattern = r'(?:pub\s+)?(\w+):\s*([^,\n]+)'
-                for field_match in re.finditer(field_pattern, fields_str):
-                    field_name = field_match.group(1)
-                    field_type = field_match.group(2).strip().rstrip(",")
-                    
-                    # Extract field doc comment
-                    field_doc = self._extract_doc_comments(fields_str, field_match.start())
-                    
-                    properties.append(ParamInfo(
-                        name=field_name,
-                        type=field_type,
-                        description=field_doc,
-                    ))
-            
-            # Parse examples from docstring
-            examples = self._extract_examples(doc)
-            
-            structs.append(ClassInfo(
-                name=name,
-                docstring=self._clean_docstring(doc),
-                properties=properties,
-                examples=examples,
-                source_file=source_file,
-                source_line=line_no,
-            ))
-        
-        return structs
-    
-    def _parse_traits(self, content: str) -> List[TraitInfo]:
-        """Parse trait definitions."""
-        traits = []
-        
-        # Match: pub trait Name { ... }
-        pattern = r'pub\s+trait\s+(\w+)(?:<[^>]*>)?(?:\s*:\s*([^{]+))?\s*\{([^}]*(?:\{[^}]*\}[^}]*)*)\}'
-        
-        for match in re.finditer(pattern, content, re.DOTALL):
-            name = match.group(1)
-            supertraits_str = match.group(2) or ""
-            body = match.group(3)
-            
-            # Extract doc comments
-            doc = self._extract_doc_comments(content, match.start())
-            
-            # Parse supertraits
-            supertraits = []
-            if supertraits_str:
-                supertraits = [s.strip() for s in supertraits_str.split("+")]
-            
-            # Parse required methods
-            methods = self._parse_trait_methods(body)
-            
-            traits.append(TraitInfo(
-                name=name,
-                docstring=self._clean_docstring(doc),
-                methods=methods,
-                supertraits=supertraits,
-            ))
-        
-        return traits
-    
-    def _parse_trait_methods(self, body: str) -> List[MethodInfo]:
-        """Parse methods from a trait body."""
-        methods = []
-        
-        # Match method signatures: fn name(...) -> Type;
-        pattern = r'fn\s+(\w+)\s*\(([^)]*)\)(?:\s*->\s*([^;{]+))?[;{]'
-        
-        for match in re.finditer(pattern, body):
-            name = match.group(1)
-            params_str = match.group(2)
-            return_type = match.group(3) or "()".strip() if match.group(3) else "()"
-            
-            # Extract doc comment
-            doc = self._extract_doc_comments(body, match.start())
-            
-            # Parse parameters
-            params = self._parse_params(params_str)
-            
-            methods.append(MethodInfo(
-                name=name,
-                signature=params_str,
-                return_type=return_type.strip(),
-                docstring=self._clean_docstring(doc),
-                params=params,
-                is_async="async" in body[max(0, match.start()-10):match.start()],
-            ))
-        
-        return methods
-    
-    def _parse_enums(self, content: str) -> List[EnumInfo]:
-        """Parse enum definitions."""
-        enums = []
-        
-        # Match: pub enum Name { ... }
-        pattern = r'pub\s+enum\s+(\w+)(?:<[^>]*>)?\s*\{([^}]*(?:\{[^}]*\}[^}]*)*)\}'
-        
-        for match in re.finditer(pattern, content, re.DOTALL):
-            name = match.group(1)
-            body = match.group(2)
-            
-            # Extract doc comments
-            doc = self._extract_doc_comments(content, match.start())
-            
-            # Parse variants
-            variants = []
-            variant_pattern = r'(\w+)(?:\s*\{[^}]*\}|\s*\([^)]*\))?'
-            for var_match in re.finditer(variant_pattern, body):
-                var_name = var_match.group(1)
-                if var_name in ("pub", "struct", "fn"):
-                    continue
-                var_doc = self._extract_doc_comments(body, var_match.start())
-                variants.append((var_name, var_doc))
-            
-            enums.append(EnumInfo(
-                name=name,
-                docstring=self._clean_docstring(doc),
-                variants=variants,
-            ))
-        
-        return enums
-    
-    def _parse_functions(self, content: str, source_file: str = "") -> List[FunctionInfo]:
-        """Parse standalone public functions."""
-        functions = []
-        
-        # Match: pub fn name(...) -> Type { ... }
-        # Exclude functions inside impl blocks by checking context
-        pattern = r'^pub\s+(?:async\s+)?fn\s+(\w+)\s*(?:<[^>]*>)?\s*\(([^)]*)\)(?:\s*->\s*([^{]+))?\s*\{'
-        
-        for match in re.finditer(pattern, content, re.MULTILINE):
-            name = match.group(1)
-            params_str = match.group(2)
-            return_type = match.group(3).strip() if match.group(3) else "()"
-            
-            # Skip if this looks like it's inside an impl block
-            # (simple heuristic: check if there's an unmatched `impl` before this)
-            before = content[:match.start()]
-            impl_count = before.count("impl ")
-            close_brace_count = before.count("}")
-            if impl_count > close_brace_count:
-                continue
-            
-            # Calculate line number
-            line_no = content[:match.start()].count('\n') + 1
-            
-            # Extract doc comments
-            doc = self._extract_doc_comments(content, match.start())
-            
-            # Parse parameters
-            params = self._parse_params(params_str)
-            
-            # Check if async
-            is_async = "async" in match.group(0)
-            
-            # Extract function body for calls
-            func_body = self._extract_brace_content(content, match.end() - 1)
-            try:
-                calls = extract_rust_function_calls(func_body) if func_body else []
-            except:
-                calls = []
-            
-            functions.append(FunctionInfo(
-                name=name,
-                signature=params_str,
-                return_type=return_type,
-                docstring=self._clean_docstring(doc),
-                params=params,
-                is_async=is_async,
-                examples=self._extract_examples(doc),
-                source_file=source_file,
-                source_line=line_no,
-                calls=calls,
-            ))
-        
-        return functions
-    
-    def _parse_impl_blocks(self, content: str, module_info: ModuleInfo) -> None:
-        """Parse impl blocks and attach methods to corresponding structs."""
-        
-        # Find all impl block starts
-        impl_pattern = r'impl(?:<[^>]*>)?\s+(?:(\w+)\s+for\s+)?(\w+)(?:<[^>]*>)?\s*\{'
-        
-        for match in re.finditer(impl_pattern, content):
-            match.group(1)  # For trait impls
-            struct_name = match.group(2)
-            
-            # Find the matching closing brace using brace counting
-            start_pos = match.end() - 1  # Position of opening brace
-            body = self._extract_brace_content(content, start_pos)
-            
-            if body:
-                # Parse methods from impl body
-                methods = self._parse_impl_methods(body)
-                
-                # Find the corresponding struct and add methods
-                for cls in module_info.classes:
-                    if cls.name == struct_name:
-                        cls.methods.extend(methods)
-                        break
-    
-    def _extract_brace_content(self, content: str, start_pos: int) -> str:
-        """Extract content within braces, handling nested braces correctly."""
-        if content[start_pos] != '{':
-            return ""
-        
-        depth = 0
-        i = start_pos
-        
-        while i < len(content):
-            if content[i] == '{':
-                depth += 1
-            elif content[i] == '}':
-                depth -= 1
-                if depth == 0:
-                    return content[start_pos + 1:i]
-            i += 1
-        
-        return content[start_pos + 1:]  # No closing brace found
-    
-    def _parse_impl_methods(self, body: str) -> List[MethodInfo]:
-        """Parse methods from an impl block body."""
-        methods = []
-        
-        # Match: pub fn name(...) -> Type { ... }
-        pattern = r'pub\s+(?:async\s+)?fn\s+(\w+)\s*\(([^)]*)\)(?:\s*->\s*([^{]+))?\s*\{'
-        
-        for match in re.finditer(pattern, body):
-            name = match.group(1)
-            params_str = match.group(2)
-            return_type = match.group(3).strip() if match.group(3) else "()"
-            
-            # Skip internal methods
-            if name.startswith("_") or name in SKIP_METHODS:
-                continue
-            
-            # Extract doc comments
-            doc = self._extract_doc_comments(body, match.start())
-            
-            # Parse parameters
-            params = self._parse_params(params_str)
-            
-            # Detect method type
-            is_static = "&self" not in params_str and "self" not in params_str
-            is_async = "async" in match.group(0)
-            
-            methods.append(MethodInfo(
-                name=name,
-                signature=params_str,
-                return_type=return_type,
-                docstring=self._clean_docstring(doc),
-                params=params,
-                is_async=is_async,
-                is_static=is_static,
-                examples=self._extract_examples(doc),
-            ))
-        
-        return methods
-    
-    def _parse_params(self, params_str: str) -> List[ParamInfo]:
-        """Parse function/method parameters."""
-        params = []
-        
-        if not params_str.strip():
-            return params
-        
-        # Simple parameter parsing
-        # Handle: name: Type, &self, &mut self, self
-        for part in params_str.split(","):
-            part = part.strip()
-            if not part:
-                continue
-            
-            # Skip self parameters
-            if part in ("self", "&self", "&mut self"):
-                continue
-            
-            # Match: name: Type
-            param_match = re.match(r'(\w+)\s*:\s*(.+)', part)
-            if param_match:
-                name = param_match.group(1)
-                type_str = param_match.group(2).strip()
-                
-                # Check for default (= value)
-                default = None
-                if "=" in type_str:
-                    type_str, default = type_str.split("=", 1)
-                    type_str = type_str.strip()
-                    default = default.strip()
-                
-                params.append(ParamInfo(
-                    name=name,
-                    type=type_str,
-                    default=default,
-                    required=default is None,
-                ))
-        
-        return params
-    
-    def _extract_examples(self, doc: str) -> List[str]:
-        """Extract code examples from documentation."""
-        examples = []
-        
-        # Find ```rust or ``` blocks
-        pattern = r'```(?:rust(?:,ignore)?)?\n(.*?)```'
-        for match in re.finditer(pattern, doc, re.DOTALL):
-            examples.append(match.group(1).strip())
-        
-        return examples
-    
-    def _clean_docstring(self, doc: str) -> str:
-        """Clean up a docstring for display."""
-        # Remove example blocks for the main description
-        doc = re.sub(r'# Example.*', '', doc, flags=re.DOTALL | re.IGNORECASE)
-        doc = re.sub(r'```.*?```', '', doc, flags=re.DOTALL)
-        
-        # Clean up whitespace
-        lines = [line.strip() for line in doc.split("\n")]
-        lines = [line for line in lines if line]
-        
-        return " ".join(lines)
-
-
-# =============================================================================
-# PROC-MACRO PARSER
-# =============================================================================
-
-class ProcMacroParser(RustDocParser):
-    """Parser specialized for proc-macro crates (like praisonai-derive)."""
-    
-    def parse_macros(self) -> List[MacroInfo]:
-        """Parse proc-macro definitions."""
-        macros = []
-        
-        lib_rs = self.src_path / "lib.rs"
-        if not lib_rs.exists():
-            return macros
-        
-        content = self._read_file(lib_rs)
-        
-        # Match: #[proc_macro_attribute] pub fn name(...) { ... }
-        pattern = r'#\[proc_macro(?:_attribute|_derive)?\]\s*pub\s+fn\s+(\w+)'
-        
-        for match in re.finditer(pattern, content):
-            name = match.group(1)
-            
-            # Extract doc comments
-            doc = self._extract_doc_comments(content, match.start())
-            
-            macros.append(MacroInfo(
-                name=name,
-                docstring=self._clean_docstring(doc),
-                examples=self._extract_examples(doc),
-            ))
-        
-        return macros
-
-
-# =============================================================================
-# WORKSPACE PARSER
-# =============================================================================
-
-class RustWorkspaceParser:
-    """Parse an entire Rust workspace with multiple crates."""
-    
-    def __init__(self, workspace_path: Path):
-        """Initialize the workspace parser.
-        
-        Args:
-            workspace_path: Path to workspace root (containing Cargo.toml with [workspace])
-        """
-        self.workspace_path = workspace_path
-        self.crates: Dict[str, RustDocParser] = {}
-        self._discover_crates()
-    
-    def _discover_crates(self) -> None:
-        """Discover crates in the workspace."""
-        # Parse workspace Cargo.toml
-        cargo_toml = self.workspace_path / "Cargo.toml"
-        if not cargo_toml.exists():
-            return
-        
-        content = cargo_toml.read_text()
-        
-        # Find workspace members
-        members_match = re.search(r'members\s*=\s*\[(.*?)\]', content, re.DOTALL)
-        if members_match:
-            members_str = members_match.group(1)
-            members = re.findall(r'"([^"]+)"', members_str)
-            
-            for member in members:
-                member_path = self.workspace_path / member
-                if member_path.exists():
-                    crate_name = member.replace("-", "_")
-                    
-                    # Use ProcMacroParser for derive crate
-                    if "derive" in member:
-                        self.crates[crate_name] = ProcMacroParser(member_path, crate_name)
-                    else:
-                        self.crates[crate_name] = RustDocParser(member_path, crate_name)
-    
-    def get_all_modules(self) -> Dict[str, List[str]]:
-        """Get all modules from all crates.
-        
-        Returns:
-            Dict mapping crate name to list of module paths
-        """
-        result = {}
-        for crate_name, parser in self.crates.items():
-            result[crate_name] = parser.get_modules()
-        return result
-    
-    def parse_all(self) -> Dict[str, List[ModuleInfo]]:
-        """Parse all modules from all crates.
-        
-        Returns:
-            Dict mapping crate name to list of ModuleInfo
-        """
-        result = {}
-        for crate_name, parser in self.crates.items():
-            modules = []
-            for module_path in parser.get_modules():
-                module_info = parser.parse_module(module_path)
-                if module_info:
-                    modules.append(module_info)
-            result[crate_name] = modules
-        return result

praisonai_tools/fcp_tool/__init__.py

@@ -1,267 +0,0 @@
-"""
-FCP Tool - Final Cut Pro automation tools for Agent Recipes.
-
-This module provides tools for:
-- EditIntent schema validation and generation
-- FCPXML generation from EditIntent
-- CommandPost integration for auto-import
-- Injection daemon for batch processing
-- Health checks and diagnostics
-
-Usage:
-    from praisonai_tools.fcp_tool import (
-        fcp_autoedit,
-        fcp_doctor,
-        EditIntent,
-        FCPXMLGenerator,
-    )
-"""
-
-from .intent import (
-    EditIntent,
-    Asset,
-    Segment,
-    Marker,
-    Timeline,
-    Project,
-    ProjectFormat,
-    AudioSettings,
-    AudioRole,
-    AudioLayout,
-    Operations,
-    seconds_to_rational,
-    rational_to_seconds,
-    get_format_preset,
-    FORMAT_PRESETS,
-)
-from .fcpxml import FCPXMLGenerator, generate_fcpxml, validate_fcpxml_structure
-from .commandpost import CommandPostBridge, CommandPostStatus
-from .injector import Injector, InjectionJob, JobStatus, DaemonState
-from .doctor import FCPDoctor, CheckResult
-from .prompting import generate_edit_intent, create_simple_intent
-
-__all__ = [
-    # Intent schema
-    "EditIntent",
-    "Asset",
-    "Segment",
-    "Marker",
-    "Timeline",
-    "Project",
-    "ProjectFormat",
-    "AudioSettings",
-    "AudioRole",
-    "AudioLayout",
-    "Operations",
-    "seconds_to_rational",
-    "rational_to_seconds",
-    "get_format_preset",
-    "FORMAT_PRESETS",
-    # FCPXML
-    "FCPXMLGenerator",
-    "generate_fcpxml",
-    "validate_fcpxml_structure",
-    # CommandPost
-    "CommandPostBridge",
-    "CommandPostStatus",
-    # Injector
-    "Injector",
-    "InjectionJob",
-    "JobStatus",
-    "DaemonState",
-    # Doctor
-    "FCPDoctor",
-    "CheckResult",
-    # Prompting
-    "generate_edit_intent",
-    "create_simple_intent",
-]
-
-
-def fcp_doctor() -> dict:
-    """
-    Run health checks for FCP integration.
-    
-    Returns:
-        dict: Health check results with 'all_passed' boolean and 'checks' list
-    """
-    doctor = FCPDoctor()
-    results = doctor.run_all_checks()
-    summary = doctor.get_summary(results)
-    return {
-        "success": True,
-        "all_passed": summary["all_passed"],
-        "passed": summary["passed"],
-        "failed": summary["failed"],
-        "checks": summary["checks"],
-    }
-
-
-def fcp_autoedit(
-    action: str,
-    instruction: str = None,
-    media_paths: list = None,
-    project_name: str = "AI Generated Project",
-    format_preset: str = "1080p25",
-    intent_json: str = None,
-    fcpxml_content: str = None,
-    watch_folder: str = None,
-    dry_run: bool = False,
-    model: str = "gpt-4o",
-) -> dict:
-    """
-    FCP AutoEdit tool for generating and injecting FCPXML.
-    
-    Actions:
-    - "generate_intent": Generate EditIntent from instruction
-    - "generate_fcpxml": Generate FCPXML from EditIntent
-    - "inject": Inject FCPXML into Final Cut Pro
-    - "full_pipeline": Run the complete pipeline
-    
-    Args:
-        action: The action to perform
-        instruction: Natural language editing instruction
-        media_paths: List of media file paths
-        project_name: Project name
-        format_preset: Format preset (e.g., "1080p25")
-        intent_json: EditIntent JSON string (for generate_fcpxml)
-        fcpxml_content: FCPXML content (for inject)
-        watch_folder: Watch folder path
-        dry_run: If True, don't inject into FCP
-        model: OpenAI model to use
-        
-    Returns:
-        dict: Result with success status and relevant data
-    """
-    import json
-    import os
-    
-    if action == "generate_intent":
-        if not instruction:
-            return {"success": False, "error": "instruction is required"}
-        if not media_paths:
-            return {"success": False, "error": "media_paths is required"}
-        
-        abs_paths = [os.path.abspath(p) for p in media_paths]
-        missing = [p for p in abs_paths if not os.path.exists(p)]
-        if missing:
-            return {"success": False, "error": f"Media files not found: {missing}"}
-        
-        intent, warnings = generate_edit_intent(
-            instruction=instruction,
-            media_paths=abs_paths,
-            project_name=project_name,
-            format_preset=format_preset,
-            model=model,
-        )
-        
-        return {
-            "success": True,
-            "intent_json": intent.model_dump_json(),
-            "intent": intent.model_dump(),
-            "warnings": warnings,
-            "assets_count": len(intent.assets),
-            "segments_count": len(intent.timeline.segments),
-        }
-    
-    elif action == "generate_fcpxml":
-        if not intent_json:
-            return {"success": False, "error": "intent_json is required"}
-        
-        try:
-            intent_data = json.loads(intent_json)
-            intent = EditIntent.model_validate(intent_data)
-        except json.JSONDecodeError as e:
-            return {"success": False, "error": f"Invalid JSON: {e}"}
-        except Exception as e:
-            return {"success": False, "error": f"Invalid EditIntent: {e}"}
-        
-        generator = FCPXMLGenerator(intent)
-        fcpxml = generator.generate()
-        warnings = generator.get_warnings()
-        
-        return {
-            "success": True,
-            "fcpxml": fcpxml,
-            "fcpxml_length": len(fcpxml),
-            "warnings": warnings,
-        }
-    
-    elif action == "inject":
-        if not fcpxml_content:
-            return {"success": False, "error": "fcpxml_content is required"}
-        
-        commandpost = CommandPostBridge(watch_folder=watch_folder)
-        injector = Injector(watch_folder=watch_folder, commandpost=commandpost)
-        
-        job_id, fcpxml_path, messages = injector.inject_one_shot(
-            fcpxml_content=fcpxml_content,
-            instruction=instruction or "",
-            intent_json=intent_json,
-        )
-        
-        return {
-            "success": True,
-            "job_id": job_id,
-            "fcpxml_path": fcpxml_path,
-            "messages": messages,
-        }
-    
-    elif action == "full_pipeline":
-        # Generate intent
-        intent_result = fcp_autoedit(
-            action="generate_intent",
-            instruction=instruction,
-            media_paths=media_paths,
-            project_name=project_name,
-            format_preset=format_preset,
-            model=model,
-        )
-        if not intent_result["success"]:
-            return intent_result
-        
-        # Generate FCPXML
-        fcpxml_result = fcp_autoedit(
-            action="generate_fcpxml",
-            intent_json=intent_result["intent_json"],
-        )
-        if not fcpxml_result["success"]:
-            return fcpxml_result
-        
-        all_warnings = intent_result.get("warnings", []) + fcpxml_result.get("warnings", [])
-        
-        if dry_run:
-            return {
-                "success": True,
-                "dry_run": True,
-                "intent": intent_result["intent"],
-                "fcpxml_length": fcpxml_result["fcpxml_length"],
-                "warnings": all_warnings,
-            }
-        
-        # Inject
-        inject_result = fcp_autoedit(
-            action="inject",
-            fcpxml_content=fcpxml_result["fcpxml"],
-            instruction=instruction,
-            intent_json=intent_result["intent_json"],
-            watch_folder=watch_folder,
-        )
-        if not inject_result["success"]:
-            return inject_result
-        
-        return {
-            "success": True,
-            "job_id": inject_result["job_id"],
-            "fcpxml_path": inject_result["fcpxml_path"],
-            "assets_count": intent_result["assets_count"],
-            "segments_count": intent_result["segments_count"],
-            "warnings": all_warnings,
-            "messages": inject_result["messages"],
-        }
-    
-    else:
-        return {
-            "success": False,
-            "error": f"Unknown action: {action}. Valid: generate_intent, generate_fcpxml, inject, full_pipeline",
-        }

praisonai_tools/fcp_tool/cli.py

@@ -1,310 +0,0 @@
-"""
-CLI for FCP Tool - Final Cut Pro automation commands.
-
-Usage:
-    python -m praisonai_tools.fcp_tool.cli doctor
-    python -m praisonai_tools.fcp_tool.cli autoedit --instruction "..." --media /path/to/file.mov
-    python -m praisonai_tools.fcp_tool.cli daemon start|stop|status
-    python -m praisonai_tools.fcp_tool.cli bootstrap
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import sys
-from typing import Optional
-
-
-def main(args: Optional[list[str]] = None) -> int:
-    """Main CLI entry point."""
-    parser = argparse.ArgumentParser(
-        prog="praisonai-tools-fcp",
-        description="Final Cut Pro automation tools",
-    )
-    subparsers = parser.add_subparsers(dest="command", help="Available commands")
-
-    # Doctor command
-    doctor_parser = subparsers.add_parser("doctor", help="Run health checks")
-    doctor_parser.add_argument("--json", action="store_true", help="Output as JSON")
-
-    # Autoedit command
-    autoedit_parser = subparsers.add_parser("autoedit", help="Generate and inject FCPXML")
-    autoedit_parser.add_argument(
-        "--instruction", "-i", type=str, help="Editing instruction"
-    )
-    autoedit_parser.add_argument(
-        "--instruction-file", "-f", type=str, help="File containing instruction"
-    )
-    autoedit_parser.add_argument(
-        "--media", "-m", action="append", dest="media_paths", help="Media file path (repeatable)"
-    )
-    autoedit_parser.add_argument(
-        "--project-name", "-n", type=str, default="Untitled Project", help="Project name"
-    )
-    autoedit_parser.add_argument(
-        "--format", type=str, default="1080p25", help="Format preset"
-    )
-    autoedit_parser.add_argument(
-        "--model", type=str, default="gpt-4o", help="OpenAI model"
-    )
-    autoedit_parser.add_argument(
-        "--dry-run", action="store_true", help="Generate without injecting"
-    )
-    autoedit_parser.add_argument(
-        "--simple", action="store_true", help="Use simple concatenation (no LLM)"
-    )
-    autoedit_parser.add_argument(
-        "--print-intent", action="store_true", help="Print EditIntent JSON"
-    )
-    autoedit_parser.add_argument(
-        "--print-fcpxml", action="store_true", help="Print generated FCPXML"
-    )
-    autoedit_parser.add_argument("--json", action="store_true", help="Output as JSON")
-
-    # Daemon command
-    daemon_parser = subparsers.add_parser("daemon", help="Manage injection daemon")
-    daemon_parser.add_argument(
-        "action", choices=["start", "stop", "status"], help="Daemon action"
-    )
-    daemon_parser.add_argument("--json", action="store_true", help="Output as JSON")
-
-    # Bootstrap command
-    bootstrap_parser = subparsers.add_parser(
-        "bootstrap", help="One-time CommandPost setup"
-    )
-    bootstrap_parser.add_argument("--json", action="store_true", help="Output as JSON")
-
-    parsed = parser.parse_args(args)
-
-    if not parsed.command:
-        parser.print_help()
-        return 1
-
-    if parsed.command == "doctor":
-        return cmd_doctor(parsed)
-    elif parsed.command == "autoedit":
-        return cmd_autoedit(parsed)
-    elif parsed.command == "daemon":
-        return cmd_daemon(parsed)
-    elif parsed.command == "bootstrap":
-        return cmd_bootstrap(parsed)
-
-    return 1
-
-
-def cmd_doctor(args: argparse.Namespace) -> int:
-    """Run health checks."""
-    from .doctor import FCPDoctor
-
-    doctor = FCPDoctor()
-    results = doctor.run_all_checks()
-
-    if args.json:
-        summary = doctor.get_summary(results)
-        print(json.dumps(summary, indent=2))
-    else:
-        doctor.print_report(results)
-
-    return 0
-
-
-def cmd_autoedit(args: argparse.Namespace) -> int:
-    """Generate and inject FCPXML."""
-    import os
-    from .fcpxml import FCPXMLGenerator
-    from .prompting import generate_edit_intent, create_simple_intent
-    from .injector import Injector
-    from .commandpost import CommandPostBridge
-
-    instruction = args.instruction
-    if args.instruction_file:
-        with open(args.instruction_file, "r") as f:
-            instruction = f.read().strip()
-
-    media_paths = args.media_paths or []
-
-    if not media_paths:
-        print("Error: At least one --media path is required", file=sys.stderr)
-        return 1
-
-    abs_paths = [os.path.abspath(p) for p in media_paths]
-    missing = [p for p in abs_paths if not os.path.exists(p)]
-    if missing:
-        print(f"Error: Media files not found: {missing}", file=sys.stderr)
-        return 1
-
-    try:
-        if args.simple or not instruction:
-            if not instruction:
-                print("No instruction provided, using simple concatenation mode")
-            intent = create_simple_intent(
-                media_paths=abs_paths,
-                project_name=args.project_name,
-                format_preset=args.format,
-            )
-            warnings = []
-        else:
-            intent, warnings = generate_edit_intent(
-                instruction=instruction,
-                media_paths=abs_paths,
-                project_name=args.project_name,
-                format_preset=args.format,
-                model=args.model,
-            )
-
-        if args.print_intent:
-            print("\n=== EditIntent JSON ===")
-            print(intent.model_dump_json(indent=2))
-
-        generator = FCPXMLGenerator(intent)
-        fcpxml = generator.generate()
-        warnings.extend(generator.get_warnings())
-
-        if args.print_fcpxml:
-            print("\n=== Generated FCPXML ===")
-            print(fcpxml)
-
-        if warnings:
-            print("\n=== Warnings ===")
-            for w in warnings:
-                print(f"  ⚠ {w}")
-
-        if args.dry_run:
-            if args.json:
-                print(json.dumps({
-                    "success": True,
-                    "dry_run": True,
-                    "intent": intent.model_dump(),
-                    "fcpxml_length": len(fcpxml),
-                    "warnings": warnings,
-                }, indent=2))
-            else:
-                print("\n=== Dry Run Complete ===")
-                print(f"FCPXML generated: {len(fcpxml)} characters")
-                print("No injection performed (--dry-run)")
-            return 0
-
-        commandpost = CommandPostBridge()
-        injector = Injector(commandpost=commandpost)
-
-        job_id, fcpxml_path, messages = injector.inject_one_shot(
-            fcpxml_content=fcpxml,
-            instruction=instruction or "Simple concatenation",
-            intent_json=intent.model_dump_json(),
-        )
-
-        if args.json:
-            print(json.dumps({
-                "success": True,
-                "job_id": job_id,
-                "fcpxml_path": fcpxml_path,
-                "messages": messages,
-                "warnings": warnings,
-            }, indent=2))
-        else:
-            print("\n=== Injection Complete ===")
-            print(f"Job ID: {job_id}")
-            print(f"FCPXML: {fcpxml_path}")
-            for msg in messages:
-                print(f"  • {msg}")
-
-        return 0
-
-    except Exception as e:
-        if args.json:
-            print(json.dumps({"success": False, "error": str(e)}, indent=2))
-        else:
-            print(f"Error: {e}", file=sys.stderr)
-        return 1
-
-
-def cmd_daemon(args: argparse.Namespace) -> int:
-    """Manage injection daemon."""
-    from .injector import Injector
-
-    injector = Injector()
-
-    if args.action == "start":
-        if injector.is_daemon_running():
-            msg = "Daemon is already running"
-            if args.json:
-                print(json.dumps({"success": False, "error": msg}))
-            else:
-                print(msg)
-            return 1
-
-        print("Starting daemon...")
-        success = injector.start_daemon(detach=True)
-        if args.json:
-            print(json.dumps({"success": success}))
-        else:
-            print("Daemon started" if success else "Failed to start daemon")
-        return 0 if success else 1
-
-    elif args.action == "stop":
-        success = injector.stop_daemon()
-        if args.json:
-            print(json.dumps({"success": success}))
-        else:
-            print("Daemon stopped" if success else "Daemon not running or failed to stop")
-        return 0 if success else 1
-
-    elif args.action == "status":
-        running = injector.is_daemon_running()
-        state = injector.get_daemon_state()
-
-        if args.json:
-            print(json.dumps({
-                "running": running,
-                "state": {
-                    "pid": state.pid if state else None,
-                    "started_at": state.started_at if state else None,
-                    "watch_folder": state.watch_folder if state else None,
-                    "jobs_processed": state.jobs_processed if state else 0,
-                    "last_job_at": state.last_job_at if state else None,
-                } if state else None,
-            }, indent=2))
-        else:
-            if running and state:
-                print(f"Daemon running (PID: {state.pid})")
-                print(f"  Started: {state.started_at}")
-                print(f"  Watch folder: {state.watch_folder}")
-                print(f"  Jobs processed: {state.jobs_processed}")
-                if state.last_job_at:
-                    print(f"  Last job: {state.last_job_at}")
-            else:
-                print("Daemon not running")
-        return 0
-
-    return 1
-
-
-def cmd_bootstrap(args: argparse.Namespace) -> int:
-    """One-time CommandPost setup."""
-    from .commandpost import CommandPostBridge
-
-    bridge = CommandPostBridge()
-    results = bridge.bootstrap()
-
-    if args.json:
-        print(json.dumps({
-            "results": [{"success": s, "message": m} for s, m in results],
-            "all_success": all(s for s, _ in results),
-        }, indent=2))
-    else:
-        print("\n=== CommandPost Bootstrap ===")
-        for success, message in results:
-            status = "✓" if success else "✗"
-            print(f"  {status} {message}")
-        print()
-        if all(s for s, _ in results):
-            print("Bootstrap complete!")
-        else:
-            print("Some steps failed. See above for details.")
-
-    return 0 if all(s for s, _ in results) else 1
-
-
-if __name__ == "__main__":
-    sys.exit(main())

praisonai_tools/fcp_tool/commandpost.py

@@ -1,361 +0,0 @@
-"""
-CommandPost Bridge for Final Cut Pro Integration
-
-Interfaces with CommandPost's cmdpost CLI to configure watch folders
-and trigger FCPXML auto-import into Final Cut Pro.
-"""
-
-from __future__ import annotations
-
-import os
-import platform
-import shutil
-import subprocess
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Optional
-
-
-@dataclass
-class CommandPostStatus:
-    """Status of CommandPost installation and configuration."""
-    is_macos: bool
-    commandpost_installed: bool
-    cmdpost_available: bool
-    cmdpost_path: Optional[str]
-    watch_folder_configured: bool
-    watch_folder_path: Optional[str]
-    auto_import_enabled: bool
-    errors: list[str]
-
-    @property
-    def is_ready(self) -> bool:
-        """Check if CommandPost is ready for auto-import."""
-        return (
-            self.is_macos
-            and self.commandpost_installed
-            and self.cmdpost_available
-            and self.watch_folder_configured
-            and self.auto_import_enabled
-        )
-
-
-class CommandPostBridge:
-    """
-    Bridge to CommandPost for FCPXML auto-import.
-    
-    CommandPost is a macOS app that extends Final Cut Pro with automation
-    capabilities. This bridge uses the cmdpost CLI to configure watch folders
-    and enable automatic FCPXML import.
-    """
-
-    KNOWN_CMDPOST_PATHS = [
-        "/Applications/CommandPost.app/Contents/Resources/extensions/hs/cmdpost",
-        "/usr/local/bin/cmdpost",
-        os.path.expanduser("~/Applications/CommandPost.app/Contents/Resources/extensions/hs/cmdpost"),
-    ]
-
-    COMMANDPOST_APP_PATHS = [
-        "/Applications/CommandPost.app",
-        os.path.expanduser("~/Applications/CommandPost.app"),
-    ]
-
-    DEFAULT_WATCH_FOLDER = os.path.expanduser("~/.praison/fcp/watch/fcpxml")
-
-    def __init__(self, cmdpost_path: Optional[str] = None, watch_folder: Optional[str] = None):
-        """
-        Initialize the CommandPost bridge.
-        
-        Args:
-            cmdpost_path: Path to cmdpost CLI (auto-detected if not provided)
-            watch_folder: Watch folder path (uses default if not provided)
-        """
-        self.cmdpost_path = cmdpost_path or self._find_cmdpost()
-        self.watch_folder = watch_folder or self.DEFAULT_WATCH_FOLDER
-
-    def _find_cmdpost(self) -> Optional[str]:
-        """Find the cmdpost CLI binary."""
-        cmdpost_in_path = shutil.which("cmdpost")
-        if cmdpost_in_path:
-            return cmdpost_in_path
-
-        for path in self.KNOWN_CMDPOST_PATHS:
-            if os.path.isfile(path) and os.access(path, os.X_OK):
-                return path
-
-        return None
-
-    def _is_commandpost_installed(self) -> bool:
-        """Check if CommandPost app is installed."""
-        for path in self.COMMANDPOST_APP_PATHS:
-            if os.path.isdir(path):
-                return True
-        return False
-
-    def get_status(self) -> CommandPostStatus:
-        """Get the current status of CommandPost configuration."""
-        is_macos = platform.system() == "Darwin"
-        errors = []
-
-        if not is_macos:
-            errors.append("CommandPost is only available on macOS")
-            return CommandPostStatus(
-                is_macos=False,
-                commandpost_installed=False,
-                cmdpost_available=False,
-                cmdpost_path=None,
-                watch_folder_configured=False,
-                watch_folder_path=None,
-                auto_import_enabled=False,
-                errors=errors,
-            )
-
-        commandpost_installed = self._is_commandpost_installed()
-        if not commandpost_installed:
-            errors.append("CommandPost is not installed. Download from https://commandpost.io")
-
-        cmdpost_available = self.cmdpost_path is not None
-        if not cmdpost_available:
-            errors.append("cmdpost CLI not found. Ensure CommandPost is installed and running.")
-
-        watch_folder_configured = os.path.isdir(self.watch_folder)
-        auto_import_enabled = False
-
-        if cmdpost_available and watch_folder_configured:
-            auto_import_enabled = self._check_auto_import_enabled()
-
-        return CommandPostStatus(
-            is_macos=is_macos,
-            commandpost_installed=commandpost_installed,
-            cmdpost_available=cmdpost_available,
-            cmdpost_path=self.cmdpost_path,
-            watch_folder_configured=watch_folder_configured,
-            watch_folder_path=self.watch_folder if watch_folder_configured else None,
-            auto_import_enabled=auto_import_enabled,
-            errors=errors,
-        )
-
-    def _check_auto_import_enabled(self) -> bool:
-        """Check if auto-import is enabled in CommandPost."""
-        if not self.cmdpost_path:
-            return False
-
-        lua_script = '''
-local fcpxmlPlugin = require("cp.plugins")("finalcutpro.watchfolders.fcpxml")
-if fcpxmlPlugin and fcpxmlPlugin.automaticallyImport then
-    print(fcpxmlPlugin.automaticallyImport() and "true" or "false")
-else
-    print("unknown")
-end
-'''
-        try:
-            result = subprocess.run(
-                [self.cmdpost_path, "-c", lua_script],
-                capture_output=True,
-                text=True,
-                timeout=10,
-            )
-            return result.stdout.strip() == "true"
-        except (subprocess.TimeoutExpired, subprocess.SubprocessError):
-            return False
-
-    def setup_watch_folder(self) -> tuple[bool, str]:
-        """
-        Set up the watch folder for FCPXML auto-import.
-        
-        Returns:
-            Tuple of (success, message)
-        """
-        try:
-            Path(self.watch_folder).mkdir(parents=True, exist_ok=True)
-            return True, f"Watch folder created: {self.watch_folder}"
-        except OSError as e:
-            return False, f"Failed to create watch folder: {e}"
-
-    def configure_auto_import(self, enable: bool = True) -> tuple[bool, str]:
-        """
-        Configure CommandPost to automatically import FCPXML files.
-        
-        Args:
-            enable: Whether to enable auto-import
-            
-        Returns:
-            Tuple of (success, message)
-        """
-        if not self.cmdpost_path:
-            return False, "cmdpost CLI not available"
-
-        lua_script = f'''
-local fcpxmlPlugin = require("cp.plugins")("finalcutpro.watchfolders.fcpxml")
-if fcpxmlPlugin and fcpxmlPlugin.automaticallyImport then
-    fcpxmlPlugin.automaticallyImport({"true" if enable else "false"})
-    print("configured")
-else
-    print("plugin_not_found")
-end
-'''
-        try:
-            result = subprocess.run(
-                [self.cmdpost_path, "-c", lua_script],
-                capture_output=True,
-                text=True,
-                timeout=10,
-            )
-            if "configured" in result.stdout:
-                return True, f"Auto-import {'enabled' if enable else 'disabled'}"
-            elif "plugin_not_found" in result.stdout:
-                return False, "FCPXML watch folder plugin not found in CommandPost"
-            else:
-                return False, f"Unexpected response: {result.stdout}"
-        except subprocess.TimeoutExpired:
-            return False, "CommandPost command timed out"
-        except subprocess.SubprocessError as e:
-            return False, f"Failed to configure CommandPost: {e}"
-
-    def add_watch_folder(self) -> tuple[bool, str]:
-        """
-        Add the watch folder to CommandPost's FCPXML watch list.
-        
-        Returns:
-            Tuple of (success, message)
-        """
-        if not self.cmdpost_path:
-            return False, "cmdpost CLI not available"
-
-        escaped_path = self.watch_folder.replace('"', '\\"')
-        lua_script = f'''
-local fcpxmlPlugin = require("cp.plugins")("finalcutpro.watchfolders.fcpxml")
-if fcpxmlPlugin and fcpxmlPlugin.addWatchFolder then
-    fcpxmlPlugin.addWatchFolder("{escaped_path}")
-    print("added")
-elseif fcpxmlPlugin then
-    -- Try alternative method
-    local watchFolders = fcpxmlPlugin.watchFolders
-    if watchFolders and watchFolders.addFolder then
-        watchFolders.addFolder("{escaped_path}")
-        print("added")
-    else
-        print("method_not_found")
-    end
-else
-    print("plugin_not_found")
-end
-'''
-        try:
-            result = subprocess.run(
-                [self.cmdpost_path, "-c", lua_script],
-                capture_output=True,
-                text=True,
-                timeout=10,
-            )
-            if "added" in result.stdout:
-                return True, f"Watch folder added: {self.watch_folder}"
-            elif "method_not_found" in result.stdout:
-                return False, (
-                    "Watch folder API not available. "
-                    "Please add the watch folder manually in CommandPost preferences."
-                )
-            elif "plugin_not_found" in result.stdout:
-                return False, "FCPXML watch folder plugin not found in CommandPost"
-            else:
-                return False, f"Unexpected response: {result.stdout}"
-        except subprocess.TimeoutExpired:
-            return False, "CommandPost command timed out"
-        except subprocess.SubprocessError as e:
-            return False, f"Failed to add watch folder: {e}"
-
-    def trigger_import(self, fcpxml_path: str) -> tuple[bool, str]:
-        """
-        Trigger import of a specific FCPXML file.
-        
-        This is an alternative to watch folder auto-import that directly
-        calls CommandPost to import a file.
-        
-        Args:
-            fcpxml_path: Path to the FCPXML file
-            
-        Returns:
-            Tuple of (success, message)
-        """
-        if not self.cmdpost_path:
-            return False, "cmdpost CLI not available"
-
-        if not os.path.isfile(fcpxml_path):
-            return False, f"FCPXML file not found: {fcpxml_path}"
-
-        escaped_path = fcpxml_path.replace('"', '\\"')
-        lua_script = f'''
-local fcpxmlPlugin = require("cp.plugins")("finalcutpro.watchfolders.fcpxml")
-if fcpxmlPlugin and fcpxmlPlugin.insertFilesIntoFinalCutPro then
-    fcpxmlPlugin.insertFilesIntoFinalCutPro({{"{escaped_path}"}})
-    print("imported")
-elseif fcpxmlPlugin and fcpxmlPlugin.importFile then
-    fcpxmlPlugin.importFile("{escaped_path}")
-    print("imported")
-else
-    print("import_not_available")
-end
-'''
-        try:
-            result = subprocess.run(
-                [self.cmdpost_path, "-c", lua_script],
-                capture_output=True,
-                text=True,
-                timeout=30,
-            )
-            if "imported" in result.stdout:
-                return True, f"FCPXML imported: {fcpxml_path}"
-            elif "import_not_available" in result.stdout:
-                return False, (
-                    "Direct import not available. "
-                    "File will be imported via watch folder if configured."
-                )
-            else:
-                return False, f"Import may have failed: {result.stdout}"
-        except subprocess.TimeoutExpired:
-            return False, "Import command timed out"
-        except subprocess.SubprocessError as e:
-            return False, f"Failed to import: {e}"
-
-    def bring_fcp_to_front(self) -> tuple[bool, str]:
-        """
-        Bring Final Cut Pro to the foreground.
-        
-        Returns:
-            Tuple of (success, message)
-        """
-        if platform.system() != "Darwin":
-            return False, "Only available on macOS"
-
-        try:
-            subprocess.run(
-                ["osascript", "-e", 'tell application "Final Cut Pro" to activate'],
-                capture_output=True,
-                timeout=5,
-            )
-            return True, "Final Cut Pro activated"
-        except subprocess.SubprocessError as e:
-            return False, f"Failed to activate Final Cut Pro: {e}"
-
-    def bootstrap(self) -> list[tuple[bool, str]]:
-        """
-        Perform one-time bootstrap setup for CommandPost integration.
-        
-        Returns:
-            List of (success, message) tuples for each setup step
-        """
-        results = []
-
-        success, msg = self.setup_watch_folder()
-        results.append((success, msg))
-
-        if self.cmdpost_path:
-            success, msg = self.configure_auto_import(True)
-            results.append((success, msg))
-
-            success, msg = self.add_watch_folder()
-            results.append((success, msg))
-        else:
-            results.append((False, "cmdpost CLI not available - skipping configuration"))
-
-        return results

praisonai_tools/fcp_tool/doctor.py

@@ -1,285 +0,0 @@
-"""
-FCP Doctor - Health Checks for Final Cut Pro Integration
-
-Provides diagnostic checks and remediation guidance for the FCP integration.
-"""
-
-from __future__ import annotations
-
-import os
-import platform
-import shutil
-from dataclasses import dataclass
-from typing import Optional
-
-from .commandpost import CommandPostBridge
-
-
-@dataclass
-class CheckResult:
-    """Result of a health check."""
-    name: str
-    passed: bool
-    message: str
-    remediation: Optional[str] = None
-
-
-class FCPDoctor:
-    """
-    Diagnostic tool for FCP integration health checks.
-    
-    Checks for:
-    - macOS environment
-    - Final Cut Pro installation
-    - CommandPost installation
-    - cmdpost CLI availability
-    - Watch folder configuration
-    """
-
-    def __init__(self, commandpost: Optional[CommandPostBridge] = None):
-        """
-        Initialize the doctor.
-        
-        Args:
-            commandpost: CommandPost bridge instance
-        """
-        self.commandpost = commandpost or CommandPostBridge()
-
-    def run_all_checks(self) -> list[CheckResult]:
-        """Run all health checks."""
-        return [
-            self.check_macos(),
-            self.check_final_cut_pro(),
-            self.check_commandpost_app(),
-            self.check_cmdpost_cli(),
-            self.check_watch_folder(),
-            self.check_auto_import(),
-            self.check_openai_api_key(),
-        ]
-
-    def check_macos(self) -> CheckResult:
-        """Check if running on macOS."""
-        is_macos = platform.system() == "Darwin"
-        if is_macos:
-            version = platform.mac_ver()[0]
-            return CheckResult(
-                name="macOS",
-                passed=True,
-                message=f"Running on macOS {version}",
-            )
-        else:
-            return CheckResult(
-                name="macOS",
-                passed=False,
-                message=f"Running on {platform.system()}, not macOS",
-                remediation="FCP integration requires macOS. Other platforms can generate FCPXML but cannot auto-import.",
-            )
-
-    def check_final_cut_pro(self) -> CheckResult:
-        """Check if Final Cut Pro is installed."""
-        fcp_paths = [
-            "/Applications/Final Cut Pro.app",
-            os.path.expanduser("~/Applications/Final Cut Pro.app"),
-        ]
-
-        for path in fcp_paths:
-            if os.path.isdir(path):
-                return CheckResult(
-                    name="Final Cut Pro",
-                    passed=True,
-                    message=f"Found at {path}",
-                )
-
-        return CheckResult(
-            name="Final Cut Pro",
-            passed=False,
-            message="Final Cut Pro not found",
-            remediation="Install Final Cut Pro from the Mac App Store.",
-        )
-
-    def check_commandpost_app(self) -> CheckResult:
-        """Check if CommandPost app is installed."""
-        cp_paths = [
-            "/Applications/CommandPost.app",
-            "/Applications/Utilities/CommandPost.app",
-            os.path.expanduser("~/Applications/CommandPost.app"),
-        ]
-
-        for path in cp_paths:
-            if os.path.isdir(path):
-                return CheckResult(
-                    name="CommandPost App",
-                    passed=True,
-                    message=f"Found at {path}",
-                )
-
-        return CheckResult(
-            name="CommandPost App",
-            passed=False,
-            message="CommandPost not found",
-            remediation="Download CommandPost from https://commandpost.io",
-        )
-
-    def check_cmdpost_cli(self) -> CheckResult:
-        """Check if cmdpost CLI is available."""
-        cmdpost_path = self.commandpost.cmdpost_path
-
-        if cmdpost_path:
-            return CheckResult(
-                name="cmdpost CLI",
-                passed=True,
-                message=f"Found at {cmdpost_path}",
-            )
-
-        cmdpost_in_path = shutil.which("cmdpost")
-        if cmdpost_in_path:
-            return CheckResult(
-                name="cmdpost CLI",
-                passed=True,
-                message=f"Found in PATH at {cmdpost_in_path}",
-            )
-
-        return CheckResult(
-            name="cmdpost CLI",
-            passed=False,
-            message="cmdpost CLI not found",
-            remediation=(
-                "Ensure CommandPost is installed and running. "
-                "The cmdpost CLI is bundled with CommandPost at: "
-                "/Applications/CommandPost.app/Contents/Resources/extensions/hs/cmdpost"
-            ),
-        )
-
-    def check_watch_folder(self) -> CheckResult:
-        """Check if watch folder exists."""
-        watch_folder = self.commandpost.watch_folder
-
-        if os.path.isdir(watch_folder):
-            return CheckResult(
-                name="Watch Folder",
-                passed=True,
-                message=f"Exists at {watch_folder}",
-            )
-
-        return CheckResult(
-            name="Watch Folder",
-            passed=False,
-            message=f"Watch folder not found: {watch_folder}",
-            remediation=f"Run: mkdir -p {watch_folder}",
-        )
-
-    def check_auto_import(self) -> CheckResult:
-        """Check if auto-import is enabled in CommandPost."""
-        status = self.commandpost.get_status()
-
-        if not status.cmdpost_available:
-            return CheckResult(
-                name="Auto-Import",
-                passed=False,
-                message="Cannot check - cmdpost CLI not available",
-                remediation="Install and run CommandPost first.",
-            )
-
-        if status.auto_import_enabled:
-            return CheckResult(
-                name="Auto-Import",
-                passed=True,
-                message="Enabled in CommandPost",
-            )
-
-        return CheckResult(
-            name="Auto-Import",
-            passed=False,
-            message="Auto-import not enabled or could not be verified",
-            remediation=(
-                "Run: praisonai-tools fcp bootstrap\n"
-                "Or manually enable in CommandPost preferences."
-            ),
-        )
-
-    def check_openai_api_key(self) -> CheckResult:
-        """Check if OpenAI API key is configured."""
-        api_key = os.environ.get("OPENAI_API_KEY")
-
-        if api_key:
-            masked = f"...{api_key[-4:]}" if len(api_key) > 4 else "****"
-            return CheckResult(
-                name="OpenAI API Key",
-                passed=True,
-                message=f"Set (ending in {masked})",
-            )
-
-        return CheckResult(
-            name="OpenAI API Key",
-            passed=False,
-            message="OPENAI_API_KEY not set",
-            remediation="Set with: export OPENAI_API_KEY=your-key",
-        )
-
-    def print_report(self, results: Optional[list[CheckResult]] = None) -> bool:
-        """
-        Print a formatted health check report.
-        
-        Args:
-            results: Check results (runs all checks if not provided)
-            
-        Returns:
-            True if all checks passed
-        """
-        if results is None:
-            results = self.run_all_checks()
-
-        print("\n" + "=" * 60)
-        print("FCP Integration Health Check")
-        print("=" * 60 + "\n")
-
-        all_passed = True
-        for result in results:
-            status = "✓" if result.passed else "✗"
-            print(f"  {status} {result.name}: {result.message}")
-            if not result.passed:
-                all_passed = False
-                if result.remediation:
-                    print(f"    → {result.remediation}")
-            print()
-
-        print("=" * 60)
-        if all_passed:
-            print("All checks passed! FCP integration is ready.")
-        else:
-            print("Some checks failed. See remediation steps above.")
-        print("=" * 60 + "\n")
-
-        return all_passed
-
-    def get_summary(self, results: Optional[list[CheckResult]] = None) -> dict:
-        """
-        Get a summary of health check results.
-        
-        Args:
-            results: Check results (runs all checks if not provided)
-            
-        Returns:
-            Summary dictionary
-        """
-        if results is None:
-            results = self.run_all_checks()
-
-        passed = [r for r in results if r.passed]
-        failed = [r for r in results if not r.passed]
-
-        return {
-            "total": len(results),
-            "passed": len(passed),
-            "failed": len(failed),
-            "all_passed": len(failed) == 0,
-            "checks": [
-                {
-                    "name": r.name,
-                    "passed": r.passed,
-                    "message": r.message,
-                    "remediation": r.remediation,
-                }
-                for r in results
-            ],
-        }

praisonai_tools/fcp_tool/fcpxml.py

@@ -1,419 +0,0 @@
-"""
-FCPXML Generator for Final Cut Pro Integration
-
-Converts EditIntent to valid FCPXML 1.11 format.
-"""
-
-from __future__ import annotations
-
-import xml.etree.ElementTree as ET
-from typing import Optional
-from xml.dom import minidom
-
-from .intent import EditIntent, Segment
-
-
-class FCPXMLGenerator:
-    """
-    Generates FCPXML from EditIntent.
-    
-    FCPXML is the interchange format for Final Cut Pro. This generator
-    produces valid FCPXML 1.11 that can be imported into FCP.
-    """
-
-    FCPXML_VERSION = "1.11"
-
-    def __init__(self, intent: EditIntent):
-        """
-        Initialize the generator with an EditIntent.
-        
-        Args:
-            intent: The EditIntent to convert to FCPXML
-        """
-        self.intent = intent
-        self._warnings: list[str] = []
-
-    def generate(self, pretty_print: bool = True) -> str:
-        """
-        Generate FCPXML string from the intent.
-        
-        Args:
-            pretty_print: Whether to format the XML with indentation
-            
-        Returns:
-            FCPXML string
-        """
-        self._warnings = list(self.intent.get_warnings())
-
-        root = ET.Element("fcpxml", version=self.FCPXML_VERSION)
-
-        resources = ET.SubElement(root, "resources")
-        self._add_format_resource(resources)
-        self._add_asset_resources(resources)
-
-        library = ET.SubElement(root, "library")
-        event = ET.SubElement(library, "event", name=self.intent.project.name)
-        self._add_project(event)
-
-        xml_str = ET.tostring(root, encoding="unicode")
-
-        if pretty_print:
-            xml_str = self._pretty_print(xml_str)
-
-        xml_declaration = '<?xml version="1.0" encoding="UTF-8"?>\n'
-        doctype = '<!DOCTYPE fcpxml>\n'
-
-        return xml_declaration + doctype + xml_str
-
-    def get_warnings(self) -> list[str]:
-        """Get warnings generated during FCPXML generation."""
-        return self._warnings
-
-    def _add_format_resource(self, resources: ET.Element) -> None:
-        """Add the format resource definition."""
-        fmt = self.intent.project.format
-
-        ET.SubElement(
-            resources,
-            "format",
-            id="r1",
-            name=f"FFVideoFormat{fmt.height}p{int(fmt.fps)}",
-            frameDuration=fmt.frame_duration_rational,
-            width=str(fmt.width),
-            height=str(fmt.height),
-        )
-        # Note: audioChannels and audioSampleRate are NOT valid on format element per DTD
-
-    def _add_asset_resources(self, resources: ET.Element) -> None:
-        """Add asset resource definitions."""
-        for asset in self.intent.assets:
-            asset_elem = ET.SubElement(
-                resources,
-                "asset",
-                id=asset.id,
-                name=asset.name,
-                uid=asset.uid,
-            )
-            # Note: src is NOT valid on asset element per DTD - only on media-rep
-
-            if asset.has_video:
-                asset_elem.set("hasVideo", "1")
-                if asset.format_ref:
-                    asset_elem.set("format", asset.format_ref)
-                else:
-                    asset_elem.set("format", "r1")
-
-            if asset.has_audio:
-                asset_elem.set("hasAudio", "1")
-                asset_elem.set("audioSources", "1")
-                asset_elem.set("audioChannels", str(asset.audio_channels))
-                # audioRate must be one of: 32k, 44.1k, 48k, 88.2k, 96k, 176.4k, 192k
-                audio_rate_map = {
-                    32000: "32k", 44100: "44.1k", 48000: "48k",
-                    88200: "88.2k", 96000: "96k", 176400: "176.4k", 192000: "192k"
-                }
-                asset_elem.set("audioRate", audio_rate_map.get(asset.audio_rate, "48k"))
-
-            if asset.duration_rational:
-                asset_elem.set("duration", asset.duration_rational)
-
-            ET.SubElement(
-                asset_elem,
-                "media-rep",
-                kind="original-media",
-                src=asset.get_src_url(),
-            )
-
-    def _add_project(self, event: ET.Element) -> None:
-        """Add the project with sequence and spine."""
-        project = ET.SubElement(
-            event,
-            "project",
-            name=self.intent.project.name,
-        )
-
-        total_duration = self._calculate_total_duration()
-
-        sequence = ET.SubElement(
-            project,
-            "sequence",
-            format="r1",
-            duration=total_duration,
-            tcStart="0/2500s",
-            tcFormat="NDF",
-        )
-        sequence.set("audioLayout", self.intent.project.audio.layout.value)
-        # audioRate must be one of: 32k, 44.1k, 48k, 88.2k, 96k, 176.4k, 192k
-        audio_rate_map = {
-            32000: "32k", 44100: "44.1k", 48000: "48k",
-            88200: "88.2k", 96000: "96k", 176400: "176.4k", 192000: "192k"
-        }
-        sequence.set("audioRate", audio_rate_map.get(self.intent.project.audio.rate, "48k"))
-
-        spine = ET.SubElement(sequence, "spine")
-
-        self._add_timeline_clips(spine)
-        # Note: markers are added inside asset-clips, not on sequence
-        # Sequence content must be (note?, spine, metadata?) per DTD
-
-    def _add_timeline_clips(self, spine: ET.Element) -> None:
-        """Add clips to the spine based on timeline segments."""
-        primary_segments = [s for s in self.intent.timeline.segments if s.lane == 0]
-        connected_segments = [s for s in self.intent.timeline.segments if s.lane != 0]
-
-        primary_segments.sort(key=lambda s: self._rational_to_frames(s.offset))
-
-        for i, segment in enumerate(primary_segments):
-            self._add_asset_clip(spine, segment, is_first=(i == 0))
-
-        for segment in connected_segments:
-            self._warnings.append(
-                f"Connected clip (lane={segment.lane}) for asset '{segment.asset_id}' "
-                "added as primary storyline clip - connected clips not fully supported in v1"
-            )
-            self._add_asset_clip(spine, segment)
-
-    def _add_asset_clip(self, parent: ET.Element, segment: Segment, is_first: bool = False) -> None:
-        """Add an asset-clip element for a segment."""
-        asset = self.intent.get_asset_by_id(segment.asset_id)
-        if not asset:
-            self._warnings.append(f"Asset not found for segment: {segment.asset_id}")
-            return
-
-        clip_name = segment.name or asset.name
-
-        clip = ET.SubElement(
-            parent,
-            "asset-clip",
-            name=clip_name,
-            ref=asset.id,
-            offset=segment.offset,
-            duration=segment.duration,
-            start=segment.start,
-        )
-
-        if asset.has_audio:
-            clip.set("audioRole", segment.role.value if segment.role else "dialogue")
-
-        if segment.volume is not None and segment.volume != 1.0:
-            adjust = ET.SubElement(clip, "adjust-volume")
-            adjust.set("amount", f"{segment.volume * 100 - 100:+.1f}dB")
-
-        # Add markers inside the clip (first clip gets all markers)
-        if is_first:
-            for marker in self.intent.timeline.markers:
-                ET.SubElement(
-                    clip,
-                    "marker",
-                    start=marker.start,
-                    duration=marker.duration,
-                    value=marker.name,
-                )
-
-    # Markers are now added inside asset-clips in _add_asset_clip method
-
-    def _calculate_total_duration(self) -> str:
-        """Calculate total timeline duration from segments."""
-        if not self.intent.timeline.segments:
-            return "0/2500s"
-
-        max_end = 0
-        for segment in self.intent.timeline.segments:
-            offset_frames = self._rational_to_frames(segment.offset)
-            duration_frames = self._rational_to_frames(segment.duration)
-            end_frames = offset_frames + duration_frames
-            max_end = max(max_end, end_frames)
-
-        timescale = self.intent.project.format.get_timescale()
-        return f"{max_end}/{timescale}s"
-
-    def _rational_to_frames(self, rational: str) -> int:
-        """Convert rational time to frame count."""
-        parts = rational.rstrip("s").split("/")
-        if len(parts) != 2:
-            return 0
-        return int(parts[0])
-
-    def _pretty_print(self, xml_str: str) -> str:
-        """Format XML with proper indentation."""
-        try:
-            dom = minidom.parseString(xml_str)
-            pretty = dom.toprettyxml(indent="  ")
-            lines = pretty.split("\n")
-            lines = [line for line in lines if line.strip() and not line.startswith("<?xml")]
-            return "\n".join(lines)
-        except Exception:
-            return xml_str
-
-
-def generate_fcpxml(intent: EditIntent, pretty_print: bool = True) -> tuple[str, list[str]]:
-    """
-    Convenience function to generate FCPXML from an EditIntent.
-    
-    Args:
-        intent: The EditIntent to convert
-        pretty_print: Whether to format the XML
-        
-    Returns:
-        Tuple of (fcpxml_string, warnings_list)
-    """
-    generator = FCPXMLGenerator(intent)
-    xml = generator.generate(pretty_print=pretty_print)
-    return xml, generator.get_warnings()
-
-
-def validate_fcpxml_structure(xml_str: str) -> tuple[bool, Optional[str]]:
-    """
-    Validate basic FCPXML structure.
-    
-    Args:
-        xml_str: FCPXML string to validate
-        
-    Returns:
-        Tuple of (is_valid, error_message)
-    """
-    try:
-        lines = xml_str.split("\n")
-        xml_content = "\n".join(
-            line for line in lines 
-            if not line.strip().startswith("<!DOCTYPE")
-        )
-        root = ET.fromstring(xml_content)
-
-        if root.tag != "fcpxml":
-            return False, f"Root element must be 'fcpxml', got '{root.tag}'"
-
-        resources = root.find("resources")
-        if resources is None:
-            return False, "Missing 'resources' element"
-
-        library = root.find("library")
-        if library is None:
-            return False, "Missing 'library' element"
-
-        event = library.find("event")
-        if event is None:
-            return False, "Missing 'event' element in library"
-
-        project = event.find("project")
-        if project is None:
-            return False, "Missing 'project' element in event"
-
-        sequence = project.find("sequence")
-        if sequence is None:
-            return False, "Missing 'sequence' element in project"
-
-        spine = sequence.find("spine")
-        if spine is None:
-            return False, "Missing 'spine' element in sequence"
-
-        return True, None
-
-    except ET.ParseError as e:
-        return False, f"XML parse error: {e}"
-
-
-def align_to_frame_boundary(time_str: str, fps: float = 30.0) -> str:
-    """
-    Align a rational time string to the nearest frame boundary.
-    
-    Args:
-        time_str: Rational time string like "12345/3000s"
-        fps: Frames per second (default 30.0)
-        
-    Returns:
-        Frame-aligned rational time string
-    """
-    parts = time_str.rstrip("s").split("/")
-    if len(parts) != 2:
-        return time_str
-    
-    numerator = int(parts[0])
-    timescale = int(parts[1])
-    
-    # Calculate frame duration in timescale units
-    frame_duration = int(timescale / fps)
-    
-    # Round to nearest frame boundary
-    aligned = round(numerator / frame_duration) * frame_duration
-    
-    return f"{aligned}/{timescale}s"
-
-
-def import_fcpxml_silent(fcpxml_path: str, timeout: int = 30) -> dict:
-    """
-    Import FCPXML into Final Cut Pro with automatic dialog handling.
-    
-    Args:
-        fcpxml_path: Path to the FCPXML file
-        timeout: Maximum seconds to wait for import
-        
-    Returns:
-        Dict with success status and any messages
-    """
-    import subprocess
-    import time
-    import os
-    from datetime import datetime
-    
-    result = {
-        "success": False,
-        "fcpxml_path": fcpxml_path,
-        "messages": [],
-    }
-    
-    if not os.path.exists(fcpxml_path):
-        result["messages"].append(f"File not found: {fcpxml_path}")
-        return result
-    
-    # Open FCPXML in FCP
-    subprocess.run(['open', '-a', 'Final Cut Pro', fcpxml_path])
-    time.sleep(3)
-    
-    # Auto-handle dialogs
-    auto_script = '''
-    tell application "System Events"
-        tell process "Final Cut Pro"
-            set frontmost to true
-            delay 0.5
-            
-            repeat 10 times
-                set windowNames to name of every window
-                
-                if (windowNames as string) contains "Import XML" then
-                    keystroke tab
-                    delay 0.2
-                    keystroke return
-                    delay 2
-                else if (windowNames as string) contains "Open Library" then
-                    key code 53
-                    delay 0.5
-                else if (count of windowNames) > 1 then
-                    keystroke return
-                    delay 1
-                else
-                    exit repeat
-                end if
-            end repeat
-            
-            return "done"
-        end tell
-    end tell
-    '''
-    
-    try:
-        subprocess.run(['osascript', '-e', auto_script], capture_output=True, timeout=timeout)
-        result["messages"].append("Dialog handling completed")
-    except subprocess.TimeoutExpired:
-        result["messages"].append("Dialog handling timed out")
-    
-    # Check if library was modified
-    lib_path = os.path.expanduser("~/Movies/Untitled.fcpbundle/CurrentVersion.flexolibrary")
-    if os.path.exists(lib_path):
-        mtime = datetime.fromtimestamp(os.path.getmtime(lib_path))
-        age = (datetime.now() - mtime).total_seconds()
-        if age < 60:
-            result["success"] = True
-            result["messages"].append(f"Library modified {age:.0f}s ago")
-    
-    return result

praisonai_tools/fcp_tool/injector.py

@@ -1,453 +0,0 @@
-"""
-FCPXML Injector for Final Cut Pro Integration
-
-Manages watch folder delivery and daemon mode for automatic FCPXML injection.
-"""
-
-from __future__ import annotations
-
-import json
-import os
-import signal
-import sys
-import tempfile
-import time
-import uuid
-from dataclasses import asdict, dataclass
-from datetime import datetime
-from enum import Enum
-from pathlib import Path
-from typing import Callable, Optional
-
-from .commandpost import CommandPostBridge
-
-
-class JobStatus(str, Enum):
-    """Job processing status."""
-    PENDING = "pending"
-    PROCESSING = "processing"
-    INJECTED = "injected"
-    FAILED = "failed"
-
-
-@dataclass
-class InjectionJob:
-    """Represents an FCPXML injection job."""
-    job_id: str
-    created_at: str
-    instruction: str
-    intent_path: Optional[str] = None
-    fcpxml_path: Optional[str] = None
-    status: JobStatus = JobStatus.PENDING
-    error: Optional[str] = None
-    completed_at: Optional[str] = None
-
-    def to_dict(self) -> dict:
-        """Convert to dictionary."""
-        d = asdict(self)
-        d["status"] = self.status.value
-        return d
-
-    @classmethod
-    def from_dict(cls, d: dict) -> "InjectionJob":
-        """Create from dictionary."""
-        d = d.copy()
-        d["status"] = JobStatus(d["status"])
-        return cls(**d)
-
-
-@dataclass
-class DaemonState:
-    """State of the daemon process."""
-    pid: int
-    started_at: str
-    watch_folder: str
-    jobs_processed: int = 0
-    last_job_at: Optional[str] = None
-
-
-class Injector:
-    """
-    Manages FCPXML injection into Final Cut Pro via CommandPost.
-    
-    Supports both one-shot injection and daemon mode with watch folder.
-    """
-
-    DEFAULT_BASE_DIR = os.path.expanduser("~/.praison/fcp")
-
-    def __init__(
-        self,
-        base_dir: Optional[str] = None,
-        watch_folder: Optional[str] = None,
-        commandpost: Optional[CommandPostBridge] = None,
-    ):
-        """
-        Initialize the injector.
-        
-        Args:
-            base_dir: Base directory for jobs and outputs
-            watch_folder: Watch folder for CommandPost
-            commandpost: CommandPost bridge instance
-        """
-        self.base_dir = Path(base_dir or self.DEFAULT_BASE_DIR)
-        self.jobs_dir = self.base_dir / "jobs"
-        self.out_dir = self.base_dir / "out"
-        self.watch_folder = Path(watch_folder or self.base_dir / "watch" / "fcpxml")
-        self.pid_file = self.base_dir / "daemon.pid"
-        self.state_file = self.base_dir / "daemon.state"
-
-        self.commandpost = commandpost or CommandPostBridge(
-            watch_folder=str(self.watch_folder)
-        )
-
-        self._ensure_directories()
-
-    def _ensure_directories(self) -> None:
-        """Create necessary directories."""
-        self.jobs_dir.mkdir(parents=True, exist_ok=True)
-        self.out_dir.mkdir(parents=True, exist_ok=True)
-        self.watch_folder.mkdir(parents=True, exist_ok=True)
-
-    def inject_one_shot(
-        self,
-        fcpxml_content: str,
-        job_id: Optional[str] = None,
-        instruction: str = "",
-        intent_json: Optional[str] = None,
-        delete_after_import: bool = False,
-    ) -> tuple[str, str, list[str]]:
-        """
-        Perform one-shot FCPXML injection.
-        
-        Args:
-            fcpxml_content: FCPXML content to inject
-            job_id: Optional job ID (generated if not provided)
-            instruction: Original instruction for logging
-            intent_json: Optional EditIntent JSON for logging
-            delete_after_import: Whether to delete file after import
-            
-        Returns:
-            Tuple of (job_id, fcpxml_path, messages)
-        """
-        job_id = job_id or str(uuid.uuid4())
-        messages = []
-
-        job = InjectionJob(
-            job_id=job_id,
-            created_at=datetime.utcnow().isoformat(),
-            instruction=instruction,
-        )
-
-        if intent_json:
-            intent_path = self.jobs_dir / f"{job_id}_intent.json"
-            intent_path.write_text(intent_json)
-            job.intent_path = str(intent_path)
-            messages.append(f"Intent saved: {intent_path}")
-
-        fcpxml_path = self._write_fcpxml_atomic(fcpxml_content, job_id)
-        job.fcpxml_path = str(fcpxml_path)
-        messages.append(f"FCPXML written: {fcpxml_path}")
-
-        watch_path = self._deliver_to_watch_folder(fcpxml_path, job_id)
-        messages.append(f"Delivered to watch folder: {watch_path}")
-
-        job.status = JobStatus.INJECTED
-        job.completed_at = datetime.utcnow().isoformat()
-
-        job_path = self.jobs_dir / f"{job_id}.json"
-        job_path.write_text(json.dumps(job.to_dict(), indent=2))
-        messages.append(f"Job logged: {job_path}")
-
-        if delete_after_import:
-            try:
-                os.remove(fcpxml_path)
-                messages.append(f"Cleaned up: {fcpxml_path}")
-            except OSError:
-                pass
-
-        return job_id, str(watch_path), messages
-
-    def _write_fcpxml_atomic(self, content: str, job_id: str) -> Path:
-        """
-        Write FCPXML content atomically (write to temp, then rename).
-        
-        Args:
-            content: FCPXML content
-            job_id: Job ID for filename
-            
-        Returns:
-            Path to the written file
-        """
-        final_path = self.out_dir / f"{job_id}.fcpxml"
-
-        fd, tmp_path = tempfile.mkstemp(
-            suffix=".fcpxml.tmp",
-            dir=str(self.out_dir),
-        )
-        try:
-            with os.fdopen(fd, "w", encoding="utf-8") as f:
-                f.write(content)
-            os.rename(tmp_path, final_path)
-        except Exception:
-            if os.path.exists(tmp_path):
-                os.remove(tmp_path)
-            raise
-
-        return final_path
-
-    def _deliver_to_watch_folder(self, source_path: Path, job_id: str) -> Path:
-        """
-        Deliver FCPXML to the watch folder with atomic rename.
-        
-        Args:
-            source_path: Source FCPXML file
-            job_id: Job ID for filename
-            
-        Returns:
-            Path in watch folder
-        """
-        watch_path = self.watch_folder / f"{job_id}.fcpxml"
-
-        fd, tmp_path = tempfile.mkstemp(
-            suffix=".fcpxml.tmp",
-            dir=str(self.watch_folder),
-        )
-        try:
-            with os.fdopen(fd, "w", encoding="utf-8") as f:
-                f.write(source_path.read_text())
-            os.rename(tmp_path, watch_path)
-        except Exception:
-            if os.path.exists(tmp_path):
-                os.remove(tmp_path)
-            raise
-
-        return watch_path
-
-    def get_job(self, job_id: str) -> Optional[InjectionJob]:
-        """Get a job by ID."""
-        job_path = self.jobs_dir / f"{job_id}.json"
-        if not job_path.exists():
-            return None
-        try:
-            return InjectionJob.from_dict(json.loads(job_path.read_text()))
-        except (json.JSONDecodeError, KeyError):
-            return None
-
-    def list_jobs(self, limit: int = 10) -> list[InjectionJob]:
-        """List recent jobs."""
-        jobs = []
-        for job_file in sorted(
-            self.jobs_dir.glob("*.json"),
-            key=lambda p: p.stat().st_mtime,
-            reverse=True,
-        )[:limit]:
-            if "_intent" not in job_file.name:
-                try:
-                    jobs.append(InjectionJob.from_dict(json.loads(job_file.read_text())))
-                except (json.JSONDecodeError, KeyError):
-                    pass
-        return jobs
-
-    def is_daemon_running(self) -> bool:
-        """Check if daemon is running."""
-        if not self.pid_file.exists():
-            return False
-
-        try:
-            pid = int(self.pid_file.read_text().strip())
-            os.kill(pid, 0)
-            return True
-        except (ValueError, ProcessLookupError, PermissionError):
-            return False
-
-    def get_daemon_state(self) -> Optional[DaemonState]:
-        """Get daemon state if running."""
-        if not self.is_daemon_running():
-            return None
-
-        if not self.state_file.exists():
-            return None
-
-        try:
-            data = json.loads(self.state_file.read_text())
-            return DaemonState(**data)
-        except (json.JSONDecodeError, TypeError):
-            return None
-
-    def start_daemon(
-        self,
-        detach: bool = False,
-        poll_interval: float = 1.0,
-        on_job: Optional[Callable[[InjectionJob], None]] = None,
-    ) -> bool:
-        """
-        Start the daemon process.
-        
-        Args:
-            detach: Whether to detach from terminal
-            poll_interval: Seconds between poll cycles
-            on_job: Callback when job is processed
-            
-        Returns:
-            True if started successfully
-        """
-        if self.is_daemon_running():
-            return False
-
-        if detach:
-            pid = os.fork()
-            if pid > 0:
-                return True
-            os.setsid()
-            pid = os.fork()
-            if pid > 0:
-                sys.exit(0)
-
-        self.pid_file.write_text(str(os.getpid()))
-
-        state = DaemonState(
-            pid=os.getpid(),
-            started_at=datetime.utcnow().isoformat(),
-            watch_folder=str(self.watch_folder),
-        )
-        self.state_file.write_text(json.dumps(asdict(state)))
-
-        self._running = True
-
-        def handle_signal(signum, frame):
-            self._running = False
-
-        signal.signal(signal.SIGTERM, handle_signal)
-        signal.signal(signal.SIGINT, handle_signal)
-
-        try:
-            self._daemon_loop(poll_interval, on_job, state)
-        finally:
-            self._cleanup_daemon()
-
-        return True
-
-    def _daemon_loop(
-        self,
-        poll_interval: float,
-        on_job: Optional[Callable[[InjectionJob], None]],
-        state: DaemonState,
-    ) -> None:
-        """Main daemon loop."""
-        pending_dir = self.base_dir / "pending"
-        pending_dir.mkdir(exist_ok=True)
-
-        while self._running:
-            for pending_file in pending_dir.glob("*.json"):
-                try:
-                    job_data = json.loads(pending_file.read_text())
-                    job = InjectionJob.from_dict(job_data)
-
-                    if job.fcpxml_path and os.path.exists(job.fcpxml_path):
-                        self._deliver_to_watch_folder(
-                            Path(job.fcpxml_path),
-                            job.job_id,
-                        )
-                        job.status = JobStatus.INJECTED
-                        job.completed_at = datetime.utcnow().isoformat()
-                    else:
-                        job.status = JobStatus.FAILED
-                        job.error = "FCPXML file not found"
-
-                    job_path = self.jobs_dir / f"{job.job_id}.json"
-                    job_path.write_text(json.dumps(job.to_dict(), indent=2))
-
-                    pending_file.unlink()
-
-                    state.jobs_processed += 1
-                    state.last_job_at = datetime.utcnow().isoformat()
-                    self.state_file.write_text(json.dumps(asdict(state)))
-
-                    if on_job:
-                        on_job(job)
-
-                except Exception as e:
-                    try:
-                        error_path = pending_dir / f"{pending_file.stem}_error.txt"
-                        error_path.write_text(str(e))
-                        pending_file.unlink()
-                    except Exception:
-                        pass
-
-            time.sleep(poll_interval)
-
-    def _cleanup_daemon(self) -> None:
-        """Clean up daemon files."""
-        try:
-            self.pid_file.unlink(missing_ok=True)
-        except Exception:
-            pass
-
-    def stop_daemon(self) -> bool:
-        """
-        Stop the daemon process.
-        
-        Returns:
-            True if stopped successfully
-        """
-        if not self.pid_file.exists():
-            return False
-
-        try:
-            pid = int(self.pid_file.read_text().strip())
-            os.kill(pid, signal.SIGTERM)
-
-            for _ in range(50):
-                time.sleep(0.1)
-                try:
-                    os.kill(pid, 0)
-                except ProcessLookupError:
-                    break
-
-            self.pid_file.unlink(missing_ok=True)
-            return True
-
-        except (ValueError, ProcessLookupError, PermissionError):
-            self.pid_file.unlink(missing_ok=True)
-            return False
-
-    def submit_job(
-        self,
-        fcpxml_content: str,
-        instruction: str = "",
-        intent_json: Optional[str] = None,
-    ) -> str:
-        """
-        Submit a job to the daemon queue.
-        
-        Args:
-            fcpxml_content: FCPXML content
-            instruction: Original instruction
-            intent_json: Optional EditIntent JSON
-            
-        Returns:
-            Job ID
-        """
-        job_id = str(uuid.uuid4())
-
-        fcpxml_path = self._write_fcpxml_atomic(fcpxml_content, job_id)
-
-        job = InjectionJob(
-            job_id=job_id,
-            created_at=datetime.utcnow().isoformat(),
-            instruction=instruction,
-            fcpxml_path=str(fcpxml_path),
-        )
-
-        if intent_json:
-            intent_path = self.jobs_dir / f"{job_id}_intent.json"
-            intent_path.write_text(intent_json)
-            job.intent_path = str(intent_path)
-
-        pending_dir = self.base_dir / "pending"
-        pending_dir.mkdir(exist_ok=True)
-
-        pending_path = pending_dir / f"{job_id}.json"
-        pending_path.write_text(json.dumps(job.to_dict(), indent=2))
-
-        return job_id

praisonai_tools/fcp_tool/intent.py

@@ -1,337 +0,0 @@
-"""
-EditIntent Pydantic Schema for Final Cut Pro Integration
-
-This module defines the structured data models for representing video editing
-intents that can be converted to FCPXML for Final Cut Pro.
-"""
-
-from __future__ import annotations
-
-import hashlib
-import os
-import re
-from enum import Enum
-from typing import Any, Optional
-from urllib.parse import quote
-
-from pydantic import BaseModel, Field, field_validator, model_validator
-
-
-class AudioRole(str, Enum):
-    """Audio roles supported in FCPXML."""
-    DIALOGUE = "dialogue"
-    MUSIC = "music"
-    EFFECTS = "effects"
-
-
-class AudioLayout(str, Enum):
-    """Audio channel layouts."""
-    MONO = "mono"
-    STEREO = "stereo"
-    SURROUND = "surround"
-
-
-class ProjectFormat(BaseModel):
-    """Video format specification for the project."""
-    width: int = Field(default=1920, ge=1, description="Frame width in pixels")
-    height: int = Field(default=1080, ge=1, description="Frame height in pixels")
-    fps: float = Field(default=25.0, gt=0, description="Frames per second")
-    frame_duration_rational: Optional[str] = Field(
-        default=None,
-        description="Rational frame duration (e.g., '100/2500s'). Auto-calculated if not provided."
-    )
-
-    @model_validator(mode="after")
-    def calculate_frame_duration(self) -> "ProjectFormat":
-        """Auto-calculate rational frame duration from fps if not provided."""
-        if self.frame_duration_rational is None:
-            timescale = int(self.fps * 100)
-            self.frame_duration_rational = f"100/{timescale}s"
-        return self
-
-    def get_timescale(self) -> int:
-        """Get the timescale for rational time calculations."""
-        return int(self.fps * 100)
-
-
-class AudioSettings(BaseModel):
-    """Audio settings for the project."""
-    layout: AudioLayout = Field(default=AudioLayout.STEREO)
-    rate: int = Field(default=48000, description="Audio sample rate in Hz")
-    channels: int = Field(default=2, ge=1, description="Number of audio channels")
-
-
-class Asset(BaseModel):
-    """Represents a media asset (video/audio file)."""
-    id: str = Field(description="Internal reference ID (e.g., 'r2', 'r3')")
-    name: str = Field(description="Display name for the asset")
-    path: str = Field(description="Absolute local file path")
-    uid: Optional[str] = Field(default=None, description="Stable hash UID for the asset")
-    has_video: bool = Field(default=True, description="Whether asset contains video")
-    has_audio: bool = Field(default=True, description="Whether asset contains audio")
-    format_ref: Optional[str] = Field(
-        default=None,
-        description="Reference to format definition (e.g., 'r1')"
-    )
-    audio_rate: int = Field(default=48000, description="Audio sample rate")
-    audio_channels: int = Field(default=2, description="Number of audio channels")
-    duration_rational: Optional[str] = Field(
-        default=None,
-        description="Asset duration as rational time (e.g., '12000/2500s')"
-    )
-
-    @field_validator("path")
-    @classmethod
-    def validate_path(cls, v: str) -> str:
-        """Validate that path is absolute."""
-        if not os.path.isabs(v):
-            raise ValueError(f"Asset path must be absolute: {v}")
-        return v
-
-    @model_validator(mode="after")
-    def generate_uid(self) -> "Asset":
-        """Generate stable UID from path if not provided."""
-        if self.uid is None:
-            self.uid = hashlib.sha256(self.path.encode()).hexdigest()[:32].upper()
-        return self
-
-    def get_src_url(self) -> str:
-        """Get file:// URL for the asset path."""
-        return f"file://{quote(self.path, safe='/:')}"
-
-
-class Segment(BaseModel):
-    """Represents a timeline segment (clip placement)."""
-    asset_id: str = Field(description="Reference to asset ID")
-    offset: str = Field(
-        description="Timeline position as rational time (e.g., '0/2500s')"
-    )
-    start: str = Field(
-        default="0/2500s",
-        description="Source start point as rational time"
-    )
-    duration: str = Field(
-        description="Segment duration as rational time (e.g., '5000/2500s')"
-    )
-    lane: int = Field(default=0, description="Timeline lane (0 = primary storyline)")
-    role: Optional[AudioRole] = Field(default=None, description="Audio role assignment")
-    volume: Optional[float] = Field(
-        default=None,
-        ge=0.0,
-        le=2.0,
-        description="Volume adjustment (1.0 = unity gain)"
-    )
-    name: Optional[str] = Field(default=None, description="Optional clip name override")
-
-    @field_validator("offset", "start", "duration")
-    @classmethod
-    def validate_rational_time(cls, v: str) -> str:
-        """Validate rational time format."""
-        if not re.match(r"^\d+/\d+s$", v):
-            raise ValueError(f"Invalid rational time format: {v}. Expected format: '1234/2500s'")
-        return v
-
-
-class Marker(BaseModel):
-    """Represents a timeline marker (chapter point)."""
-    name: str = Field(description="Marker name/label")
-    start: str = Field(description="Marker position as rational time")
-    duration: str = Field(default="0/2500s", description="Marker duration")
-
-    @field_validator("start", "duration")
-    @classmethod
-    def validate_rational_time(cls, v: str) -> str:
-        """Validate rational time format."""
-        if not re.match(r"^\d+/\d+s$", v):
-            raise ValueError(f"Invalid rational time format: {v}. Expected format: '1234/2500s'")
-        return v
-
-
-class Operations(BaseModel):
-    """Optional processing operations (may not all be implemented)."""
-    remove_pauses_over_seconds: Optional[float] = Field(
-        default=None,
-        ge=0.0,
-        description="Remove pauses longer than this duration (NOT IMPLEMENTED)"
-    )
-    loudness_target_lufs: Optional[float] = Field(
-        default=None,
-        le=0.0,
-        description="Target loudness in LUFS (NOT IMPLEMENTED)"
-    )
-    zoom_punches: Optional[list[dict[str, Any]]] = Field(
-        default=None,
-        description="Zoom punch-in effects (NOT IMPLEMENTED)"
-    )
-
-    def get_unimplemented_warnings(self) -> list[str]:
-        """Return warnings for operations that are requested but not implemented."""
-        warnings = []
-        if self.remove_pauses_over_seconds is not None:
-            warnings.append(
-                f"Operation 'remove_pauses_over_seconds={self.remove_pauses_over_seconds}' "
-                "is NOT IMPLEMENTED - pauses will not be removed"
-            )
-        if self.loudness_target_lufs is not None:
-            warnings.append(
-                f"Operation 'loudness_target_lufs={self.loudness_target_lufs}' "
-                "is NOT IMPLEMENTED - loudness will not be adjusted"
-            )
-        if self.zoom_punches:
-            warnings.append(
-                f"Operation 'zoom_punches' with {len(self.zoom_punches)} entries "
-                "is NOT IMPLEMENTED - zoom effects will not be applied"
-            )
-        return warnings
-
-
-class Timeline(BaseModel):
-    """Timeline structure containing segments and markers."""
-    segments: list[Segment] = Field(default_factory=list)
-    markers: list[Marker] = Field(default_factory=list)
-
-
-class Project(BaseModel):
-    """Project metadata."""
-    name: str = Field(description="Project name")
-    format: ProjectFormat = Field(default_factory=ProjectFormat)
-    audio: AudioSettings = Field(default_factory=AudioSettings)
-
-
-class EditIntent(BaseModel):
-    """
-    Complete edit intent specification.
-    
-    This is the top-level schema that represents a complete video editing
-    instruction that can be converted to FCPXML.
-    """
-    version: str = Field(default="1", description="Schema version")
-    project: Project = Field(description="Project metadata")
-    assets: list[Asset] = Field(default_factory=list, description="Media assets")
-    timeline: Timeline = Field(default_factory=Timeline, description="Timeline structure")
-    operations: Optional[Operations] = Field(
-        default=None,
-        description="Optional processing operations"
-    )
-    agent_id: Optional[str] = Field(default=None, description="Agent ID for attribution")
-    run_id: Optional[str] = Field(default=None, description="Run ID for attribution")
-    session_id: Optional[str] = Field(default=None, description="Session ID for attribution")
-    missing_inputs: Optional[list[str]] = Field(
-        default=None,
-        description="List of missing inputs that were requested but not provided"
-    )
-    needs_user_timestamps: bool = Field(
-        default=False,
-        description="True if user needs to provide specific timestamps"
-    )
-
-    @model_validator(mode="after")
-    def validate_asset_references(self) -> "EditIntent":
-        """Validate that all segment asset_ids reference valid assets."""
-        asset_ids = {asset.id for asset in self.assets}
-        for segment in self.timeline.segments:
-            if segment.asset_id not in asset_ids:
-                raise ValueError(
-                    f"Segment references unknown asset_id '{segment.asset_id}'. "
-                    f"Valid asset IDs: {asset_ids}"
-                )
-        return self
-
-    def get_warnings(self) -> list[str]:
-        """Get all warnings for this intent."""
-        warnings = []
-        if self.operations:
-            warnings.extend(self.operations.get_unimplemented_warnings())
-        if self.missing_inputs:
-            warnings.append(
-                f"Missing inputs requested: {', '.join(self.missing_inputs)}"
-            )
-        if self.needs_user_timestamps:
-            warnings.append(
-                "User timestamps needed - using default concatenation order"
-            )
-        return warnings
-
-    def get_asset_by_id(self, asset_id: str) -> Optional[Asset]:
-        """Get an asset by its ID."""
-        for asset in self.assets:
-            if asset.id == asset_id:
-                return asset
-        return None
-
-    @classmethod
-    def get_json_schema(cls) -> dict:
-        """Get the JSON schema for this model."""
-        return cls.model_json_schema()
-
-
-def seconds_to_rational(seconds: float, fps: float = 25.0) -> str:
-    """
-    Convert seconds to rational time string.
-    
-    Args:
-        seconds: Time in seconds
-        fps: Frames per second for timescale calculation
-        
-    Returns:
-        Rational time string (e.g., '2500/2500s' for 1 second at 25fps)
-    """
-    timescale = int(fps * 100)
-    frames = int(round(seconds * timescale))
-    return f"{frames}/{timescale}s"
-
-
-def rational_to_seconds(rational: str) -> float:
-    """
-    Convert rational time string to seconds.
-    
-    Args:
-        rational: Rational time string (e.g., '2500/2500s')
-        
-    Returns:
-        Time in seconds
-    """
-    match = re.match(r"^(\d+)/(\d+)s$", rational)
-    if not match:
-        raise ValueError(f"Invalid rational time format: {rational}")
-    numerator = int(match.group(1))
-    denominator = int(match.group(2))
-    return numerator / denominator
-
-
-FORMAT_PRESETS = {
-    "1080p25": ProjectFormat(width=1920, height=1080, fps=25.0),
-    "1080p30": ProjectFormat(width=1920, height=1080, fps=30.0),
-    "1080p24": ProjectFormat(width=1920, height=1080, fps=24.0),
-    "1080p50": ProjectFormat(width=1920, height=1080, fps=50.0),
-    "1080p60": ProjectFormat(width=1920, height=1080, fps=60.0),
-    "4k25": ProjectFormat(width=3840, height=2160, fps=25.0),
-    "4k30": ProjectFormat(width=3840, height=2160, fps=30.0),
-    "4k24": ProjectFormat(width=3840, height=2160, fps=24.0),
-    "4k50": ProjectFormat(width=3840, height=2160, fps=50.0),
-    "4k60": ProjectFormat(width=3840, height=2160, fps=60.0),
-    "720p25": ProjectFormat(width=1280, height=720, fps=25.0),
-    "720p30": ProjectFormat(width=1280, height=720, fps=30.0),
-}
-
-
-def get_format_preset(name: str) -> ProjectFormat:
-    """Get a format preset by name."""
-    if name not in FORMAT_PRESETS:
-        raise ValueError(
-            f"Unknown format preset: {name}. "
-            f"Available presets: {', '.join(FORMAT_PRESETS.keys())}"
-        )
-    return FORMAT_PRESETS[name].model_copy()
-
-
-# Rebuild models to resolve forward references
-ProjectFormat.model_rebuild()
-AudioSettings.model_rebuild()
-Asset.model_rebuild()
-Segment.model_rebuild()
-Marker.model_rebuild()
-Operations.model_rebuild()
-Timeline.model_rebuild()
-Project.model_rebuild()
-EditIntent.model_rebuild()

praisonai_tools/fcp_tool/prompting.py

@@ -1,401 +0,0 @@
-"""
-LLM Prompting for Final Cut Pro Integration
-
-Converts natural language instructions to EditIntent JSON using OpenAI SDK.
-"""
-
-from __future__ import annotations
-
-import json
-import os
-from typing import Any, Optional
-
-from pydantic import ValidationError
-
-from .intent import (
-    Asset,
-    AudioRole,
-    AudioSettings,
-    EditIntent,
-    Project,
-    ProjectFormat,
-    Segment,
-    Timeline,
-    get_format_preset,
-)
-
-
-EDIT_INTENT_SCHEMA = EditIntent.model_json_schema()
-
-SYSTEM_PROMPT = """You are an expert video editor assistant that converts natural language editing instructions into structured JSON for Final Cut Pro.
-
-You MUST output ONLY valid JSON that conforms to the EditIntent schema. No prose, no explanations, just JSON.
-
-## CRITICAL RULES:
-1. ONLY use media files from the provided media_paths list. NEVER invent file paths.
-2. If the user requests media not in the list, add it to "missing_inputs" array.
-3. For time-based edits without specific timestamps, set "needs_user_timestamps": true and use default concatenation order.
-4. Use rational time format for all time values: "numerator/denominators" (e.g., "2500/2500s" = 1 second at 25fps)
-5. Default timescale is fps * 100 (e.g., 25fps → 2500)
-
-## CAPABILITIES (v1):
-- Create projects with primary storyline
-- Place asset clips in sequence
-- Set audio roles (dialogue, music, effects)
-- Add chapter markers
-- Basic volume adjustments
-
-## NOT IMPLEMENTED (store in operations but will warn):
-- remove_pauses_over_seconds: Silence/pause removal
-- loudness_target_lufs: Audio normalization
-- zoom_punches: Ken Burns / zoom effects
-
-## SCHEMA STRUCTURE:
-{schema}
-
-## EXAMPLE OUTPUT:
-```json
-{{
-  "version": "1",
-  "project": {{
-    "name": "My Edit",
-    "format": {{"width": 1920, "height": 1080, "fps": 25.0}},
-    "audio": {{"layout": "stereo", "rate": 48000, "channels": 2}}
-  }},
-  "assets": [
-    {{
-      "id": "r2",
-      "name": "interview",
-      "path": "/path/to/interview.mov",
-      "has_video": true,
-      "has_audio": true
-    }}
-  ],
-  "timeline": {{
-    "segments": [
-      {{
-        "asset_id": "r2",
-        "offset": "0/2500s",
-        "start": "0/2500s",
-        "duration": "5000/2500s",
-        "lane": 0,
-        "role": "dialogue"
-      }}
-    ],
-    "markers": []
-  }}
-}}
-```
-"""
-
-USER_PROMPT_TEMPLATE = """## INSTRUCTION:
-{instruction}
-
-## AVAILABLE MEDIA FILES:
-{media_list}
-
-## PROJECT SETTINGS:
-- Project name: {project_name}
-- Format: {format_info}
-
-Generate the EditIntent JSON now. Output ONLY the JSON, no other text."""
-
-
-def _get_openai_client():
-    """Lazy import and create OpenAI client."""
-    try:
-        from openai import OpenAI
-    except ImportError:
-        raise ImportError(
-            "OpenAI SDK not installed. Install with: pip install openai"
-        )
-
-    api_key = os.environ.get("OPENAI_API_KEY")
-    if not api_key:
-        raise ValueError(
-            "OPENAI_API_KEY environment variable not set. "
-            "Set it with: export OPENAI_API_KEY=your-key"
-        )
-
-    return OpenAI(api_key=api_key)
-
-
-def generate_edit_intent(
-    instruction: str,
-    media_paths: list[str],
-    project_name: str = "Untitled Project",
-    format_preset: Optional[str] = None,
-    width: Optional[int] = None,
-    height: Optional[int] = None,
-    fps: Optional[float] = None,
-    model: str = "gpt-4o",
-    max_retries: int = 3,
-    agent_id: Optional[str] = None,
-    run_id: Optional[str] = None,
-    session_id: Optional[str] = None,
-) -> tuple[EditIntent, list[str]]:
-    """
-    Generate EditIntent from natural language instruction using OpenAI.
-    
-    Args:
-        instruction: Natural language editing instruction
-        media_paths: List of available media file paths
-        project_name: Name for the project
-        format_preset: Format preset name (e.g., "1080p25")
-        width: Custom width (overrides preset)
-        height: Custom height (overrides preset)
-        fps: Custom fps (overrides preset)
-        model: OpenAI model to use
-        max_retries: Maximum retry attempts for validation
-        agent_id: Agent ID for attribution
-        run_id: Run ID for attribution
-        session_id: Session ID for attribution
-        
-    Returns:
-        Tuple of (EditIntent, warnings)
-    """
-    client = _get_openai_client()
-    warnings = []
-
-    for path in media_paths:
-        if not os.path.isabs(path):
-            raise ValueError(f"Media path must be absolute: {path}")
-        if not os.path.exists(path):
-            warnings.append(f"Media file not found (will be referenced anyway): {path}")
-
-    if format_preset:
-        project_format = get_format_preset(format_preset)
-    else:
-        project_format = ProjectFormat(
-            width=width or 1920,
-            height=height or 1080,
-            fps=fps or 25.0,
-        )
-
-    if width:
-        project_format.width = width
-    if height:
-        project_format.height = height
-    if fps:
-        project_format.fps = fps
-
-    media_list = "\n".join(f"- {path}" for path in media_paths) or "No media files provided"
-    format_info = f"{project_format.width}x{project_format.height} @ {project_format.fps}fps"
-
-    system_prompt = SYSTEM_PROMPT.format(schema=json.dumps(EDIT_INTENT_SCHEMA, indent=2))
-    user_prompt = USER_PROMPT_TEMPLATE.format(
-        instruction=instruction,
-        media_list=media_list,
-        project_name=project_name,
-        format_info=format_info,
-    )
-
-    last_error = None
-    for attempt in range(max_retries):
-        try:
-            if attempt > 0:
-                user_prompt = f"{user_prompt}\n\n## PREVIOUS ERROR (attempt {attempt}):\n{last_error}\n\nPlease fix the JSON and try again."
-
-            response = client.chat.completions.create(
-                model=model,
-                messages=[
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": user_prompt},
-                ],
-                response_format={"type": "json_object"},
-                temperature=0.1,
-            )
-
-            content = response.choices[0].message.content
-            if not content:
-                last_error = "Empty response from LLM"
-                continue
-
-            try:
-                data = json.loads(content)
-            except json.JSONDecodeError as e:
-                last_error = f"Invalid JSON: {e}"
-                continue
-
-            data = _normalize_intent_data(data, media_paths, project_name, project_format)
-
-            intent = EditIntent.model_validate(data)
-
-            intent.agent_id = agent_id
-            intent.run_id = run_id
-            intent.session_id = session_id
-
-            warnings.extend(intent.get_warnings())
-
-            return intent, warnings
-
-        except ValidationError as e:
-            last_error = f"Schema validation error: {e}"
-            continue
-        except Exception as e:
-            last_error = f"Error: {e}"
-            continue
-
-    raise ValueError(
-        f"Failed to generate valid EditIntent after {max_retries} attempts. "
-        f"Last error: {last_error}"
-    )
-
-
-def _normalize_intent_data(
-    data: dict[str, Any],
-    media_paths: list[str],
-    project_name: str,
-    project_format: ProjectFormat,
-) -> dict[str, Any]:
-    """
-    Normalize and fill in missing fields in the intent data.
-    
-    Args:
-        data: Raw data from LLM
-        media_paths: Available media paths
-        project_name: Project name
-        project_format: Project format settings
-        
-    Returns:
-        Normalized data dictionary
-    """
-    if "version" not in data:
-        data["version"] = "1"
-
-    if "project" not in data:
-        data["project"] = {}
-
-    if "name" not in data["project"]:
-        data["project"]["name"] = project_name
-
-    if "format" not in data["project"]:
-        data["project"]["format"] = project_format.model_dump()
-    else:
-        fmt = data["project"]["format"]
-        if "width" not in fmt:
-            fmt["width"] = project_format.width
-        if "height" not in fmt:
-            fmt["height"] = project_format.height
-        if "fps" not in fmt:
-            fmt["fps"] = project_format.fps
-
-    if "audio" not in data["project"]:
-        data["project"]["audio"] = {"layout": "stereo", "rate": 48000, "channels": 2}
-
-    if "assets" not in data or not data["assets"]:
-        data["assets"] = []
-        for i, path in enumerate(media_paths):
-            ext = os.path.splitext(path)[1].lower()
-            has_video = ext in (".mov", ".mp4", ".m4v", ".avi", ".mkv", ".webm")
-            has_audio = ext in (".wav", ".mp3", ".aac", ".m4a", ".aiff") or has_video
-
-            data["assets"].append({
-                "id": f"r{i + 2}",
-                "name": os.path.basename(path),
-                "path": path,
-                "has_video": has_video,
-                "has_audio": has_audio,
-            })
-
-    for asset in data["assets"]:
-        if "id" not in asset:
-            asset["id"] = f"r{data['assets'].index(asset) + 2}"
-        if "name" not in asset:
-            asset["name"] = os.path.basename(asset.get("path", "unknown"))
-
-    if "timeline" not in data:
-        data["timeline"] = {"segments": [], "markers": []}
-
-    if "segments" not in data["timeline"]:
-        data["timeline"]["segments"] = []
-
-    if "markers" not in data["timeline"]:
-        data["timeline"]["markers"] = []
-
-    if not data["timeline"]["segments"] and data["assets"]:
-        fps = data["project"]["format"].get("fps", 25.0)
-        timescale = int(fps * 100)
-        offset = 0
-
-        for asset in data["assets"]:
-            duration_seconds = 10.0
-            duration_frames = int(duration_seconds * timescale)
-
-            data["timeline"]["segments"].append({
-                "asset_id": asset["id"],
-                "offset": f"{offset}/{timescale}s",
-                "start": f"0/{timescale}s",
-                "duration": f"{duration_frames}/{timescale}s",
-                "lane": 0,
-            })
-            offset += duration_frames
-
-    return data
-
-
-def create_simple_intent(
-    media_paths: list[str],
-    project_name: str = "Simple Project",
-    format_preset: str = "1080p25",
-) -> EditIntent:
-    """
-    Create a simple EditIntent that concatenates all media files.
-    
-    This is useful for testing or when LLM is not available.
-    
-    Args:
-        media_paths: List of media file paths
-        project_name: Project name
-        format_preset: Format preset name
-        
-    Returns:
-        EditIntent with all media concatenated
-    """
-    project_format = get_format_preset(format_preset)
-    timescale = project_format.get_timescale()
-
-    assets = []
-    segments = []
-    offset = 0
-
-    for i, path in enumerate(media_paths):
-        if not os.path.isabs(path):
-            raise ValueError(f"Media path must be absolute: {path}")
-
-        ext = os.path.splitext(path)[1].lower()
-        has_video = ext in (".mov", ".mp4", ".m4v", ".avi", ".mkv", ".webm")
-        has_audio = ext in (".wav", ".mp3", ".aac", ".m4a", ".aiff") or has_video
-
-        asset_id = f"r{i + 2}"
-        assets.append(Asset(
-            id=asset_id,
-            name=os.path.basename(path),
-            path=path,
-            has_video=has_video,
-            has_audio=has_audio,
-            format_ref="r1" if has_video else None,
-        ))
-
-        duration_frames = 10 * timescale
-
-        segments.append(Segment(
-            asset_id=asset_id,
-            offset=f"{offset}/{timescale}s",
-            start=f"0/{timescale}s",
-            duration=f"{duration_frames}/{timescale}s",
-            lane=0,
-            role=AudioRole.DIALOGUE if has_audio else None,
-        ))
-        offset += duration_frames
-
-    return EditIntent(
-        version="1",
-        project=Project(
-            name=project_name,
-            format=project_format,
-            audio=AudioSettings(),
-        ),
-        assets=assets,
-        timeline=Timeline(segments=segments, markers=[]),
-    )

praisonai_tools/n8n/__init__.py

@@ -1,23 +0,0 @@
-"""n8n Workflow Automation Tools for PraisonAI Agents.
-
-Bidirectional integration between PraisonAI agents and n8n workflow automation platform.
-
-Usage:
-    from praisonai_tools.n8n import n8n_workflow
-    
-    # Agent uses n8n workflow
-    result = n8n_workflow(
-        workflow_id="my-workflow-id",
-        input_data={"message": "Hello from PraisonAI"},
-        n8n_url="http://localhost:5678",
-        api_key="your-n8n-api-key"
-    )
-"""
-
-from .n8n_workflow import n8n_workflow, n8n_list_workflows, N8nWorkflowTool
-
-__all__ = [
-    "n8n_workflow",
-    "n8n_list_workflows",
-    "N8nWorkflowTool",
-]

praisonai_tools/n8n/n8n_workflow.py

@@ -1,278 +0,0 @@
-"""n8n Workflow Execution Tool for PraisonAI Agents.
-
-Enables PraisonAI agents to execute n8n workflows, providing access to
-n8n's 400+ integrations (Slack, Gmail, Notion, databases, APIs, etc.).
-
-Usage:
-    from praisonai_tools.n8n import n8n_workflow
-    
-    # Execute n8n workflow
-    result = n8n_workflow(
-        workflow_id="workflow-123",
-        input_data={"message": "Hello from PraisonAI"},
-        n8n_url="http://localhost:5678",
-        api_key="your-api-key"
-    )
-
-Environment Variables:
-    N8N_URL: n8n instance URL (default: http://localhost:5678)
-    N8N_API_KEY: n8n API key for authentication
-"""
-
-import os
-import logging
-from typing import Any, Dict, Optional, Union
-
-from praisonai_tools.tools.base import BaseTool
-from praisonai_tools.tools.decorator import tool
-
-logger = logging.getLogger(__name__)
-
-
-class N8nWorkflowTool(BaseTool):
-    """Tool for executing n8n workflows from PraisonAI agents."""
-    
-    name = "n8n_workflow"
-    description = "Execute n8n workflows to access 400+ integrations (Slack, Gmail, Notion, databases, APIs, etc.)"
-    
-    def __init__(
-        self,
-        n8n_url: Optional[str] = None,
-        api_key: Optional[str] = None,
-        timeout: float = 60.0,
-    ):
-        self.n8n_url = (n8n_url or os.getenv("N8N_URL", "http://localhost:5678")).rstrip('/')
-        self.api_key = api_key or os.getenv("N8N_API_KEY")
-        self.timeout = timeout
-        super().__init__()
-    
-    def run(
-        self,
-        workflow_id: str,
-        input_data: Optional[Dict[str, Any]] = None,
-        wait_for_completion: bool = True,
-        **kwargs
-    ) -> Dict[str, Any]:
-        """Execute an n8n workflow via webhook trigger.
-        
-        Args:
-            workflow_id: The n8n workflow ID to execute
-            input_data: Input data to pass to the workflow
-            wait_for_completion: Whether to wait for workflow completion (ignored for webhook execution)
-            
-        Returns:
-            Workflow execution result
-        """
-        if not workflow_id:
-            return {"error": "workflow_id is required"}
-            
-        try:
-            # Lazy import httpx to avoid import-time overhead
-            import httpx
-        except ImportError:
-            return {"error": "httpx not installed. Install with: pip install 'praisonai-tools[n8n]'"}
-        
-        # Fetch workflow to find webhook trigger
-        webhook_url = self._get_webhook_url(workflow_id)
-        if isinstance(webhook_url, dict) and "error" in webhook_url:
-            return webhook_url  # Return error from _get_webhook_url
-        
-        # Execute workflow via webhook (no API key needed for webhooks)
-        try:
-            with httpx.Client(timeout=self.timeout) as client:
-                response = client.post(
-                    webhook_url,
-                    json=input_data or {},
-                    headers={"Content-Type": "application/json"},
-                )
-                response.raise_for_status()
-                
-                # Try to parse JSON response, fallback to text if not JSON
-                try:
-                    return response.json()
-                except ValueError:
-                    return {"result": response.text, "status_code": response.status_code}
-                
-        except httpx.TimeoutException:
-            logger.error(f"n8n workflow {workflow_id} webhook timed out after {self.timeout}s")
-            return {"error": f"Workflow webhook execution timed out after {self.timeout} seconds"}
-        except httpx.HTTPStatusError as e:
-            logger.error(f"n8n webhook error: {e.response.status_code} - {e.response.text}")
-            return {"error": f"HTTP {e.response.status_code}: {e.response.text}"}
-        except Exception as e:
-            logger.error(f"n8n workflow webhook execution error: {e}")
-            return {"error": str(e)}
-    
-    def _get_webhook_url(self, workflow_id: str) -> Union[str, Dict[str, Any]]:
-        """Get webhook URL for a workflow by finding its webhook trigger node.
-        
-        Args:
-            workflow_id: The n8n workflow ID
-            
-        Returns:
-            Webhook URL string or error dict
-        """
-        try:
-            import httpx
-        except ImportError:
-            return {"error": "httpx not installed"}
-        
-        # Prepare headers for API calls
-        headers = {"Content-Type": "application/json"}
-        if self.api_key:
-            headers["X-N8N-API-KEY"] = self.api_key
-        
-        try:
-            with httpx.Client(timeout=30.0) as client:
-                # Fetch workflow definition
-                response = client.get(
-                    f"{self.n8n_url}/api/v1/workflows/{workflow_id}",
-                    headers=headers,
-                )
-                response.raise_for_status()
-                workflow = response.json()
-                
-                # Find webhook trigger node
-                nodes = workflow.get("nodes", [])
-                webhook_node = None
-                for node in nodes:
-                    if node.get("type") == "n8n-nodes-base.webhook":
-                        webhook_node = node
-                        break
-                
-                if not webhook_node:
-                    return {"error": "Workflow has no Webhook trigger node. Add a Webhook trigger to enable external execution."}
-                
-                # Extract webhook path
-                parameters = webhook_node.get("parameters", {})
-                path = parameters.get("path", "")
-                if not path:
-                    return {"error": "Webhook trigger node has no path configured"}
-                
-                # Ensure workflow is active
-                if not workflow.get("active"):
-                    activate_response = client.patch(
-                        f"{self.n8n_url}/api/v1/workflows/{workflow_id}",
-                        json={"active": True},
-                        headers=headers,
-                    )
-                    activate_response.raise_for_status()
-                    logger.info(f"Activated workflow {workflow_id}")
-                
-                # Construct webhook URL
-                # Use /webhook-test/ for test mode, /webhook/ for production
-                webhook_url = f"{self.n8n_url}/webhook/{path}"
-                return webhook_url
-                
-        except httpx.HTTPStatusError as e:
-            logger.error(f"n8n API error fetching workflow {workflow_id}: {e.response.status_code} - {e.response.text}")
-            return {"error": f"HTTP {e.response.status_code}: {e.response.text}"}
-        except Exception as e:
-            logger.error(f"Error fetching workflow {workflow_id}: {e}")
-            return {"error": str(e)}
-    
-    def list_workflows(self) -> Dict[str, Any]:
-        """List available n8n workflows."""
-        try:
-            import httpx
-        except ImportError:
-            return {"error": "httpx not installed. Install with: pip install 'praisonai-tools[n8n]'"}
-        
-        headers = {"Content-Type": "application/json"}
-        if self.api_key:
-            headers["X-N8N-API-KEY"] = self.api_key
-        
-        try:
-            with httpx.Client(timeout=30.0) as client:
-                response = client.get(
-                    f"{self.n8n_url}/api/v1/workflows",
-                    headers=headers,
-                )
-                response.raise_for_status()
-                return response.json()
-                
-        except httpx.HTTPStatusError as e:
-            logger.error(f"n8n API error: {e.response.status_code} - {e.response.text}")
-            return {"error": f"HTTP {e.response.status_code}: {e.response.text}"}
-        except Exception as e:
-            logger.error(f"n8n list workflows error: {e}")
-            return {"error": str(e)}
-
-
-@tool
-def n8n_workflow(
-    workflow_id: str,
-    input_data: Optional[Dict[str, Any]] = None,
-    n8n_url: Optional[str] = None,
-    api_key: Optional[str] = None,
-    timeout: float = 60.0,
-    wait_for_completion: bool = True,
-) -> Dict[str, Any]:
-    """Execute an n8n workflow and return the result.
-    
-    Provides PraisonAI agents access to n8n's 400+ integrations including:
-    - Communication: Slack, Discord, Telegram, Gmail, Outlook
-    - Productivity: Notion, Google Sheets, Airtable, Trello
-    - Databases: PostgreSQL, MongoDB, MySQL, Redis
-    - APIs: REST, GraphQL, webhooks
-    - And 400+ more integrations
-    
-    Args:
-        workflow_id: The n8n workflow ID to execute
-        input_data: Input data to pass to the workflow (optional)
-        n8n_url: n8n instance URL (defaults to N8N_URL env var or http://localhost:5678)
-        api_key: n8n API key (defaults to N8N_API_KEY env var)
-        timeout: Request timeout in seconds (default: 60)
-        wait_for_completion: Wait for workflow to complete (default: True)
-        
-    Returns:
-        Dict containing workflow execution result or error information
-        
-    Example:
-        # Send Slack message via n8n workflow
-        result = n8n_workflow(
-            workflow_id="slack-notify",
-            input_data={
-                "channel": "#general",
-                "message": "Hello from PraisonAI!"
-            }
-        )
-        
-        # Create Notion page via n8n workflow
-        result = n8n_workflow(
-            workflow_id="notion-create-page",
-            input_data={
-                "title": "Meeting Notes",
-                "content": "Discussion about project timeline"
-            }
-        )
-    """
-    tool = N8nWorkflowTool(
-        n8n_url=n8n_url,
-        api_key=api_key,
-        timeout=timeout,
-    )
-    
-    return tool.run(
-        workflow_id=workflow_id,
-        input_data=input_data,
-        wait_for_completion=wait_for_completion,
-    )
-
-
-@tool
-def n8n_list_workflows(
-    n8n_url: Optional[str] = None,
-    api_key: Optional[str] = None,
-) -> Dict[str, Any]:
-    """List available n8n workflows.
-    
-    Args:
-        n8n_url: n8n instance URL (defaults to N8N_URL env var or http://localhost:5678)
-        api_key: n8n API key (defaults to N8N_API_KEY env var)
-        
-    Returns:
-        Dict containing list of available workflows or error information
-    """
-    tool = N8nWorkflowTool(n8n_url=n8n_url, api_key=api_key)
-    return tool.list_workflows()

praisonai_tools/observability/__init__.py

@@ -1,88 +0,0 @@
-"""
-PraisonAI Observability Suite
-
-Unified observability integrations for PraisonAI agents.
-All imports are lazy-loaded for zero performance impact when not in use.
-
-Usage:
-    from praisonai_tools.observability import obs
-    
-    # Auto-detect and configure
-    obs.init()
-    
-    # Or use specific provider
-    obs.init(provider="langfuse")
-"""
-
-__all__ = [
-    # Core
-    "obs",
-    "ObservabilityManager",
-    "ObservabilityConfig",
-    # Providers
-    "AgentOpsProvider",
-    "LangfuseProvider",
-    "LangSmithProvider",
-    "TraceloopProvider",
-    "ArizePhoenixProvider",
-    "OpenLITProvider",
-    "LangtraceProvider",
-    "LangWatchProvider",
-    "DatadogProvider",
-    "MLflowProvider",
-    "OpikProvider",
-    "PortkeyProvider",
-    "BraintrustProvider",
-    "MaximProvider",
-    "WeaveProvider",
-    "NeatlogsProvider",
-    "LangDBProvider",
-    "AtlaProvider",
-    "PatronusProvider",
-    "TrueFoundryProvider",
-]
-
-# Lazy loading implementation
-def __getattr__(name: str):
-    """Lazy load observability components."""
-    if name == "obs" or name == "ObservabilityManager":
-        from praisonai_tools.observability.manager import ObservabilityManager
-        if name == "obs":
-            return ObservabilityManager()
-        return ObservabilityManager
-    
-    if name == "ObservabilityConfig":
-        from praisonai_tools.observability.config import ObservabilityConfig
-        return ObservabilityConfig
-    
-    # Provider lazy loading
-    provider_map = {
-        "AgentOpsProvider": ("agentops_provider", "AgentOpsProvider"),
-        "LangfuseProvider": ("langfuse_provider", "LangfuseProvider"),
-        "LangSmithProvider": ("langsmith_provider", "LangSmithProvider"),
-        "TraceloopProvider": ("traceloop_provider", "TraceloopProvider"),
-        "ArizePhoenixProvider": ("arize_phoenix_provider", "ArizePhoenixProvider"),
-        "OpenLITProvider": ("openlit_provider", "OpenLITProvider"),
-        "LangtraceProvider": ("langtrace_provider", "LangtraceProvider"),
-        "LangWatchProvider": ("langwatch_provider", "LangWatchProvider"),
-        "DatadogProvider": ("datadog_provider", "DatadogProvider"),
-        "MLflowProvider": ("mlflow_provider", "MLflowProvider"),
-        "OpikProvider": ("opik_provider", "OpikProvider"),
-        "PortkeyProvider": ("portkey_provider", "PortkeyProvider"),
-        "BraintrustProvider": ("braintrust_provider", "BraintrustProvider"),
-        "MaximProvider": ("maxim_provider", "MaximProvider"),
-        "WeaveProvider": ("weave_provider", "WeaveProvider"),
-        "NeatlogsProvider": ("neatlogs_provider", "NeatlogsProvider"),
-        "LangDBProvider": ("langdb_provider", "LangDBProvider"),
-        "AtlaProvider": ("atla_provider", "AtlaProvider"),
-        "PatronusProvider": ("patronus_provider", "PatronusProvider"),
-        "TrueFoundryProvider": ("truefoundry_provider", "TrueFoundryProvider"),
-    }
-    
-    if name in provider_map:
-        module_name, class_name = provider_map[name]
-        import importlib
-        module = importlib.import_module(f"praisonai_tools.observability.providers.{module_name}")
-        return getattr(module, class_name)
-    
-    raise AttributeError(f"module 'praisonai_tools.observability' has no attribute '{name}'")

praisonai_tools/observability/base.py

@@ -1,447 +0,0 @@
-"""
-Base Provider Interface
-
-Abstract base class for all observability providers.
-"""
-
-import contextvars
-import uuid
-from abc import ABC, abstractmethod
-from contextlib import contextmanager
-from dataclasses import dataclass, field
-from datetime import datetime, timezone
-from enum import Enum
-from typing import Any, Callable, Dict, Generator, List, Optional
-
-from praisonai_tools.observability.config import ObservabilityConfig
-
-
-class SpanKind(Enum):
-    """Types of spans in the observability system."""
-    AGENT = "agent"
-    LLM = "llm"
-    TOOL = "tool"
-    RETRIEVAL = "retrieval"
-    CHAIN = "chain"
-    WORKFLOW = "workflow"
-    CUSTOM = "custom"
-
-
-class SpanStatus(Enum):
-    """Status of a span."""
-    UNSET = "unset"
-    OK = "ok"
-    ERROR = "error"
-
-
-@dataclass
-class SpanContext:
-    """Context for span propagation."""
-    trace_id: str
-    span_id: str
-    parent_span_id: Optional[str] = None
-    
-    @classmethod
-    def new(cls, parent: Optional["SpanContext"] = None) -> "SpanContext":
-        """Create a new span context."""
-        return cls(
-            trace_id=parent.trace_id if parent else str(uuid.uuid4()),
-            span_id=str(uuid.uuid4()),
-            parent_span_id=parent.span_id if parent else None,
-        )
-
-
-@dataclass
-class Span:
-    """Represents a single span in a trace."""
-    name: str
-    kind: SpanKind
-    context: SpanContext
-    start_time: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
-    end_time: Optional[datetime] = None
-    status: SpanStatus = SpanStatus.UNSET
-    attributes: Dict[str, Any] = field(default_factory=dict)
-    events: List[Dict[str, Any]] = field(default_factory=list)
-    
-    # LLM-specific
-    model: Optional[str] = None
-    input_tokens: Optional[int] = None
-    output_tokens: Optional[int] = None
-    total_tokens: Optional[int] = None
-    
-    # Tool-specific
-    tool_name: Optional[str] = None
-    tool_input: Optional[Any] = None
-    tool_output: Optional[Any] = None
-    
-    # Error info
-    error_message: Optional[str] = None
-    error_type: Optional[str] = None
-    
-    def end(self, status: SpanStatus = SpanStatus.OK) -> None:
-        """End the span."""
-        self.end_time = datetime.now(timezone.utc)
-        self.status = status
-    
-    def set_error(self, error: Exception) -> None:
-        """Set error information."""
-        self.status = SpanStatus.ERROR
-        self.error_message = str(error)
-        self.error_type = type(error).__name__
-    
-    def add_event(self, name: str, attributes: Optional[Dict[str, Any]] = None) -> None:
-        """Add an event to the span."""
-        self.events.append({
-            "name": name,
-            "timestamp": datetime.utcnow().isoformat(),
-            "attributes": attributes or {},
-        })
-    
-    @property
-    def duration_ms(self) -> Optional[float]:
-        """Get span duration in milliseconds."""
-        if self.end_time and self.start_time:
-            return (self.end_time - self.start_time).total_seconds() * 1000
-        return None
-    
-    def to_dict(self) -> Dict[str, Any]:
-        """Convert span to dictionary."""
-        return {
-            "name": self.name,
-            "kind": self.kind.value,
-            "trace_id": self.context.trace_id,
-            "span_id": self.context.span_id,
-            "parent_span_id": self.context.parent_span_id,
-            "start_time": self.start_time.isoformat(),
-            "end_time": self.end_time.isoformat() if self.end_time else None,
-            "status": self.status.value,
-            "attributes": self.attributes,
-            "events": self.events,
-            "model": self.model,
-            "input_tokens": self.input_tokens,
-            "output_tokens": self.output_tokens,
-            "total_tokens": self.total_tokens,
-            "tool_name": self.tool_name,
-            "tool_input": self.tool_input,
-            "tool_output": self.tool_output,
-            "error_message": self.error_message,
-            "error_type": self.error_type,
-            "duration_ms": self.duration_ms,
-        }
-
-
-@dataclass
-class Trace:
-    """Represents a complete trace."""
-    trace_id: str
-    name: str
-    spans: List[Span] = field(default_factory=list)
-    start_time: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
-    end_time: Optional[datetime] = None
-    metadata: Dict[str, Any] = field(default_factory=dict)
-    
-    # Context
-    session_id: Optional[str] = None
-    user_id: Optional[str] = None
-    agent_id: Optional[str] = None
-    
-    def add_span(self, span: Span) -> None:
-        """Add a span to the trace."""
-        self.spans.append(span)
-    
-    def end(self) -> None:
-        """End the trace."""
-        self.end_time = datetime.now(timezone.utc)
-    
-    @property
-    def duration_ms(self) -> Optional[float]:
-        """Get trace duration in milliseconds."""
-        if self.end_time and self.start_time:
-            return (self.end_time - self.start_time).total_seconds() * 1000
-        return None
-    
-    def to_dict(self) -> Dict[str, Any]:
-        """Convert trace to dictionary."""
-        return {
-            "trace_id": self.trace_id,
-            "name": self.name,
-            "spans": [s.to_dict() for s in self.spans],
-            "start_time": self.start_time.isoformat(),
-            "end_time": self.end_time.isoformat() if self.end_time else None,
-            "metadata": self.metadata,
-            "session_id": self.session_id,
-            "user_id": self.user_id,
-            "agent_id": self.agent_id,
-            "duration_ms": self.duration_ms,
-        }
-
-
-class BaseObservabilityProvider(ABC):
-    """Abstract base class for observability providers."""
-    
-    name: str = "base"
-    
-    # Per-context span stack and trace for async safety
-    _ctx_span_stack: contextvars.ContextVar = contextvars.ContextVar(
-        'obs_span_stack', default=None
-    )
-    _ctx_current_trace: contextvars.ContextVar = contextvars.ContextVar(
-        'obs_current_trace', default=None
-    )
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize the provider."""
-        self.config = config or ObservabilityConfig()
-        self._initialized = False
-    
-    @property
-    def _current_trace(self) -> Optional[Trace]:
-        return self._ctx_current_trace.get(None)
-    
-    @_current_trace.setter
-    def _current_trace(self, value: Optional[Trace]):
-        self._ctx_current_trace.set(value)
-    
-    @property
-    def _span_stack(self) -> List[Span]:
-        stack = self._ctx_span_stack.get(None)
-        if stack is None:
-            stack = []
-            self._ctx_span_stack.set(stack)
-        return stack
-    
-    @_span_stack.setter
-    def _span_stack(self, value: List[Span]):
-        self._ctx_span_stack.set(value)
-    
-    @abstractmethod
-    def init(self, **kwargs) -> bool:
-        """Initialize the provider. Returns True if successful."""
-        pass
-    
-    @abstractmethod
-    def shutdown(self) -> None:
-        """Shutdown the provider and flush any pending data."""
-        pass
-    
-    @abstractmethod
-    def is_available(self) -> bool:
-        """Check if the provider SDK is available."""
-        pass
-    
-    @abstractmethod
-    def check_connection(self) -> tuple[bool, str]:
-        """Check connection to the provider. Returns (success, message)."""
-        pass
-    
-    @abstractmethod
-    def export_span(self, span: Span) -> bool:
-        """Export a span to the provider."""
-        pass
-    
-    @abstractmethod
-    def export_trace(self, trace: Trace) -> bool:
-        """Export a complete trace to the provider."""
-        pass
-    
-    def start_trace(
-        self,
-        name: str,
-        session_id: Optional[str] = None,
-        user_id: Optional[str] = None,
-        agent_id: Optional[str] = None,
-        metadata: Optional[Dict[str, Any]] = None,
-    ) -> Trace:
-        """Start a new trace."""
-        trace = Trace(
-            trace_id=str(uuid.uuid4()),
-            name=name,
-            session_id=session_id or self.config.session_id,
-            user_id=user_id or self.config.user_id,
-            metadata=metadata or {},
-        )
-        trace.agent_id = agent_id
-        self._current_trace = trace
-        return trace
-    
-    def end_trace(self, trace: Optional[Trace] = None) -> None:
-        """End a trace and export it."""
-        trace = trace or self._current_trace
-        if trace:
-            trace.end()
-            self.export_trace(trace)
-            if trace == self._current_trace:
-                self._current_trace = None
-    
-    def start_span(
-        self,
-        name: str,
-        kind: SpanKind = SpanKind.CUSTOM,
-        parent: Optional[Span] = None,
-        attributes: Optional[Dict[str, Any]] = None,
-    ) -> Span:
-        """Start a new span."""
-        parent_context = None
-        if parent:
-            parent_context = parent.context
-        elif self._span_stack:
-            parent_context = self._span_stack[-1].context
-        elif self._current_trace:
-            parent_context = SpanContext(
-                trace_id=self._current_trace.trace_id,
-                span_id="root",
-            )
-        
-        span = Span(
-            name=name,
-            kind=kind,
-            context=SpanContext.new(parent_context),
-            attributes=attributes or {},
-        )
-        self._span_stack.append(span)
-        
-        if self._current_trace:
-            self._current_trace.add_span(span)
-        
-        return span
-    
-    def end_span(
-        self,
-        span: Optional[Span] = None,
-        status: SpanStatus = SpanStatus.OK,
-    ) -> None:
-        """End a span and export it."""
-        span = span or (self._span_stack[-1] if self._span_stack else None)
-        if span:
-            span.end(status)
-            self.export_span(span)
-            if span in self._span_stack:
-                self._span_stack.remove(span)
-    
-    @contextmanager
-    def trace(
-        self,
-        name: str,
-        session_id: Optional[str] = None,
-        user_id: Optional[str] = None,
-        agent_id: Optional[str] = None,
-        metadata: Optional[Dict[str, Any]] = None,
-    ) -> Generator[Trace, None, None]:
-        """Context manager for traces."""
-        trace = self.start_trace(name, session_id, user_id, agent_id, metadata)
-        try:
-            yield trace
-        except Exception as e:
-            trace.metadata["error"] = str(e)
-            raise
-        finally:
-            self.end_trace(trace)
-    
-    @contextmanager
-    def span(
-        self,
-        name: str,
-        kind: SpanKind = SpanKind.CUSTOM,
-        attributes: Optional[Dict[str, Any]] = None,
-    ) -> Generator[Span, None, None]:
-        """Context manager for spans."""
-        span = self.start_span(name, kind, attributes=attributes)
-        try:
-            yield span
-        except Exception as e:
-            span.set_error(e)
-            raise
-        finally:
-            self.end_span(span)
-    
-    def log_llm_call(
-        self,
-        model: str,
-        input_messages: Any,
-        output: Any,
-        input_tokens: Optional[int] = None,
-        output_tokens: Optional[int] = None,
-        duration_ms: Optional[float] = None,
-        error: Optional[Exception] = None,
-        metadata: Optional[Dict[str, Any]] = None,
-    ) -> Span:
-        """Log an LLM call."""
-        span = self.start_span(f"llm.{model}", SpanKind.LLM)
-        span.model = model
-        span.input_tokens = input_tokens
-        span.output_tokens = output_tokens
-        span.total_tokens = (input_tokens or 0) + (output_tokens or 0) if input_tokens or output_tokens else None
-        span.attributes["input"] = str(input_messages) if not self.config.redact_inputs else "[REDACTED]"
-        span.attributes["output"] = str(output) if not self.config.redact_outputs else "[REDACTED]"
-        span.attributes.update(metadata or {})
-        
-        if error:
-            span.set_error(error)
-        
-        self.end_span(span, SpanStatus.ERROR if error else SpanStatus.OK)
-        return span
-    
-    def log_tool_call(
-        self,
-        tool_name: str,
-        tool_input: Any,
-        tool_output: Any,
-        duration_ms: Optional[float] = None,
-        error: Optional[Exception] = None,
-        metadata: Optional[Dict[str, Any]] = None,
-    ) -> Span:
-        """Log a tool call."""
-        span = self.start_span(f"tool.{tool_name}", SpanKind.TOOL)
-        span.tool_name = tool_name
-        span.tool_input = tool_input if not self.config.redact_inputs else "[REDACTED]"
-        span.tool_output = tool_output if not self.config.redact_outputs else "[REDACTED]"
-        span.attributes.update(metadata or {})
-        
-        if error:
-            span.set_error(error)
-        
-        self.end_span(span, SpanStatus.ERROR if error else SpanStatus.OK)
-        return span
-    
-    def log_agent_step(
-        self,
-        agent_name: str,
-        step_name: str,
-        input_data: Any,
-        output_data: Any,
-        error: Optional[Exception] = None,
-        metadata: Optional[Dict[str, Any]] = None,
-    ) -> Span:
-        """Log an agent step."""
-        span = self.start_span(f"agent.{agent_name}.{step_name}", SpanKind.AGENT)
-        span.attributes["agent_name"] = agent_name
-        span.attributes["step_name"] = step_name
-        span.attributes["input"] = str(input_data) if not self.config.redact_inputs else "[REDACTED]"
-        span.attributes["output"] = str(output_data) if not self.config.redact_outputs else "[REDACTED]"
-        span.attributes.update(metadata or {})
-        
-        if error:
-            span.set_error(error)
-        
-        self.end_span(span, SpanStatus.ERROR if error else SpanStatus.OK)
-        return span
-    
-    def decorator(self, name: Optional[str] = None, kind: SpanKind = SpanKind.CUSTOM):
-        """Decorator to trace a function."""
-        def wrapper(func: Callable) -> Callable:
-            span_name = name or func.__name__
-            
-            def wrapped(*args, **kwargs):
-                with self.span(span_name, kind):
-                    return func(*args, **kwargs)
-            
-            async def async_wrapped(*args, **kwargs):
-                with self.span(span_name, kind):
-                    return await func(*args, **kwargs)
-            
-            import asyncio
-            if asyncio.iscoroutinefunction(func):
-                return async_wrapped
-            return wrapped
-        
-        return wrapper

praisonai_tools/observability/bridge.py

@@ -1,207 +0,0 @@
-"""
-Observability Bridge
-
-Bridges ContextTraceEmitter events from praisonaiagents core SDK
-to the ObservabilityManager's provider (e.g., LangSmith, Langfuse).
-
-This implements the ContextTraceSinkProtocol so the core SDK's
-internal trace events (LLM calls, tool calls, agent lifecycle)
-are forwarded to external observability providers.
-
-Zero Performance Impact:
-- Only active when obs.init() is called
-- Lazy imports for praisonaiagents types
-- No overhead when not used
-"""
-
-from typing import Any, TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class ObservabilitySink:
-    """
-    Bridges ContextTraceEmitter events to ObservabilityManager spans.
-    
-    Implements the ContextTraceSinkProtocol from praisonaiagents.trace.context_events.
-    When registered as the active sink, all agent/LLM/tool events are forwarded
-    to the configured observability provider.
-    """
-    
-    def __init__(self, manager: "ObservabilityManager"):
-        self._manager = manager
-        self._active_llm_span = None
-        self._active_tool_span = None
-        self._active_agent_span = None
-    
-    def emit(self, event: Any) -> None:
-        """
-        Receive a ContextEvent and forward to the observability provider.
-        
-        Args:
-            event: A ContextEvent from praisonaiagents.trace.context_events
-        """
-        try:
-            from praisonaiagents.trace.context_events import ContextEventType
-        except ImportError:
-            return
-        
-        if not self._manager or not self._manager.enabled:
-            return
-        
-        event_type = event.event_type
-        
-        # AGENT_START/AGENT_END are handled by _wrap_agent_class in manager.py
-        # with full input/output data — skip bridge duplicates to avoid empty spans
-        if event_type == ContextEventType.LLM_REQUEST:
-            self._handle_llm_request(event)
-        elif event_type == ContextEventType.LLM_RESPONSE:
-            self._handle_llm_response(event)
-        elif event_type == ContextEventType.TOOL_CALL_START:
-            self._handle_tool_start(event)
-        elif event_type == ContextEventType.TOOL_CALL_END:
-            self._handle_tool_end(event)
-    
-    def _handle_agent_start(self, event: Any) -> None:
-        """Create an agent span."""
-        from praisonai_tools.observability.base import SpanKind
-        
-        agent_name = getattr(event, 'agent_name', 'unknown')
-        data = getattr(event, 'data', {}) or {}
-        
-        attrs = {}
-        if data.get('role'):
-            attrs['agent.role'] = str(data['role'])
-        if data.get('goal'):
-            attrs['agent.goal'] = str(data['goal'])
-        
-        span = self._manager.start_span(
-            f"agent.{agent_name}",
-            kind=SpanKind.AGENT,
-            attributes=attrs,
-        )
-        self._active_agent_span = span
-    
-    def _handle_agent_end(self, event: Any) -> None:
-        """End the agent span."""
-        if self._active_agent_span:
-            self._manager.end_span(self._active_agent_span)
-            self._active_agent_span = None
-    
-    def _handle_llm_request(self, event: Any) -> None:
-        """Create an LLM span with input messages."""
-        from praisonai_tools.observability.base import SpanKind
-        
-        data = getattr(event, 'data', {}) or {}
-        model = data.get('model', 'unknown')
-        messages = data.get('messages', [])
-        
-        attrs = {}
-        attrs['gen_ai.request.model'] = str(model)
-        attrs['gen_ai.system'] = 'openai'
-        
-        # Set input messages as GenAI prompt attributes
-        if messages:
-            for i, msg in enumerate(messages):
-                if isinstance(msg, dict):
-                    role = msg.get('role', '')
-                    content = msg.get('content', '')
-                    attrs[f'gen_ai.prompt.{i}.role'] = str(role)
-                    if content:
-                        # Truncate very long content
-                        content_str = str(content)
-                        if len(content_str) > 10000:
-                            content_str = content_str[:10000] + "..."
-                        attrs[f'gen_ai.prompt.{i}.content'] = content_str
-        
-        span = self._manager.start_span(
-            str(model),
-            kind=SpanKind.LLM,
-            attributes=attrs,
-        )
-        if span:
-            span.model = str(model)
-        self._active_llm_span = span
-    
-    def _handle_llm_response(self, event: Any) -> None:
-        """Populate LLM span with response data and end it."""
-        data = getattr(event, 'data', {}) or {}
-        prompt_tokens = getattr(event, 'prompt_tokens', 0) or 0
-        completion_tokens = getattr(event, 'completion_tokens', 0) or 0
-        cost_usd = getattr(event, 'cost_usd', 0.0) or 0.0
-        
-        if self._active_llm_span:
-            self._active_llm_span.input_tokens = prompt_tokens
-            self._active_llm_span.output_tokens = completion_tokens
-            self._active_llm_span.total_tokens = prompt_tokens + completion_tokens
-            
-            # Set GenAI usage attributes
-            self._active_llm_span.attributes['gen_ai.usage.prompt_tokens'] = prompt_tokens
-            self._active_llm_span.attributes['gen_ai.usage.completion_tokens'] = completion_tokens
-            self._active_llm_span.attributes['gen_ai.usage.total_tokens'] = prompt_tokens + completion_tokens
-            
-            if cost_usd:
-                self._active_llm_span.attributes['gen_ai.usage.cost'] = cost_usd
-            
-            finish_reason = data.get('finish_reason')
-            if finish_reason:
-                self._active_llm_span.attributes['gen_ai.response.finish_reason'] = str(finish_reason)
-            
-            response_content = data.get('response_content')
-            if response_content:
-                content_str = str(response_content)
-                if len(content_str) > 10000:
-                    content_str = content_str[:10000] + "..."
-                self._active_llm_span.attributes['gen_ai.completion.0.content'] = content_str
-                self._active_llm_span.attributes['gen_ai.completion.0.role'] = 'assistant'
-            
-            self._manager.end_span(self._active_llm_span)
-            self._active_llm_span = None
-    
-    def _handle_tool_start(self, event: Any) -> None:
-        """Create a tool span."""
-        from praisonai_tools.observability.base import SpanKind
-        
-        data = getattr(event, 'data', {}) or {}
-        tool_name = data.get('tool_name', 'unknown')
-        arguments = data.get('arguments', {})
-        
-        attrs = {
-            'tool.name': str(tool_name),
-        }
-        if arguments:
-            attrs['tool.arguments'] = str(arguments)
-        
-        span = self._manager.start_span(
-            str(tool_name),
-            kind=SpanKind.TOOL,
-            attributes=attrs,
-        )
-        if span:
-            span.tool_name = str(tool_name)
-            span.tool_input = arguments
-        self._active_tool_span = span
-    
-    def _handle_tool_end(self, event: Any) -> None:
-        """End the tool span with result."""
-        data = getattr(event, 'data', {}) or {}
-        
-        if self._active_tool_span:
-            result = data.get('result')
-            error = data.get('error')
-            
-            if result is not None:
-                result_str = str(result)
-                if len(result_str) > 10000:
-                    result_str = result_str[:10000] + "..."
-                self._active_tool_span.tool_output = result_str
-            
-            if error:
-                from praisonai_tools.observability.base import SpanStatus
-                self._active_tool_span.error_message = str(error)
-                self._manager.end_span(self._active_tool_span, SpanStatus.ERROR)
-            else:
-                self._manager.end_span(self._active_tool_span)
-            
-            self._active_tool_span = None

praisonai_tools/observability/cli.py

@@ -1,252 +0,0 @@
-"""
-PraisonAI Observability CLI.
-
-Lightweight CLI that lives in praisonai_tools, close to the observability code.
-Can be run standalone: python -m praisonai_tools.observability.cli doctor
-Or via the praisonai wrapper: praisonai obs doctor
-"""
-
-
-import typer
-
-
-app = typer.Typer(help="Observability diagnostics and management")
-
-
-def _get_obs():
-    """Lazy-import obs singleton."""
-    try:
-        from praisonai_tools.observability import obs
-        return obs
-    except ImportError:
-        from rich.console import Console
-        Console(stderr=True).print(
-            "[red]✗ praisonai-tools observability not available.[/red]"
-        )
-        raise typer.Exit(1)
-
-
-def _print_doctor_table(results: dict) -> None:
-    """Pretty-print doctor results as a Rich table."""
-    from rich.console import Console
-    from rich.table import Table
-
-    console = Console()
-    console.print("\n[bold cyan]🔍 PraisonAI Observability Doctor[/bold cyan]\n")
-
-    table = Table(show_header=True, header_style="bold")
-    table.add_column("Check", style="dim", min_width=20)
-    table.add_column("Result")
-
-    # Enabled
-    enabled = results.get("enabled", False)
-    table.add_row(
-        "Enabled",
-        "[green]✓ Yes[/green]" if enabled else "[yellow]✗ No[/yellow]",
-    )
-
-    # Provider
-    provider = results.get("provider")
-    table.add_row(
-        "Active Provider",
-        f"[green]{provider}[/green]" if provider else "[dim]None[/dim]",
-    )
-
-    # Connection
-    conn_status = results.get("connection_status")
-    conn_msg = results.get("connection_message", "")
-    if conn_status is True:
-        table.add_row("Connection", f"[green]✓ {conn_msg}[/green]")
-    elif conn_status is False:
-        table.add_row("Connection", f"[red]✗ {conn_msg}[/red]")
-    else:
-        table.add_row("Connection", "[dim]N/A[/dim]")
-
-    # Available providers
-    available = results.get("available_providers", [])
-    table.add_row(
-        "Available Providers",
-        ", ".join(available) if available else "[dim]None[/dim]",
-    )
-
-    # Registered providers
-    registered = results.get("registered_providers", [])
-    table.add_row(
-        "Registered Providers",
-        ", ".join(registered) if registered else "[dim]None[/dim]",
-    )
-
-    console.print(table)
-    console.print()
-
-
-@app.command("doctor")
-def obs_doctor(
-    json_output: bool = typer.Option(False, "--json", help="Output as JSON"),
-):
-    """
-    Run observability health checks.
-
-    Checks provider status, connection, and available providers.
-
-    Examples:
-        python -m praisonai_tools.observability.cli doctor
-        praisonai obs doctor
-        praisonai obs doctor --json
-    """
-    obs = _get_obs()
-    results = obs.doctor()
-
-    if json_output:
-        import json
-        typer.echo(json.dumps(results, indent=2, default=str))
-        raise typer.Exit(0)
-
-    _print_doctor_table(results)
-    raise typer.Exit(0)
-
-
-@app.command("verify")
-def obs_verify(
-    provider: str = typer.Option("langsmith", help="Provider to verify"),
-    project: str = typer.Option("default", help="Project name to check"),
-    limit: int = typer.Option(5, help="Number of recent runs to check"),
-    json_output: bool = typer.Option(False, "--json", help="Output as JSON"),
-):
-    """
-    Verify traces are recorded in the observability backend.
-
-    Uses the provider's SDK to fetch recent traces and check for
-    PraisonAI branding attributes.
-
-    Examples:
-        praisonai obs verify --provider langsmith --project "My First App"
-        python -m praisonai_tools.observability.cli verify --json
-    """
-    from rich.console import Console
-
-    console = Console()
-
-    if provider == "langsmith":
-        _verify_langsmith(console, project, limit, json_output)
-    else:
-        console.print(f"[yellow]Verify not yet supported for provider: {provider}[/yellow]")
-        raise typer.Exit(1)
-
-
-def _verify_langsmith(console, project: str, limit: int, json_output: bool):
-    """Verify traces in LangSmith using the LangSmith SDK."""
-    import os
-    from rich.table import Table
-
-    try:
-        from langsmith import Client
-    except ImportError:
-        console.print("[red]✗ langsmith SDK not installed.[/red]")
-        console.print("[dim]Install with: pip install langsmith[/dim]")
-        raise typer.Exit(1)
-
-    api_key = os.getenv("LANGSMITH_API_KEY")
-    endpoint = os.getenv("LANGSMITH_ENDPOINT", "https://api.smith.langchain.com")
-
-    if not api_key:
-        console.print("[red]✗ LANGSMITH_API_KEY not set.[/red]")
-        raise typer.Exit(1)
-
-    ls = Client(api_url=endpoint, api_key=api_key)
-
-    try:
-        runs = list(ls.list_runs(
-            project_name=project,
-            execution_order=1,  # root runs only
-            limit=limit,
-        ))
-    except Exception as e:
-        console.print(f"[red]✗ Failed to fetch runs: {e}[/red]")
-        raise typer.Exit(1)
-
-    # Collect results
-    results = []
-    for run in runs:
-        extra = run.extra or {}
-        metadata = extra.get("metadata", {})
-        results.append({
-            "name": run.name,
-            "id": str(run.id),
-            "status": run.status,
-            "run_type": run.run_type,
-            "has_inputs": bool(run.inputs),
-            "has_outputs": bool(run.outputs),
-            "praisonai.version": metadata.get("praisonai.version"),
-            "praisonai.framework": metadata.get("praisonai.framework"),
-            "start_time": str(run.start_time) if run.start_time else None,
-        })
-
-    if json_output:
-        import json
-        typer.echo(json.dumps({
-            "project": project,
-            "endpoint": endpoint,
-            "runs_checked": len(results),
-            "runs": results,
-        }, indent=2, default=str))
-        raise typer.Exit(0)
-
-    # Pretty print
-    console.print("\n[bold cyan]🔍 PraisonAI Trace Verification[/bold cyan]")
-    console.print(f"[dim]Project: {project} | Endpoint: {endpoint}[/dim]\n")
-
-    if not results:
-        console.print("[yellow]No runs found in project.[/yellow]")
-        raise typer.Exit(0)
-
-    table = Table(show_header=True, header_style="bold")
-    table.add_column("Name", min_width=15)
-    table.add_column("Type")
-    table.add_column("Status")
-    table.add_column("I/O")
-    table.add_column("Version")
-    table.add_column("Framework")
-
-    branded_count = 0
-    for r in results:
-        version = r["praisonai.version"]
-        framework = r["praisonai.framework"]
-        has_branding = version is not None and framework is not None
-        if has_branding:
-            branded_count += 1
-
-        table.add_row(
-            r["name"],
-            r["run_type"] or "—",
-            f"[green]{r['status']}[/green]" if r["status"] == "success" else r["status"] or "—",
-            "[green]✓[/green]" if r["has_inputs"] and r["has_outputs"] else "[yellow]partial[/yellow]",
-            f"[green]{version}[/green]" if version else "[dim]—[/dim]",
-            f"[green]{framework}[/green]" if framework else "[dim]—[/dim]",
-        )
-
-    console.print(table)
-    console.print()
-
-    # Summary
-    total = len(results)
-    if branded_count == total:
-        console.print(f"[green]✓ All {total} runs have PraisonAI branding[/green]")
-    elif branded_count > 0:
-        console.print(f"[yellow]⚠ {branded_count}/{total} runs have PraisonAI branding[/yellow]")
-    else:
-        console.print("[red]✗ No runs have PraisonAI branding[/red]")
-
-    console.print()
-    raise typer.Exit(0)
-
-
-@app.callback(invoke_without_command=True)
-def obs_callback(ctx: typer.Context):
-    """Observability diagnostics and management."""
-    if ctx.invoked_subcommand is None:
-        ctx.invoke(obs_doctor)
-
-
-if __name__ == "__main__":
-    app()

praisonai_tools/observability/config.py

@@ -1,125 +0,0 @@
-"""
-Observability Configuration
-
-Unified configuration for all observability providers.
-"""
-
-import os
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional
-
-
-@dataclass
-class ObservabilityConfig:
-    """Configuration for observability providers."""
-    
-    # Provider selection
-    provider: Optional[str] = None  # Auto-detect if None
-    
-    # Common settings
-    project_name: Optional[str] = None
-    session_id: Optional[str] = None
-    run_id: Optional[str] = None
-    user_id: Optional[str] = None
-    
-    # Tracing settings
-    enabled: bool = True
-    trace_llm_calls: bool = True
-    trace_tool_calls: bool = True
-    trace_agent_steps: bool = True
-    
-    # Multi-agent settings
-    propagate_context: bool = True
-    parent_trace_id: Optional[str] = None
-    
-    # Performance settings
-    batch_size: int = 100
-    flush_interval: float = 5.0
-    async_export: bool = True
-    
-    # Redaction settings
-    redact_inputs: bool = False
-    redact_outputs: bool = False
-    redaction_patterns: List[str] = field(default_factory=list)
-    
-    # Provider-specific settings
-    extra: Dict[str, Any] = field(default_factory=dict)
-    
-    @classmethod
-    def from_env(cls) -> "ObservabilityConfig":
-        """Create config from environment variables."""
-        return cls(
-            provider=os.getenv("PRAISONAI_OBS_PROVIDER"),
-            project_name=os.getenv("PRAISONAI_OBS_PROJECT"),
-            session_id=os.getenv("PRAISONAI_OBS_SESSION_ID"),
-            run_id=os.getenv("PRAISONAI_OBS_RUN_ID"),
-            user_id=os.getenv("PRAISONAI_OBS_USER_ID"),
-            enabled=os.getenv("PRAISONAI_OBS_ENABLED", "true").lower() == "true",
-            trace_llm_calls=os.getenv("PRAISONAI_OBS_TRACE_LLM", "true").lower() == "true",
-            trace_tool_calls=os.getenv("PRAISONAI_OBS_TRACE_TOOLS", "true").lower() == "true",
-            trace_agent_steps=os.getenv("PRAISONAI_OBS_TRACE_AGENTS", "true").lower() == "true",
-            propagate_context=os.getenv("PRAISONAI_OBS_PROPAGATE", "true").lower() == "true",
-            batch_size=int(os.getenv("PRAISONAI_OBS_BATCH_SIZE", "100")),
-            flush_interval=float(os.getenv("PRAISONAI_OBS_FLUSH_INTERVAL", "5.0")),
-            async_export=os.getenv("PRAISONAI_OBS_ASYNC", "true").lower() == "true",
-            redact_inputs=os.getenv("PRAISONAI_OBS_REDACT_INPUTS", "false").lower() == "true",
-            redact_outputs=os.getenv("PRAISONAI_OBS_REDACT_OUTPUTS", "false").lower() == "true",
-        )
-    
-    def merge(self, **kwargs) -> "ObservabilityConfig":
-        """Create a new config with merged values."""
-        current = {
-            "provider": self.provider,
-            "project_name": self.project_name,
-            "session_id": self.session_id,
-            "run_id": self.run_id,
-            "user_id": self.user_id,
-            "enabled": self.enabled,
-            "trace_llm_calls": self.trace_llm_calls,
-            "trace_tool_calls": self.trace_tool_calls,
-            "trace_agent_steps": self.trace_agent_steps,
-            "propagate_context": self.propagate_context,
-            "parent_trace_id": self.parent_trace_id,
-            "batch_size": self.batch_size,
-            "flush_interval": self.flush_interval,
-            "async_export": self.async_export,
-            "redact_inputs": self.redact_inputs,
-            "redact_outputs": self.redact_outputs,
-            "redaction_patterns": self.redaction_patterns.copy(),
-            "extra": self.extra.copy(),
-        }
-        current.update({k: v for k, v in kwargs.items() if v is not None})
-        return ObservabilityConfig(**current)
-
-
-# Provider environment variable mappings
-PROVIDER_ENV_KEYS = {
-    "agentops": ["AGENTOPS_API_KEY"],
-    "langfuse": ["LANGFUSE_PUBLIC_KEY", "LANGFUSE_SECRET_KEY"],
-    "langsmith": ["LANGSMITH_API_KEY"],
-    "traceloop": ["TRACELOOP_API_KEY"],
-    "arize_phoenix": ["PHOENIX_API_KEY"],
-    "openlit": ["OPENLIT_API_KEY"],
-    "langtrace": ["LANGTRACE_API_KEY"],
-    "langwatch": ["LANGWATCH_API_KEY"],
-    "datadog": ["DD_API_KEY"],
-    "mlflow": ["MLFLOW_TRACKING_URI"],
-    "opik": ["OPIK_API_KEY", "COMET_API_KEY"],
-    "portkey": ["PORTKEY_API_KEY"],
-    "braintrust": ["BRAINTRUST_API_KEY"],
-    "maxim": ["MAXIM_API_KEY"],
-    "weave": ["WANDB_API_KEY"],
-    "neatlogs": ["NEATLOGS_API_KEY"],
-    "langdb": ["LANGDB_API_KEY"],
-    "atla": ["ATLA_API_KEY"],
-    "patronus": ["PATRONUS_API_KEY"],
-    "truefoundry": ["TRUEFOUNDRY_API_KEY"],
-}
-
-
-def detect_provider() -> Optional[str]:
-    """Auto-detect available provider from environment variables."""
-    for provider, keys in PROVIDER_ENV_KEYS.items():
-        if all(os.getenv(key) for key in keys):
-            return provider
-    return None

praisonai_tools/observability/manager.py

@@ -1,672 +0,0 @@
-"""
-Observability Manager
-
-Central manager for all observability providers.
-"""
-
-import os
-from typing import Any, Dict, List, Optional, Type
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    SpanKind,
-    SpanStatus,
-    Trace,
-)
-from praisonai_tools.observability.config import (
-    ObservabilityConfig,
-    PROVIDER_ENV_KEYS,
-    detect_provider,
-)
-
-
-class ObservabilityManager:
-    """
-    Central manager for observability providers.
-    
-    Usage:
-        from praisonai_tools.observability import obs
-        
-        # Auto-detect provider from env vars
-        obs.init()
-        
-        # Or specify provider
-        obs.init(provider="langfuse")
-        
-        # Use context managers
-        with obs.trace("my-workflow"):
-            with obs.span("step-1", kind=SpanKind.AGENT):
-                # do work
-                pass
-    """
-    
-    _instance: Optional["ObservabilityManager"] = None
-    _providers: Dict[str, Type[BaseObservabilityProvider]] = {}
-    
-    def __new__(cls) -> "ObservabilityManager":
-        """Singleton pattern."""
-        if cls._instance is None:
-            cls._instance = super().__new__(cls)
-            cls._instance._initialized = False
-        return cls._instance
-    
-    def __init__(self):
-        """Initialize the manager."""
-        if self._initialized:
-            return
-        
-        self.config: Optional[ObservabilityConfig] = None
-        self._provider: Optional[BaseObservabilityProvider] = None
-        self._initialized = True
-    
-    @classmethod
-    def register_provider(cls, name: str, provider_class: Type[BaseObservabilityProvider]) -> None:
-        """Register a provider class."""
-        cls._providers[name] = provider_class
-    
-    @classmethod
-    def list_providers(cls) -> List[str]:
-        """List all registered providers."""
-        return list(cls._providers.keys())
-    
-    @classmethod
-    def get_available_providers(cls) -> List[str]:
-        """Get providers that have required env vars set."""
-        available = []
-        for name in cls._providers:
-            if name in PROVIDER_ENV_KEYS:
-                keys = PROVIDER_ENV_KEYS[name]
-                if all(os.getenv(key) for key in keys):
-                    available.append(name)
-        return available
-    
-    def init(
-        self,
-        provider: Optional[str] = None,
-        project_name: Optional[str] = None,
-        session_id: Optional[str] = None,
-        auto_instrument: bool = True,
-        **kwargs
-    ) -> bool:
-        """
-        Initialize observability with a provider.
-        
-        Args:
-            provider: Provider name (auto-detect if None)
-            project_name: Project/app name for traces
-            session_id: Session identifier
-            auto_instrument: If True (default), auto-instruments Agent/Agents/Workflow
-                           classes so all operations are traced without explicit
-                           obs.trace() wrappers
-            **kwargs: Additional provider-specific config
-            
-        Returns:
-            True if initialization successful
-            
-        Example:
-            # Simplest usage - auto-detect provider and auto-instrument
-            obs.init()
-            
-            # Explicit provider
-            obs.init(provider="langfuse")
-            
-            # Disable auto-instrumentation for manual control
-            obs.init(auto_instrument=False)
-        """
-        # Build config
-        self.config = ObservabilityConfig.from_env().merge(
-            provider=provider,
-            project_name=project_name,
-            session_id=session_id,
-            extra=kwargs,
-        )
-        
-        # Auto-detect provider if not specified
-        provider_name = self.config.provider or detect_provider()
-        
-        if not provider_name:
-            # No provider configured - silent no-op mode
-            return False
-        
-        # Get provider class
-        if provider_name not in self._providers:
-            # Try to load provider dynamically
-            self._load_provider(provider_name)
-        
-        if provider_name not in self._providers:
-            return False
-        
-        # Instantiate and initialize provider
-        provider_class = self._providers[provider_name]
-        self._provider = provider_class(self.config)
-        
-        result = self._provider.init(**kwargs)
-        
-        # Auto-instrument Agent classes after successful init
-        if result and auto_instrument:
-            self._auto_instrument_agents()
-        
-        return result
-    
-    # Mapping from obs provider names to litellm callback names
-    _LITELLM_CALLBACK_MAP = {
-        "langsmith": "langsmith",
-        "langfuse": "langfuse",
-        "datadog": "datadog",
-        "opik": "opik",
-        "braintrust": "braintrust",
-        "mlflow": "mlflow",
-        "langwatch": "langwatch",
-    }
-    
-    def _get_litellm_callback_name(self, provider_name: str) -> Optional[str]:
-        """Get the litellm callback name for a provider."""
-        return self._LITELLM_CALLBACK_MAP.get(provider_name)
-    
-    def _auto_instrument_agents(self) -> None:
-        """
-        Auto-instrument Agent/Agents/Workflow classes to create spans automatically.
-        
-        This patches the main lifecycle methods (chat, start, run, astart) to
-        create traces and spans without requiring explicit obs.trace() wrappers.
-        Also sets litellm callbacks for providers that support it, and registers
-        the ObservabilitySink bridge for ContextTraceEmitter events.
-        """
-        if not self._provider or not self._provider._initialized:
-            return
-        
-        try:
-            # Set litellm callbacks for the active provider
-            self._setup_litellm_callbacks()
-            
-            # Register bridge sink for ContextTraceEmitter events
-            self._setup_bridge_sink()
-            
-            
-            # Try to import praisonaiagents classes
-            try:
-                from praisonaiagents.agent.agent import Agent
-                self._wrap_agent_class(Agent)
-            except ImportError:
-                pass
-            
-            try:
-                from praisonaiagents.agents.agents import Agents
-                self._wrap_workflow_class(Agents)
-            except ImportError:
-                pass
-                
-        except Exception:
-            # Silently fail - don't break user code if instrumentation fails
-            pass
-    
-    def _setup_litellm_callbacks(self) -> None:
-        """Set litellm callbacks for the active provider."""
-        provider_name = self._provider.name if self._provider else None
-        if not provider_name:
-            return
-        
-        callback_name = self._get_litellm_callback_name(provider_name)
-        if not callback_name:
-            return
-        
-        try:
-            import litellm
-            
-            # Add callback if not already present
-            if not hasattr(litellm, 'callbacks') or litellm.callbacks is None:
-                litellm.callbacks = []
-            if callback_name not in litellm.callbacks:
-                litellm.callbacks.append(callback_name)
-            
-            if not hasattr(litellm, 'success_callback') or litellm.success_callback is None:
-                litellm.success_callback = []
-            if callback_name not in litellm.success_callback:
-                litellm.success_callback.append(callback_name)
-        except ImportError:
-            pass
-    
-    def _setup_bridge_sink(self) -> None:
-        """Register ObservabilitySink as the ContextTraceEmitter sink."""
-        try:
-            from praisonai_tools.observability.bridge import ObservabilitySink
-            from praisonaiagents.trace.context_events import (
-                ContextTraceEmitter,
-                set_context_emitter,
-            )
-            
-            sink = ObservabilitySink(self)
-            emitter = ContextTraceEmitter(
-                sink=sink,
-                session_id=self.config.session_id or "",
-                enabled=True,
-            )
-            set_context_emitter(emitter)
-        except ImportError:
-            pass
-    
-    @staticmethod
-    def _get_version():
-        """Get praisonai-tools version lazily."""
-        try:
-            from praisonai_tools import __version__
-            return __version__
-        except Exception:
-            return "unknown"
-    
-    @staticmethod
-    def _set_branding_attrs(span):
-        """Set framework branding attributes on a span."""
-        if span is None:
-            return
-        try:
-            span.attributes["praisonai.version"] = ObservabilityManager._get_version()
-            span.attributes["praisonai.framework"] = "praisonai"
-        except Exception:
-            pass
-    
-    def _wrap_agent_class(self, agent_cls: type) -> None:
-        """Wrap Agent class methods to auto-create spans with input/output data."""
-        from functools import wraps
-        import json
-        
-        # Skip if already instrumented
-        if getattr(agent_cls, '_obs_instrumented', False):
-            return
-        
-        manager = self
-        
-        def _extract_chat_input(agent_self, args, kwargs):
-            """Extract the input prompt from chat() arguments."""
-            prompt = args[0] if args else kwargs.get('prompt', kwargs.get('message', ''))
-            agent_name = getattr(agent_self, 'name', 'unknown')
-            instructions = getattr(agent_self, 'instructions', '')
-            model = getattr(agent_self, 'model', None)
-            # Use a dict format that LangSmith can display
-            input_data = {"input": str(prompt) if prompt else ""}
-            if instructions:
-                input_data["instructions"] = str(instructions)
-            if agent_name:
-                input_data["agent_name"] = agent_name
-            if model:
-                input_data["model"] = str(model)
-            return input_data
-        
-        def _set_span_io(span, input_data, output_data):
-            """Set input/output attributes on a span for LangSmith display."""
-            if span is None:
-                return
-            try:
-                # LangSmith recognizes these attributes for chain/agent spans
-                if input_data:
-                    input_str = json.dumps(input_data) if isinstance(input_data, dict) else str(input_data)
-                    span.attributes["input.value"] = input_str
-                    span.attributes["input"] = input_str
-                if output_data:
-                    output_str = str(output_data) if not isinstance(output_data, str) else output_data
-                    span.attributes["output.value"] = output_str
-                    span.attributes["output"] = output_str
-            except Exception:
-                pass
-        
-        # Wrap chat method
-        if hasattr(agent_cls, 'chat'):
-            original_chat = agent_cls.chat
-            
-            @wraps(original_chat)
-            def instrumented_chat(self, *args, **kwargs):
-                agent_name = getattr(self, 'name', 'unknown')
-                input_data = _extract_chat_input(self, args, kwargs)
-                with manager.trace(agent_name):
-                    span = manager.start_span(agent_name, kind=SpanKind.AGENT)
-                    _set_branding_attrs = ObservabilityManager._set_branding_attrs
-                    _set_branding_attrs(span)
-                    try:
-                        # Set input before the call
-                        _set_span_io(span, input_data, None)
-                        result = original_chat(self, *args, **kwargs)
-                        # Set output after the call
-                        output_str = str(result) if result else ""
-                        # Truncate very long outputs for span attributes
-                        if len(output_str) > 4000:
-                            output_str = output_str[:4000] + "..."
-                        _set_span_io(span, None, output_str)
-                        return result
-                    except Exception as e:
-                        if span:
-                            span.set_error(e)
-                        raise
-                    finally:
-                        manager.end_span(span)
-            
-            agent_cls.chat = instrumented_chat
-        
-        # Wrap start method
-        if hasattr(agent_cls, 'start'):
-            original_start = agent_cls.start
-            
-            @wraps(original_start)
-            def instrumented_start(self, *args, **kwargs):
-                import types
-                agent_name = getattr(self, 'name', 'unknown')
-                
-                result = original_start(self, *args, **kwargs)
-                
-                # Handle generator (streaming) mode
-                if isinstance(result, types.GeneratorType):
-                    def traced_generator():
-                        with manager.trace(agent_name):
-                            span_ctx = manager.span(agent_name, kind=SpanKind.AGENT)
-                            with span_ctx as span:
-                                ObservabilityManager._set_branding_attrs(span)
-                                for chunk in result:
-                                    yield chunk
-                    return traced_generator()
-                else:
-                    return result
-            
-            agent_cls.start = instrumented_start
-        
-        # Wrap run method
-        if hasattr(agent_cls, 'run'):
-            original_run = agent_cls.run
-            
-            @wraps(original_run)
-            def instrumented_run(self, *args, **kwargs):
-                agent_name = getattr(self, 'name', 'unknown')
-                with manager.trace(agent_name):
-                    with manager.span(agent_name, kind=SpanKind.AGENT) as span:
-                        ObservabilityManager._set_branding_attrs(span)
-                        return original_run(self, *args, **kwargs)
-            
-            agent_cls.run = instrumented_run
-        
-        # Mark as instrumented
-        agent_cls._obs_instrumented = True
-    
-    def _wrap_workflow_class(self, workflow_cls: type) -> None:
-        """Wrap Agents (workflow) class methods to auto-create spans."""
-        from functools import wraps
-        
-        # Skip if already instrumented
-        if getattr(workflow_cls, '_obs_instrumented', False):
-            return
-        
-        manager = self
-        
-        # Wrap start method
-        if hasattr(workflow_cls, 'start'):
-            original_start = workflow_cls.start
-            
-            @wraps(original_start)
-            def instrumented_start(self, *args, **kwargs):
-                workflow_name = getattr(self, 'name', None) or 'workflow'
-                with manager.trace(workflow_name):
-                    with manager.span(workflow_name, kind=SpanKind.WORKFLOW) as span:
-                        ObservabilityManager._set_branding_attrs(span)
-                        # Capture input: task descriptions
-                        try:
-                            tasks_raw = getattr(self, 'tasks', []) or []
-                            # Agents stores tasks as dict {idx: Task}, not a list
-                            task_items = tasks_raw.values() if isinstance(tasks_raw, dict) else tasks_raw
-                            agents_raw = getattr(self, 'agents', []) or []
-                            agent_items = agents_raw.values() if isinstance(agents_raw, dict) else agents_raw
-                            input_data = {
-                                "workflow": workflow_name,
-                                "agents": [getattr(a, 'name', str(a)) for a in agent_items],
-                                "tasks": [getattr(t, 'description', str(t)) for t in task_items],
-                            }
-                            import json
-                            input_str = json.dumps(input_data)
-                            if span:
-                                span.attributes["input.value"] = input_str
-                                span.attributes["input"] = input_str
-                        except Exception:
-                            pass
-                        result = original_start(self, *args, **kwargs)
-                        # Capture output
-                        try:
-                            if span and result:
-                                output_str = str(result)
-                                if len(output_str) > 4000:
-                                    output_str = output_str[:4000] + "..."
-                                span.attributes["output.value"] = output_str
-                                span.attributes["output"] = output_str
-                        except Exception:
-                            pass
-                        return result
-            
-            workflow_cls.start = instrumented_start
-        
-        # Wrap astart (async) method
-        if hasattr(workflow_cls, 'astart'):
-            original_astart = workflow_cls.astart
-            
-            @wraps(original_astart)
-            async def instrumented_astart(self, *args, **kwargs):
-                workflow_name = getattr(self, 'name', None) or 'workflow'
-                with manager.trace(workflow_name):
-                    with manager.span(workflow_name, kind=SpanKind.WORKFLOW) as span:
-                        ObservabilityManager._set_branding_attrs(span)
-                        # Capture input
-                        try:
-                            tasks_raw = getattr(self, 'tasks', []) or []
-                            task_items = tasks_raw.values() if isinstance(tasks_raw, dict) else tasks_raw
-                            agents_raw = getattr(self, 'agents', []) or []
-                            agent_items = agents_raw.values() if isinstance(agents_raw, dict) else agents_raw
-                            input_data = {
-                                "workflow": workflow_name,
-                                "agents": [getattr(a, 'name', str(a)) for a in agent_items],
-                                "tasks": [getattr(t, 'description', str(t)) for t in task_items],
-                            }
-                            import json
-                            input_str = json.dumps(input_data)
-                            if span:
-                                span.attributes["input.value"] = input_str
-                                span.attributes["input"] = input_str
-                        except Exception:
-                            pass
-                        result = await original_astart(self, *args, **kwargs)
-                        # Capture output
-                        try:
-                            if span and result:
-                                output_str = str(result)
-                                if len(output_str) > 4000:
-                                    output_str = output_str[:4000] + "..."
-                                span.attributes["output.value"] = output_str
-                                span.attributes["output"] = output_str
-                        except Exception:
-                            pass
-                        return result
-            
-            workflow_cls.astart = instrumented_astart
-        
-        # Mark as instrumented
-        workflow_cls._obs_instrumented = True
-    
-    def _load_provider(self, name: str) -> None:
-        """Dynamically load a provider module."""
-        provider_modules = {
-            "agentops": "agentops_provider",
-            "langfuse": "langfuse_provider",
-            "langsmith": "langsmith_provider",
-            "traceloop": "traceloop_provider",
-            "arize_phoenix": "arize_phoenix_provider",
-            "openlit": "openlit_provider",
-            "langtrace": "langtrace_provider",
-            "langwatch": "langwatch_provider",
-            "datadog": "datadog_provider",
-            "mlflow": "mlflow_provider",
-            "opik": "opik_provider",
-            "portkey": "portkey_provider",
-            "braintrust": "braintrust_provider",
-            "maxim": "maxim_provider",
-            "weave": "weave_provider",
-            "neatlogs": "neatlogs_provider",
-            "langdb": "langdb_provider",
-            "atla": "atla_provider",
-            "patronus": "patronus_provider",
-            "truefoundry": "truefoundry_provider",
-        }
-        
-        if name in provider_modules:
-            try:
-                import importlib
-                importlib.import_module(
-                    f"praisonai_tools.observability.providers.{provider_modules[name]}"
-                )
-                # Provider should auto-register on import
-            except ImportError:
-                pass
-    
-    def shutdown(self) -> None:
-        """Shutdown the current provider."""
-        if self._provider:
-            self._provider.shutdown()
-            self._provider = None
-    
-    @property
-    def provider(self) -> Optional[BaseObservabilityProvider]:
-        """Get the current provider."""
-        return self._provider
-    
-    @property
-    def enabled(self) -> bool:
-        """Check if observability is enabled."""
-        return self._provider is not None and self._provider._initialized
-    
-    def doctor(self) -> Dict[str, Any]:
-        """
-        Run diagnostics on observability setup.
-        
-        Returns:
-            Dict with diagnostic results
-        """
-        results = {
-            "enabled": self.enabled,
-            "provider": self._provider.name if self._provider else None,
-            "available_providers": self.get_available_providers(),
-            "registered_providers": self.list_providers(),
-            "connection_status": None,
-            "connection_message": None,
-        }
-        
-        if self._provider:
-            success, message = self._provider.check_connection()
-            results["connection_status"] = success
-            results["connection_message"] = message
-        
-        return results
-    
-    # Delegate methods to provider
-    
-    def trace(self, name: str, **kwargs):
-        """Start a trace context manager."""
-        if self._provider:
-            return self._provider.trace(name, **kwargs)
-        return _NoOpContextManager()
-    
-    def span(self, name: str, kind: SpanKind = SpanKind.CUSTOM, **kwargs):
-        """Start a span context manager."""
-        if self._provider:
-            return self._provider.span(name, kind, **kwargs)
-        return _NoOpContextManager()
-    
-    def start_trace(self, name: str, **kwargs) -> Optional[Trace]:
-        """Start a new trace."""
-        if self._provider:
-            return self._provider.start_trace(name, **kwargs)
-        return None
-    
-    def end_trace(self, trace: Optional[Trace] = None) -> None:
-        """End a trace."""
-        if self._provider:
-            self._provider.end_trace(trace)
-    
-    def start_span(self, name: str, kind: SpanKind = SpanKind.CUSTOM, **kwargs) -> Optional[Span]:
-        """Start a new span."""
-        if self._provider:
-            return self._provider.start_span(name, kind, **kwargs)
-        return None
-    
-    def end_span(self, span: Optional[Span] = None, status: SpanStatus = SpanStatus.OK) -> None:
-        """End a span."""
-        if self._provider:
-            self._provider.end_span(span, status)
-    
-    def log_llm_call(self, model: str, input_messages: Any, output: Any, **kwargs) -> Optional[Span]:
-        """Log an LLM call."""
-        if self._provider:
-            return self._provider.log_llm_call(model, input_messages, output, **kwargs)
-        return None
-    
-    def log_tool_call(self, tool_name: str, tool_input: Any, tool_output: Any, **kwargs) -> Optional[Span]:
-        """Log a tool call."""
-        if self._provider:
-            return self._provider.log_tool_call(tool_name, tool_input, tool_output, **kwargs)
-        return None
-    
-    def log_agent_step(self, agent_name: str, step_name: str, input_data: Any, output_data: Any, **kwargs) -> Optional[Span]:
-        """Log an agent step."""
-        if self._provider:
-            return self._provider.log_agent_step(agent_name, step_name, input_data, output_data, **kwargs)
-        return None
-    
-    def decorator(self, name: Optional[str] = None, kind: SpanKind = SpanKind.CUSTOM):
-        """Decorator to trace a function."""
-        if self._provider:
-            return self._provider.decorator(name, kind)
-        
-        # No-op decorator
-        def noop(func):
-            return func
-        return noop
-
-
-class _NoOpContextManager:
-    """No-op context manager when observability is disabled."""
-    
-    def __enter__(self):
-        return None
-    
-    def __exit__(self, *args):
-        pass
-
-
-# Auto-register built-in providers on first access
-def _auto_register_providers():
-    """Auto-register all built-in providers."""
-    provider_classes = [
-        ("agentops", "AgentOpsProvider", "agentops_provider"),
-        ("langfuse", "LangfuseProvider", "langfuse_provider"),
-        ("langsmith", "LangSmithProvider", "langsmith_provider"),
-        ("traceloop", "TraceloopProvider", "traceloop_provider"),
-        ("arize_phoenix", "ArizePhoenixProvider", "arize_phoenix_provider"),
-        ("openlit", "OpenLITProvider", "openlit_provider"),
-        ("langtrace", "LangtraceProvider", "langtrace_provider"),
-        ("langwatch", "LangWatchProvider", "langwatch_provider"),
-        ("datadog", "DatadogProvider", "datadog_provider"),
-        ("mlflow", "MLflowProvider", "mlflow_provider"),
-        ("opik", "OpikProvider", "opik_provider"),
-        ("portkey", "PortkeyProvider", "portkey_provider"),
-        ("braintrust", "BraintrustProvider", "braintrust_provider"),
-        ("maxim", "MaximProvider", "maxim_provider"),
-        ("weave", "WeaveProvider", "weave_provider"),
-        ("neatlogs", "NeatlogsProvider", "neatlogs_provider"),
-        ("langdb", "LangDBProvider", "langdb_provider"),
-        ("atla", "AtlaProvider", "atla_provider"),
-        ("patronus", "PatronusProvider", "patronus_provider"),
-        ("truefoundry", "TrueFoundryProvider", "truefoundry_provider"),
-    ]
-    
-    for name, class_name, module_name in provider_classes:
-        try:
-            import importlib
-            module = importlib.import_module(
-                f"praisonai_tools.observability.providers.{module_name}"
-            )
-            provider_class = getattr(module, class_name)
-            ObservabilityManager.register_provider(name, provider_class)
-        except (ImportError, AttributeError):
-            pass  # Provider not available

praisonai_tools/observability/providers/__init__.py

@@ -1,63 +0,0 @@
-"""
-Observability Providers
-
-All provider implementations for the observability suite.
-"""
-
-__all__ = [
-    "AgentOpsProvider",
-    "LangfuseProvider",
-    "LangSmithProvider",
-    "TraceloopProvider",
-    "ArizePhoenixProvider",
-    "OpenLITProvider",
-    "LangtraceProvider",
-    "LangWatchProvider",
-    "DatadogProvider",
-    "MLflowProvider",
-    "OpikProvider",
-    "PortkeyProvider",
-    "BraintrustProvider",
-    "MaximProvider",
-    "WeaveProvider",
-    "NeatlogsProvider",
-    "LangDBProvider",
-    "AtlaProvider",
-    "PatronusProvider",
-    "TrueFoundryProvider",
-]
-
-
-def __getattr__(name: str):
-    """Lazy load provider classes."""
-    provider_map = {
-        "AgentOpsProvider": "agentops_provider",
-        "LangfuseProvider": "langfuse_provider",
-        "LangSmithProvider": "langsmith_provider",
-        "TraceloopProvider": "traceloop_provider",
-        "ArizePhoenixProvider": "arize_phoenix_provider",
-        "OpenLITProvider": "openlit_provider",
-        "LangtraceProvider": "langtrace_provider",
-        "LangWatchProvider": "langwatch_provider",
-        "DatadogProvider": "datadog_provider",
-        "MLflowProvider": "mlflow_provider",
-        "OpikProvider": "opik_provider",
-        "PortkeyProvider": "portkey_provider",
-        "BraintrustProvider": "braintrust_provider",
-        "MaximProvider": "maxim_provider",
-        "WeaveProvider": "weave_provider",
-        "NeatlogsProvider": "neatlogs_provider",
-        "LangDBProvider": "langdb_provider",
-        "AtlaProvider": "atla_provider",
-        "PatronusProvider": "patronus_provider",
-        "TrueFoundryProvider": "truefoundry_provider",
-    }
-    
-    if name in provider_map:
-        import importlib
-        module = importlib.import_module(
-            f"praisonai_tools.observability.providers.{provider_map[name]}"
-        )
-        return getattr(module, name)
-    
-    raise AttributeError(f"module 'praisonai_tools.observability.providers' has no attribute '{name}'")

praisonai_tools/observability/providers/agentops_provider.py

@@ -1,119 +0,0 @@
-"""
-AgentOps Provider
-
-Integration with AgentOps for agent observability.
-https://agentops.ai/
-"""
-
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class AgentOpsProvider(BaseObservabilityProvider):
-    """AgentOps observability provider."""
-    
-    name = "agentops"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize AgentOps provider."""
-        super().__init__(config)
-        self._client = None
-        self._session = None
-    
-    def is_available(self) -> bool:
-        """Check if agentops SDK is available."""
-        try:
-            import agentops
-            return True
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize AgentOps."""
-        if not self.is_available():
-            return False
-        
-        try:
-            import agentops
-            
-            api_key = kwargs.get("api_key") or os.getenv("AGENTOPS_API_KEY")
-            
-            if not api_key:
-                return False
-            
-            # Initialize AgentOps
-            agentops.init(
-                api_key=api_key,
-                default_tags=kwargs.get("tags", []),
-                auto_start_session=kwargs.get("auto_start_session", True),
-            )
-            
-            self._client = agentops
-            self._initialized = True
-            return True
-            
-        except Exception as e:
-            print(f"AgentOps init failed: {e}")
-            return False
-    
-    def shutdown(self) -> None:
-        """Shutdown AgentOps."""
-        if self._client and self._session:
-            try:
-                self._client.end_session()
-            except Exception:
-                pass
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check AgentOps connection."""
-        if not self._initialized:
-            return False, "AgentOps not initialized"
-        
-        try:
-            # AgentOps doesn't have a direct ping method
-            # Check if we have a valid API key
-            api_key = os.getenv("AGENTOPS_API_KEY")
-            if api_key:
-                return True, "AgentOps API key configured"
-            return False, "AGENTOPS_API_KEY not set"
-        except Exception as e:
-            return False, str(e)
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to AgentOps."""
-        if not self._initialized or not self._client:
-            return False
-        
-        try:
-            # AgentOps auto-instruments, but we can record events
-            if span.error_message:
-                self._client.record(
-                    self._client.ErrorEvent(
-                        error_type=span.error_type or "Error",
-                        details=span.error_message,
-                    )
-                )
-            return True
-        except Exception:
-            return False
-    
-    def export_trace(self, trace: Trace) -> bool:
-        """Export trace to AgentOps."""
-        if not self._initialized:
-            return False
-        
-        # AgentOps handles traces automatically via sessions
-        return True
-
-
-# Auto-register provider
-ObservabilityManager.register_provider("agentops", AgentOpsProvider)

praisonai_tools/observability/providers/arize_phoenix_provider.py

@@ -1,104 +0,0 @@
-"""
-Arize Phoenix Provider
-
-Integration with Arize Phoenix for LLM observability.
-https://phoenix.arize.com/
-"""
-
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class ArizePhoenixProvider(BaseObservabilityProvider):
-    """Arize Phoenix observability provider."""
-    
-    name = "arize_phoenix"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize Arize Phoenix provider."""
-        super().__init__(config)
-        self._tracer_provider = None
-    
-    def is_available(self) -> bool:
-        """Check if phoenix SDK is available."""
-        try:
-            import importlib.util
-            return importlib.util.find_spec("phoenix.otel") is not None
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize Arize Phoenix."""
-        if not self.is_available():
-            return False
-        
-        try:
-            from phoenix.otel import register
-            
-            api_key = kwargs.get("api_key") or os.getenv("PHOENIX_API_KEY")
-            endpoint = kwargs.get("endpoint") or os.getenv(
-                "PHOENIX_COLLECTOR_ENDPOINT", "https://app.phoenix.arize.com/"
-            )
-            project_name = kwargs.get("project_name") or self.config.project_name or "default"
-            
-            # Set environment variables
-            if api_key:
-                os.environ["PHOENIX_API_KEY"] = api_key
-            os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = endpoint
-            
-            # Register Phoenix tracer
-            self._tracer_provider = register(
-                project_name=project_name,
-                auto_instrument=kwargs.get("auto_instrument", True),
-            )
-            
-            self._initialized = True
-            return True
-            
-        except Exception as e:
-            print(f"Arize Phoenix init failed: {e}")
-            return False
-    
-    def shutdown(self) -> None:
-        """Shutdown Arize Phoenix provider."""
-        if self._tracer_provider:
-            try:
-                self._tracer_provider.shutdown()
-            except Exception:
-                pass
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check Arize Phoenix connection."""
-        if not self._initialized:
-            return False, "Arize Phoenix not initialized"
-        
-        api_key = os.getenv("PHOENIX_API_KEY")
-        if api_key:
-            return True, "Phoenix API key configured"
-        return False, "PHOENIX_API_KEY not set"
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to Arize Phoenix."""
-        if not self._initialized:
-            return False
-        # Phoenix auto-instruments via OpenInference
-        return True
-    
-    def export_trace(self, trace_obj: Trace) -> bool:
-        """Export trace to Arize Phoenix."""
-        if not self._initialized:
-            return False
-        return True
-
-
-# Auto-register provider
-ObservabilityManager.register_provider("arize_phoenix", ArizePhoenixProvider)

praisonai_tools/observability/providers/atla_provider.py

@@ -1,95 +0,0 @@
-"""
-Atla Provider
-
-Integration with Atla for LLM observability.
-https://app.atla-ai.com/
-"""
-
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class AtlaProvider(BaseObservabilityProvider):
-    """Atla observability provider."""
-    
-    name = "atla"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize Atla provider."""
-        super().__init__(config)
-        self._atla = None
-    
-    def is_available(self) -> bool:
-        """Check if atla SDK is available."""
-        try:
-            import importlib.util
-            return importlib.util.find_spec("atla_insights") is not None
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize Atla."""
-        if not self.is_available():
-            return False
-        
-        try:
-            from atla_insights import configure
-            
-            api_key = kwargs.get("api_key") or os.getenv("ATLA_API_KEY")
-            
-            if not api_key:
-                return False
-            
-            configure(token=api_key)
-            self._initialized = True
-            return True
-            
-        except Exception as e:
-            print(f"Atla init failed: {e}")
-            return False
-    
-    def shutdown(self) -> None:
-        """Shutdown Atla provider."""
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check Atla connection."""
-        if not self._initialized:
-            return False, "Atla not initialized"
-        
-        api_key = os.getenv("ATLA_API_KEY")
-        if api_key:
-            return True, "Atla API key configured"
-        return False, "ATLA_API_KEY not set"
-    
-    def instrument(self, provider: str = "openai"):
-        """Get Atla instrumentation context manager."""
-        if not self.is_available():
-            from contextlib import nullcontext
-            return nullcontext()
-        
-        try:
-            from atla_insights import instrument_agno
-            return instrument_agno(provider)
-        except ImportError:
-            from contextlib import nullcontext
-            return nullcontext()
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to Atla."""
-        return self._initialized
-    
-    def export_trace(self, trace_obj: Trace) -> bool:
-        """Export trace to Atla."""
-        return self._initialized
-
-
-ObservabilityManager.register_provider("atla", AtlaProvider)

praisonai_tools/observability/providers/braintrust_provider.py

@@ -1,106 +0,0 @@
-"""
-Braintrust Provider
-
-Integration with Braintrust for LLM observability and evaluation.
-https://www.braintrust.dev/
-"""
-
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class BraintrustProvider(BaseObservabilityProvider):
-    """Braintrust observability provider."""
-    
-    name = "braintrust"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize Braintrust provider."""
-        super().__init__(config)
-        self._tracer_provider = None
-    
-    def is_available(self) -> bool:
-        """Check if braintrust SDK is available."""
-        try:
-            import importlib.util
-            return importlib.util.find_spec("braintrust") is not None
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize Braintrust."""
-        if not self.is_available():
-            return False
-        
-        try:
-            from opentelemetry import trace
-            from opentelemetry.sdk.trace import TracerProvider
-            from braintrust.otel import BraintrustSpanProcessor
-            
-            api_key = kwargs.get("api_key") or os.getenv("BRAINTRUST_API_KEY")
-            project = kwargs.get("project") or os.getenv("BRAINTRUST_PARENT", "praisonai")
-            
-            if not api_key:
-                return False
-            
-            os.environ["BRAINTRUST_API_KEY"] = api_key
-            os.environ["BRAINTRUST_PARENT"] = f"project_name:{project}"
-            
-            current_provider = trace.get_tracer_provider()
-            if isinstance(current_provider, TracerProvider):
-                provider = current_provider
-            else:
-                provider = TracerProvider()
-                trace.set_tracer_provider(provider)
-            
-            provider.add_span_processor(BraintrustSpanProcessor())
-            self._tracer_provider = provider
-            
-            self._initialized = True
-            return True
-            
-        except Exception as e:
-            print(f"Braintrust init failed: {e}")
-            return False
-    
-    def shutdown(self) -> None:
-        """Shutdown Braintrust provider."""
-        if self._tracer_provider:
-            try:
-                self._tracer_provider.shutdown()
-            except Exception:
-                pass
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check Braintrust connection."""
-        if not self._initialized:
-            return False, "Braintrust not initialized"
-        
-        api_key = os.getenv("BRAINTRUST_API_KEY")
-        if api_key:
-            return True, "Braintrust API key configured"
-        return False, "BRAINTRUST_API_KEY not set"
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to Braintrust."""
-        if not self._initialized:
-            return False
-        return True
-    
-    def export_trace(self, trace_obj: Trace) -> bool:
-        """Export trace to Braintrust."""
-        if not self._initialized:
-            return False
-        return True
-
-
-ObservabilityManager.register_provider("braintrust", BraintrustProvider)

praisonai_tools/observability/providers/datadog_provider.py

@@ -1,91 +0,0 @@
-"""
-Datadog Provider
-
-Integration with Datadog LLM Observability.
-https://www.datadoghq.com/product/llm-observability/
-"""
-
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class DatadogProvider(BaseObservabilityProvider):
-    """Datadog LLM Observability provider."""
-    
-    name = "datadog"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize Datadog provider."""
-        super().__init__(config)
-        self._ddtrace = None
-    
-    def is_available(self) -> bool:
-        """Check if ddtrace SDK is available."""
-        try:
-            import importlib.util
-            return importlib.util.find_spec("ddtrace") is not None
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize Datadog."""
-        if not self.is_available():
-            return False
-        
-        try:
-            api_key = kwargs.get("api_key") or os.getenv("DD_API_KEY")
-            site = kwargs.get("site") or os.getenv("DD_SITE", "datadoghq.com")
-            ml_app = kwargs.get("ml_app") or os.getenv("DD_LLMOBS_ML_APP", "praisonai")
-            
-            if not api_key:
-                return False
-            
-            os.environ["DD_API_KEY"] = api_key
-            os.environ["DD_SITE"] = site
-            os.environ["DD_LLMOBS_ENABLED"] = "true"
-            os.environ["DD_LLMOBS_ML_APP"] = ml_app
-            os.environ["DD_LLMOBS_AGENTLESS_ENABLED"] = "true"
-            
-            self._initialized = True
-            return True
-            
-        except Exception as e:
-            print(f"Datadog init failed: {e}")
-            return False
-    
-    def shutdown(self) -> None:
-        """Shutdown Datadog provider."""
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check Datadog connection."""
-        if not self._initialized:
-            return False, "Datadog not initialized"
-        
-        api_key = os.getenv("DD_API_KEY")
-        if api_key:
-            return True, "Datadog API key configured"
-        return False, "DD_API_KEY not set"
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to Datadog."""
-        if not self._initialized:
-            return False
-        return True
-    
-    def export_trace(self, trace_obj: Trace) -> bool:
-        """Export trace to Datadog."""
-        if not self._initialized:
-            return False
-        return True
-
-
-ObservabilityManager.register_provider("datadog", DatadogProvider)

praisonai_tools/observability/providers/langdb_provider.py

@@ -1,63 +0,0 @@
-"""
-LangDB Provider
-
-Integration with LangDB for SQL-based LLM observability.
-"""
-
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class LangDBProvider(BaseObservabilityProvider):
-    """LangDB observability provider."""
-    
-    name = "langdb"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize LangDB provider."""
-        super().__init__(config)
-    
-    def is_available(self) -> bool:
-        """Check if langdb SDK is available."""
-        try:
-            import importlib.util
-            return importlib.util.find_spec("langdb") is not None
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize LangDB."""
-        api_key = kwargs.get("api_key") or os.getenv("LANGDB_API_KEY")
-        if not api_key:
-            return False
-        self._initialized = True
-        return True
-    
-    def shutdown(self) -> None:
-        """Shutdown LangDB provider."""
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check LangDB connection."""
-        if not self._initialized:
-            return False, "LangDB not initialized"
-        return True, "LangDB configured"
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to LangDB."""
-        return self._initialized
-    
-    def export_trace(self, trace_obj: Trace) -> bool:
-        """Export trace to LangDB."""
-        return self._initialized
-
-
-ObservabilityManager.register_provider("langdb", LangDBProvider)

praisonai_tools/observability/providers/langfuse_provider.py

@@ -1,159 +0,0 @@
-"""
-Langfuse Provider
-
-Integration with Langfuse for LLM observability.
-https://langfuse.com/
-"""
-
-import base64
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class LangfuseProvider(BaseObservabilityProvider):
-    """Langfuse observability provider using OpenTelemetry."""
-    
-    name = "langfuse"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize Langfuse provider."""
-        super().__init__(config)
-        self._tracer_provider = None
-        self._tracer = None
-    
-    def is_available(self) -> bool:
-        """Check if required SDKs are available."""
-        try:
-            from opentelemetry.sdk.trace import TracerProvider
-            from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
-            return True
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize Langfuse via OpenTelemetry."""
-        if not self.is_available():
-            return False
-        
-        try:
-            from opentelemetry import trace
-            from opentelemetry.sdk.trace import TracerProvider
-            from opentelemetry.sdk.trace.export import SimpleSpanProcessor
-            from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
-            from opentelemetry.sdk.resources import Resource
-            
-            public_key = kwargs.get("public_key") or os.getenv("LANGFUSE_PUBLIC_KEY")
-            secret_key = kwargs.get("secret_key") or os.getenv("LANGFUSE_SECRET_KEY")
-            host = kwargs.get("host") or os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com")
-            
-            if not public_key or not secret_key:
-                return False
-            
-            # Create auth header
-            auth = base64.b64encode(f"{public_key}:{secret_key}".encode()).decode()
-            
-            # Configure OTLP endpoint
-            endpoint = f"{host}/api/public/otel/v1/traces"
-            headers = {"Authorization": f"Basic {auth}"}
-            
-            # Get praisonai version for resource attributes
-            _version = "unknown"
-            try:
-                import praisonai_tools
-                _version = getattr(praisonai_tools, "__version__", "unknown")
-            except Exception:
-                pass
-            
-            # Setup tracer provider with PraisonAI resource branding
-            resource = Resource.create({
-                "service.name": "praisonai",
-                "service.version": _version,
-                "praisonai.version": _version,
-                "praisonai.framework": "praisonai",
-            })
-            self._tracer_provider = TracerProvider(resource=resource)
-            self._tracer_provider.add_span_processor(
-                SimpleSpanProcessor(OTLPSpanExporter(endpoint=endpoint, headers=headers))
-            )
-            trace.set_tracer_provider(self._tracer_provider)
-            self._tracer = trace.get_tracer("praisonai.observability")
-            
-            self._initialized = True
-            return True
-            
-        except Exception as e:
-            print(f"Langfuse init failed: {e}")
-            return False
-    
-    def shutdown(self) -> None:
-        """Shutdown Langfuse provider."""
-        if self._tracer_provider:
-            try:
-                self._tracer_provider.shutdown()
-            except Exception:
-                pass
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check Langfuse connection."""
-        if not self._initialized:
-            return False, "Langfuse not initialized"
-        
-        public_key = os.getenv("LANGFUSE_PUBLIC_KEY")
-        secret_key = os.getenv("LANGFUSE_SECRET_KEY")
-        
-        if public_key and secret_key:
-            return True, "Langfuse credentials configured"
-        return False, "LANGFUSE_PUBLIC_KEY or LANGFUSE_SECRET_KEY not set"
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to Langfuse via OTel."""
-        if not self._initialized or not self._tracer:
-            return False
-        
-        try:
-            with self._tracer.start_as_current_span(span.name) as otel_span:
-                # Set attributes
-                otel_span.set_attribute("span.kind", span.kind.value)
-                
-                if span.model:
-                    otel_span.set_attribute("llm.model", span.model)
-                if span.input_tokens:
-                    otel_span.set_attribute("llm.input_tokens", span.input_tokens)
-                if span.output_tokens:
-                    otel_span.set_attribute("llm.output_tokens", span.output_tokens)
-                if span.tool_name:
-                    otel_span.set_attribute("tool.name", span.tool_name)
-                
-                for key, value in span.attributes.items():
-                    otel_span.set_attribute(key, str(value))
-                
-                if span.error_message:
-                    from opentelemetry.trace import Status, StatusCode
-                    otel_span.set_status(
-                        Status(StatusCode.ERROR, span.error_message)
-                    )
-            
-            return True
-        except Exception:
-            return False
-    
-    def export_trace(self, trace_obj: Trace) -> bool:
-        """Export trace to Langfuse."""
-        if not self._initialized:
-            return False
-        
-        # Spans are exported individually via OTel
-        return True
-
-
-# Auto-register provider
-ObservabilityManager.register_provider("langfuse", LangfuseProvider)

praisonai_tools/observability/providers/langsmith_provider.py

@@ -1,186 +0,0 @@
-"""
-LangSmith Provider
-
-Integration with LangSmith for LLM observability.
-https://smith.langchain.com/
-"""
-
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class LangSmithProvider(BaseObservabilityProvider):
-    """LangSmith observability provider using OpenTelemetry."""
-    
-    name = "langsmith"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize LangSmith provider."""
-        super().__init__(config)
-        self._tracer_provider = None
-        self._tracer = None
-    
-    def is_available(self) -> bool:
-        """Check if required SDKs are available."""
-        try:
-            from opentelemetry.sdk.trace import TracerProvider
-            from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
-            return True
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize LangSmith via OpenTelemetry."""
-        if not self.is_available():
-            return False
-        
-        try:
-            from opentelemetry import trace
-            from opentelemetry.sdk.trace import TracerProvider
-            from opentelemetry.sdk.trace.export import SimpleSpanProcessor
-            from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
-            from opentelemetry.sdk.resources import Resource
-            
-            api_key = kwargs.get("api_key") or os.getenv("LANGSMITH_API_KEY")
-            project = kwargs.get("project") or os.getenv("LANGSMITH_PROJECT", "default")
-            endpoint = kwargs.get("endpoint") or os.getenv(
-                "LANGSMITH_ENDPOINT", "https://api.smith.langchain.com"
-            )
-            
-            if not api_key:
-                return False
-            
-            # Configure OTLP endpoint for LangSmith
-            otlp_endpoint = f"{endpoint}/otel/v1/traces"
-            headers = {
-                "x-api-key": api_key,
-                "Langsmith-Project": project,
-            }
-            
-            # Get praisonai version for resource attributes
-            _version = "unknown"
-            try:
-                import praisonai_tools
-                _version = getattr(praisonai_tools, "__version__", "unknown")
-            except Exception:
-                pass
-            
-            # Setup tracer provider with PraisonAI resource branding
-            resource = Resource.create({
-                "service.name": "praisonai",
-                "service.version": _version,
-                "praisonai.version": _version,
-                "praisonai.framework": "praisonai",
-            })
-            self._tracer_provider = TracerProvider(resource=resource)
-            self._tracer_provider.add_span_processor(
-                SimpleSpanProcessor(OTLPSpanExporter(endpoint=otlp_endpoint, headers=headers))
-            )
-            trace.set_tracer_provider(self._tracer_provider)
-            self._tracer = trace.get_tracer("praisonai.observability")
-            
-            self._initialized = True
-            return True
-            
-        except Exception as e:
-            print(f"LangSmith init failed: {e}")
-            return False
-    
-    def shutdown(self) -> None:
-        """Shutdown LangSmith provider."""
-        if self._tracer_provider:
-            try:
-                self._tracer_provider.shutdown()
-            except Exception:
-                pass
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check LangSmith connection."""
-        if not self._initialized:
-            return False, "LangSmith not initialized"
-        
-        api_key = os.getenv("LANGSMITH_API_KEY")
-        if api_key:
-            return True, "LangSmith API key configured"
-        return False, "LANGSMITH_API_KEY not set"
-    
-    # Map SpanKind to LangSmith span kind values
-    _LANGSMITH_SPAN_KINDS = {
-        "llm": "llm",
-        "tool": "tool",
-        "agent": "chain",
-        "chain": "chain",
-        "workflow": "chain",
-        "retrieval": "retriever",
-        "custom": "chain",
-    }
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to LangSmith via OTel with GenAI semantic conventions."""
-        if not self._initialized or not self._tracer:
-            return False
-        
-        try:
-            with self._tracer.start_as_current_span(span.name) as otel_span:
-                # LangSmith-specific span kind
-                ls_kind = self._LANGSMITH_SPAN_KINDS.get(span.kind.value, "chain")
-                otel_span.set_attribute("langsmith.span.kind", ls_kind)
-                
-                # GenAI model attributes
-                if span.model:
-                    otel_span.set_attribute("gen_ai.request.model", span.model)
-                    otel_span.set_attribute("gen_ai.response.model", span.model)
-                    otel_span.set_attribute("gen_ai.system", "openai")
-                
-                # GenAI usage metrics
-                if span.input_tokens:
-                    otel_span.set_attribute("gen_ai.usage.prompt_tokens", span.input_tokens)
-                if span.output_tokens:
-                    otel_span.set_attribute("gen_ai.usage.completion_tokens", span.output_tokens)
-                if span.input_tokens or span.output_tokens:
-                    otel_span.set_attribute(
-                        "gen_ai.usage.total_tokens",
-                        (span.input_tokens or 0) + (span.output_tokens or 0),
-                    )
-                
-                # Tool attributes
-                if span.tool_name:
-                    otel_span.set_attribute("gen_ai.tool.name", span.tool_name)
-                    otel_span.set_attribute("tool.name", span.tool_name)
-                
-                # Forward all custom attributes (includes gen_ai.prompt.*, gen_ai.completion.*, etc.)
-                for key, value in span.attributes.items():
-                    try:
-                        otel_span.set_attribute(key, str(value))
-                        # LangSmith maps langsmith.metadata.* to run metadata
-                        if key.startswith("praisonai."):
-                            otel_span.set_attribute(f"langsmith.metadata.{key}", str(value))
-                    except Exception:
-                        pass
-                
-                if span.error_message:
-                    from opentelemetry.trace import Status, StatusCode
-                    otel_span.set_status(Status(StatusCode.ERROR, span.error_message))
-            
-            return True
-        except Exception:
-            return False
-    
-    def export_trace(self, trace_obj: Trace) -> bool:
-        """Export trace to LangSmith."""
-        if not self._initialized:
-            return False
-        return True
-
-
-# Auto-register provider
-ObservabilityManager.register_provider("langsmith", LangSmithProvider)

praisonai_tools/observability/providers/langtrace_provider.py

@@ -1,89 +0,0 @@
-"""
-Langtrace Provider
-
-Integration with Langtrace for LLM observability.
-https://langtrace.ai/
-"""
-
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class LangtraceProvider(BaseObservabilityProvider):
-    """Langtrace observability provider."""
-    
-    name = "langtrace"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize Langtrace provider."""
-        super().__init__(config)
-        self._langtrace = None
-    
-    def is_available(self) -> bool:
-        """Check if langtrace SDK is available."""
-        try:
-            import importlib.util
-            return importlib.util.find_spec("langtrace_python_sdk") is not None
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize Langtrace."""
-        if not self.is_available():
-            return False
-        
-        try:
-            from langtrace_python_sdk import langtrace
-            
-            api_key = kwargs.get("api_key") or os.getenv("LANGTRACE_API_KEY")
-            
-            if api_key:
-                os.environ["LANGTRACE_API_KEY"] = api_key
-            
-            langtrace.init()
-            
-            self._langtrace = langtrace
-            self._initialized = True
-            return True
-            
-        except Exception as e:
-            print(f"Langtrace init failed: {e}")
-            return False
-    
-    def shutdown(self) -> None:
-        """Shutdown Langtrace provider."""
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check Langtrace connection."""
-        if not self._initialized:
-            return False, "Langtrace not initialized"
-        
-        api_key = os.getenv("LANGTRACE_API_KEY")
-        if api_key:
-            return True, "Langtrace API key configured"
-        return False, "LANGTRACE_API_KEY not set"
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to Langtrace."""
-        if not self._initialized:
-            return False
-        return True
-    
-    def export_trace(self, trace_obj: Trace) -> bool:
-        """Export trace to Langtrace."""
-        if not self._initialized:
-            return False
-        return True
-
-
-# Auto-register provider
-ObservabilityManager.register_provider("langtrace", LangtraceProvider)

praisonai_tools/observability/providers/langwatch_provider.py

@@ -1,90 +0,0 @@
-"""
-LangWatch Provider
-
-Integration with LangWatch for LLM observability.
-https://langwatch.ai/
-"""
-
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class LangWatchProvider(BaseObservabilityProvider):
-    """LangWatch observability provider."""
-    
-    name = "langwatch"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize LangWatch provider."""
-        super().__init__(config)
-        self._langwatch = None
-    
-    def is_available(self) -> bool:
-        """Check if langwatch SDK is available."""
-        try:
-            import importlib.util
-            return importlib.util.find_spec("langwatch") is not None
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize LangWatch."""
-        if not self.is_available():
-            return False
-        
-        try:
-            import langwatch
-            
-            api_key = kwargs.get("api_key") or os.getenv("LANGWATCH_API_KEY")
-            
-            if api_key:
-                os.environ["LANGWATCH_API_KEY"] = api_key
-            
-            instrumentors = kwargs.get("instrumentors", [])
-            langwatch.setup(instrumentors=instrumentors)
-            
-            self._langwatch = langwatch
-            self._initialized = True
-            return True
-            
-        except Exception as e:
-            print(f"LangWatch init failed: {e}")
-            return False
-    
-    def shutdown(self) -> None:
-        """Shutdown LangWatch provider."""
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check LangWatch connection."""
-        if not self._initialized:
-            return False, "LangWatch not initialized"
-        
-        api_key = os.getenv("LANGWATCH_API_KEY")
-        if api_key:
-            return True, "LangWatch API key configured"
-        return False, "LANGWATCH_API_KEY not set"
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to LangWatch."""
-        if not self._initialized:
-            return False
-        return True
-    
-    def export_trace(self, trace_obj: Trace) -> bool:
-        """Export trace to LangWatch."""
-        if not self._initialized:
-            return False
-        return True
-
-
-# Auto-register provider
-ObservabilityManager.register_provider("langwatch", LangWatchProvider)

praisonai_tools/observability/providers/maxim_provider.py

@@ -1,92 +0,0 @@
-"""
-Maxim Provider
-
-Integration with Maxim for LLM observability.
-https://getmaxim.ai/
-"""
-
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class MaximProvider(BaseObservabilityProvider):
-    """Maxim observability provider."""
-    
-    name = "maxim"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize Maxim provider."""
-        super().__init__(config)
-        self._maxim = None
-        self._logger = None
-    
-    def is_available(self) -> bool:
-        """Check if maxim SDK is available."""
-        try:
-            import importlib.util
-            return importlib.util.find_spec("maxim") is not None
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize Maxim."""
-        if not self.is_available():
-            return False
-        
-        try:
-            from maxim import Maxim
-            
-            api_key = kwargs.get("api_key") or os.getenv("MAXIM_API_KEY")
-            repo_id = kwargs.get("repo_id") or os.getenv("MAXIM_LOG_REPO_ID")
-            
-            if api_key:
-                os.environ["MAXIM_API_KEY"] = api_key
-            if repo_id:
-                os.environ["MAXIM_LOG_REPO_ID"] = repo_id
-            
-            self._maxim = Maxim()
-            self._logger = self._maxim.logger()
-            
-            self._initialized = True
-            return True
-            
-        except Exception as e:
-            print(f"Maxim init failed: {e}")
-            return False
-    
-    def shutdown(self) -> None:
-        """Shutdown Maxim provider."""
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check Maxim connection."""
-        if not self._initialized:
-            return False, "Maxim not initialized"
-        
-        api_key = os.getenv("MAXIM_API_KEY")
-        if api_key:
-            return True, "Maxim API key configured"
-        return False, "MAXIM_API_KEY not set"
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to Maxim."""
-        if not self._initialized:
-            return False
-        return True
-    
-    def export_trace(self, trace_obj: Trace) -> bool:
-        """Export trace to Maxim."""
-        if not self._initialized:
-            return False
-        return True
-
-
-ObservabilityManager.register_provider("maxim", MaximProvider)

praisonai_tools/observability/providers/mlflow_provider.py

@@ -1,89 +0,0 @@
-"""
-MLflow Provider
-
-Integration with MLflow for LLM observability.
-https://mlflow.org/
-"""
-
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class MLflowProvider(BaseObservabilityProvider):
-    """MLflow observability provider."""
-    
-    name = "mlflow"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize MLflow provider."""
-        super().__init__(config)
-        self._mlflow = None
-    
-    def is_available(self) -> bool:
-        """Check if mlflow SDK is available."""
-        try:
-            import importlib.util
-            return importlib.util.find_spec("mlflow") is not None
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize MLflow."""
-        if not self.is_available():
-            return False
-        
-        try:
-            import mlflow
-            
-            tracking_uri = kwargs.get("tracking_uri") or os.getenv("MLFLOW_TRACKING_URI")
-            experiment_name = kwargs.get("experiment") or self.config.project_name or "PraisonAI"
-            
-            if tracking_uri:
-                mlflow.set_tracking_uri(tracking_uri)
-            
-            mlflow.set_experiment(experiment_name)
-            
-            self._mlflow = mlflow
-            self._initialized = True
-            return True
-            
-        except Exception as e:
-            print(f"MLflow init failed: {e}")
-            return False
-    
-    def shutdown(self) -> None:
-        """Shutdown MLflow provider."""
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check MLflow connection."""
-        if not self._initialized:
-            return False, "MLflow not initialized"
-        
-        tracking_uri = os.getenv("MLFLOW_TRACKING_URI")
-        if tracking_uri:
-            return True, f"MLflow tracking URI: {tracking_uri}"
-        return True, "MLflow initialized with default tracking"
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to MLflow."""
-        if not self._initialized or not self._mlflow:
-            return False
-        return True
-    
-    def export_trace(self, trace_obj: Trace) -> bool:
-        """Export trace to MLflow."""
-        if not self._initialized:
-            return False
-        return True
-
-
-ObservabilityManager.register_provider("mlflow", MLflowProvider)

praisonai_tools/observability/providers/neatlogs_provider.py

@@ -1,63 +0,0 @@
-"""
-Neatlogs Provider
-
-Integration with Neatlogs for LLM observability.
-"""
-
-import os
-from typing import Optional
-
-from praisonai_tools.observability.base import (
-    BaseObservabilityProvider,
-    Span,
-    Trace,
-)
-from praisonai_tools.observability.config import ObservabilityConfig
-from praisonai_tools.observability.manager import ObservabilityManager
-
-
-class NeatlogsProvider(BaseObservabilityProvider):
-    """Neatlogs observability provider."""
-    
-    name = "neatlogs"
-    
-    def __init__(self, config: Optional[ObservabilityConfig] = None):
-        """Initialize Neatlogs provider."""
-        super().__init__(config)
-    
-    def is_available(self) -> bool:
-        """Check if neatlogs SDK is available."""
-        try:
-            import importlib.util
-            return importlib.util.find_spec("neatlogs") is not None
-        except ImportError:
-            return False
-    
-    def init(self, **kwargs) -> bool:
-        """Initialize Neatlogs."""
-        api_key = kwargs.get("api_key") or os.getenv("NEATLOGS_API_KEY")
-        if not api_key:
-            return False
-        self._initialized = True
-        return True
-    
-    def shutdown(self) -> None:
-        """Shutdown Neatlogs provider."""
-        self._initialized = False
-    
-    def check_connection(self) -> tuple:
-        """Check Neatlogs connection."""
-        if not self._initialized:
-            return False, "Neatlogs not initialized"
-        return True, "Neatlogs configured"
-    
-    def export_span(self, span: Span) -> bool:
-        """Export span to Neatlogs."""
-        return self._initialized
-    
-    def export_trace(self, trace_obj: Trace) -> bool:
-        """Export trace to Neatlogs."""
-        return self._initialized
-
-
-ObservabilityManager.register_provider("neatlogs", NeatlogsProvider)