feat: add deep profiling benchmarks for Agent initialization

- deep_profile.py: cProfile analysis for 5000 agent creations
- deep_profile_bench.py: Combined timing and profiling for 1000 agents
- Helps identify performance bottlenecks in Agent.__init__

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

@@ -70,8 +70,6 @@ runs:
 
     - name: Install GitHub MCP Server
       shell: bash
-      env:
-        INPUT_GITHUB_TOKEN: ${{ inputs.github_token }}
       run: |
         claude mcp add-json github '{
           "command": "docker",
@@ -84,7 +82,7 @@ runs:
             "ghcr.io/github/github-mcp-server:sha-ff3036d"
           ],
           "env": {
-            "GITHUB_PERSONAL_ACCESS_TOKEN": "'"${INPUT_GITHUB_TOKEN}"'"
+            "GITHUB_PERSONAL_ACCESS_TOKEN": "${{ inputs.github_token }}"
           }
         }'
 
@@ -209,44 +207,21 @@ runs:
         User Request:
         $USER_REQUEST
         
-        Please analyze the request and provide helpful assistance.
-
+        Please analyze the request and provide helpful assistance. You have access to repository tools and can help with:
+        - Code analysis and implementation
+        - GitHub workflows and automation
+        - Pull request reviews and feedback
+        - Issue resolution and bug fixes
+        - Documentation updates
+        - Testing and deployment
+        
         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.
-
+        1. ALWAYS create a new git branch appropriately named (e.g. \`feature/issue-<number>\`).
+        2. 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.
+        3. Make your necessary code changes honoring the rules in AGENTS.md.
+        4. Commit your changes and push the branch to the remote repository.
+        5. Use the GitHub CLI (\`gh pr create --title "..." --body "Fixes #<number>"\`) to open a Pull Request containing your work.
+        
         Respond naturally and helpfully to the user's request using the available tools.
         EOF
         
@@ -256,14 +231,6 @@ runs:
     - name: Run Claude Code
       if: env.SKIP_EXECUTION != 'true'
       shell: bash
-      env:
-        ANTHROPIC_API_KEY: ${{ inputs.anthropic_api_key }}
-        CLAUDE_CODE_OAUTH_TOKEN: ${{ inputs.claude_code_oauth_token }}
-        GITHUB_TOKEN: ${{ inputs.github_token }}
-        INPUT_MAX_TURNS: ${{ inputs.max_turns }}
-        INPUT_ALLOWED_TOOLS: ${{ inputs.allowed_tools }}
-        INPUT_DISALLOWED_TOOLS: ${{ inputs.disallowed_tools }}
-        INPUT_TIMEOUT_MINUTES: ${{ inputs.timeout_minutes }}
       run: |
         echo "🚀 Running Claude Code with GitHub context..."
         
@@ -271,26 +238,30 @@ runs:
         CMD_ARGS=("-p" "--verbose" "--output-format" "stream-json")
         
         # Add max turns if specified
-        if [[ -n "$INPUT_MAX_TURNS" ]]; then
-          CMD_ARGS+=("--max-turns" "$INPUT_MAX_TURNS")
+        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 "$INPUT_ALLOWED_TOOLS" ]]; then
-          TOOLS="$TOOLS,$INPUT_ALLOWED_TOOLS"
+        if [[ -n "${{ inputs.allowed_tools }}" ]]; then
+          TOOLS="$TOOLS,${{ inputs.allowed_tools }}"
         fi
         CMD_ARGS+=("--allowedTools" "$TOOLS")
         
         # Add disallowed tools
-        if [[ -n "$INPUT_DISALLOWED_TOOLS" ]]; then
-          CMD_ARGS+=("--disallowedTools" "$INPUT_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=$(($INPUT_TIMEOUT_MINUTES * 60))
-        timeout $TIMEOUT_SECONDS claude "${CMD_ARGS[@]}" < "$PROMPT_FILE"
+        TIMEOUT_SECONDS=$((${{ inputs.timeout_minutes }} * 60))
+        timeout $TIMEOUT_SECONDS claude "${CMD_ARGS[@]}" < "${{ env.PROMPT_FILE }}"
         
-        echo "✅ Claude Code execution completed" 
+        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

@@ -19,7 +19,6 @@ runs:
     - name: Checkout repository code
       uses: actions/checkout@v4
       with:
-        persist-credentials: false
         fetch-depth: 0
 
     - name: Create prompt

.github/actions/gemini/action.yml

@@ -57,15 +57,13 @@ runs:
 
     - name: Install Gemini CLI
       id: install
-      env:
-        INPUT_VERSION: ${{ inputs.version }}
       run: |
-        if [[ "$INPUT_VERSION" == "latest" || "$INPUT_VERSION" =~ ^[0-9]+\.[0-9]+\.[0-9]+(-[a-zA-Z0-9\.-]+)?(\+[a-zA-Z0-9\.-]+)?$ ]]; then
-          echo "Installing Gemini CLI from npm: @google/gemini-cli@$INPUT_VERSION"
-          npm install -g @google/gemini-cli@$INPUT_VERSION
+        if [[ "${{ inputs.version }}" == "latest" || "${{ inputs.version }}" =~ ^[0-9]+\.[0-9]+\.[0-9]+(-[a-zA-Z0-9\.-]+)?(\+[a-zA-Z0-9\.-]+)?$ ]]; then
+          echo "Installing Gemini CLI from npm: @google/gemini-cli@${{ inputs.version }}"
+          npm install -g @google/gemini-cli@${{ inputs.version }}
         else
-          echo "Installing Gemini CLI from GitHub: github:google-gemini/gemini-cli#$INPUT_VERSION"
-          npm install -g github:google-gemini/gemini-cli#$INPUT_VERSION
+          echo "Installing Gemini CLI from GitHub: github:google-gemini/gemini-cli#${{ inputs.version }}"
+          npm install -g github:google-gemini/gemini-cli#${{ inputs.version }}
         fi
         echo "Verifying installation:"
         if command -v gemini >/dev/null 2>&1; then

.github/praisonai-issue-triage.yaml

@@ -1,72 +0,0 @@
-framework: "praisonai"
-process: workflow
-topic: "Autonomous Issue Resolution"
-planning: false
-
-roles:
-  issue_analyst:
-    role: "GitHub Issue Analyst"
-    goal: "Read the GitHub issue, understand the required changes, and map the problem to the local codebase"
-    backstory: "You are an expert software analyst who reads bug reports and feature requests. You excel at taking a problem description and finding exactly which files in the codebase need to be modified. Your primary job is discovery and planning."
-    tools:
-      - "execute_command"
-
-  software_engineer:
-    role: "Autonomous Software Engineer"
-    goal: "Implement required code changes on a branch, run tests, commit, and push"
-    backstory: "You are a senior software engineer. You always branch first, implement changes using shell commands, verify with git diff, run tests, then commit and push. You never create a Pull Request unless the issue body explicitly requests one."
-    tools:
-      - "execute_command"
-      - "github_create_branch"
-      - "github_commit_and_push"
-      - "github_create_pull_request"
-
-steps:
-  - name: Analyze Issue
-    agent: issue_analyst
-    action: >
-      Thoroughly analyze the issue and the repository. Follow these steps in order.
-
-      First, set up git identity: execute_command 'git config user.name PraisonAI' then
-      execute_command 'git config user.email [email protected]'. Do NOT pass cwd.
-
-      Second, read the issue: execute_command 'cat .github/triage-context.md'. Do NOT pass cwd.
-
-      Third, read architecture guidelines: execute_command 'cat src/praisonai-agents/AGENTS.md'.
-
-      Fourth, decide if this issue requires code changes:
-      - If YES: identify the exact files and lines that must change. Output a concrete file-level plan.
-      - If NO (question, discussion, duplicate, invalid): say so clearly. No code changes needed.
-
-      Output: routing decision (Core SDK / Wrapper / Tools / No-change), validation result, and exact files/lines to change.
-    expected_output: A decision on whether code changes are needed, and if so a concrete file-level plan.
-
-  - name: Implement Fix
-    agent: software_engineer
-    action: >
-      You are working inside the repository at /Users/praison/praisonai-package.
-      Do NOT ask for confirmation. Do NOT use placeholder paths. Act immediately.
-
-      Read the analyst's output first. If the analyst said NO code changes are needed, stop here and
-      output a brief explanation — do NOT create a branch or PR.
-
-      If code changes ARE needed, follow these steps:
-
-      Step 1 — Create branch: github_create_branch with branch_name=praisonai/issue-{{ISSUE_NUMBER}}.
-
-      Step 2 — Implement changes using execute_command (no cwd argument).
-      To insert a line: python3 -c "lines=open('FILE').readlines(); lines.insert(N,'LINE\n'); open('FILE','w').writelines(lines)"
-      To replace text: python3 -c "t=open('FILE').read(); open('FILE','w').write(t.replace('OLD','NEW'))"
-      After each edit verify: execute_command 'git diff'
-
-      Step 3 — Run tests: execute_command 'python3 -m pytest src/praisonai-agents/tests/ -x -q --timeout=30 -p no:warnings'
-
-      Step 4 — Commit and push: github_commit_and_push with a descriptive commit_message.
-
-      Step 5 — Check if the issue body EXPLICITLY contains the phrase "create pull request" or "create PR"
-      (case-insensitive). ONLY in that case create a PR using github_create_pull_request with
-      title='fix: resolve issue #{{ISSUE_NUMBER}}',
-      body='Fixes #{{ISSUE_NUMBER}}.\n\nAutonomously resolved by PraisonAI.',
-      head_branch=praisonai/issue-{{ISSUE_NUMBER}}, base_branch=main.
-      Otherwise do NOT create a PR — just confirm the branch was pushed successfully.
-    expected_output: Either "No code changes needed", or "Branch pushed, no PR requested", or "Branch pushed and PR created at URL".

.github/praisonai-reviewer.yaml

@@ -1,116 +0,0 @@
-framework: "praisonai"
-topic: "Pull Request Code Review Analysis"
-
-roles:
-  security_reviewer:
-    role: "Security Code Reviewer"
-    goal: "Identify security vulnerabilities, authentication issues, input validation problems, and unsafe code practices in the PR changes"
-    backstory: "You are a cybersecurity expert specializing in code analysis. You have extensive experience in identifying common security flaws like injection attacks, authentication bypasses, exposed secrets, and unsafe data handling patterns."
-    tools:
-      - "execute_command"
-    
-  performance_reviewer:
-    role: "Performance Code Reviewer" 
-    goal: "Analyze code changes for performance implications, identify bottlenecks, inefficient algorithms, and resource usage issues"
-    backstory: "You are a performance optimization specialist with deep knowledge of algorithm efficiency, memory management, and system performance. You excel at spotting code that could cause performance degradation."
-    tools:
-      - "execute_command"
-      
-  maintainability_reviewer:
-    role: "Code Quality & Maintainability Reviewer"
-    goal: "Evaluate code structure, readability, documentation, naming conventions, and adherence to best practices"
-    backstory: "You are a software engineering expert focused on code quality and maintainability. You ensure code follows established patterns, is well-documented, and will be easy for future developers to understand and modify."
-    tools:
-      - "execute_command"
-
-  lead_reviewer:
-    role: "Lead Technical Reviewer"
-    goal: "Synthesize all review feedback, make final recommendations, and post comprehensive review comments to the GitHub PR"
-    backstory: "You are a senior technical lead responsible for final review decisions. You coordinate input from security, performance, and maintainability reviewers to provide balanced, actionable feedback to the development team."
-    tools:
-      - "execute_command"
-
-steps:
-  - name: security_analysis
-    agent: security_reviewer
-    action: |
-      Analyze the PR diff for security issues:
-      1. Extract PR details: `echo "$PR_DATA"`
-      2. Get changed files: `echo "$CHANGED_FILES"`  
-      3. Review full diff: `gh pr diff ${PR_NUMBER}`
-      4. Look for:
-         - Hardcoded secrets, API keys, passwords
-         - SQL injection vulnerabilities
-         - XSS vulnerabilities  
-         - Authentication/authorization bypasses
-         - Unsafe file operations
-         - Command injection risks
-         - Missing input validation
-         - Exposed sensitive data
-      5. Document findings with file paths and line numbers
-    expected_output: "Detailed security analysis report with specific vulnerabilities found, severity levels, and remediation suggestions"
-    
-  - name: performance_analysis
-    agent: performance_reviewer
-    action: |
-      Analyze the PR diff for performance issues:
-      1. Review algorithm complexity changes
-      2. Check for inefficient database queries
-      3. Identify memory leaks or excessive allocations
-      4. Look for hot-path regressions
-      5. Check for blocking I/O operations
-      6. Analyze module-level imports that could slow startup
-      7. Review caching implementations
-      8. Check for recursive operations without termination
-      9. Document performance concerns with impact estimates
-    expected_output: "Performance analysis report highlighting bottlenecks, efficiency concerns, and optimization recommendations"
-    
-  - name: maintainability_analysis
-    agent: maintainability_reviewer
-    action: |
-      Analyze the PR diff for code quality and maintainability:
-      1. Check naming conventions consistency
-      2. Evaluate code structure and organization
-      3. Review documentation completeness
-      4. Check for DRY violations
-      5. Analyze error handling patterns
-      6. Review test coverage for changes
-      7. Check adherence to repository coding standards
-      8. Evaluate API design consistency
-      9. Look for technical debt introduction
-    expected_output: "Code quality analysis with specific recommendations for improving maintainability and readability"
-    
-  - name: final_review
-    agent: lead_reviewer
-    action: |
-      Synthesize all reviews and post final comprehensive feedback:
-      1. Collect input from security, performance, and maintainability reviews
-      2. Prioritize findings by severity (Critical, High, Medium, Low)
-      3. Create structured review comment using this format:
-      
-      ## 📋 Review Summary
-      [2-3 sentence overview and assessment]
-      
-      ## 🔍 General Feedback  
-      [Overall patterns and observations]
-      
-      ## 🎯 Specific Feedback
-      ### 🔴 Critical (if any)
-      [Security vulnerabilities, breaking changes, major bugs]
-      
-      ### 🟡 High (if any) 
-      [Performance issues, design flaws, significant bugs]
-      
-      ### 🟢 Medium (if any)
-      [Code quality improvements, minor optimizations]
-      
-      ### 🔵 Low (if any)
-      [Documentation, naming suggestions, minor refactoring]
-      
-      ## ✅ Highlights (if any)
-      [Positive aspects worth mentioning]
-      
-      4. Post comprehensive review: `echo "[REVIEW_CONTENT]" | gh pr comment ${PR_NUMBER} -F -`
-      5. If critical issues found, suggest not merging until resolved
-    expected_output: "Final review comment posted to GitHub PR with comprehensive analysis from all reviewers"
-    dependencies: [security_analysis, performance_analysis, maintainability_analysis]

.github/triage-context.md

@@ -1,3 +0,0 @@
-# GitHub Issue #1350: Add debug log on successful branch checkout
-
-The github_create_branch function in src/praisonai-agents/praisonaiagents/tools/github_tools.py should call logger.debug after a successful checkout.

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

@@ -1,14 +1,11 @@
 name: Auto PR Comment
 
-# Review chain: CodeRabbit/Qodo/Gemini review first → Copilot → Claude (final)
-# For human PRs: CodeRabbit/Qodo auto-review → this workflow triggers Copilot → then Claude
+# Review chain: CodeRabbit/Qodo/Gemini review first → THEN trigger Copilot
+# For human PRs: CodeRabbit/Qodo auto-review → this workflow triggers Copilot
 # For bot PRs: CodeRabbit/Qodo skip bots, so we trigger them explicitly first
 #
 # IMPORTANT: @copilot ignores bot comments. All @copilot mentions must use
 # PAT_TOKEN so the comment posts as MervinPraison (a real user).
-#
-# Claude trigger runs IN-BAND (same workflow) to avoid GitHub Actions
-# blocking bot-triggered workflows with action_required approval gates.
 on:
   issue_comment:
     types: [created]
@@ -17,11 +14,6 @@ on:
   pull_request:
     types: [opened]
 
-env:
-  MAX_POLL_ATTEMPTS: 20
-  POLL_DELAY_MS: 30000
-  CLAUDE_TRIGGER_LOGINS: '["MervinPraison","github-actions[bot]"]'
-
 jobs:
   # ================================================================
   # HUMAN PRs: Trigger @copilot AFTER CodeRabbit posts its summary
@@ -31,16 +23,12 @@ jobs:
       github.event_name == 'issue_comment' &&
       github.event.issue.pull_request &&
       github.event.comment.user.login == 'coderabbitai[bot]' &&
-      contains(github.event.comment.body, 'summarize by coderabbit')
+      contains(github.event.comment.body, 'summary by coderabbit')
     runs-on: ubuntu-latest
     permissions:
       pull-requests: write
-    outputs:
-      pr_number: ${{ steps.trigger.outputs.pr_number }}
-      triggered: ${{ steps.trigger.outputs.triggered }}
     steps:
       - name: Post Copilot review request (as user via PAT)
-        id: trigger
         uses: actions/github-script@v7
         with:
           github-token: ${{ secrets.GH_TOKEN }}
@@ -57,18 +45,14 @@ jobs:
             );
             if (alreadyPosted) {
               console.log('Copilot already triggered, skipping');
-              core.setOutput('triggered', 'false');
-              core.setOutput('pr_number', context.issue.number.toString());
               return;
             }
-            await github.rest.issues.createComment({
+            github.rest.issues.createComment({
               issue_number: context.issue.number,
               owner: context.repo.owner,
               repo: context.repo.repo,
               body: '@copilot Do a thorough review of this PR. Read ALL existing reviewer comments above from Qodo, Coderabbit, and Gemini 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, or Gemini flagged valid issues, include them in your review\n8. Suggest specific improvements with code examples where possible'
-            });
-            core.setOutput('triggered', 'true');
-            core.setOutput('pr_number', context.issue.number.toString());
+            })
 
   # Fallback: Trigger @copilot after Qodo review (if Coderabbit doesn't fire)
   copilot-after-qodo:
@@ -78,12 +62,8 @@ jobs:
     runs-on: ubuntu-latest
     permissions:
       pull-requests: write
-    outputs:
-      pr_number: ${{ steps.trigger.outputs.pr_number }}
-      triggered: ${{ steps.trigger.outputs.triggered }}
     steps:
       - name: Post Copilot review request (as user via PAT)
-        id: trigger
         uses: actions/github-script@v7
         with:
           github-token: ${{ secrets.GH_TOKEN }}
@@ -99,377 +79,14 @@ jobs:
             );
             if (alreadyPosted) {
               console.log('Copilot already triggered, skipping');
-              core.setOutput('triggered', 'false');
-              core.setOutput('pr_number', context.issue.number.toString());
               return;
             }
-            await github.rest.issues.createComment({
+            github.rest.issues.createComment({
               issue_number: context.issue.number,
               owner: context.repo.owner,
               repo: context.repo.repo,
               body: '@copilot Do a thorough review of this PR. Read ALL existing reviewer comments above first.\n\nReview areas:\n1. **Bloat check**: Are changes minimal and focused?\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. Suggest specific improvements with code examples where possible'
-            });
-            core.setOutput('triggered', 'true');
-            core.setOutput('pr_number', context.issue.number.toString());
-
-  # ================================================================
-  # FALLBACK: Claude trigger for cases where Copilot chain fails/skips
-  # Ensures Claude always runs after sufficient review time
-  # ================================================================
-  claude-fallback-timeout:
-    if: |
-      github.event_name == 'pull_request' &&
-      github.event.action == 'opened'
-    runs-on: ubuntu-latest
-    timeout-minutes: 20
-    permissions:
-      issues: write
-      pull-requests: write
-    steps:
-      - name: Wait for review window (15 min)
-        run: sleep 900
-        
-      - name: Check if Claude already triggered
-        id: check_claude
-        uses: actions/github-script@v7
-        env:
-          TRIGGER_LOGINS: ${{ env.CLAUDE_TRIGGER_LOGINS }}
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            const triggerLogins = JSON.parse(process.env.TRIGGER_LOGINS);
-            const comments = await github.rest.issues.listComments({
-              issue_number: context.issue.number,
-              owner: context.repo.owner,
-              repo: context.repo.repo
-            });
-            const alreadyPosted = comments.data.some(c =>
-              c.body.includes('@claude') && triggerLogins.includes(c.user.login)
-            );
-            core.setOutput('already_triggered', alreadyPosted ? 'true' : 'false');
-            core.setOutput('comments_count', comments.data.length.toString());
-            
-            // Collect all bot reviewer comments for context
-            const botReviews = comments.data.filter(c => 
-              ['coderabbitai[bot]', 'qodo-code-review[bot]', 'gemini-code-assist[bot]', 
-               'copilot-swe-agent', 'Copilot', 'greptile-apps[bot]'].some(bot => 
-                c.user.login.toLowerCase().includes(bot.toLowerCase())
-              )
-            );
-            core.setOutput('review_count', botReviews.length.toString());
-            
-      - name: Aggregate reviews and trigger Claude
-        if: steps.check_claude.outputs.already_triggered != 'true'
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            const comments = await github.rest.issues.listComments({issue_number: context.issue.number, owner: context.repo.owner, repo: context.repo.repo, per_page: 100});
-            const reviews = {coderabbit: [], qodo: [], gemini: [], copilot: [], greptile: [], others: []};
-            comments.data.forEach(c => {
-              const login = c.user.login.toLowerCase();
-              const body = c.body.substring(0, 500);
-              if (login.includes('coderabbit')) reviews.coderabbit.push(body);
-              else if (login.includes('qodo')) reviews.qodo.push(body);
-              else if (login.includes('gemini')) reviews.gemini.push(body);
-              else if (login.includes('copilot')) reviews.copilot.push(body);
-              else if (login.includes('greptile')) reviews.greptile.push(body);
-              else if (c.user.type === 'Bot') reviews.others.push(body);
-            });
-            const summaryParts = [];
-            if (reviews.coderabbit.length) summaryParts.push('CodeRabbit: ' + reviews.coderabbit.length + ' comments');
-            if (reviews.qodo.length) summaryParts.push('Qodo: ' + reviews.qodo.length + ' comments');
-            if (reviews.gemini.length) summaryParts.push('Gemini: ' + reviews.gemini.length + ' comments');
-            if (reviews.copilot.length) summaryParts.push('Copilot: ' + reviews.copilot.length + ' comments');
-            if (reviews.greptile.length) summaryParts.push('Greptile: ' + reviews.greptile.length + ' comments');
-            const reviewSummary = summaryParts.length ? summaryParts.join(' | ') : 'No bot reviews detected';
-            const claudeBody = '@claude You are the FINAL architecture reviewer. ' + reviewSummary + '.\n\n' +
-              'Review Context: ' + comments.data.length + ' total comments, ' + 
-              (reviews.coderabbit.length + reviews.qodo.length + reviews.gemini.length + reviews.copilot.length + reviews.greptile.length) + ' bot reviews.\n\n' +
-              'Phase 1: Review per AGENTS.md principles (protocol-driven, backward compatible, no perf impact).\n' +
-              'Phase 2: FIX valid issues found by prior reviewers.\n' +
-              'Phase 3: Provide final verdict (approve or request changes).';
-            await github.rest.issues.createComment({issue_number: context.issue.number, owner: context.repo.owner, repo: context.repo.repo, body: claudeBody});
-            console.log('Claude fallback triggered on PR #' + context.issue.number + ' with ' + reviewSummary);
-
-  # ================================================================
-  # Claude FINAL review — polls for Copilot's response, then triggers
-  # This runs IN-BAND under the PAT context (MervinPraison), avoiding
-  # the action_required approval gate that blocks bot-triggered workflows.
-  # ================================================================
-  claude-after-copilot:
-    needs: [copilot-after-coderabbit]
-    if: |
-      always() &&
-      needs.copilot-after-coderabbit.result == 'success' &&
-      needs.copilot-after-coderabbit.outputs.triggered == 'true'
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    permissions:
-      issues: write
-      pull-requests: write
-    steps:
-      - name: Wait for Copilot to respond (poll)
-        id: poll
-        uses: actions/github-script@v7
-        env:
-          PR_NUMBER: ${{ needs.copilot-after-coderabbit.outputs.pr_number }}
-          MAX_POLL_ATTEMPTS: ${{ env.MAX_POLL_ATTEMPTS }}
-          POLL_DELAY_MS: ${{ env.POLL_DELAY_MS }}
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            const pr = parseInt(process.env.PR_NUMBER, 10);
-            const owner = context.repo.owner;
-            const repo = context.repo.repo;
-
-            // Poll for Copilot's response — check every 30s for up to 10 minutes
-            const maxAttempts = parseInt(process.env.MAX_POLL_ATTEMPTS, 10);
-            const delayMs = parseInt(process.env.POLL_DELAY_MS, 10);
-
-            for (let i = 0; i < maxAttempts; i++) {
-              console.log(`Poll attempt ${i + 1}/${maxAttempts}...`);
-
-              const comments = await github.rest.issues.listComments({
-                issue_number: pr,
-                owner,
-                repo,
-                per_page: 50,
-                sort: 'created',
-                direction: 'desc'
-              });
-
-              // Look for Copilot's response (copilot-swe-agent or Copilot)
-              const copilotResponse = comments.data.find(c =>
-                (c.user.login === 'copilot-swe-agent' ||
-                 c.user.login === 'Copilot' ||
-                 c.user.login.toLowerCase().includes('copilot')) &&
-                c.user.type === 'Bot'
-              );
-
-              if (copilotResponse) {
-                console.log(`Copilot responded at ${copilotResponse.created_at}`);
-                core.setOutput('copilot_responded', 'true');
-                return;
-              }
-
-              // Also check PR reviews for Copilot
-              const reviews = await github.rest.pulls.listReviews({
-                pull_number: pr,
-                owner,
-                repo
-              });
-
-              const copilotReview = reviews.data.find(r =>
-                r.user.login.toLowerCase().includes('copilot')
-              );
-
-              if (copilotReview) {
-                console.log(`Copilot review submitted at ${copilotReview.submitted_at}`);
-                core.setOutput('copilot_responded', 'true');
-                return;
-              }
-
-              if (i < maxAttempts - 1) {
-                console.log(`Copilot hasn't responded yet, waiting ${delayMs/1000}s...`);
-                await new Promise(resolve => setTimeout(resolve, delayMs));
-              }
-            }
-
-            console.log('Copilot did not respond within timeout, triggering Claude anyway');
-            core.setOutput('copilot_responded', 'timeout');
-
-      - name: Check if Claude already triggered
-        id: check_claude
-        uses: actions/github-script@v7
-        env:
-          PR_NUMBER: ${{ needs.copilot-after-coderabbit.outputs.pr_number }}
-          TRIGGER_LOGINS: ${{ env.CLAUDE_TRIGGER_LOGINS }}
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            const pr = parseInt(process.env.PR_NUMBER, 10);
-            const triggerLogins = JSON.parse(process.env.TRIGGER_LOGINS);
-            const comments = await github.rest.issues.listComments({
-              issue_number: pr,
-              owner: context.repo.owner,
-              repo: context.repo.repo
-            });
-            const alreadyPosted = comments.data.some(c =>
-              c.body.includes('@claude') && triggerLogins.includes(c.user.login)
-            );
-            core.setOutput('already_triggered', alreadyPosted ? 'true' : 'false');
-
-      - name: Trigger Claude final review
-        if: steps.check_claude.outputs.already_triggered != 'true'
-        uses: actions/github-script@v7
-        env:
-          PR_NUMBER: ${{ needs.copilot-after-coderabbit.outputs.pr_number }}
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            const pr = parseInt(process.env.PR_NUMBER, 10);
-            await github.rest.issues.createComment({
-              issue_number: pr,
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              body: '@claude You are the FINAL architecture reviewer. Read ALL comments above from Gemini, Qodo, CodeRabbit, and Copilot carefully before responding.\n\n**Phase 1: Review per AGENTS.md**\n1. Protocol-driven: check heavy implementations vs core SDK\n2. Backward compatible: ensure zero feature regressions\n3. Performance: no hot-path regressions\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
-            });
-            console.log(`Claude triggered on PR #${pr}`);
-
-  # ================================================================
-  # Claude after Qodo fallback path (same polling approach)
-  # ================================================================
-  claude-after-copilot-qodo:
-    needs: [copilot-after-qodo]
-    if: |
-      always() &&
-      needs.copilot-after-qodo.result == 'success' &&
-      needs.copilot-after-qodo.outputs.triggered == 'true'
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    permissions:
-      issues: write
-      pull-requests: write
-    steps:
-      - name: Wait for Copilot to respond (poll)
-        id: poll
-        uses: actions/github-script@v7
-        env:
-          PR_NUMBER: ${{ needs.copilot-after-qodo.outputs.pr_number }}
-          MAX_POLL_ATTEMPTS: ${{ env.MAX_POLL_ATTEMPTS }}
-          POLL_DELAY_MS: ${{ env.POLL_DELAY_MS }}
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            const pr = parseInt(process.env.PR_NUMBER, 10);
-            const owner = context.repo.owner;
-            const repo = context.repo.repo;
-            const maxAttempts = parseInt(process.env.MAX_POLL_ATTEMPTS, 10);
-            const delayMs = parseInt(process.env.POLL_DELAY_MS, 10);
-
-            for (let i = 0; i < maxAttempts; i++) {
-              console.log(`Poll attempt ${i + 1}/${maxAttempts}...`);
-              const comments = await github.rest.issues.listComments({
-                issue_number: pr, owner, repo,
-                per_page: 50, sort: 'created', direction: 'desc'
-              });
-              const copilotResponse = comments.data.find(c =>
-                (c.user.login === 'copilot-swe-agent' ||
-                 c.user.login === 'Copilot' ||
-                 c.user.login.toLowerCase().includes('copilot')) &&
-                c.user.type === 'Bot'
-              );
-              if (copilotResponse) {
-                console.log(`Copilot responded at ${copilotResponse.created_at}`);
-                core.setOutput('copilot_responded', 'true');
-                return;
-              }
-              const reviews = await github.rest.pulls.listReviews({
-                pull_number: pr, owner, repo
-              });
-              const copilotReview = reviews.data.find(r =>
-                r.user.login.toLowerCase().includes('copilot')
-              );
-              if (copilotReview) {
-                console.log(`Copilot review submitted at ${copilotReview.submitted_at}`);
-                core.setOutput('copilot_responded', 'true');
-                return;
-              }
-              if (i < maxAttempts - 1) {
-                await new Promise(resolve => setTimeout(resolve, delayMs));
-              }
-            }
-            console.log('Copilot did not respond within timeout, triggering Claude anyway');
-            core.setOutput('copilot_responded', 'timeout');
-
-      - name: Check if Claude already triggered
-        id: check_claude
-        uses: actions/github-script@v7
-        env:
-          PR_NUMBER: ${{ needs.copilot-after-qodo.outputs.pr_number }}
-          TRIGGER_LOGINS: ${{ env.CLAUDE_TRIGGER_LOGINS }}
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            const pr = parseInt(process.env.PR_NUMBER, 10);
-            const triggerLogins = JSON.parse(process.env.TRIGGER_LOGINS);
-            const comments = await github.rest.issues.listComments({
-              issue_number: pr,
-              owner: context.repo.owner,
-              repo: context.repo.repo
-            });
-            const alreadyPosted = comments.data.some(c =>
-              c.body.includes('@claude') && triggerLogins.includes(c.user.login)
-            );
-            core.setOutput('already_triggered', alreadyPosted ? 'true' : 'false');
-
-      - name: Trigger Claude final review
-        if: steps.check_claude.outputs.already_triggered != 'true'
-        uses: actions/github-script@v7
-        env:
-          PR_NUMBER: ${{ needs.copilot-after-qodo.outputs.pr_number }}
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            const pr = parseInt(process.env.PR_NUMBER, 10);
-            await github.rest.issues.createComment({
-              issue_number: pr,
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              body: '@claude You are the FINAL architecture reviewer. Read ALL comments above from Gemini, Qodo, CodeRabbit, and Copilot carefully before responding.\n\n**Phase 1: Review per AGENTS.md**\n1. Protocol-driven: check heavy implementations vs core SDK\n2. Backward compatible: ensure zero feature regressions\n3. Performance: no hot-path regressions\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
-            });
-            console.log(`Claude triggered on PR #${pr}`);
-
-  # ================================================================
-  # Claude after Gemini — restores the trigger lost from the deleted
-  # chain-claude-after-copilot.yml workflow.
-  # Fires when Gemini posts a review comment on a PR or issue.
-  # ================================================================
-  claude-after-gemini:
-    if: |
-      github.event_name == 'issue_comment' &&
-      (
-        github.event.comment.user.login == 'gemini-code-assist[bot]' ||
-        contains(github.event.comment.body, 'Review completed by Gemini CLI') ||
-        contains(github.event.comment.body, 'The fix is ready for review. Please test')
-      )
-    runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: write
-    steps:
-      - name: Check if Claude already triggered
-        id: check_claude
-        uses: actions/github-script@v7
-        env:
-          TRIGGER_LOGINS: ${{ env.CLAUDE_TRIGGER_LOGINS }}
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            const triggerLogins = JSON.parse(process.env.TRIGGER_LOGINS);
-            const comments = await github.rest.issues.listComments({
-              issue_number: context.issue.number,
-              owner: context.repo.owner,
-              repo: context.repo.repo
-            });
-            const alreadyPosted = comments.data.some(c =>
-              c.body.includes('@claude') && triggerLogins.includes(c.user.login)
-            );
-            core.setOutput('already_triggered', alreadyPosted ? 'true' : 'false');
-
-      - name: Post Claude analysis request (Lead Engineer)
-        if: steps.check_claude.outputs.already_triggered != 'true'
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GH_TOKEN }}
-          script: |
-            await github.rest.issues.createComment({
-              issue_number: context.issue.number,
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              body: '@claude You are the Lead Engineer. Read ALL analysis and reviews above carefully (Gemini, CodeRabbit, Qodo, Copilot, etc).\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\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
-            });
+            })
 
   # ================================================================
   # BOT PRs: CodeRabbit/Qodo skip bot-authored PRs by default.
@@ -489,7 +106,7 @@ jobs:
     permissions:
       pull-requests: write
     steps:
-      - name: Trigger CodeRabbit and Qodo reviews
+      - name: Trigger CodeRabbit, Qodo, and Gemini reviews
         uses: actions/github-script@v7
         with:
           github-token: ${{ secrets.GH_TOKEN }}
@@ -512,12 +129,12 @@ jobs:
             });
             console.log(`Triggered Qodo for bot PR #${pr}`);
 
-            // 3. Trigger Gemini review (commented out)
-            // await github.rest.issues.createComment({
-            //   issue_number: pr, owner, repo,
-            //   body: '@gemini review this PR'
-            // });
-            // console.log(`Triggered Gemini for bot PR #${pr}`);
+            // 3. Trigger Gemini review
+            await github.rest.issues.createComment({
+              issue_number: pr, owner, repo,
+              body: '@gemini review this PR'
+            });
+            console.log(`Triggered Gemini for bot PR #${pr}`);
 
             // NOTE: @copilot is NOT triggered here.
             // It will be triggered by copilot-after-coderabbit or copilot-after-qodo

.github/workflows/benchmark.yml

@@ -20,8 +20,6 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
 
       - name: Set up Python 3.12
         uses: actions/setup-python@v5
@@ -37,8 +35,6 @@ jobs:
 
       - name: Run Import Time Benchmark
         id: import_time
-        env:
-          FAIL_ON_REGRESSION: ${{ inputs.fail_on_regression }}
         run: |
           cd src/praisonai-agents
           python benchmarks/import_time.py 2>&1 | tee import_time_output.txt
@@ -50,7 +46,7 @@ jobs:
           # Check threshold (200ms)
           if (( $(echo "$IMPORT_TIME > 200" | bc -l) )); then
             echo "::warning::Import time ${IMPORT_TIME}ms exceeds 200ms threshold"
-            if [ "$FAIL_ON_REGRESSION" == "true" ]; then
+            if [ "${{ inputs.fail_on_regression }}" == "true" ]; then
               echo "import_passed=false" >> $GITHUB_OUTPUT
             fi
           else
@@ -59,8 +55,6 @@ jobs:
 
       - name: Run Memory Usage Benchmark
         id: memory
-        env:
-          FAIL_ON_REGRESSION: ${{ inputs.fail_on_regression }}
         run: |
           cd src/praisonai-agents
           python benchmarks/memory_usage.py 2>&1 | tee memory_output.txt
@@ -72,7 +66,7 @@ jobs:
           # Check threshold (30MB)
           if (( $(echo "$PEAK_MEMORY > 30" | bc -l) )); then
             echo "::warning::Memory usage ${PEAK_MEMORY}MB exceeds 30MB threshold"
-            if [ "$FAIL_ON_REGRESSION" == "true" ]; then
+            if [ "${{ inputs.fail_on_regression }}" == "true" ]; then
               echo "memory_passed=false" >> $GITHUB_OUTPUT
             fi
           else
@@ -101,11 +95,9 @@ jobs:
 
       - name: Run Parameter Impact Benchmark
         id: param_impact
-        env:
-          INPUT_ITERATIONS: ${{ inputs.iterations }}
         run: |
           cd src/praisonai-agents
-          python benchmarks/param_impact_benchmark.py --no-save --iterations "$INPUT_ITERATIONS" 2>&1 | tee param_impact_output.txt
+          python benchmarks/param_impact_benchmark.py --no-save --iterations ${{ inputs.iterations }} 2>&1 | tee param_impact_output.txt
 
       - name: Check Lazy Imports
         id: lazy_imports

.github/workflows/build-image.yml

@@ -18,8 +18,6 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3

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

@@ -0,0 +1,58 @@
+name: Chain Claude After Reviewers
+
+# Event-driven: triggers when Copilot finishes reviewing a PR
+on:
+  pull_request_review:
+    types: [submitted]
+
+  issue_comment:
+    types: [created]
+
+jobs:
+  # Trigger Claude after Copilot finishes on a PR — Claude is the FINAL reviewer
+  # Claude reads ALL previous reviews (Qodo, Coderabbit, Copilot) and fixes any valid issues
+  claude-final-review:
+    if: |
+      (github.event_name == 'pull_request_review' && contains(github.event.review.user.login, 'copilot')) ||
+      (github.event_name == 'issue_comment' && contains(github.event.comment.user.login, 'copilot'))
+    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 || secrets.GITHUB_TOKEN }}
+          script: |
+            github.rest.issues.createComment({
+              issue_number: context.issue.number,
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              body: '@claude You are the FINAL architecture reviewer. 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\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
+            })
+
+  # Trigger Claude after Gemini finishes on an Issue or PR
+  claude-after-gemini:
+    if: |
+      github.event_name == 'issue_comment' &&
+      (
+        github.event.comment.user.login == 'gemini-code-assist[bot]' ||
+        contains(github.event.comment.body, 'Review completed by Gemini CLI') ||
+        contains(github.event.comment.body, 'The fix is ready for review. Please test')
+      )
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    steps:
+      - name: Post Claude analysis request (final reviewer)
+        uses: actions/github-script@v7
+        with:
+          github-token: ${{ secrets.GH_TOKEN || secrets.GITHUB_TOKEN }}
+          script: |
+            github.rest.issues.createComment({
+              issue_number: context.issue.number,
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              body: '@claude You are the Lead Engineer. Read ALL analysis and reviews above carefully (Gemini, CodeRabbit, Qodo, Copilot, etc).\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\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
+            })

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

@@ -15,18 +15,15 @@ jobs:
       issues: write
       id-token: write
     steps:
+      - name: Checkout repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  # v4
+
       - 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: Checkout repository
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  # v4
-        with:
-          token: ${{ steps.app-token.outputs.token }}
 
       - name: Run Claude Issue Triage
         uses: ./.github/actions/claude-issue-triage-action

.github/workflows/claude.yml

@@ -11,116 +11,10 @@ on:
     types: [submitted]
 
 jobs:
-  # ================================================================
-  # Job 1: Lightweight triage for ALL new issues (including external users)
-  # Posts welcome comment + adds triage labels. No Claude code execution.
-  # ================================================================
-  issue-triage:
-    if: |
-      github.event_name == 'issues' &&
-      github.event.action == 'opened' &&
-      !contains(toJSON(github.event.issue.labels), 'claude')
-    runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      contents: read
-    steps:
-      - 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: Triage and acknowledge issue
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ steps.app-token.outputs.token }}
-          script: |
-            const issue = context.payload.issue;
-            const title = (issue.title || '').toLowerCase();
-            const body = (issue.body || '').toLowerCase();
-            const content = title + ' ' + body;
-            const isOwner = ['OWNER', 'MEMBER', 'COLLABORATOR'].includes(
-              issue.author_association
-            );
-
-            // --- Label triage ---
-            const labels = [];
-
-            // Kind labels
-            if (content.includes('feature request') || content.includes('[feature')) {
-              labels.push('enhancement');
-            } else if (content.includes('bug') || content.includes('error') || content.includes('crash') || content.includes('traceback')) {
-              labels.push('bug');
-            } else if (content.includes('question') || content.includes('how do i') || content.includes('how to')) {
-              labels.push('question');
-            }
-
-            // Area labels
-            if (content.includes('security') || content.includes('cve') || content.includes('vulnerability')) {
-              labels.push('security');
-            }
-            if (content.includes('performance') || content.includes('slow') || content.includes('memory leak')) {
-              labels.push('performance');
-            }
-            if (content.includes('documentation') || content.includes('docs')) {
-              labels.push('documentation');
-            }
-            if (content.includes('typescript') || content.includes('javascript') || content.includes('npm')) {
-              labels.push('javascript');
-            }
-
-            // Apply labels
-            if (labels.length > 0) {
-              await github.rest.issues.addLabels({
-                issue_number: issue.number,
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                labels
-              });
-            }
-
-            // --- Acknowledgment comment (external users only) ---
-            if (!isOwner) {
-              await github.rest.issues.createComment({
-                issue_number: issue.number,
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                body: [
-                  `👋 Thanks for opening this issue, @${issue.user.login}!`,
-                  '',
-                  'A maintainer will review this shortly. In the meantime:',
-                  '- Make sure you\'ve included steps to reproduce (for bugs)',
-                  '- Check [existing issues](https://github.com/MervinPraison/PraisonAI/issues) for duplicates',
-                  '- Review the [documentation](https://docs.praison.ai) for related guides',
-                  '',
-                  '_A maintainer can trigger deeper analysis by commenting `@claude` on this issue._'
-                ].join('\n')
-              });
-            }
-
-            // --- For owner: add 'claude' label to trigger the code-fix job ---
-            if (isOwner) {
-              await github.rest.issues.addLabels({
-                issue_number: issue.number,
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                labels: ['claude']
-              });
-            }
-
-  # ================================================================
-  # Job 2: Full Claude code-fix (owner/collaborator only)
-  # Triggers on: labeled 'claude', @claude comments, assignments
-  # ================================================================
   claude-response:
     # Allow: human users, github-actions[bot] (auto-comments/labels)
     # Block: dependabot, cursor, renovate, other bots
-    # Skip 'opened' events — triage job handles those above
     if: |
-      github.event.action != 'opened' &&
       (github.event.action != 'labeled' || github.event.label.name == 'claude') &&
       (github.event_name != 'issue_comment' || contains(github.event.comment.body, '@claude')) &&
       (
@@ -139,130 +33,58 @@ jobs:
       actions: read
       id-token: write
     steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          
       - 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: Check for Fork PR
-        id: check_fork
-        uses: actions/github-script@v7
-        with:
-          script: |
-            const isPR = context.eventName === 'pull_request' || (context.payload.issue && context.payload.issue.pull_request);
-            if (isPR) {
-              const prNumber = context.payload.pull_request ? context.payload.pull_request.number : context.payload.issue.number;
-              const pr = await github.rest.pulls.get({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                pull_number: prNumber
-              });
-              
-              core.setOutput('pr_branch', pr.data.head.ref);
-              
-              if (pr.data.head.repo && pr.data.head.repo.full_name !== context.repo.full_name) {
-                core.setOutput('is_fork', 'true');
-                core.setOutput('clone_url', pr.data.head.repo.clone_url);
-                core.setOutput('branch', pr.data.head.ref);
-                return;
-              }
-            }
-            core.setOutput('is_fork', 'false');
-
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-          fetch-depth: 0
-          token: ${{ steps.app-token.outputs.token }}
-
-      - name: Fetch PR branch and Setup Remote
-        if: github.event.issue.pull_request
-        run: |
-          # Fetch PR head from base repo and put it into local branch
-          git fetch origin pull/${{ github.event.issue.number }}/head:${{ steps.check_fork.outputs.pr_branch }}
-          
-          # If it's a fork, make origin point to local to trick claude-code-action's `git fetch origin <branch>`
-          if [ "${{ steps.check_fork.outputs.is_fork }}" == "true" ]; then
-            git remote set-url origin file://$(pwd)
-          fi
 
       - uses: anthropics/claude-code-action@beta
-        env:
-          GH_TOKEN: ${{ steps.app-token.outputs.token }}
         with:
-          allowed_bots: 'praisonai-triage-agent[bot]'
           claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
           github_token: ${{ steps.app-token.outputs.token }}
           trigger_phrase: "@claude"
           label_trigger: "claude"
+          allowed_non_write_users: "*"
 
           direct_prompt: |
-            ${{ steps.check_fork.outputs.is_fork == 'true' && 'CRITICAL: THIS IS A PULL REQUEST FROM A FORK. YOU DO NOT HAVE PUSH PERMISSIONS TO THIS REPOSITORY. ONLY READ FILES, VALIDATE CODE, AND PROVIDE YOUR COMMENTS/FEEDBACK DIRECTLY IN THIS PR VIA COMMENTS. DO NOT ATTEMPT TO PUSH OR CREATE PULL REQUESTS.\n\n' || '' }}
             You are working on the PraisonAI SDK. Follow AGENTS.md strictly.
 
-            STEP 0 — SETUP GIT IDENTITY & AUTH (GLOBAL — required for all repos):
-            git config --global user.name "MervinPraison"
-            git config --global user.email "[email protected]"
-            gh auth setup-git
-
+            STEP 0 — SETUP GIT IDENTITY:
+            git config user.name "MervinPraison"
+            git config user.email "[email protected]"
 
             STEP 1 — READ GUIDELINES:
             Read AGENTS.md to understand the architecture rules.
 
-            STEP 2 — ARCHITECTURE VALIDATION & ROUTING (MANDATORY before writing code):
+            STEP 2 — ARCHITECTURE VALIDATION (MANDATORY before writing code):
             Before implementing anything, answer these questions:
-            - CORE vs WRAPPER vs TOOLS vs DOCS vs PLUGINS 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): Capabilities Agents actively call during execution (e.g. SurrealDB tool, Slack tool).
-              4. Documentation (PraisonAIDocs): Documentation pages, guides, API references.
-              5. Plugins (PraisonAI-Plugins): Framework lifecycle extensions replacing systemic behavior (e.g. tracing, logging, metrics, hooks, guardrails).
-              For items routed to an EXTERNAL repository (Tools, Docs, Plugins), follow STEP 3-ALT below.
+            - Does this add heavy implementations to the core SDK (praisonaiagents)? If YES → move it to the wrapper (praisonai) or make it a plugin.
             - 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.
+            If ANY of these checks fail, add a comment to the issue explaining why and close it. Do NOT create a PR.
 
-            STEP 3 — IMPLEMENT (for changes in THIS repo — PraisonAI):
+            STEP 3 — IMPLEMENT:
             - Create a fix branch and implement a minimal, focused fix
             - Follow protocol-driven design: protocols in core SDK, heavy implementations in wrapper
             - Keep changes small and backward-compatible
 
-            STEP 3-ALT — IMPLEMENT IN EXTERNAL REPO (for PraisonAI-Tools, PraisonAIDocs, PraisonAI-Plugins):
-            When work must happen in a different repository, follow these steps EXACTLY. Do NOT attempt to use `cd`, as directory state is not preserved. Use `git -C` for all commands.
-            a) Clone the repository:
-               gh repo clone MervinPraison/<REPO_NAME> /tmp/<REPO_NAME>
-            b) Copy GitHub authentication from the main repository so push works seamlessly:
-               git -C /tmp/<REPO_NAME> config http."https://github.com/".extraheader "$(git config --get http."https://github.com/".extraheader)"
-            c) Create a feature branch (NEVER commit to main):
-               git -C /tmp/<REPO_NAME> checkout -b claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
-            d) Make your changes using absolute paths (e.g. edit /tmp/<REPO_NAME>/docs.json)
-            e) Commit:
-               git -C /tmp/<REPO_NAME> add -A
-               git -C /tmp/<REPO_NAME> commit -m "feat: <description> (fixes #$ISSUE_NUMBER)"
-            f) Push the branch:
-               git -C /tmp/<REPO_NAME> push origin claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
-            g) Create PR in the EXTERNAL repo:
-               gh pr create -R MervinPraison/<REPO_NAME> --head claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d) --title "feat: <title>" --body "Fixes MervinPraison/PraisonAI#$ISSUE_NUMBER
-
-               <body>"
-
             STEP 4 — TEST:
-            - For SDK changes: cd src/praisonai-agents && PYTHONPATH=. python -m pytest tests/ -x -q --timeout=30
-            - For docs changes: verify files exist and are valid markdown/MDX
+            - 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, and create PR using `gh pr create`
-            - For external repos, use `gh pr create -R MervinPraison/<REPO_NAME>`
-            CRITICAL: You MUST create the PR automatically using `gh pr create`. Do NOT just provide a link or say "manual push required".
-            NOTE: If you worked on an external repo, the wrapper action will report "No commits" for the main repo at the end. This is expected, do not worry about it.
+            CRITICAL: You MUST create the PR automatically using `gh pr create`. Do NOT just provide a link.
           allowed_tools: |
             Bash(git:*)
             Bash(python:*)

.github/workflows/docker-publish.yml

@@ -20,10 +20,6 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3

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

@@ -34,10 +34,6 @@ jobs:
 
       - name: Checkout repository
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
         with:
           token: ${{ steps.generate_token.outputs.token }}
 

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

@@ -35,10 +35,6 @@ jobs:
 
       - name: Checkout repository
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
         with:
           token: ${{ steps.generate_token.outputs.token }}
 

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

@@ -46,10 +46,6 @@ jobs:
 
       - name: Checkout PR code
         uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
         with:
           token: ${{ steps.generate_token.outputs.token }}
           ref: ${{ steps.checkout_ref.outputs.ref }}

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

@@ -1,72 +0,0 @@
-name: PraisonAI Issue Triage
-
-on:
-  issue_comment:
-    types: [created]
-  workflow_dispatch:
-    inputs:
-      issue_number:
-        description: 'Issue Number'
-        required: true
-        type: string
-
-jobs:
-  triage:
-    runs-on: ubuntu-latest
-    if: >
-      github.event_name == 'workflow_dispatch' ||
-      (github.event_name == 'issue_comment' && 
-      !github.event.issue.pull_request && 
-      contains(github.event.comment.body, '@praisonai') &&
-      github.actor != 'github-actions[bot]' &&
-      github.event.comment.user.type != 'Bot' &&
-      (github.event.comment.author_association == 'OWNER' ||
-       github.event.comment.author_association == 'MEMBER' ||
-       github.event.comment.author_association == 'COLLABORATOR'))
-    
-    steps:
-      - name: Checkout Repository
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
-        with:
-          fetch-depth: 0
-          
-      - name: Generate GitHub App Token
-        id: generate_token
-        uses: tibdex/github-app-token@v2
-        with:
-          app_id: ${{ secrets.PRAISONAI_APP_ID }}
-          private_key: ${{ secrets.PRAISONAI_APP_PRIVATE_KEY }}
-          
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-          
-      - name: Set up Git
-        run: |
-          git config --local user.email "[email protected]"
-          git config --local user.name "PraisonAI Bot"
-          
-      - name: Install PraisonAI
-        run: |
-          pip install uv
-          cd src/praisonai-agents && uv pip install --system -e ".[all]"
-          cd ../praisonai && uv pip install --system -e ".[all]"
-        
-      - name: Run PraisonAI Issue Triage
-        env:
-          GITHUB_TOKEN: ${{ steps.generate_token.outputs.token }}
-          GH_TOKEN: ${{ steps.generate_token.outputs.token }}
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          PRAISONAI_AUTO_APPROVE: "true"
-        run: |
-          if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
-            export ISSUE_NUMBER="${{ inputs.issue_number }}"
-          else
-            export ISSUE_NUMBER="${{ github.event.issue.number }}"
-          fi
-          praisonai github triage --issue $ISSUE_NUMBER --agent-file .github/praisonai-issue-triage.yaml

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

@@ -1,72 +0,0 @@
-name: PraisonAI PR Reviewer
-
-on:
-  pull_request:
-    types: [opened, synchronize, reopened, ready_for_review]
-  issue_comment:
-    types: [created]
-  workflow_dispatch:
-    inputs:
-      pr_number:
-        description: 'Pull Request Number'
-        required: true
-        type: string
-
-jobs:
-  review:
-    runs-on: ubuntu-latest
-    if: >
-      (github.event_name == 'pull_request' && github.event.pull_request.draft == false) ||
-      github.event_name == 'workflow_dispatch' ||
-      (github.event_name == 'issue_comment' && 
-       github.event.issue.pull_request && 
-       contains(github.event.comment.body, '@praisonai') &&
-       github.actor != 'github-actions[bot]' &&
-       github.event.comment.user.type != 'Bot' &&
-       (github.event.comment.author_association == 'OWNER' ||
-        github.event.comment.author_association == 'MEMBER' ||
-        github.event.comment.author_association == 'COLLABORATOR'))
-    
-    steps:
-      - name: Determine checkout ref
-        id: dest
-        run: |
-          if [ "${{ github.event_name }}" = "issue_comment" ]; then
-            echo "ref=refs/pull/${{ github.event.issue.number }}/head" >> "$GITHUB_OUTPUT"
-          elif [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
-            echo "ref=refs/pull/${{ inputs.pr_number }}/head" >> "$GITHUB_OUTPUT"
-          else
-            echo "ref=${{ github.event.pull_request.head.sha }}" >> "$GITHUB_OUTPUT"
-          fi
-
-      - name: Checkout Repository
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
-        with:
-          ref: ${{ steps.dest.outputs.ref }}
-          fetch-depth: 0
-          
-      - name: Generate GitHub App Token
-        id: generate_token
-        uses: tibdex/github-app-token@v2
-        with:
-          app_id: ${{ secrets.PRAISONAI_APP_ID }}
-          private_key: ${{ secrets.PRAISONAI_APP_PRIVATE_KEY }}
-          
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-          
-      - name: Install PraisonAI
-        run: pip install "praisonai[all]"
-        
-      - name: Run PraisonAI PR Review
-        env:
-          GITHUB_TOKEN: ${{ steps.generate_token.outputs.token }}
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-        run: |
-          praisonai workflow run .github/praisonai-reviewer.yaml

.github/workflows/python-package.yml

@@ -15,8 +15,7 @@ jobs:
 
     steps:
     - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
+
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5
       with:

.github/workflows/python-publish.yml

@@ -22,8 +22,6 @@ jobs:
 
     steps:
     - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
     - name: Set up Python
       uses: actions/setup-python@v5
       with:

.github/workflows/release.yml

@@ -26,10 +26,6 @@ jobs:
     # First checkout the upstream repo at the specified tag
     - name: Checkout upstream repo
       uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
       with:
         repository: MervinPraison/PraisonAI
         ref: ${{ github.event.inputs.tag }}
@@ -38,20 +34,14 @@ jobs:
     # Get the commit hash from the tag
     - name: Get commit hash
       id: get_commit
-      env:
-        INPUT_TAG: ${{ github.event.inputs.tag }}
       run: |
         COMMIT_HASH=$(git rev-parse HEAD)
         echo "commit_hash=$COMMIT_HASH" >> $GITHUB_OUTPUT
-        echo "Found commit: $COMMIT_HASH for tag $INPUT_TAG"
+        echo "Found commit: $COMMIT_HASH for tag ${{ github.event.inputs.tag }}"
 
     # Now checkout the fork at main branch
     - name: Checkout fork
       uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
       with:
         repository: ${{ github.event.inputs.fork_owner }}/PraisonAI
         ref: main
@@ -70,11 +60,8 @@ jobs:
         # Fetch from upstream
         git fetch upstream --tags --force
         
-    - name: Hard reset to tag commit
-      env:
-        COMMIT_HASH: ${{ steps.get_commit.outputs.commit_hash }}
-      run: |
-        git reset --hard $COMMIT_HASH
+        # Reset to upstream commit
+        git reset --hard ${{ steps.get_commit.outputs.commit_hash }}
 
     - name: Setup Python
       uses: actions/setup-python@v5
@@ -449,10 +436,6 @@ jobs:
     # First checkout the fork's main branch
     - name: Checkout fork repo
       uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
       with:
         fetch-depth: 0
         token: ${{ secrets.PAT_TOKEN }}
@@ -462,64 +445,55 @@ jobs:
     # Add upstream and get commit from tag
     - name: Get upstream tag commit
       id: get_upstream_commit
-      env:
-        INPUT_TAG: ${{ github.event.inputs.tag }}
       run: |
         # Add upstream remote
         git remote add upstream https://github.com/MervinPraison/PraisonAI.git
         
         # Delete local tag if it exists
-        git tag -d "$INPUT_TAG" || true
+        git tag -d ${{ github.event.inputs.tag }} || true
         
         # Fetch from upstream including tags with force
         git fetch upstream --tags --force
         
         # Verify tag exists in upstream
-        if ! git ls-remote --tags upstream | grep -q "refs/tags/$INPUT_TAG$"; then
-          echo "::error::Tag $INPUT_TAG not found in upstream repository"
+        if ! git ls-remote --tags upstream | grep -q "refs/tags/${{ github.event.inputs.tag }}$"; then
+          echo "::error::Tag ${{ github.event.inputs.tag }} not found in upstream repository"
           exit 1
         fi
         
         # Get the commit hash from the upstream tag
-        UPSTREAM_COMMIT=$(git rev-list -n 1 refs/tags/"$INPUT_TAG")
+        UPSTREAM_COMMIT=$(git rev-list -n 1 refs/tags/${{ github.event.inputs.tag }})
         if [ -z "$UPSTREAM_COMMIT" ]; then
-          echo "::error::Could not find commit for tag $INPUT_TAG"
+          echo "::error::Could not find commit for tag ${{ github.event.inputs.tag }}"
           exit 1
         fi
         echo "upstream_commit=$UPSTREAM_COMMIT" >> $GITHUB_OUTPUT
-        echo "Found commit: $UPSTREAM_COMMIT for tag $INPUT_TAG"
+        echo "Found commit: $UPSTREAM_COMMIT for tag ${{ github.event.inputs.tag }}"
 
     # Create tag at the same commit
     - name: Create tag at commit
-      env:
-        GIT_ASKPASS: /bin/echo
-        PAT_TOKEN: ${{ secrets.PAT_TOKEN }}
-        INPUT_TAG: ${{ github.event.inputs.tag }}
       run: |
         git config --global user.name "MervinPraison"
         git config --global user.email "[email protected]"
         
         # Remove tag if it exists locally
-        git tag -d "$INPUT_TAG" || true
+        git tag -d ${{ github.event.inputs.tag }} || true
         
         # Remove tag if it exists remotely
-        git push origin :refs/tags/"$INPUT_TAG" || true
+        git push origin :refs/tags/${{ github.event.inputs.tag }} || true
         
-    - name: Fetch tag from upstream
-      env:
-        UPSTREAM_COMMIT: ${{ steps.get_upstream_commit.outputs.upstream_commit }}
-      run: |
-        git fetch upstream $UPSTREAM_COMMIT --force
+        # Fetch the specific commit from upstream
+        git fetch upstream ${{ steps.get_upstream_commit.outputs.upstream_commit }} --force
         
-    - name: Reset to upstream commit
-      env:
-        UPSTREAM_COMMIT: ${{ steps.get_upstream_commit.outputs.upstream_commit }}
-      run: |
-        git reset --hard $UPSTREAM_COMMIT
+        # Reset to upstream commit
+        git reset --hard ${{ steps.get_upstream_commit.outputs.upstream_commit }}
         
         # Create and push tag
-        git tag -a "$INPUT_TAG" -m "Release $INPUT_TAG (synced from upstream MervinPraison/PraisonAI@$INPUT_TAG)"
-        git push origin "$INPUT_TAG"
+        git tag -a ${{ github.event.inputs.tag }} -m "Release ${{ github.event.inputs.tag }} (synced from upstream MervinPraison/PraisonAI@${{ github.event.inputs.tag }})"
+        git push origin ${{ github.event.inputs.tag }}
+      env:
+        GIT_ASKPASS: /bin/echo
+        PAT_TOKEN: ${{ secrets.PAT_TOKEN }}
 
     # Create the release
     - name: Create Release

.github/workflows/test-comprehensive.yml

@@ -37,10 +37,6 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
 
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5
@@ -71,11 +67,9 @@ jobs:
         fi
 
     - name: Run Comprehensive Test Suite
-      env:
-        INPUT_TEST_TYPE: ${{ github.event.inputs.test_type || 'all' }}
       run: |
         # Determine test type from input or default to 'all'
-        TEST_TYPE="$INPUT_TEST_TYPE"
+        TEST_TYPE="${{ github.event.inputs.test_type || 'all' }}"
         
         echo "🧪 Running comprehensive test suite (type: $TEST_TYPE)"
         
@@ -108,16 +102,13 @@ jobs:
 
     - name: Generate Comprehensive Test Report
       if: always()
-      env:
-        INPUT_TEST_TYPE: ${{ github.event.inputs.test_type || 'all' }}
-        EVENT_NAME: ${{ github.event_name }}
       run: |
         echo "# 📋 Comprehensive Test Report" > comprehensive_report.md
         echo "" >> comprehensive_report.md
         echo "**Python Version:** ${{ matrix.python-version }}" >> comprehensive_report.md
-        echo "**Test Type:** $INPUT_TEST_TYPE" >> comprehensive_report.md
-        echo "**Trigger:** $EVENT_NAME" >> comprehensive_report.md
-        echo "**Status:** 🟢 All Tests Passed" >> comprehensive_report.md
+        echo "**Test Type:** ${{ github.event.inputs.test_type || 'all' }}" >> comprehensive_report.md
+        echo "**Trigger:** ${{ github.event_name }}" >> comprehensive_report.md
+        echo "**Date:** $(date -u)" >> comprehensive_report.md
         echo "" >> comprehensive_report.md
         
         echo "## 🧪 Test Categories Covered:" >> comprehensive_report.md

.github/workflows/test-core.yml

@@ -22,8 +22,7 @@ jobs:
 
     steps:
     - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
+
     - name: Set up Python 3.11
       uses: actions/setup-python@v5
       with:

.github/workflows/test-extended.yml

@@ -13,10 +13,6 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
 
     - name: Set up Python
       uses: actions/setup-python@v5
@@ -81,10 +77,6 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
 
     - name: Set up Python
       uses: actions/setup-python@v5

.github/workflows/test-frameworks.yml

@@ -33,10 +33,6 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
 
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5

.github/workflows/test-optimized.yml

@@ -45,8 +45,7 @@ jobs:
     timeout-minutes: 5
     steps:
     - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
+    
     - name: Set up Python 3.11
       uses: actions/setup-python@v5
       with:
@@ -105,8 +104,7 @@ jobs:
     
     steps:
     - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
+    
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5
       with:
@@ -187,8 +185,6 @@ jobs:
         fi
     
     - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       if: steps.check-key.outputs.has_key == 'true'
     
     - name: Set up Python 3.11
@@ -259,8 +255,6 @@ jobs:
         fi
     
     - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       if: steps.check-key.outputs.has_key == 'true'
     
     - name: Set up Python 3.11
@@ -318,8 +312,6 @@ jobs:
         fi
     
     - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       if: steps.check-key.outputs.has_key == 'true'
     
     - name: Set up Python 3.11
@@ -377,8 +369,6 @@ jobs:
         fi
     
     - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       if: steps.check-key.outputs.has_key == 'true'
     
     - name: Set up Python 3.11
@@ -436,8 +426,6 @@ jobs:
         fi
     
     - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       if: steps.check-key.outputs.has_key == 'true'
     
     - name: Set up Python 3.11
@@ -495,8 +483,6 @@ jobs:
         fi
     
     - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
       if: steps.check-key.outputs.has_key == 'true'
     
     - name: Set up Python 3.11

.github/workflows/test-real.yml

@@ -34,21 +34,14 @@ jobs:
     
     steps:
     - name: 🚨 Cost Warning
-      env:
-        INPUT_FRAMEWORK: ${{ github.event.inputs.framework }}
-        INPUT_CONFIRM_COSTS: ${{ github.event.inputs.confirm_costs }}
       run: |
         echo "⚠️  WARNING: This workflow will make real API calls!"
         echo "💰 This may incur charges on your API accounts"
-        echo "🎯 Framework: $INPUT_FRAMEWORK"
-        echo "✅ Cost confirmation: $INPUT_CONFIRM_COSTS"
+        echo "🎯 Framework: ${{ github.event.inputs.framework }}"
+        echo "✅ Cost confirmation: ${{ github.event.inputs.confirm_costs }}"
         
     - name: Checkout code
       uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-        with:
-          persist-credentials: false
 
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5
@@ -100,23 +93,21 @@ jobs:
 
     - name: Generate Real Test Report
       if: always()
-      env:
-        INPUT_FRAMEWORK: ${{ github.event.inputs.framework }}
       run: |
         echo "# 🔥 Real End-to-End Test Report" > real_test_report.md
         echo "" >> real_test_report.md
         echo "⚠️  **WARNING: This report represents tests that made real API calls**" >> real_test_report.md
         echo "" >> real_test_report.md
-        echo "**Framework Tested:** $INPUT_FRAMEWORK" >> real_test_report.md
+        echo "**Framework Tested:** ${{ github.event.inputs.framework }}" >> real_test_report.md
         echo "**Python Version:** ${{ matrix.python-version }}" >> real_test_report.md
         echo "**Date:** $(date -u)" >> real_test_report.md
-        echo "**Triggered by:** $GITHUB_ACTOR" >> real_test_report.md
+        echo "**Triggered by:** ${{ github.actor }}" >> real_test_report.md
         echo "" >> real_test_report.md
         
         echo "## 🧪 Test Results" >> real_test_report.md
         echo "" >> real_test_report.md
         
-        if [ "$INPUT_FRAMEWORK" == "autogen" ] || [ "$INPUT_FRAMEWORK" == "all" ]; then
+        if [ "${{ github.event.inputs.framework }}" == "autogen" ] || [ "${{ github.event.inputs.framework }}" == "all" ]; then
           echo "### AutoGen Real Tests:" >> real_test_report.md
           echo "- Environment verification" >> real_test_report.md
           echo "- Agent creation with real API calls" >> real_test_report.md
@@ -124,7 +115,7 @@ jobs:
           echo "" >> real_test_report.md
         fi
         
-        if [ "$INPUT_FRAMEWORK" == "crewai" ] || [ "$INPUT_FRAMEWORK" == "all" ]; then
+        if [ "${{ github.event.inputs.framework }}" == "crewai" ] || [ "${{ github.event.inputs.framework }}" == "all" ]; then
           echo "### CrewAI Real Tests:" >> real_test_report.md
           echo "- Environment verification" >> real_test_report.md
           echo "- Crew creation with real API calls" >> real_test_report.md
@@ -159,14 +150,11 @@ jobs:
     
     steps:
     - name: 🛡️ Safety Check Failed
-      env:
-        INPUT_FRAMEWORK: ${{ github.event.inputs.framework }}
-        INPUT_CONFIRM_COSTS: ${{ github.event.inputs.confirm_costs }}
       run: |
         echo "🚨 Real tests not executed due to safety checks:"
         echo ""
-        echo "✅ Costs confirmed: $INPUT_CONFIRM_COSTS"
-        echo "✅ Framework selected: $INPUT_FRAMEWORK"
+        echo "✅ Costs confirmed: ${{ github.event.inputs.confirm_costs }}"
+        echo "✅ Framework selected: ${{ github.event.inputs.framework }}"
         echo ""
         echo "To run real tests:"
         echo "1. Select a framework (autogen, crewai, or all)"

.github/workflows/test-windows.yml

@@ -26,8 +26,7 @@ jobs:
     
     steps:
     - uses: actions/checkout@v4
-        with:
-          persist-credentials: false
+    
     - name: Set up Python 3.11
       uses: actions/setup-python@v5
       with:

.gitignore

@@ -94,7 +94,7 @@ Cargo.lock
 **/*.rs.bk
 *.pdb
 .windsurf
+.agent
 
 src/praisonai/build*.egg-info/
 src/praisonai/PraisonAI.egg-info/
-jobs/*

README.md

@@ -23,13 +23,14 @@
 
 </div>
 
-PraisonAI 🦞 — **Hire a 24/7 AI Workforce.** Stop writing boilerplate and start shipping autonomous agents that research, plan, and execute tasks across your apps. From one agent to an entire organization, deployed in 5 lines of code.
+PraisonAI 🦞 — Automate and solve complex challenges with AI agent teams that plan, research, code, and deliver results to Telegram, Discord, and WhatsApp — running 24/7. A low-code, production-ready multi-agent framework with handoffs, guardrails, memory, RAG, and 100+ LLM providers, built around simplicity, customisation, and effective human-agent collaboration.
 
 <div align="center">
   <br>
   <a href="https://x.com/elonmusk/status/1893870468249141688" target="_blank">
     <img src="https://img.shields.io/badge/Highlighted_by_Elon_Musk-000000?style=for-the-badge&logo=x&logoColor=white" alt="Highlighted by Elon Musk" />
   </a>
+  <p><em>"Grok 3 customer support" — <a href="https://x.com/elonmusk/status/1893870468249141688">Elon Musk quoting PraisonAI's tutorial</a></em></p>
   <br>
 </div>
 
@@ -68,6 +69,26 @@ PraisonAI 🦞 — **Hire a 24/7 AI Workforce.** Stop writing boilerplate and st
 
 ---
 
+> **Quick Paths:**
+> - 🆕 **New here?** → [Quick Start](#-quick-start) *(1 minute to first agent)*
+> - 📦 **Installing?** → [Installation](#-installation)
+> - 🐍 **Python SDK?** → [Python Examples](#-using-python-code)
+> - 📄 **YAML/No-Code?** → [YAML Examples](#-using-yaml-no-code)
+> - 🎯 **CLI user?** → [CLI Quick Reference](#-cli-quick-reference)
+> - 🤝 **Contributing?** → [Contributing](#-contributing)
+
+---
+
+## ⚡ Performance
+
+PraisonAI is built for speed, with agent instantiation in under 4μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.
+
+| Performance Metric | PraisonAI |
+|--------------------|-----------|
+| Avg Instantiation Time | **3.77 μs** |
+
+---
+
 ## 🎯 Use Cases
 
 AI agents solving real-world problems across industries:
@@ -83,44 +104,9 @@ AI agents solving real-world problems across industries:
 
 ---
 
-## 🚀 Meet your first Agent (Under 1 Minute)
+##  Supported Providers
 
-1. Install the lightweight core SDK:
-```bash
-pip install praisonaiagents
-export OPENAI_API_KEY="your-api-key"
-```
-
-2. Run your first autonomous agent:
-```python
-from praisonaiagents import Agent
-
-# Give your agent a goal, and watch it work.
-agent = Agent(instructions="You are a senior data analyst.")
-agent.start("Analyze the top 3 tech trends of 2026 and format as a markdown table.")
-```
-
----
-
-## 🌌 The PraisonAI Ecosystem
-
-Start simple with the core SDK, or expand to full visual builders and dashboards when you're ready.
-
-*   **Core SDK (`praisonaiagents`)**: For pure Python development. `pip install praisonaiagents`
-*   💻 **PraisonAI CLI (`praisonai`)**: For terminal-based developers. `pip install praisonai`
-*   🦞 **Claw Dashboard**: Connect agents directly to Telegram, Slack, or Discord. `pip install "praisonai[claw]"`
-*   🔗 **Flow Visual Builder**: Drag-and-drop workflow creation. `pip install "praisonai[flow]"`
-*   🤖 **PraisonAI UI**: Clean chat interface. `pip install "praisonai[ui]"`
-
-### JavaScript SDK
-
-```bash
-npm install praisonai
-```
-
-## 🧠 Supported Providers & Features
-
-Powered by 100+ LLMs (OpenAI, Anthropic, Gemini & local models).
+PraisonAI supports 100+ LLM providers through seamless integration:
 
 <p align="center">
 <img src="https://img.shields.io/badge/OpenAI-412991?style=flat&logo=openai&logoColor=white" alt="OpenAI" />
@@ -178,14 +164,6 @@ Powered by 100+ LLMs (OpenAI, Anthropic, Gemini & local models).
 
 </details>
 
-<div align="center">
-  <a href="https://x.com/elonmusk/status/1893870468249141688" target="_blank">
-    <img src=".github/images/elon_musk_praisonai.png" alt="Highlighted by Elon Musk" width="600" />
-  </a>
-  <p><em>"Grok 3 customer support" — <a href="https://x.com/elonmusk/status/1893870468249141688">Elon Musk quoting PraisonAI's tutorial</a></em></p>
-</div>
-<br>
-
 ---
 
 ## 🌟 Why PraisonAI?
@@ -227,6 +205,78 @@ Powered by 100+ LLMs (OpenAI, Anthropic, Gemini & local models).
 </details>
 
 
+<div align="center">
+  <a href="https://x.com/elonmusk/status/1893870468249141688" target="_blank">
+    <img src=".github/images/elon_musk_praisonai.png" alt="Highlighted by Elon Musk" width="600" />
+  </a>
+  <p><em>"Grok 3 customer support" — <a href="https://x.com/elonmusk/status/1893870468249141688">Elon Musk quoting PraisonAI's tutorial</a></em></p>
+</div>
+<br>
+
+---
+
+## 🚀 Quick Start
+
+Get started with PraisonAI in under 1 minute:
+
+```bash
+# Install
+pip install praisonaiagents
+
+# Set API key
+export OPENAI_API_KEY=your_key_here
+
+# Create a simple agent
+python -c "from praisonaiagents import Agent; Agent(instructions='You are a helpful AI assistant').start('Write a haiku about AI')"
+```
+
+> **Next Steps:** [Single Agent Example](#1-single-agent) | [Multi Agents](#2-multi-agents) | [Full Docs](https://docs.praison.ai)
+
+---
+
+## 📦 Installation
+
+### Python SDK
+
+Lightweight package dedicated for coding:
+
+```bash
+pip install praisonaiagents
+```
+
+For the full framework with CLI support:
+
+```bash
+pip install praisonai
+```
+
+🦞 **PraisonAI Claw** — full UI with bots, memory, knowledge, and gateway:
+
+```bash
+pip install "praisonai[claw]"
+praisonai claw
+```
+
+🔗 **PraisonAI Flow** — Langflow Visual Flow Builder:
+
+```bash
+pip install "praisonai[flow]"
+praisonai flow
+```
+
+🤖 **PraisonAI UI** — Clean chat interface:
+
+```bash
+pip install "praisonai[ui]"
+praisonai ui
+```
+
+### JavaScript SDK
+
+```bash
+npm install praisonai
+```
+
 
 
 ---
@@ -650,7 +700,6 @@ praisonai agents.yaml
 | Auto-Save Sessions | [Docs](https://docs.praison.ai/docs/cli/session) | [📖](https://docs.praison.ai/docs/cli/session) |
 | History in Context | [Docs](https://docs.praison.ai/docs/cli/session) | [📖](https://docs.praison.ai/docs/cli/session) |
 | Telemetry | [Example](examples/python/telemetry/production-telemetry-example.py) | [📖](https://docs.praison.ai/docs/features/telemetry) |
-| Langfuse Tracing | [Docs](https://docs.praison.ai/docs/observability/langfuse) | [📖](https://docs.praison.ai/docs/observability/langfuse) |
 | Project Docs (.praison/docs/) | [Docs](https://docs.praison.ai/docs/cli/docs) | [📖](https://docs.praison.ai/docs/cli/docs) |
 | AI Commit Messages | [Docs](https://docs.praison.ai/docs/cli/commit) | [📖](https://docs.praison.ai/docs/cli/commit) |
 | @Mentions in Prompts | [Docs](https://docs.praison.ai/docs/cli/mentions) | [📖](https://docs.praison.ai/docs/cli/mentions) |
@@ -725,20 +774,6 @@ const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
 agent.start('Write a movie script about a robot in Mars');
 ```
 
----
-
-## ⚡ Performance
-
-PraisonAI is built for speed, with agent instantiation in under 4μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.
-
-| Performance Metric | PraisonAI |
-|--------------------|-----------|
-| Avg Instantiation Time | **3.77 μs** |
-
----
-
-
-
 ---
 
 ## ⭐ Star History
@@ -747,19 +782,6 @@ PraisonAI is built for speed, with agent instantiation in under 4μs. This reduc
 
 ---
 
-## 🔍 Langfuse Tracing
-
-```bash
-pip install "praisonai[langfuse]"
-praisonai langfuse
-```
-
-<p align="center">
-  <img src=".github/images/langfuse.png" alt="PraisonAI Langfuse Tracing" width="800" />
-</p>
-
----
-
 ## 🎓 Video Tutorials
 
 Learn PraisonAI through our comprehensive video series:

api.md

@@ -28,19 +28,15 @@ from praisonaiagents import Agent, AutoAgents, AutoRagAgent, ContextAgent, DeepR
 
 Methods:
 
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">aclose</a>() -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">agent_id</a>() -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">analyze_prompt</a>(prompt: str) -> set</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">auto_memory</a>() -> Optional[bool]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">auto_memory</a>(value: Optional[bool]) -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">background</a>() -> Optional[bool]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">background</a>(value: Optional[bool]) -> None</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">chat_history</a>()</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">chat_history</a>(value)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">chat_with_context</a>(message: str, context: 'ContextPack', **kwargs) -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">checkpoints</a>() -> Optional[bool]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">checkpoints</a>(value: Optional[bool]) -> None</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">close</a>() -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">console</a>() -> Optional[Any]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">context_manager</a>() -> Optional[Any]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">context_manager</a>(value)</code>
@@ -56,7 +52,6 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_skills_prompt</a>() -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">handoff_to</a>(target_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None, config: Optional['HandoffConfig'] = None) -> 'HandoffResult'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">handoff_to_async</a>(target_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None, config: Optional['HandoffConfig'] = None) -> 'HandoffResult'</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">is_closed</a>() -> bool</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">llm_model</a>() -> Optional[str]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">output_style</a>() -> Optional[str]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">output_style</a>(value: Optional[str]) -> None</code>
@@ -364,9 +359,6 @@ Methods:
 
 * <code title="class GuardrailConfig">GuardrailConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="class LLMGuardrail">LLMGuardrail.<a href="./src/praisonai-agents/praisonaiagents/guardrails/llm_guardrail.py">__call__</a>(task_output) -> Tuple[bool, Union[str, 'TaskOutput']]</code>
-* <code title="class LLMGuardrail">LLMGuardrail.<a href="./src/praisonai-agents/praisonaiagents/guardrails/llm_guardrail.py">validate_input</a>(content: str, **kwargs) -> Tuple[bool, str]</code>
-* <code title="class LLMGuardrail">LLMGuardrail.<a href="./src/praisonai-agents/praisonaiagents/guardrails/llm_guardrail.py">validate_output</a>(content: str, **kwargs) -> Tuple[bool, str]</code>
-* <code title="class LLMGuardrail">LLMGuardrail.<a href="./src/praisonai-agents/praisonaiagents/guardrails/llm_guardrail.py">validate_tool_call</a>(tool_name: str, arguments: Dict[str, Any], **kwargs) -> Tuple[bool, Dict[str, Any]]</code>
 
 # Planning
 
@@ -555,7 +547,7 @@ Methods:
 
 Types:
 ```python
-from praisonaiagents import FlowDisplay, track_workflow
+from praisonaiagents import FlowDisplay, obs, track_workflow
 ```
 
 Methods:
@@ -741,7 +733,7 @@ Methods:
 
 Types:
 ```python
-from praisonai import AgentApp, AgentOS, AnthropicManagedAgent, CloudProvider, DB, Deploy, DeployConfig, DeployType, LocalManagedAgent, LocalManagedConfig, ManagedAgent, ManagedConfig, PraisonAI, __version__
+from praisonai import AgentApp, AgentOS, CloudProvider, Deploy, DeployConfig, DeployType, PraisonAI, __version__
 ```
 
 # CLI
@@ -863,7 +855,6 @@ Methods:
 * <code title="cli">praisonai gateway send <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai gateway start <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai gateway status <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
-* <code title="cli">praisonai github triage <a href="./src/praisonai/praisonai/cli/commands/github.py">--help</a></code>
 * <code title="cli">praisonai hooks add <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
 * <code title="cli">praisonai hooks list <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
 * <code title="cli">praisonai hooks remove <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
@@ -871,17 +862,6 @@ Methods:
 * <code title="cli">praisonai knowledge index <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
 * <code title="cli">praisonai knowledge list <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
 * <code title="cli">praisonai knowledge search <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
-* <code title="cli">praisonai langfuse config <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
-* <code title="cli">praisonai langfuse connect <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
-* <code title="cli">praisonai langfuse init <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
-* <code title="cli">praisonai langfuse langfuse-start <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
-* <code title="cli">praisonai langfuse sessions <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
-* <code title="cli">praisonai langfuse show <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
-* <code title="cli">praisonai langfuse status <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
-* <code title="cli">praisonai langfuse stop <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
-* <code title="cli">praisonai langfuse test <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
-* <code title="cli">praisonai langfuse traces <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
-* <code title="cli">praisonai langfuse version <a href="./src/praisonai/praisonai/cli/commands/langfuse.py">--help</a></code>
 * <code title="cli">praisonai loop help <a href="./src/praisonai/praisonai/cli/commands/loop.py">--help</a></code>
 * <code title="cli">praisonai loop loop-main <a href="./src/praisonai/praisonai/cli/commands/loop.py">--help</a></code>
 * <code title="cli">praisonai lsp logs <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
@@ -889,16 +869,6 @@ Methods:
 * <code title="cli">praisonai lsp start <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
 * <code title="cli">praisonai lsp status <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
 * <code title="cli">praisonai lsp stop <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
-* <code title="cli">praisonai managed get <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
-* <code title="cli">praisonai managed list <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
-* <code title="cli">praisonai managed managed-callback <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
-* <code title="cli">praisonai managed multi <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
-* <code title="cli">praisonai managed restore <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
-* <code title="cli">praisonai managed resume <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
-* <code title="cli">praisonai managed run <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
-* <code title="cli">praisonai managed save <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
-* <code title="cli">praisonai managed show <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
-* <code title="cli">praisonai managed update <a href="./src/praisonai/praisonai/cli/commands/managed.py">--help</a></code>
 * <code title="cli">praisonai mcp add <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
 * <code title="cli">praisonai mcp auth <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
 * <code title="cli">praisonai mcp describe <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
@@ -931,10 +901,6 @@ Methods:
 * <code title="cli">praisonai plugins list <a href="./src/praisonai/praisonai/cli/commands/plugins.py">--help</a></code>
 * <code title="cli">praisonai plugins plugins-callback <a href="./src/praisonai/praisonai/cli/commands/plugins.py">--help</a></code>
 * <code title="cli">praisonai plugins remove <a href="./src/praisonai/praisonai/cli/commands/plugins.py">--help</a></code>
-* <code title="cli">praisonai port check <a href="./src/praisonai/praisonai/cli/commands/port.py">--help</a></code>
-* <code title="cli">praisonai port kill <a href="./src/praisonai/praisonai/cli/commands/port.py">--help</a></code>
-* <code title="cli">praisonai port list <a href="./src/praisonai/praisonai/cli/commands/port.py">--help</a></code>
-* <code title="cli">praisonai port port-callback <a href="./src/praisonai/praisonai/cli/commands/port.py">--help</a></code>
 * <code title="cli">praisonai profile imports <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
 * <code title="cli">praisonai profile optimize <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
 * <code title="cli">praisonai profile profile-callback <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.2" \
+    "praisonai>=4.5.112" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.2" \
+    "praisonai>=4.5.112" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.2" \
+    "praisonai>=4.5.112" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/managed-agents/persistence/clickhouse_managed.py

@@ -1,115 +0,0 @@
-#!/usr/bin/env python3
-"""
-ManagedAgent + ClickHouse Persistence — Real conversation with session resume.
-
-ClickHouse stores analytics/state; SQLite stores conversation history.
-
-Prerequisites:
-    pip install praisonai praisonaiagents clickhouse-connect
-    export OPENAI_API_KEY="sk-..."
-    # ClickHouse running on localhost:8123
-"""
-
-import json
-import os
-import sys
-import tempfile
-
-import clickhouse_connect
-
-if not os.getenv("OPENAI_API_KEY"):
-    sys.exit("ERROR: OPENAI_API_KEY not set.")
-
-from praisonai import ManagedAgent, LocalManagedConfig, DB
-from praisonaiagents import Agent
-
-CH_HOST = os.getenv("CH_HOST", "localhost")
-CH_PORT = int(os.getenv("CH_PORT", "8123"))
-CH_USER = os.getenv("CH_USER", "clickhouse")
-CH_PASSWORD = os.getenv("CH_PASSWORD", "clickhouse")
-SQLITE_PATH = os.path.join(tempfile.gettempdir(), "managed_ch_conv.db")
-print(f"ManagedAgent + ClickHouse Persistence\nCH: {CH_HOST}:{CH_PORT}\nSQLite: {SQLITE_PATH}\n")
-
-# Setup ClickHouse table
-client = clickhouse_connect.get_client(host=CH_HOST, port=CH_PORT, username=CH_USER, password=CH_PASSWORD)
-TABLE = "managed_agent_sessions"
-client.command(f"""
-    CREATE TABLE IF NOT EXISTS {TABLE} (
-        session_id String, agent_id String,
-        input_tokens UInt64, output_tokens UInt64,
-        state_json String, created_at DateTime DEFAULT now()
-    ) ENGINE = MergeTree() ORDER BY (session_id, created_at)
-""")
-
-# ── Phase 1: Create agent, teach facts ──
-print("=== Phase 1: First Session ===")
-
-db = DB(database_url=SQLITE_PATH)
-managed = ManagedAgent(
-    provider="local", db=db,
-    config=LocalManagedConfig(
-        model="gpt-4o-mini", name="ClickHouse Memory Agent",
-        system="You are a helpful assistant. Remember all facts the user tells you.",
-    ),
-)
-agent = Agent(name="User", backend=managed)
-
-result1 = agent.run("Remember: My favourite movie is Interstellar by Christopher Nolan, 2014. Confirm.")
-print(f"Agent: {result1[:200]}...")
-print(f"Session ID: {managed.session_id}")
-
-result2 = agent.run("Also remember: The Hans Zimmer soundtrack is my favourite film score. Confirm.")
-print(f"Agent: {result2[:200]}...")
-
-# Store to ClickHouse
-session_id = managed.session_id
-client.insert(TABLE, [[
-    session_id, managed.agent_id or "",
-    managed.total_input_tokens, managed.total_output_tokens,
-    json.dumps({"environment_id": managed.environment_id}),
-]], column_names=["session_id", "agent_id", "input_tokens", "output_tokens", "state_json"])
-
-# ── Phase 2: ClickHouse Verification ──
-print("\n=== Phase 2: ClickHouse Verification ===")
-rows = client.query(f"SELECT session_id, agent_id FROM {TABLE} WHERE session_id=%(sid)s LIMIT 1", parameters={"sid": session_id})
-for row in rows.result_rows:
-    print(f"  session_id: {row[0]}, agent_id: {row[1]}")
-
-# ── Phase 3: Destroy instance ──
-print("\n=== Phase 3: Instance Goes Idle ===")
-saved_ids = managed.save_ids()
-print(f"Saved IDs: {saved_ids}")
-del agent, managed, db
-print("Agent destroyed.\n")
-
-# ── Phase 4: Resume — no config needed ──
-print("=== Phase 4: Resume Session ===")
-db2 = DB(database_url=SQLITE_PATH)
-managed2 = ManagedAgent(provider="local", db=db2)
-managed2.resume_session(session_id)
-print(f"Resumed session: {managed2.session_id}")
-
-agent2 = Agent(name="User", backend=managed2)
-result3 = agent2.run("What is my favourite movie, director, year, and soundtrack composer?")
-print(f"Agent: {result3[:300]}...")
-
-# ── Phase 5: Validate ──
-print("\n=== Phase 5: Validation ===")
-r = result3.lower()
-checks = {
-    "Remembers 'Interstellar'": "interstellar" in r,
-    "Remembers 'Nolan'": "nolan" in r,
-    "Remembers '2014'": "2014" in r,
-    "Remembers 'Zimmer'": "zimmer" in r,
-    "Session ID continuity": managed2.session_id == session_id,
-}
-all_passed = True
-for check, ok in checks.items():
-    print(f"  [{'PASS' if ok else 'FAIL'}] {check}")
-    if not ok:
-        all_passed = False
-
-client.command(f"ALTER TABLE {TABLE} DELETE WHERE session_id = '{session_id}'")
-client.close()
-os.unlink(SQLITE_PATH)
-print(f"\n{'PASS' if all_passed else 'FAIL'}: ClickHouse ManagedAgent persistence test")

examples/managed-agents/persistence/cockroachdb_managed.py

@@ -1,25 +0,0 @@
-"""
-CockroachDB Serverless Persistence Example.
-
-CockroachDB Serverless provides distributed SQL with scale-to-zero.
-Auto-handles serialization retries (error 40001) and SSL.
-
-Setup:
-  1. Create free CockroachDB Serverless cluster at https://cockroachlabs.cloud
-  2. Copy connection string from dashboard
-  3. Set env var: export COCKROACHDB_URL="postgresql://user:[email protected]:26257/mydb?sslmode=verify-full"
-  4. pip install praisonai[cockroachdb]
-"""
-
-import os
-from praisonaiagents import Agent
-
-agent = Agent(
-    name="CockroachDB Agent",
-    instructions="You are a helpful assistant.",
-    db={"database_url": os.getenv("COCKROACHDB_URL")},
-    session_id="crdb-demo-session",
-)
-
-result = agent.start("What is distributed SQL? Explain in 2 sentences.")
-print(result)

examples/managed-agents/persistence/json_managed.py

@@ -1,92 +0,0 @@
-#!/usr/bin/env python3
-"""
-ManagedAgent + JSON File Persistence — Real conversation with session resume.
-
-Uses DefaultSessionStore (JSON files on disk). No external database required.
-
-Prerequisites:
-    pip install praisonai praisonaiagents
-    export OPENAI_API_KEY="sk-..."
-"""
-
-import json
-import os
-import shutil
-import sys
-import tempfile
-
-if not os.getenv("OPENAI_API_KEY"):
-    sys.exit("ERROR: OPENAI_API_KEY not set.")
-
-from praisonai import ManagedAgent, LocalManagedConfig
-from praisonaiagents import Agent
-from praisonaiagents.session.store import DefaultSessionStore
-
-SESSION_DIR = os.path.join(tempfile.gettempdir(), "managed_json_sessions")
-print(f"ManagedAgent + JSON File Persistence\nSession dir: {SESSION_DIR}\n")
-
-# ── Phase 1: Create agent, teach facts ──
-print("=== Phase 1: First Session ===")
-
-store = DefaultSessionStore(session_dir=SESSION_DIR)
-managed = ManagedAgent(
-    provider="local", session_store=store,
-    config=LocalManagedConfig(
-        model="gpt-4o-mini", name="JSON Memory Agent",
-        system="You are a helpful assistant. Remember all facts the user tells you.",
-    ),
-)
-agent = Agent(name="User", backend=managed)
-
-result1 = agent.run("Remember: My favourite city is Barcelona, Spain. I love the Sagrada Familia. Confirm.")
-print(f"Agent: {result1[:200]}...")
-print(f"Session ID: {managed.session_id}")
-
-result2 = agent.run("Also remember: I always stay at Hotel Arts in Barcelona. Confirm.")
-print(f"Agent: {result2[:200]}...")
-
-# ── Phase 2: Verify JSON files ──
-print("\n=== Phase 2: JSON File Verification ===")
-json_files = [f for f in os.listdir(SESSION_DIR) if f.endswith(".json")]
-print(f"Files: {json_files}")
-for fname in json_files:
-    with open(os.path.join(SESSION_DIR, fname), "r") as f:
-        data = json.load(f)
-    print(f"  {fname}: {len(data.get('messages', []))} messages")
-
-# ── Phase 3: Destroy instance ──
-print("\n=== Phase 3: Instance Goes Idle ===")
-saved_ids = managed.save_ids()
-session_id = saved_ids["session_id"]
-print(f"Saved IDs: {saved_ids}")
-del agent, managed, store
-print("Agent destroyed.\n")
-
-# ── Phase 4: Resume — no config needed ──
-print("=== Phase 4: Resume Session ===")
-store2 = DefaultSessionStore(session_dir=SESSION_DIR)
-managed2 = ManagedAgent(provider="local", session_store=store2)
-managed2.resume_session(session_id)
-print(f"Resumed session: {managed2.session_id}")
-
-agent2 = Agent(name="User", backend=managed2)
-result3 = agent2.run("What is my favourite city, landmark, and hotel?")
-print(f"Agent: {result3[:300]}...")
-
-# ── Phase 5: Validate ──
-print("\n=== Phase 5: Validation ===")
-r = result3.lower()
-checks = {
-    "Remembers 'Barcelona'": "barcelona" in r,
-    "Remembers 'Sagrada Familia'": "sagrada" in r,
-    "Remembers 'Hotel Arts'": "hotel arts" in r or "arts" in r,
-    "Session ID continuity": managed2.session_id == session_id,
-}
-all_passed = True
-for check, ok in checks.items():
-    print(f"  [{'PASS' if ok else 'FAIL'}] {check}")
-    if not ok:
-        all_passed = False
-
-shutil.rmtree(SESSION_DIR)
-print(f"\n{'PASS' if all_passed else 'FAIL'}: JSON File ManagedAgent persistence test")

examples/managed-agents/persistence/mongodb_managed.py

@@ -1,102 +0,0 @@
-#!/usr/bin/env python3
-"""
-ManagedAgent + MongoDB Persistence — Real conversation with session resume.
-
-MongoDB stores state metadata; SQLite stores conversation history.
-
-Prerequisites:
-    pip install praisonai praisonaiagents pymongo
-    export OPENAI_API_KEY="sk-..."
-    # MongoDB running on localhost:27017
-"""
-
-import os
-import sys
-import tempfile
-
-import pymongo
-
-if not os.getenv("OPENAI_API_KEY"):
-    sys.exit("ERROR: OPENAI_API_KEY not set.")
-
-from praisonai import ManagedAgent, LocalManagedConfig, DB
-from praisonaiagents import Agent
-
-MONGO_URL = os.getenv("MONGO_URL", "mongodb://localhost:27017")
-SQLITE_PATH = os.path.join(tempfile.gettempdir(), "managed_mongo_conv.db")
-print(f"ManagedAgent + MongoDB Persistence\nMongoDB: {MONGO_URL}\nSQLite: {SQLITE_PATH}\n")
-
-# ── Phase 1: Create agent, teach facts ──
-print("=== Phase 1: First Session ===")
-
-db = DB(database_url=SQLITE_PATH)
-managed = ManagedAgent(
-    provider="local", db=db,
-    config=LocalManagedConfig(
-        model="gpt-4o-mini", name="MongoDB Memory Agent",
-        system="You are a helpful assistant. Remember all facts the user tells you.",
-    ),
-)
-agent = Agent(name="User", backend=managed)
-
-result1 = agent.run("Remember: My favourite book is 'Dune' by Frank Herbert, published 1965. Confirm.")
-print(f"Agent: {result1[:200]}...")
-print(f"Session ID: {managed.session_id}")
-
-result2 = agent.run("Also remember: 'Dune Messiah' is my second favourite. Confirm.")
-print(f"Agent: {result2[:200]}...")
-
-# Store session metadata into MongoDB for verification
-mongo_client = pymongo.MongoClient(MONGO_URL)
-mongo_col = mongo_client["praisonai_managed"]["sessions"]
-state_doc = {
-    "_id": managed.session_id,
-    "agent_id": managed.agent_id,
-    "total_input_tokens": managed.total_input_tokens,
-}
-mongo_col.replace_one({"_id": managed.session_id}, state_doc, upsert=True)
-
-# ── Phase 2: MongoDB Verification ──
-print("\n=== Phase 2: MongoDB Verification ===")
-doc = mongo_col.find_one({"_id": managed.session_id})
-print(f"  Session in MongoDB: {doc['_id']}" if doc else "  WARNING: not found")
-
-# ── Phase 3: Destroy instance ──
-print("\n=== Phase 3: Instance Goes Idle ===")
-saved_ids = managed.save_ids()
-session_id = saved_ids["session_id"]
-print(f"Saved IDs: {saved_ids}")
-del agent, managed, db
-print("Agent destroyed.\n")
-
-# ── Phase 4: Resume — no config needed ──
-print("=== Phase 4: Resume Session ===")
-db2 = DB(database_url=SQLITE_PATH)
-managed2 = ManagedAgent(provider="local", db=db2)
-managed2.resume_session(session_id)
-print(f"Resumed session: {managed2.session_id}")
-
-agent2 = Agent(name="User", backend=managed2)
-result3 = agent2.run("What is my favourite book, author, year, and second favourite?")
-print(f"Agent: {result3[:300]}...")
-
-# ── Phase 5: Validate ──
-print("\n=== Phase 5: Validation ===")
-r = result3.lower()
-checks = {
-    "Remembers 'Dune'": "dune" in r,
-    "Remembers 'Herbert'": "herbert" in r,
-    "Remembers '1965'": "1965" in r,
-    "Remembers 'Messiah'": "messiah" in r,
-    "Session ID continuity": managed2.session_id == session_id,
-}
-all_passed = True
-for check, ok in checks.items():
-    print(f"  [{'PASS' if ok else 'FAIL'}] {check}")
-    if not ok:
-        all_passed = False
-
-mongo_col.delete_one({"_id": session_id})
-mongo_client.close()
-os.unlink(SQLITE_PATH)
-print(f"\n{'PASS' if all_passed else 'FAIL'}: MongoDB ManagedAgent persistence test")

examples/managed-agents/persistence/mysql_managed.py

@@ -1,92 +0,0 @@
-#!/usr/bin/env python3
-"""
-ManagedAgent + MySQL Persistence — Real conversation with session resume.
-
-Prerequisites:
-    pip install praisonai praisonaiagents mysql-connector-python
-    export OPENAI_API_KEY="sk-..."
-    # MySQL running on localhost:3307
-"""
-
-import os
-import sys
-
-import mysql.connector
-
-if not os.getenv("OPENAI_API_KEY"):
-    sys.exit("ERROR: OPENAI_API_KEY not set.")
-
-from praisonai import ManagedAgent, LocalManagedConfig, DB
-from praisonaiagents import Agent
-
-MYSQL_URL = os.getenv("MYSQL_URL", "mysql://root:praisontest@localhost:3307/praisonai")
-print(f"ManagedAgent + MySQL Persistence\nDB: {MYSQL_URL}\n")
-
-# ── Phase 1: Create agent, teach facts ──
-print("=== Phase 1: First Session ===")
-
-db = DB(database_url=MYSQL_URL)
-managed = ManagedAgent(
-    provider="local", db=db,
-    config=LocalManagedConfig(
-        model="gpt-4o-mini", name="MySQL Memory Agent",
-        system="You are a helpful assistant. Remember all facts the user tells you.",
-    ),
-)
-agent = Agent(name="User", backend=managed)
-
-result1 = agent.run("Remember: I live in Tokyo, Japan and love ramen from Ichiran. Confirm.")
-print(f"Agent: {result1[:200]}...")
-print(f"Session ID: {managed.session_id}")
-
-result2 = agent.run("Also remember: I work as a machine learning engineer at a robotics company. Confirm.")
-print(f"Agent: {result2[:200]}...")
-
-# ── Phase 2: Direct MySQL Verification ──
-print("\n=== Phase 2: MySQL Verification ===")
-conn = mysql.connector.connect(host="localhost", port=3307, user="root", password="praisontest", database="praisonai")
-cursor = conn.cursor()
-cursor.execute("SELECT table_name FROM information_schema.tables WHERE table_schema='praisonai' AND table_name LIKE '%message%'")
-msg_tables = [r[0] for r in cursor.fetchall()]
-print(f"Message tables: {msg_tables}")
-for t in msg_tables:
-    cursor.execute(f"SELECT COUNT(*) FROM `{t}`")
-    print(f"  {t}: {cursor.fetchone()[0]} rows")
-conn.close()
-
-# ── Phase 3: Destroy instance ──
-print("\n=== Phase 3: Instance Goes Idle ===")
-saved_ids = managed.save_ids()
-print(f"Saved IDs: {saved_ids}")
-del agent, managed, db
-print("Agent destroyed.\n")
-
-# ── Phase 4: Resume — no config needed ──
-print("=== Phase 4: Resume Session ===")
-db2 = DB(database_url=MYSQL_URL)
-managed2 = ManagedAgent(provider="local", db=db2)
-managed2.resume_session(saved_ids["session_id"])
-print(f"Resumed session: {managed2.session_id}")
-
-agent2 = Agent(name="User", backend=managed2)
-result3 = agent2.run("Where do I live, what's my favourite food, and what do I do for work?")
-print(f"Agent: {result3[:300]}...")
-
-# ── Phase 5: Validate ──
-print("\n=== Phase 5: Validation ===")
-r = result3.lower()
-checks = {
-    "Remembers 'Tokyo'": "tokyo" in r,
-    "Remembers 'ramen'": "ramen" in r,
-    "Remembers 'Ichiran'": "ichiran" in r,
-    "Remembers 'machine learning'": "machine learning" in r or "ml" in r,
-    "Remembers 'robotics'": "robotic" in r,
-    "Session ID continuity": managed2.session_id == saved_ids["session_id"],
-}
-all_passed = True
-for check, ok in checks.items():
-    print(f"  [{'PASS' if ok else 'FAIL'}] {check}")
-    if not ok:
-        all_passed = False
-
-print(f"\n{'PASS' if all_passed else 'FAIL'}: MySQL ManagedAgent persistence test")

examples/managed-agents/persistence/neon_managed.py

@@ -1,34 +0,0 @@
-"""
-Neon Serverless PostgreSQL Persistence Example.
-
-Neon auto-idles after inactivity — you only pay when active.
-The PraisonAI adapter auto-handles cold-start retries and SSL.
-
-Setup:
-  1. Create free Neon project at https://neon.tech
-  2. Copy connection string from dashboard
-  3. Set env var: export NEON_DATABASE_URL="postgresql://user:[email protected]/dbname?sslmode=require"
-  4. pip install praisonai[neon]
-"""
-
-import os
-from praisonaiagents import Agent
-
-# Option 1: Use NEON_DATABASE_URL env var (recommended)
-agent = Agent(
-    name="Neon Agent",
-    instructions="You are a helpful assistant.",
-    db={"database_url": os.getenv("NEON_DATABASE_URL")},
-    session_id="neon-demo-session",
-)
-
-# Option 2: Direct URL
-# agent = Agent(
-#     name="Neon Agent",
-#     instructions="You are a helpful assistant.",
-#     db={"database_url": "postgresql://user:[email protected]/dbname?sslmode=require"},
-#     session_id="neon-demo-session",
-# )
-
-result = agent.start("What is serverless PostgreSQL? Explain in 2 sentences.")
-print(result)

examples/managed-agents/persistence/postgres_managed.py

@@ -1,94 +0,0 @@
-#!/usr/bin/env python3
-"""
-ManagedAgent + PostgreSQL Persistence — Real conversation with session resume.
-
-Prerequisites:
-    pip install praisonai praisonaiagents psycopg2-binary
-    export OPENAI_API_KEY="sk-..."
-    # PostgreSQL running on localhost:5432
-"""
-
-import os
-import sys
-
-import psycopg2
-
-if not os.getenv("OPENAI_API_KEY"):
-    sys.exit("ERROR: OPENAI_API_KEY not set.")
-
-from praisonai import ManagedAgent, LocalManagedConfig, DB
-from praisonaiagents import Agent
-
-PG_URL = os.getenv("PG_URL", "postgresql://postgres:postgres@localhost:5432/postgres")
-print(f"ManagedAgent + PostgreSQL Persistence\nDB: {PG_URL}\n")
-
-# ── Phase 1: Create agent, teach facts ──
-print("=== Phase 1: First Session ===")
-
-db = DB(database_url=PG_URL)
-managed = ManagedAgent(
-    provider="local", db=db,
-    config=LocalManagedConfig(
-        model="gpt-4o-mini", name="PG Memory Agent",
-        system="You are a helpful assistant. Remember all facts the user tells you.",
-    ),
-)
-agent = Agent(name="User", backend=managed)
-
-result1 = agent.run("Remember: My favourite language is Rust, 3 years experience. Confirm.")
-print(f"Agent: {result1[:200]}...")
-print(f"Session ID: {managed.session_id}")
-
-result2 = agent.run("Also remember: I'm building a web crawler with the reqwest crate. Confirm.")
-print(f"Agent: {result2[:200]}...")
-
-# ── Phase 2: Direct PostgreSQL Verification ──
-print("\n=== Phase 2: PostgreSQL Verification ===")
-conn = psycopg2.connect(PG_URL)
-cursor = conn.cursor()
-cursor.execute("""
-    SELECT table_name FROM information_schema.tables
-    WHERE table_schema = 'public' AND table_name LIKE '%message%'
-""")
-msg_tables = [r[0] for r in cursor.fetchall()]
-print(f"Message tables: {msg_tables}")
-for t in msg_tables:
-    cursor.execute(f"SELECT COUNT(*) FROM {t}")
-    print(f"  {t}: {cursor.fetchone()[0]} rows")
-conn.close()
-
-# ── Phase 3: Destroy instance ──
-print("\n=== Phase 3: Instance Goes Idle ===")
-saved_ids = managed.save_ids()
-print(f"Saved IDs: {saved_ids}")
-del agent, managed, db
-print("Agent destroyed.\n")
-
-# ── Phase 4: Resume — no config needed ──
-print("=== Phase 4: Resume Session ===")
-db2 = DB(database_url=PG_URL)
-managed2 = ManagedAgent(provider="local", db=db2)
-managed2.resume_session(saved_ids["session_id"])
-print(f"Resumed session: {managed2.session_id}")
-
-agent2 = Agent(name="User", backend=managed2)
-result3 = agent2.run("What is my favourite language, experience, and what am I building?")
-print(f"Agent: {result3[:300]}...")
-
-# ── Phase 5: Validate ──
-print("\n=== Phase 5: Validation ===")
-r = result3.lower()
-checks = {
-    "Remembers 'Rust'": "rust" in r,
-    "Remembers '3 years'": "3" in r and "year" in r,
-    "Remembers 'crawler'": "crawler" in r or "crawl" in r,
-    "Remembers 'reqwest'": "reqwest" in r,
-    "Session ID continuity": managed2.session_id == saved_ids["session_id"],
-}
-all_passed = True
-for check, ok in checks.items():
-    print(f"  [{'PASS' if ok else 'FAIL'}] {check}")
-    if not ok:
-        all_passed = False
-
-print(f"\n{'PASS' if all_passed else 'FAIL'}: PostgreSQL ManagedAgent persistence test")

examples/managed-agents/persistence/redis_managed.py

@@ -1,90 +0,0 @@
-#!/usr/bin/env python3
-"""
-ManagedAgent + Redis Persistence — Real conversation with session resume.
-
-Redis stores state/metadata; SQLite stores conversation history.
-
-Prerequisites:
-    pip install praisonai praisonaiagents redis
-    export OPENAI_API_KEY="sk-..."
-    # Redis running on localhost:6379
-"""
-
-import os
-import sys
-import tempfile
-
-import redis as redis_lib
-
-if not os.getenv("OPENAI_API_KEY"):
-    sys.exit("ERROR: OPENAI_API_KEY not set.")
-
-from praisonai import ManagedAgent, LocalManagedConfig, DB
-from praisonaiagents import Agent
-
-REDIS_URL = os.getenv("REDIS_URL", "redis://:myredissecret@localhost:6379/0")
-SQLITE_PATH = os.path.join(tempfile.gettempdir(), "managed_redis_conv.db")
-print(f"ManagedAgent + Redis Persistence\nRedis: {REDIS_URL}\nSQLite: {SQLITE_PATH}\n")
-
-# ── Phase 1: Create agent, teach facts ──
-print("=== Phase 1: First Session ===")
-
-db = DB(database_url=SQLITE_PATH, state_url=REDIS_URL)
-managed = ManagedAgent(
-    provider="local", db=db,
-    config=LocalManagedConfig(
-        model="gpt-4o-mini", name="Redis Memory Agent",
-        system="You are a helpful assistant. Remember all facts the user tells you.",
-    ),
-)
-agent = Agent(name="User", backend=managed)
-
-result1 = agent.run("Remember: My cat Luna is a Siamese who loves tuna treats. Confirm.")
-print(f"Agent: {result1[:200]}...")
-print(f"Session ID: {managed.session_id}")
-
-result2 = agent.run("Also remember: Luna was adopted from a shelter in Portland, Oregon. Confirm.")
-print(f"Agent: {result2[:200]}...")
-
-# ── Phase 2: Direct Redis Verification ──
-print("\n=== Phase 2: Redis Verification ===")
-rc = redis_lib.Redis(host="localhost", port=6379, password="myredissecret", decode_responses=True)
-all_keys = rc.keys("*")
-print(f"Total Redis keys: {len(all_keys)}")
-
-# ── Phase 3: Destroy instance ──
-print("\n=== Phase 3: Instance Goes Idle ===")
-saved_ids = managed.save_ids()
-print(f"Saved IDs: {saved_ids}")
-del agent, managed, db
-print("Agent destroyed.\n")
-
-# ── Phase 4: Resume — no config needed ──
-print("=== Phase 4: Resume Session ===")
-db2 = DB(database_url=SQLITE_PATH, state_url=REDIS_URL)
-managed2 = ManagedAgent(provider="local", db=db2)
-managed2.resume_session(saved_ids["session_id"])
-print(f"Resumed session: {managed2.session_id}")
-
-agent2 = Agent(name="User", backend=managed2)
-result3 = agent2.run("What is my cat's name, breed, favourite treat, and adoption city?")
-print(f"Agent: {result3[:300]}...")
-
-# ── Phase 5: Validate ──
-print("\n=== Phase 5: Validation ===")
-r = result3.lower()
-checks = {
-    "Remembers 'Luna'": "luna" in r,
-    "Remembers 'Siamese'": "siamese" in r,
-    "Remembers 'tuna'": "tuna" in r,
-    "Remembers 'Portland'": "portland" in r,
-    "Session ID continuity": managed2.session_id == saved_ids["session_id"],
-}
-all_passed = True
-for check, ok in checks.items():
-    print(f"  [{'PASS' if ok else 'FAIL'}] {check}")
-    if not ok:
-        all_passed = False
-
-os.unlink(SQLITE_PATH)
-print(f"\n{'PASS' if all_passed else 'FAIL'}: Redis ManagedAgent persistence test")

examples/managed-agents/persistence/sqlite_managed.py

@@ -1,92 +0,0 @@
-#!/usr/bin/env python3
-"""
-ManagedAgent + SQLite Persistence — Real conversation with session resume.
-
-Prerequisites:
-    pip install praisonai praisonaiagents
-    export OPENAI_API_KEY="sk-..."
-"""
-
-import os
-import sqlite3
-import sys
-import tempfile
-
-if not os.getenv("OPENAI_API_KEY"):
-    sys.exit("ERROR: OPENAI_API_KEY not set.")
-
-from praisonai import ManagedAgent, LocalManagedConfig, DB
-from praisonaiagents import Agent
-
-DB_PATH = os.path.join(tempfile.gettempdir(), "managed_sqlite_test.db")
-print(f"ManagedAgent + SQLite Persistence\nDB: {DB_PATH}\n")
-
-# ── Phase 1: Create agent, teach facts ──
-print("=== Phase 1: First Session ===")
-
-db = DB(database_url=DB_PATH)
-managed = ManagedAgent(
-    provider="local", db=db,
-    config=LocalManagedConfig(
-        model="gpt-4o-mini", name="SQLite Memory Agent",
-        system="You are a helpful assistant. Remember all facts the user tells you.",
-    ),
-)
-
-agent = Agent(name="User", backend=managed)
-
-result1 = agent.run("Remember: My dog's name is Biscuit, a golden retriever. Confirm.")
-print(f"Agent: {result1[:200]}...")
-print(f"Session ID: {managed.session_id}")
-
-result2 = agent.run("Also remember: Biscuit's favourite toy is a blue squeaky ball. Confirm.")
-print(f"Agent: {result2[:200]}...")
-
-# ── Phase 2: Verify SQLite directly ──
-print("\n=== Phase 2: SQLite Verification ===")
-conn = sqlite3.connect(DB_PATH)
-cursor = conn.cursor()
-cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
-tables = [r[0] for r in cursor.fetchall()]
-print(f"Tables: {tables}")
-for t in tables:
-    if "message" in t.lower():
-        cursor.execute(f"SELECT COUNT(*) FROM [{t}]")
-        print(f"  {t}: {cursor.fetchone()[0]} rows")
-conn.close()
-
-# ── Phase 3: Destroy instance (simulate idle) ──
-print("\n=== Phase 3: Instance Goes Idle ===")
-saved_ids = managed.save_ids()
-print(f"Saved IDs: {saved_ids}")
-del agent, managed, db
-print("Agent destroyed.\n")
-
-# ── Phase 4: Resume — no config needed, restored from DB ──
-print("=== Phase 4: Resume Session ===")
-db2 = DB(database_url=DB_PATH)
-managed2 = ManagedAgent(provider="local", db=db2)
-managed2.resume_session(saved_ids["session_id"])
-print(f"Resumed session: {managed2.session_id}")
-
-agent2 = Agent(name="User", backend=managed2)
-result3 = agent2.run("What is my dog's name, breed, and favourite toy?")
-print(f"Agent: {result3[:300]}...")
-
-# ── Phase 5: Validate ──
-print("\n=== Phase 5: Validation ===")
-r = result3.lower()
-checks = {
-    "Remembers 'Biscuit'": "biscuit" in r,
-    "Remembers 'golden retriever'": "golden retriever" in r,
-    "Remembers toy": "blue" in r or "squeaky" in r,
-    "Session ID continuity": managed2.session_id == saved_ids["session_id"],
-}
-all_passed = True
-for check, ok in checks.items():
-    print(f"  [{'PASS' if ok else 'FAIL'}] {check}")
-    if not ok:
-        all_passed = False
-
-os.unlink(DB_PATH)
-print(f"\n{'PASS' if all_passed else 'FAIL'}: SQLite ManagedAgent persistence test")

examples/managed-agents/persistence/supabase_direct_managed.py

@@ -1,28 +0,0 @@
-"""
-Supabase Direct PostgreSQL Persistence Example.
-
-Supabase offers two connection modes:
-  1. REST API (via supabase-py) — set SUPABASE_URL + SUPABASE_KEY
-  2. Direct PostgreSQL (via psycopg2) — use Postgres connection string
-
-This example uses direct PostgreSQL mode with auto-retry for paused projects.
-
-Setup:
-  1. Create free Supabase project at https://supabase.com
-  2. Go to Settings > Database > Connection string (URI)
-  3. Set env var: export SUPABASE_DATABASE_URL="postgresql://postgres.xxx:[pass]@xxx.pooler.supabase.com:6543/postgres"
-  4. pip install praisonai[postgresql]
-"""
-
-import os
-from praisonaiagents import Agent
-
-agent = Agent(
-    name="Supabase Agent",
-    instructions="You are a helpful assistant.",
-    db={"database_url": os.getenv("SUPABASE_DATABASE_URL")},
-    session_id="supabase-demo-session",
-)
-
-result = agent.start("What is Supabase? Explain in 2 sentences.")
-print(result)

examples/managed-agents/persistence/turso_managed.py

@@ -1,29 +0,0 @@
-"""
-Turso/libSQL Persistence Example.
-
-Turso provides SQLite-at-the-edge with automatic sync and scale-to-zero.
-
-Setup:
-  1. Create free Turso database at https://turso.tech
-  2. Run: turso db tokens create <db-name>
-  3. Set env vars:
-     export TURSO_DATABASE_URL="libsql://mydb-user.turso.io"
-     export TURSO_AUTH_TOKEN="eyJ..."
-  4. pip install praisonai[turso]
-"""
-
-import os
-from praisonaiagents import Agent
-
-agent = Agent(
-    name="Turso Agent",
-    instructions="You are a helpful assistant.",
-    db={
-        "database_url": os.getenv("TURSO_DATABASE_URL"),
-        "auth_token": os.getenv("TURSO_AUTH_TOKEN"),
-    },
-    session_id="turso-demo-session",
-)
-
-result = agent.start("What is edge computing? Explain in 2 sentences.")
-print(result)

examples/persistence/clickhouse_persistence.py

@@ -1,156 +0,0 @@
-"""
-ClickHouse — Persistence Example
-
-Demonstrates:
-  - Connecting to ClickHouse
-  - Creating tables for agent state
-  - Writing and reading agent metadata
-  - JSON state storage and retrieval
-  - Session resume after simulated restart
-
-Requirements:
-    pip install clickhouse-connect
-    Docker: ClickHouse on localhost:8123 (user=clickhouse, password=clickhouse)
-
-Run:
-    python clickhouse_persistence.py
-"""
-
-import json
-import uuid
-import clickhouse_connect
-
-CH_HOST = "localhost"
-CH_PORT = 8123
-CH_USER = "clickhouse"
-CH_PASSWORD = "clickhouse"
-
-print(f"ClickHouse: {CH_HOST}:{CH_PORT}\n")
-
-client = clickhouse_connect.get_client(
-    host=CH_HOST, port=CH_PORT, username=CH_USER, password=CH_PASSWORD,
-)
-
-TABLE_SESSIONS = f"praison_sessions_{uuid.uuid4().hex[:8]}"
-TABLE_STATE = f"praison_state_{uuid.uuid4().hex[:8]}"
-
-# --- Phase 1: Create tables and insert session data ---
-print("=== Phase 1: Create Tables & Insert Sessions ===")
-client.command(f"""
-    CREATE TABLE IF NOT EXISTS {TABLE_SESSIONS} (
-        session_id String,
-        agent_id String,
-        agent_version UInt32,
-        total_input_tokens UInt64,
-        total_output_tokens UInt64,
-        model String,
-        created_at DateTime DEFAULT now()
-    ) ENGINE = MergeTree()
-    ORDER BY session_id
-""")
-
-sessions = [
-    ["session_ch_001", "agent_demo_001", 3, 500, 200, "gpt-4o"],
-    ["session_ch_002", "agent_demo_002", 1, 1000, 400, "gpt-4o-mini"],
-    ["session_ch_003", "agent_demo_001", 3, 750, 300, "gpt-4o"],
-]
-
-client.insert(
-    TABLE_SESSIONS, sessions,
-    column_names=["session_id", "agent_id", "agent_version", "total_input_tokens", "total_output_tokens", "model"],
-)
-
-for s in sessions:
-    print(f"  Inserted: {s[0]} (agent={s[1]}, tokens_in={s[3]})")
-
-print()
-
-# --- Phase 2: Query and verify ---
-print("=== Phase 2: Query & Verify ===")
-result = client.query(f"SELECT * FROM {TABLE_SESSIONS} ORDER BY session_id")
-print(f"  Total sessions: {len(result.result_rows)}")
-for row in result.result_rows:
-    print(f"    {row[0]}: agent={row[1]}, v{row[2]}, tokens_in={row[3]}, model={row[5]}")
-
-assert len(result.result_rows) == 3
-assert result.result_rows[0][0] == "session_ch_001"
-
-# Aggregate query
-agg = client.query(f"SELECT agent_id, SUM(total_input_tokens) as total_in FROM {TABLE_SESSIONS} GROUP BY agent_id ORDER BY agent_id")
-print("\n  Tokens by agent:")
-for row in agg.result_rows:
-    print(f"    {row[0]}: {row[1]} input tokens")
-
-print()
-
-# --- Phase 3: JSON state storage ---
-print("=== Phase 3: JSON State Storage ===")
-client.command(f"""
-    CREATE TABLE IF NOT EXISTS {TABLE_STATE} (
-        session_id String,
-        state_json String,
-        updated_at DateTime DEFAULT now()
-    ) ENGINE = ReplacingMergeTree(updated_at)
-    ORDER BY session_id
-""")
-
-state = {
-    "agent_id": "agent_demo_001",
-    "agent_version": 3,
-    "environment_id": "env_ch_001",
-    "total_input_tokens": 2000,
-    "total_output_tokens": 800,
-    "compute_instance_id": "flyio_ch_001",
-    "session_history": [
-        {"id": "session_ch_001", "status": "completed"},
-        {"id": "session_ch_003", "status": "idle"},
-    ],
-}
-
-client.insert(
-    TABLE_STATE,
-    [["session_ch_001", json.dumps(state)]],
-    column_names=["session_id", "state_json"],
-)
-print("  Stored JSON state for session_ch_001")
-
-# Retrieve
-result = client.query(f"SELECT state_json FROM {TABLE_STATE} WHERE session_id = 'session_ch_001'")
-recovered = json.loads(result.result_rows[0][0])
-print(f"  Recovered agent_id: {recovered['agent_id']}")
-print(f"  Recovered tokens: in={recovered['total_input_tokens']}, out={recovered['total_output_tokens']}")
-print(f"  Recovered compute: {recovered['compute_instance_id']}")
-assert recovered["total_input_tokens"] == 2000
-
-print()
-
-# --- Phase 4: Session Resume ---
-print("=== Phase 4: Session Resume (Simulating Restart) ===")
-client.close()
-
-client2 = clickhouse_connect.get_client(
-    host=CH_HOST, port=CH_PORT, username=CH_USER, password=CH_PASSWORD,
-)
-
-result2 = client2.query(f"SELECT state_json FROM {TABLE_STATE} WHERE session_id = 'session_ch_001'")
-assert len(result2.result_rows) > 0, "State not found after restart!"
-recovered2 = json.loads(result2.result_rows[0][0])
-print(f"  Recovered after restart: agent={recovered2['agent_id']}, tokens_in={recovered2['total_input_tokens']}")
-
-# Update state
-recovered2["total_input_tokens"] += 500
-client2.insert(
-    TABLE_STATE,
-    [["session_ch_001", json.dumps(recovered2)]],
-    column_names=["session_id", "state_json"],
-)
-print(f"  Updated tokens_in: {recovered2['total_input_tokens']}")  # noqa: F541
-
-# --- Cleanup ---
-print("\n=== Cleanup ===")
-client2.command(f"DROP TABLE IF EXISTS {TABLE_SESSIONS}")
-client2.command(f"DROP TABLE IF EXISTS {TABLE_STATE}")
-client2.close()
-print("  Tables dropped.")
-
-print("\n✅ ClickHouse Persistence — All tests passed!")

examples/persistence/json_file_session_store.py

@@ -1,128 +0,0 @@
-"""
-JSON File DefaultSessionStore — Full Persistence Example
-
-Demonstrates:
-  - Creating a DefaultSessionStore (JSON file-backed)
-  - Adding messages and metadata
-  - Verifying data on disk
-  - Session resume after simulated restart
-  - Full managed agent state roundtrip (7 data categories)
-
-Requirements:
-    pip install praisonaiagents
-
-Run:
-    python json_file_session_store.py
-"""
-
-import json
-import os
-import shutil
-import tempfile
-import uuid
-from praisonaiagents.session.store import DefaultSessionStore
-
-# --- Setup ---
-session_dir = os.path.join(tempfile.gettempdir(), f"praison_json_example_{uuid.uuid4().hex[:6]}")
-os.makedirs(session_dir, exist_ok=True)
-print(f"Session directory: {session_dir}\n")
-
-store = DefaultSessionStore(session_dir=session_dir)
-SESSION_ID = f"json-demo-{uuid.uuid4().hex[:8]}"
-
-# --- Phase 1: Add messages ---
-print("=== Phase 1: Add Messages ===")
-messages = [
-    ("user", "What is machine learning?"),
-    ("assistant", "Machine learning is a subset of AI that enables systems to learn from data."),
-    ("user", "Give me an example."),
-    ("assistant", "Spam filters use ML to classify emails as spam or not spam."),
-]
-
-for role, content in messages:
-    store.add_message(SESSION_ID, role, content)
-    print(f"  Added [{role}]: {content[:60]}")
-
-print()
-
-# --- Phase 2: Store metadata (simulating managed agent state) ---
-print("=== Phase 2: Store Metadata ===")
-session = store.get_session(SESSION_ID)
-session.metadata = {
-    "agent_id": "json_demo_agent_001",
-    "agent_version": 2,
-    "environment_id": "env_local_json",
-    "total_input_tokens": 450,
-    "total_output_tokens": 180,
-    "compute_instance_id": "local_json_demo",
-    "session_history": [
-        {"id": SESSION_ID, "status": "idle", "title": "ML chat"},
-    ],
-}
-store._save_session(session)
-
-print(f"  agent_id: {session.metadata['agent_id']}")
-print(f"  tokens: in={session.metadata['total_input_tokens']}, out={session.metadata['total_output_tokens']}")
-print(f"  compute: {session.metadata['compute_instance_id']}")
-
-print()
-
-# --- Phase 3: Verify on disk ---
-print("=== Phase 3: Verify JSON File on Disk ===")
-json_file = os.path.join(session_dir, f"{SESSION_ID}.json")
-assert os.path.exists(json_file), f"JSON file not found: {json_file}"
-
-with open(json_file) as f:
-    data = json.load(f)
-
-print(f"  File: {json_file}")
-print(f"  Session ID: {data['session_id']}")
-print(f"  Messages: {len(data.get('messages', []))}")
-print(f"  Metadata agent_id: {data['metadata']['agent_id']}")
-print(f"  Metadata tokens_in: {data['metadata']['total_input_tokens']}")
-
-assert data["metadata"]["agent_id"] == "json_demo_agent_001"
-assert data["metadata"]["total_input_tokens"] == 450
-assert len(data["messages"]) == 4
-
-print()
-
-# --- Phase 4: Session Resume ---
-print("=== Phase 4: Session Resume (Simulating Restart) ===")
-del store
-
-store2 = DefaultSessionStore(session_dir=session_dir)
-
-# Verify session exists
-assert store2.session_exists(SESSION_ID), "Session not found after restart!"
-
-# Recover history
-history = store2.get_chat_history(SESSION_ID)
-print(f"  Recovered messages: {len(history)}")
-assert len(history) == 4
-
-# Recover metadata
-session2 = store2.get_session(SESSION_ID)
-meta = session2.metadata
-print(f"  Recovered agent_id: {meta['agent_id']}")
-print(f"  Recovered tokens: in={meta['total_input_tokens']}, out={meta['total_output_tokens']}")
-print(f"  Recovered compute: {meta['compute_instance_id']}")
-print(f"  Recovered session_history: {len(meta['session_history'])} entries")
-
-assert meta["agent_id"] == "json_demo_agent_001"
-assert meta["total_input_tokens"] == 450
-assert meta["compute_instance_id"] == "local_json_demo"
-
-# Continue conversation
-store2.add_message(SESSION_ID, "user", "What about deep learning?")
-store2.add_message(SESSION_ID, "assistant", "Deep learning uses neural networks with many layers.")
-final_history = store2.get_chat_history(SESSION_ID)
-print(f"  Messages after resume: {len(final_history)}")
-assert len(final_history) == 6
-
-# --- Cleanup ---
-print("\n=== Cleanup ===")
-shutil.rmtree(session_dir)
-print("  Session directory removed.")
-
-print("\n✅ JSON File DefaultSessionStore — All tests passed!")

examples/persistence/mongodb_state_store.py

@@ -1,123 +0,0 @@
-"""
-MongoDB StateStore — Full Persistence Example
-
-Demonstrates:
-  - Creating a MongoDB StateStore
-  - Key-value operations (get/set/delete/exists)
-  - Complex state persistence (agent metadata, usage tokens)
-  - Direct MongoDB verification
-  - Session resume after simulated restart
-
-Requirements:
-    pip install praisonai pymongo
-    Docker: MongoDB on localhost:27017 (no auth)
-
-Run:
-    python mongodb_state_store.py
-"""
-
-import uuid
-import pymongo
-from praisonai.persistence.state.mongodb import MongoDBStateStore
-
-MONGO_URL = "mongodb://localhost:27017"
-DATABASE = "praisonai_example"
-COLLECTION = f"state_{uuid.uuid4().hex[:8]}"
-
-print(f"MongoDB: {MONGO_URL}")
-print(f"Database: {DATABASE}, Collection: {COLLECTION}\n")
-
-store = MongoDBStateStore(url=MONGO_URL, database=DATABASE, collection=COLLECTION)
-
-# --- Phase 1: Basic key-value operations ---
-print("=== Phase 1: Basic Key-Value Operations ===")
-store.set("agent_id", "mongo_demo_agent_001")
-store.set("model", "gpt-4o-mini")
-store.set("counter", 42)
-
-print(f"  agent_id: {store.get('agent_id')}")
-print(f"  model: {store.get('model')}")
-print(f"  counter: {store.get('counter')}")
-print(f"  exists('agent_id'): {store.exists('agent_id')}")
-print(f"  exists('missing'): {store.exists('missing')}")
-
-assert store.get("agent_id") == "mongo_demo_agent_001"
-assert store.get("counter") == 42
-
-print()
-
-# --- Phase 2: Complex state persistence ---
-print("=== Phase 2: Complex State Persistence ===")
-managed_state = {
-    "agent_id": "mongo_demo_agent_001",
-    "agent_version": 4,
-    "environment_id": "env_mongo_001",
-    "total_input_tokens": 800,
-    "total_output_tokens": 300,
-    "compute_instance_id": "e2b_mongo_demo",
-    "session_history": [
-        {"id": "session_m1", "status": "completed"},
-        {"id": "session_m2", "status": "idle"},
-    ],
-}
-
-store.set("managed_state", managed_state)
-recovered = store.get("managed_state")
-
-print(f"  agent_id: {recovered['agent_id']}")
-print(f"  agent_version: {recovered['agent_version']}")
-print(f"  total_input_tokens: {recovered['total_input_tokens']}")
-print(f"  compute_instance_id: {recovered['compute_instance_id']}")
-print(f"  session_history count: {len(recovered['session_history'])}")
-
-assert recovered["agent_id"] == "mongo_demo_agent_001"
-assert recovered["total_input_tokens"] == 800
-
-print()
-
-# --- Phase 3: Direct MongoDB Verification ---
-print("=== Phase 3: Direct MongoDB Verification ===")
-client = pymongo.MongoClient(MONGO_URL)
-doc = client[DATABASE][COLLECTION].find_one({"_id": "managed_state"})
-print(f"  Raw doc _id: {doc['_id']}")
-print(f"  Raw doc value.agent_id: {doc['value']['agent_id']}")
-print(f"  Raw doc value.total_input_tokens: {doc['value']['total_input_tokens']}")
-assert doc["value"]["compute_instance_id"] == "e2b_mongo_demo"
-client.close()
-
-print()
-
-# --- Phase 4: Session Resume ---
-print("=== Phase 4: Session Resume (Simulating Restart) ===")
-store.close()
-
-store2 = MongoDBStateStore(url=MONGO_URL, database=DATABASE, collection=COLLECTION)
-
-recovered2 = store2.get("managed_state")
-assert recovered2 is not None, "State not found after restart!"
-print(f"  Recovered agent_id: {recovered2['agent_id']}")
-print(f"  Recovered tokens: in={recovered2['total_input_tokens']}, out={recovered2['total_output_tokens']}")
-print(f"  Recovered compute: {recovered2['compute_instance_id']}")
-
-# Update
-recovered2["total_input_tokens"] += 400
-store2.set("managed_state", recovered2)
-final = store2.get("managed_state")
-print(f"  Updated input_tokens: {final['total_input_tokens']}")
-assert final["total_input_tokens"] == 1200
-
-# Delete test
-store2.delete("counter")
-assert not store2.exists("counter")
-print("  Delete verified: counter removed")
-
-store2.close()
-
-# --- Cleanup ---
-print("\n=== Cleanup ===")
-client = pymongo.MongoClient(MONGO_URL)
-client[DATABASE].drop_collection(COLLECTION)
-client.close()
-print("  Collection dropped.")
-
-print("\n✅ MongoDB StateStore — All tests passed!")

examples/persistence/mysql_conversation_store.py

@@ -1,136 +0,0 @@
-"""
-MySQL ConversationStore — Full Persistence Example
-
-Demonstrates:
-  - Creating a MySQL ConversationStore
-  - Creating sessions, adding messages
-  - Direct SQL verification that data is in MySQL
-  - Session resume after simulated restart
-
-Requirements:
-    pip install praisonai mysql-connector-python
-    Docker: MySQL on localhost:3307 (user=root, password=praisontest, database=praisonai)
-
-Run:
-    python mysql_conversation_store.py
-"""
-
-import uuid
-import mysql.connector
-from praisonai.persistence.conversation.mysql import MySQLConversationStore
-from praisonai.persistence.conversation.base import ConversationSession, ConversationMessage
-
-MYSQL_HOST = "localhost"
-MYSQL_PORT = 3307
-MYSQL_USER = "root"
-MYSQL_PASSWORD = "praisontest"
-MYSQL_DB = "praisonai"
-TABLE_PREFIX = f"example_{uuid.uuid4().hex[:6]}_"
-
-print(f"MySQL: {MYSQL_USER}@{MYSQL_HOST}:{MYSQL_PORT}/{MYSQL_DB}")
-print(f"Table prefix: {TABLE_PREFIX}\n")
-
-store = MySQLConversationStore(
-    host=MYSQL_HOST, port=MYSQL_PORT, user=MYSQL_USER,
-    password=MYSQL_PASSWORD, database=MYSQL_DB, table_prefix=TABLE_PREFIX,
-)
-SESSION_ID = f"mysql-demo-{uuid.uuid4().hex[:8]}"
-
-# --- Phase 1: Create session and add messages ---
-print("=== Phase 1: Create Session & Add Messages ===")
-session = ConversationSession(
-    session_id=SESSION_ID,
-    agent_id="mysql_demo_agent",
-    name="MySQL Demo",
-    metadata={"agent_version": 7, "model": "gpt-4o-mini"},
-)
-store.create_session(session)
-
-messages = [
-    ("user", "What makes MySQL popular?"),
-    ("assistant", "MySQL is popular for its speed, reliability, and ease of use in web applications."),
-    ("user", "What about scalability?"),
-    ("assistant", "MySQL supports replication, sharding, and clustering for horizontal scalability."),
-]
-
-for role, content in messages:
-    msg = ConversationMessage(session_id=SESSION_ID, role=role, content=content)
-    store.add_message(SESSION_ID, msg)
-    print(f"  Added [{role}]: {content[:60]}")
-
-print()
-
-# --- Phase 2: Verify data ---
-print("=== Phase 2: Verify Data in MySQL ===")
-retrieved = store.get_session(SESSION_ID)
-print(f"  Session: {retrieved.session_id}")
-print(f"  Agent ID: {retrieved.agent_id}")
-print(f"  Metadata: {retrieved.metadata}")
-
-stored = store.get_messages(SESSION_ID)
-print(f"  Messages: {len(stored)}")
-for msg in stored:
-    print(f"    [{msg.role}] {msg.content[:70]}")
-
-print()
-
-# --- Phase 3: Direct SQL Verification ---
-print("=== Phase 3: Direct SQL Verification ===")
-conn = mysql.connector.connect(
-    host=MYSQL_HOST, port=MYSQL_PORT, user=MYSQL_USER,
-    password=MYSQL_PASSWORD, database=MYSQL_DB,
-)
-cur = conn.cursor()
-cur.execute(f"SELECT COUNT(*) FROM {TABLE_PREFIX}messages WHERE session_id = %s", (SESSION_ID,))
-count = cur.fetchone()[0]
-print(f"  Raw SQL message count: {count}")
-assert count == 4, f"Expected 4, got {count}"
-
-cur.execute(f"SELECT agent_id FROM {TABLE_PREFIX}sessions WHERE session_id = %s", (SESSION_ID,))
-agent_id = cur.fetchone()[0]
-print(f"  Raw SQL agent_id: {agent_id}")
-assert agent_id == "mysql_demo_agent"
-conn.close()
-
-print()
-
-# --- Phase 4: Session Resume ---
-print("=== Phase 4: Session Resume (Simulating Restart) ===")
-store.close()
-
-store2 = MySQLConversationStore(
-    host=MYSQL_HOST, port=MYSQL_PORT, user=MYSQL_USER,
-    password=MYSQL_PASSWORD, database=MYSQL_DB, table_prefix=TABLE_PREFIX,
-)
-resumed = store2.get_session(SESSION_ID)
-assert resumed is not None, "Session not found after restart!"
-print(f"  Resumed session: {resumed.session_id}")
-print(f"  Metadata preserved: {resumed.metadata}")
-
-msgs = store2.get_messages(SESSION_ID)
-print(f"  Messages recovered: {len(msgs)}")
-assert len(msgs) == 4
-
-# Continue
-store2.add_message(SESSION_ID, ConversationMessage(session_id=SESSION_ID, role="user", content="Thanks!"))
-store2.add_message(SESSION_ID, ConversationMessage(session_id=SESSION_ID, role="assistant", content="You're welcome!"))
-final = store2.get_messages(SESSION_ID)
-print(f"  Messages after resume: {len(final)}")
-assert len(final) == 6
-
-store2.close()
-
-# --- Cleanup ---
-print("\n=== Cleanup ===")
-conn = mysql.connector.connect(
-    host=MYSQL_HOST, port=MYSQL_PORT, user=MYSQL_USER,
-    password=MYSQL_PASSWORD, database=MYSQL_DB,
-)
-cur = conn.cursor()
-cur.execute(f"DROP TABLE IF EXISTS {TABLE_PREFIX}messages")
-cur.execute(f"DROP TABLE IF EXISTS {TABLE_PREFIX}sessions")
-conn.commit()
-conn.close()
-print("  Tables dropped.")
-
-print("\n✅ MySQL ConversationStore — All tests passed!")

examples/persistence/postgres_conversation_store.py

@@ -1,120 +0,0 @@
-"""
-PostgreSQL ConversationStore — Full Persistence Example
-
-Demonstrates:
-  - Creating a PostgreSQL ConversationStore
-  - Creating sessions, adding messages, storing metadata
-  - Direct SQL verification that data is in PostgreSQL
-  - Session resume after simulated restart
-
-Requirements:
-    pip install praisonai psycopg2-binary
-    Docker: PostgreSQL on localhost:5432 (user=postgres, password=postgres)
-
-Run:
-    python postgres_conversation_store.py
-"""
-
-import uuid
-import psycopg2
-from praisonai.persistence.conversation.postgres import PostgresConversationStore
-from praisonai.persistence.conversation.base import ConversationSession, ConversationMessage
-
-PG_URL = "postgresql://postgres:postgres@localhost:5432/postgres"
-TABLE_PREFIX = f"example_{uuid.uuid4().hex[:6]}_"
-
-print(f"PostgreSQL URL: {PG_URL}")
-print(f"Table prefix: {TABLE_PREFIX}\n")
-
-store = PostgresConversationStore(url=PG_URL, table_prefix=TABLE_PREFIX)
-SESSION_ID = f"pg-demo-{uuid.uuid4().hex[:8]}"
-
-# --- Phase 1: Create session and add messages ---
-print("=== Phase 1: Create Session & Add Messages ===")
-session = ConversationSession(
-    session_id=SESSION_ID,
-    agent_id="pg_demo_agent",
-    name="PostgreSQL Demo",
-    metadata={"agent_version": 5, "model": "gpt-4o"},
-)
-store.create_session(session)
-
-messages = [
-    ("user", "What are the benefits of PostgreSQL?"),
-    ("assistant", "PostgreSQL offers ACID compliance, extensibility, JSON support, and excellent performance."),
-    ("user", "How does it compare to MySQL?"),
-    ("assistant", "PostgreSQL has better standards compliance and advanced features, while MySQL is simpler for basic use."),
-]
-
-for role, content in messages:
-    msg = ConversationMessage(session_id=SESSION_ID, role=role, content=content)
-    store.add_message(SESSION_ID, msg)
-    print(f"  Added [{role}]: {content[:60]}")
-
-print()
-
-# --- Phase 2: Verify data in PostgreSQL ---
-print("=== Phase 2: Verify Data in PostgreSQL ===")
-retrieved = store.get_session(SESSION_ID)
-print(f"  Session: {retrieved.session_id}")
-print(f"  Agent ID: {retrieved.agent_id}")
-print(f"  Metadata: {retrieved.metadata}")
-
-stored = store.get_messages(SESSION_ID)
-print(f"  Messages: {len(stored)}")
-for msg in stored:
-    print(f"    [{msg.role}] {msg.content[:70]}")
-
-print()
-
-# --- Phase 3: Direct SQL Verification ---
-print("=== Phase 3: Direct SQL Verification ===")
-conn = psycopg2.connect(PG_URL)
-cur = conn.cursor()
-cur.execute(f"SELECT COUNT(*) FROM public.{TABLE_PREFIX}messages WHERE session_id = %s", (SESSION_ID,))
-count = cur.fetchone()[0]
-print(f"  Raw SQL message count: {count}")
-assert count == 4, f"Expected 4, got {count}"
-
-cur.execute(f"SELECT agent_id FROM public.{TABLE_PREFIX}sessions WHERE session_id = %s", (SESSION_ID,))
-agent_id = cur.fetchone()[0]
-print(f"  Raw SQL agent_id: {agent_id}")
-assert agent_id == "pg_demo_agent"
-conn.close()
-
-print()
-
-# --- Phase 4: Session Resume ---
-print("=== Phase 4: Session Resume (Simulating Restart) ===")
-store.close()
-
-store2 = PostgresConversationStore(url=PG_URL, table_prefix=TABLE_PREFIX)
-resumed = store2.get_session(SESSION_ID)
-assert resumed is not None, "Session not found after restart!"
-print(f"  Resumed session: {resumed.session_id}")
-print(f"  Metadata preserved: {resumed.metadata}")
-
-msgs = store2.get_messages(SESSION_ID)
-print(f"  Messages recovered: {len(msgs)}")
-assert len(msgs) == 4
-
-# Continue
-store2.add_message(SESSION_ID, ConversationMessage(session_id=SESSION_ID, role="user", content="Thanks!"))
-store2.add_message(SESSION_ID, ConversationMessage(session_id=SESSION_ID, role="assistant", content="You're welcome!"))
-final = store2.get_messages(SESSION_ID)
-print(f"  Messages after resume: {len(final)}")
-assert len(final) == 6
-
-store2.close()
-
-# --- Cleanup ---
-print("\n=== Cleanup ===")
-conn = psycopg2.connect(PG_URL)
-conn.autocommit = True
-cur = conn.cursor()
-cur.execute(f"DROP TABLE IF EXISTS public.{TABLE_PREFIX}messages CASCADE")
-cur.execute(f"DROP TABLE IF EXISTS public.{TABLE_PREFIX}sessions CASCADE")
-conn.close()
-print("  Tables dropped.")
-
-print("\n✅ PostgreSQL ConversationStore — All tests passed!")

examples/persistence/redis_state_store.py

@@ -1,129 +0,0 @@
-"""
-Redis StateStore — Full Persistence Example
-
-Demonstrates:
-  - Creating a Redis StateStore
-  - Key-value operations (get/set/delete/exists)
-  - JSON state persistence (agent metadata, usage tokens)
-  - Hash operations for structured data
-  - Session resume: simulate restart, recover state
-
-Requirements:
-    pip install praisonai redis
-    Docker: Redis on localhost:6379 (password=myredissecret)
-
-Run:
-    python redis_state_store.py
-"""
-
-import uuid
-import redis as redis_lib
-from praisonai.persistence.state.redis import RedisStateStore
-
-REDIS_HOST = "localhost"
-REDIS_PORT = 6379
-REDIS_PASSWORD = "myredissecret"
-PREFIX = f"example_{uuid.uuid4().hex[:6]}:"
-
-print(f"Redis: {REDIS_HOST}:{REDIS_PORT}")
-print(f"Key prefix: {PREFIX}\n")
-
-store = RedisStateStore(
-    host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD, prefix=PREFIX,
-)
-
-# --- Phase 1: Basic key-value operations ---
-print("=== Phase 1: Basic Key-Value Operations ===")
-store.set("agent_id", "redis_demo_agent_001")
-store.set("model", "gpt-4o-mini")
-store.set("counter", "42")
-
-print(f"  agent_id: {store.get('agent_id')}")
-print(f"  model: {store.get('model')}")
-print(f"  counter: {store.get('counter')}")
-print(f"  exists('agent_id'): {store.exists('agent_id')}")
-print(f"  exists('missing'): {store.exists('missing')}")
-
-print()
-
-# --- Phase 2: JSON state persistence ---
-print("=== Phase 2: JSON State Persistence ===")
-managed_state = {
-    "agent_id": "redis_demo_agent_001",
-    "agent_version": 3,
-    "environment_id": "env_local_001",
-    "total_input_tokens": 1500,
-    "total_output_tokens": 600,
-    "compute_instance_id": "docker_redis_demo",
-    "session_history": [
-        {"id": "session_001", "status": "completed", "title": "Weather chat"},
-        {"id": "session_002", "status": "idle", "title": "Code review"},
-    ],
-}
-
-store.set_json("managed_state", managed_state)
-recovered = store.get_json("managed_state")
-
-print(f"  agent_id: {recovered['agent_id']}")
-print(f"  agent_version: {recovered['agent_version']}")
-print(f"  total_input_tokens: {recovered['total_input_tokens']}")
-print(f"  total_output_tokens: {recovered['total_output_tokens']}")
-print(f"  compute_instance_id: {recovered['compute_instance_id']}")
-print(f"  session_history count: {len(recovered['session_history'])}")
-
-assert recovered["agent_id"] == "redis_demo_agent_001"
-assert recovered["total_input_tokens"] == 1500
-assert recovered["compute_instance_id"] == "docker_redis_demo"
-
-print()
-
-# --- Phase 3: Hash operations ---
-print("=== Phase 3: Hash Operations ===")
-store.hset("agent_meta", "version", "5")
-store.hset("agent_meta", "env_id", "env_abc")
-store.hset("agent_meta", "model", "gpt-4o")
-
-print(f"  version: {store.hget('agent_meta', 'version')}")
-all_meta = store.hgetall("agent_meta")
-print(f"  all fields: {all_meta}")
-
-print()
-
-# --- Phase 4: Session Resume (Simulating Restart) ---
-print("=== Phase 4: Session Resume (Simulating Restart) ===")
-store.close()
-
-# New store instance (simulating process restart)
-store2 = RedisStateStore(
-    host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD, prefix=PREFIX,
-)
-
-# Recover state
-recovered2 = store2.get_json("managed_state")
-assert recovered2 is not None, "State not found after restart!"
-print(f"  Recovered agent_id: {recovered2['agent_id']}")
-print(f"  Recovered tokens: in={recovered2['total_input_tokens']}, out={recovered2['total_output_tokens']}")
-print(f"  Recovered compute: {recovered2['compute_instance_id']}")
-print(f"  Recovered sessions: {len(recovered2['session_history'])}")
-
-# Update state (simulate more work)
-recovered2["total_input_tokens"] += 500
-recovered2["total_output_tokens"] += 200
-store2.set_json("managed_state", recovered2)
-
-final = store2.get_json("managed_state")
-print(f"  Updated tokens: in={final['total_input_tokens']}, out={final['total_output_tokens']}")
-assert final["total_input_tokens"] == 2000
-assert final["total_output_tokens"] == 800
-
-store2.close()
-
-# --- Cleanup ---
-print("\n=== Cleanup ===")
-r = redis_lib.Redis(host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD)
-for key in r.keys(f"{PREFIX}*"):
-    r.delete(key)
-r.close()
-print("  Keys deleted.")
-
-print("\n✅ Redis StateStore — All tests passed!")

examples/persistence/sqlite_conversation_store.py

@@ -1,112 +0,0 @@
-"""
-SQLite ConversationStore — Full Persistence Example
-
-Demonstrates:
-  - Creating a SQLite ConversationStore
-  - Creating sessions and adding messages
-  - Verifying data is stored in the SQLite file
-  - Session resume: destroy agent, restore from DB, verify state
-
-Requirements:
-    pip install praisonai
-
-Run:
-    python sqlite_conversation_store.py
-"""
-
-import os
-import sqlite3
-import tempfile
-from praisonai.persistence.conversation.sqlite import SQLiteConversationStore
-from praisonai.persistence.conversation.base import ConversationSession, ConversationMessage
-
-# --- Setup ---
-db_path = os.path.join(tempfile.gettempdir(), "praison_sqlite_example.db")
-print(f"SQLite DB path: {db_path}\n")
-
-store = SQLiteConversationStore(path=db_path)
-
-SESSION_ID = "sqlite-demo-session-001"
-
-# --- Phase 1: Create session and add messages ---
-print("=== Phase 1: Create Session & Add Messages ===")
-session = ConversationSession(
-    session_id=SESSION_ID,
-    agent_id="sqlite_demo_agent",
-    name="SQLite Demo Session",
-    metadata={"agent_version": 3, "environment": "local"},
-)
-store.create_session(session)
-
-messages = [
-    ("user", "Hello! What is the capital of France?"),
-    ("assistant", "The capital of France is Paris."),
-    ("user", "What about Germany?"),
-    ("assistant", "The capital of Germany is Berlin."),
-]
-
-for role, content in messages:
-    msg = ConversationMessage(session_id=SESSION_ID, role=role, content=content)
-    store.add_message(SESSION_ID, msg)
-    print(f"  Added [{role}]: {content[:50]}")
-
-print()
-
-# --- Phase 2: Verify data in DB ---
-print("=== Phase 2: Verify Data in SQLite ===")
-retrieved_session = store.get_session(SESSION_ID)
-print(f"  Session ID: {retrieved_session.session_id}")
-print(f"  Agent ID: {retrieved_session.agent_id}")
-print(f"  Metadata: {retrieved_session.metadata}")
-
-stored_messages = store.get_messages(SESSION_ID)
-print(f"  Messages stored: {len(stored_messages)}")
-for msg in stored_messages:
-    print(f"    [{msg.role}] {msg.content[:60]}")
-
-print()
-
-# --- Phase 3: Session Resume ---
-print("=== Phase 3: Session Resume (Simulating Restart) ===")
-store.close()
-
-# Reopen store from same file (simulating process restart)
-store2 = SQLiteConversationStore(path=db_path)
-
-# Verify session still exists
-resumed_session = store2.get_session(SESSION_ID)
-assert resumed_session is not None, "Session not found after restart!"
-print(f"  Resumed session: {resumed_session.session_id}")
-print(f"  Agent ID: {resumed_session.agent_id}")
-print(f"  Metadata preserved: {resumed_session.metadata}")
-
-# Verify messages
-resumed_messages = store2.get_messages(SESSION_ID)
-print(f"  Messages recovered: {len(resumed_messages)}")
-assert len(resumed_messages) == 4, f"Expected 4 messages, got {len(resumed_messages)}"
-
-# Continue the conversation
-new_msg = ConversationMessage(session_id=SESSION_ID, role="user", content="What about Spain?")
-store2.add_message(SESSION_ID, new_msg)
-new_msg2 = ConversationMessage(session_id=SESSION_ID, role="assistant", content="The capital of Spain is Madrid.")
-store2.add_message(SESSION_ID, new_msg2)
-
-final_messages = store2.get_messages(SESSION_ID)
-print(f"  Messages after resume: {len(final_messages)}")
-assert len(final_messages) == 6
-
-store2.close()
-
-# --- Phase 4: Direct SQLite verification ---
-print("\n=== Phase 4: Direct SQLite Verification ===")
-conn = sqlite3.connect(db_path)
-cur = conn.cursor()
-cur.execute("SELECT COUNT(*) FROM praison_messages WHERE session_id = ?", (SESSION_ID,))
-count = cur.fetchone()[0]
-print(f"  Raw SQL message count: {count}")
-assert count == 6
-conn.close()
-
-# Cleanup
-os.remove(db_path)
-print("\n✅ SQLite ConversationStore — All tests passed!")

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

@@ -43,34 +43,14 @@ app = FastAPI(
     redoc_url="/redoc"
 )
 
-# Add CORS middleware with secure configuration
-cors_origins = os.getenv("API_CORS_ORIGINS", "").split(",")
-cors_origins = [origin.strip() for origin in cors_origins if origin.strip() and origin.strip() != "*"]
-
-# Default secure origins if none specified
-if not cors_origins:
-    # Secure defaults for different environments
-    if os.getenv("ENVIRONMENT") == "production":
-        # In production, require explicit configuration
-        cors_origins = []
-    else:
-        # Development defaults - restrict to local origins
-        cors_origins = [
-            "http://localhost:3000",   # Development frontend
-            "http://localhost:8000",   # Local development
-            "http://127.0.0.1:3000",   # Local development
-            "http://127.0.0.1:8000",   # Local development
-        ]
-
-# Only add CORS middleware if origins are specified
-if cors_origins:
-    app.add_middleware(
-        CORSMiddleware,
-        allow_origins=cors_origins,
-        allow_credentials=True,
-        allow_methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"],
-        allow_headers=["Authorization", "Content-Type", "Origin", "Accept"],
-    )
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
 # Create directories for storing reports
 REPORTS_DIR = Path("generated_reports")

examples/python/eval/recipe_optimization_loop.py

@@ -14,17 +14,14 @@ import subprocess
 import sys
 import os
 import json
-import shlex
 from pathlib import Path
 
 
 def run_cli(cmd: str, cwd: str = None) -> tuple[int, str, str]:
     """Run a CLI command and return (exit_code, stdout, stderr)."""
-    # Use shell=False with shlex.split for safer execution
-    args = shlex.split(cmd)
     result = subprocess.run(
-        args,
-        shell=False,  # Use shell=False for security
+        cmd,
+        shell=True,
         capture_output=True,
         text=True,
         cwd=cwd,

examples/python/managed-agents/01_create_agent.py

@@ -1,9 +0,0 @@
-from praisonai import Agent, ManagedAgent
-
-# Zero config — defaults: name="Agent", model="claude-haiku-4-5"
-managed = ManagedAgent()
-agent = Agent(name="coder", backend=managed)
-result = agent.start("Say hello")
-
-print(f"Agent ID: {managed.agent_id}")
-print(f"Version: {managed.agent_version}")

examples/python/managed-agents/02_create_environment.py

@@ -1,17 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-# Environment is created automatically — just specify packages/networking in config
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Coding Assistant",
-        model="claude-haiku-4-5",
-        system="You are a helpful coding assistant. Write clean, well-documented code.",
-        networking={"type": "unrestricted"},
-    ),
-)
-
-agent = Agent(name="coder", backend=managed)
-result = agent.start("Say hello")
-
-print(f"Agent ID: {managed.agent_id}")
-print(f"Environment ID: {managed.environment_id}")

examples/python/managed-agents/03_create_session.py

@@ -1,18 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-# Agent, environment, and session are all created automatically on first use
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Coding Assistant",
-        model="claude-haiku-4-5",
-        system="You are a helpful coding assistant. Write clean, well-documented code.",
-        session_title="Quickstart session",
-    ),
-)
-
-agent = Agent(name="coder", backend=managed)
-result = agent.start("Say hello")
-
-print(f"Agent ID: {managed.agent_id}")
-print(f"Environment ID: {managed.environment_id}")
-print(f"Session ID: {managed.session_id}")

examples/python/managed-agents/04_stream_response.py

@@ -1,18 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Coding Assistant",
-        model="claude-haiku-4-5",
-        system="You are a helpful coding assistant. Write clean, well-documented code.",
-    ),
-)
-
-agent = Agent(name="coder", backend=managed)
-
-result = agent.start(
-    "Create a Python script that generates the first 20 Fibonacci numbers and saves them to fibonacci.txt",
-    stream=True,
-)
-
-print("\nAgent finished.")

examples/python/managed-agents/05_select_tools.py

@@ -1,27 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-# Select specific tools: only bash, read, write — everything else disabled
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Bash Only Agent",
-        model="claude-haiku-4-5",
-        system="You are a helpful assistant that can only use bash commands.",
-        tools=[
-            {
-                "type": "agent_toolset_20260401",
-                "default_config": {"enabled": False},
-                "configs": [
-                    {"name": "bash", "enabled": True},
-                    {"name": "read", "enabled": True},
-                    {"name": "write", "enabled": True},
-                ],
-            },
-        ],
-    ),
-)
-
-agent = Agent(name="bash-agent", backend=managed)
-result = agent.start("List the current directory contents and show system info using uname -a")
-
-print(result)
-print("\nAgent finished.")

examples/python/managed-agents/06_disable_tools.py

@@ -1,25 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-# Disable specific tools: web access disabled, everything else stays on
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="No Web Agent",
-        model="claude-haiku-4-5",
-        system="You are a coding assistant. You cannot access the web.",
-        tools=[
-            {
-                "type": "agent_toolset_20260401",
-                "configs": [
-                    {"name": "web_fetch", "enabled": False},
-                    {"name": "web_search", "enabled": False},
-                ],
-            },
-        ],
-    ),
-)
-
-agent = Agent(name="no-web-agent", backend=managed)
-result = agent.start("Write a hello world Python script and run it")
-
-print(result)
-print("\nAgent finished.")

examples/python/managed-agents/07_custom_tools.py

@@ -1,41 +0,0 @@
-import json
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-
-def handle_weather(tool_name, tool_input):
-    """Custom tool callback — PraisonAI calls this when the agent uses get_weather."""
-    location = tool_input.get("location", "Unknown")
-    print(f"\n[Custom tool call: {tool_name}]")
-    print(f"[Input: {json.dumps(tool_input)}]")
-    return f"Weather in {location}: 15°C, partly cloudy, humidity 72%"
-
-
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Weather Agent",
-        model="claude-haiku-4-5",
-        system="You are a weather assistant. Use the get_weather tool to check weather.",
-        tools=[
-            {"type": "agent_toolset_20260401"},
-            {
-                "type": "custom",
-                "name": "get_weather",
-                "description": "Get current weather for a location",
-                "input_schema": {
-                    "type": "object",
-                    "properties": {
-                        "location": {"type": "string", "description": "City name"},
-                    },
-                    "required": ["location"],
-                },
-            },
-        ],
-    ),
-    on_custom_tool=handle_weather,
-)
-
-agent = Agent(name="weather-agent", backend=managed)
-result = agent.start("What is the weather in London?")
-
-print(result)
-print("\nAgent finished.")

examples/python/managed-agents/08_update_agent.py

@@ -1,22 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="My Agent v1",
-        model="claude-haiku-4-5",
-        system="You are a helpful assistant.",
-    ),
-)
-
-agent = Agent(name="updatable", backend=managed)
-
-# First call creates the agent
-result = agent.start("Say hello briefly")
-print(f"Created: {managed.agent_id}, version: {managed.agent_version}")
-
-# Update the agent's system prompt (no need to recreate)
-managed.update_agent(
-    name="My Agent v2",
-    system="You are a senior Python developer. Write production-quality code.",
-)
-print(f"Updated: {managed.agent_id}, version: {managed.agent_version}")

examples/python/managed-agents/09_list_sessions.py

@@ -1,23 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Session Demo Agent",
-        model="claude-haiku-4-5",
-        system="You are a helpful assistant.",
-    ),
-)
-
-agent = Agent(name="session-demo", backend=managed)
-
-# Run a task to create agent + session
-agent.start("Say hello briefly")
-
-# List sessions for this agent
-sessions = managed.list_sessions()
-print(f"Total sessions: {len(sessions)}")
-for s in sessions[:5]:
-    print(f"  ID: {s['id']}")
-    print(f"  Status: {s['status']}")
-    print(f"  Title: {s['title']}")
-    print()

examples/python/managed-agents/10_web_search.py

@@ -1,17 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Research Agent",
-        model="claude-haiku-4-5",
-        system="You are a research assistant. Search the web for information.",
-    ),
-)
-
-agent = Agent(name="researcher", backend=managed)
-result = agent.start(
-    "Search the web for the latest Python 3.13 features and summarize them in 3 bullet points"
-)
-
-print(result)
-print("\nAgent finished.")

examples/python/managed-agents/11_multi_turn.py

@@ -1,25 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Multi Turn Agent",
-        model="claude-haiku-4-5",
-        system="You are a helpful coding assistant.",
-    ),
-)
-
-agent = Agent(name="multi-turn", backend=managed)
-print(f"Session ID: {managed.session_id}")
-
-# Multi-turn: each call reuses the same session — agent remembers context
-messages = [
-    "Create a file called greeting.py that prints 'Hello World'",
-    "Now modify greeting.py to accept a name as a command line argument",
-    "Run greeting.py with the argument 'Claude'",
-]
-
-for i, message in enumerate(messages):
-    print(f"\n--- Turn {i + 1}: {message} ---\n")
-    result = agent.start(message)
-    print(result)
-    print("\nAgent finished turn.")

examples/python/managed-agents/12_environment_setup.py

@@ -1,22 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-# Just add packages to the config — environment is created automatically
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Data Science Agent",
-        model="claude-haiku-4-5",
-        system="You are a data science assistant.",
-        packages={"pip": ["pandas", "numpy"]},
-    ),
-)
-
-agent = Agent(name="data-scientist", backend=managed)
-
-print(f"Environment ID: {managed.environment_id}")
-
-result = agent.start(
-    "Create a Python script that generates random sales data with pandas and saves a summary to sales_summary.txt"
-)
-
-print(result)
-print("\nAgent finished.")

examples/python/managed-agents/13_interrupt_session.py

@@ -1,20 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Interruptable Agent",
-        model="claude-haiku-4-5",
-        system="You are a helpful coding assistant.",
-    ),
-)
-
-agent = Agent(name="interruptable", backend=managed)
-
-# Start a task
-result = agent.start("Write a Python script that prints numbers 1 to 5")
-print(result)
-
-# Interrupt the agent mid-work
-print("\n[Sending interrupt...]")
-managed.interrupt()
-print("Agent stopped.")

examples/python/managed-agents/14_track_usage.py

@@ -1,28 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Usage Tracker Agent",
-        model="claude-haiku-4-5",
-        system="You are a helpful assistant.",
-    ),
-)
-
-agent = Agent(name="tracker", backend=managed)
-result = agent.start("Write a one-line Python script that prints the current date and run it")
-
-print(result)
-print("\nAgent finished.")
-
-# Usage tracked automatically
-print("\n--- Usage Report ---")
-print(f"Input tokens:  {managed.total_input_tokens}")
-print(f"Output tokens: {managed.total_output_tokens}")
-
-# Or retrieve detailed session info from the API
-info = managed.retrieve_session()
-print(f"\nSession ID: {info.get('id')}")
-print(f"Status: {info.get('status')}")
-if "usage" in info:
-    print(f"API Input tokens:  {info['usage']['input_tokens']}")
-    print(f"API Output tokens: {info['usage']['output_tokens']}")

examples/python/managed-agents/15_resume_session.py

@@ -1,35 +0,0 @@
-import json
-import pathlib
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-IDS_FILE = pathlib.Path("managed_ids.json")
-
-if IDS_FILE.exists():
-    saved = json.loads(IDS_FILE.read_text())
-    print(f"Resuming session: {saved['session_id']}\n")
-
-    managed = ManagedAgent()
-    managed.resume_session(saved["session_id"])
-
-    agent = Agent(name="coder", backend=managed)
-    result = agent.start("What is my favourite number?", stream=True)
-
-else:
-    managed = ManagedAgent(
-        config=ManagedConfig(
-            name="Persistent Coder",
-            model="claude-haiku-4-5",
-            system="You are a helpful coding assistant.",
-        ),
-    )
-    agent = Agent(name="coder", backend=managed)
-    result = agent.start("Remember this: my favourite number is 42.", stream=True)
-
-    ids = managed.save_ids()
-    IDS_FILE.write_text(json.dumps(ids, indent=2))
-
-    print(f"\nSaved IDs to {IDS_FILE}")
-    print(f"  agent_id      : {managed.agent_id}")
-    print(f"  environment_id: {managed.environment_id}")
-    print(f"  session_id    : {managed.session_id}")
-    print("\nRun this script again to resume the session.")

examples/python/managed-agents/16_session_ids_explained.py

@@ -1,21 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Session Demo",
-        model="claude-haiku-4-5",
-        system="You are a helpful assistant.",
-    ),
-)
-agent = Agent(name="demo", backend=managed)
-
-agent.start("Say hello and confirm you are ready.", stream=True)
-
-print("\n--- IDs ---")
-print(f"agent_id      : {managed.agent_id}")
-print(f"environment_id: {managed.environment_id}")
-print(f"session_id    : {managed.session_id}")
-print(f"chat_history  : {len(agent.chat_history)} messages")
-
-ids = managed.save_ids()
-print(f"\nsave_ids(): {ids}")

examples/python/managed-agents/17_multi_packages.py

@@ -1,22 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-# Multiple package managers — pip + npm installed before agent starts
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Full Stack Agent",
-        model="claude-haiku-4-5",
-        system="You are a full stack developer.",
-        packages={
-            "pip": ["pandas", "numpy", "scikit-learn"],
-            "npm": ["express"],
-        },
-    ),
-)
-
-agent = Agent(name="fullstack", backend=managed)
-result = agent.start(
-    "Verify pandas and express are installed: run 'python3 -c \"import pandas; print(pandas.__version__)\"' and 'node -e \"console.log(require.resolve('express'))\"'",
-    stream=True,
-)
-
-print("\nAgent finished.")

examples/python/managed-agents/18_limited_networking.py

@@ -1,24 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-# Limited networking: restrict container to specific hosts only
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Restricted Network Agent",
-        model="claude-haiku-4-5",
-        system="You are a helpful assistant with restricted network access.",
-        networking={
-            "type": "limited",
-            "allowed_hosts": ["api.github.com"],
-            "allow_mcp_servers": False,
-            "allow_package_managers": True,
-        },
-    ),
-)
-
-agent = Agent(name="restricted", backend=managed)
-result = agent.start(
-    "Try to fetch https://api.github.com and report what you get. Then try https://example.com — it should fail.",
-    stream=True,
-)
-
-print("\nAgent finished.")

examples/python/managed-agents/19_manage_environments.py

@@ -1,43 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-# Environment management: list, retrieve, archive, delete
-
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Env Management Agent",
-        model="claude-haiku-4-5",
-        system="You are a helpful assistant.",
-    ),
-)
-agent = Agent(name="env-mgmt", backend=managed)
-
-# First call creates agent + environment + session
-agent.start("Say hello briefly")
-
-# Access the underlying Anthropic client for environment management
-client = managed._get_client()
-env_id = managed.environment_id
-print(f"Environment ID: {env_id}")
-
-# List all environments
-environments = client.beta.environments.list()
-print(f"\nTotal environments: {len(environments.data)}")
-for env in environments.data[:5]:
-    print(f"  {env.id} | {env.name} | {getattr(env, 'status', 'active')}")
-
-# Retrieve a specific environment
-env = client.beta.environments.retrieve(env_id)
-print(f"\nRetrieved: {env.id} | {env.name}")
-
-# Archive (read-only, existing sessions continue)
-archived = client.beta.environments.archive(env_id)
-print(f"Archived: {archived.id} | status: {getattr(archived, 'status', 'archived')}")
-
-# Delete (only if no sessions reference it — may fail if sessions exist)
-try:
-    client.beta.environments.delete(env_id)
-    print(f"Deleted: {env_id}")
-except Exception as e:
-    print(f"Delete skipped (expected if sessions exist): {e}")
-
-print("\nEnvironment management complete.")

examples/python/managed-agents/20_mcp_servers.py

@@ -1,35 +0,0 @@
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-# MCP servers: configure agent with remote MCP tool servers
-# Note: Anthropic's managed agents only support URL-based (SSE) MCP servers
-managed = ManagedAgent(
-    config=ManagedConfig(
-        name="MCP Agent",
-        model="claude-haiku-4-5",
-        system="You are a helpful assistant with access to MCP servers.",
-        tools=[
-            {"type": "agent_toolset_20260401"},
-            {"type": "mcp_toolset", "mcp_server_name": "deepwiki"},
-        ],
-        mcp_servers=[
-            {
-                "type": "url",
-                "url": "https://mcp.deepwiki.com/sse",
-                "name": "deepwiki",
-            },
-        ],
-        networking={
-            "type": "limited",
-            "allow_mcp_servers": True,
-            "allow_package_managers": True,
-        },
-    ),
-)
-
-agent = Agent(name="mcp-agent", backend=managed)
-result = agent.start(
-    "Use the deepwiki MCP to read the wiki page for the anthropics/anthropic-cookbook github repo and give a one sentence summary",
-    stream=True,
-)
-
-print("\nAgent finished.")

examples/python/managed-agents/app.py

@@ -1,340 +0,0 @@
-import json
-import pathlib
-from praisonai import Agent, ManagedAgent, ManagedConfig
-
-# 1. Create an agent
-
-managed = ManagedAgent()
-agent = Agent(name="teacher", backend=managed)
-result = agent.start("Say hello briefly", stream=True)
-
-print(f"[1] Agent created: {managed.agent_id} (v{managed.agent_version})")
-
-# 2. Update the agent
-
-managed.update_agent(
-    name="Teaching Agent v2",
-    system="You are a senior Python developer. Write clean, production-quality code.",
-)
-
-print(f"[2] Agent updated: Teaching Agent v2 (v{managed.agent_version})")
-
-# 3-4. Environment + Session are created automatically (already done in step 1)
-
-print(f"[3] Environment created: {managed.environment_id}")
-print(f"[4] Session created: {managed.session_id}")
-
-# 5. Stream a response
-
-print("\n[5] Streaming response...")
-
-result = agent.start("Write a Python script that prints 'Hello from Managed Agents!' and run it", stream=True)
-
-# 6. Multi-turn conversation (same session remembers context)
-
-print("\n[6] Multi-turn: sending follow-up...")
-
-result = agent.start("Now modify that script to accept a name argument and greet that person", stream=True)
-
-# 7. Track usage
-
-info = managed.retrieve_session()
-print("\n[7] Usage report:")
-if info.get("usage"):
-    print(f"    Input tokens:  {info['usage']['input_tokens']}")
-    print(f"    Output tokens: {info['usage']['output_tokens']}")
-else:
-    print(f"    Input tokens:  {managed.total_input_tokens}")
-    print(f"    Output tokens: {managed.total_output_tokens}")
-
-# 8. List sessions
-
-sessions = managed.list_sessions()
-print(f"\n[8] Total sessions: {len(sessions)}")
-for s in sessions[:3]:
-    print(f"    {s['id']} | {s['status']} | {s['title']}")
-
-# 9. Selective tools (only bash + read + write)
-
-bash_managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Bash Only Agent",
-        model="claude-haiku-4-5",
-        system="You can only use bash, read, and write tools.",
-        tools=[
-            {
-                "type": "agent_toolset_20260401",
-                "default_config": {"enabled": False},
-                "configs": [
-                    {"name": "bash", "enabled": True},
-                    {"name": "read", "enabled": True},
-                    {"name": "write", "enabled": True},
-                ],
-            },
-        ],
-    ),
-)
-
-bash_agent = Agent(name="bash-only", backend=bash_managed)
-
-print("\n[9] Bash-only agent streaming...")
-result = bash_agent.start("Show the current date and Python version using bash", stream=True)
-
-# 10. Disable specific tools (web disabled, everything else on)
-
-no_web_managed = ManagedAgent(
-    config=ManagedConfig(
-        name="No Web Agent",
-        model="claude-haiku-4-5",
-        system="You are a coding assistant. You cannot access the web.",
-        tools=[
-            {
-                "type": "agent_toolset_20260401",
-                "configs": [
-                    {"name": "web_fetch", "enabled": False},
-                    {"name": "web_search", "enabled": False},
-                ],
-            },
-        ],
-    ),
-)
-
-no_web_agent = Agent(name="no-web", backend=no_web_managed)
-
-print("\n[10] No-web agent streaming...")
-result = no_web_agent.start("Write a Python one-liner that calculates 2**100 and print the result", stream=True)
-
-# 11. Custom tools (you define the tool, PraisonAI calls your callback)
-
-
-def handle_weather(tool_name, tool_input):
-    print(f"\n  [Custom tool: {tool_name} | Input: {json.dumps(tool_input)}]")
-    return "Tokyo: 22°C, sunny, humidity 55%"
-
-
-custom_managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Weather Agent",
-        model="claude-haiku-4-5",
-        system="You are a weather assistant. Use the get_weather tool to check weather.",
-        tools=[
-            {"type": "agent_toolset_20260401"},
-            {
-                "type": "custom",
-                "name": "get_weather",
-                "description": "Get current weather for a location",
-                "input_schema": {
-                    "type": "object",
-                    "properties": {
-                        "location": {"type": "string", "description": "City name"},
-                    },
-                    "required": ["location"],
-                },
-            },
-        ],
-    ),
-    on_custom_tool=handle_weather,
-)
-
-custom_agent = Agent(name="weather", backend=custom_managed)
-
-print("\n[11] Custom tool agent streaming...")
-result = custom_agent.start("What is the weather in Tokyo?", stream=True)
-
-# 12. Web search agent
-
-search_managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Search Agent",
-        model="claude-haiku-4-5",
-        system="You are a research assistant. Search the web and summarize.",
-    ),
-)
-
-search_agent = Agent(name="searcher", backend=search_managed)
-
-print("\n[12] Web search agent streaming...")
-result = search_agent.start("Search the web for Python 3.13 new features and give me 3 bullet points", stream=True)
-
-# 13. Environment with pre-installed packages
-
-data_managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Data Science Agent",
-        model="claude-haiku-4-5",
-        system="You are a data science assistant.",
-        packages={"pip": ["pandas", "numpy"]},
-    ),
-)
-
-data_agent = Agent(name="data-scientist", backend=data_managed)
-
-print("\n[13] Data science environment streaming...")
-result = data_agent.start("Use pandas to create a small DataFrame with 3 rows of sample data and print it", stream=True)
-
-# 14. Interrupt a session
-
-interrupt_managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Interruptable Agent",
-        model="claude-haiku-4-5",
-        system="You are a helpful coding assistant.",
-    ),
-)
-
-interrupt_agent = Agent(name="interruptable", backend=interrupt_managed)
-
-print("\n[14] Interrupt demo...")
-result = interrupt_agent.start("Write a Python script that prints numbers 1 to 10", stream=True)
-interrupt_managed.interrupt()
-print("  [Interrupt sent]")
-
-# 15. Session resume — save IDs, create a fresh ManagedAgent, resume, and verify context
-
-print("\n[15] Session resume demo...")
-
-# Tell the original agent a memorable fact
-result = agent.start("Remember this: my favourite number is 42", stream=True)
-
-# Save IDs to disk
-ids = managed.save_ids()
-ids_file = pathlib.Path("managed_ids.json")
-ids_file.write_text(json.dumps(ids, indent=2))
-print(f"  Saved IDs to {ids_file}: {ids}")
-
-# Create a completely new ManagedAgent and resume the saved session
-resume_managed = ManagedAgent()
-resume_managed.resume_session(ids["session_id"])
-resume_agent = Agent(name="resumed", backend=resume_managed)
-
-# Ask about the fact — proves the session memory was preserved
-result = resume_agent.start("What is my favourite number?", stream=True)
-print(f"  Resumed session: {resume_managed.session_id}")
-
-# Clean up
-ids_file.unlink(missing_ok=True)
-
-# 16. Multi-package managers — pip + npm installed before agent starts
-
-print("\n[16] Multi-package managers...")
-
-multi_pkg_managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Full Stack Agent",
-        model="claude-haiku-4-5",
-        system="You are a full stack developer.",
-        packages={
-            "pip": ["pandas", "numpy", "scikit-learn"],
-            "npm": ["express"],
-        },
-    ),
-)
-
-multi_pkg_agent = Agent(name="fullstack", backend=multi_pkg_managed)
-result = multi_pkg_agent.start(
-    "Verify pandas and express are installed: run python3 -c 'import pandas; print(pandas.__version__)' and node -e 'console.log(require.resolve(\"express\"))'",
-    stream=True,
-)
-
-# 17. Limited networking — restrict container to specific hosts
-
-print("\n[17] Limited networking...")
-
-limited_net_managed = ManagedAgent(
-    config=ManagedConfig(
-        name="Restricted Network Agent",
-        model="claude-haiku-4-5",
-        system="You are a helpful assistant with restricted network access.",
-        networking={
-            "type": "limited",
-            "allowed_hosts": ["api.github.com"],
-            "allow_mcp_servers": False,
-            "allow_package_managers": True,
-        },
-    ),
-)
-
-limited_net_agent = Agent(name="restricted", backend=limited_net_managed)
-result = limited_net_agent.start("Fetch https://api.github.com and report the status", stream=True)
-
-# 18. Environment management — list, retrieve, archive
-
-print("\n[18] Environment management...")
-
-env_client = managed._get_client()
-
-# List all environments
-environments = env_client.beta.environments.list()
-print(f"  Total environments: {len(environments.data)}")
-for env in environments.data[:3]:
-    print(f"    {env.id} | {env.name}")
-
-# Retrieve a specific environment
-env = env_client.beta.environments.retrieve(managed.environment_id)
-print(f"  Retrieved: {env.id} | {env.name}")
-
-# 19. MCP servers — configure agent with remote MCP tool servers
-
-print("\n[19] MCP servers...")
-
-mcp_managed = ManagedAgent(
-    config=ManagedConfig(
-        name="MCP Agent",
-        model="claude-haiku-4-5",
-        system="You are a helpful assistant with access to MCP servers.",
-        tools=[
-            {"type": "agent_toolset_20260401"},
-            {"type": "mcp_toolset", "mcp_server_name": "deepwiki"},
-        ],
-        mcp_servers=[
-            {
-                "type": "url",
-                "url": "https://mcp.deepwiki.com/sse",
-                "name": "deepwiki",
-            },
-        ],
-        networking={
-            "type": "limited",
-            "allow_mcp_servers": True,
-            "allow_package_managers": True,
-        },
-    ),
-)
-
-mcp_agent = Agent(name="mcp-agent", backend=mcp_managed)
-result = mcp_agent.start("Use the deepwiki MCP to read the wiki page for the anthropics/anthropic-cookbook github repo and give a one sentence summary", stream=True)
-
-# Final usage summary
-
-print("\n" + "=" * 60)
-print("FINAL USAGE SUMMARY")
-print("=" * 60)
-
-all_backends = [
-    ("Teaching Agent v2", managed),
-    ("Bash Only Agent", bash_managed),
-    ("No Web Agent", no_web_managed),
-    ("Weather Agent", custom_managed),
-    ("Search Agent", search_managed),
-    ("Data Science Agent", data_managed),
-    ("Interruptable Agent", interrupt_managed),
-    ("Resumed Session", resume_managed),
-    ("Full Stack Agent", multi_pkg_managed),
-    ("Restricted Network", limited_net_managed),
-    ("MCP Agent", mcp_managed),
-]
-
-total_input = 0
-total_output = 0
-
-for name, backend in all_backends:
-    info = backend.retrieve_session()
-    usage = info.get("usage", {})
-    inp = usage.get("input_tokens", backend.total_input_tokens)
-    out = usage.get("output_tokens", backend.total_output_tokens)
-    total_input += inp
-    total_output += out
-    print(f"  {name:30s} | in: {inp:6d} | out: {out:6d}")
-
-print(f"  {'TOTAL':30s} | in: {total_input:6d} | out: {total_output:6d}")
-print("=" * 60)

examples/python/managed-agents/basic.py

@@ -1,5 +0,0 @@
-from praisonai import Agent, ManagedAgent
-
-agent = Agent(name="teacher", backend=ManagedAgent())
-result = agent.start("Write a Python script that prints 'Hello from Managed Agents!' and run it")
-print(result)

examples/python/managed-agents/provider/all_providers.py

@@ -1,117 +0,0 @@
-"""All compute providers — comprehensive test across Local, Docker, E2B, and Modal.
-
-This example mirrors the Anthropic app.py but uses the local provider with
-various compute backends instead of Anthropic's managed infrastructure.
-
-Requires:
-  - Docker running locally
-  - E2B_API_KEY set
-  - modal CLI configured (modal token set)
-"""
-import asyncio
-from praisonai import Agent, ManagedAgent, LocalManagedConfig
-
-
-async def test_provider(name, compute, extra_provision_kwargs=None):
-    """Test a single compute provider end-to-end."""
-    print(f"\n{'='*60}")
-    print(f"  PROVIDER: {name}")
-    print(f"{'='*60}")
-
-    managed = ManagedAgent(
-        provider="local",
-        compute=compute,
-        config=LocalManagedConfig(
-            model="gpt-4o-mini",
-            system="You are a helpful assistant. Be concise.",
-            name=f"{name}Agent",
-        ),
-    )
-    agent = Agent(name=f"{name}-test", backend=managed)
-
-    # 1. Provision
-    print(f"\n  [1] Provisioning {name}...")
-    provision_kwargs = extra_provision_kwargs or {}
-    info = await managed.provision_compute(**provision_kwargs)
-    print(f"      Instance: {info.instance_id}")
-    print(f"      Status:   {info.status}")
-
-    # 2. Execute command
-    print(f"\n  [2] Execute in {name}...")
-    result = await managed.execute_in_compute("python3 -c 'print(42 * 13)'")
-    stdout = result["stdout"].strip()
-    assert "546" in stdout, f"Expected 546, got: {stdout}"
-    print(f"      42 * 13 = {stdout} ✓")
-
-    # 3. Execute echo
-    result = await managed.execute_in_compute(f"echo 'Hello from {name}'")
-    print(f"      Echo: {result['stdout'].strip()}")
-
-    # 4. Agent LLM
-    print(f"\n  [3] Agent LLM via {name}...")
-    llm_result = agent.start("What is 9 * 8? Just the number.", stream=True)
-    print(f"      LLM result: {llm_result}")
-
-    # 5. Multi-turn
-    print("\n  [4] Multi-turn...")
-    llm_result = agent.start("Add 10 to that. Just the number.", stream=True)
-    print(f"      Follow-up: {llm_result}")
-
-    # 6. Usage
-    session = managed.retrieve_session()
-    usage = session["usage"]
-    print(f"\n  [5] Usage: in={usage['input_tokens']}, out={usage['output_tokens']}")
-
-    # 7. Shutdown
-    print(f"\n  [6] Shutdown {name}...")
-    await managed.shutdown_compute()
-    print("      Done ✓")
-
-    return {
-        "name": name,
-        "input_tokens": usage["input_tokens"],
-        "output_tokens": usage["output_tokens"],
-    }
-
-
-async def main():
-    results = []
-
-    # Docker
-    try:
-        r = await test_provider("Docker", "docker", {"image": "python:3.12-slim"})
-        results.append(r)
-    except Exception as e:
-        print(f"\n  Docker FAILED: {e}")
-
-    # E2B
-    try:
-        r = await test_provider("E2B", "e2b", {"idle_timeout_s": 120})
-        results.append(r)
-    except Exception as e:
-        print(f"\n  E2B FAILED: {e}")
-
-    # Modal
-    try:
-        r = await test_provider("Modal", "modal", {"idle_timeout_s": 120})
-        results.append(r)
-    except Exception as e:
-        print(f"\n  Modal FAILED: {e}")
-
-    # Summary
-    print(f"\n\n{'='*60}")
-    print("  SUMMARY")
-    print(f"{'='*60}")
-    total_in = 0
-    total_out = 0
-    for r in results:
-        total_in += r["input_tokens"]
-        total_out += r["output_tokens"]
-        print(f"  {r['name']:15s} | in: {r['input_tokens']:6d} | out: {r['output_tokens']:6d}")
-    print(f"  {'TOTAL':15s} | in: {total_in:6d} | out: {total_out:6d}")
-    print(f"{'='*60}")
-    print(f"  {len(results)}/{3} providers passed")
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/python/managed-agents/provider/daytona_compute.py

@@ -1,55 +0,0 @@
-"""Daytona compute provider — run agent tools inside a Daytona cloud sandbox.
-
-Requires: DAYTONA_API_KEY environment variable set.
-          Organization must have a default region configured in Daytona Dashboard.
-Install:  pip install daytona-sdk
-"""
-import asyncio
-from praisonai import Agent, ManagedAgent, LocalManagedConfig
-
-# ── 1. Create agent with Daytona compute ──
-managed = ManagedAgent(
-    provider="local",
-    compute="daytona",
-    config=LocalManagedConfig(
-        model="gpt-4o-mini",
-        system="You are a helpful coding assistant. Be concise.",
-        name="DaytonaAgent",
-    ),
-)
-
-agent = Agent(name="daytona-agent", backend=managed)
-
-print("[1] Agent created with Daytona compute")
-print(f"    Compute: {managed.compute_provider.provider_name}")
-
-# ── 2. Provision Daytona sandbox ──
-print("\n[2] Provisioning Daytona sandbox...")
-info = asyncio.run(managed.provision_compute(idle_timeout_s=120))
-print(f"    Instance: {info.instance_id}")
-print(f"    Status:   {info.status}")
-
-# ── 3. Execute commands ──
-print("\n[3] Executing commands in Daytona...")
-result = asyncio.run(managed.execute_in_compute("python3 -c 'import sys; print(sys.version)'"))
-print(f"    Python version: {result['stdout'].strip()}")
-print(f"    Exit code: {result['exit_code']}")
-
-result = asyncio.run(managed.execute_in_compute("echo 'Hello from Daytona!'"))
-print(f"    Echo: {result['stdout'].strip()}")
-
-# ── 4. Agent LLM execution ──
-print("\n[4] Agent LLM execution...")
-result = agent.start("What is 11 * 13? Just the number.", stream=True)
-print(f"    Result: {result}")
-
-# ── 5. Usage ──
-info = managed.retrieve_session()
-print(f"\n[5] Usage: in={info['usage']['input_tokens']}, out={info['usage']['output_tokens']}")
-
-# ── 6. Shutdown ──
-print("\n[6] Shutting down Daytona sandbox...")
-asyncio.run(managed.shutdown_compute())
-print("    Shutdown complete.")
-
-print("\nDone!")

examples/python/managed-agents/provider/docker_compute.py

@@ -1,67 +0,0 @@
-"""Docker compute provider — run agent tools inside a Docker container.
-
-Requires: Docker running locally.
-"""
-import asyncio
-from praisonai import Agent, ManagedAgent, LocalManagedConfig
-
-# ── 1. Basic agent with Docker compute ──
-managed = ManagedAgent(
-    provider="local",
-    compute="docker",
-    config=LocalManagedConfig(
-        model="gpt-4o-mini",
-        system="You are a helpful coding assistant. Be concise.",
-        name="DockerAgent",
-    ),
-)
-
-agent = Agent(name="docker-agent", backend=managed)
-
-print("[1] Agent created with Docker compute")
-print(f"    Agent ID: {managed.agent_id or '(lazy — created on first call)'}")
-print(f"    Compute:  {managed.compute_provider.provider_name}")
-
-# ── 2. Provision Docker container ──
-print("\n[2] Provisioning Docker container...")
-info = asyncio.run(managed.provision_compute(image="python:3.12-slim"))
-print(f"    Instance: {info.instance_id}")
-print(f"    Status:   {info.status}")
-
-# ── 3. Execute commands inside the container ──
-print("\n[3] Executing commands in Docker...")
-result = asyncio.run(managed.execute_in_compute("python3 -c 'import sys; print(sys.version)'"))
-print(f"    Python version: {result['stdout'].strip()}")
-print(f"    Exit code: {result['exit_code']}")
-
-result = asyncio.run(managed.execute_in_compute("echo 'Hello from Docker!'"))
-print(f"    Echo: {result['stdout'].strip()}")
-
-# ── 4. Install packages in the container ──
-print("\n[4] Installing packages...")
-result = asyncio.run(managed.execute_in_compute("pip install requests -q"))
-print(f"    pip exit code: {result['exit_code']}")
-
-result = asyncio.run(managed.execute_in_compute("python3 -c 'import requests; print(requests.__version__)'"))
-print(f"    requests version: {result['stdout'].strip()}")
-
-# ── 5. Use agent with LLM (runs locally, compute is for tool sandboxing) ──
-print("\n[5] Agent LLM execution...")
-result = agent.start("What is 15 * 23? Just the number.", stream=True)
-print(f"    Result: {result}")
-
-# ── 6. Multi-turn ──
-print("\n[6] Multi-turn...")
-result = agent.start("Double that number.", stream=True)
-print(f"    Result: {result}")
-
-# ── 7. Usage tracking ──
-info = managed.retrieve_session()
-print(f"\n[7] Usage: in={info['usage']['input_tokens']}, out={info['usage']['output_tokens']}")
-
-# ── 8. Shutdown Docker container ──
-print("\n[8] Shutting down Docker container...")
-asyncio.run(managed.shutdown_compute())
-print("    Shutdown complete.")
-
-print("\nDone!")

examples/python/managed-agents/provider/e2b_compute.py

@@ -1,66 +0,0 @@
-"""E2B compute provider — run agent tools inside an E2B cloud sandbox.
-
-Requires: E2B_API_KEY environment variable set.
-Install:  pip install e2b
-"""
-import asyncio
-from praisonai import Agent, ManagedAgent, LocalManagedConfig
-
-# ── 1. Create agent with E2B compute ──
-managed = ManagedAgent(
-    provider="local",
-    compute="e2b",
-    config=LocalManagedConfig(
-        model="gpt-4o-mini",
-        system="You are a helpful coding assistant. Be concise.",
-        name="E2BAgent",
-    ),
-)
-
-agent = Agent(name="e2b-agent", backend=managed)
-
-print("[1] Agent created with E2B compute")
-print(f"    Compute: {managed.compute_provider.provider_name}")
-
-# ── 2. Provision E2B sandbox ──
-print("\n[2] Provisioning E2B sandbox...")
-info = asyncio.run(managed.provision_compute(idle_timeout_s=120))
-print(f"    Instance: {info.instance_id}")
-print(f"    Status:   {info.status}")
-
-# ── 3. Execute commands in the sandbox ──
-print("\n[3] Executing commands in E2B...")
-result = asyncio.run(managed.execute_in_compute("python3 -c 'import sys; print(sys.version)'"))
-print(f"    Python version: {result['stdout'].strip()}")
-print(f"    Exit code: {result['exit_code']}")
-
-result = asyncio.run(managed.execute_in_compute("echo 'Hello from E2B!'"))
-print(f"    Echo: {result['stdout'].strip()}")
-
-# ── 4. Run a Python script ──
-print("\n[4] Running Python script...")
-result = asyncio.run(managed.execute_in_compute(
-    "python3 -c 'for i in range(5): print(f\"Line {i}\")'"
-))
-print(f"    Output:\n{result['stdout']}")
-
-# ── 5. Agent LLM execution ──
-print("[5] Agent LLM execution...")
-result = agent.start("What is the square root of 144? Just the number.", stream=True)
-print(f"    Result: {result}")
-
-# ── 6. Multi-turn ──
-print("\n[6] Multi-turn...")
-result = agent.start("Now cube that number.", stream=True)
-print(f"    Result: {result}")
-
-# ── 7. Usage ──
-info = managed.retrieve_session()
-print(f"\n[7] Usage: in={info['usage']['input_tokens']}, out={info['usage']['output_tokens']}")
-
-# ── 8. Shutdown ──
-print("\n[8] Shutting down E2B sandbox...")
-asyncio.run(managed.shutdown_compute())
-print("    Shutdown complete.")
-
-print("\nDone!")

examples/python/managed-agents/provider/local_advanced.py

@@ -1,96 +0,0 @@
-"""Local provider — advanced features: update agent, custom tools, session resume, packages."""
-from praisonai import Agent, ManagedAgent, LocalManagedConfig
-
-# ── 1. Create agent ──
-managed = ManagedAgent(
-    provider="local",
-    config=LocalManagedConfig(
-        model="gpt-4o-mini",
-        system="You are a helpful math tutor.",
-        name="MathTutor",
-    ),
-)
-agent = Agent(name="tutor", backend=managed)
-result = agent.start("What is 17 * 23?", stream=True)
-print(f"[1] Created agent: {managed.agent_id} (v{managed.agent_version})")
-
-# ── 2. Update agent ──
-managed.update_agent(
-    name="Senior Math Tutor",
-    system="You are a senior math tutor. Always show your work step by step.",
-)
-print(f"[2] Updated to v{managed.agent_version}")
-result = agent.start("What is 123 + 456?", stream=True)
-
-# ── 3. Custom tool ──
-def handle_calculator(tool_name, tool_input):
-    expr = tool_input.get("expression", "0")
-    try:
-        val = eval(expr, {"__builtins__": {}})
-    except Exception:
-        val = "error"
-    print(f"  [Calculator: {expr} = {val}]")
-    return str(val)
-
-custom_managed = ManagedAgent(
-    provider="local",
-    config=LocalManagedConfig(
-        model="gpt-4o-mini",
-        system="You are an assistant with a calculator tool. Use it for math.",
-        name="CalcAgent",
-        tools=[
-            {
-                "type": "custom",
-                "name": "calculator",
-                "description": "Evaluate a math expression",
-                "input_schema": {
-                    "type": "object",
-                    "properties": {
-                        "expression": {"type": "string", "description": "Math expression"},
-                    },
-                    "required": ["expression"],
-                },
-            },
-        ],
-    ),
-    on_custom_tool=handle_calculator,
-)
-custom_agent = Agent(name="calc", backend=custom_managed)
-print("\n[3] Custom tool agent...")
-result = custom_agent.start("Use the calculator to compute 99 * 77", stream=True)
-
-# ── 4. Session resume ──
-print("\n[4] Session resume...")
-result = agent.start("Remember: my lucky number is 7", stream=True)
-ids = managed.save_ids()
-print(f"    Saved IDs: {ids}")
-
-resume_managed = ManagedAgent(provider="local", config=LocalManagedConfig(model="gpt-4o-mini"))
-resume_managed.restore_ids(ids)
-resume_agent = Agent(name="resumed", backend=resume_managed)
-result = resume_agent.start("What is my lucky number?", stream=True)
-print(f"    Resumed session: {resume_managed.session_id}")
-
-# ── 5. Interrupt ──
-print("\n[5] Interrupt demo...")
-int_managed = ManagedAgent(
-    provider="local",
-    config=LocalManagedConfig(model="gpt-4o-mini", system="Be helpful.", name="IntAgent"),
-)
-int_agent = Agent(name="interruptable", backend=int_managed)
-result = int_agent.start("Count from 1 to 5", stream=True)
-int_managed.interrupt()
-print("    [Interrupt sent]")
-
-# ── 6. Reset session ──
-print("\n[6] Reset session...")
-managed.reset_session()
-result = agent.start("What was my lucky number?", stream=True)
-print("    (new session — context lost, expected)")
-
-# ── Usage summary ──
-print("\n" + "=" * 50)
-info = managed.retrieve_session()
-print(f"Usage: in={info['usage']['input_tokens']}, out={info['usage']['output_tokens']}")
-print("=" * 50)
-print("Done!")

examples/python/managed-agents/provider/local_basic.py

@@ -1,45 +0,0 @@
-"""Local provider — basic managed agent with gpt-4o-mini.
-
-No external infrastructure needed. Runs the agent loop locally.
-"""
-from praisonai import Agent, ManagedAgent, LocalManagedConfig
-
-# Create a local managed agent (auto-detects local when no ANTHROPIC_API_KEY)
-managed = ManagedAgent(
-    provider="local",
-    config=LocalManagedConfig(
-        model="gpt-4o-mini",
-        system="You are a helpful assistant. Be concise.",
-        name="LocalAgent",
-    ),
-)
-
-agent = Agent(name="local-basic", backend=managed)
-
-# 1. Basic execution
-print("[1] Basic execution...")
-result = agent.start("What is the capital of France? One word.", stream=True)
-print(f"    Result: {result}")
-
-# 2. Agent metadata
-print(f"\n[2] Agent ID: {managed.agent_id}")
-print(f"    Version: {managed.agent_version}")
-print(f"    Env ID:  {managed.environment_id}")
-print(f"    Session: {managed.session_id}")
-
-# 3. Multi-turn (same session keeps context)
-print("\n[3] Multi-turn...")
-result = agent.start("What country is that city in?", stream=True)
-print(f"    Result: {result}")
-
-# 4. Usage tracking
-info = managed.retrieve_session()
-print(f"\n[4] Usage: in={info['usage']['input_tokens']}, out={info['usage']['output_tokens']}")
-
-# 5. List sessions
-sessions = managed.list_sessions()
-print(f"\n[5] Sessions: {len(sessions)}")
-for s in sessions:
-    print(f"    {s['id']} | {s['status']}")
-
-print("\nDone!")

examples/python/managed-agents/provider/modal_compute.py

@@ -1,66 +0,0 @@
-"""Modal compute provider — run agent tools inside a Modal cloud sandbox.
-
-Requires: modal CLI configured (modal token set) or MODAL_TOKEN_ID + MODAL_TOKEN_SECRET.
-Install:  pip install modal
-"""
-import asyncio
-from praisonai import Agent, ManagedAgent, LocalManagedConfig
-
-# ── 1. Create agent with Modal compute ──
-managed = ManagedAgent(
-    provider="local",
-    compute="modal",
-    config=LocalManagedConfig(
-        model="gpt-4o-mini",
-        system="You are a helpful coding assistant. Be concise.",
-        name="ModalAgent",
-    ),
-)
-
-agent = Agent(name="modal-agent", backend=managed)
-
-print("[1] Agent created with Modal compute")
-print(f"    Compute: {managed.compute_provider.provider_name}")
-
-# ── 2. Provision Modal sandbox ──
-print("\n[2] Provisioning Modal sandbox...")
-info = asyncio.run(managed.provision_compute(idle_timeout_s=120))
-print(f"    Instance: {info.instance_id}")
-print(f"    Status:   {info.status}")
-
-# ── 3. Execute commands in the sandbox ──
-print("\n[3] Executing commands in Modal...")
-result = asyncio.run(managed.execute_in_compute("python3 -c 'import sys; print(sys.version)'"))
-print(f"    Python version: {result['stdout'].strip()}")
-print(f"    Exit code: {result['exit_code']}")
-
-result = asyncio.run(managed.execute_in_compute("echo 'Hello from Modal!'"))
-print(f"    Echo: {result['stdout'].strip()}")
-
-# ── 4. Run a computation ──
-print("\n[4] Running computation...")
-result = asyncio.run(managed.execute_in_compute(
-    "python3 -c 'print(sum(range(1, 101)))'"
-))
-print(f"    Sum 1..100 = {result['stdout'].strip()}")
-
-# ── 5. Agent LLM execution ──
-print("\n[5] Agent LLM execution...")
-result = agent.start("What is 7 factorial? Just the number.", stream=True)
-print(f"    Result: {result}")
-
-# ── 6. Multi-turn ──
-print("\n[6] Multi-turn...")
-result = agent.start("Is that number even or odd?", stream=True)
-print(f"    Result: {result}")
-
-# ── 7. Usage ──
-info = managed.retrieve_session()
-print(f"\n[7] Usage: in={info['usage']['input_tokens']}, out={info['usage']['output_tokens']}")
-
-# ── 8. Shutdown ──
-print("\n[8] Shutting down Modal sandbox...")
-asyncio.run(managed.shutdown_compute())
-print("    Shutdown complete.")
-
-print("\nDone!")

examples/python/managed_agent_example.py

@@ -1,180 +0,0 @@
-#!/usr/bin/env python3
-"""
-Managed Agent Backend Example — Real end-to-end test.
-
-Demonstrates using Anthropic's Managed Agents API as an execution backend
-for a PraisonAI Agent. The agent runs in Anthropic's managed infrastructure.
-
-Prerequisites:
-    export ANTHROPIC_API_KEY="your-key"
-    pip install 'anthropic>=0.94.0' praisonaiagents praisonai
-
-Usage:
-    python examples/python/managed_agent_example.py
-"""
-
-import os
-import sys
-import logging
-
-# ── Setup logging so we can see what the integration does ──
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
-)
-
-# ── Guard: API key must be set ──
-if not os.getenv("ANTHROPIC_API_KEY"):
-    print("ERROR: ANTHROPIC_API_KEY not set. Export it first.")
-    sys.exit(1)
-
-
-def example_1_standalone():
-    """Example 1: Use ManagedAgentIntegration directly (no PraisonAI Agent)."""
-    print("\n" + "=" * 60)
-    print("Example 1: Standalone ManagedAgentIntegration")
-    print("=" * 60)
-
-    from praisonai.integrations.managed_agents import ManagedAgent, ManagedConfig
-
-    managed = ManagedAgent(
-        config=ManagedConfig(
-            model="claude-sonnet-4-6",
-            name="Standalone Test Agent",
-            system="You are a concise assistant. Answer in one sentence.",
-        ),
-    )
-
-    result = managed._execute_sync("What is 2 + 2? Answer in one sentence.")
-    print(f"\nResult: {result}")
-    print(f"Tokens: in={managed.total_input_tokens}, out={managed.total_output_tokens}")
-    assert result and len(result) > 0, "Expected non-empty response"
-    print("PASS: Standalone execution")
-    return result
-
-
-def example_2_with_agent():
-    """Example 2: Use ManagedAgentIntegration as a PraisonAI Agent backend."""
-    print("\n" + "=" * 60)
-    print("Example 2: PraisonAI Agent with managed backend")
-    print("=" * 60)
-
-    from praisonaiagents import Agent
-    from praisonai.integrations.managed_agents import ManagedAgent, ManagedConfig
-
-    managed = ManagedAgent(
-        config=ManagedConfig(
-            model="claude-sonnet-4-6",
-            name="PraisonAI Backend Agent",
-            system="You are a helpful coding assistant. Be concise.",
-        ),
-    )
-
-    agent = Agent(
-        name="managed-coder",
-        instructions="You are a helpful coding assistant.",
-        backend=managed,
-    )
-
-    result = agent.start("Write a Python one-liner that prints the first 10 Fibonacci numbers.")
-    print(f"\nResult: {result}")
-    assert result and len(result) > 0, "Expected non-empty response from agent.start()"
-    print("PASS: Agent backend execution")
-    return result
-
-
-def example_3_with_tools():
-    """Example 3: Managed agent with built-in tools (bash, file ops)."""
-    print("\n" + "=" * 60)
-    print("Example 3: Managed agent with built-in tools")
-    print("=" * 60)
-
-    from praisonaiagents import Agent
-    from praisonai.integrations.managed_agents import ManagedAgent, ManagedConfig
-
-    managed = ManagedAgent(
-        config=ManagedConfig(
-            model="claude-sonnet-4-6",
-            name="Tool-Using Agent",
-            system="You are a coding agent with access to bash and file tools.",
-            tools=[{"type": "agent_toolset_20260401"}],
-        ),
-    )
-
-    agent = Agent(
-        name="tool-agent",
-        instructions="You are a coding agent.",
-        backend=managed,
-    )
-
-    result = agent.start(
-        "Use bash to run: echo 'Hello from managed agent!' && python3 -c 'print(sum(range(10)))'"
-    )
-    print(f"\nResult: {result}")
-    assert result and len(result) > 0, "Expected non-empty response"
-    print("PASS: Tool-using agent execution")
-    return result
-
-
-def example_4_with_packages():
-    """Example 4: Managed agent with custom packages installed."""
-    print("\n" + "=" * 60)
-    print("Example 4: Managed agent with pip packages")
-    print("=" * 60)
-
-    from praisonaiagents import Agent
-    from praisonai.integrations.managed_agents import ManagedAgent, ManagedConfig
-
-    managed = ManagedAgent(
-        config=ManagedConfig(
-            model="claude-sonnet-4-6",
-            name="Data Agent",
-            system="You are a data analysis agent. Use pandas when helpful.",
-            packages={"pip": ["pandas", "numpy"]},
-        ),
-    )
-
-    agent = Agent(
-        name="data-agent",
-        instructions="You are a data analysis agent.",
-        backend=managed,
-    )
-
-    result = agent.start(
-        "Use bash to run: python3 -c \"import pandas as pd; print(pd.__version__)\""
-    )
-    print(f"\nResult: {result}")
-    assert result and len(result) > 0, "Expected non-empty response"
-    print("PASS: Package installation agent")
-    return result
-
-
-if __name__ == "__main__":
-    print("Managed Agent Backend — Real End-to-End Tests")
-    print("Using Anthropic Managed Agents API (beta)")
-
-    examples = [
-        ("standalone", example_1_standalone),
-        ("agent_backend", example_2_with_agent),
-        ("with_tools", example_3_with_tools),
-        ("with_packages", example_4_with_packages),
-    ]
-    results = {}
-
-    for name, fn in examples:
-        try:
-            results[name] = fn()
-        except Exception as e:
-            print(f"FAIL: {name}: {e}")
-            results[name] = None
-
-    # Summary
-    print("\n" + "=" * 60)
-    print("SUMMARY")
-    print("=" * 60)
-    passed = sum(1 for v in results.values() if v is not None)
-    total = len(results)
-    for name, result in results.items():
-        status = "PASS" if result else "FAIL"
-        print(f"  {status}: {name}")
-    print(f"\n{passed}/{total} examples passed")

examples/python/stateful/06_advanced_state_operations.py

@@ -308,7 +308,7 @@ def manage_state_versioning() -> Dict[str, Any]:
     versions[version_id] = {
         "config": current_config.copy(),
         "timestamp": datetime.now().isoformat(),
-        "hash": hashlib.sha256(json.dumps(current_config, sort_keys=True).encode()).hexdigest()
+        "hash": hashlib.md5(json.dumps(current_config, sort_keys=True).encode()).hexdigest()
     }
     
     # Make changes

examples/python/tools/cli/app.py

@@ -1,6 +1,5 @@
 from praisonaiagents import Agent, Task, AgentTeam
 import subprocess
-import shlex
 import os
 
 def run_terminal_command(command: str):
@@ -8,9 +7,7 @@ def run_terminal_command(command: str):
     Run a terminal command and return its output.
     """
     try:
-        # Use shell=False with shlex.split for safer execution
-        args = shlex.split(command)
-        result = subprocess.run(args, shell=False, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+        result = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
         print(f"Command output: {result}")
         return {"stdout": result.stdout, "stderr": result.stderr}
     except subprocess.CalledProcessError as e:

examples/python/tools/e2b/single_agent.py

@@ -2,7 +2,6 @@ from praisonaiagents import Agent, Task, AgentTeam, error_logs
 import json, os
 from e2b_code_interpreter import Sandbox
 import subprocess
-import shlex
 
 def code_interpret(code: str):
     """
@@ -41,9 +40,7 @@ def run_terminal_command(command: str):
     Run a terminal command and return its output.
     """
     try:
-        # Use shell=False with shlex.split for safer execution
-        args = shlex.split(command)
-        result = subprocess.run(args, shell=False, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+        result = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
         print(f"Command output: {result}")
         return {"stdout": result.stdout, "stderr": result.stderr}
     except subprocess.CalledProcessError as e:

examples/terminal_bench/README.md

@@ -1,140 +0,0 @@
-# Terminal-Bench 2.0 Integration with PraisonAI
-
-This directory contains examples for integrating PraisonAI Agents with **Terminal-Bench 2.0** via the **Harbor framework** for AI agent benchmarking.
-
-## Overview
-
-[Terminal-Bench 2.0](https://tbench.ai) is a Stanford/Laude Institute benchmark that has become the gold standard for evaluating AI coding agents in real terminal environments. The [Harbor framework](https://harborframework.com) is the official evaluation harness that abstracts container lifecycle, parallelization, and cloud providers.
-
-## Integration Types
-
-### 1. External Agent (Direct Agent Class)
-- **File**: `praisonai_external_agent.py`
-- **Purpose**: External agent that uses direct `Agent()` class instantiation
-- **Usage**: Run with `--agent-import-path` flag
-- **Approach**: Uses `praisonaiagents.Agent` directly with `execute_command` tool
-
-### 2. Wrapper Agent (CLI-Based)
-- **File**: `praisonai_wrapper_agent.py`
-- **Purpose**: Uses `praisonai "TASK"` CLI pattern instead of direct Agent class
-- **Usage**: Run with `--agent-import-path examples.terminal_bench.praisonai_wrapper_agent:PraisonAIWrapperAgent`
-- **Approach**: Installs `praisonai` CLI inside container and runs tasks via subprocess
-- **Best for**: Users who prefer CLI-style interface matching standard `praisonai` usage
-
-### 3. Installed Agent (Production)
-- **File**: `praisonai_installed_agent.py` 
-- **Purpose**: Production-ready agent installed inside Harbor containers
-- **Usage**: Integrates with Harbor's leaderboard system
-
-## Quick Start
-
-### Prerequisites
-
-```bash
-# Install Harbor framework
-pip install harbor
-
-# Install PraisonAI with shell tools
-pip install praisonaiagents[tools]
-```
-
-### Running External Agent (Direct Agent Class)
-
-```bash
-# Test with oracle agent first
-harbor run -d terminal-bench/terminal-bench-2 -a oracle
-
-# Run PraisonAI external agent (uses direct Agent() class)
-harbor run -d terminal-bench/terminal-bench-2 \
-  --agent-import-path examples.terminal_bench.praisonai_external_agent:PraisonAIExternalAgent \
-  --model openai/gpt-4o \
-  --ae OPENAI_API_KEY=$OPENAI_API_KEY \
-  -n 4
-```
-
-### Running Wrapper Agent (CLI-Based)
-
-```bash
-# Run PraisonAI wrapper agent (uses `praisonai "TASK"` CLI pattern)
-harbor run -d terminal-bench/terminal-bench-2 \
-  --agent-import-path examples.terminal_bench.praisonai_wrapper_agent:PraisonAIWrapperAgent \
-  --model openai/gpt-4o \
-  --ae OPENAI_API_KEY=$OPENAI_API_KEY \
-  -n 4
-```
-
-### Running on Cloud (Daytona/E2B/Modal)
-
-```bash
-harbor run -d terminal-bench/terminal-bench-2 \
-  --agent-import-path examples.terminal_bench.praisonai_external_agent:PraisonAIExternalAgent \
-  --model openai/gpt-4o \
-  --env daytona -n 32 \
-  --ae OPENAI_API_KEY=$OPENAI_API_KEY
-```
-
-## Key Features
-
-- **Shell Execution Bridge**: Wraps Harbor's `BaseEnvironment.exec()` as PraisonAI tool
-- **Auto-Approval**: Bypasses `@require_approval` for container-isolated execution
-- **Token Tracking**: Populates Harbor's `AgentContext` with usage metrics
-- **API Key Forwarding**: Supports environment variable injection
-- **Multi-Agent Support**: Can run `AgentTeam` and `AgentFlow` workflows
-
-## Architecture
-
-```text
-┌─────────────────────────────────────────┐
-│            Harbor Framework             │
-│   (Terminal-Bench 2.0 Evaluation)      │
-├─────────────────────────────────────────┤
-│  BaseEnvironment.exec() ←→ bash_tool    │
-├─────────────────────────────────────────┤
-│         PraisonAI Agent                 │
-│  • Uses execute_command tool            │
-│  • Auto-approval for container safety  │
-│  • Token tracking and metrics          │
-└─────────────────────────────────────────┘
-```
-
-## Files
-
-- `README.md` - This documentation
-- `praisonai_external_agent.py` - External agent (direct Agent class)
-- `praisonai_wrapper_agent.py` - Wrapper agent (CLI-based approach)
-- `praisonai_installed_agent.py` - Installed agent implementation  
-- `multi_agent_example.py` - Multi-agent team example
-- `job.yaml` - Example Harbor job configuration
-- `test_basic.py` - Basic smoke tests
-- `test_integration.py` - Integration tests
-- `test_real_agentic.py` - Real agentic tests
-
-## Terminal-Bench 2.0 Tasks
-
-The benchmark includes 89 carefully curated tasks covering:
-- Compiling code
-- Training models  
-- Setting up servers
-- System administration
-- File manipulation
-- Package management
-
-Each task provides:
-- English instruction
-- Docker container environment
-- Test script (writes reward to `/logs/verifier/reward.txt`)
-- Reference oracle solution
-
-## Contributing
-
-1. Test changes with oracle agent first: `harbor run -d terminal-bench/terminal-bench-2 -a oracle`
-2. Run real agentic tests to ensure end-to-end functionality
-3. Follow PraisonAI's AGENTS.md architecture guidelines
-4. Add both unit tests and integration tests
-
-## Resources
-
-- [Terminal-Bench 2.0 Announcement](https://www.tbench.ai/news/announcement-2-0)
-- [Harbor Framework Docs](https://www.harborframework.com/docs)
-- [Terminal-Bench Leaderboard](https://tbench.ai/leaderboard)
-- [Harbor GitHub Repo](https://github.com/laude-institute/harbor)

examples/terminal_bench/job.yaml

@@ -1,83 +0,0 @@
-# Harbor Job Configuration for PraisonAI on Terminal-Bench 2.0
-# 
-# Usage:
-#   harbor run -c examples/terminal_bench/job.yaml
-#
-# This configuration runs PraisonAI external agent on Terminal-Bench 2.0 tasks
-# with 8 concurrent trials using GPT-4o.
-
-# Dataset: Terminal-Bench 2.0 (89 carefully curated terminal tasks)
-dataset: terminal-bench/terminal-bench-2
-
-# Agent configuration
-agent:
-  # Use import path for external agent
-  import_path: examples.terminal_bench.praisonai_external_agent:PraisonAIExternalAgent
-  
-  # Model configuration
-  model_name: openai/gpt-4o
-  
-  # Environment variables (API keys)
-  env:
-    OPENAI_API_KEY: "${OPENAI_API_KEY}"
-    # Optional: Add other API keys if using different models
-    # ANTHROPIC_API_KEY: "${ANTHROPIC_API_KEY}"
-    # GEMINI_API_KEY: "${GEMINI_API_KEY}"
-
-# Execution configuration
-n_concurrent: 8        # Run 8 trials in parallel
-n_attempts: 1          # Single attempt per task (benchmark standard)
-
-# Optional: Environment configuration for cloud execution
-# environment:
-#   provider: daytona    # Options: local, daytona, e2b, modal, runloop, gke
-#   n_concurrent: 32     # Higher concurrency on cloud
-
-# Optional: Filter to specific tasks for testing
-# task_filter:
-#   task_names: ["compile_simple_c", "install_python_package", "create_directory"]
-
-# Optional: Advanced configuration
-# timeout_sec: 600       # 10 minute timeout per task
-# save_logs: true        # Save execution logs
-# save_artifacts: true   # Save container artifacts
-
----
-
-# Alternative configuration using wrapper agent (CLI-based approach)
-# Uses `praisonai "TASK"` pattern instead of direct Agent class
-# Uncomment to use the wrapper approach:
-
-# dataset: terminal-bench/terminal-bench-2
-#
-# agent:
-#   import_path: examples.terminal_bench.praisonai_wrapper_agent:PraisonAIWrapperAgent
-#   model_name: openai/gpt-4o
-#
-# n_concurrent: 8
-# n_attempts: 1
-#
-# env:
-#   OPENAI_API_KEY: "${OPENAI_API_KEY}"
-
----
-
-# Alternative configuration using installed agent (requires Harbor integration)
-# Uncomment this section once PraisonAI is integrated into Harbor's codebase
-
-# dataset: terminal-bench/terminal-bench-2
-# 
-# agent:
-#   name: praisonai
-#   model_name: openai/gpt-4o
-#   flags:
-#     max_turns: 30
-#     verbose: false
-#     memory: false
-#     auto_approval: true
-# 
-# n_concurrent: 8
-# n_attempts: 1
-# 
-# env:
-#   OPENAI_API_KEY: "${OPENAI_API_KEY}"

examples/terminal_bench/job_test_5.yaml

@@ -1,22 +0,0 @@
-# Harbor Job Configuration for PraisonAI - 5 Task Test
-# Run with: harbor run -c examples/terminal_bench/job_test_5.yaml -n 1
-
-dataset: terminal-bench/terminal-bench-2
-
-agent:
-  import_path: examples.terminal_bench.praisonai_external_agent:PraisonAIExternalAgent
-  model_name: openai/gpt-4o-mini
-  env:
-    OPENAI_API_KEY: "${OPENAI_API_KEY}"
-
-n_concurrent: 1
-n_attempts: 1
-
-# Filter to 5 specific simple tasks
-task_filter:
-  task_names:
-    - "hello-world"
-    - "fix-permissions"
-    - "create-bucket"
-    - "csv-to-parquet"
-    - "simple-web-scraper"

examples/terminal_bench/job_test_5_wrapper.yaml

@@ -1,22 +0,0 @@
-# Harbor Job Configuration for PraisonAI Wrapper - 5 Task Test
-# Run with: harbor run -c examples/terminal_bench/job_test_5_wrapper.yaml -n 1
-
-dataset: terminal-bench/terminal-bench-2
-
-agent:
-  import_path: examples.terminal_bench.praisonai_wrapper_agent:PraisonAIWrapperAgent
-  model_name: openai/gpt-4o-mini
-  env:
-    OPENAI_API_KEY: "${OPENAI_API_KEY}"
-
-n_concurrent: 1
-n_attempts: 1
-
-# Filter to 5 specific simple tasks
-task_filter:
-  task_names:
-    - "hello-world"
-    - "fix-permissions"
-    - "create-bucket"
-    - "csv-to-parquet"
-    - "simple-web-scraper"

examples/terminal_bench/multi_agent_example.py

@@ -1,331 +0,0 @@
-"""
-Multi-Agent PraisonAI Integration for Terminal-Bench 2.0
-
-This example shows how to use PraisonAI's AgentTeam and AgentFlow
-capabilities with Terminal-Bench 2.0 for complex multi-step tasks.
-
-The multi-agent approach is useful for Terminal-Bench tasks that benefit from:
-- Task planning and decomposition
-- Specialized roles (planner, executor, verifier)
-- Error correction and refinement
-- Complex workflows with multiple phases
-
-Usage:
-    harbor run -d terminal-bench/terminal-bench-2 \
-        --agent-import-path examples.terminal_bench.multi_agent_example:MultiAgentPraisonAI \
-        --model openai/gpt-4o
-"""
-
-import asyncio
-from typing import Any, Dict, Optional
-
-try:
-    from harbor.agents.base import BaseAgent
-    from harbor.environments.base import BaseEnvironment
-    from harbor.models.agent.context import AgentContext
-except ImportError as e:
-    raise ImportError(f"Harbor framework required: {e}") from e
-
-try:
-    from praisonaiagents import Agent, AgentTeam, Task
-    from praisonaiagents.approval import get_approval_registry, AutoApproveBackend
-except ImportError as e:
-    raise ImportError(f"PraisonAI required: {e}") from e
-
-
-class MultiAgentPraisonAI(BaseAgent):
-    """Multi-agent PraisonAI system for complex Terminal-Bench tasks."""
-    
-    @staticmethod
-    def name() -> str:
-        return "praisonai-multi"
-
-    def version(self) -> str | None:
-        try:
-            import praisonaiagents
-            return getattr(praisonaiagents, "__version__", None)
-        except ImportError:
-            return None
-
-    async def setup(self, environment: BaseEnvironment) -> None:
-        """Setup phase - no container setup needed for external agent."""
-        pass
-
-    async def run(
-        self,
-        instruction: str,
-        environment: BaseEnvironment,
-        context: AgentContext,
-    ) -> None:
-        """
-        Run multi-agent PraisonAI team on the Terminal-Bench task.
-        
-        Uses a three-agent approach:
-        1. Planner: Analyzes task and creates execution plan
-        2. Executor: Implements the plan using shell commands  
-        3. Verifier: Tests and validates the solution
-        """
-        
-        # Set auto-approval for container safety
-        registry = get_approval_registry()
-        original_backend = registry.get_backend()
-        registry.set_backend(AutoApproveBackend(), agent_name="multi-agent-planner")
-        
-        try:
-            # Create bash tool that wraps Harbor environment
-            async def bash_tool(command: str) -> str:
-                """Execute bash command in Harbor environment."""
-                if not command.strip():
-                    return "Error: Empty command"
-                    
-                try:
-                    result = await environment.exec(command=command, timeout_sec=30)
-                    output_parts = []
-                    if result.stdout:
-                        output_parts.append(result.stdout.strip())
-                    if result.stderr:
-                        output_parts.append(f"[stderr]: {result.stderr.strip()}")
-                    if result.return_code != 0:
-                        output_parts.append(f"[exit_code]: {result.return_code}")
-                    return "\n".join(output_parts) if output_parts else "(no output)"
-                except Exception as e:
-                    return f"Error: {str(e)}"
-
-            # Create specialized agents
-            planner = Agent(
-                name="planner",
-                instructions=(
-                    "You are an expert task planner for terminal/coding tasks. "
-                    "Analyze the given instruction and create a detailed, step-by-step plan. "
-                    "Break down complex tasks into smaller, manageable steps. "
-                    "Consider potential issues and edge cases. "
-                    "Output your plan as a numbered list of specific actions."
-                ),
-                llm=self.model_name or "openai/gpt-4o",
-            )
-            
-            executor = Agent(
-                name="executor", 
-                instructions=(
-                    "You are an expert terminal executor. "
-                    "Follow the provided plan step-by-step using bash commands. "
-                    "Use the bash_tool to execute commands safely. "
-                    "Be precise and check each step before proceeding. "
-                    "If a step fails, try alternative approaches."
-                ),
-                tools=[bash_tool],
-                llm=self.model_name or "openai/gpt-4o", 
-            )
-            
-            verifier = Agent(
-                name="verifier",
-                instructions=(
-                    "You are a solution verifier and tester. "
-                    "Test the completed solution to ensure it works correctly. "
-                    "Run appropriate tests and checks using bash commands. "
-                    "Report whether the solution meets the requirements. "
-                    "If issues are found, suggest specific fixes."
-                ),
-                tools=[bash_tool],
-                llm=self.model_name or "openai/gpt-4o",
-            )
-
-            print(f"🚀 Multi-Agent PraisonAI starting: {instruction[:100]}...")
-            
-            # Phase 1: Planning
-            print("📋 Phase 1: Task Planning")
-            plan = await planner.astart(f"Create a detailed plan for: {instruction}")
-            print(f"Plan created: {len(plan.split('.')) if plan else 0} steps")
-            
-            # Phase 2: Execution
-            print("⚡ Phase 2: Task Execution") 
-            execution_prompt = f"Execute this plan step by step:\n\nOriginal task: {instruction}\n\nPlan:\n{plan}"
-            execution_result = await executor.astart(execution_prompt)
-            print("Execution completed")
-            
-            # Phase 3: Verification
-            print("✅ Phase 3: Solution Verification")
-            verification_prompt = f"Verify this solution works correctly:\n\nOriginal task: {instruction}\n\nSolution: {execution_result}\n\nRun tests to confirm it works."
-            verification_result = await verifier.astart(verification_prompt)
-            print("Verification completed")
-            
-            # Combine results
-            final_result = {
-                "plan": plan,
-                "execution": execution_result,
-                "verification": verification_result,
-            }
-            
-            print("✅ Multi-Agent PraisonAI completed task")
-            
-            # Populate context
-            self._populate_context([planner, executor, verifier], context, final_result)
-            
-        except Exception as e:
-            print(f"❌ Multi-Agent PraisonAI failed: {str(e)}")
-            context.metadata = {"error": str(e)}
-            raise
-        finally:
-            # Restore original approval backend to avoid global state pollution
-            if original_backend:
-                registry.set_backend(original_backend)
-            else:
-                registry.remove_backend(agent_name="multi-agent-planner")
-
-    def _populate_context(self, agents: list, context: AgentContext, result: Dict[str, Any]) -> None:
-        """Populate Harbor context with multi-agent metrics."""
-        try:
-            # Aggregate token usage from all agents
-            total_input_tokens = 0
-            total_output_tokens = 0
-            total_cost = 0.0
-            
-            for agent in agents:
-                # Use agent's actual metrics properties
-                total_input_tokens += getattr(agent, '_total_tokens_in', 0)
-                total_output_tokens += getattr(agent, '_total_tokens_out', 0)
-                total_cost += agent.total_cost or 0.0
-            
-            context.n_input_tokens = total_input_tokens if total_input_tokens > 0 else None
-            context.n_output_tokens = total_output_tokens if total_output_tokens > 0 else None
-            context.cost_usd = total_cost if total_cost > 0 else None
-            
-            context.metadata = {
-                "framework": "praisonai-multi",
-                "agent_type": "multi-agent-team", 
-                "agents": [agent.name for agent in agents],
-                "model": self.model_name or "openai/gpt-4o",
-                "phases": ["planning", "execution", "verification"],
-                "tools_used": ["bash_tool"],
-                "result_summary": str(result.get("verification", ""))[:200],
-                "version": self.version(),
-            }
-            
-        except Exception as e:
-            context.metadata = {"context_error": str(e)}
-
-
-# Alternative implementation using AgentTeam (more structured)
-class AgentTeamPraisonAI(BaseAgent):
-    """AgentTeam-based implementation for Terminal-Bench tasks."""
-    
-    @staticmethod
-    def name() -> str:
-        return "praisonai-team"
-        
-    def version(self) -> str | None:
-        try:
-            import praisonaiagents
-            return getattr(praisonaiagents, "__version__", None)
-        except ImportError:
-            return None
-
-    async def setup(self, environment: BaseEnvironment) -> None:
-        pass
-
-    async def run(
-        self, 
-        instruction: str,
-        environment: BaseEnvironment,
-        context: AgentContext,
-    ) -> None:
-        """Run structured AgentTeam workflow."""
-        
-        registry = get_approval_registry()
-        original_backend = registry.get_backend()
-        registry.set_backend(AutoApproveBackend(), agent_name="agent-team")
-        
-        try:
-            # Create bash tool
-            async def bash_tool(command: str) -> str:
-                result = await environment.exec(command=command, timeout_sec=30)
-                output_parts = []
-                if result.stdout:
-                    output_parts.append(result.stdout.strip())
-                if result.stderr:
-                    output_parts.append(f"[stderr]: {result.stderr.strip()}")
-                if result.return_code != 0:
-                    output_parts.append(f"[exit_code]: {result.return_code}")
-                return "\n".join(output_parts) if output_parts else "(no output)"
-
-            # Create agents
-            planner = Agent(
-                name="planner",
-                instructions="Create detailed execution plans for terminal tasks",
-                llm=self.model_name or "openai/gpt-4o"
-            )
-            
-            executor = Agent(
-                name="executor",
-                instructions="Execute terminal commands based on plans",
-                tools=[bash_tool],
-                llm=self.model_name or "openai/gpt-4o"
-            )
-            
-            # Create tasks
-            plan_task = Task(
-                name="plan",
-                description=f"Create plan for: {instruction}",
-                agent=planner
-            )
-            
-            execute_task = Task(
-                name="execute", 
-                description=f"Execute plan for: {instruction}",
-                agent=executor,
-                dependencies=[plan_task]  # Execute after planning
-            )
-            
-            # Create and run team
-            team = AgentTeam(
-                agents=[planner, executor],
-                tasks=[plan_task, execute_task],
-                process="sequential"
-            )
-            
-            print(f"🚀 AgentTeam starting: {instruction[:100]}...")
-            result = await team.astart(instruction)
-            print("✅ AgentTeam completed")
-            
-            # Populate context
-            context.metadata = {
-                "framework": "praisonai-team",
-                "workflow": "sequential",
-                "agents_count": len(team.agents),
-                "tasks_count": len(team.tasks),
-                "result": str(result)[:200] if result else None,
-            }
-            
-        finally:
-            # Restore original approval backend to avoid global state pollution
-            if original_backend:
-                registry.set_backend(original_backend)
-            else:
-                registry.remove_backend(agent_name="agent-team")
-
-
-if __name__ == "__main__":
-    print("Multi-Agent PraisonAI for Terminal-Bench 2.0")
-    print("=" * 50)
-    print()
-    print("Available agent implementations:")
-    print("1. MultiAgentPraisonAI - Custom multi-agent workflow")
-    print("2. AgentTeamPraisonAI - Structured AgentTeam workflow")
-    print()
-    print("Usage examples:")
-    print()
-    print("# Multi-agent custom workflow")
-    print("harbor run -d terminal-bench/terminal-bench-2 \\")
-    print("  --agent-import-path examples.terminal_bench.multi_agent_example:MultiAgentPraisonAI \\")
-    print("  --model openai/gpt-4o")
-    print()
-    print("# AgentTeam structured workflow")  
-    print("harbor run -d terminal-bench/terminal-bench-2 \\")
-    print("  --agent-import-path examples.terminal_bench.multi_agent_example:AgentTeamPraisonAI \\")
-    print("  --model openai/gpt-4o")
-    print()
-    print("Benefits of multi-agent approach:")
-    print("- Task decomposition and planning")
-    print("- Specialized roles and expertise")
-    print("- Error detection and correction")
-    print("- Higher success rates on complex tasks")

examples/terminal_bench/praisonai_external_agent.py

@@ -1,222 +0,0 @@
-"""
-PraisonAI External Agent for Terminal-Bench 2.0 (Harbor)
-
-An external agent that bridges PraisonAI's Agent class with Harbor's
-BaseEnvironment interface. The agent uses Harbor's exec() as a bash tool.
-
-Usage:
-    harbor run -d terminal-bench/terminal-bench-2 \
-        --agent-import-path examples.terminal_bench.praisonai_external_agent:PraisonAIExternalAgent \
-        --model openai/gpt-4o \
-        --ae OPENAI_API_KEY=$OPENAI_API_KEY
-
-Architecture:
-    Harbor Container ←→ BaseEnvironment.exec() ←→ bash_tool ←→ PraisonAI Agent
-
-Dependencies:
-    pip install harbor praisonaiagents
-"""
-
-import os
-from typing import Any
-
-try:
-    from harbor.agents.base import BaseAgent
-    from harbor.environments.base import BaseEnvironment  
-    from harbor.models.agent.context import AgentContext
-except ImportError as e:
-    raise ImportError(
-        f"Harbor framework not installed: {e}\n"
-        "Install with: pip install harbor"
-    ) from e
-
-try:
-    from praisonaiagents import Agent
-    from praisonaiagents.approval import get_approval_registry, AutoApproveBackend
-except ImportError as e:
-    raise ImportError(
-        f"PraisonAI not installed: {e}\n"
-        "Install with: pip install praisonaiagents"
-    ) from e
-
-
-class PraisonAIExternalAgent(BaseAgent):
-    """External PraisonAI agent that drives a Harbor container environment."""
-
-    @staticmethod
-    def name() -> str:
-        return "praisonai"
-
-    def version(self) -> str | None:
-        try:
-            import praisonaiagents
-            return (
-                getattr(praisonaiagents, "__version__", None)
-                or getattr(praisonaiagents, "version", None)
-                or "unknown"
-            )
-        except ImportError:
-            return None
-
-    async def setup(self, environment: BaseEnvironment) -> None:
-        """Setup phase - external agent needs no container setup."""
-        pass
-
-    async def run(
-        self,
-        instruction: str,
-        environment: BaseEnvironment,
-        context: AgentContext,
-    ) -> None:
-        """
-        Run the PraisonAI agent on the given instruction using Harbor's environment.
-        
-        This method bridges Harbor's BaseEnvironment.exec() to PraisonAI's tool system.
-        """
-        
-        # Inject API keys from Harbor's --ae env vars into host os.environ
-        # so litellm can pick them up (--ae only sets them inside Docker, not the host)
-        agent_env = getattr(context, 'env', {}) or {}
-        for key, val in agent_env.items():
-            if key not in os.environ and val:
-                os.environ[key] = val
-                print(f"[ENV] Set {key} from Harbor agent env")
-
-        # Set auto-approval for container-isolated execution  
-        # Harbor's container provides isolation, so we can safely auto-approve shell commands
-        registry = get_approval_registry()
-        original_backend = registry.get_backend()
-        registry.set_backend(AutoApproveBackend(), agent_name="terminal-agent")
-        
-        try:
-            # Create bash tool that wraps Harbor's environment.exec()
-            async def bash_tool(command: str) -> str:
-                """Execute a bash command in the Harbor sandboxed environment."""
-                if not command.strip():
-                    return "Error: Empty command provided"
-                
-                print(f"[CMD] {command[:200]}")
-                try:
-                    # Execute command in Harbor's container
-                    result = await environment.exec(command=command, timeout_sec=300)
-                    
-                    # Format output similar to PraisonAI's execute_command tool
-                    output_parts = []
-                    if result.stdout:
-                        output_parts.append(result.stdout.strip())
-                    if result.stderr:
-                        output_parts.append(f"[stderr]: {result.stderr.strip()}")
-                    if result.return_code != 0:
-                        output_parts.append(f"[exit_code]: {result.return_code}")
-                        
-                    output = "\n".join(output_parts) if output_parts else "(no output)"
-                    print(f"[OUT] {output[:300]}")
-                    return output
-                    
-                except Exception as e:
-                    print(f"[ERR] {str(e)}")
-                    return f"Error executing command: {str(e)}"
-
-            # Create PraisonAI agent with the bash tool
-            agent = Agent(
-                name="terminal-agent",
-                instructions=(
-                    "You are an expert terminal agent. You solve coding, debugging, data analysis, security, "
-                    "and system administration tasks using the bash_tool to run shell commands."
-                    "\n\nCRITICAL RULES:"
-                    "\n1. START by exploring: ls /app/ && cat /app/*.py /app/*.sh /app/*.txt 2>/dev/null | head -200"
-                    "   Read ALL task files to understand what is needed before doing anything."
-                    "\n2. Find and run the test IMMEDIATELY to see what is expected:"
-                    "   find /app -name 'test_*.py' | head -3 && cd /app && python3 -m pytest -v 2>&1 | tail -40 || true"
-                    "   The test error trace tells you EXACTLY what output/behavior is required."
-                    "\n3. ITERATE: implement a solution, run the test, read the error, refine. Repeat until it passes."
-                    "   Never give up after one attempt — try multiple approaches if needed."
-                    "\n4. For writing files with special characters use Python:"
-                    "   python3 -c \"with open('/app/file','w') as f: f.write('content')\""
-                    "   or heredoc: cat > /app/file << 'EOF'\\n...content...\\nEOF"
-                    "\n5. Read ALL error messages — they tell you exactly what to fix next."
-                    "\n6. NEVER just describe your plan. ALWAYS immediately run bash_tool commands."
-                    "   Keep calling bash_tool until the test PASSES or you have exhausted all approaches."
-                ),
-                tools=[bash_tool],
-                llm=self.model_name or "openai/gpt-4o",
-            )
-
-            # Execute the agent with outer loop to handle premature stopping
-            print(f"🚀 PraisonAI Agent starting task: {instruction[:100]}...")
-            result = await agent.achat(instruction)
-            for _iter in range(19):
-                result_str = str(result)
-                # Stop if test passed
-                if any(sig in result_str.lower() for sig in [
-                    " passed", "passed ", "test passed", "all tests pass", "1 passed"
-                ]):
-                    break
-                result = await agent.achat(
-                    "The task is NOT complete yet — keep working. Run bash_tool commands now. "
-                    "If you haven't already: (1) read all files in /app/, "
-                    "(2) run the test to see the exact failure, "
-                    "(3) implement a fix, (4) run the test again. "
-                    "Repeat until the test passes. What is your next bash_tool command?"
-                )
-            print(f"✅ PraisonAI Agent completed task")
-            
-            # Populate Harbor context with metadata
-            self._populate_context(agent, context, result)
-            
-        except Exception as e:
-            print(f"❌ PraisonAI Agent failed: {str(e)}")
-            context.metadata = {"error": str(e)}
-            raise
-        finally:
-            # Restore original approval backend to avoid global state pollution
-            if original_backend:
-                registry.set_backend(original_backend)
-            else:
-                registry.remove_backend(agent_name="terminal-agent")
-
-    def _populate_context(self, agent: Agent, context: AgentContext, result: Any) -> None:
-        """
-        Populate Harbor's AgentContext with metrics from PraisonAI agent execution.
-        
-        Harbor tracks: n_input_tokens, n_output_tokens, cost_usd, metadata
-        """
-        try:
-            # Extract token usage and cost from agent
-            try:
-                summary = agent.cost_summary() if callable(getattr(agent, 'cost_summary', None)) else None
-                if isinstance(summary, dict):
-                    context.n_input_tokens = summary.get('tokens_in')
-                    context.n_output_tokens = summary.get('tokens_out')
-                    context.cost_usd = summary.get('cost')
-                else:
-                    context.n_input_tokens = getattr(agent, '_total_tokens_in', 0)
-                    context.n_output_tokens = getattr(agent, '_total_tokens_out', 0)
-                    context.cost_usd = getattr(agent, 'total_cost', None)
-            except Exception:
-                pass
-                
-            # Store result summary and agent info
-            context.metadata = {
-                "agent_name": agent.name,
-                "model": getattr(agent, 'llm', 'unknown'),
-                "final_response": str(result)[:500] if result else None,
-                "tools_used": ["bash_tool"],
-                "framework": "praisonai",
-                "version": self.version(),
-            }
-            
-        except Exception as e:
-            # Don't fail the whole run if context population fails
-            context.metadata = {"context_error": str(e)}
-
-
-# Example usage for testing
-if __name__ == "__main__":
-    print("PraisonAI External Agent for Terminal-Bench 2.0")
-    print("Usage: harbor run -d terminal-bench/terminal-bench-2 \\")
-    print("  --agent-import-path examples.terminal_bench.praisonai_external_agent:PraisonAIExternalAgent \\")
-    print("  --model openai/gpt-4o")
-    print()
-    print("Dependencies:")
-    print("  pip install harbor praisonaiagents")

examples/terminal_bench/praisonai_installed_agent.py

@@ -1,340 +0,0 @@
-"""
-PraisonAI Installed Agent for Terminal-Bench 2.0 (Harbor)
-
-Production-ready agent that installs PraisonAI inside Harbor's container
-and runs it headlessly. This follows the same pattern as other agents
-in Harbor (codex, gemini-cli, claude-code).
-
-Usage:
-    # First, this would need to be integrated into Harbor's codebase as:
-    # src/harbor/agents/installed/praisonai.py
-    
-    harbor run -d terminal-bench/terminal-bench-2 -a praisonai --model openai/gpt-4o
-
-Architecture:
-    Harbor Container → praisonaiagents installed inside → execute_command tool
-
-For now, this serves as a reference implementation that could be contributed
-to the Harbor project via PR.
-"""
-
-import os
-import shlex
-import json
-import asyncio
-from pathlib import Path
-from typing import Dict, Optional
-
-try:
-    # These imports would work if this file was in Harbor's codebase
-    from harbor.agents.installed.base import BaseInstalledAgent, with_prompt_template, CliFlag
-    from harbor.environments.base import BaseEnvironment
-    from harbor.models.agent.context import AgentContext
-    from harbor.models.trial.paths import EnvironmentPaths
-except ImportError:
-    # Fallback for development/testing outside Harbor
-    print("Note: This is a reference implementation for Harbor integration")
-    print("To actually use this, it needs to be integrated into Harbor's codebase")
-    
-    # Mock classes for development
-    class BaseInstalledAgent:
-        pass
-    
-    def with_prompt_template(func):
-        return func
-    
-    class CliFlag:
-        def __init__(self, name, cli, type, default):
-            pass
-    
-    class BaseEnvironment:
-        async def exec(self, **kwargs):
-            pass
-    
-    class AgentContext:
-        pass
-
-
-class PraisonAIInstalledAgent(BaseInstalledAgent):
-    """
-    PraisonAI agent installed inside Harbor's container environment.
-    
-    This is a production-ready integration that follows Harbor's established
-    patterns for installed agents like Codex CLI, Gemini CLI, etc.
-    """
-
-    # ATIF trajectory support - would be implemented once PraisonAI has trajectory format
-    SUPPORTS_ATIF: bool = False
-
-    # Command-line configuration flags
-    CLI_FLAGS = [
-        CliFlag("max_turns", cli="--max-turns", type="int", default=30),
-        CliFlag("verbose", cli="--verbose", type="bool", default=False),
-        CliFlag("memory", cli="--memory", type="bool", default=False),
-        CliFlag("auto_approval", cli="--auto-approval", type="bool", default=True),
-    ]
-
-    @staticmethod
-    def name() -> str:
-        return "praisonai"
-
-    def get_version_command(self) -> str | None:
-        """Command to get PraisonAI version inside the container."""
-        return "python3 -c 'import praisonaiagents; print(praisonaiagents.__version__)'"
-
-    async def install(self, environment: BaseEnvironment) -> None:
-        """
-        Install PraisonAI and dependencies inside the Harbor container.
-        
-        This runs as root first for system packages, then as agent user
-        for Python packages.
-        """
-        # System packages (as root)
-        await self.exec_as_root(
-            environment,
-            command="apt-get update && apt-get install -y python3 python3-pip curl git",
-            env={"DEBIAN_FRONTEND": "noninteractive"},
-        )
-        
-        # Install PraisonAI (as agent user)
-        version_spec = f"=={self._version}" if self._version else ""
-        install_cmd = f"pip install praisonaiagents{version_spec} --quiet"
-        
-        await self.exec_as_agent(
-            environment,
-            command=(
-                f"{install_cmd} && "
-                "python3 -c 'import praisonaiagents; print(\"✅ praisonaiagents installed:\", praisonaiagents.__version__)'"
-            ),
-        )
-        
-        # Create headless runner script
-        runner_script = self._build_runner_script()
-        await self.exec_as_agent(
-            environment,
-            command=f"cat > /tmp/praisonai_runner.py << 'SCRIPT_EOF'\n{runner_script}\nSCRIPT_EOF",
-        )
-        
-        print("✅ PraisonAI installation completed")
-
-    def _build_runner_script(self) -> str:
-        """
-        Build the Python runner script that will execute inside the container.
-        
-        This script handles:
-        - Setting up auto-approval for container safety
-        - Creating agent with shell tools
-        - Executing the instruction
-        - Outputting results in JSON format
-        """
-        return '''#!/usr/bin/env python3
-"""
-PraisonAI Headless Runner for Harbor Terminal-Bench 2.0
-"""
-import sys
-import json
-import os
-from typing import Dict, Any
-
-def main():
-    if len(sys.argv) < 2:
-        print(json.dumps({"error": "No instruction provided"}))
-        sys.exit(1)
-        
-    instruction = sys.argv[1]
-    model = sys.argv[2] if len(sys.argv) > 2 else "openai/gpt-4o"
-    max_turns = int(sys.argv[3]) if len(sys.argv) > 3 else 30
-    try:
-        from praisonaiagents import Agent
-        from praisonaiagents.tools import execute_command
-        from praisonaiagents.approval import get_approval_registry, AutoApproveBackend
-        
-        # Set auto-approval for container-isolated execution
-        get_approval_registry().set_backend(AutoApproveBackend())
-        
-        # Create terminal agent with shell execution capabilities
-        agent = Agent(
-            name="terminal-agent",
-            instructions=(
-                "You are an expert terminal agent working on coding and system administration tasks. "
-                "Use the execute_command tool to run shell commands safely. "
-                "Be precise, verify your work, and complete the task step by step. "
-                "Always test your solution to ensure it works correctly."
-            ),
-            tools=[execute_command],
-            llm=model,
-        )
-        
-        # Execute the instruction
-        result = agent.start(instruction)
-        
-        # Extract metrics
-        metrics = {
-            "result": str(result) if result else None,
-            "agent_name": agent.name,
-            "model": model,
-            "tools_used": ["execute_command"],
-        }
-        
-        # Add token usage if available
-        if hasattr(agent, '_usage'):
-            usage = agent._usage
-            if usage:
-                metrics.update({
-                    "input_tokens": getattr(usage, 'input_tokens', None),
-                    "output_tokens": getattr(usage, 'output_tokens', None),
-                })
-                
-        # Add cost if available
-        if hasattr(agent, '_cost'):
-            metrics["cost_usd"] = agent._cost
-            
-        print(json.dumps(metrics))
-        
-    except Exception as e:
-        error_result = {
-            "error": str(e),
-            "error_type": type(e).__name__,
-        }
-        print(json.dumps(error_result))
-        sys.exit(1)
-
-if __name__ == "__main__":
-    main()
-'''
-
-    @with_prompt_template
-    async def run(
-        self,
-        instruction: str,
-        environment: BaseEnvironment,
-        context: AgentContext,
-    ) -> None:
-        """
-        Run the PraisonAI agent on the given instruction.
-        
-        This executes the headless runner script with the instruction,
-        similar to how other installed agents work in Harbor.
-        """
-        model = self.model_name or "openai/gpt-4o"
-        max_turns = getattr(self, 'max_turns', 30)
-        
-        # Build command to run PraisonAI
-        cmd_args = [
-            "python3", "/tmp/praisonai_runner.py",
-            shlex.quote(instruction),
-            shlex.quote(model),
-            str(max_turns),
-        ]
-        
-        command = " ".join(cmd_args)
-        
-        try:
-            # Execute the agent
-            await self.exec_as_agent(
-                environment,
-                command=command,
-                env=self._get_environment_vars(),
-            )
-        except Exception as e:
-            # Store error in context for Harbor's reporting
-            context.metadata = {"execution_error": str(e)}
-            raise
-
-    def _get_environment_vars(self) -> Dict[str, str]:
-        """Get environment variables to pass to the agent execution."""
-        env_vars = {}
-        
-        # Common API keys that should be forwarded
-        api_keys = [
-            "OPENAI_API_KEY",
-            "ANTHROPIC_API_KEY", 
-            "GEMINI_API_KEY",
-            "GOOGLE_API_KEY",
-            "AZURE_OPENAI_API_KEY",
-            "GROQ_API_KEY",
-        ]
-        
-        for key in api_keys:
-            value = os.environ.get(key)
-            if value:
-                env_vars[key] = value
-                
-        return env_vars
-
-    def populate_context_post_run(self, context: AgentContext) -> None:
-        """
-        Parse the agent's output and populate Harbor's AgentContext.
-        
-        This extracts token counts, costs, and other metrics from the
-        JSON output of the headless runner script.
-        """
-        try:
-            # Parse the last stdout output for JSON metrics
-            # In Harbor's model, the last execution output should contain our JSON
-            last_output = getattr(context, '_last_stdout', None)
-            
-            if last_output:
-                try:
-                    metrics = json.loads(last_output.strip())
-                    
-                    # Extract metrics with safe defaults
-                    context.n_input_tokens = metrics.get('input_tokens') 
-                    context.n_output_tokens = metrics.get('output_tokens')
-                    context.cost_usd = metrics.get('cost_usd')
-                    
-                    # Store additional metadata
-                    context.metadata = {
-                        "framework": "praisonai",
-                        "agent_type": "installed",
-                        "agent_name": metrics.get('agent_name', 'terminal-agent'),
-                        "model": metrics.get('model'),
-                        "tools_used": metrics.get('tools_used', []),
-                        "version": self.get_version() if hasattr(self, 'get_version') else None,
-                    }
-                    
-                except (json.JSONDecodeError, ValueError) as e:
-                    # If JSON parsing fails, store basic metadata
-                    context.metadata = {
-                        "framework": "praisonai", 
-                        "agent_type": "installed",
-                        "parse_error": str(e),
-                        "raw_output": str(last_output)[:200] if last_output else None,
-                    }
-            else:
-                # No output to parse
-                context.metadata = {
-                    "framework": "praisonai",
-                    "agent_type": "installed", 
-                    "status": "no_output",
-                }
-                
-        except Exception as e:
-            context.metadata = {"context_population_error": str(e)}
-
-
-# Factory function for Harbor integration
-def create_praisonai_agent(**kwargs) -> PraisonAIInstalledAgent:
-    """Factory function to create PraisonAI installed agent."""
-    return PraisonAIInstalledAgent(**kwargs)
-
-
-# Example usage and testing
-if __name__ == "__main__":
-    print("PraisonAI Installed Agent for Terminal-Bench 2.0")
-    print()
-    print("This is a reference implementation for Harbor integration.")
-    print("To use this in production:")
-    print()
-    print("1. Add this file to Harbor's codebase as:")
-    print("   src/harbor/agents/installed/praisonai.py")
-    print()
-    print("2. Register in Harbor's agent factory:")
-    print("   - Add 'PRAISONAI = \"praisonai\"' to AgentName enum")
-    print("   - Add PraisonAI to _AGENTS list and _AGENT_MAP")
-    print()
-    print("3. Run with Harbor:")
-    print("   harbor run -d terminal-bench/terminal-bench-2 -a praisonai --model openai/gpt-4o")
-    print()
-    print("Dependencies:")
-    print("   pip install harbor praisonaiagents")

examples/terminal_bench/praisonai_wrapper_agent.py

@@ -1,233 +0,0 @@
-"""
-PraisonAI Wrapper Agent for Terminal-Bench 2.0 (Harbor)
-
-An external agent that uses the PraisonAI CLI wrapper instead of
-direct Agent class instantiation. This provides a higher-level interface
-that matches the standard `praisonai "TASK"` CLI usage pattern.
-
-Usage:
-    harbor run -d terminal-bench/terminal-bench-2 \
-        --agent-import-path examples.terminal_bench.praisonai_wrapper_agent:PraisonAIWrapperAgent \
-        --model openai/gpt-4o \
-        --ae OPENAI_API_KEY=$OPENAI_API_KEY \
-        -n 4
-
-Architecture:
-    Harbor Container ←→ BaseEnvironment.exec() ←→ praisonai CLI subprocess
-
-Dependencies:
-    pip install harbor praisonai
-"""
-
-import asyncio
-import shlex
-import json
-import os
-from typing import Any, Dict, Optional
-
-# These imports require Harbor to be installed
-try:
-    from harbor.agents.base import BaseAgent
-    from harbor.environments.base import BaseEnvironment
-    from harbor.models.agent.context import AgentContext
-except ImportError as e:
-    raise ImportError(
-        f"Harbor framework not installed: {e}\n"
-        "Install with: pip install harbor"
-    ) from e
-
-
-class PraisonAIWrapperAgent(BaseAgent):
-    """
-    PraisonAI wrapper agent that uses the CLI/subprocess approach.
-    
-    This agent installs PraisonAI inside the Harbor container and runs
-    tasks using the `praisonai "TASK"` pattern, matching standard CLI usage.
-    """
-
-    @staticmethod
-    def name() -> str:
-        return "praisonai-wrapper"
-
-    def version(self) -> str | None:
-        """Get PraisonAI version from container."""
-        try:
-            import praisonai
-            return getattr(praisonai, "__version__", None)
-        except ImportError:
-            return None
-
-    async def setup(self, environment: BaseEnvironment) -> None:
-        """
-        Setup phase - install PraisonAI wrapper inside the container.
-        """
-        print("🔧 Installing PraisonAI wrapper inside container...")
-        
-        # Install system dependencies
-        await self._exec_as_root(
-            environment,
-            command="apt-get update && apt-get install -y python3 python3-pip curl git",
-            env={"DEBIAN_FRONTEND": "noninteractive"},
-        )
-        
-        # Install PraisonAI wrapper
-        version_spec = f"=={self._version}" if getattr(self, '_version', None) else ""
-        install_cmd = f"pip install praisonai{version_spec} --quiet"
-        
-        await self._exec_as_agent(
-            environment,
-            command=(
-                f"{install_cmd} && "
-                "python3 -c 'import praisonai; print(\"✅ praisonai installed:\", praisonai.__version__)'"
-            ),
-        )
-        
-        print("✅ PraisonAI wrapper setup completed")
-
-    async def run(
-        self,
-        instruction: str,
-        environment: BaseEnvironment,
-        context: AgentContext,
-    ) -> None:
-        """
-        Run the PraisonAI wrapper agent on the given instruction.
-        
-        Uses the `praisonai "TASK"` CLI pattern inside the Harbor container.
-        """
-        model = self.model_name or "openai/gpt-4o"
-        
-        # Build the praisonai CLI command
-        # Format: praisonai "TASK" --model MODEL
-        cmd_parts = [
-            "praisonai",
-            shlex.quote(instruction),
-            "--model", shlex.quote(model),
-        ]
-        
-        # Add API key to environment
-        env_vars = self._get_environment_vars()
-        
-        command = " ".join(cmd_parts)
-        
-        try:
-            print(f"🚀 PraisonAI wrapper starting task: {instruction[:100]}...")
-            
-            # Execute praisonai CLI inside the container
-            result = await environment.exec(
-                command=command,
-                timeout_sec=600,  # 10 minute timeout for complex tasks
-                env=env_vars,
-            )
-            
-            print(f"✅ PraisonAI wrapper completed task")
-            
-            # Populate Harbor context with results
-            self._populate_context(result, context, instruction)
-            
-        except Exception as e:
-            print(f"❌ PraisonAI wrapper failed: {str(e)}")
-            context.metadata = {"error": str(e)}
-            raise
-
-    def _populate_context(
-        self,
-        result: Any,
-        context: AgentContext,
-        instruction: str
-    ) -> None:
-        """
-        Populate Harbor's AgentContext with execution results.
-        """
-        try:
-            # Extract output from result
-            stdout = getattr(result, 'stdout', '') or ''
-            stderr = getattr(result, 'stderr', '') or ''
-            return_code = getattr(result, 'return_code', 0)
-            
-            # Store result summary
-            context.metadata = {
-                "agent_name": "praisonai-wrapper",
-                "model": self.model_name or "openai/gpt-4o",
-                "framework": "praisonai",
-                "wrapper_type": "cli",
-                "instruction_preview": instruction[:200],
-                "return_code": return_code,
-                "stderr_preview": stderr[:500] if stderr else None,
-            }
-            
-            # Try to parse any JSON output for metrics
-            try:
-                # Look for JSON in the output
-                lines = stdout.strip().split('\n')
-                for line in reversed(lines):
-                    if line.strip().startswith('{'):
-                        metrics = json.loads(line.strip())
-                        if isinstance(metrics, dict):
-                            context.n_input_tokens = metrics.get('input_tokens')
-                            context.n_output_tokens = metrics.get('output_tokens')
-                            context.cost_usd = metrics.get('cost_usd')
-                            context.metadata['parsed_metrics'] = metrics
-                            break
-            except (json.JSONDecodeError, ValueError):
-                pass  # No JSON metrics found
-            
-            # Store final response (last non-empty line or full stdout)
-            final_lines = [l for l in stdout.split('\n') if l.strip()]
-            context.metadata['final_response'] = final_lines[-1][:500] if final_lines else stdout[:500]
-            
-        except Exception as e:
-            context.metadata = {"context_error": str(e)}
-
-    def _get_environment_vars(self) -> Dict[str, str]:
-        """Get environment variables to pass to the agent execution."""
-        env_vars = {}
-        
-        # Forward API keys
-        api_keys = [
-            "OPENAI_API_KEY",
-            "ANTHROPIC_API_KEY",
-            "GEMINI_API_KEY",
-            "GOOGLE_API_KEY",
-            "AZURE_OPENAI_API_KEY",
-            "GROQ_API_KEY",
-        ]
-        
-        for key in api_keys:
-            value = os.environ.get(key)
-            if value:
-                env_vars[key] = value
-        
-        # Set LOGLEVEL to reduce noise
-        env_vars["LOGLEVEL"] = "WARNING"
-        
-        return env_vars
-
-    async def _exec_as_root(self, environment: BaseEnvironment, command: str, env: Optional[Dict] = None) -> Any:
-        """Execute command as root in the container."""
-        if hasattr(environment, 'exec_as_root'):
-            return await environment.exec_as_root(command=command, env=env or {})
-        else:
-            # Fallback: use sudo
-            return await environment.exec(command=f"sudo {command}", env=env or {})
-
-    async def _exec_as_agent(self, environment: BaseEnvironment, command: str) -> Any:
-        """Execute command as the agent user in the container."""
-        return await environment.exec(command=command)
-
-
-# Example usage for testing
-if __name__ == "__main__":
-    print("PraisonAI Wrapper Agent for Terminal-Bench 2.0")
-    print("=" * 50)
-    print()
-    print("This agent uses the PraisonAI CLI wrapper approach:")
-    print('  praisonai "TASK" --model MODEL')
-    print()
-    print("Usage with Harbor:")
-    print("  harbor run -d terminal-bench/terminal-bench-2 \\")
-    print("    --agent-import-path examples.terminal_bench.praisonai_wrapper_agent:PraisonAIWrapperAgent \\")
-    print("    --model openai/gpt-4o")
-    print()
-    print("Dependencies:")
-    print("  pip install harbor praisonai")

examples/terminal_bench/test_agent_comparison.py

@@ -1,347 +0,0 @@
-#!/usr/bin/env python3
-"""
-Agent Comparison Test - Run 5 tasks with both wrapper and direct agent
-
-This test compares:
-1. Direct Agent approach (praisonai_external_agent pattern)
-2. CLI Wrapper approach (praisonai_wrapper_agent pattern)
-
-Usage:
-    python test_agent_comparison.py
-"""
-import sys
-import os
-import time
-import asyncio
-from datetime import datetime
-
-# Add paths
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'praisonai-agents'))
-
-# 5 Test Tasks (simple to moderately complex)
-TEST_TASKS = [
-    {
-        "name": "list_directory",
-        "instruction": "List all files in the current directory and count how many Python files exist",
-        "expected_artifacts": ["file count", ".py count"]
-    },
-    {
-        "name": "create_structure",
-        "instruction": "Create a directory called 'test_workspace', create a file inside it named 'readme.txt' with content 'Hello World', then verify it exists",
-        "expected_artifacts": ["test_workspace/", "readme.txt"]
-    },
-    {
-        "name": "file_operations",
-        "instruction": "Create a file called 'numbers.txt' with numbers 1-10 each on a new line, then display the first 3 lines",
-        "expected_artifacts": ["numbers.txt"]
-    },
-    {
-        "name": "system_info",
-        "instruction": "Show the current working directory, current user, and Python version",
-        "expected_artifacts": ["pwd output", "whoami output"]
-    },
-    {
-        "name": "text_processing",
-        "instruction": "Create a file 'words.txt' with 5 words (apple, banana, cherry, date, elderberry), then count the total characters in the file",
-        "expected_artifacts": ["words.txt", "character count"]
-    }
-]
-
-
-class DirectAgentRunner:
-    """Runs tasks using direct Agent class approach."""
-    
-    def __init__(self):
-        from praisonaiagents import Agent
-        from praisonaiagents.tools import execute_command
-        from praisonaiagents.approval import get_approval_registry, AutoApproveBackend
-        
-        self.Agent = Agent
-        self.execute_command = execute_command
-        self.registry = get_approval_registry()
-        self.registry.set_backend(AutoApproveBackend())
-        self.results = []
-    
-    def run_task(self, task: dict) -> dict:
-        """Run a single task and return metrics."""
-        print(f"\n🎯 Direct Agent - Running: {task['name']}")
-        print(f"   Instruction: {task['instruction'][:60]}...")
-        
-        start_time = time.time()
-        
-        try:
-            agent = self.Agent(
-                name=f"direct-{task['name']}",
-                instructions="You are a terminal task agent. Use execute_command tool to complete tasks.",
-                tools=[self.execute_command],
-                llm="gpt-4o-mini"
-            )
-            
-            result = agent.start(task['instruction'])
-            elapsed = time.time() - start_time
-            
-            # Get cost if available
-            cost = getattr(agent, 'total_cost', 0) or 0
-            
-            print(f"   ✅ Completed in {elapsed:.2f}s")
-            print(f"   💰 Cost: ${cost:.6f}")
-            
-            return {
-                "task": task['name'],
-                "success": True,
-                "time": elapsed,
-                "cost": cost,
-                "result_preview": str(result)[:100] if result else "No output",
-                "error": None
-            }
-            
-        except Exception as e:
-            elapsed = time.time() - start_time
-            print(f"   ❌ Failed in {elapsed:.2f}s: {e}")
-            return {
-                "task": task['name'],
-                "success": False,
-                "time": elapsed,
-                "cost": 0,
-                "result_preview": None,
-                "error": str(e)
-            }
-
-
-class WrapperAgentRunner:
-    """Runs tasks using CLI wrapper approach via subprocess."""
-    
-    def __init__(self):
-        self.results = []
-    
-    def run_task(self, task: dict) -> dict:
-        """Run a single task using praisonai CLI via subprocess."""
-        print(f"\n🎯 Wrapper Agent - Running: {task['name']}")
-        print(f"   Instruction: {task['instruction'][:60]}...")
-        
-        start_time = time.time()
-        
-        try:
-            import subprocess
-            import shlex
-            
-            # Build the praisonai CLI command
-            # Format: praisonai "TASK" --model MODEL
-            cmd = [
-                "praisonai",
-                task['instruction'],
-                "--model", "gpt-4o-mini"
-            ]
-            
-            # Run the command
-            result = subprocess.run(
-                cmd,
-                capture_output=True,
-                text=True,
-                timeout=300,  # 5 minute timeout
-                env={**os.environ, "LOGLEVEL": "WARNING"}
-            )
-            
-            elapsed = time.time() - start_time
-            
-            if result.returncode == 0:
-                print(f"   ✅ Completed in {elapsed:.2f}s")
-                return {
-                    "task": task['name'],
-                    "success": True,
-                    "time": elapsed,
-                    "cost": 0,  # Cost tracking not available in CLI mode
-                    "result_preview": result.stdout[:100] if result.stdout else "No output",
-                    "error": None
-                }
-            else:
-                print(f"   ❌ Failed with exit code {result.returncode} in {elapsed:.2f}s")
-                return {
-                    "task": task['name'],
-                    "success": False,
-                    "time": elapsed,
-                    "cost": 0,
-                    "result_preview": None,
-                    "error": f"Exit code {result.returncode}: {result.stderr[:200]}"
-                }
-            
-        except subprocess.TimeoutExpired:
-            elapsed = time.time() - start_time
-            print(f"   ⏱️  Timeout after {elapsed:.2f}s")
-            return {
-                "task": task['name'],
-                "success": False,
-                "time": elapsed,
-                "cost": 0,
-                "result_preview": None,
-                "error": "Timeout after 300s"
-            }
-            
-        except FileNotFoundError:
-            elapsed = time.time() - start_time
-            print(f"   ⚠️ praisonai CLI not found")
-            return {
-                "task": task['name'],
-                "success": False,
-                "time": elapsed,
-                "cost": 0,
-                "result_preview": None,
-                "error": "praisonai CLI not installed (pip install praisonai)"
-            }
-            
-        except Exception as e:
-            elapsed = time.time() - start_time
-            print(f"   ❌ Failed in {elapsed:.2f}s: {e}")
-            return {
-                "task": task['name'],
-                "success": False,
-                "time": elapsed,
-                "cost": 0,
-                "result_preview": None,
-                "error": str(e)
-            }
-
-
-def compare_results(direct_results: list, wrapper_results: list):
-    """Compare results from both approaches."""
-    print("\n" + "=" * 70)
-    print("📊 COMPARISON RESULTS")
-    print("=" * 70)
-    
-    # Calculate metrics
-    direct_success = sum(1 for r in direct_results if r['success'])
-    wrapper_success = sum(1 for r in wrapper_results if r['success'])
-    
-    direct_total_time = sum(r['time'] for r in direct_results)
-    wrapper_total_time = sum(r['time'] for r in wrapper_results)
-    
-    direct_total_cost = sum(r['cost'] for r in direct_results)
-    
-    print(f"\n✅ Success Rate:")
-    print(f"   Direct Agent:  {direct_success}/5 ({direct_success*20}%)")
-    print(f"   Wrapper Agent: {wrapper_success}/5 ({wrapper_success*20}%)")
-    
-    print(f"\n⏱️  Total Time:")
-    print(f"   Direct Agent:  {direct_total_time:.2f}s (avg: {direct_total_time/5:.2f}s per task)")
-    print(f"   Wrapper Agent: {wrapper_total_time:.2f}s (avg: {wrapper_total_time/5:.2f}s per task)")
-    
-    print(f"\n💰 Total Cost (Direct Agent only):")
-    print(f"   ${direct_total_cost:.6f} (avg: ${direct_total_cost/5:.6f} per task)")
-    
-    print(f"\n📋 Detailed Results:")
-    print(f"   {'Task':<20} {'Direct':<12} {'Wrapper':<12} {'Winner':<10}")
-    print(f"   {'-'*56}")
-    
-    for d, w in zip(direct_results, wrapper_results):
-        d_status = "✅" if d['success'] else "❌"
-        w_status = "✅" if w['success'] else "❌"
-        
-        if d['success'] and w['success']:
-            winner = "Direct" if d['time'] < w['time'] else "Wrapper"
-        elif d['success']:
-            winner = "Direct"
-        elif w['success']:
-            winner = "Wrapper"
-        else:
-            winner = "None"
-        
-        print(f"   {d['task']:<20} {d_status} {d['time']:>6.2f}s  {w_status} {w['time']:>6.2f}s  {winner}")
-    
-    # Overall winner
-    print(f"\n🏆 OVERALL WINNER:")
-    if direct_success > wrapper_success:
-        print(f"   🥇 Direct Agent (higher success rate)")
-    elif wrapper_success > direct_success:
-        print(f"   🥇 Wrapper Agent (higher success rate)")
-    elif direct_success == wrapper_success == 5:
-        if direct_total_time < wrapper_total_time:
-            print(f"   🥇 Direct Agent (faster, same success rate)")
-        else:
-            print(f"   🥇 Wrapper Agent (faster, same success rate)")
-    else:
-        print(f"   ⚠️  Mixed results - both had failures")
-    
-    print("\n" + "=" * 70)
-
-
-def main():
-    print("=" * 70)
-    print("🔬 AGENT COMPARISON TEST - 5 Tasks")
-    print("=" * 70)
-    print(f"\nTesting {len(TEST_TASKS)} tasks with both approaches...")
-    print("Model: gpt-4o-mini")
-    print(f"Start time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-    
-    # Clean up any previous test artifacts
-    print("\n🧹 Cleaning up previous test artifacts...")
-    cleanup_commands = [
-        "rm -rf test_workspace numbers.txt words.txt readme.txt 2>/dev/null; true"
-    ]
-    for cmd in cleanup_commands:
-        os.system(cmd)
-    
-    # Run with Direct Agent
-    print("\n" + "=" * 70)
-    print("🤖 RUNNING WITH DIRECT AGENT (Agent class)")
-    print("=" * 70)
-    
-    direct_runner = DirectAgentRunner()
-    direct_results = []
-    
-    for task in TEST_TASKS:
-        result = direct_runner.run_task(task)
-        direct_results.append(result)
-    
-    # Clean up between runs
-    print("\n🧹 Cleaning up before wrapper test...")
-    for cmd in cleanup_commands:
-        os.system(cmd)
-    
-    # Run with Wrapper Agent
-    print("\n" + "=" * 70)
-    print("📦 RUNNING WITH WRAPPER AGENT (CLI approach)")
-    print("=" * 70)
-    
-    wrapper_runner = WrapperAgentRunner()
-    wrapper_results = []
-    
-    for task in TEST_TASKS:
-        result = wrapper_runner.run_task(task)
-        wrapper_results.append(result)
-    
-    # Compare results
-    compare_results(direct_results, wrapper_results)
-    
-    # Final cleanup
-    print("\n🧹 Final cleanup...")
-    for cmd in cleanup_commands:
-        os.system(cmd)
-    
-    print(f"\n✅ Comparison complete! End time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-    
-    return direct_results, wrapper_results
-
-
-if __name__ == "__main__":
-    try:
-        direct_results, wrapper_results = main()
-        
-        # Exit with error if both failed completely
-        direct_success = sum(1 for r in direct_results if r['success'])
-        wrapper_success = sum(1 for r in wrapper_results if r['success'])
-        
-        if direct_success == 0 and wrapper_success == 0:
-            print("\n❌ Both approaches failed completely!")
-            sys.exit(1)
-        else:
-            print("\n✅ At least one approach succeeded on some tasks")
-            sys.exit(0)
-            
-    except KeyboardInterrupt:
-        print("\n\n⚠️  Test interrupted by user")
-        sys.exit(130)
-    except Exception as e:
-        print(f"\n❌ Fatal error: {e}")
-        import traceback
-        traceback.print_exc()
-        sys.exit(1)

examples/terminal_bench/test_basic.py

@@ -1,170 +0,0 @@
-#!/usr/bin/env python3
-"""
-Basic smoke tests for Terminal-Bench Integration
-
-Tests basic functionality without requiring LLM calls or external dependencies.
-"""
-import sys
-import os
-
-# Add the package to path
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'praisonai-agents'))
-
-def test_imports():
-    """Test that all required modules can be imported."""
-    try:
-        from praisonaiagents import Agent
-        print("✅ Agent imported successfully")
-        
-        from praisonaiagents.tools import execute_command
-        print("✅ execute_command tool imported successfully")
-        
-        from praisonaiagents.approval import get_approval_registry, AutoApproveBackend
-        print("✅ Approval system imported successfully")
-        
-        return True
-        
-    except ImportError as e:
-        print(f"❌ Import failed: {e}")
-        return False
-
-def test_agent_creation():
-    """Test that Agent can be created without issues."""
-    try:
-        from praisonaiagents import Agent
-        
-        # Create agent without calling LLM
-        agent = Agent(
-            name='test-agent',
-            instructions='Test agent for terminal tasks'
-        )
-        
-        assert agent.name == 'test-agent'
-        print("✅ Agent creation successful")
-        return True
-        
-    except Exception as e:
-        print(f"❌ Agent creation failed: {e}")
-        return False
-
-def test_approval_system():
-    """Test approval system configuration."""
-    try:
-        from praisonaiagents.approval import get_approval_registry, AutoApproveBackend
-        
-        registry = get_approval_registry()
-        registry.set_backend(AutoApproveBackend())
-        
-        print("✅ Approval system configuration successful")
-        return True
-        
-    except Exception as e:
-        print(f"❌ Approval system test failed: {e}")
-        return False
-
-def test_external_agent_import():
-    """Test that our external agent can be imported."""
-    try:
-        # Mock Harbor imports for testing
-        import sys
-        from unittest.mock import MagicMock
-        
-        sys.modules['harbor'] = MagicMock()
-        sys.modules['harbor.agents'] = MagicMock() 
-        sys.modules['harbor.agents.base'] = MagicMock()
-        sys.modules['harbor.environments'] = MagicMock()
-        sys.modules['harbor.environments.base'] = MagicMock()
-        sys.modules['harbor.models'] = MagicMock()
-        sys.modules['harbor.models.agent'] = MagicMock()
-        sys.modules['harbor.models.agent.context'] = MagicMock()
-        
-        # Now try to import our agent
-        import praisonai_external_agent
-        print("✅ External agent import successful")
-        return True
-        
-    except Exception as e:
-        print(f"❌ External agent import failed: {e}")
-        return False
-
-def test_multi_agent_import():
-    """Test that multi-agent example can be imported.""" 
-    try:
-        import sys
-        from unittest.mock import MagicMock
-        
-        # Mock Harbor for testing
-        sys.modules['harbor'] = MagicMock()
-        sys.modules['harbor.agents'] = MagicMock()
-        sys.modules['harbor.agents.base'] = MagicMock()
-        sys.modules['harbor.environments'] = MagicMock()
-        sys.modules['harbor.environments.base'] = MagicMock()
-        sys.modules['harbor.models'] = MagicMock()
-        sys.modules['harbor.models.agent'] = MagicMock()
-        sys.modules['harbor.models.agent.context'] = MagicMock()
-        
-        import multi_agent_example
-        print("✅ Multi-agent example import successful")
-        return True
-        
-    except Exception as e:
-        print(f"❌ Multi-agent example import failed: {e}")
-        return False
-
-def test_wrapper_agent_import():
-    """Test that wrapper agent can be imported."""
-    try:
-        import sys
-        from unittest.mock import MagicMock
-        
-        # Mock Harbor for testing
-        sys.modules['harbor'] = MagicMock()
-        sys.modules['harbor.agents'] = MagicMock()
-        sys.modules['harbor.agents.base'] = MagicMock()
-        sys.modules['harbor.environments'] = MagicMock()
-        sys.modules['harbor.environments.base'] = MagicMock()
-        sys.modules['harbor.models'] = MagicMock()
-        sys.modules['harbor.models.agent'] = MagicMock()
-        sys.modules['harbor.models.agent.context'] = MagicMock()
-        
-        import praisonai_wrapper_agent
-        print("✅ Wrapper agent import successful (CLI-based approach)")
-        return True
-        
-    except Exception as e:
-        print(f"❌ Wrapper agent import failed: {e}")
-        return False
-
-if __name__ == "__main__":
-    print("PraisonAI Terminal-Bench Integration - Basic Tests")
-    print("=" * 55)
-    
-    tests = [
-        ("Basic Imports", test_imports),
-        ("Agent Creation", test_agent_creation), 
-        ("Approval System", test_approval_system),
-        ("External Agent Import", test_external_agent_import),
-        ("Multi-Agent Import", test_multi_agent_import),
-        ("Wrapper Agent Import", test_wrapper_agent_import),
-    ]
-    
-    passed = 0
-    total = len(tests)
-    
-    for test_name, test_func in tests:
-        print(f"\n🧪 Testing {test_name}...")
-        try:
-            if test_func():
-                passed += 1
-        except Exception as e:
-            print(f"❌ Test {test_name} crashed: {e}")
-    
-    print("\n" + "=" * 55)
-    print(f"Results: {passed}/{total} tests passed")
-    
-    if passed == total:
-        print("🎉 ALL BASIC TESTS PASSED!")
-        print("The Terminal-Bench integration components are properly structured.")
-    else:
-        print("❌ Some tests failed - check output above")
-        sys.exit(1)

examples/terminal_bench/test_integration.py

@@ -1,274 +0,0 @@
-"""
-Tests for PraisonAI Terminal-Bench 2.0 Integration
-
-This module contains both unit tests and integration tests for the
-PraisonAI Harbor integration.
-
-Run tests with:
-    python -m pytest examples/terminal_bench/test_integration.py -v
-
-Requirements:
-    pip install pytest praisonaiagents
-    # Harbor is optional for unit tests, required for integration tests
-"""
-
-import pytest
-import asyncio
-from unittest.mock import Mock, AsyncMock, patch
-from pathlib import Path
-
-
-class TestPraisonAIExternalAgent:
-    """Test the external agent implementation."""
-    
-    def test_agent_metadata(self):
-        """Test agent name and version reporting."""
-        try:
-            from .praisonai_external_agent import PraisonAIExternalAgent
-        except ImportError:
-            pytest.skip("Harbor not installed - skipping Harbor-specific tests")
-            
-        agent = PraisonAIExternalAgent()
-        assert agent.name() == "praisonai"
-        
-        # Version should be None if praisonaiagents not installed, or actual version
-        version = agent.version()
-        assert version is None or isinstance(version, str)
-
-    @pytest.mark.asyncio
-    async def test_setup(self):
-        """Test agent setup phase."""
-        try:
-            from .praisonai_external_agent import PraisonAIExternalAgent
-        except ImportError:
-            pytest.skip("Harbor not installed")
-            
-        agent = PraisonAIExternalAgent()
-        mock_env = Mock()
-        
-        # Setup should complete without error (external agent needs no setup)
-        await agent.setup(mock_env)
-
-    @pytest.mark.asyncio
-    async def test_bash_tool_execution(self):
-        """Test the bash tool wrapper around Harbor's exec()."""
-        try:
-            from .praisonai_external_agent import PraisonAIExternalAgent
-        except ImportError:
-            pytest.skip("Harbor not installed")
-            
-        # Mock Harbor environment
-        mock_env = Mock()
-        mock_result = Mock()
-        mock_result.stdout = "Hello, World!"
-        mock_result.stderr = ""
-        mock_result.return_code = 0
-        mock_env.exec = AsyncMock(return_value=mock_result)
-        
-        # Mock agent context
-        mock_context = Mock()
-        mock_context.metadata = {}
-        
-        agent = PraisonAIExternalAgent()
-        
-        # Mock PraisonAI Agent to avoid LLM calls in tests
-        with patch('praisonaiagents.Agent') as mock_agent_class:
-            mock_agent_instance = Mock()
-            mock_agent_instance.start.return_value = "Task completed successfully"
-            mock_agent_instance.name = "terminal-agent"
-            mock_agent_class.return_value = mock_agent_instance
-            
-            # Mock approval backend
-            with patch('praisonaiagents.approval.set_approval_backend'):
-                await agent.run("echo 'Hello, World!'", mock_env, mock_context)
-            
-            # Verify agent was created with correct parameters
-            mock_agent_class.assert_called_once()
-            args, kwargs = mock_agent_class.call_args
-            
-            assert kwargs['name'] == 'terminal-agent'
-            assert 'tools' in kwargs
-            assert len(kwargs['tools']) == 1  # bash_tool
-            assert kwargs['verbose'] is False
-            assert kwargs['memory'] is False
-
-    def test_context_population(self):
-        """Test that agent context is properly populated."""
-        try:
-            from .praisonai_external_agent import PraisonAIExternalAgent
-        except ImportError:
-            pytest.skip("Harbor not installed")
-            
-        agent_impl = PraisonAIExternalAgent()
-        
-        # Mock agent with usage data
-        mock_agent = Mock()
-        mock_agent.name = "test-agent"
-        mock_agent.llm = "gpt-4o"
-        mock_agent._usage = Mock()
-        mock_agent._usage.input_tokens = 100
-        mock_agent._usage.output_tokens = 50
-        mock_agent._cost = 0.01
-        
-        mock_context = Mock()
-        mock_context.metadata = {}
-        
-        # Test context population
-        agent_impl._populate_context(mock_agent, mock_context, "Test result")
-        
-        assert mock_context.n_input_tokens == 100
-        assert mock_context.n_output_tokens == 50
-        assert mock_context.cost_usd == 0.01
-        assert mock_context.metadata['agent_name'] == 'test-agent'
-        assert mock_context.metadata['model'] == 'gpt-4o'
-        assert mock_context.metadata['framework'] == 'praisonai'
-
-
-class TestPraisonAIInstalledAgent:
-    """Test the installed agent implementation."""
-    
-    def test_agent_configuration(self):
-        """Test agent CLI flags and configuration."""
-        try:
-            from .praisonai_installed_agent import PraisonAIInstalledAgent
-        except ImportError:
-            pytest.skip("Harbor not installed")
-            
-        agent = PraisonAIInstalledAgent()
-        assert agent.name() == "praisonai"
-        assert agent.SUPPORTS_ATIF is False  # Until trajectory format is implemented
-        
-        # Check CLI flags
-        flag_names = [flag.name if hasattr(flag, 'name') else str(flag) for flag in agent.CLI_FLAGS]
-        expected_flags = ['max_turns', 'verbose', 'memory', 'auto_approval']
-        for expected in expected_flags:
-            assert any(expected in flag_name for flag_name in flag_names)
-
-    def test_version_command(self):
-        """Test version detection command."""
-        try:
-            from .praisonai_installed_agent import PraisonAIInstalledAgent
-        except ImportError:
-            pytest.skip("Harbor not installed")
-            
-        agent = PraisonAIInstalledAgent()
-        version_cmd = agent.get_version_command()
-        
-        assert version_cmd is not None
-        assert "praisonaiagents" in version_cmd
-        assert "__version__" in version_cmd
-
-    def test_runner_script_generation(self):
-        """Test that the headless runner script is properly generated."""
-        try:
-            from .praisonai_installed_agent import PraisonAIInstalledAgent
-        except ImportError:
-            pytest.skip("Harbor not installed")
-            
-        agent = PraisonAIInstalledAgent()
-        script = agent._build_runner_script()
-        
-        assert "import praisonaiagents" in script
-        assert "Agent" in script
-        assert "execute_command" in script
-        assert "AutoApproveBackend" in script
-        assert "json.dumps" in script
-
-
-class TestIntegration:
-    """Integration tests that require both PraisonAI and Harbor."""
-    
-    @pytest.mark.integration
-    def test_praisonai_agent_real(self):
-        """
-        Real agentic test - agent must call LLM end-to-end.
-        
-        This is a MANDATORY test per AGENTS.md §9.4.
-        Agent MUST call agent.start() with real prompt and produce LLM output.
-        """
-        try:
-            from praisonaiagents import Agent
-        except ImportError:
-            pytest.skip("PraisonAI not installed")
-            
-        # Create real agent that will call LLM
-        agent = Agent(
-            name="test-terminal-agent", 
-            instructions="You are a helpful terminal assistant"
-        )
-        
-        # Real agentic test - agent must call LLM and produce text response
-        result = agent.start("Say hello in one sentence and mention you can help with terminal tasks")
-        
-        # Verify we got actual LLM output
-        assert result is not None
-        assert isinstance(result, str)
-        assert len(result) > 0
-        
-        # Print output for manual verification
-        print("✅ Real agentic test result:")
-        print(result)
-
-    @pytest.mark.integration
-    @pytest.mark.asyncio
-    async def test_bash_tool_real_execution(self):
-        """Test bash tool with real command execution (if safe)."""
-        # This would test real bash execution in a safe environment
-        # For now, we'll mock it to avoid system changes
-        
-        mock_result = Mock()
-        mock_result.stdout = "PraisonAI Terminal Test\n"
-        mock_result.stderr = ""
-        mock_result.return_code = 0
-        
-        # Test that our bash tool wrapper works correctly
-        async def mock_exec(command, timeout_sec=30):
-            assert "echo" in command  # Ensure we're testing echo command
-            return mock_result
-            
-        # This simulates Harbor's BaseEnvironment.exec()
-        result = await mock_exec("echo 'PraisonAI Terminal Test'")
-        
-        assert result.stdout.strip() == "PraisonAI Terminal Test"
-        assert result.return_code == 0
-
-    @pytest.mark.integration
-    def test_auto_approval_setup(self):
-        """Test that auto-approval backend works correctly."""
-        try:
-            from praisonaiagents.approval import set_approval_backend, AutoApproveBackend
-        except ImportError:
-            pytest.skip("PraisonAI approval system not available")
-            
-        # Test setting and restoring approval backend
-        original = set_approval_backend(AutoApproveBackend())
-        new_backend = set_approval_backend(original)
-        
-        assert isinstance(new_backend, AutoApproveBackend)
-
-
-if __name__ == "__main__":
-    # Allow running tests directly
-    import sys
-    
-    print("PraisonAI Terminal-Bench 2.0 Integration Tests")
-    print("=" * 50)
-    
-    # Check dependencies
-    try:
-        import praisonaiagents
-        print(f"✅ PraisonAI version: {praisonaiagents.__version__}")
-    except ImportError:
-        print("❌ PraisonAI not installed: pip install praisonaiagents")
-        sys.exit(1)
-    
-    try:
-        import harbor
-        print("✅ Harbor framework available")
-    except ImportError:
-        print("⚠️  Harbor not installed: pip install harbor")
-        print("   (Some tests will be skipped)")
-    
-    print()
-    print("Run tests with: python -m pytest examples/terminal_bench/test_integration.py -v")
-    print("Run real agentic test: python -m pytest examples/terminal_bench/test_integration.py::TestIntegration::test_praisonai_agent_real -v -s")

examples/terminal_bench/test_real_agentic.py

@@ -1,105 +0,0 @@
-#!/usr/bin/env python3
-"""
-Real Agentic Test for Terminal-Bench Integration
-
-This is a MANDATORY test per AGENTS.md §9.4.
-The agent MUST call agent.start() with a real prompt and produce LLM output.
-"""
-import sys
-import os
-
-# Add the package to path
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'praisonai-agents'))
-
-def test_real_agentic():
-    """Real agentic test - agent must call LLM end-to-end."""
-    try:
-        from praisonaiagents import Agent
-    except ImportError as e:
-        print(f"❌ Cannot import praisonaiagents: {e}")
-        print("Make sure you're running from the correct directory")
-        return False
-    
-    try:
-        # Create agent that will call LLM
-        agent = Agent(
-            name='terminal-bench-test-agent',
-            instructions='You are a helpful assistant that can help with terminal and coding tasks'
-        )
-        
-        # MUST call agent.start() with real prompt - this calls the LLM
-        print("🚀 Starting real agentic test (calling LLM)...")
-        result = agent.start('Say hello and briefly mention you can help with terminal tasks. Keep it to one sentence.')
-        
-        # Verify we got actual LLM output
-        assert result is not None, "Agent returned None"
-        assert isinstance(result, str), f"Agent returned {type(result)}, expected str" 
-        assert len(result) > 0, "Agent returned empty string"
-        
-        print("✅ Real agentic test PASSED!")
-        print(f"📊 Result length: {len(result)} characters")
-        print(f"🤖 Agent response: {result}")
-        
-        return True
-        
-    except Exception as e:
-        print(f"❌ Real agentic test FAILED: {e}")
-        return False
-
-def test_shell_tools():
-    """Test that shell tools are available and importable."""
-    try:
-        from praisonaiagents.tools import execute_command
-        print("✅ Shell tools imported successfully")
-        
-        # Test tool metadata
-        print(f"📋 execute_command available: {callable(execute_command)}")
-        return True
-        
-    except ImportError as e:
-        print(f"❌ Cannot import shell tools: {e}")
-        return False
-
-def test_approval_system():
-    """Test that approval system works for auto-approval."""
-    try:
-        from praisonaiagents.approval import get_approval_registry, AutoApproveBackend
-        
-        # Test setting auto-approval backend
-        registry = get_approval_registry()
-        registry.set_backend(AutoApproveBackend())
-        
-        print("✅ Approval system works correctly")
-        return True
-        
-    except ImportError as e:
-        print(f"❌ Cannot import approval system: {e}")
-        return False
-
-if __name__ == "__main__":
-    print("PraisonAI Terminal-Bench Integration - Real Agentic Test")
-    print("=" * 60)
-    
-    all_passed = True
-    
-    # Test 1: Shell tools
-    print("\n🧪 Test 1: Shell Tools Import")
-    if not test_shell_tools():
-        all_passed = False
-    
-    # Test 2: Approval system
-    print("\n🧪 Test 2: Approval System")
-    if not test_approval_system():
-        all_passed = False
-    
-    # Test 3: Real agentic test (MANDATORY)
-    print("\n🧪 Test 3: Real Agentic Test (MANDATORY)")
-    if not test_real_agentic():
-        all_passed = False
-    
-    print("\n" + "=" * 60)
-    if all_passed:
-        print("🎉 ALL TESTS PASSED - Terminal-Bench integration is ready!")
-    else:
-        print("❌ Some tests failed - check output above")
-        sys.exit(1)