fix(examples): phase-3 API alignment for consolidated params on v4.6.52 (#1864)

Co-authored-by: Dhivya-Bharathy <[email protected]>

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

@@ -15,7 +15,7 @@ on:
   pull_request_review:
     types: [submitted]
   pull_request:
-    types: [opened, synchronize]
+    types: [opened]
 
 env:
   MAX_POLL_ATTEMPTS: 20
@@ -143,12 +143,9 @@ jobs:
               owner: context.repo.owner,
               repo: context.repo.repo
             });
-            const alreadyPosted = comments.data.some(c => {
-              const body = (c.body || '').toLowerCase();
-              if (!body.includes('@claude') || !triggerLogins.includes(c.user.login)) return false;
-              if (body.includes('merge conflict')) return false;
-              return body.includes('final architecture reviewer') || body.includes('lead engineer');
-            });
+            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());
             
@@ -189,7 +186,7 @@ jobs:
             const claudeBody = '@claude You are the FINAL architecture reviewer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. ' + 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, SDK value gate — no feature bloat).\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});
@@ -298,12 +295,9 @@ jobs:
               owner: context.repo.owner,
               repo: context.repo.repo
             });
-            const alreadyPosted = comments.data.some(c => {
-              const body = (c.body || '').toLowerCase();
-              if (!body.includes('@claude') || !triggerLogins.includes(c.user.login)) return false;
-              if (body.includes('merge conflict')) return false;
-              return body.includes('final architecture reviewer') || body.includes('lead engineer');
-            });
+            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
@@ -319,7 +313,7 @@ jobs:
               issue_number: pr,
               owner: context.repo.owner,
               repo: context.repo.repo,
-              body: '@claude You are the FINAL architecture reviewer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. 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\n4. SDK value: review in depth whether the change genuinely adds value to the SDK — never add features for the sake of adding them. It must strengthen the SDK (simpler, more user-friendly, robust, world-class, secure). If it does not clearly add value, request changes or recommend rejecting/closing rather than merging scope creep\n\n**Phase 2: FIX Valid Issues**\n5. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n6. Push all code fixes directly to THIS branch (do NOT create a new PR)\n7. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n8. If all issues are resolved, approve the PR / close the Issue\n9. If blocking issues remain, request changes / leave clear action items'
+              body: '@claude You are the FINAL architecture reviewer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. 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}`);
 
@@ -405,12 +399,9 @@ jobs:
               owner: context.repo.owner,
               repo: context.repo.repo
             });
-            const alreadyPosted = comments.data.some(c => {
-              const body = (c.body || '').toLowerCase();
-              if (!body.includes('@claude') || !triggerLogins.includes(c.user.login)) return false;
-              if (body.includes('merge conflict')) return false;
-              return body.includes('final architecture reviewer') || body.includes('lead engineer');
-            });
+            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
@@ -426,7 +417,7 @@ jobs:
               issue_number: pr,
               owner: context.repo.owner,
               repo: context.repo.repo,
-              body: '@claude You are the FINAL architecture reviewer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. 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\n4. SDK value: review in depth whether the change genuinely adds value to the SDK — never add features for the sake of adding them. It must strengthen the SDK (simpler, more user-friendly, robust, world-class, secure). If it does not clearly add value, request changes or recommend rejecting/closing rather than merging scope creep\n\n**Phase 2: FIX Valid Issues**\n5. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n6. Push all code fixes directly to THIS branch (do NOT create a new PR)\n7. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n8. If all issues are resolved, approve the PR / close the Issue\n9. If blocking issues remain, request changes / leave clear action items'
+              body: '@claude You are the FINAL architecture reviewer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. 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}`);
 
@@ -462,12 +453,9 @@ jobs:
               owner: context.repo.owner,
               repo: context.repo.repo
             });
-            const alreadyPosted = comments.data.some(c => {
-              const body = (c.body || '').toLowerCase();
-              if (!body.includes('@claude') || !triggerLogins.includes(c.user.login)) return false;
-              if (body.includes('merge conflict')) return false;
-              return body.includes('final architecture reviewer') || body.includes('lead engineer');
-            });
+            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)
@@ -480,65 +468,9 @@ jobs:
               issue_number: context.issue.number,
               owner: context.repo.owner,
               repo: context.repo.repo,
-              body: '@claude You are the Lead Engineer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. 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\n4. SDK value: review in depth whether the change genuinely adds value to the SDK — never add features for the sake of adding them. It must strengthen the SDK (simpler, more user-friendly, robust, world-class, secure). If it does not clearly add value, request changes or recommend rejecting/closing rather than merging scope creep\n\n**Phase 2: FIX Valid Issues**\n5. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n6. Push all code fixes directly to THIS branch (do NOT create a new PR)\n7. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n8. If all issues are resolved, approve the PR / close the Issue\n9. If blocking issues remain, request changes / leave clear action items'
+              body: '@claude You are the Lead Engineer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. 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'
             });
 
-  # ================================================================
-  # Recovery: post FINAL @claude if merge-conflict comment blocked the chain
-  # (e.g. conflict fired before claude-after-copilot could post review prompt)
-  # ================================================================
-  claude-review-recovery:
-    if: |
-      github.event_name == 'pull_request' &&
-      github.event.action == 'synchronize'
-    runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: read
-    steps:
-      - name: Post missing FINAL Claude review after rebase
-        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 pr = context.payload.pull_request.number;
-            const owner = context.repo.owner;
-            const repo = context.repo.repo;
-
-            const { data: comments } = await github.rest.issues.listComments({
-              issue_number: pr, owner, repo, per_page: 100,
-            });
-
-            const hasFinalReview = comments.some(c => {
-              const body = (c.body || '').toLowerCase();
-              if (!body.includes('@claude') || !triggerLogins.includes(c.user.login)) return false;
-              if (body.includes('merge conflict')) return false;
-              return body.includes('final architecture reviewer') || body.includes('lead engineer');
-            });
-            if (hasFinalReview) {
-              core.info('FINAL Claude review already posted, skipping recovery');
-              return;
-            }
-
-            const hasReviewerActivity = comments.some(c => {
-              const login = c.user.login.toLowerCase();
-              return login.includes('coderabbit') || login.includes('copilot') ||
-                login.includes('greptile') || login.includes('qodo') || login.includes('gemini');
-            });
-            if (!hasReviewerActivity) {
-              core.info('No bot reviewer activity yet, skipping recovery');
-              return;
-            }
-
-            await github.rest.issues.createComment({
-              issue_number: pr, owner, repo,
-              body: '@claude You are the FINAL architecture reviewer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. 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\n4. SDK value: review in depth whether the change genuinely adds value to the SDK — never add features for the sake of adding them. It must strengthen the SDK (simpler, more user-friendly, robust, world-class, secure). If it does not clearly add value, request changes or recommend rejecting/closing rather than merging scope creep\n\n**Phase 2: FIX Valid Issues**\n5. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n6. Push all code fixes directly to THIS branch (do NOT create a new PR)\n7. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n8. If all issues are resolved, approve the PR / close the Issue\n9. If blocking issues remain, request changes / leave clear action items'
-            });
-            core.info(`Posted recovery FINAL Claude review on PR #${pr}`);
-
   # ================================================================
   # BOT PRs: CodeRabbit/Qodo skip bot-authored PRs by default.
   # Trigger them explicitly. Do NOT trigger Copilot here — it will

.github/workflows/claude.yml

@@ -151,67 +151,26 @@ jobs:
         id: check_fork
         uses: actions/github-script@v7
         with:
-          github-token: ${{ steps.app-token.outputs.token }}
           script: |
-            const isPR = context.eventName === 'pull_request'
-              || context.eventName === 'pull_request_review'
-              || context.eventName === 'pull_request_review_comment'
-              || (context.payload.issue && context.payload.issue.pull_request);
-
-            if (!isPR) {
-              core.setOutput('is_pr', 'false');
-              core.setOutput('is_fork', 'false');
-              core.setOutput('can_push_fork', 'false');
-              return;
-            }
-
-            const prNumber = context.payload.pull_request
-              ? context.payload.pull_request.number
-              : context.payload.issue.number;
-            const pr = await github.rest.pulls.get({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              pull_number: prNumber
-            });
-
-            let mergeState = '';
-            try {
-              const gql = await github.graphql(
-                `query($owner: String!, $repo: String!, $number: Int!) {
-                  repository(owner: $owner, name: $repo) {
-                    pullRequest(number: $number) { mergeStateStatus }
-                  }
-                }`,
-                { owner: context.repo.owner, repo: context.repo.repo, number: prNumber }
-              );
-              mergeState = (gql.repository.pullRequest.mergeStateStatus || '').toUpperCase();
-            } catch (e) {
-              core.warning(`GraphQL mergeStateStatus failed: ${e.message}`);
-            }
-            if (!mergeState) {
-              mergeState = (pr.data.mergeable_state || pr.data.merge_state_status || '').toUpperCase();
-            }
-
-            core.setOutput('is_pr', 'true');
-            core.setOutput('pr_number', String(prNumber));
-            core.setOutput('pr_branch', pr.data.head.ref);
-            core.setOutput('head_repo', pr.data.head.repo.full_name);
-            core.setOutput('merge_state', mergeState);
-
-            const baseRepo = `${context.repo.owner}/${context.repo.repo}`;
-            const maintainerCanModify = pr.data.maintainer_can_modify === true;
-            core.setOutput('maintainer_can_modify', String(maintainerCanModify));
-
-            if (pr.data.head.repo && pr.data.head.repo.full_name !== baseRepo) {
-              core.setOutput('is_fork', 'true');
-              core.setOutput('can_push_fork', String(maintainerCanModify));
-              core.setOutput('fork_repo', pr.data.head.repo.full_name);
-              core.setOutput('clone_url', pr.data.head.repo.clone_url);
-              core.setOutput('branch', pr.data.head.ref);
-              return;
+            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');
-            core.setOutput('can_push_fork', 'false');
 
       - name: Checkout repository
         uses: actions/checkout@v4
@@ -221,40 +180,31 @@ jobs:
           token: ${{ steps.app-token.outputs.token }}
 
       - name: Fetch PR branch and Setup Remote
-        if: steps.check_fork.outputs.is_pr == 'true'
+        if: github.event.issue.pull_request
         env:
           PR_BRANCH: ${{ steps.check_fork.outputs.pr_branch }}
-          PR_NUMBER: ${{ steps.check_fork.outputs.pr_number }}
           IS_FORK: ${{ steps.check_fork.outputs.is_fork }}
-          CAN_PUSH_FORK: ${{ steps.check_fork.outputs.can_push_fork }}
-          FORK_REPO: ${{ steps.check_fork.outputs.fork_repo }}
-          GH_TOKEN: ${{ secrets.GH_TOKEN }}
         run: |
-          git fetch origin "pull/${PR_NUMBER}/head:${PR_BRANCH}"
-          git checkout "${PR_BRANCH}"
-          git fetch origin main
-
-          if [ "${IS_FORK}" = "true" ] && [ "${CAN_PUSH_FORK}" = "true" ]; then
-            git remote set-url origin "https://x-access-token:${GH_TOKEN}@github.com/${FORK_REPO}.git"
-          elif [ "${IS_FORK}" = "true" ]; then
-            # Block push: trick claude-code-action's `git fetch origin <branch>`
-            git remote set-url origin "file://$(pwd)"
+          # Fetch PR head from base repo and put it into local branch
+          git fetch origin pull/${{ github.event.issue.number }}/head:"${PR_BRANCH}"
+          
+          # If it's a fork, make origin point to local to trick claude-code-action's `git fetch origin <branch>`
+          if [ "${IS_FORK}" = "true" ]; then
+            git remote set-url origin file://$(pwd)
           fi
 
       - uses: anthropics/claude-code-action@beta
         env:
-          GH_TOKEN: ${{ steps.check_fork.outputs.can_push_fork == 'true' && secrets.GH_TOKEN || steps.app-token.outputs.token }}
+          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 }}
-          model: claude-opus-4-8
           trigger_phrase: "@claude"
           label_trigger: "claude"
 
           direct_prompt: |
-            ${{ steps.check_fork.outputs.is_fork == 'true' && steps.check_fork.outputs.can_push_fork != 'true' && 'CRITICAL: THIS IS A PULL REQUEST FROM A FORK WITHOUT MAINTAINER EDITS. YOU DO NOT HAVE PUSH PERMISSIONS. ONLY READ FILES, VALIDATE CODE, AND PROVIDE COMMENTS/FEEDBACK ON THIS PR. DO NOT ATTEMPT TO PUSH OR CREATE PULL REQUESTS.\n\n' || (steps.check_fork.outputs.can_push_fork == 'true' && format('NOTE: Fork PR with maintainer edits allowed. Push directly to fork {0} branch {1}.\n\n', steps.check_fork.outputs.fork_repo, steps.check_fork.outputs.pr_branch) || (steps.check_fork.outputs.is_pr == 'true' && steps.check_fork.outputs.is_fork != 'true' && 'NOTE: The branch is under MervinPraison/PraisonAI (not a fork). You are able to make modifications and push directly to this branch.\n\n' || (steps.check_fork.outputs.is_pr != 'true' && 'NOTE: This is an issue (not a PR). Create a new fix branch per STEP 3 below.\n\n' || ''))) }}
-            ${{ steps.check_fork.outputs.is_pr == 'true' && format('CONTEXT: PR #{0} on branch {1}. head.repo={2}, is_fork={3}, can_push_fork={4}, merge_state={5}. NEVER treat cursor/*, claude/* branch names or bot authors as forks — only is_fork=true means fork.\n\n', steps.check_fork.outputs.pr_number, steps.check_fork.outputs.pr_branch, steps.check_fork.outputs.head_repo, steps.check_fork.outputs.is_fork, steps.check_fork.outputs.can_push_fork, steps.check_fork.outputs.merge_state) || '' }}
+            ${{ 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' || 'NOTE: The branch is under MervinPraison/PraisonAI (not a fork). You are able to make modifications and push directly to this branch.\n\n' }}
             You are working on the PraisonAI SDK. Follow AGENTS.md strictly.
 
             STEP 0 — SETUP GIT IDENTITY & AUTH (GLOBAL — required for all repos):
@@ -283,9 +233,15 @@ jobs:
             - 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.
 
-            ${{ steps.check_fork.outputs.is_pr == 'true' && (steps.check_fork.outputs.is_fork != 'true' || steps.check_fork.outputs.can_push_fork == 'true') && steps.check_fork.outputs.merge_state == 'DIRTY' && format('STEP 3 — RESOLVE MERGE CONFLICTS ON PR (branch: {0}, PR #{1}{2}):\n- DO NOT create a new branch or PR\n- git fetch origin main && git rebase origin/main\n- Resolve conflicts: keep this PR intent, merge in main newer logic\n- git add -A && git rebase --continue (repeat until done)\n- git push --force-with-lease origin {0}\n- Run targeted pytest on changed paths\n- Comment files resolved and what you skipped\n', steps.check_fork.outputs.pr_branch, steps.check_fork.outputs.pr_number, steps.check_fork.outputs.can_push_fork == 'true' && format(', fork: {0}', steps.check_fork.outputs.fork_repo) || '') || '' }}
-            ${{ steps.check_fork.outputs.is_pr == 'true' && (steps.check_fork.outputs.is_fork != 'true' || steps.check_fork.outputs.can_push_fork == 'true') && steps.check_fork.outputs.merge_state != 'DIRTY' && format('STEP 3 — IMPLEMENT ON EXISTING PR (branch: {0}, PR #{1}{2}):\n- DO NOT create a new branch or PR\n- git checkout {0}\n- Implement minimal, focused fixes from reviewer feedback\n- Follow protocol-driven design: protocols in core SDK, heavy implementations in wrapper\n- Keep changes small and backward-compatible\n- Commit: git add -A && git commit -m "fix: <description>"\n- Push: git push origin {0}\n- Comment a summary of exact files modified and what you skipped\n', steps.check_fork.outputs.pr_branch, steps.check_fork.outputs.pr_number, steps.check_fork.outputs.can_push_fork == 'true' && format(', fork: {0}', steps.check_fork.outputs.fork_repo) || '') || '' }}
-            ${{ steps.check_fork.outputs.is_pr != 'true' && 'STEP 3 — IMPLEMENT (for changes in THIS repo — PraisonAI):\n- Create a fix branch: git checkout -b claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)\n- Implement a minimal, focused fix\n- Follow protocol-driven design: protocols in core SDK, heavy implementations in wrapper\n- Keep changes small and backward-compatible\n- Commit: git add -A && git commit -m "fix: <description> (fixes #$ISSUE_NUMBER)"\n- Push: git push origin claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)\nCRITICAL: After pushing, you MUST immediately run: gh pr create --title "fix: <title>" --body "Fixes #$ISSUE_NUMBER" --head claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)\nDo NOT stop after pushing. Do NOT say "manually create a PR". Always create the PR automatically.\n' || '' }}
+            STEP 3 — IMPLEMENT (for changes in THIS repo — PraisonAI):
+            - Create a fix branch: git checkout -b claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
+            - Implement a minimal, focused fix
+            - Follow protocol-driven design: protocols in core SDK, heavy implementations in wrapper
+            - Keep changes small and backward-compatible
+            - Commit: git add -A && git commit -m "fix: <description> (fixes #$ISSUE_NUMBER)"
+            - Push: git push origin claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
+            CRITICAL: After pushing, you MUST immediately run: gh pr create --title "fix: <title>" --body "Fixes #$ISSUE_NUMBER" --head claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
+            Do NOT stop after pushing. Do NOT say "manually create a PR". Always create the PR automatically.
 
             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.
@@ -311,7 +267,10 @@ jobs:
             - For docs changes: verify files exist and are valid markdown/MDX
             - Ensure no regressions
 
-            ${{ steps.check_fork.outputs.is_pr == 'true' && 'STEP 5 — SKIP: Already on an open PR. Do NOT run gh pr create.\n' || 'STEP 5 — CREATE PR:\n- Commit with descriptive message, push, and create PR using `gh pr create`\n- For external repos, use `gh pr create -R MervinPraison/<REPO_NAME>`\nCRITICAL: You MUST create the PR automatically using `gh pr create`. Do NOT just provide a link or say "manual push required".\n' }}
+            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.
           allowed_tools: |
             Bash(git:*)

.github/workflows/deploy-install-scripts.yml

@@ -1,52 +0,0 @@
-name: Deploy install scripts to praison.ai
-
-# When install.sh or install.ps1 change on main, trigger praison.ai CI which
-# syncs scripts at build time and deploys to Azure AKS (existing azure-pipeline.yaml).
-
-on:
-  push:
-    branches: [main]
-    paths:
-      - 'src/praisonai/scripts/install.sh'
-      - 'src/praisonai/scripts/install.ps1'
-  workflow_dispatch:
-
-concurrency:
-  group: deploy-install-scripts
-  cancel-in-progress: false
-
-permissions:
-  contents: read
-
-jobs:
-  trigger-website-deploy:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Trigger praison.ai CI
-        env:
-          GH_TOKEN: ${{ secrets.SECRETS_ADMIN_PAT }}
-          SOURCE_SHA: ${{ github.sha }}
-        run: |
-          set -euo pipefail
-          if [ -z "${GH_TOKEN:-}" ]; then
-            echo "SECRETS_ADMIN_PAT is not configured in this repository"
-            exit 1
-          fi
-
-          gh workflow run CI \
-            --repo MervinPraison/praison.ai \
-            --ref master
-
-          echo "Triggered praison.ai CI for PraisonAI commit ${SOURCE_SHA}"
-          echo "Monitor: https://github.com/MervinPraison/praison.ai/actions/workflows/azure-pipeline.yaml"
-
-          sleep 10
-          RUN_URL=$(gh run list \
-            --repo MervinPraison/praison.ai \
-            --workflow CI \
-            --limit 1 \
-            --json url,status,createdAt \
-            --jq '.[0].url' 2>/dev/null || true)
-          if [ -n "${RUN_URL}" ]; then
-            echo "Latest praison.ai CI run: ${RUN_URL}"
-          fi

.github/workflows/docker-publish.yml

@@ -22,6 +22,8 @@ jobs:
         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

@@ -36,6 +36,9 @@ jobs:
         uses: actions/checkout@v4
         with:
           persist-credentials: false
+        with:
+          persist-credentials: false
+        with:
           token: ${{ steps.generate_token.outputs.token }}
 
       - name: Run Gemini Issue Triage

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

@@ -37,6 +37,9 @@ jobs:
         uses: actions/checkout@v4
         with:
           persist-credentials: false
+        with:
+          persist-credentials: false
+        with:
           token: ${{ steps.generate_token.outputs.token }}
 
       - name: Set up Git

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

@@ -48,6 +48,9 @@ jobs:
         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 }}
           fetch-depth: 0

.github/workflows/integration-cross-repo.yml

@@ -60,16 +60,16 @@ jobs:
     - name: Run PraisonAI integration tests
       run: |
         cd PraisonAI/src/praisonai
-        python -m pytest tests/integration/test_aiui_* -v
+        python -m pytest tests/integration/test_aiui_* -v --timeout=300
         
     - name: Run PraisonAIUI integration tests
       run: |
         cd PraisonAIUI
         if [ -f tests/integration/test_agentic_roundtrip.py ]; then
-          python -m pytest tests/integration/test_agentic_roundtrip.py -v
+          python -m pytest tests/integration/test_agentic_roundtrip.py -v --timeout=300
         fi
         if [ -f tests/test_feature_sdk_backends.py ]; then
-          python -m pytest tests/test_feature_sdk_backends.py -v
+          python -m pytest tests/test_feature_sdk_backends.py -v --timeout=300
         fi
         
     - name: Test Pattern C CLI
@@ -152,7 +152,7 @@ jobs:
       run: |
         cd PraisonAI/src/praisonai
         if [ -n "$OPENAI_API_KEY" ]; then
-          python -m pytest tests/integration/test_aiui_host_agentic.py -v
+          python -m pytest tests/integration/test_aiui_host_agentic.py -v --timeout=600
         else
           echo "OPENAI_API_KEY not available, skipping agentic tests"
         fi

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

@@ -1,183 +0,0 @@
-name: Auto Merge Conflict → Claude
-
-# Detects internal PRs with mergeStateStatus=DIRTY and posts @claude to trigger claude.yml.
-
-on:
-  push:
-    branches: [main]
-  pull_request:
-    types: [opened, synchronize, reopened]
-  workflow_dispatch:
-  schedule:
-    - cron: '0 */6 * * *'
-
-concurrency:
-  group: merge-conflict-scan-${{ github.repository }}
-  cancel-in-progress: false
-
-env:
-  # PAT posts as MervinPraison so issue_comment triggers claude.yml (GITHUB_TOKEN cannot chain workflows)
-  GH_TOKEN: ${{ secrets.GH_TOKEN || github.token }}
-
-permissions:
-  pull-requests: write
-  issues: write
-
-jobs:
-  detect-and-trigger:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Detect conflicting PRs and trigger Claude
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ env.GH_TOKEN }}
-          script: |
-            const owner = context.repo.owner;
-            const repo = context.repo.repo;
-            const baseRepo = `${owner}/${repo}`;
-            const isMainPush = context.eventName === 'push';
-            const COOLDOWN_MS = 12 * 60 * 60 * 1000;
-            const AUTO_ACTORS = ['github-actions[bot]', 'MervinPraison'];
-            const LABEL = 'claude-conflict-pending';
-            const COMMENT_BODY = [
-              '@claude this PR has merge conflicts with `main`.',
-              'Please rebase onto latest `main`, resolve conflicts (keep this PR\'s intent, merge in newer main logic),',
-              'run targeted tests, and force-push with `--force-with-lease`.',
-              'Comment which files you resolved.'
-            ].join(' ');
-
-            const sleep = (ms) => new Promise((r) => setTimeout(r, ms));
-
-            async function getMergeState(prNumber) {
-              const query = `
-                query($owner: String!, $repo: String!, $number: Int!) {
-                  repository(owner: $owner, name: $repo) {
-                    pullRequest(number: $number) {
-                      mergeStateStatus
-                      maintainerCanModify
-                      isDraft
-                      headRef { repository { nameWithOwner } }
-                    }
-                  }
-                }
-              `;
-              for (let attempt = 0; attempt < 3; attempt++) {
-                const result = await github.graphql(query, { owner, repo, number: prNumber });
-                const prGql = result.repository.pullRequest;
-                const status = (prGql?.mergeStateStatus || '').toUpperCase();
-                if (status && status !== 'UNKNOWN') {
-                  return {
-                    status,
-                    isDraft: prGql.isDraft,
-                    headRepo: prGql.headRef?.repository?.nameWithOwner,
-                    maintainerCanModify: prGql.maintainerCanModify === true,
-                  };
-                }
-                if (attempt < 2) await sleep(10000);
-              }
-              const pr = await github.rest.pulls.get({ owner, repo, pull_number: prNumber });
-              return {
-                status: (pr.data.mergeable_state || '').toUpperCase(),
-                isDraft: pr.data.draft,
-                headRepo: pr.data.head.repo?.full_name,
-                maintainerCanModify: pr.data.maintainer_can_modify === true,
-              };
-            }
-
-            function hasFinalClaudeReviewTrigger(comments) {
-              return comments.some((c) => {
-                const body = (c.body || '').toLowerCase();
-                if (!AUTO_ACTORS.includes(c.user.login)) return false;
-                if (!body.includes('@claude')) return false;
-                if (body.includes('merge conflict')) return false;
-                return body.includes('final architecture reviewer') || body.includes('lead engineer');
-              });
-            }
-
-            function hasRecentAutoComment(comments) {
-              const cutoff = Date.now() - COOLDOWN_MS;
-              return comments.some((c) => {
-                if (!AUTO_ACTORS.includes(c.user.login)) return false;
-                const body = (c.body || '').toLowerCase();
-                if (!body.includes('@claude') || !body.includes('merge conflict')) return false;
-                return new Date(c.created_at).getTime() > cutoff;
-              });
-            }
-
-            async function processPR(prNumber) {
-              const { status, isDraft, headRepo, maintainerCanModify } = await getMergeState(prNumber);
-              core.info(`PR #${prNumber}: mergeState=${status}, head=${headRepo}, draft=${isDraft}, maintainerCanModify=${maintainerCanModify}`);
-
-              const { data: issue } = await github.rest.issues.get({
-                owner,
-                repo,
-                issue_number: prNumber,
-              });
-              const labels = issue.labels.map((l) => l.name);
-
-              if (status !== 'DIRTY' && labels.includes(LABEL)) {
-                await github.rest.issues.removeLabel({
-                  owner,
-                  repo,
-                  issue_number: prNumber,
-                  name: LABEL,
-                });
-                core.info(`Removed ${LABEL} from PR #${prNumber}`);
-              }
-
-              if (status !== 'DIRTY') return { skipped: true, reason: 'not dirty' };
-              if (isDraft) return { skipped: true, reason: 'draft' };
-              if (headRepo && headRepo !== baseRepo && !maintainerCanModify) {
-                return { skipped: true, reason: 'fork without maintainer edits' };
-              }
-
-              const { data: comments } = await github.rest.issues.listComments({
-                owner,
-                repo,
-                issue_number: prNumber,
-                per_page: 100,
-              });
-              if (!isMainPush && hasRecentAutoComment(comments)) {
-                return { skipped: true, reason: 'recent auto comment' };
-              }
-
-              if (!hasFinalClaudeReviewTrigger(comments)) {
-                return { skipped: true, reason: 'awaiting final claude review trigger' };
-              }
-
-              await github.rest.issues.createComment({
-                owner,
-                repo,
-                issue_number: prNumber,
-                body: COMMENT_BODY,
-              });
-              if (!labels.includes(LABEL)) {
-                await github.rest.issues.addLabels({
-                  owner,
-                  repo,
-                  issue_number: prNumber,
-                  labels: [LABEL],
-                });
-              }
-              return { triggered: true };
-            }
-
-            let prNumbers = [];
-            if (context.eventName === 'pull_request') {
-              prNumbers = [context.payload.pull_request.number];
-            } else {
-              const prs = await github.paginate(github.rest.pulls.list, {
-                owner,
-                repo,
-                state: 'open',
-                per_page: 100,
-              });
-              prNumbers = prs.map((p) => p.number);
-            }
-
-            const results = [];
-            for (const num of prNumbers) {
-              results.push({ pr: num, ...(await processPR(num)) });
-            }
-            core.summary.addRaw('```json\n' + JSON.stringify(results, null, 2) + '\n```');
-            await core.summary.write();

.github/workflows/python-package.yml

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

.github/workflows/release.yml

@@ -26,8 +26,11 @@ 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:
-        persist-credentials: false
         repository: MervinPraison/PraisonAI
         ref: ${{ github.event.inputs.tag }}
         fetch-depth: 0
@@ -45,8 +48,11 @@ jobs:
     # Now checkout the fork at main branch
     - name: Checkout fork
       uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+        with:
+          persist-credentials: false
       with:
-        persist-credentials: false
         repository: ${{ github.event.inputs.fork_owner }}/PraisonAI
         ref: main
         fetch-depth: 0
@@ -443,8 +449,11 @@ 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:
-        persist-credentials: false
         fetch-depth: 0
         token: ${{ secrets.PAT_TOKEN }}
         repository: ${{ github.event.inputs.fork_owner }}/PraisonAI

.github/workflows/sync-secrets-to-aiui.yml

@@ -1,31 +0,0 @@
-name: Sync Claude Secrets to PraisonAIUI
-
-on:
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  sync:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Sync secrets to PraisonAIUI
-        env:
-          GH_TOKEN: ${{ secrets.SECRETS_ADMIN_PAT }}
-          TARGET: MervinPraison/PraisonAIUI
-          CLAUDE_APP_ID: ${{ secrets.CLAUDE_APP_ID }}
-          CLAUDE_APP_PRIVATE_KEY: ${{ secrets.CLAUDE_APP_PRIVATE_KEY }}
-          CLAUDE_CODE_OAUTH_TOKEN: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
-          GH_TOKEN_VALUE: ${{ secrets.GH_TOKEN }}
-        run: |
-          set -euo pipefail
-          if [ -z "${GH_TOKEN:-}" ]; then
-            echo "SECRETS_ADMIN_PAT is not configured in this repository"
-            exit 1
-          fi
-          echo -n "$CLAUDE_APP_ID" | gh secret set CLAUDE_APP_ID --repo "$TARGET"
-          echo -n "$CLAUDE_APP_PRIVATE_KEY" | gh secret set CLAUDE_APP_PRIVATE_KEY --repo "$TARGET"
-          echo -n "$CLAUDE_CODE_OAUTH_TOKEN" | gh secret set CLAUDE_CODE_OAUTH_TOKEN --repo "$TARGET"
-          echo -n "$GH_TOKEN_VALUE" | gh secret set GH_TOKEN --repo "$TARGET"
-          echo "Synced CLAUDE_APP_ID, CLAUDE_APP_PRIVATE_KEY, CLAUDE_CODE_OAUTH_TOKEN, GH_TOKEN → $TARGET"

.github/workflows/test-comprehensive.yml

@@ -37,8 +37,10 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
+        with:
+          persist-credentials: false
 
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5

.github/workflows/test-core.yml

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

.github/workflows/test-extended.yml

@@ -13,8 +13,10 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
+        with:
+          persist-credentials: false
 
     - name: Set up Python
       uses: actions/setup-python@v5
@@ -79,8 +81,10 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
+        with:
+          persist-credentials: false
 
     - name: Set up Python
       uses: actions/setup-python@v5

.github/workflows/test-frameworks.yml

@@ -33,8 +33,10 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        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 +45,8 @@ jobs:
     timeout-minutes: 5
     steps:
     - uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
     - name: Set up Python 3.11
       uses: actions/setup-python@v5
       with:
@@ -105,8 +105,8 @@ jobs:
     
     steps:
     - uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5
       with:
@@ -187,8 +187,8 @@ jobs:
         fi
     
     - uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
       if: steps.check-key.outputs.has_key == 'true'
     
     - name: Set up Python 3.11
@@ -259,8 +259,8 @@ jobs:
         fi
     
     - uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
       if: steps.check-key.outputs.has_key == 'true'
     
     - name: Set up Python 3.11
@@ -318,8 +318,8 @@ jobs:
         fi
     
     - uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
       if: steps.check-key.outputs.has_key == 'true'
     
     - name: Set up Python 3.11
@@ -377,8 +377,8 @@ jobs:
         fi
     
     - uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
       if: steps.check-key.outputs.has_key == 'true'
     
     - name: Set up Python 3.11
@@ -436,8 +436,8 @@ jobs:
         fi
     
     - uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
       if: steps.check-key.outputs.has_key == 'true'
     
     - name: Set up Python 3.11
@@ -495,8 +495,8 @@ jobs:
         fi
     
     - uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
       if: steps.check-key.outputs.has_key == 'true'
     
     - name: Set up Python 3.11

.github/workflows/test-real.yml

@@ -45,8 +45,10 @@ jobs:
         
     - name: Checkout code
       uses: actions/checkout@v4
-      with:
-        persist-credentials: false
+        with:
+          persist-credentials: false
+        with:
+          persist-credentials: false
 
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5

.gitignore

@@ -88,9 +88,6 @@ issue_details.json
 .internal
 .praisonai
 
-# Backup files
-*.bak
-
 # Rust / Cargo
 target/
 Cargo.lock

AGENTS.md

@@ -1,10 +0,0 @@
-# Agent Instructions
-
-You are working on the PraisonAI project.
-
-## Project Guidelines
-- Follow the existing code style and conventions
-- Be concise and helpful in responses  
-- Test implementation thoroughly
-- Ensure backward compatibility with existing APIs
-- Follow protocol-driven design: core protocols in praisonaiagents/, heavy implementations in praisonai/

CONTRIBUTING.md

@@ -1,14 +0,0 @@
-# Contributing to PraisonAI
-
-Thank you for your interest! Here's how to get started:
-
-1. Fork the repository
-2. Create a feature branch
-3. Make your changes
-4. Submit a pull request
-
-## Guidelines
-
-- Follow existing code style
-- Add tests for new features
-- Update documentation as needed

README.md

@@ -770,11 +770,11 @@ agent.start('Write a movie script about a robot in Mars');
 
 ## ⚡ Performance
 
-PraisonAI is built for speed, with agent instantiation in around 14μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.
+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 | **14 μs** |
+| Avg Instantiation Time | **3.77 μs** |
 
 ---
 

api.md

@@ -15,7 +15,6 @@ Methods:
 * <code title="class GuardrailResult">GuardrailResult.<a href="./src/praisonai-agents/praisonaiagents/guardrails/guardrail_result.py">from_tuple</a>(result: Tuple[bool, Any]) -> 'GuardrailResult'</code>
 * <code title="class HandoffConfig">HandoffConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">from_dict</a>(data: Dict[str, Any]) -> 'HandoffConfig'</code>
 * <code title="class HandoffConfig">HandoffConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">to_dict</a>() -> Dict[str, Any]</code>
-* <code title="class HandoffResult">HandoffResult.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">from_outcome</a>(outcome: AgentRunOutcome, target_agent: Optional[str] = None, source_agent: Optional[str] = None, handoff_depth: int = 0) -> 'HandoffResult'</code>
 * <code title="class TaskOutput">TaskOutput.<a href="./src/praisonai-agents/praisonaiagents/main.py">json</a>() -> Optional[str]</code>
 * <code title="class TaskOutput">TaskOutput.<a href="./src/praisonai-agents/praisonaiagents/main.py">to_dict</a>() -> dict</code>
 * <code title="class ToolResult">ToolResult.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">to_dict</a>() -> Dict[str, Any]</code>
@@ -59,8 +58,6 @@ Methods:
 * <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_instance</a>()</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">llm_instance</a>(value)</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>
@@ -78,7 +75,7 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_until</a>(prompt: str, criteria: str, threshold: float = 8.0, max_iterations: int = 5, mode: str = 'optimize', on_iteration: Optional[Callable[[Any], None]] = None, verbose: bool = False) -> 'EvaluationLoopResult'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_until_async</a>(prompt: str, criteria: str, threshold: float = 8.0, max_iterations: int = 5, mode: str = 'optimize', on_iteration: Optional[Callable[[Any], None]] = None, verbose: bool = False) -> 'EvaluationLoopResult'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">skill_manager</a>() -> Optional[Any]</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">store_memory</a>(content: str, memory_type: str = 'short_term', action: str = 'add', **kwargs: Any) -> None</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">store_memory</a>(content: str, memory_type: str = 'short_term', **kwargs: Any) -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">stream_emitter</a>() -> Optional[Any]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">stream_emitter</a>(value: Optional[Any]) -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">thinking_budget</a>() -> Optional[int]</code>
@@ -178,7 +175,6 @@ Methods:
 * <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_schema_roundtrip</a>() -> bool</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">__call__</a>(*args, **kwargs) -> Any</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">check_availability</a>() -> tuple[bool, str]</code>
-* <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">get_schema</a>() -> Dict[str, Any]</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">injected_params</a>() -> Dict[str, Any]</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">run</a>(**kwargs) -> Any</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">clear</a>() -> None</code>
@@ -186,18 +182,16 @@ Methods:
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">discover_single_file_plugins</a>() -> int</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get</a>(name: str) -> Optional[Union[BaseTool, Callable]]</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_all</a>() -> Dict[str, Union[BaseTool, Callable]]</code>
-* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_tool_definitions</a>() -> List[Dict[str, Any]]</code>
-* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_trust_level</a>(name: str) -> Optional[str]</code>
-* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_available_tools</a>(context: Optional[Dict[str, Any]] = None, ttl_seconds: Optional[float] = None) -> List[Union[BaseTool, Callable]]</code>
+* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_available_tools</a>(context: Optional[Dict[str, Any]] = None) -> List[Union[BaseTool, Callable]]</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_base_tools</a>() -> List[BaseTool]</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_tools</a>() -> List[str]</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_tools_with_allowed_filter</a>(context: Optional[Dict[str, Any]] = None) -> List[str]</code>
-* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None, overwrite: bool = False, trust_level: Optional[str] = None, dynamic_schema_overrides: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None) -> None</code>
+* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None, overwrite: bool = False) -> None</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">unregister</a>(name: str) -> bool</code>
 * <code title="class Tools">Tools.<a href="./src/praisonai-agents/praisonaiagents/tools/tools.py">internet_search</a>(*args, **kwargs)</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_registry</a>() -> ToolRegistry</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_tool</a>(name: str) -> Optional[Union[BaseTool, Callable]]</code>
-* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register_tool</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None, trust_level: Optional[str] = None, dynamic_schema_overrides: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None) -> None</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register_tool</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None) -> None</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">tool</a>(func: Optional[Callable] = None) -> Union[FunctionTool, Callable[[Callable], FunctionTool]]</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_tool</a>(tool: Any) -> bool</code>
 
@@ -242,7 +236,6 @@ from praisonaiagents import Memory, MemoryBackend, MemoryConfig
 
 Methods:
 
-* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">build_cache_optimized_context</a>(task_descr: str, user_id: Optional[str] = None, additional: str = '', max_items: int = 3, include_cache_boundary: bool = True, include_in_output: Optional[bool] = None) -> Dict[str, str]</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">build_context_for_task</a>(task_descr: str, user_id: Optional[str] = None, additional: str = '', max_items: int = 3, include_in_output: Optional[bool] = None) -> str</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">calculate_quality_metrics</a>(output: str, expected_output: str, llm: Optional[str] = None, custom_prompt: Optional[str] = None) -> Dict[str, float]</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">close_connections</a>()</code>
@@ -360,7 +353,7 @@ Methods:
 * <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">to_tool_function</a>(source_agent: 'Agent') -> Callable</code>
 * <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">tool_description</a>() -> str</code>
 * <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">tool_name</a>() -> str</code>
-* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">handoff</a>(agent: 'Agent', tool_name_override: Optional[str] = None, tool_description_override: Optional[str] = None, on_handoff: Optional[Callable] = None, input_type: Optional[type] = None, input_filter: Optional[Callable[[HandoffInputData], HandoffInputData]] = None, config: Optional[HandoffConfig] = None, context_policy: Optional[str] = None, timeout_seconds: Optional[float] = None, max_concurrent: Optional[int] = None, detect_cycles: Optional[bool] = None, max_depth: Optional[int] = None, tool_policy_mode: Optional[Literal['intersect', 'passthrough']] = None, blocked_tools: Optional[List[str]] = None) -> Handoff</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">handoff</a>(agent: 'Agent', tool_name_override: Optional[str] = None, tool_description_override: Optional[str] = None, on_handoff: Optional[Callable] = None, input_type: Optional[type] = None, input_filter: Optional[Callable[[HandoffInputData], HandoffInputData]] = None, config: Optional[HandoffConfig] = None, context_policy: Optional[str] = None, timeout_seconds: Optional[float] = None, max_concurrent: Optional[int] = None, detect_cycles: Optional[bool] = None, max_depth: Optional[int] = None) -> Handoff</code>
 * <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">compress_history</a>(data: HandoffInputData) -> HandoffInputData</code>
 * <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">keep_last_n_messages</a>(n: int) -> Callable[[HandoffInputData], HandoffInputData]</code>
 * <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">remove_all_tools</a>(data: HandoffInputData) -> HandoffInputData</code>
@@ -479,16 +472,11 @@ Methods:
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">activate</a>(skill: LoadedSkill) -> bool</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">activate_by_name</a>(name: str) -> bool</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">add_skill</a>(skill_path: str) -> Optional[LoadedSkill]</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">approve</a>(identifier: str) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">archive_skill</a>(name: str) -> dict</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">clear</a>() -> None</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">create</a>(name: str, content: str, category: Optional[str] = None, propose: Optional[bool] = None) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">create_skill</a>(name: str, content: str, category: str = None, agent_created: bool = True, propose: Optional[bool] = None) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">delete</a>(name: str, propose: Optional[bool] = None) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">delete_skill</a>(name: str, hard: bool = False, propose: Optional[bool] = None) -> dict</code>
+* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">create_skill</a>(name: str, content: str, category: str = None) -> dict</code>
+* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">delete_skill</a>(name: str) -> dict</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">discover</a>(skill_dirs: Optional[List[str]] = None, include_defaults: bool = True) -> int</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">edit</a>(name: str, content: str, propose: Optional[bool] = None) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">edit_skill</a>(name: str, content: str, propose: Optional[bool] = None) -> dict</code>
+* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">edit_skill</a>(name: str, content: str) -> dict</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_allowed_tools</a>(name: str) -> List[str]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_available_skills</a>() -> List[SkillMetadata]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_available_skills_by_state</a>(state: SkillState) -> List[LoadedSkill]</code>
@@ -497,24 +485,15 @@ Methods:
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_skills_diagnostics</a>() -> Dict[str, ValidationResult]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_user_invocable_skills</a>() -> List[LoadedSkill]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">invoke</a>(name: str, raw_args: str = '', session_id: Optional[str] = None, shell_exec: bool = False) -> Optional[str]</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">list_archived_skills</a>() -> List[str]</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">list_pending</a>() -> List[dict]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">load_resources</a>(name: str) -> bool</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">patch</a>(name: str, old_string: str, new_string: str, file_path: Optional[str] = None, replace_all: bool = False, propose: Optional[bool] = None) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">patch_skill</a>(name: str, old_string: str, new_string: str, file_path: str = None, replace_all: bool = False, propose: Optional[bool] = None) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">reject</a>(identifier: str) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">remove_file</a>(name: str, file_path: str, propose: Optional[bool] = None) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">remove_skill_file</a>(name: str, file_path: str, propose: Optional[bool] = None) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">restore_skill</a>(name: str, skill_dir: Optional[str] = None) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">rollback_skill</a>(name: str) -> dict</code>
+* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">patch_skill</a>(name: str, old_string: str, new_string: str, file_path: str = None, replace_all: bool = False) -> dict</code>
+* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">remove_skill_file</a>(name: str, file_path: str) -> dict</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">skill_names</a>() -> List[str]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">skills</a>() -> List[LoadedSkill]</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">to_prompt</a>() -> str</code>
 * <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">validate_skill_capabilities</a>(skill_name: str, force_refresh: bool = False) -> ValidationResult</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">write_file</a>(name: str, file_path: str, file_content: str, propose: Optional[bool] = None) -> dict</code>
-* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">write_skill_file</a>(name: str, file_path: str, file_content: str, propose: Optional[bool] = None) -> dict</code>
+* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">write_skill_file</a>(name: str, file_path: str, file_content: str) -> dict</code>
 * <code title="class SkillMetadata">SkillMetadata.<a href="./src/praisonai-agents/praisonaiagents/skills/models.py">from_properties</a>(props: SkillProperties) -> 'SkillMetadata'</code>
-* <code title="class SkillProperties">SkillProperties.<a href="./src/praisonai-agents/praisonaiagents/skills/models.py">idle_days</a>() -> Optional[float]</code>
 * <code title="class SkillProperties">SkillProperties.<a href="./src/praisonai-agents/praisonaiagents/skills/models.py">to_dict</a>() -> dict</code>
 * <code title="class SkillsConfig">SkillsConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 
@@ -532,12 +511,10 @@ Methods:
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">add_memory</a>(text: str, memory_type: str = 'long', **metadata) -> None</code>
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">chat</a>(message: str, **kwargs) -> str</code>
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">clear_memory</a>(memory_type: str = 'all') -> None</code>
-* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">close</a>() -> None</code>
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">create_agent</a>(*args, **kwargs) -> 'Agent'</code>
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">get_context</a>(query: str, max_items: int = 3) -> str</code>
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">get_state</a>(key: str, default: Any = None) -> Any</code>
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">increment_state</a>(key: str, increment: int = 1, default: int = 0) -> None</code>
-* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">is_expired</a>() -> bool</code>
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">knowledge</a>() -> 'Knowledge'</code>
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">memory</a>() -> 'Memory'</code>
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">restore_state</a>() -> Dict[str, Any]</code>
@@ -546,7 +523,6 @@ Methods:
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">search_memory</a>(query: str, memory_type: str = 'long', limit: int = 5) -> List[Dict[str, Any]]</code>
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">send_message</a>(message: str, **kwargs) -> str</code>
 * <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">set_state</a>(key: str, value: Any) -> None</code>
-* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">time_to_expiry</a>() -> Optional[float]</code>
 
 # MCP
 
@@ -629,7 +605,7 @@ Methods:
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">register_snapshot_callback</a>(callback: Callable[[SnapshotHookData], None]) -> None</code>
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">reset</a>() -> None</code>
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">set_tool_budget</a>(tool_name: str, max_tokens: int, protected: bool = False) -> None</code>
-* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">truncate_tool_output</a>(tool_name: str, output: str, tool_call_id: str = None, run_id: str = None) -> str</code>
+* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">truncate_tool_output</a>(tool_name: str, output: str) -> str</code>
 
 # UI
 
@@ -650,7 +626,6 @@ Methods:
 * <code title="class AutonomyConfig">AutonomyConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/autonomy.py">effective_track_changes</a>() -> bool</code>
 * <code title="class AutonomyConfig">AutonomyConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/autonomy.py">from_dict</a>(data: Dict[str, Any]) -> 'AutonomyConfig'</code>
 * <code title="class CachingConfig">CachingConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
-* <code title="class ExecutionConfig">ExecutionConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">from_dict</a>(data: Dict[str, Any]) -> 'ExecutionConfig'</code>
 * <code title="class ExecutionConfig">ExecutionConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 * <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 HooksConfig">HooksConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
@@ -718,7 +693,7 @@ Methods:
 
 Types:
 ```python
-from praisonaiagents import AgentAppConfig, AgentAppProtocol, AgentFlow, AgentManager, AgentOSConfig, AgentOSProtocol, AgentTeam, AutoApproveBackend, EmbeddingResult, RetryBackoffConfig, __version__, aembedding, aembeddings, embedding, embeddings, get_dimensions
+from praisonaiagents import AgentAppConfig, AgentAppProtocol, AgentFlow, AgentManager, AgentOSConfig, AgentOSProtocol, AgentTeam, AutoApproveBackend, EmbeddingResult, aembedding, aembeddings, embedding, embeddings, get_dimensions
 ```
 
 Methods:
@@ -743,16 +718,12 @@ Methods:
 * <code title="class AgentFlow">AgentFlow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">verbose</a>(value: bool)</code>
 * <code title="class AgentOSProtocol">AgentOSProtocol.<a href="./src/praisonai-agents/praisonaiagents/app/protocols.py">get_app</a>() -> Any</code>
 * <code title="class AgentOSProtocol">AgentOSProtocol.<a href="./src/praisonai-agents/praisonaiagents/app/protocols.py">serve</a>(host: Optional[str] = None, port: Optional[int] = None, reload: bool = False, **kwargs: Any) -> None</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aannounce_completion</a>(agent_id: str, task_id: str, result: Any, success: bool = True, error: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None) -> None</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">add_task</a>(task)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aexecute_task</a>(task_id)</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">announce_completion</a>(agent_id: str, task_id: str, result: Any, success: bool = True, error: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None) -> None</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">append_to_state</a>(key: str, value: Any, max_length: Optional[int] = None) -> List[Any]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_all_tasks</a>()</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_task</a>(task_id)</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aspawn_sub_agent</a>(agent: Agent, task: Any, completion_callback: Optional[Callable[[SubAgentCompletionEvent], Any]] = None, metadata: Optional[Dict[str, Any]] = None) -> SpawnedSubAgent</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">astart</a>(content = None, return_dict = False, **kwargs)</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">await_for_completions</a>(timeout: Optional[float] = None, agent_ids: Optional[List[str]] = None) -> List[SubAgentCompletionEvent]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clean_json_output</a>(output: str) -> str</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clear_state</a>() -> None</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">close</a>() -> None</code>
@@ -767,7 +738,6 @@ Methods:
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_tasks_status</a>()</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_detailed_token_report</a>() -> Dict[str, Any]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_plan_markdown</a>() -> str</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_spawned_agents</a>() -> List[SpawnedSubAgent]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_state</a>(key: str, default: Any = None) -> Any</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_details</a>(task_id)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_result</a>(task_id)</code>
@@ -784,12 +754,10 @@ Methods:
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_output_to_file</a>(task, task_output)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_session_state</a>(session_id: str, include_memory: bool = True) -> None</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">set_state</a>(key: str, value: Any) -> None</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">spawn_sub_agent</a>(agent: Agent, task: Any, completion_callback: Optional[Callable[[SubAgentCompletionEvent], Any]] = None, metadata: Optional[Dict[str, Any]] = None) -> SpawnedSubAgent</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, output = None, **kwargs)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">todo_list</a>()</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_plan_step_status</a>(step_id: str, status: str) -> bool</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_state</a>(updates: Dict) -> None</code>
-* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">wait_for_completions</a>(timeout: Optional[float] = None, agent_ids: Optional[List[str]] = None) -> List[SubAgentCompletionEvent]</code>
 * <code title="class AutoApproveBackend">AutoApproveBackend.<a href="./src/praisonai-agents/praisonaiagents/approval/backends.py">request_approval</a>(request: ApprovalRequest) -> ApprovalDecision</code>
 * <code title="class AutoApproveBackend">AutoApproveBackend.<a href="./src/praisonai-agents/praisonaiagents/approval/backends.py">request_approval_sync</a>(request: ApprovalRequest) -> ApprovalDecision</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/embedding/embed.py">aembedding</a>(input: Union[str, List[str]], model: str = 'text-embedding-3-small', dimensions: Optional[int] = None, encoding_format: str = 'float', timeout: float = 600.0, api_key: Optional[str] = None, api_base: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) -> EmbeddingResult</code>
@@ -809,17 +777,11 @@ Methods:
 
 * <code title="cli">praisonai <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
 * <code title="cli">praisonai acp acp-main <a href="./src/praisonai/praisonai/cli/commands/acp.py">--help</a></code>
-* <code title="cli">praisonai agent list <a href="./src/praisonai/praisonai/cli/commands/agent.py">--help</a></code>
-* <code title="cli">praisonai agent show <a href="./src/praisonai/praisonai/cli/commands/agent.py">--help</a></code>
 * <code title="cli">praisonai agents create <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
 * <code title="cli">praisonai agents info <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
 * <code title="cli">praisonai agents list <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
 * <code title="cli">praisonai app app <a href="./src/praisonai/praisonai/cli/commands/app.py">--help</a></code>
 * <code title="cli">praisonai audit agent-centric <a href="./src/praisonai/praisonai/cli/commands/audit.py">--help</a></code>
-* <code title="cli">praisonai auth list <a href="./src/praisonai/praisonai/cli/commands/auth.py">--help</a></code>
-* <code title="cli">praisonai auth login <a href="./src/praisonai/praisonai/cli/commands/auth.py">--help</a></code>
-* <code title="cli">praisonai auth logout <a href="./src/praisonai/praisonai/cli/commands/auth.py">--help</a></code>
-* <code title="cli">praisonai auth status <a href="./src/praisonai/praisonai/cli/commands/auth.py">--help</a></code>
 * <code title="cli">praisonai batch batch-run <a href="./src/praisonai/praisonai/cli/commands/batch.py">--help</a></code>
 * <code title="cli">praisonai batch list <a href="./src/praisonai/praisonai/cli/commands/batch.py">--help</a></code>
 * <code title="cli">praisonai batch report <a href="./src/praisonai/praisonai/cli/commands/batch.py">--help</a></code>
@@ -860,8 +822,6 @@ Methods:
 * <code title="cli">praisonai claw claw <a href="./src/praisonai/praisonai/cli/commands/claw.py">--help</a></code>
 * <code title="cli">praisonai code <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
 * <code title="cli">praisonai code code-main <a href="./src/praisonai/praisonai/cli/commands/code.py">--help</a></code>
-* <code title="cli">praisonai command list <a href="./src/praisonai/praisonai/cli/commands/command.py">--help</a></code>
-* <code title="cli">praisonai command show <a href="./src/praisonai/praisonai/cli/commands/command.py">--help</a></code>
 * <code title="cli">praisonai commit commit-main <a href="./src/praisonai/praisonai/cli/commands/commit.py">--help</a></code>
 * <code title="cli">praisonai completion bash <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
 * <code title="cli">praisonai completion completion-callback <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
@@ -874,9 +834,6 @@ Methods:
 * <code title="cli">praisonai config path <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
 * <code title="cli">praisonai config reset <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
 * <code title="cli">praisonai config set <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
-* <code title="cli">praisonai config show <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
-* <code title="cli">praisonai config sources <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
-* <code title="cli">praisonai config validate <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
 * <code title="cli">praisonai context add <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
 * <code title="cli">praisonai context clear <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
 * <code title="cli">praisonai context compact <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
@@ -907,18 +864,17 @@ Methods:
 * <code title="cli">praisonai docs serve <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
 * <code title="cli">praisonai docs stats <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
 * <code title="cli">praisonai doctor cleanup <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
-* <code title="cli">praisonai doctor docker <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
+* <code title="cli">praisonai doctor config <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
+* <code title="cli">praisonai doctor db <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
+* <code title="cli">praisonai doctor doctor-callback <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor env <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
-* <code title="cli">praisonai doctor llm-providers <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor mcp <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
-* <code title="cli">praisonai doctor memory-store <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
-* <code title="cli">praisonai doctor metadata-store <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor network <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor packaging <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor performance <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
-* <code title="cli">praisonai doctor runtime <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor selftest <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor skills <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
+* <code title="cli">praisonai doctor tools <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai doctor troubleshoot <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
 * <code title="cli">praisonai endpoints list <a href="./src/praisonai/praisonai/cli/commands/endpoints.py">--help</a></code>
 * <code title="cli">praisonai endpoints test <a href="./src/praisonai/praisonai/cli/commands/endpoints.py">--help</a></code>
@@ -946,9 +902,6 @@ Methods:
 * <code title="cli">praisonai gateway install <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai gateway logs <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <code title="cli">praisonai gateway mint-link <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
-* <code title="cli">praisonai gateway pause <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
-* <code title="cli">praisonai gateway reconnect <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
-* <code title="cli">praisonai gateway resume <a href="./src/praisonai/praisonai/cli/commands/gateway.py">--help</a></code>
 * <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>
@@ -958,11 +911,6 @@ Methods:
 * <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>
-* <code title="cli">praisonai identity import <a href="./src/praisonai/praisonai/cli/commands/identity.py">--help</a></code>
-* <code title="cli">praisonai identity link <a href="./src/praisonai/praisonai/cli/commands/identity.py">--help</a></code>
-* <code title="cli">praisonai identity list <a href="./src/praisonai/praisonai/cli/commands/identity.py">--help</a></code>
-* <code title="cli">praisonai identity unlink <a href="./src/praisonai/praisonai/cli/commands/identity.py">--help</a></code>
-* <code title="cli">praisonai init init <a href="./src/praisonai/praisonai/cli/commands/init.py">--help</a></code>
 * <code title="cli">praisonai kanban block <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
 * <code title="cli">praisonai kanban boards <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
 * <code title="cli">praisonai kanban comment <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
@@ -1027,9 +975,6 @@ Methods:
 * <code title="cli">praisonai memory search <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
 * <code title="cli">praisonai memory show <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
 * <code title="cli">praisonai memory status <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
-* <code title="cli">praisonai models describe <a href="./src/praisonai/praisonai/cli/commands/models.py">--help</a></code>
-* <code title="cli">praisonai models list <a href="./src/praisonai/praisonai/cli/commands/models.py">--help</a></code>
-* <code title="cli">praisonai models validate <a href="./src/praisonai/praisonai/cli/commands/models.py">--help</a></code>
 * <code title="cli">praisonai n8n export <a href="./src/praisonai/praisonai/cli/commands/n8n.py">--help</a></code>
 * <code title="cli">praisonai n8n import <a href="./src/praisonai/praisonai/cli/commands/n8n.py">--help</a></code>
 * <code title="cli">praisonai n8n list <a href="./src/praisonai/praisonai/cli/commands/n8n.py">--help</a></code>
@@ -1047,14 +992,6 @@ Methods:
 * <code title="cli">praisonai pairing list <a href="./src/praisonai/praisonai/cli/commands/pairing.py">--help</a></code>
 * <code title="cli">praisonai pairing revoke <a href="./src/praisonai/praisonai/cli/commands/pairing.py">--help</a></code>
 * <code title="cli">praisonai paths show <a href="./src/praisonai/praisonai/cli/commands/paths.py">--help</a></code>
-* <code title="cli">praisonai permissions allow <a href="./src/praisonai/praisonai/cli/commands/permissions.py">--help</a></code>
-* <code title="cli">praisonai permissions ask <a href="./src/praisonai/praisonai/cli/commands/permissions.py">--help</a></code>
-* <code title="cli">praisonai permissions deny <a href="./src/praisonai/praisonai/cli/commands/permissions.py">--help</a></code>
-* <code title="cli">praisonai permissions export <a href="./src/praisonai/praisonai/cli/commands/permissions.py">--help</a></code>
-* <code title="cli">praisonai permissions import-rules <a href="./src/praisonai/praisonai/cli/commands/permissions.py">--help</a></code>
-* <code title="cli">praisonai permissions list <a href="./src/praisonai/praisonai/cli/commands/permissions.py">--help</a></code>
-* <code title="cli">praisonai permissions remove <a href="./src/praisonai/praisonai/cli/commands/permissions.py">--help</a></code>
-* <code title="cli">praisonai permissions reset <a href="./src/praisonai/praisonai/cli/commands/permissions.py">--help</a></code>
 * <code title="cli">praisonai plugins create <a href="./src/praisonai/praisonai/cli/commands/plugins.py">--help</a></code>
 * <code title="cli">praisonai plugins disable <a href="./src/praisonai/praisonai/cli/commands/plugins.py">--help</a></code>
 * <code title="cli">praisonai plugins discover <a href="./src/praisonai/praisonai/cli/commands/plugins.py">--help</a></code>
@@ -1117,8 +1054,6 @@ Methods:
 * <code title="cli">praisonai sandbox sandbox-callback <a href="./src/praisonai/praisonai/cli/commands/sandbox.py">--help</a></code>
 * <code title="cli">praisonai sandbox status <a href="./src/praisonai/praisonai/cli/commands/sandbox.py">--help</a></code>
 * <code title="cli">praisonai schedule add <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
-* <code title="cli">praisonai schedule blueprint <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
-* <code title="cli">praisonai schedule blueprint-list <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule delete <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule describe <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule list <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
@@ -1128,10 +1063,6 @@ Methods:
 * <code title="cli">praisonai schedule start <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule stats <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai schedule stop <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
-* <code title="cli">praisonai schedule suggestion-accept <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
-* <code title="cli">praisonai schedule suggestion-dismiss <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
-* <code title="cli">praisonai schedule suggestion-propose <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
-* <code title="cli">praisonai schedule suggestions <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
 * <code title="cli">praisonai serve a2a <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve a2u <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve acp <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
@@ -1140,7 +1071,6 @@ Methods:
 * <code title="cli">praisonai serve gateway <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve lsp <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve mcp <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
-* <code title="cli">praisonai serve openai <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve rag <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve recipe <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
 * <code title="cli">praisonai serve registry <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
@@ -1208,9 +1138,6 @@ Methods:
 * <code title="cli">praisonai up logs <a href="./src/praisonai/praisonai/cli/commands/up.py">--help</a></code>
 * <code title="cli">praisonai up status <a href="./src/praisonai/praisonai/cli/commands/up.py">--help</a></code>
 * <code title="cli">praisonai up up-start <a href="./src/praisonai/praisonai/cli/commands/up.py">--help</a></code>
-* <code title="cli">praisonai validate check <a href="./src/praisonai/praisonai/cli/commands/validate.py">--help</a></code>
-* <code title="cli">praisonai validate schema <a href="./src/praisonai/praisonai/cli/commands/validate.py">--help</a></code>
-* <code title="cli">praisonai validate validate <a href="./src/praisonai/praisonai/cli/commands/validate.py">--help</a></code>
 * <code title="cli">praisonai version check <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
 * <code title="cli">praisonai version show <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
 * <code title="cli">praisonai version version-callback <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
@@ -1363,7 +1290,7 @@ export { // Python parity additions
 export { // Subagent Tool (agent-as-tool pattern)
   SubagentTool, BaseTool, FunctionTool, ToolRegistry, ToolResult, ToolValidationError, createDelegator, createSubagentTool, createSubagentTools, createTool, getRegistry, getTool, registerTool, tool, validateTool } from "./tools";
 export { airweaveSearch, bedrockBrowserClick, bedrockBrowserFill, bedrockBrowserNavigate, bedrockCodeInterpreter, codeExecution, codeMode, createCustomTool, exaSearch, firecrawlCrawl, firecrawlScrape, parallelSearch, perplexitySearch, registerCustomTool, registerLocalTool, registerNpmTool, superagentGuard, superagentRedact, superagentVerify, tavilyCrawl, tavilyExtract, tavilySearch, valyuBioSearch, valyuCompanyResearch, valyuEconomicsSearch, valyuFinanceSearch, valyuPaperSearch, valyuPatentSearch, valyuSecSearch, valyuWebSearch } from "./tools/builtins";
-export { BudgetExceededError, MissingDependencyError, MissingEnvVarError, ToolsRegistry, composeMiddleware, createLoggingMiddleware, createRateLimitMiddleware, createRedactionMiddleware, createRetryMiddleware, createTimeoutMiddleware, createToolsRegistry, createTracingMiddleware, createValidationMiddleware, getToolsRegistry, get_registry, get_tool, register_tool, resetToolsRegistry, validate_tool } from "./tools/registry";
+export { BudgetExceededError, MissingDependencyError, MissingEnvVarError, ToolsRegistry, composeMiddleware, createLoggingMiddleware, createRateLimitMiddleware, createRedactionMiddleware, createRetryMiddleware, createTimeoutMiddleware, createToolsRegistry, createTracingMiddleware, createValidationMiddleware, getRegistry, getTool, getToolsRegistry, get_registry, get_tool, registerTool, register_tool, resetToolsRegistry, validateTool, validate_tool } from "./tools/registry";
 export type { InstallHints, PraisonTool, RedactionHooks, RegisteredTool, ToolCapabilities, ToolExecutionContext, ToolExecutionResult, ToolFactory, ToolHooks, ToolInstallStatus, ToolLimits, ToolLogger, ToolMetadata, ToolMiddleware, ToolParameterProperty, ToolParameterSchema } from "./tools/registry";
 export { registerBuiltinTools, tools } from "./tools/tools";
 export { // Classes

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>=4.6.74" \
+    "praisonai>=4.6.52" \
     "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>=4.6.74" \
+    "praisonai>=4.6.52" \
     "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>=4.6.74" \
+    "praisonai>=4.6.52" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/approval/agent_approval.py

@@ -12,13 +12,8 @@ from praisonaiagents import Agent
 from praisonaiagents.approval import AgentApproval
 from praisonaiagents.tools.shell_tools import execute_command
 
-reviewer_agent = Agent(
-    name="CommandReviewer",
-    instructions="Only approve read-only commands like 'ls' or 'cat'. Deny destructive commands.",
-)
-
 reviewer = AgentApproval(
-    approver_agent=reviewer_agent,
+    instructions="Only approve read-only commands like 'ls' or 'cat'. Deny destructive commands.",
 )
 
 agent = Agent(

examples/consolidated_params/basic_web.py

@@ -5,12 +5,12 @@ Demonstrates web search with consolidated params.
 Presets: duckduckgo, tavily, google, bing, serper, search_only, fetch_only
 """
 
-from praisonaiagents import Agent, WebConfig
+from praisonaiagents import Agent
 
-# Basic: Enable web search with preset (search only — fetch needs Claude)
+# Basic: Enable web search with preset
 agent = Agent(
     instructions="You are a research assistant with web access.",
-    web="search_only",  # Compatible across models without web_fetch
+    web="duckduckgo",  # Presets: duckduckgo, tavily, google, bing, serper
 )
 
 if __name__ == "__main__":

examples/consolidated_params/basic_workflow.py

@@ -30,5 +30,5 @@ if __name__ == "__main__":
             sys.stdout.reconfigure(encoding="utf-8")
         except Exception:
             pass
-    result = workflow.run("AI agents")
+    result = workflow.run()
     print(result)

examples/cookbooks/Industry_Templates/README.md

@@ -1,216 +0,0 @@
-# Industry Templates for PraisonAI
-
-This directory contains industry-specific agent templates based on the SRAO Framework, providing pre-built workflows for rapid deployment of agent workforces across different industries.
-
-## Overview
-
-These templates demonstrate how PraisonAI can be used to create specialized agent teams for various industries, with **70% code reuse** across different sectors through shared patterns and abstractions.
-
-## Available Templates
-
-### 1. Manufacturing (`manufacturing_template.py`)
-**Key Agents:**
-- **ParseOrder**: Extracts and structures order information from various formats
-- **CheckInventory**: Validates material availability and supply chain status  
-- **OptimizeSchedule**: Optimizes production scheduling based on constraints
-- **DefectDetect**: Quality control and defect detection
-
-**Features:**
-- Order processing with multiple input formats
-- Real-time inventory management
-- Production optimization algorithms
-- Quality inspection with defect detection
-- Fallback strategies for each critical step
-
-### 2. Energy (`energy_template.py`)
-**Key Agents:**
-- **SCADAReader**: Processes SCADA telemetry data from wind turbines
-- **VibrationAnalyzer**: Analyzes vibration patterns for predictive maintenance
-- **PowerForecaster**: Predicts power generation based on weather and history
-- **MaintenanceScheduler**: Schedules predictive maintenance
-
-**Features:**
-- Real-time wind farm monitoring
-- Vibration-based fault detection
-- Power generation forecasting
-- Grid integration and load balancing
-- Predictive maintenance scheduling
-
-### 3. Healthcare (`healthcare_template.py`)
-**Key Agents:**
-- **VitalSignsCapture**: Processes patient vital signs from monitoring devices
-- **EMRRetrieval**: Retrieves electronic medical records with HIPAA compliance
-- **TriageRecommendation**: Provides ESI-based triage recommendations
-- **ResourceAllocator**: Manages hospital resource allocation
-
-**Features:**
-- Emergency triage workflow
-- HIPAA-compliant data handling
-- Patient safety protocols
-- Resource optimization
-- Cross-department coordination
-
-### 4. Agriculture (`agriculture_template.py`)
-**Key Agents:**
-- **MultispectralAnalyzer**: Analyzes multispectral imagery for crop health
-- **DiseaseIdentifier**: Identifies crop diseases and pests using AI
-- **SprayRecommender**: Recommends targeted spraying strategies
-- **YieldPredictor**: Predicts crop yield based on conditions
-
-**Features:**
-- Precision agriculture with drone/satellite imagery
-- Pest and disease early warning system
-- Optimized chemical application
-- Yield forecasting
-- Sustainable farming patterns
-
-### 5. Transportation (`transportation_template.py`)
-**Key Agents:**
-- **LiDARFusion**: Processes LiDAR point cloud data for infrastructure
-- **DisplacementCalculator**: Calculates structural displacement
-- **HeatmapGenerator**: Generates safety visualization heatmaps
-- **MaintenancePlanner**: Plans infrastructure maintenance
-
-**Features:**
-- Tunnel/bridge safety monitoring
-- LiDAR-based structural analysis
-- Real-time safety assessment
-- Traffic flow optimization
-- Predictive maintenance scheduling
-
-## Key Features Across All Templates
-
-### 1. I/O Schemas
-Each template includes Pydantic models for structured data handling:
-- Input validation
-- Type safety
-- Clear data contracts between agents
-
-### 2. SLA Requirements
-Every agent has defined Service Level Agreements:
-- Processing time requirements
-- Response time guarantees
-- Performance benchmarks
-
-### 3. Fallback Strategies
-Robust error handling with fallback mechanisms:
-- Graceful degradation
-- Manual intervention queues
-- Conservative estimates when systems fail
-
-### 4. Cross-Industry Patterns (70% Reuse)
-Reusable base patterns that work across industries:
-- Data parsing agents
-- Resource availability checkers
-- Optimization engines
-- Inspection/quality control agents
-
-## Usage Example
-
-```python
-from manufacturing_template import (
-    order_parser,
-    inventory_checker,
-    schedule_optimizer,
-    quality_inspector,
-    manufacturing_workflow
-)
-
-# Process a manufacturing order
-order_text = "Customer ABC needs 100 units of Product-XYZ by March 15th, high priority"
-result = manufacturing_workflow(order_text)
-print(result)
-```
-
-## Adapting Templates for Your Industry
-
-### Using Base Patterns
-Each template includes reusable pattern classes that can be adapted:
-
-```python
-from manufacturing_template import IndustryAgentPattern
-
-# Create a custom data parser for any domain
-custom_parser = IndustryAgentPattern.create_data_parser(
-    name="CustomParser",
-    domain="retail",
-    sla_seconds=20
-)
-
-# Create a resource checker for any resource type
-resource_checker = IndustryAgentPattern.create_resource_checker(
-    name="StaffAvailability",
-    resource_type="customer_service_staff",
-    sla_seconds=3
-)
-```
-
-### Creating Custom Workflows
-Combine agents from different templates or create new ones:
-
-```python
-from praisonaiagents import Agent, tool
-
-# Define custom tools
-@tool
-def custom_analysis(data: str) -> Dict:
-    """Your custom analysis logic"""
-    return {"result": "analyzed"}
-
-# Create custom agent
-custom_agent = Agent(
-    name="CustomAnalyzer",
-    instructions="Your specific instructions",
-    tools=[custom_analysis]
-)
-```
-
-## Integration with PraisonAI Core
-
-These templates are built on top of PraisonAI's core Agent framework:
-
-```python
-from praisonaiagents import Agent, tool, AgentTeam, Task
-
-# Combine multiple industry agents into a team
-team = AgentTeam(
-    agents=[order_parser, inventory_checker, schedule_optimizer],
-    tasks=[
-        Task(name="parse", agent=order_parser),
-        Task(name="check", agent=inventory_checker),
-        Task(name="optimize", agent=schedule_optimizer)
-    ]
-)
-```
-
-## Best Practices
-
-1. **Start with Templates**: Use these as starting points and customize for your specific needs
-2. **Maintain SLAs**: Define clear performance requirements for each agent
-3. **Implement Fallbacks**: Always have fallback strategies for critical operations
-4. **Use Type Safety**: Leverage Pydantic models for data validation
-5. **Monitor Performance**: Track agent performance against SLAs
-6. **Iterate and Improve**: Continuously refine based on real-world usage
-
-## Contributing
-
-To add a new industry template:
-
-1. Follow the existing template structure
-2. Include all key components:
-   - I/O Schemas (Pydantic models)
-   - Agent definitions with SLAs
-   - Tools for agent capabilities
-   - Complete workflow function
-   - Fallback strategies
-   - Cross-industry patterns
-3. Add comprehensive documentation
-4. Include usage examples
-
-## License
-
-These templates are part of the PraisonAI project and follow the same licensing terms.
-
-## References
-
-Based on the [SRAO Framework](https://github.com/beixuan577/SRAO-Framework) (MIT License)

examples/cookbooks/Industry_Templates/__init__.py

@@ -1,38 +0,0 @@
-"""
-Industry Templates for PraisonAI
-================================
-
-Pre-built agent templates for rapid deployment across different industries.
-Based on SRAO Framework with 70% cross-industry code reuse.
-
-Available templates:
-- Manufacturing: Order processing, inventory, scheduling, quality control
-- Energy: Wind farm monitoring, predictive maintenance, power forecasting
-- Healthcare: Emergency triage, EMR retrieval, resource allocation
-- Agriculture: Precision farming, pest detection, yield prediction
-- Transportation: Infrastructure monitoring, safety assessment, maintenance
-
-Example usage:
-    from Industry_Templates.manufacturing_template import manufacturing_workflow
-    
-    result = manufacturing_workflow("Customer order text")
-"""
-
-__version__ = "1.0.0"
-__author__ = "PraisonAI Team"
-
-# Import the modules so they're accessible via the package
-from . import manufacturing_template
-from . import energy_template
-from . import healthcare_template
-from . import agriculture_template
-from . import transportation_template
-
-# Export commonly used items for convenience
-__all__ = [
-    "manufacturing_template",
-    "energy_template", 
-    "healthcare_template",
-    "agriculture_template",
-    "transportation_template"
-]

examples/cookbooks/Industry_Templates/agriculture_template.py

@@ -1,551 +0,0 @@
-"""
-Agriculture Industry Template
-=============================
-Precision agriculture and pest management workflow
-Based on SRAO Framework with IoT sensor integration
-
-Key agents:
-- MultispectralAnalyzer: Analyzes multispectral imagery
-- DiseaseIdentifier: Identifies crop diseases and pests
-- SprayRecommender: Recommends targeted spraying strategies
-- YieldPredictor: Predicts crop yield based on conditions
-"""
-
-from praisonaiagents import Agent, tool
-from typing import Dict, List, Any, Tuple, Optional
-from pydantic import BaseModel
-from datetime import datetime, timedelta
-from enum import Enum
-
-
-# Crop health levels
-class CropHealthLevel(str, Enum):
-    EXCELLENT = "excellent"
-    GOOD = "good"
-    FAIR = "fair"
-    POOR = "poor"
-    CRITICAL = "critical"
-
-
-# Pest/Disease severity
-class SeverityLevel(str, Enum):
-    NONE = "none"
-    LOW = "low"
-    MODERATE = "moderate"
-    HIGH = "high"
-    SEVERE = "severe"
-
-
-# I/O Schemas
-class MultispectralData(BaseModel):
-    """Multispectral imagery analysis data"""
-    field_id: str
-    capture_time: str
-    ndvi_index: float  # Normalized Difference Vegetation Index
-    ndre_index: float  # Normalized Difference Red Edge
-    ccci_index: float  # Canopy Chlorophyll Content Index
-    moisture_level: float  # Soil moisture percentage
-    temperature: float  # Celsius
-    affected_area: float  # Hectares
-    gps_coordinates: Tuple[float, float]
-
-
-class PestDiseaseReport(BaseModel):
-    """Pest and disease identification report"""
-    report_id: str
-    field_id: str
-    detection_time: str
-    pest_type: Optional[str]
-    disease_type: Optional[str]
-    severity: SeverityLevel
-    affected_crops: List[str]
-    spread_rate: float  # % per day
-    environmental_factors: Dict[str, Any]
-    confidence_score: float
-
-
-class SprayRecommendation(BaseModel):
-    """Targeted spraying recommendation"""
-    recommendation_id: str
-    field_id: str
-    treatment_type: str  # pesticide, fungicide, herbicide, fertilizer
-    product_name: str
-    application_rate: float  # L/hectare
-    target_zones: List[Dict[str, Any]]  # GPS zones
-    optimal_time: str
-    weather_window: Dict[str, Any]
-    cost_estimate: float
-    environmental_impact: str  # low, medium, high
-
-
-class YieldForecast(BaseModel):
-    """Crop yield prediction"""
-    forecast_id: str
-    field_id: str
-    crop_type: str
-    predicted_yield: float  # tons/hectare
-    confidence_interval: Tuple[float, float]
-    harvest_window: Tuple[str, str]
-    quality_grade: str  # A, B, C
-    market_price_estimate: float
-    risk_factors: List[str]
-
-
-# Agriculture-specific tools
-@tool
-def analyze_multispectral_imagery(field_id: str, image_data: Dict) -> Dict:
-    """Analyze multispectral drone/satellite imagery for crop health"""
-    # Simulate multispectral analysis
-    ndvi = 0.75  # Healthy vegetation typically 0.6-0.9
-    
-    # Determine health based on NDVI
-    if ndvi > 0.8:
-        health = CropHealthLevel.EXCELLENT
-        affected_area = 0.0
-    elif ndvi > 0.6:
-        health = CropHealthLevel.GOOD
-        affected_area = 2.5
-    elif ndvi > 0.4:
-        health = CropHealthLevel.FAIR
-        affected_area = 5.0
-    else:
-        health = CropHealthLevel.POOR
-        affected_area = 10.0
-    
-    return {
-        "field_id": field_id,
-        "capture_time": datetime.now().isoformat(),
-        "ndvi_index": ndvi,
-        "ndre_index": 0.68,
-        "ccci_index": 0.72,
-        "moisture_level": 35.0,
-        "temperature": 24.5,
-        "affected_area": affected_area,
-        "gps_coordinates": (40.7128, -74.0060),
-        "health_level": health.value
-    }
-
-
-@tool
-def identify_pest_disease(spectral_data: Dict, historical_data: List) -> Dict:
-    """Identify pests and diseases using AI image recognition"""
-    # Simulate pest/disease detection
-    ndvi = spectral_data.get("ndvi_index", 0.75)
-    affected_area = spectral_data.get("affected_area", 0)
-    
-    if affected_area > 8.0:
-        pest_type = "aphids"
-        disease_type = "powdery_mildew"
-        severity = SeverityLevel.HIGH
-        spread_rate = 2.5
-    elif affected_area > 4.0:
-        pest_type = "whiteflies"
-        disease_type = "leaf_spot"
-        severity = SeverityLevel.MODERATE
-        spread_rate = 1.5
-    elif affected_area > 0:
-        pest_type = None
-        disease_type = "early_blight"
-        severity = SeverityLevel.LOW
-        spread_rate = 0.5
-    else:
-        pest_type = None
-        disease_type = None
-        severity = SeverityLevel.NONE
-        spread_rate = 0.0
-    
-    return {
-        "report_id": f"PDR-{datetime.now().strftime('%Y%m%d%H%M')}",
-        "field_id": spectral_data["field_id"],
-        "detection_time": datetime.now().isoformat(),
-        "pest_type": pest_type,
-        "disease_type": disease_type,
-        "severity": severity.value,
-        "affected_crops": ["wheat", "corn"],
-        "spread_rate": spread_rate,
-        "environmental_factors": {
-            "humidity": 65,
-            "temperature": spectral_data.get("temperature", 25),
-            "rainfall_last_week": 15  # mm
-        },
-        "confidence_score": 0.85
-    }
-
-
-@tool
-def calculate_spray_recommendation(pest_report: Dict, field_data: Dict, weather: Dict) -> Dict:
-    """Calculate optimal spraying strategy for pest/disease control"""
-    severity = pest_report.get("severity", "none")
-    
-    if severity == "high" or severity == "severe":
-        treatment = "pesticide"
-        product = "Imidacloprid"
-        rate = 0.5
-        impact = "medium"
-    elif severity == "moderate":
-        treatment = "fungicide"
-        product = "Azoxystrobin"
-        rate = 0.3
-        impact = "low"
-    elif severity == "low":
-        treatment = "organic_treatment"
-        product = "Neem Oil"
-        rate = 0.2
-        impact = "minimal"
-    else:
-        treatment = "fertilizer"
-        product = "NPK 20-20-20"
-        rate = 0.4
-        impact = "positive"
-    
-    # Calculate target zones based on affected area
-    affected_area = field_data.get("affected_area", 0)
-    if affected_area > 0:
-        target_zones = [
-            {
-                "zone_id": "A1",
-                "gps_boundary": [(40.71, -74.00), (40.72, -74.01)],
-                "priority": "high"
-            }
-        ]
-    else:
-        target_zones = []
-    
-    return {
-        "recommendation_id": f"SPR-{datetime.now().strftime('%Y%m%d%H%M')}",
-        "field_id": field_data["field_id"],
-        "treatment_type": treatment,
-        "product_name": product,
-        "application_rate": rate,
-        "affected_area": affected_area,
-        "target_zones": target_zones,
-        "optimal_time": (datetime.now() + timedelta(days=1)).isoformat(),
-        "weather_window": {
-            "wind_speed": weather.get("wind_speed", 5),
-            "precipitation": weather.get("precipitation", 0),
-            "suitable": True
-        },
-        "cost_estimate": rate * affected_area * 50,  # $/hectare
-        "environmental_impact": impact
-    }
-
-
-@tool
-def predict_crop_yield(field_id: str, crop_type: str, current_health: Dict, weather_forecast: Dict) -> Dict:
-    """Predict crop yield based on current conditions and forecasts"""
-    # Simulate yield prediction
-    health_level = current_health.get("health_level", "good")
-    
-    base_yield = {
-        "wheat": 3.5,
-        "corn": 9.5,
-        "soybeans": 2.8,
-        "rice": 6.0
-    }.get(crop_type, 5.0)
-    
-    # Adjust based on health
-    health_multiplier = {
-        "excellent": 1.2,
-        "good": 1.0,
-        "fair": 0.8,
-        "poor": 0.6,
-        "critical": 0.4
-    }.get(health_level, 1.0)
-    
-    predicted_yield = base_yield * health_multiplier
-    
-    # Quality grade based on conditions
-    if health_level in ["excellent", "good"]:
-        quality = "A"
-        price_multiplier = 1.2
-    elif health_level == "fair":
-        quality = "B"
-        price_multiplier = 1.0
-    else:
-        quality = "C"
-        price_multiplier = 0.8
-    
-    return {
-        "forecast_id": f"YLD-{datetime.now().strftime('%Y%m%d%H%M')}",
-        "field_id": field_id,
-        "crop_type": crop_type,
-        "predicted_yield": predicted_yield,
-        "confidence_interval": (predicted_yield * 0.9, predicted_yield * 1.1),
-        "harvest_window": (
-            (datetime.now() + timedelta(days=60)).isoformat(),
-            (datetime.now() + timedelta(days=75)).isoformat()
-        ),
-        "quality_grade": quality,
-        "market_price_estimate": predicted_yield * 250 * price_multiplier,  # $/ton
-        "risk_factors": ["weather_variability", "pest_pressure", "market_volatility"]
-    }
-
-
-# Agriculture agent definitions
-multispectral_agent = Agent(
-    name="MultispectralAnalyzer",
-    instructions="""You are a remote sensing specialist for precision agriculture.
-    Analyze multispectral imagery from drones and satellites.
-    Calculate vegetation indices (NDVI, NDRE, CCCI) to assess crop health.
-    Identify stress zones and anomalies in field conditions.
-    SLA: Process imagery within 2 minutes per field.""",
-    tools=[analyze_multispectral_imagery]
-)
-
-disease_agent = Agent(
-    name="DiseaseIdentifier",
-    instructions="""You are a plant pathology expert with AI vision capabilities.
-    Identify crop diseases, pests, and nutrient deficiencies.
-    Assess severity levels and predict spread patterns.
-    Consider environmental factors affecting disease development.
-    SLA: Complete identification within 30 seconds.""",
-    tools=[identify_pest_disease]
-)
-
-spray_agent = Agent(
-    name="SprayRecommender",
-    instructions="""You are a precision application specialist.
-    Recommend targeted spraying strategies for pest and disease control.
-    Optimize chemical usage to minimize environmental impact.
-    Consider weather windows and application regulations.
-    SLA: Generate recommendations within 1 minute.""",
-    tools=[calculate_spray_recommendation]
-)
-
-yield_agent = Agent(
-    name="YieldPredictor",
-    instructions="""You are a crop yield forecasting specialist.
-    Predict harvest yields based on current conditions and historical data.
-    Assess crop quality grades and market value estimates.
-    Identify risk factors that could impact final yield.
-    SLA: Generate forecast within 45 seconds.""",
-    tools=[predict_crop_yield]
-)
-
-
-# Precision agriculture workflow
-def precision_agriculture_workflow(field_ids: List[str], crop_type: str, weather_data: Dict):
-    """
-    Complete precision agriculture workflow from monitoring to treatment
-    Includes early warning system and sustainable farming practices
-    """
-    
-    workflow_results = {
-        "timestamp": datetime.now().isoformat(),
-        "fields_monitored": len(field_ids),
-        "alerts": [],
-        "treatments_needed": [],
-        "yield_forecasts": [],
-        "sustainability_score": 0.0
-    }
-    
-    for field_id in field_ids:
-        try:
-            # Step 1: Multispectral analysis
-            spectral_analysis = multispectral_agent.start(
-                f"Analyze multispectral imagery for field {field_id}"
-            )
-            
-            # Step 2: Disease and pest detection
-            pest_report = disease_agent.start(
-                f"Identify pests/diseases from spectral data: {spectral_analysis}"
-            )
-            
-            # Check for alerts
-            if pest_report.get("severity") in ["high", "severe"]:
-                workflow_results["alerts"].append({
-                    "field_id": field_id,
-                    "type": "pest_disease_alert",
-                    "severity": pest_report["severity"],
-                    "pest": pest_report.get("pest_type"),
-                    "disease": pest_report.get("disease_type"),
-                    "action": "immediate_treatment_required"
-                })
-                
-                # Step 3: Generate spray recommendation
-                spray_recommendation = spray_agent.start(
-                    f"Calculate spray strategy for pest report: {pest_report}, "
-                    f"field: {spectral_analysis}, weather: {weather_data}"
-                )
-                workflow_results["treatments_needed"].append(spray_recommendation)
-            
-            # Step 4: Yield prediction
-            yield_forecast = yield_agent.start(
-                f"Predict yield for field {field_id}, crop {crop_type}, "
-                f"health: {spectral_analysis}, weather: {weather_data}"
-            )
-            workflow_results["yield_forecasts"].append(yield_forecast)
-            
-        except Exception as e:
-            # Fallback: Schedule manual inspection
-            workflow_results["alerts"].append({
-                "field_id": field_id,
-                "type": "monitoring_failure",
-                "error": str(e),
-                "action": "schedule_manual_inspection"
-            })
-    
-    # Calculate sustainability score
-    total_chemical = sum(
-        t.get("application_rate", 0) * t.get("affected_area", 0) 
-        for t in workflow_results["treatments_needed"]
-    )
-    workflow_results["sustainability_score"] = max(0, 100 - (total_chemical * 10))
-    
-    return workflow_results
-
-
-# Sustainable farming patterns
-class SustainableFarmingPatterns:
-    """
-    Patterns for sustainable and regenerative agriculture
-    """
-    
-    @staticmethod
-    def integrated_pest_management(pest_level: str) -> Dict:
-        """IPM strategy based on pest pressure"""
-        strategies = {
-            "low": {
-                "method": "biological_control",
-                "actions": ["release_beneficial_insects", "plant_trap_crops"],
-                "chemical_use": 0
-            },
-            "moderate": {
-                "method": "targeted_intervention",
-                "actions": ["spot_treatment", "pheromone_traps", "minimal_spray"],
-                "chemical_use": 30
-            },
-            "high": {
-                "method": "integrated_approach",
-                "actions": ["systematic_spray", "crop_rotation", "resistant_varieties"],
-                "chemical_use": 70
-            }
-        }
-        return strategies.get(pest_level, strategies["moderate"])
-    
-    @staticmethod
-    def precision_irrigation(moisture_level: float, crop_stage: str) -> Dict:
-        """Calculate optimal irrigation based on soil moisture and crop stage"""
-        # Water requirements by growth stage (mm/day)
-        stage_requirements = {
-            "germination": 3.0,
-            "vegetative": 5.0,
-            "flowering": 7.0,
-            "grain_filling": 6.0,
-            "maturation": 2.0
-        }
-        
-        required = stage_requirements.get(crop_stage, 5.0)
-        deficit = max(0, 40 - moisture_level)  # Target 40% moisture
-        
-        return {
-            "irrigation_needed": deficit > 10,
-            "amount_mm": deficit * 2,
-            "method": "drip" if deficit < 20 else "sprinkler",
-            "schedule": "night_irrigation" if deficit > 15 else "morning_irrigation",
-            "water_saved": (100 - deficit) / 100 * 1000  # Liters per hectare
-        }
-    
-    @staticmethod
-    def crop_rotation_optimizer(current_crop: str, soil_data: Dict) -> str:
-        """Recommend next crop for rotation based on soil health"""
-        rotations = {
-            "wheat": ["soybeans", "canola"],  # Nitrogen fixation
-            "corn": ["soybeans", "alfalfa"],  # Break disease cycle
-            "soybeans": ["wheat", "corn"],  # Utilize fixed nitrogen
-            "rice": ["legumes", "vegetables"],  # Soil structure improvement
-        }
-        
-        return rotations.get(current_crop, ["cover_crops"])[0]
-
-
-# IoT sensor integration patterns
-class IoTSensorPatterns:
-    """
-    Patterns for integrating IoT sensors in smart farming
-    """
-    
-    @staticmethod
-    def create_sensor_network_agent(sensor_types: List[str]) -> Agent:
-        """Create agent for managing IoT sensor networks"""
-        return Agent(
-            name="SensorNetworkManager",
-            instructions=f"""Manage IoT sensor network with {', '.join(sensor_types)}.
-            Collect real-time data from field sensors.
-            Detect sensor anomalies and maintenance needs.
-            Aggregate data for precision agriculture decisions.
-            SLA: Data collection every 15 minutes."""
-        )
-    
-    @staticmethod
-    def process_sensor_data(sensor_readings: List[Dict]) -> Dict:
-        """Process and aggregate IoT sensor data"""
-        aggregated = {
-            "timestamp": datetime.now().isoformat(),
-            "sensor_count": len(sensor_readings),
-            "averages": {},
-            "alerts": []
-        }
-        
-        # Calculate averages
-        for reading in sensor_readings:
-            for key, value in reading.items():
-                if isinstance(value, (int, float)):
-                    if key not in aggregated["averages"]:
-                        aggregated["averages"][key] = []
-                    aggregated["averages"][key].append(value)
-        
-        # Convert to means
-        for key in aggregated["averages"]:
-            values = aggregated["averages"][key]
-            aggregated["averages"][key] = sum(values) / len(values)
-            
-            # Check for anomalies
-            if key == "soil_ph" and (aggregated["averages"][key] < 6.0 or aggregated["averages"][key] > 7.5):
-                aggregated["alerts"].append({
-                    "type": "soil_ph_anomaly",
-                    "value": aggregated["averages"][key],
-                    "action": "adjust_soil_treatment"
-                })
-        
-        return aggregated
-
-
-# Example usage
-if __name__ == "__main__":
-    # Monitor multiple fields
-    fields = ["FIELD-001", "FIELD-002", "FIELD-003"]
-    weather = {
-        "temperature": 25,
-        "humidity": 60,
-        "wind_speed": 8,
-        "precipitation": 0,
-        "forecast": "partly_cloudy"
-    }
-    
-    result = precision_agriculture_workflow(fields, "wheat", weather)
-    print("Agriculture workflow result:", result)
-    
-    # Sustainable farming example
-    ipm_strategy = SustainableFarmingPatterns.integrated_pest_management("moderate")
-    print("IPM Strategy:", ipm_strategy)
-    
-    irrigation_plan = SustainableFarmingPatterns.precision_irrigation(25.0, "flowering")
-    print("Irrigation Plan:", irrigation_plan)
-    
-    next_crop = SustainableFarmingPatterns.crop_rotation_optimizer("wheat", {})
-    print("Recommended next crop:", next_crop)
-    
-    # IoT integration example
-    sensor_agent = IoTSensorPatterns.create_sensor_network_agent(
-        ["soil_moisture", "temperature", "humidity", "light", "ph"]
-    )
-    
-    sensor_data = [
-        {"sensor_id": "S001", "soil_moisture": 35, "temperature": 24, "soil_ph": 6.8},
-        {"sensor_id": "S002", "soil_moisture": 32, "temperature": 25, "soil_ph": 7.2},
-        {"sensor_id": "S003", "soil_moisture": 38, "temperature": 23, "soil_ph": 5.8}
-    ]
-    
-    processed_data = IoTSensorPatterns.process_sensor_data(sensor_data)
-    print("Processed sensor data:", processed_data)

examples/cookbooks/Industry_Templates/energy_template.py

@@ -1,372 +0,0 @@
-"""
-Energy Industry Template
-========================
-Wind farm monitoring and power optimization workflow
-Based on SRAO Framework patterns with predictive maintenance
-
-Key agents:
-- SCADAReader: Processes SCADA telemetry data
-- VibrationAnalyzer: Analyzes turbine vibration patterns
-- PowerForecaster: Predicts power generation
-- MaintenanceScheduler: Schedules predictive maintenance
-"""
-
-from praisonaiagents import Agent, tool
-from typing import Dict, List, Any, Optional
-from pydantic import BaseModel
-from datetime import datetime, timedelta
-
-
-# I/O Schemas
-class TurbineData(BaseModel):
-    """Wind turbine telemetry data"""
-    turbine_id: str
-    timestamp: str
-    wind_speed: float  # m/s
-    power_output: float  # MW
-    rotor_speed: float  # RPM
-    blade_pitch: float  # degrees
-    nacelle_temperature: float  # Celsius
-    vibration_level: float  # mm/s
-    operational_status: str  # running, stopped, maintenance
-
-
-class VibrationAnalysis(BaseModel):
-    """Vibration analysis results"""
-    turbine_id: str
-    analysis_time: str
-    vibration_rms: float
-    frequency_peaks: List[float]
-    anomaly_detected: bool
-    failure_probability: float
-    recommended_action: str
-
-
-class PowerForecast(BaseModel):
-    """Power generation forecast"""
-    forecast_id: str
-    start_time: str
-    end_time: str
-    predicted_output: List[float]  # MW per hour
-    confidence_interval: float
-    weather_factors: Dict[str, Any]
-    grid_demand: float
-
-
-class MaintenancePlan(BaseModel):
-    """Maintenance scheduling plan"""
-    plan_id: str
-    turbine_id: str
-    maintenance_type: str  # preventive, predictive, corrective
-    scheduled_date: str
-    estimated_duration: int  # hours
-    required_parts: List[str]
-    technician_team: str
-    production_loss: float  # MW
-
-
-# Tools for energy management
-@tool
-def read_scada_telemetry(turbine_id: str, time_range: str = "last_hour") -> Dict:
-    """Read SCADA system telemetry for wind turbines"""
-    # Simulate SCADA data reading
-    return {
-        "turbine_id": turbine_id,
-        "timestamp": datetime.now().isoformat(),
-        "wind_speed": 12.5,
-        "power_output": 2.3,
-        "rotor_speed": 16.8,
-        "blade_pitch": 8.5,
-        "nacelle_temperature": 45.2,
-        "vibration_level": 3.2,
-        "operational_status": "running"
-    }
-
-
-@tool
-def analyze_vibration_patterns(turbine_data: Dict) -> Dict:
-    """Analyze vibration patterns for early fault detection"""
-    # Simulate vibration analysis
-    vibration_level = turbine_data.get("vibration_level", 0)
-    anomaly = vibration_level > 5.0  # Threshold for anomaly
-    
-    return {
-        "turbine_id": turbine_data["turbine_id"],
-        "analysis_time": datetime.now().isoformat(),
-        "vibration_rms": vibration_level,
-        "frequency_peaks": [10.5, 25.3, 50.0],  # Hz
-        "anomaly_detected": anomaly,
-        "failure_probability": min(vibration_level / 10.0, 1.0),
-        "recommended_action": "immediate_inspection" if anomaly else "continue_monitoring"
-    }
-
-
-@tool
-def forecast_power_generation(weather_data: Dict, historical_data: List) -> Dict:
-    """Forecast power generation based on weather and historical patterns"""
-    # Simulate power forecasting
-    base_forecast = [2.0 + (i * 0.1) for i in range(24)]  # 24-hour forecast
-    
-    return {
-        "forecast_id": f"FCST-{datetime.now().strftime('%Y%m%d%H%M')}",
-        "start_time": datetime.now().isoformat(),
-        "end_time": (datetime.now() + timedelta(hours=24)).isoformat(),
-        "predicted_output": base_forecast,
-        "confidence_interval": 0.85,
-        "weather_factors": weather_data,
-        "grid_demand": sum(base_forecast)
-    }
-
-
-@tool
-def schedule_maintenance(turbine_id: str, maintenance_type: str, urgency: str = "normal") -> Dict:
-    """Schedule maintenance based on condition monitoring"""
-    # Calculate maintenance window
-    if urgency == "urgent":
-        scheduled_date = (datetime.now() + timedelta(days=1)).isoformat()
-    else:
-        scheduled_date = (datetime.now() + timedelta(days=7)).isoformat()
-    
-    return {
-        "plan_id": f"MNT-{datetime.now().strftime('%Y%m%d%H%M')}",
-        "turbine_id": turbine_id,
-        "maintenance_type": maintenance_type,
-        "scheduled_date": scheduled_date,
-        "estimated_duration": 4,
-        "required_parts": ["bearing_set", "lubricant", "filter"],
-        "technician_team": "Team-A",
-        "production_loss": 8.0  # MW lost during maintenance
-    }
-
-
-# Agent Definitions with SLA requirements
-scada_reader = Agent(
-    name="SCADAReader",
-    instructions="""You are a SCADA system specialist for wind farms.
-    Process real-time telemetry data from wind turbines.
-    Identify operational anomalies and performance deviations.
-    SLA: Process telemetry within 1 second for real-time monitoring.""",
-    tools=[read_scada_telemetry]
-)
-
-vibration_analyzer = Agent(
-    name="VibrationAnalyzer",
-    instructions="""You are a vibration analysis expert for rotating machinery.
-    Analyze vibration patterns to detect early signs of mechanical failure.
-    Use FFT analysis and pattern recognition for fault diagnosis.
-    SLA: Complete analysis within 5 seconds per turbine.""",
-    tools=[analyze_vibration_patterns]
-)
-
-power_forecaster = Agent(
-    name="PowerForecaster",
-    instructions="""You are a power generation forecasting specialist.
-    Predict power output based on weather forecasts and historical patterns.
-    Consider grid demand and optimize generation schedules.
-    SLA: Generate 24-hour forecast within 30 seconds.""",
-    tools=[forecast_power_generation]
-)
-
-maintenance_scheduler = Agent(
-    name="MaintenanceScheduler",
-    instructions="""You are a predictive maintenance planning expert.
-    Schedule maintenance based on condition monitoring and failure predictions.
-    Minimize production loss while ensuring turbine reliability.
-    SLA: Create maintenance plan within 1 minute.""",
-    tools=[schedule_maintenance]
-)
-
-
-# Energy workflow with grid integration
-def energy_monitoring_workflow(turbine_ids: List[str], weather_forecast: Dict):
-    """
-    Complete energy monitoring and optimization workflow
-    Includes predictive maintenance and grid integration
-    """
-    
-    results = {
-        "timestamp": datetime.now().isoformat(),
-        "turbines_monitored": len(turbine_ids),
-        "alerts": [],
-        "maintenance_required": [],
-        "power_forecast": None
-    }
-    
-    # Step 1: Monitor all turbines
-    for turbine_id in turbine_ids:
-        try:
-            # Read SCADA data
-            scada_data = scada_reader.start(
-                f"Read telemetry for turbine {turbine_id}"
-            )
-            
-            # Analyze vibrations
-            vibration_result = vibration_analyzer.start(
-                f"Analyze vibrations for data: {scada_data}"
-            )
-            
-            # Check for anomalies
-            if vibration_result.get("anomaly_detected"):
-                results["alerts"].append({
-                    "turbine_id": turbine_id,
-                    "type": "vibration_anomaly",
-                    "severity": "high" if vibration_result.get("failure_probability", 0) > 0.7 else "medium"
-                })
-                
-                # Schedule maintenance if needed
-                maintenance_plan = maintenance_scheduler.start(
-                    f"Schedule maintenance for turbine {turbine_id} with urgency based on {vibration_result}"
-                )
-                results["maintenance_required"].append(maintenance_plan)
-                
-        except Exception as e:
-            # Fallback: Mark turbine for manual inspection
-            results["alerts"].append({
-                "turbine_id": turbine_id,
-                "type": "monitoring_failure",
-                "error": str(e),
-                "action": "manual_inspection_required"
-            })
-    
-    # Step 2: Power generation forecast
-    try:
-        historical_data = []  # Would be fetched from database
-        power_forecast = power_forecaster.start(
-            f"Forecast power with weather {weather_forecast} and history {historical_data}"
-        )
-        results["power_forecast"] = power_forecast
-        
-        # Grid integration check
-        if power_forecast.get("predicted_output"):
-            total_predicted = sum(power_forecast["predicted_output"])
-            grid_demand = power_forecast.get("grid_demand", 0)
-            
-            if total_predicted < grid_demand * 0.9:
-                results["alerts"].append({
-                    "type": "supply_shortage",
-                    "predicted": total_predicted,
-                    "demand": grid_demand,
-                    "action": "consider_backup_sources"
-                })
-                
-    except Exception as e:
-        # Fallback: Use conservative forecast
-        results["power_forecast"] = {
-            "status": "fallback_mode",
-            "conservative_estimate": len(turbine_ids) * 1.5,  # MW
-            "error": str(e)
-        }
-    
-    return results
-
-
-# Cross-industry pattern adaptation
-class EnergyPatternAdapter:
-    """
-    Adapts manufacturing patterns for energy sector
-    Demonstrates 70% code reuse across industries
-    """
-    
-    @staticmethod
-    def adapt_for_solar_farm():
-        """Adapt wind farm agents for solar farm monitoring"""
-        return Agent(
-            name="SolarMonitor",
-            instructions="""Monitor solar panel efficiency and temperature.
-            Track irradiance levels and predict power output.
-            Detect panel degradation and soiling issues.
-            SLA: Real-time monitoring with 2-second updates."""
-        )
-    
-    @staticmethod
-    def adapt_for_power_grid():
-        """Adapt for power grid management"""
-        return Agent(
-            name="GridBalancer",
-            instructions="""Balance power supply and demand across the grid.
-            Manage load distribution and prevent blackouts.
-            Coordinate with renewable and traditional sources.
-            SLA: Sub-second response for grid stability."""
-        )
-    
-    @staticmethod
-    def adapt_for_battery_storage():
-        """Adapt for battery energy storage systems"""
-        return Agent(
-            name="BatteryOptimizer",
-            instructions="""Optimize battery charge/discharge cycles.
-            Predict battery degradation and schedule replacements.
-            Maximize energy arbitrage opportunities.
-            SLA: Optimization decision within 10 seconds."""
-        )
-
-
-# Fallback strategies for critical operations
-class EnergyFallbackStrategies:
-    """
-    Fallback strategies for maintaining grid stability
-    """
-    
-    @staticmethod
-    def turbine_failure_fallback(failed_turbine_id: str, available_turbines: List[str]):
-        """Handle turbine failure by redistributing load"""
-        return {
-            "strategy": "load_redistribution",
-            "failed_turbine": failed_turbine_id,
-            "compensating_turbines": available_turbines[:3],  # Use top 3 turbines
-            "increase_output_by": 0.3,  # MW per turbine
-            "duration": "until_maintenance_complete"
-        }
-    
-    @staticmethod
-    def forecast_failure_fallback(last_known_forecast: Optional[Dict]):
-        """Handle forecasting system failure"""
-        if last_known_forecast:
-            return {
-                "strategy": "use_last_known",
-                "forecast": last_known_forecast,
-                "confidence": 0.6,
-                "validity": "4_hours"
-            }
-        else:
-            return {
-                "strategy": "seasonal_average",
-                "estimated_output": 50.0,  # MW based on seasonal average
-                "confidence": 0.4,
-                "recommendation": "increase_reserves"
-            }
-    
-    @staticmethod
-    def communication_failure_fallback():
-        """Handle SCADA communication failure"""
-        return {
-            "strategy": "local_control_mode",
-            "actions": [
-                "Switch turbines to autonomous mode",
-                "Use local safety thresholds",
-                "Log data locally for later sync",
-                "Alert maintenance teams"
-            ],
-            "recovery_check_interval": 300  # seconds
-        }
-
-
-# Example usage
-if __name__ == "__main__":
-    # Monitor wind farm
-    turbine_list = [f"WT-{i:03d}" for i in range(1, 11)]  # 10 turbines
-    weather = {"wind_speed": 14.0, "direction": "NW", "temperature": 15.0}
-    
-    result = energy_monitoring_workflow(turbine_list, weather)
-    print("Energy monitoring result:", result)
-    
-    # Demonstrate cross-industry adaptation
-    solar_monitor = EnergyPatternAdapter.adapt_for_solar_farm()
-    grid_balancer = EnergyPatternAdapter.adapt_for_power_grid()
-    battery_optimizer = EnergyPatternAdapter.adapt_for_battery_storage()
-    
-    # Fallback examples
-    turbine_fallback = EnergyFallbackStrategies.turbine_failure_fallback(
-        "WT-003", ["WT-001", "WT-002", "WT-004", "WT-005"]
-    )
-    print("Turbine failure fallback:", turbine_fallback)

examples/cookbooks/Industry_Templates/healthcare_template.py

@@ -1,478 +0,0 @@
-"""
-Healthcare Industry Template
-============================
-Emergency triage and patient care coordination workflow
-Based on SRAO Framework with HIPAA-compliant patterns
-
-Key agents:
-- VitalSignsCapture: Processes patient vital signs
-- EMRRetrieval: Retrieves electronic medical records
-- TriageRecommendation: Provides triage recommendations
-- ResourceAllocator: Manages hospital resource allocation
-"""
-
-from praisonaiagents import Agent, tool
-from typing import Dict, List, Any, Optional
-from pydantic import BaseModel
-from datetime import datetime
-from enum import Enum
-
-
-# Triage levels following Emergency Severity Index (ESI)
-class TriageLevel(str, Enum):
-    RESUSCITATION = "1_resuscitation"  # Immediate life-saving intervention
-    EMERGENT = "2_emergent"  # High risk, severe pain/distress
-    URGENT = "3_urgent"  # Stable but needs multiple resources
-    LESS_URGENT = "4_less_urgent"  # Stable, one resource needed
-    NON_URGENT = "5_non_urgent"  # Stable, no resources needed
-
-
-# I/O Schemas
-class VitalSigns(BaseModel):
-    """Patient vital signs data"""
-    patient_id: str
-    timestamp: str
-    heart_rate: int  # bpm
-    blood_pressure_systolic: int  # mmHg
-    blood_pressure_diastolic: int  # mmHg
-    respiratory_rate: int  # breaths/min
-    temperature: float  # Celsius
-    oxygen_saturation: int  # percentage
-    pain_scale: int  # 0-10
-    consciousness_level: str  # alert, verbal, pain, unresponsive (AVPU)
-
-
-class MedicalHistory(BaseModel):
-    """Patient medical history from EMR"""
-    patient_id: str
-    allergies: List[str]
-    chronic_conditions: List[str]
-    current_medications: List[Dict[str, str]]
-    recent_visits: List[Dict[str, Any]]
-    emergency_contacts: List[Dict[str, str]]
-    insurance_status: str
-
-
-class TriageAssessment(BaseModel):
-    """Triage assessment and recommendations"""
-    assessment_id: str
-    patient_id: str
-    triage_level: TriageLevel
-    chief_complaint: str
-    recommended_department: str
-    estimated_wait_time: int  # minutes
-    required_resources: List[str]
-    clinical_notes: str
-    red_flags: List[str]
-
-
-class ResourceAllocation(BaseModel):
-    """Hospital resource allocation"""
-    allocation_id: str
-    patient_id: str
-    assigned_bed: Optional[str]
-    assigned_staff: List[str]
-    equipment_needed: List[str]
-    department: str
-    priority_score: float
-    estimated_treatment_time: int  # minutes
-
-
-# Healthcare-specific tools
-@tool
-def capture_vital_signs(patient_id: str) -> Dict:
-    """Capture patient vital signs from monitoring devices"""
-    # Simulate vital signs capture
-    return {
-        "patient_id": patient_id,
-        "timestamp": datetime.now().isoformat(),
-        "heart_rate": 78,
-        "blood_pressure_systolic": 130,
-        "blood_pressure_diastolic": 85,
-        "respiratory_rate": 16,
-        "temperature": 37.2,
-        "oxygen_saturation": 98,
-        "pain_scale": 6,
-        "consciousness_level": "alert"
-    }
-
-
-@tool
-def retrieve_medical_history(patient_id: str, consent_verified: bool = True) -> Dict:
-    """Retrieve patient medical history from EMR system"""
-    if not consent_verified:
-        return {"error": "consent_required", "message": "Patient consent required for EMR access"}
-    
-    # Simulate EMR retrieval
-    return {
-        "patient_id": patient_id,
-        "allergies": ["penicillin", "latex"],
-        "chronic_conditions": ["hypertension", "diabetes_type2"],
-        "current_medications": [
-            {"name": "metformin", "dose": "500mg", "frequency": "twice daily"},
-            {"name": "lisinopril", "dose": "10mg", "frequency": "once daily"}
-        ],
-        "recent_visits": [
-            {"date": "2024-02-15", "reason": "routine_checkup", "department": "primary_care"}
-        ],
-        "emergency_contacts": [
-            {"name": "Jane Doe", "relationship": "spouse", "phone": "555-0123"}
-        ],
-        "insurance_status": "active"
-    }
-
-
-@tool
-def calculate_triage_priority(vital_signs: Dict, medical_history: Dict, chief_complaint: str) -> Dict:
-    """Calculate triage priority based on clinical indicators"""
-    # Simulate triage algorithm
-    priority_score = 0
-    red_flags = []
-    
-    # Check vital signs
-    if vital_signs["heart_rate"] > 120 or vital_signs["heart_rate"] < 50:
-        priority_score += 3
-        red_flags.append("abnormal_heart_rate")
-    
-    if vital_signs["oxygen_saturation"] < 92:
-        priority_score += 4
-        red_flags.append("low_oxygen")
-    
-    if vital_signs["consciousness_level"] != "alert":
-        priority_score += 5
-        red_flags.append("altered_consciousness")
-    
-    # Determine triage level
-    if priority_score >= 8:
-        triage_level = TriageLevel.RESUSCITATION
-        department = "emergency"
-        wait_time = 0
-    elif priority_score >= 5:
-        triage_level = TriageLevel.EMERGENT
-        department = "emergency"
-        wait_time = 10
-    elif priority_score >= 3:
-        triage_level = TriageLevel.URGENT
-        department = "urgent_care"
-        wait_time = 30
-    else:
-        triage_level = TriageLevel.LESS_URGENT
-        department = "primary_care"
-        wait_time = 60
-    
-    return {
-        "assessment_id": f"TRG-{datetime.now().strftime('%Y%m%d%H%M%S')}",
-        "patient_id": vital_signs["patient_id"],
-        "triage_level": triage_level.value,
-        "chief_complaint": chief_complaint,
-        "recommended_department": department,
-        "estimated_wait_time": wait_time,
-        "required_resources": ["physician", "nurse", "monitoring"],
-        "clinical_notes": f"Priority score: {priority_score}",
-        "red_flags": red_flags
-    }
-
-
-@tool
-def allocate_resources(patient_id: str, triage_level: str, department: str) -> Dict:
-    """Allocate hospital resources based on triage assessment"""
-    # Simulate resource allocation
-    if "resuscitation" in triage_level:
-        bed = "ER-01"
-        staff = ["Dr. Smith", "Nurse Johnson", "Nurse Williams"]
-        equipment = ["cardiac_monitor", "defibrillator", "crash_cart"]
-        priority = 10.0
-    elif "emergent" in triage_level:
-        bed = "ER-05"
-        staff = ["Dr. Brown", "Nurse Davis"]
-        equipment = ["vital_signs_monitor", "IV_pump"]
-        priority = 8.0
-    else:
-        bed = "UC-12"
-        staff = ["PA Miller", "Nurse Garcia"]
-        equipment = ["basic_monitor"]
-        priority = 5.0
-    
-    return {
-        "allocation_id": f"RES-{datetime.now().strftime('%Y%m%d%H%M%S')}",
-        "patient_id": patient_id,
-        "assigned_bed": bed,
-        "assigned_staff": staff,
-        "equipment_needed": equipment,
-        "department": department,
-        "priority_score": priority,
-        "estimated_treatment_time": 45
-    }
-
-
-# Healthcare agent definitions with SLA requirements
-vital_signs_agent = Agent(
-    name="VitalSignsCapture",
-    instructions="""You are a clinical vital signs specialist.
-    Capture and interpret patient vital signs from various monitoring devices.
-    Identify abnormal values and trends that require immediate attention.
-    Follow HIPAA guidelines for patient data handling.
-    SLA: Capture and process within 30 seconds.""",
-    tools=[capture_vital_signs]
-)
-
-emr_agent = Agent(
-    name="EMRRetrieval",
-    instructions="""You are an EMR system specialist with HIPAA compliance expertise.
-    Retrieve relevant patient medical history while ensuring data privacy.
-    Verify patient consent and maintain audit trails.
-    Highlight relevant allergies and contraindications.
-    SLA: Retrieve records within 5 seconds.""",
-    tools=[retrieve_medical_history]
-)
-
-triage_agent = Agent(
-    name="TriageRecommendation",
-    instructions="""You are an emergency triage specialist following ESI guidelines.
-    Assess patient severity and provide triage recommendations.
-    Consider vital signs, medical history, and presenting symptoms.
-    Identify red flags that require immediate intervention.
-    SLA: Complete assessment within 1 minute.""",
-    tools=[calculate_triage_priority]
-)
-
-resource_agent = Agent(
-    name="ResourceAllocator",
-    instructions="""You are a hospital resource management specialist.
-    Allocate beds, staff, and equipment based on patient needs and priority.
-    Optimize resource utilization while maintaining quality of care.
-    Coordinate between departments for efficient patient flow.
-    SLA: Allocate resources within 30 seconds.""",
-    tools=[allocate_resources]
-)
-
-
-# Healthcare workflow with safety protocols
-def emergency_triage_workflow(patient_id: str, chief_complaint: str):
-    """
-    Complete emergency triage workflow from arrival to resource allocation
-    Includes safety checks and fallback protocols
-    """
-    
-    workflow_result = {
-        "patient_id": patient_id,
-        "timestamp": datetime.now().isoformat(),
-        "status": "in_progress",
-        "safety_checks": [],
-        "clinical_pathway": []
-    }
-    
-    # Step 1: Vital signs capture with validation
-    try:
-        vital_signs = vital_signs_agent.start(
-            f"Capture vital signs for patient {patient_id}"
-        )
-        workflow_result["clinical_pathway"].append({
-            "step": "vital_signs",
-            "status": "completed",
-            "data": vital_signs
-        })
-        
-        # Safety check: Validate vital signs are within possible ranges
-        if vital_signs.get("heart_rate", 0) < 20 or vital_signs.get("heart_rate", 0) > 300:
-            workflow_result["safety_checks"].append({
-                "type": "vital_signs_validation",
-                "issue": "impossible_heart_rate",
-                "action": "manual_verification_required"
-            })
-    except Exception as e:
-        # Fallback: Manual vital signs entry
-        workflow_result["clinical_pathway"].append({
-            "step": "vital_signs",
-            "status": "manual_entry_required",
-            "error": str(e)
-        })
-        vital_signs = {"manual_entry": True}
-    
-    # Step 2: EMR retrieval with consent check
-    try:
-        medical_history = emr_agent.start(
-            f"Retrieve medical history for patient {patient_id} with verified consent"
-        )
-        workflow_result["clinical_pathway"].append({
-            "step": "emr_retrieval",
-            "status": "completed",
-            "records_retrieved": True
-        })
-        
-        # Safety check: Allergy alerts
-        if medical_history.get("allergies"):
-            workflow_result["safety_checks"].append({
-                "type": "allergy_alert",
-                "allergies": medical_history["allergies"],
-                "action": "notify_all_providers"
-            })
-    except Exception as e:
-        # Fallback: Proceed with limited information
-        medical_history = {
-            "patient_id": patient_id,
-            "allergies": [],
-            "note": "EMR_unavailable_proceed_with_caution"
-        }
-        workflow_result["safety_checks"].append({
-            "type": "emr_unavailable",
-            "action": "collect_history_manually"
-        })
-    
-    # Step 3: Triage assessment
-    try:
-        triage_assessment = triage_agent.start(
-            f"Assess triage for patient with vitals {vital_signs}, "
-            f"history {medical_history}, complaint: {chief_complaint}"
-        )
-        workflow_result["clinical_pathway"].append({
-            "step": "triage_assessment",
-            "status": "completed",
-            "triage_level": triage_assessment.get("triage_level")
-        })
-        
-        # Safety check: Red flags requiring immediate action
-        if triage_assessment.get("red_flags"):
-            workflow_result["safety_checks"].append({
-                "type": "clinical_red_flags",
-                "flags": triage_assessment["red_flags"],
-                "action": "immediate_physician_notification"
-            })
-    except Exception as e:
-        # Fallback: Conservative triage (assume urgent)
-        triage_assessment = {
-            "triage_level": TriageLevel.URGENT.value,
-            "recommended_department": "emergency",
-            "fallback": True
-        }
-    
-    # Step 4: Resource allocation
-    try:
-        resource_allocation = resource_agent.start(
-            f"Allocate resources for patient {patient_id} "
-            f"with triage {triage_assessment.get('triage_level')} "
-            f"to {triage_assessment.get('recommended_department')}"
-        )
-        workflow_result["clinical_pathway"].append({
-            "step": "resource_allocation",
-            "status": "completed",
-            "bed_assigned": resource_allocation.get("assigned_bed"),
-            "staff_assigned": resource_allocation.get("assigned_staff")
-        })
-    except Exception as e:
-        # Fallback: Queue for next available
-        workflow_result["clinical_pathway"].append({
-            "step": "resource_allocation",
-            "status": "queued",
-            "queue_position": "next_available",
-            "error": str(e)
-        })
-    
-    workflow_result["status"] = "completed"
-    return workflow_result
-
-
-# HIPAA-compliant data handling patterns
-class HIPAACompliancePatterns:
-    """
-    Reusable patterns for HIPAA-compliant healthcare workflows
-    """
-    
-    @staticmethod
-    def anonymize_patient_data(data: Dict) -> Dict:
-        """Remove PHI for analytics and reporting"""
-        safe_fields = ["triage_level", "department", "wait_time", "resource_type"]
-        return {k: v for k, v in data.items() if k in safe_fields}
-    
-    @staticmethod
-    def audit_log_access(user_id: str, patient_id: str, action: str, reason: str):
-        """Create audit trail for EMR access"""
-        return {
-            "timestamp": datetime.now().isoformat(),
-            "user_id": user_id,
-            "patient_id": patient_id,
-            "action": action,
-            "reason": reason,
-            "ip_address": "10.0.0.1",  # Would be actual IP
-            "session_id": "SEC-" + datetime.now().strftime('%Y%m%d%H%M%S')
-        }
-    
-    @staticmethod
-    def encrypt_sensitive_data(data: Dict) -> Dict:
-        """Encrypt sensitive fields (placeholder for real encryption)"""
-        sensitive_fields = ["ssn", "dob", "address", "phone"]
-        encrypted = data.copy()
-        for field in sensitive_fields:
-            if field in encrypted:
-                encrypted[field] = f"ENCRYPTED_{field.upper()}"
-        return encrypted
-
-
-# Cross-department coordination patterns
-class HealthcareCoordinationPatterns:
-    """
-    Patterns for coordinating across healthcare departments
-    """
-    
-    @staticmethod
-    def coordinate_lab_orders(patient_id: str, triage_level: str) -> Agent:
-        """Create agent for lab order coordination"""
-        return Agent(
-            name="LabCoordinator",
-            instructions=f"""Coordinate laboratory orders for patient {patient_id}.
-            Priority level: {triage_level}.
-            Ensure STAT orders for critical patients.
-            Track specimen collection and result delivery.
-            SLA: Order placement within 2 minutes."""
-        )
-    
-    @staticmethod
-    def coordinate_radiology(patient_id: str, imaging_type: str) -> Agent:
-        """Create agent for radiology coordination"""
-        return Agent(
-            name="RadiologyCoordinator",
-            instructions=f"""Schedule {imaging_type} imaging for patient {patient_id}.
-            Check for contrast allergies and pregnancy status.
-            Coordinate with transport for patient movement.
-            SLA: Schedule within 5 minutes for urgent cases."""
-        )
-    
-    @staticmethod
-    def coordinate_pharmacy(patient_id: str, medications: List[str]) -> Agent:
-        """Create agent for pharmacy coordination"""
-        return Agent(
-            name="PharmacyCoordinator",
-            instructions=f"""Verify and dispense medications for patient {patient_id}.
-            Check for drug interactions and allergies.
-            Ensure proper dosing based on patient parameters.
-            SLA: Medication verification within 3 minutes."""
-        )
-
-
-# Example usage
-if __name__ == "__main__":
-    # Process emergency patient
-    patient_id = "PT-2024-00123"
-    chief_complaint = "severe chest pain radiating to left arm"
-    
-    result = emergency_triage_workflow(patient_id, chief_complaint)
-    print("Triage workflow result:", result)
-    
-    # Create specialized coordinators
-    lab_coordinator = HealthcareCoordinationPatterns.coordinate_lab_orders(
-        patient_id, TriageLevel.EMERGENT.value
-    )
-    
-    radiology_coordinator = HealthcareCoordinationPatterns.coordinate_radiology(
-        patient_id, "chest_xray"
-    )
-    
-    pharmacy_coordinator = HealthcareCoordinationPatterns.coordinate_pharmacy(
-        patient_id, ["aspirin", "nitroglycerin"]
-    )
-    
-    # HIPAA compliance example
-    audit_log = HIPAACompliancePatterns.audit_log_access(
-        "DR-001", patient_id, "EMR_ACCESS", "emergency_triage"
-    )
-    print("Audit log created:", audit_log)

examples/cookbooks/Industry_Templates/manufacturing_template.py

@@ -1,298 +0,0 @@
-"""
-Manufacturing Industry Template
-================================
-Order scheduling, inventory management, and quality inspection workflow
-Based on SRAO Framework patterns with 70% cross-industry reuse
-
-Key agents:
-- ParseOrder: Extracts order details from various formats
-- CheckInventory: Validates material availability
-- OptimizeSchedule: Optimizes production scheduling
-- DefectDetect: Quality control and defect detection
-"""
-
-from praisonaiagents import Agent, tool
-from typing import Dict, List, Any
-from pydantic import BaseModel
-
-
-# I/O Schemas
-class OrderDetails(BaseModel):
-    """Structured order information"""
-    order_id: str
-    customer_id: str
-    product_id: str
-    quantity: int
-    priority: str = "normal"  # normal, high, urgent
-    due_date: str
-    requirements: Dict[str, Any] = {}
-
-
-class InventoryStatus(BaseModel):
-    """Material availability status"""
-    material_id: str
-    available_quantity: int
-    reorder_level: int
-    supplier_lead_time: int
-    location: str
-
-
-class ProductionSchedule(BaseModel):
-    """Optimized production schedule"""
-    schedule_id: str
-    production_line: str
-    start_time: str
-    end_time: str
-    assigned_workers: List[str]
-    estimated_completion: str
-    bottleneck_analysis: Dict[str, Any] = {}
-
-
-class QualityReport(BaseModel):
-    """Quality inspection results"""
-    batch_id: str
-    inspection_time: str
-    defect_rate: float
-    defect_types: List[str]
-    passed: bool
-    recommendations: List[str]
-
-
-# Tools for agents
-@tool
-def extract_order_details(text: str) -> Dict:
-    """Extract structured order information from unstructured text"""
-    # Simulate order parsing logic
-    return {
-        "order_id": "ORD-2024-001",
-        "customer_id": "CUST-123",
-        "product_id": "PROD-456",
-        "quantity": 100,
-        "priority": "high",
-        "due_date": "2024-03-15",
-        "requirements": {"material": "steel", "finish": "polished"}
-    }
-
-
-@tool
-def check_material_availability(product_id: str, quantity: int) -> Dict:
-    """Check if materials are available for production"""
-    # Simulate inventory check
-    return {
-        "material_id": "MAT-789",
-        "available_quantity": 500,
-        "reorder_level": 100,
-        "supplier_lead_time": 7,
-        "location": "Warehouse-A"
-    }
-
-
-@tool
-def calculate_optimal_schedule(order: Dict, inventory: Dict) -> Dict:
-    """Calculate optimal production schedule based on constraints"""
-    # Simulate scheduling optimization
-    return {
-        "schedule_id": "SCH-2024-001",
-        "production_line": "Line-3",
-        "start_time": "2024-03-10T08:00:00",
-        "end_time": "2024-03-14T17:00:00",
-        "assigned_workers": ["Worker-001", "Worker-002"],
-        "estimated_completion": "2024-03-14T16:30:00",
-        "bottleneck_analysis": {
-            "critical_path": "Assembly",
-            "buffer_time": 2.5
-        }
-    }
-
-
-@tool
-def perform_quality_inspection(batch_id: str) -> Dict:
-    """Perform automated quality inspection on production batch"""
-    # Simulate quality control
-    return {
-        "batch_id": batch_id,
-        "inspection_time": "2024-03-14T17:00:00",
-        "defect_rate": 0.02,
-        "defect_types": ["surface_scratch", "dimension_variance"],
-        "passed": True,
-        "recommendations": [
-            "Adjust machine calibration on Line-3",
-            "Increase inspection frequency for next batch"
-        ]
-    }
-
-
-# Agent Definitions with SLA requirements
-order_parser = Agent(
-    name="ParseOrder",
-    instructions="""You are an order parsing specialist. Extract and structure order information 
-    from various formats (email, PDF, API). Ensure all required fields are captured and validated.
-    SLA: Process within 30 seconds per order.""",
-    tools=[extract_order_details]
-)
-
-inventory_checker = Agent(
-    name="CheckInventory",
-    instructions="""You are an inventory management specialist. Verify material availability,
-    check reorder points, and identify potential supply chain issues.
-    SLA: Real-time response within 5 seconds.""",
-    tools=[check_material_availability]
-)
-
-schedule_optimizer = Agent(
-    name="OptimizeSchedule",
-    instructions="""You are a production scheduling expert. Optimize production schedules
-    based on order priority, resource availability, and constraints. Minimize idle time
-    and maximize throughput.
-    SLA: Generate schedule within 2 minutes for complex orders.""",
-    tools=[calculate_optimal_schedule]
-)
-
-quality_inspector = Agent(
-    name="DefectDetect",
-    instructions="""You are a quality control specialist. Perform automated inspections,
-    detect defects using vision analysis, and provide improvement recommendations.
-    SLA: Complete inspection within 1 minute per batch.""",
-    tools=[perform_quality_inspection]
-)
-
-
-# Workflow with fallback strategies
-def manufacturing_workflow(order_text: str):
-    """
-    Complete manufacturing workflow from order to quality control
-    Includes fallback strategies for each critical step
-    """
-    
-    # Step 1: Parse order with fallback
-    try:
-        order_details = order_parser.start(f"Parse this order: {order_text}")
-        if not order_details:
-            # Fallback: Manual review queue
-            return {"status": "manual_review_required", "reason": "order_parsing_failed"}
-    except Exception as e:
-        return {"status": "error", "stage": "order_parsing", "error": str(e)}
-    
-    # Step 2: Check inventory with fallback
-    try:
-        inventory_status = inventory_checker.start(
-            f"Check materials for order: {order_details}"
-        )
-        if not inventory_status:
-            # Fallback: Alternative supplier check
-            return {"status": "alternative_supplier_needed", "order": order_details}
-    except Exception as e:
-        # Fallback: Conservative estimate with safety margin
-        inventory_status = {"estimated": True, "safety_margin": 1.5}
-    
-    # Step 3: Optimize schedule with fallback
-    try:
-        schedule = schedule_optimizer.start(
-            f"Create schedule for order {order_details} with inventory {inventory_status}"
-        )
-        if not schedule:
-            # Fallback: Standard scheduling template
-            schedule = {"template": "standard", "priority": "normal"}
-    except Exception as e:
-        # Fallback: Queue for next available slot
-        schedule = {"status": "queued", "estimated_start": "next_available"}
-    
-    # Step 4: Quality inspection with fallback
-    try:
-        quality_report = quality_inspector.start(
-            f"Inspect batch for schedule {schedule}"
-        )
-        if not quality_report:
-            # Fallback: Manual inspection required
-            quality_report = {"requires_manual": True, "inspector_assigned": "QC-Team-1"}
-    except Exception as e:
-        # Fallback: Quarantine for review
-        quality_report = {"status": "quarantined", "review_required": True}
-    
-    return {
-        "status": "completed",
-        "order": order_details,
-        "inventory": inventory_status,
-        "schedule": schedule,
-        "quality": quality_report
-    }
-
-
-# Cross-industry reusable patterns (70% reuse)
-class IndustryAgentPattern:
-    """
-    Base pattern reusable across industries
-    Provides common agent capabilities that can be specialized
-    """
-    
-    @staticmethod
-    def create_data_parser(name: str, domain: str, sla_seconds: int = 30):
-        """Create a data parsing agent for any domain"""
-        return Agent(
-            name=name,
-            instructions=f"""You are a {domain} data parsing specialist.
-            Extract and structure information from various formats.
-            Validate all required fields according to {domain} standards.
-            SLA: Process within {sla_seconds} seconds."""
-        )
-    
-    @staticmethod
-    def create_resource_checker(name: str, resource_type: str, sla_seconds: int = 5):
-        """Create a resource availability checker for any resource type"""
-        return Agent(
-            name=name,
-            instructions=f"""You are a {resource_type} availability specialist.
-            Verify {resource_type} availability and identify constraints.
-            Provide real-time status and alternative options if needed.
-            SLA: Response within {sla_seconds} seconds."""
-        )
-    
-    @staticmethod
-    def create_optimizer(name: str, optimization_target: str, sla_minutes: int = 2):
-        """Create an optimization agent for any target metric"""
-        return Agent(
-            name=name,
-            instructions=f"""You are an optimization expert for {optimization_target}.
-            Find optimal solutions considering all constraints and priorities.
-            Minimize waste and maximize efficiency.
-            SLA: Generate solution within {sla_minutes} minutes."""
-        )
-    
-    @staticmethod
-    def create_inspector(name: str, inspection_type: str, sla_minutes: int = 1):
-        """Create an inspection agent for any quality metric"""
-        return Agent(
-            name=name,
-            instructions=f"""You are a {inspection_type} inspection specialist.
-            Perform automated analysis and detect anomalies.
-            Provide actionable recommendations for improvement.
-            SLA: Complete inspection within {sla_minutes} minute(s)."""
-        )
-
-
-# Example usage
-if __name__ == "__main__":
-    # Create a manufacturing workflow instance
-    order = "Customer ABC needs 100 units of Product-XYZ by March 15th, high priority"
-    
-    # Option 1: Use specialized agents
-    result = manufacturing_workflow(order)
-    print("Manufacturing workflow result:", result)
-    
-    # Option 2: Use reusable patterns for other industries
-    # These patterns can be adapted for Energy, Healthcare, Agriculture, Transportation
-    energy_parser = IndustryAgentPattern.create_data_parser(
-        "EnergyDataParser", "wind farm telemetry", sla_seconds=10
-    )
-    
-    healthcare_checker = IndustryAgentPattern.create_resource_checker(
-        "BedAvailability", "hospital bed", sla_seconds=3
-    )
-    
-    agriculture_optimizer = IndustryAgentPattern.create_optimizer(
-        "IrrigationOptimizer", "water usage", sla_minutes=5
-    )
-    
-    transport_inspector = IndustryAgentPattern.create_inspector(
-        "TunnelSafety", "structural integrity", sla_minutes=2
-    )

examples/cookbooks/Industry_Templates/transportation_template.py

@@ -1,599 +0,0 @@
-"""
-Transportation Industry Template
-================================
-Tunnel safety monitoring and infrastructure management workflow
-Based on SRAO Framework with LiDAR and sensor fusion
-
-Key agents:
-- LiDARFusion: Processes LiDAR point cloud data
-- DisplacementCalculator: Calculates structural displacement
-- HeatmapGenerator: Generates safety heatmaps
-- MaintenancePlanner: Plans infrastructure maintenance
-"""
-
-from praisonaiagents import Agent, tool
-from typing import Dict, List, Any, Tuple, Optional
-from pydantic import BaseModel
-from datetime import datetime, timedelta
-from enum import Enum
-import math
-
-
-# Safety levels for infrastructure
-class SafetyLevel(str, Enum):
-    SAFE = "safe"
-    CAUTION = "caution"
-    WARNING = "warning"
-    DANGER = "danger"
-    CRITICAL = "critical"
-
-
-# Infrastructure types
-class InfrastructureType(str, Enum):
-    TUNNEL = "tunnel"
-    BRIDGE = "bridge"
-    HIGHWAY = "highway"
-    RAILWAY = "railway"
-    AIRPORT = "airport"
-
-
-# I/O Schemas
-class LiDARScan(BaseModel):
-    """LiDAR scan data for infrastructure"""
-    scan_id: str
-    infrastructure_id: str
-    scan_time: str
-    point_cloud_size: int  # Number of points
-    scan_resolution: float  # Points per square meter
-    coverage_area: float  # Square meters
-    anomaly_points: List[Tuple[float, float, float]]  # XYZ coordinates
-    reference_baseline: Optional[Dict[str, Any]]
-
-
-class DisplacementAnalysis(BaseModel):
-    """Structural displacement analysis"""
-    analysis_id: str
-    infrastructure_id: str
-    analysis_time: str
-    max_displacement: float  # millimeters
-    displacement_vector: Tuple[float, float, float]  # XYZ displacement
-    critical_zones: List[Dict[str, Any]]
-    settlement_rate: float  # mm/month
-    tilt_angle: float  # degrees
-    safety_factor: float
-
-
-class SafetyHeatmap(BaseModel):
-    """Infrastructure safety heatmap"""
-    heatmap_id: str
-    infrastructure_id: str
-    generation_time: str
-    grid_resolution: float  # meters
-    safety_scores: List[List[float]]  # 2D grid of safety scores
-    critical_areas: List[Dict[str, Any]]
-    overall_safety: SafetyLevel
-    risk_trends: Dict[str, Any]
-
-
-class MaintenanceSchedule(BaseModel):
-    """Maintenance planning schedule"""
-    schedule_id: str
-    infrastructure_id: str
-    maintenance_type: str  # preventive, corrective, emergency
-    priority_level: str  # low, medium, high, critical
-    scheduled_date: str
-    estimated_duration: int  # hours
-    required_resources: List[str]
-    traffic_impact: str
-    cost_estimate: float
-
-
-# Transportation-specific tools
-@tool
-def process_lidar_scan(infrastructure_id: str, scan_type: str = "full") -> Dict:
-    """Process LiDAR point cloud data for structural analysis"""
-    # Simulate LiDAR processing
-    return {
-        "scan_id": f"LIDAR-{datetime.now().strftime('%Y%m%d%H%M')}",
-        "infrastructure_id": infrastructure_id,
-        "scan_time": datetime.now().isoformat(),
-        "point_cloud_size": 5000000,  # 5 million points
-        "scan_resolution": 100.0,  # points/m²
-        "coverage_area": 50000.0,  # m²
-        "anomaly_points": [
-            (10.5, 20.3, 5.1),
-            (15.2, 25.8, 5.3),
-            (20.1, 30.5, 5.5)
-        ],
-        "reference_baseline": {
-            "date": "2024-01-01",
-            "baseline_id": "BSL-001"
-        }
-    }
-
-
-@tool
-def calculate_displacement(current_scan: Dict, baseline_scan: Dict) -> Dict:
-    """Calculate structural displacement from LiDAR scans"""
-    # Simulate displacement calculation
-    anomalies = current_scan.get("anomaly_points", [])
-    
-    if len(anomalies) > 5:
-        max_displacement = 15.0
-        safety_factor = 0.6
-        safety_level = SafetyLevel.WARNING
-    elif len(anomalies) > 2:
-        max_displacement = 8.0
-        safety_factor = 0.8
-        safety_level = SafetyLevel.CAUTION
-    else:
-        max_displacement = 3.0
-        safety_factor = 0.95
-        safety_level = SafetyLevel.SAFE
-    
-    return {
-        "analysis_id": f"DISP-{datetime.now().strftime('%Y%m%d%H%M')}",
-        "infrastructure_id": current_scan["infrastructure_id"],
-        "analysis_time": datetime.now().isoformat(),
-        "max_displacement": max_displacement,
-        "displacement_vector": (2.5, 1.8, max_displacement),
-        "critical_zones": [
-            {
-                "zone_id": "CZ-001",
-                "location": "tunnel_crown",
-                "displacement": max_displacement * 0.8,
-                "risk": "high" if max_displacement > 10 else "medium"
-            }
-        ],
-        "settlement_rate": max_displacement / 30,  # mm/month
-        "tilt_angle": math.degrees(math.atan(max_displacement / 1000)),
-        "safety_factor": safety_factor
-    }
-
-
-@tool
-def generate_safety_heatmap(displacement_data: Dict, sensor_data: List[Dict]) -> Dict:
-    """Generate safety heatmap visualization for infrastructure"""
-    # Simulate heatmap generation
-    safety_factor = displacement_data.get("safety_factor", 1.0)
-    
-    # Create grid (simplified 10x10)
-    grid_size = 10
-    safety_grid = []
-    critical_areas = []
-    
-    for i in range(grid_size):
-        row = []
-        for j in range(grid_size):
-            # Generate safety score based on position and displacement
-            base_score = safety_factor
-            distance_factor = math.sqrt((i-5)**2 + (j-5)**2) / 10
-            safety_score = min(1.0, base_score + (0.2 * distance_factor))
-            row.append(safety_score)
-            
-            if safety_score < 0.7:
-                critical_areas.append({
-                    "grid_position": (i, j),
-                    "safety_score": safety_score,
-                    "recommended_action": "immediate_inspection"
-                })
-        safety_grid.append(row)
-    
-    # Determine overall safety
-    avg_safety = sum(sum(row) for row in safety_grid) / (grid_size * grid_size)
-    if avg_safety > 0.9:
-        overall = SafetyLevel.SAFE
-    elif avg_safety > 0.8:
-        overall = SafetyLevel.CAUTION
-    elif avg_safety > 0.7:
-        overall = SafetyLevel.WARNING
-    else:
-        overall = SafetyLevel.DANGER
-    
-    return {
-        "heatmap_id": f"HEAT-{datetime.now().strftime('%Y%m%d%H%M')}",
-        "infrastructure_id": displacement_data["infrastructure_id"],
-        "generation_time": datetime.now().isoformat(),
-        "grid_resolution": 5.0,  # meters
-        "safety_scores": safety_grid,
-        "critical_areas": critical_areas,
-        "overall_safety": overall.value,
-        "risk_trends": {
-            "trend": "deteriorating" if avg_safety < 0.8 else "stable",
-            "rate": -0.02 if avg_safety < 0.8 else 0.0
-        }
-    }
-
-
-@tool
-def plan_maintenance(infrastructure_id: str, safety_analysis: Dict, traffic_data: Dict) -> Dict:
-    """Plan maintenance based on safety analysis and traffic patterns"""
-    safety_level = safety_analysis.get("overall_safety", "safe")
-    
-    # Determine maintenance urgency
-    if safety_level in ["danger", "critical"]:
-        maintenance_type = "emergency"
-        priority = "critical"
-        days_until = 1
-        duration = 48
-    elif safety_level == "warning":
-        maintenance_type = "corrective"
-        priority = "high"
-        days_until = 7
-        duration = 24
-    elif safety_level == "caution":
-        maintenance_type = "preventive"
-        priority = "medium"
-        days_until = 30
-        duration = 12
-    else:
-        maintenance_type = "preventive"
-        priority = "low"
-        days_until = 90
-        duration = 8
-    
-    # Traffic impact assessment
-    peak_traffic = traffic_data.get("peak_volume", 1000)
-    if peak_traffic > 5000:
-        traffic_impact = "severe"
-        cost_multiplier = 2.0
-    elif peak_traffic > 2000:
-        traffic_impact = "moderate"
-        cost_multiplier = 1.5
-    else:
-        traffic_impact = "minimal"
-        cost_multiplier = 1.0
-    
-    return {
-        "schedule_id": f"MAINT-{datetime.now().strftime('%Y%m%d%H%M')}",
-        "infrastructure_id": infrastructure_id,
-        "maintenance_type": maintenance_type,
-        "priority_level": priority,
-        "scheduled_date": (datetime.now() + timedelta(days=days_until)).isoformat(),
-        "estimated_duration": duration,
-        "required_resources": [
-            "inspection_team",
-            "repair_crew",
-            "safety_equipment",
-            "traffic_control"
-        ],
-        "traffic_impact": traffic_impact,
-        "cost_estimate": 50000 * cost_multiplier * (duration / 24)
-    }
-
-
-# Transportation agent definitions
-lidar_agent = Agent(
-    name="LiDARFusion",
-    instructions="""You are a LiDAR data processing specialist for infrastructure.
-    Process point cloud data from mobile and terrestrial LiDAR scanners.
-    Perform data fusion from multiple sensor sources.
-    Identify structural anomalies and deformations.
-    SLA: Process scan data within 5 minutes.""",
-    tools=[process_lidar_scan]
-)
-
-displacement_agent = Agent(
-    name="DisplacementCalculator",
-    instructions="""You are a structural displacement analysis expert.
-    Calculate precise displacement measurements from temporal scans.
-    Analyze settlement patterns and structural movements.
-    Predict future displacement trends using ML models.
-    SLA: Complete analysis within 2 minutes.""",
-    tools=[calculate_displacement]
-)
-
-heatmap_agent = Agent(
-    name="HeatmapGenerator",
-    instructions="""You are a safety visualization specialist.
-    Generate intuitive safety heatmaps for infrastructure monitoring.
-    Integrate multiple data sources for comprehensive risk assessment.
-    Highlight critical areas requiring immediate attention.
-    SLA: Generate heatmap within 1 minute.""",
-    tools=[generate_safety_heatmap]
-)
-
-maintenance_agent = Agent(
-    name="MaintenancePlanner",
-    instructions="""You are an infrastructure maintenance planning expert.
-    Schedule maintenance based on structural health and traffic patterns.
-    Optimize maintenance windows to minimize disruption.
-    Coordinate resources and estimate costs.
-    SLA: Create maintenance plan within 3 minutes.""",
-    tools=[plan_maintenance]
-)
-
-
-# Transportation infrastructure monitoring workflow
-def infrastructure_monitoring_workflow(
-    infrastructure_ids: List[str],
-    infrastructure_type: InfrastructureType,
-    traffic_data: Dict
-):
-    """
-    Complete infrastructure monitoring workflow from scanning to maintenance
-    Includes safety assessment and predictive maintenance
-    """
-    
-    monitoring_results = {
-        "timestamp": datetime.now().isoformat(),
-        "infrastructure_type": infrastructure_type.value,
-        "structures_monitored": len(infrastructure_ids),
-        "safety_assessments": [],
-        "maintenance_required": [],
-        "emergency_alerts": [],
-        "network_health_score": 0.0
-    }
-    
-    total_safety_score = 0.0
-    
-    for infra_id in infrastructure_ids:
-        try:
-            # Step 1: LiDAR scanning
-            lidar_scan = lidar_agent.start(
-                f"Process LiDAR scan for {infrastructure_type.value} {infra_id}"
-            )
-            
-            # Step 2: Displacement analysis
-            # Get baseline (would be from database in real scenario)
-            baseline = {"scan_id": "BSL-001", "point_cloud": []}
-            
-            displacement_analysis = displacement_agent.start(
-                f"Calculate displacement for scan: {lidar_scan} against baseline: {baseline}"
-            )
-            
-            # Step 3: Generate safety heatmap
-            sensor_data = []  # Would include IoT sensor readings
-            safety_heatmap = heatmap_agent.start(
-                f"Generate heatmap for displacement: {displacement_analysis} "
-                f"with sensors: {sensor_data}"
-            )
-            
-            monitoring_results["safety_assessments"].append({
-                "infrastructure_id": infra_id,
-                "safety_level": safety_heatmap.get("overall_safety"),
-                "max_displacement": displacement_analysis.get("max_displacement"),
-                "critical_zones": len(safety_heatmap.get("critical_areas", []))
-            })
-            
-            # Check for emergency conditions
-            if safety_heatmap.get("overall_safety") in ["danger", "critical"]:
-                monitoring_results["emergency_alerts"].append({
-                    "infrastructure_id": infra_id,
-                    "alert_level": "emergency",
-                    "safety_status": safety_heatmap["overall_safety"],
-                    "immediate_action": "close_to_traffic",
-                    "notification_sent": True
-                })
-                
-                # Step 4: Emergency maintenance planning
-                maintenance_plan = maintenance_agent.start(
-                    f"Plan emergency maintenance for {infra_id} "
-                    f"with safety: {safety_heatmap} and traffic: {traffic_data}"
-                )
-                monitoring_results["maintenance_required"].append(maintenance_plan)
-            
-            elif safety_heatmap.get("overall_safety") in ["warning", "caution"]:
-                # Regular maintenance planning
-                maintenance_plan = maintenance_agent.start(
-                    f"Plan maintenance for {infra_id} "
-                    f"with safety: {safety_heatmap} and traffic: {traffic_data}"
-                )
-                monitoring_results["maintenance_required"].append(maintenance_plan)
-            
-            # Calculate safety score contribution
-            safety_mapping = {
-                "safe": 1.0, "caution": 0.8, "warning": 0.6,
-                "danger": 0.3, "critical": 0.1
-            }
-            total_safety_score += safety_mapping.get(
-                safety_heatmap.get("overall_safety", "safe"), 0.5
-            )
-            
-        except Exception as e:
-            # Fallback: Manual inspection
-            monitoring_results["emergency_alerts"].append({
-                "infrastructure_id": infra_id,
-                "alert_level": "system_failure",
-                "error": str(e),
-                "action": "dispatch_manual_inspection_team"
-            })
-    
-    # Calculate network health score
-    if infrastructure_ids:
-        monitoring_results["network_health_score"] = (
-            total_safety_score / len(infrastructure_ids) * 100
-        )
-    
-    return monitoring_results
-
-
-# Multi-modal transportation patterns
-class MultiModalTransportPatterns:
-    """
-    Patterns for different transportation infrastructure types
-    Demonstrates cross-industry reuse for transportation sector
-    """
-    
-    @staticmethod
-    def adapt_for_bridge_monitoring() -> Agent:
-        """Adapt tunnel monitoring for bridge structures"""
-        return Agent(
-            name="BridgeMonitor",
-            instructions="""Monitor bridge structural health using sensors.
-            Track deck deflection, cable tension, and vibration patterns.
-            Detect fatigue cracks and corrosion.
-            Consider wind and seismic loads.
-            SLA: Real-time monitoring with 10-second updates."""
-        )
-    
-    @staticmethod
-    def adapt_for_railway_tracks() -> Agent:
-        """Adapt for railway track monitoring"""
-        return Agent(
-            name="RailwayMonitor",
-            instructions="""Monitor railway track geometry and condition.
-            Detect rail defects, gauge variations, and ballast degradation.
-            Analyze wheel-rail interaction forces.
-            Predict track maintenance needs.
-            SLA: Process track inspection data within 3 minutes per km."""
-        )
-    
-    @staticmethod
-    def adapt_for_airport_runways() -> Agent:
-        """Adapt for airport runway monitoring"""
-        return Agent(
-            name="RunwayMonitor",
-            instructions="""Monitor airport runway surface conditions.
-            Detect FOD (Foreign Object Debris) and surface irregularities.
-            Assess pavement condition and friction levels.
-            Monitor drainage and lighting systems.
-            SLA: Complete runway scan within 10 minutes."""
-        )
-
-
-# Intelligent traffic management integration
-class TrafficManagementPatterns:
-    """
-    Patterns for integrating with intelligent traffic systems
-    """
-    
-    @staticmethod
-    def optimize_traffic_flow(maintenance_window: Dict, traffic_patterns: Dict) -> Dict:
-        """Optimize traffic flow during maintenance"""
-        peak_hours = traffic_patterns.get("peak_hours", [7, 9, 17, 19])
-        maintenance_start = maintenance_window.get("start_time", "02:00")
-        
-        # Calculate optimal diversion routes
-        return {
-            "strategy": "dynamic_routing",
-            "diversion_routes": [
-                {"route_id": "ALT-1", "capacity": 0.6, "added_time": 15},
-                {"route_id": "ALT-2", "capacity": 0.3, "added_time": 20}
-            ],
-            "lane_management": {
-                "reversible_lanes": True,
-                "peak_direction": "inbound" if int(maintenance_start[:2]) < 12 else "outbound"
-            },
-            "public_transport": {
-                "increase_frequency": True,
-                "additional_services": 4
-            },
-            "estimated_delay": 10  # minutes
-        }
-    
-    @staticmethod
-    def incident_response_plan(incident_type: str, location: Dict) -> Dict:
-        """Generate incident response plan"""
-        response_times = {
-            "accident": 5,
-            "breakdown": 10,
-            "infrastructure_failure": 2,
-            "weather_hazard": 15
-        }
-        
-        return {
-            "incident_id": f"INC-{datetime.now().strftime('%Y%m%d%H%M')}",
-            "response_time": response_times.get(incident_type, 10),
-            "resources_dispatched": [
-                r for r in [
-                    "emergency_response_team",
-                    "traffic_control_unit",
-                    "maintenance_crew" if "infrastructure" in incident_type else None
-                ] if r is not None
-            ],
-            "traffic_management": {
-                "lane_closures": 2,
-                "speed_reduction": 30,  # km/h
-                "message_signs": ["Incident ahead", "Reduce speed", "Merge left"]
-            },
-            "estimated_clearance": 45  # minutes
-        }
-
-
-# Predictive maintenance patterns
-class PredictiveMaintenancePatterns:
-    """
-    ML-based predictive maintenance for transportation infrastructure
-    """
-    
-    @staticmethod
-    def predict_failure_probability(
-        displacement_trend: List[float],
-        environmental_factors: Dict
-    ) -> float:
-        """Predict probability of structural failure"""
-        # Simplified prediction model
-        trend_slope = (displacement_trend[-1] - displacement_trend[0]) / len(displacement_trend)
-        temperature_factor = abs(environmental_factors.get("temp_variation", 0)) / 50
-        moisture_factor = environmental_factors.get("humidity", 50) / 100
-        
-        base_probability = 0.1
-        trend_contribution = max(0, trend_slope * 10)
-        env_contribution = (temperature_factor + moisture_factor) * 0.2
-        
-        return min(1.0, base_probability + trend_contribution + env_contribution)
-    
-    @staticmethod
-    def optimize_maintenance_schedule(
-        infrastructure_conditions: List[Dict],
-        budget_constraint: float
-    ) -> List[Dict]:
-        """Optimize maintenance schedule across infrastructure network"""
-        # Sort by urgency (safety_factor * cost_efficiency)
-        prioritized = sorted(
-            infrastructure_conditions,
-            key=lambda x: (1 - x.get("safety_factor", 1)) * (1 / x.get("cost", 1)),
-            reverse=True
-        )
-        
-        scheduled = []
-        total_cost = 0
-        
-        for infra in prioritized:
-            if total_cost + infra.get("cost", 0) <= budget_constraint:
-                scheduled.append({
-                    "infrastructure_id": infra["id"],
-                    "scheduled_date": infra["recommended_date"],
-                    "priority": "high" if infra["safety_factor"] < 0.7 else "medium"
-                })
-                total_cost += infra.get("cost", 0)
-        
-        return scheduled
-
-
-# Example usage
-if __name__ == "__main__":
-    # Monitor tunnel network
-    tunnel_ids = [f"TUN-{i:03d}" for i in range(1, 6)]
-    traffic = {
-        "peak_volume": 3000,
-        "off_peak_volume": 800,
-        "peak_hours": [7, 9, 17, 19]
-    }
-    
-    result = infrastructure_monitoring_workflow(
-        tunnel_ids,
-        InfrastructureType.TUNNEL,
-        traffic
-    )
-    print("Infrastructure monitoring result:", result)
-    
-    # Multi-modal adaptation examples
-    bridge_monitor = MultiModalTransportPatterns.adapt_for_bridge_monitoring()
-    railway_monitor = MultiModalTransportPatterns.adapt_for_railway_tracks()
-    runway_monitor = MultiModalTransportPatterns.adapt_for_airport_runways()
-    
-    # Traffic management example
-    maintenance = {"start_time": "22:00", "duration": 8}
-    traffic_plan = TrafficManagementPatterns.optimize_traffic_flow(maintenance, traffic)
-    print("Traffic optimization plan:", traffic_plan)
-    
-    # Predictive maintenance example
-    displacement_history = [2.0, 2.3, 2.8, 3.5, 4.3]  # mm over months
-    environment = {"temp_variation": 30, "humidity": 75}
-    
-    failure_prob = PredictiveMaintenancePatterns.predict_failure_probability(
-        displacement_history, environment
-    )
-    print(f"Failure probability: {failure_prob:.2%}")

examples/knowledge/context_required_example.py

@@ -80,7 +80,7 @@ Be precise and quote exact values when asked about codes or numbers.""",
         print(f"\nAnswer: {response}\n")
         
         # Verify the answer contains the unique code
-        if "ZEBRA-71" in str(response or "").upper():
+        if "ZEBRA-71" in response.upper():
             print("✅ VERIFIED: Agent correctly retrieved the unique code from context!\n")
         else:
             print("❌ WARNING: Agent did not find the code - check retrieval\n")
@@ -104,7 +104,7 @@ Be precise and quote exact values when asked about codes or numbers.""",
         response = agent.chat("What is the security incident code?")
         print(f"\nAnswer: {response}\n")
         
-        if "HAWK-88" in str(response or "").upper():
+        if "HAWK-88" in response.upper():
             print("✅ VERIFIED: Agent correctly retrieved the security code!\n")
         else:
             print("❌ WARNING: Agent did not find the code - check retrieval\n")

examples/observability/multi_agent_tracing.py

@@ -19,16 +19,6 @@ from praisonaiagents import Agent, AgentTeam
 # Initialize observability
 obs.init()
 
-
-def set_span_attr(span, key, value):
-    """Set span attribute safely across observability implementations."""
-    if hasattr(span, "set_attribute"):
-        span.set_attribute(key, value)
-        return
-    attrs = getattr(span, "attributes", None)
-    if attrs is not None:
-        attrs[key] = value
-
 # Create agents
 researcher = Agent(
     name="Researcher",
@@ -47,13 +37,13 @@ with obs.trace("content-creation", metadata={"workflow": "research-write"}):
     
     # Research phase
     with obs.span("research-phase", kind=SpanKind.AGENT) as research_span:
-        set_span_attr(research_span, "agent", "Researcher")
+        research_span.attributes["agent"] = "Researcher"
         research_result = researcher.chat("Research the benefits of AI in healthcare")
     
     # Writing phase  
     with obs.span("writing-phase", kind=SpanKind.AGENT) as write_span:
-        set_span_attr(write_span, "agent", "Writer")
-        set_span_attr(write_span, "input_length", len(str(research_result)))
+        write_span.attributes["agent"] = "Writer"
+        write_span.attributes["input_length"] = len(str(research_result))
         final_content = writer.chat(f"Write a blog post based on: {research_result}")
 
 print("Final Content:")

examples/persistence/minimal_agent_db.py

@@ -13,8 +13,7 @@ Expected output:
     - Session can be resumed later with same session_id
 """
 
-from praisonaiagents import Agent
-from praisonaiagents.db import db
+from praisonaiagents import Agent, db
 
 # Create database connection (PostgreSQL + Redis)
 my_db = db(
@@ -26,12 +25,10 @@ my_db = db(
 agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant. Be concise.",
-    memory={
-        "db": my_db,
-        # session_id is optional - defaults to per-hour ID (YYYYMMDDHH-hash)
-        # Set explicitly for session continuity across runs:
-        "session_id": "my-persistent-session",
-    },
+    db=my_db,
+    # session_id is optional - defaults to per-hour ID (YYYYMMDDHH-hash)
+    # Set explicitly for session continuity across runs:
+    session_id="my-persistent-session"
 )
 
 # Chat - messages are automatically persisted

examples/persistence/postgres_runs_traces.py

@@ -12,8 +12,7 @@ Expected output:
     - Export shows full session data
 """
 
-from praisonaiagents import Agent
-from praisonaiagents.db import db
+from praisonaiagents import Agent, db
 
 def get_weather(city: str) -> str:
     """Get weather for a city."""
@@ -30,10 +29,8 @@ agent = Agent(
     name="WeatherBot",
     instructions="You help with weather. Use the get_weather tool when asked.",
     tools=[get_weather],
-    memory={
-        "db": my_db,
-        "session_id": "weather-session-001",
-    },
+    db=my_db,
+    session_id="weather-session-001"
 )
 
 # First chat - triggers run tracking

examples/persistence/redis_state.py

@@ -13,8 +13,7 @@ Expected output:
     Agent responds with state persisted to Redis
 """
 
-from praisonaiagents import Agent
-from praisonaiagents.db import db
+from praisonaiagents import Agent, db
 
 print("=== Redis State Store (Agent-First) ===")
 
@@ -27,10 +26,8 @@ my_db = db(
 agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant.",
-    memory={
-        "db": my_db,
-        "session_id": "redis-state-session",
-    },
+    db=my_db,
+    session_id="redis-state-session"
 )
 
 # Chat - state is automatically managed via Redis

examples/persistence/session_resume.py

@@ -11,8 +11,7 @@ Expected output:
     - Second run: Resumes with history
 """
 
-from praisonaiagents import Agent
-from praisonaiagents.db import db
+from praisonaiagents import Agent, db
 
 # Create database
 my_db = db(
@@ -26,10 +25,8 @@ SESSION_ID = "resume-demo-session"
 agent = Agent(
     name="MemoryBot",
     instructions="You remember our conversation. Be helpful and concise.",
-    memory={
-        "db": my_db,
-        "session_id": SESSION_ID,
-    },
+    db=my_db,
+    session_id=SESSION_ID
 )
 
 # Check if this is a new or resumed session

examples/persistence/simple_db_agent.py

@@ -26,8 +26,7 @@ Expected Output:
     Messages persisted: 2
 """
 
-from praisonaiagents import Agent
-from praisonaiagents.db import db
+from praisonaiagents import Agent, db
 
 # Create database adapter (simplified import)
 db_instance = db(
@@ -38,10 +37,8 @@ db_instance = db(
 agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant. Keep responses brief.",
-    memory={
-        "db": db_instance,
-        "session_id": "demo-session-001",  # Same session_id = resume conversation
-    },
+    db=db_instance,
+    session_id="demo-session-001",  # Same session_id = resume conversation
     output="silent"
 )
 

examples/persistence/sqlite_local.py

@@ -11,8 +11,7 @@ Expected output:
     - Data persisted to local SQLite file
 """
 
-from praisonaiagents import Agent
-from praisonaiagents.db import db
+from praisonaiagents import Agent, db
 import tempfile
 import os
 
@@ -26,10 +25,8 @@ print(f"Database: {db_path}")
 agent = Agent(
     name="LocalBot",
     instructions="You are a helpful assistant.",
-    memory={
-        "db": my_db,
-        "session_id": "local-session",
-    },
+    db=my_db,
+    session_id="local-session"
 )
 
 # Chat

examples/python/agents/autoagents-hierarchical-generation.py

@@ -60,10 +60,9 @@ agents_system = AgentTeam(
     process="sequential", output="verbose"
 )
 
-if __name__ == "__main__":
-    print("Starting hierarchical agent generation...")
-    result = agents_system.start()
+print("Starting hierarchical agent generation...")
+result = agents_system.start()
 
-    print(f"\nHierarchical Result: {str(result)[:200]}...")
-    print("\n✅ AutoAgents hierarchical generation complete!")
-    print("Demonstrated coordinated hierarchical workflow between manager and specialist agents.")
+print(f"\nHierarchical Result: {result[:200]}...")
+print("\n✅ AutoAgents hierarchical generation complete!")
+print("Demonstrated coordinated hierarchical workflow between manager and specialist agents.")

examples/python/agents/code-agent.py

@@ -6,18 +6,10 @@ It includes self-reflection for improving code quality and multiple specialized
 """
 
 from praisonaiagents import Agent, Task, AgentTeam
+from praisonaiagents.tools import CodeInterpreter
 
-def run_python_code(code: str) -> str:
-    """Execute Python code safely and return stdout or errors."""
-    import io
-    import contextlib
-    try:
-        buf = io.StringIO()
-        with contextlib.redirect_stdout(buf):
-            exec(code, {"__builtins__": __builtins__})
-        return buf.getvalue() or "Executed successfully (no output)"
-    except Exception as e:
-        return f"Execution error: {e}"
+# Initialize code execution tool
+code_interpreter = CodeInterpreter()
 
 # Custom code analysis tool
 def analyze_code_complexity(code: str) -> str:
@@ -40,7 +32,9 @@ code_writer = Agent(
     goal="Write clean, efficient, and well-documented Python code",
     backstory="You are a senior Python developer with 10+ years of experience in writing production-quality code.",
     instructions="Write Python code that follows PEP 8 standards, includes proper error handling, and has clear documentation.",
-    output="silent",
+    reflection=True,
+    
+    
 )
 
 code_reviewer = Agent(
@@ -58,7 +52,7 @@ code_executor = Agent(
     goal="Safely execute and test Python code",
     backstory="You are responsible for running code and reporting results or errors.",
     instructions="Execute the provided code safely and report the output or any errors encountered.",
-    tools=[run_python_code]
+    tools=[code_interpreter]
 )
 
 # Example tasks
@@ -93,7 +87,7 @@ def demonstrate_code_agent():
     workflow = AgentTeam(
         agents=[code_writer, code_reviewer, code_executor],
         tasks=[write_task, review_task, execute_task],
-        process="sequential", output="silent"
+        process="sequential", output="verbose"
     )
     
     # Run the workflow
@@ -112,8 +106,10 @@ general_code_agent = Agent(
 3. Explain code concepts
 4. Refactor code for better performance
 5. Add tests and documentation""",
-    tools=[run_python_code, analyze_code_complexity],
-    output="silent",
+    tools=[code_interpreter, analyze_code_complexity],
+    reflection=True,
+    
+    
 )
 
 if __name__ == "__main__":

examples/python/agents/math-agent.py

@@ -41,25 +41,21 @@ def calculate_statistics(numbers: list) -> str:
     """Calculate basic statistics for a list of numbers."""
     if not numbers:
         return "No numbers provided"
-    try:
-        nums = [float(x) for x in numbers]
-    except (TypeError, ValueError):
-        return "Error: all values must be numbers"
-
-    n = len(nums)
-    mean = sum(nums) / n
-    sorted_nums = sorted(nums)
+    
+    n = len(numbers)
+    mean = sum(numbers) / n
+    sorted_nums = sorted(numbers)
     median = sorted_nums[n//2] if n % 2 else (sorted_nums[n//2-1] + sorted_nums[n//2]) / 2
-    variance = sum((x - mean)**2 for x in nums) / n
+    variance = sum((x - mean)**2 for x in numbers) / n
     std_dev = math.sqrt(variance)
-
+    
     return f"""Statistics:
 - Count: {n}
 - Mean: {mean:.4f}
 - Median: {median:.4f}
 - Standard Deviation: {std_dev:.4f}
-- Min: {min(nums)}
-- Max: {max(nums)}"""
+- Min: {min(numbers)}
+- Max: {max(numbers)}"""
 
 # Create specialized math agents
 calculation_agent = Agent(
@@ -70,7 +66,8 @@ calculation_agent = Agent(
     instructions="Perform the requested calculations step by step, showing your work clearly.",
     tools=[basic_calculator, solve_quadratic, calculate_statistics],
     reflection=True,
-    output="silent",
+    
+    
 )
 
 problem_solver_agent = Agent(
@@ -85,7 +82,8 @@ problem_solver_agent = Agent(
 4. Show each step of the solution
 5. Verify the answer makes sense""",
     reflection=True,
-    output="silent",
+    
+    
 )
 
 math_tutor_agent = Agent(
@@ -93,8 +91,7 @@ math_tutor_agent = Agent(
     role="Mathematics tutor and explainer",
     goal="Explain mathematical concepts clearly and help users understand math",
     backstory="You are a patient math tutor who can explain concepts at any level from elementary to advanced.",
-    instructions="Explain mathematical concepts using simple language, provide examples, and check for understanding.",
-    output="silent",
+    instructions="Explain mathematical concepts using simple language, provide examples, and check for understanding."
 )
 
 # Example workflow for solving complex problems
@@ -129,8 +126,7 @@ def solve_math_problem_workflow():
     workflow = AgentTeam(
         agents=[problem_solver_agent, calculation_agent, math_tutor_agent],
         tasks=[understand_task, solve_task, verify_task],
-        process="sequential",
-        output="silent",
+        process="sequential", output="verbose"
     )
     
     return workflow.start()
@@ -152,7 +148,8 @@ math_assistant = Agent(
 Always show your work step by step.""",
     tools=[basic_calculator, solve_quadratic, calculate_statistics],
     reflection=True,
-    output="silent",
+    
+    
 )
 
 if __name__ == "__main__":

examples/python/api/simple-mcp-server.py

@@ -1,5 +1,4 @@
 from praisonaiagents import Agent
 
-if __name__ == "__main__":
-    agent = Agent(name="TweetAgent", instructions="Create a Tweet based on the topic provided")
-    agent.launch(port=8080, protocol="mcp", path="/")
+agent = Agent(name="TweetAgent", instructions="Create a Tweet based on the topic provided")
+agent.launch(port=8080, protocol="mcp")

examples/python/bot_run_control_example.py

@@ -1,238 +0,0 @@
-#!/usr/bin/env python3
-"""
-Example: Bot In-Flight Run Control
-
-Demonstrates how to use SessionRunControl to provide better UX for bots
-during long-running agent operations. Shows busy feedback, pending message
-handling, and /stop command support.
-
-This example shows the "before" and "after" behavior described in issue #1914.
-"""
-
-import asyncio
-import time
-from typing import Optional
-
-# Mock agent for demonstration
-class MockAgent:
-    """Simple mock agent that simulates long-running tasks."""
-    
-    def __init__(self, name="assistant"):
-        self.name = name
-        self.chat_history = []
-        self._interrupt_controller = None
-    
-    def chat(self, prompt: str) -> str:
-        """Simulate a long-running chat response."""
-        print(f"[Agent] Starting to process: '{prompt[:50]}...'")
-        
-        # Simulate long processing with interrupt checking
-        for i in range(30):  # 3 second task (30 x 0.1s)
-            time.sleep(0.1)
-            
-            # Check for interruption
-            if (self._interrupt_controller and 
-                hasattr(self._interrupt_controller, 'is_set') and
-                self._interrupt_controller.is_set()):
-                reason = getattr(self._interrupt_controller, 'reason', 'unknown')
-                print(f"[Agent] ⚠️ Interrupted: {reason}")
-                raise InterruptedError(f"Task cancelled: {reason}")
-                
-        response = f"Completed analysis of: {prompt}"
-        print(f"[Agent] ✅ Finished: {response}")
-        return response
-
-
-async def demo_without_run_control():
-    """Demonstrate the old behavior (silent blocking)."""
-    print("\n" + "="*60)
-    print("🔴 BEFORE: Without Run Control (Silent Blocking)")
-    print("="*60)
-    
-    from praisonai.bots._session import BotSessionManager
-    
-    # Standard session manager (no run control)
-    session_mgr = BotSessionManager()
-    agent = MockAgent("research-agent")
-    user_id = "user123"
-    
-    print("\n1. User sends long task:")
-    print("   User: 'research quantum computing trends'")
-    
-    # Start first task (this will run for 3 seconds)
-    task1 = asyncio.create_task(
-        session_mgr.chat(agent, user_id, "research quantum computing trends")
-    )
-    
-    # Simulate user sending follow-up messages during execution
-    await asyncio.sleep(0.5)
-    print("\n2. User sends follow-up (this will block silently):")
-    print("   User: 'actually focus on business impact'")
-    
-    task2 = asyncio.create_task(
-        session_mgr.chat(agent, user_id, "actually focus on business impact")  
-    )
-    
-    await asyncio.sleep(0.5)
-    print("\n3. User tries to send /stop (this will also block):")
-    print("   User: '/stop'")
-    
-    task3 = asyncio.create_task(
-        session_mgr.chat(agent, user_id, "/stop")
-    )
-    
-    print("\n⏳ Waiting for all messages to complete...")
-    results = await asyncio.gather(task1, task2, task3)
-    
-    print("\n📊 Results (all processed sequentially with no feedback):")
-    for i, result in enumerate(results, 1):
-        print(f"   {i}. {result}")
-    
-    print("\n❌ Problems with this approach:")
-    print("   - No immediate feedback on follow-up messages")
-    print("   - No way to cancel long-running tasks") 
-    print("   - User doesn't know if bot is working or broken")
-
-
-async def demo_with_run_control():
-    """Demonstrate the new behavior with run control."""
-    print("\n" + "="*60)
-    print("🟢 AFTER: With Run Control (Better UX)")  
-    print("="*60)
-    
-    from praisonai.bots._run_control import SessionRunControl
-    from praisonai.bots._session import BotSessionManager
-    from praisonai.bots._commands import handle_stop_command
-    
-    # Session manager with run control
-    run_control = SessionRunControl(
-        busy_mode="queue",
-        busy_ack_template="⏳ {action} — will process after current task finishes"
-    )
-    session_mgr = BotSessionManager(run_control=run_control)
-    agent = MockAgent("research-agent")
-    user_id = "user123"
-    
-    print("\n1. User sends long task:")
-    print("   User: 'research quantum computing trends'")
-    
-    # Start first task
-    task1 = asyncio.create_task(
-        session_mgr.chat_with_run_control(agent, user_id, "research quantum computing trends")
-    )
-    
-    # Immediate follow-up messages get acknowledgments
-    await asyncio.sleep(0.5)
-    print("\n2. User sends follow-up (gets immediate feedback):")
-    print("   User: 'actually focus on business impact'")
-    
-    result2 = await session_mgr.chat_with_run_control(
-        agent, user_id, "actually focus on business impact"
-    )
-    print(f"   Bot: {result2['response']}")
-    print(f"   Metadata: {result2['metadata']}")
-    
-    await asyncio.sleep(0.5)
-    print("\n3. User decides to stop current task:")
-    print("   User: '/stop'")
-    
-    stop_response = await handle_stop_command(user_id, run_control)
-    print(f"   Bot: {stop_response}")
-    
-    # Wait for first task to complete (it should be cancelled)
-    try:
-        result1 = await task1
-        print(f"\n📊 Task 1 result: {result1['response']}")
-        print(f"   Metadata: {result1['metadata']}")
-    except asyncio.CancelledError:
-        print("\n📊 Task 1 was cancelled as expected")
-    
-    print("\n4. User sends fresh request after stopping:")
-    print("   User: 'what's the weather like?'")
-    
-    result4 = await session_mgr.chat_with_run_control(
-        agent, user_id, "what's the weather like?"
-    )
-    print(f"   Bot: {result4['response']}")
-    
-    print("\n✅ Benefits of this approach:")
-    print("   - Immediate feedback on all messages")
-    print("   - Can cancel long-running tasks with /stop")
-    print("   - Pending messages are queued and processed in order")
-    print("   - User always knows what's happening")
-
-
-async def demo_interrupt_mode():
-    """Demonstrate interrupt mode."""
-    print("\n" + "="*60)
-    print("⚡ BONUS: Interrupt Mode (Cancel and Restart)")
-    print("="*60)
-    
-    from praisonai.bots._run_control import SessionRunControl
-    from praisonai.bots._session import BotSessionManager
-    
-    # Use interrupt mode instead of queue mode
-    run_control = SessionRunControl(busy_mode="interrupt")
-    session_mgr = BotSessionManager(run_control=run_control)
-    agent = MockAgent("research-agent")
-    user_id = "user123"
-    
-    print("\n1. User starts long task:")
-    print("   User: 'research renewable energy for 30 pages'")
-    
-    task1 = asyncio.create_task(
-        session_mgr.chat_with_run_control(agent, user_id, "research renewable energy for 30 pages")
-    )
-    
-    await asyncio.sleep(0.5)
-    print("\n2. User changes mind (interrupts and restarts):")
-    print("   User: 'actually just summarize solar panel efficiency'")
-    
-    result2 = await session_mgr.chat_with_run_control(
-        agent, user_id, "actually just summarize solar panel efficiency"
-    )
-    print(f"   Bot: {result2['response']}")
-    
-    # Wait for original task (should be interrupted)
-    try:
-        result1 = await task1
-        print(f"\n📊 Original task result: {result1['response']}")
-        if result1['metadata'].get('interrupted'):
-            print("   ✅ Original task was properly interrupted")
-    except:
-        print("\n📊 Original task was cancelled")
-    
-    print("\n✅ Interrupt mode is great for:")
-    print("   - When users frequently change direction")
-    print("   - Interactive sessions where latest intent matters most")
-    print("   - Real-time collaboration scenarios")
-
-
-async def main():
-    """Run all demonstrations."""
-    print("🚀 Bot In-Flight Run Control Demo")
-    print("Solving the silent lock problem described in issue #1914")
-    
-    # Show the problem
-    await demo_without_run_control()
-    
-    # Show the solution  
-    await demo_with_run_control()
-    
-    # Show advanced feature
-    await demo_interrupt_mode()
-    
-    print("\n" + "="*60)
-    print("🎯 Summary")
-    print("="*60)
-    print("SessionRunControl solves the silent blocking problem by providing:")
-    print("1. Immediate feedback for mid-run messages")
-    print("2. /stop command support via InterruptController")
-    print("3. Configurable policies (queue/interrupt/steer)")
-    print("4. Run generation tracking to prevent race conditions")
-    print("5. Pending message merging and ordering")
-    print("\nThis makes bot interactions feel responsive and predictable!")
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/python/camera/camera-basic.py

@@ -90,18 +90,9 @@ def main():
     
     if result:
         print("\n🔍 Analysis Results:")
-        task_results = result.get("task_results") if isinstance(result, dict) else result
-        if isinstance(task_results, dict):
-            iterable = task_results.items()
-        elif isinstance(task_results, list):
-            iterable = enumerate(task_results)
-        else:
-            iterable = [("result", task_results)]
-
-        for task_id, task_result in iterable:
+        for task_id, task_result in result["task_results"].items():
             print(f"\nTask {task_id}:")
-            raw_value = getattr(task_result, "raw", None)
-            print(raw_value if raw_value is not None else str(task_result))
+            print(task_result.raw)
     else:
         print("❌ Failed to capture and analyze camera feed")
 

examples/python/camera/camera-multi-agent.py

@@ -148,19 +148,10 @@ def main():
             "scene_analysis": "🌍 SCENE ANALYSIS"
         }
         
-        task_results = result.get("task_results") if isinstance(result, dict) else result
-        if isinstance(task_results, dict):
-            iterable = task_results.items()
-        elif isinstance(task_results, list):
-            iterable = enumerate(task_results)
-        else:
-            iterable = [("result", task_results)]
-
-        for task_id, task_result in iterable:
+        for task_id, task_result in result["task_results"].items():
             print(f"\n{agent_names.get(task_id, f'Task {task_id}')}:")
             print("-" * 40)
-            raw_value = getattr(task_result, "raw", None)
-            print(raw_value if raw_value is not None else str(task_result))
+            print(task_result.raw)
             
         print("\n" + "="*60)
         print("✅ Multi-agent analysis complete!")

examples/python/cli/autonomy_modes_example.py

@@ -34,5 +34,5 @@ print(f"\nChanged to: {handler.get_mode()}")
 print("File edits now auto-approved, shell commands still require approval")
 
 # Get statistics
-stats = manager.get_stats()
+stats = handler.get_stats()
 print(f"\nStats: {stats}")

examples/python/cli/cost_tracking_example.py

@@ -31,4 +31,4 @@ print(f"Total cost: ${summary['total_cost']:.4f}")
 print(f"Avg cost/request: ${summary['avg_cost_per_request']:.4f}")
 
 # Display formatted (uses Rich if available)
-handler.print_summary()
+handler.display_summary()

examples/python/cli/slash_commands_example.py

@@ -41,9 +41,9 @@ def my_custom_handler(args, context):
 custom_cmd = SlashCommand(
     name="mycommand",
     description="My custom command",
-    action=my_custom_handler,
-    kind=CommandKind.CUSTOM,
-    alt_names=["mc"]
+    handler=my_custom_handler,
+    kind=CommandKind.ACTION,
+    aliases=["mc"]
 )
 
 handler.register(custom_cmd)

examples/python/concepts/knowledge-agents-task.py

@@ -1,9 +1,8 @@
-from pathlib import Path
-
-from praisonaiagents import Agent, Task, AgentTeam
-import logging
+import tempfile
 import os
 import sys
+import logging
+from praisonaiagents import Agent, Task, AgentTeam
 
 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -20,9 +19,11 @@ config = {
     }
 }
 
-_sample_path = Path(__file__).with_name("sample_knowledge.txt")
-if not _sample_path.exists():
-    _sample_path.write_text("Mervin Praison is the creator of PraisonAI.\n", encoding="utf-8")
+# Create sample file in system temp directory
+_sample_path = os.path.join(tempfile.gettempdir(), "sample_knowledge.txt")
+if not os.path.exists(_sample_path):
+    with open(_sample_path, "w", encoding="utf-8") as f:
+        f.write("Mervin Praison is the creator of PraisonAI.\n")
 
 # Create an agent with knowledge capabilities
 knowledge_agent = Agent(
@@ -30,7 +31,7 @@ knowledge_agent = Agent(
     role="Information Specialist",
     goal="Store and retrieve knowledge efficiently",
     backstory="Expert in managing and utilizing stored knowledge",
-    knowledge={**config, "sources": [str(_sample_path)]},
+    knowledge={**config, "sources": [_sample_path]},
 )
 
 # Define a task for the agent

examples/python/concepts/repetitive-agents.py

@@ -19,5 +19,4 @@ agents = AgentTeam(
     execution={"max_iter": 30},
 )
 
-if __name__ == "__main__":
-    agents.start()
+agents.start()

examples/python/concepts/self-reflection-optimization-patterns.py

@@ -5,8 +5,7 @@ This example demonstrates self-reflection capabilities using PraisonAI's
 built-in reflection features for iterative improvement and quality optimization.
 """
 
-from praisonaiagents import Agent, Task
-from praisonaiagents.agents import AgentTeam
+from praisonaiagents import Agent, Task, AgentTeam
 from praisonaiagents.tools import internet_search
 
 print("=== Self-Reflection Optimization Patterns Example ===\n")
@@ -34,16 +33,9 @@ reflection_task = Task(
 )
 
 # Run with self-reflection optimization
-if __name__ == "__main__":
-    print("Starting self-reflection optimization demonstration...")
-    agents = AgentTeam(
-        agents=[reflection_agent],
-        tasks=[reflection_task],
-        process="sequential",
-        output="verbose",
-    )
-    result = agents.start()
+print("Starting self-reflection optimization demonstration...")
+result = reflection_agent.execute_task(reflection_task)
 
-    print(f"\nSelf-Reflection Result: {str(result)[:200]}...")
-    print("\n✅ Self-reflection optimization complete!")
-    print("Agent demonstrated iterative improvement through built-in self-reflection capabilities.")
+print(f"\nSelf-Reflection Result: {result[:200]}...")
+print("\n✅ Self-reflection optimization complete!")
+print("Agent demonstrated iterative improvement through built-in self-reflection capabilities.")

examples/python/data/cot-agents.py

@@ -136,5 +136,4 @@ agents = AgentTeam(
     output="minimal",
 )
 
-if __name__ == "__main__":
-    agents.start()
+agents.start()

examples/python/gateway_server_prototype.py

@@ -1,201 +0,0 @@
-"""
-Prototype implementation of Gateway Server with backpressure and flow control.
-
-This demonstrates how the bounded queues and slow consumer detection would be
-implemented in the wrapper layer (praisonai package) using the configuration
-from the core SDK (praisonaiagents).
-"""
-
-from __future__ import annotations
-
-import asyncio
-import logging
-from dataclasses import dataclass, field
-from typing import Any, Dict, Optional
-
-# For the prototype, we'll define minimal config inline
-# from praisonaiagents.gateway import GatewayConfig
-
-@dataclass 
-class SessionConfig:
-    max_inbox: int = 256
-    
-@dataclass
-class GatewayConfig:
-    session_config: SessionConfig = field(default_factory=SessionConfig)
-    max_buffered_bytes: int = 1024 * 1024  # 1MB default
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class GatewaySession:
-    """Gateway session with bounded inbox for backpressure."""
-    
-    _session_id: str
-    _agent_id: str
-    _client_id: Optional[str] = None
-    _inbox: asyncio.Queue = field(default_factory=asyncio.Queue)
-    _max_inbox: int = 256
-    _is_executing: bool = False
-    
-    def setup_inbox(self, max_inbox: int = 256) -> None:
-        """Set up the inbox with proper bounds."""
-        self._max_inbox = max_inbox
-        if max_inbox > 0:
-            self._inbox = asyncio.Queue(maxsize=max_inbox)
-        else:
-            self._inbox = asyncio.Queue()  # Unbounded for backward compat if 0
-    
-    async def queue_message(self, message: str) -> bool:
-        """Queue a user message with backpressure handling.
-        
-        Returns:
-            True if message was queued, False if inbox is full.
-        """
-        if self._max_inbox > 0:
-            # Bounded queue - check if full
-            try:
-                self._inbox.put_nowait(message)
-                return True
-            except asyncio.QueueFull:
-                return False
-        else:
-            # Unbounded queue for backward compatibility
-            await self._inbox.put(message)
-            return True
-
-
-class GatewayServer:
-    """Gateway server with flow control."""
-    
-    def __init__(self, config: GatewayConfig):
-        self.config = config
-        self._sessions: Dict[str, GatewaySession] = {}
-        self._clients: Dict[str, Any] = {}  # client_id -> WebSocket
-        
-    def create_session(self, session_id: str, agent_id: str, client_id: str) -> GatewaySession:
-        """Create a new session with bounded inbox."""
-        session = GatewaySession(
-            _session_id=session_id,
-            _agent_id=agent_id,
-            _client_id=client_id,
-        )
-        # Set up inbox with configured bounds
-        session.setup_inbox(self.config.session_config.max_inbox)
-        self._sessions[session_id] = session
-        return session
-        
-    async def handle_message(self, session: GatewaySession, message: str, client_id: str) -> str:
-        """Handle incoming message with inbox overflow protection."""
-        # Try to queue the message
-        queued = await session.queue_message(message)
-        if not queued:
-            # Inbox is full, send error response
-            await self._send_to_client(
-                client_id,
-                {
-                    "type": "error",
-                    "code": "inbox_full",
-                    "message": "Message queue is full. Please wait for current messages to be processed."
-                }
-            )
-            return "Inbox full - message rejected."
-        
-        # Start processing if not already running
-        if not session._is_executing:
-            session._is_executing = True
-            asyncio.create_task(self._drain_session_queue(session, client_id))
-            return "Processing started."
-        
-        return "Message queued."
-    
-    async def _drain_session_queue(self, session: GatewaySession, client_id: str) -> None:
-        """Process all messages from the session's inbox queue."""
-        try:
-            while not session._inbox.empty():
-                msg = await session._inbox.get()
-                # Process message here (simplified for prototype)
-                logger.info(f"Processing message for session {session._session_id}: {msg}")
-                
-                # Simulate processing delay
-                await asyncio.sleep(0.1)
-                
-                # Send response to client (simplified)
-                await self._send_to_client(
-                    client_id,
-                    {
-                        "type": "response",
-                        "message": f"Processed: {msg}"
-                    }
-                )
-        except Exception as e:
-            logger.error(f"Error processing queue for session {session._session_id}: {e}")
-        finally:
-            session._is_executing = False
-    
-    async def _send_to_client(self, client_id: str, data: Dict[str, Any]) -> None:
-        """Send data to client with slow consumer detection."""
-        ws = self._clients.get(client_id)
-        if ws:
-            try:
-                # Check for slow consumer (buffered bytes)
-                if self.config.max_buffered_bytes > 0:
-                    # Try to get transport buffer size
-                    transport = getattr(ws, 'transport', None) or getattr(ws, '_transport', None)
-                    if transport and hasattr(transport, 'get_write_buffer_size'):
-                        buffered_bytes = transport.get_write_buffer_size()
-                        if buffered_bytes > self.config.max_buffered_bytes:
-                            # Check if this is a droppable event type
-                            event_type = data.get("type", "")
-                            droppable_types = {"presence", "typing", "status"}
-                            
-                            if event_type in droppable_types:
-                                # Drop the message for slow consumers
-                                logger.warning(f"Dropping {event_type} event for slow consumer {client_id} (buffered: {buffered_bytes} bytes)")
-                                return
-                            else:
-                                # Critical event - close the connection
-                                logger.warning(f"Closing slow consumer {client_id} (buffered: {buffered_bytes} bytes > {self.config.max_buffered_bytes})")
-                                await ws.close(code=1013, reason="slow consumer")
-                                return
-                
-                # Send the message
-                await ws.send_json(data)
-            except Exception as e:
-                logger.error(f"Error sending to client {client_id}: {e}", exc_info=True)
-
-
-# Example usage demonstrating the flow control
-async def demo():
-    """Demonstrate the flow control implementation."""
-    
-    # Create config with bounded inbox and slow consumer detection
-    config = GatewayConfig()
-    config.session_config.max_inbox = 10  # Small inbox for demo
-    config.max_buffered_bytes = 1024  # 1KB for demo
-    
-    # Create server and session
-    server = GatewayServer(config)
-    session = server.create_session("sess1", "agent1", "client1")
-    
-    # Simulate filling up the inbox
-    print(f"Inbox max size: {session._inbox.maxsize}")
-    
-    # Fill the inbox
-    for i in range(10):
-        success = await session.queue_message(f"Message {i}")
-        print(f"Queued message {i}: {success}")
-    
-    # Try to add one more - should fail
-    success = await session.queue_message("Overflow message")
-    print(f"Overflow message queued: {success}")
-    
-    # Process messages
-    while not session._inbox.empty():
-        msg = await session._inbox.get()
-        print(f"Processing: {msg}")
-
-
-if __name__ == "__main__":
-    asyncio.run(demo())

examples/python/general/multimodal.py

@@ -49,20 +49,10 @@ agents = AgentTeam(
     process="sequential"
 )
 
-if __name__ == "__main__":
-    # Run all tasks
-    result = agents.start()
+# Run all tasks
+result = agents.start()
 
-    # Print results
-    task_results = result.get("task_results") if isinstance(result, dict) else result
-    if isinstance(task_results, dict):
-        iterable = task_results.items()
-    elif isinstance(task_results, list):
-        iterable = enumerate(task_results)
-    else:
-        iterable = [("result", task_results)]
-
-    for task_id, task_result in iterable:
-        print(f"\nTask {task_id} Result:")
-        raw_value = getattr(task_result, "raw", None)
-        print(raw_value if raw_value is not None else str(task_result))
+# Print results
+for task_id, task_result in result["task_results"].items():
+    print(f"\nTask {task_id} Result:")
+    print(task_result.raw)

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

@@ -14,33 +14,30 @@ agent = Agent(name="env-mgmt", backend=managed)
 # First call creates agent + environment + session
 agent.start("Say hello briefly")
 
-if hasattr(managed, "_get_client"):
-    # Access the underlying Anthropic client for environment management
-    client = managed._get_client()
-    env_id = managed.environment_id
-    print(f"Environment ID: {env_id}")
+# 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')}")
+# 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}")
+# 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')}")
+# 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}")
-else:
-    print("Environment management demo skipped: backend does not expose _get_client().")
+# 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/mcp/mcp-resumability.py

@@ -15,6 +15,13 @@ Protocol: MCP 2025-11-25
 
 from praisonaiagents import Agent, MCP
 
+# Agent with resumability support (automatic)
+agent = Agent(
+    name="Resumable Assistant",
+    instructions="You are a helpful assistant with resumable connections.",
+    tools=MCP("https://api.example.com/mcp")
+)
+
 # Resumability is automatic - the MCP client:
 # 1. Tracks event IDs from SSE streams (id: field)
 # 2. Stores last event ID per stream
@@ -22,12 +29,6 @@ from praisonaiagents import Agent, MCP
 # 4. Respects retry delay from server (retry: field)
 
 if __name__ == "__main__":
-    agent = Agent(
-        name="Resumable Assistant",
-        instructions="You are a helpful assistant with resumable connections.",
-        tools=MCP("https://api.example.com/mcp"),
-    )
-
     print("MCP Resumability Example")
     print("=" * 40)
     print()

examples/python/mcp/mcp-session-management.py

@@ -15,6 +15,13 @@ Protocol: MCP 2025-11-25
 
 from praisonaiagents import Agent, MCP
 
+# Basic agent with automatic session management
+agent = Agent(
+    name="Session Assistant",
+    instructions="You are a helpful assistant with session support.",
+    tools=MCP("https://api.example.com/mcp")
+)
+
 # Session management is automatic - the MCP client:
 # 1. Receives Mcp-Session-Id from server during initialization
 # 2. Includes session ID in all subsequent requests
@@ -22,12 +29,6 @@ from praisonaiagents import Agent, MCP
 # 4. Can terminate session explicitly when done
 
 if __name__ == "__main__":
-    agent = Agent(
-        name="Session Assistant",
-        instructions="You are a helpful assistant with session support.",
-        tools=MCP("https://api.example.com/mcp"),
-    )
-
     print("MCP Session Management Example")
     print("=" * 40)
     print()

examples/python/mcp/streamable-http-mcp.py

@@ -16,31 +16,34 @@ Protocol: MCP 2025-11-25
 
 from praisonaiagents import Agent, MCP
 
+# Basic Streamable HTTP connection
+agent = Agent(
+    name="HTTP Stream Assistant",
+    instructions="You are a helpful assistant connected via Streamable HTTP.",
+    tools=MCP("https://api.example.com/mcp")
+)
+
+# With authentication headers
+agent_auth = Agent(
+    name="Authenticated Assistant",
+    instructions="You are a helpful assistant with authenticated connection.",
+    tools=MCP(
+        "https://api.example.com/mcp",
+        headers={"Authorization": "Bearer your-token"}
+    )
+)
+
+# With timeout and debug
+agent_debug = Agent(
+    name="Debug Assistant",
+    tools=MCP(
+        "https://api.example.com/mcp",
+        timeout=120,
+        debug=True
+    )
+)
+
 if __name__ == "__main__":
-    agent = Agent(
-        name="HTTP Stream Assistant",
-        instructions="You are a helpful assistant connected via Streamable HTTP.",
-        tools=MCP("https://api.example.com/mcp"),
-    )
-
-    agent_auth = Agent(
-        name="Authenticated Assistant",
-        instructions="You are a helpful assistant with authenticated connection.",
-        tools=MCP(
-            "https://api.example.com/mcp",
-            headers={"Authorization": "Bearer your-token"},
-        ),
-    )
-
-    agent_debug = Agent(
-        name="Debug Assistant",
-        tools=MCP(
-            "https://api.example.com/mcp",
-            timeout=120,
-            debug=True,
-        ),
-    )
-
     print("Streamable HTTP MCP Transport Example")
     print("=" * 40)
     print("This example requires a Streamable HTTP MCP server running.")

examples/python/mcp/websocket-mcp.py

@@ -16,33 +16,36 @@ Protocol: SEP-1288 (WebSocket Transport)
 
 from praisonaiagents import Agent, MCP
 
+# Basic WebSocket connection
+agent = Agent(
+    name="WebSocket Assistant",
+    instructions="You are a helpful assistant connected via WebSocket.",
+    tools=MCP("ws://localhost:8080/mcp")
+)
+
+# Secure WebSocket with authentication
+agent_secure = Agent(
+    name="Secure WebSocket Assistant",
+    instructions="You are a helpful assistant with secure WebSocket connection.",
+    tools=MCP(
+        "wss://api.example.com/mcp",
+        auth_token="Bearer your-secret-token",
+        timeout=60
+    )
+)
+
+# WebSocket with custom options
+agent_custom = Agent(
+    name="Custom WebSocket Assistant",
+    tools=MCP(
+        "wss://api.example.com/mcp",
+        auth_token="your-token",
+        timeout=120,
+        debug=True
+    )
+)
+
 if __name__ == "__main__":
-    agent = Agent(
-        name="WebSocket Assistant",
-        instructions="You are a helpful assistant connected via WebSocket.",
-        tools=MCP("ws://localhost:8080/mcp"),
-    )
-
-    agent_secure = Agent(
-        name="Secure WebSocket Assistant",
-        instructions="You are a helpful assistant with secure WebSocket connection.",
-        tools=MCP(
-            "wss://api.example.com/mcp",
-            auth_token="Bearer your-secret-token",
-            timeout=60,
-        ),
-    )
-
-    agent_custom = Agent(
-        name="Custom WebSocket Assistant",
-        tools=MCP(
-            "wss://api.example.com/mcp",
-            auth_token="your-token",
-            timeout=120,
-            debug=True,
-        ),
-    )
-
     # Note: Requires a running WebSocket MCP server
     # Example server: https://github.com/modelcontextprotocol/servers
     

examples/python/message_steering_example.py

@@ -1,173 +0,0 @@
-#!/usr/bin/env python3
-"""
-Message Steering Example
-
-Demonstrates real-time message steering for agents during execution.
-Shows how to send guidance messages to agents while they're running tasks.
-
-Usage:
-    # Python API
-    python message_steering_example.py
-    
-    # CLI with steering enabled
-    praisonai --message-steering "Write a detailed report about AI trends" 
-    
-    # YAML (add message_steering: true to agent config)
-"""
-
-import asyncio
-import threading
-import time
-from praisonaiagents import Agent
-
-
-def basic_message_steering():
-    """Basic message steering example - enable steering and send guidance."""
-    print("🎯 Basic Message Steering Example")
-    print("=" * 50)
-    
-    # Create agent with message steering enabled
-    agent = Agent(
-        name="researcher", 
-        instructions="You are a helpful research assistant",
-        message_steering=True,  # Enable real-time message steering
-        llm="gpt-4o-mini"
-    )
-    
-    print("✅ Agent created with message steering enabled")
-    print(f"📊 Steering status: {agent.get_steering_status()}")
-    
-    # Send some guidance
-    msg_id_1 = agent.steer("Focus on recent developments from 2024")
-    msg_id_2 = agent.steer("Keep response under 200 words", priority=10)
-    
-    print(f"📨 Sent steering messages: {msg_id_1}, {msg_id_2}")
-    print(f"📊 Steering status: {agent.get_steering_status()}")
-    
-    # Execute task - steering will be automatically injected
-    result = agent.start("Summarize the latest AI trends")
-    
-    print("\n📝 Result:")
-    print(result)
-
-
-def threaded_message_steering():
-    """Advanced example - send steering messages while agent is running."""
-    print("\n🚀 Threaded Message Steering Example")
-    print("=" * 50)
-    
-    agent = Agent(
-        name="writer",
-        instructions="You are a content writer", 
-        message_steering=True,
-        llm="gpt-4o-mini"
-    )
-    
-    result_holder = [None]
-    
-    def run_agent():
-        """Run agent in background thread."""
-        result = agent.start("Write a comprehensive article about quantum computing")
-        result_holder[0] = result
-    
-    # Start agent in background
-    thread = threading.Thread(target=run_agent)
-    thread.start()
-    
-    # Send steering messages while it's running
-    time.sleep(0.5)  # Let execution start
-    agent.steer("Make it accessible to beginners")
-    
-    time.sleep(1.0) 
-    agent.steer("Include practical applications", priority=15)
-    
-    time.sleep(1.5)
-    agent.steer("Keep it under 300 words total", priority=20)
-    
-    # Wait for completion
-    thread.join(timeout=60)
-    
-    if result_holder[0]:
-        print("\n📝 Final Result:")
-        print(result_holder[0])
-    else:
-        print("⚠️ Agent execution timed out")
-
-
-async def async_message_steering():
-    """Async example showing message steering with async execution."""
-    print("\n⚡ Async Message Steering Example")
-    print("=" * 50)
-    
-    agent = Agent(
-        name="analyst",
-        instructions="You are a data analyst",
-        message_steering=True,
-        llm="gpt-4o-mini"
-    )
-    
-    # Start async task
-    async def run_analysis():
-        return await agent.astart("Analyze the impact of AI on job markets")
-    
-    # Create task
-    task = asyncio.create_task(run_analysis())
-    
-    # Send steering while running
-    await asyncio.sleep(0.3)
-    agent.steer("Focus on positive opportunities")
-    
-    await asyncio.sleep(0.7)  
-    agent.steer("Include specific examples", priority=12)
-    
-    # Wait for result
-    result = await task
-    
-    print("\n📝 Analysis Result:")
-    print(result)
-
-
-def cli_yaml_examples():
-    """Show CLI and YAML usage examples."""
-    print("\n💻 CLI & YAML Examples")
-    print("=" * 50)
-    
-    print("🔧 CLI Usage:")
-    print("  praisonai --message-steering 'Your task here'")
-    print("  # Then in another terminal:")
-    print("  # agent.steer('Additional guidance')")
-    
-    print("\n📄 YAML Configuration:")
-    yaml_example = """
-# agents.yaml
-framework: praisonai
-roles:
-  researcher:
-    role: Research Assistant
-    goal: Conduct thorough research
-    backstory: Expert researcher with steering support
-    message_steering: true    # Enable steering for this agent
-    tools:
-      - internet_search
-      - write_file
-    tasks:
-      research_task:
-        description: Research AI trends and write report
-        expected_output: Comprehensive research report
-"""
-    print(yaml_example.strip())
-
-
-if __name__ == "__main__":
-    # Run examples
-    basic_message_steering()
-    threaded_message_steering() 
-    
-    # Run async example
-    asyncio.run(async_message_steering())
-    
-    # Show CLI/YAML usage
-    cli_yaml_examples()
-    
-    print("\n✨ Message steering examples completed!")
-    print("📚 See docs for more advanced patterns and priority levels")

examples/python/rag/auto_rag_agent_basic.py

@@ -12,6 +12,7 @@ Environment:
 """
 
 import os
+import tempfile
 from praisonaiagents import Agent, AutoRagAgent
 
 # Ensure API key is set
@@ -20,8 +21,7 @@ if not os.environ.get("OPENAI_API_KEY"):
     exit(1)
 
 # Create a sample document for testing
-from pathlib import Path
-sample_doc = str(Path(__file__).with_name("sample_doc.txt"))
+sample_doc = os.path.join(tempfile.gettempdir(), "sample_doc.txt")
 with open(sample_doc, "w", encoding="utf-8") as f:
     f.write("""
 PraisonAI Framework Documentation

examples/python/save_output/03_workflow_output_file.py

@@ -4,7 +4,7 @@ Example 3: Save Agent Output Using Workflow output_file
 Workflow step output is automatically saved with variable substitution.
 """
 
-from praisonaiagents import Agent, AgentFlow, Task
+from praisonaiagents import AgentFlow
 
 # Define workflow with output_file
 workflow_config = {
@@ -33,30 +33,9 @@ workflow_config = {
     ]
 }
 
-# Build agent and workflow from config dict
-writer_cfg = workflow_config["agents"]["writer"]
-writer = Agent(
-    name="writer",
-    role=writer_cfg["role"],
-    goal=writer_cfg["goal"],
-    llm=writer_cfg["llm"],
-)
-
-step_cfg = workflow_config["steps"][0]
-workflow = AgentFlow(
-    name=workflow_config["metadata"]["name"],
-    variables=workflow_config["variables"],
-    steps=[
-        Task(
-            name="write_tutorial",
-            description=step_cfg["action"],
-            expected_output=step_cfg["expected_output"],
-            output_file=step_cfg["output_file"],
-            agent=writer,
-        )
-    ],
-)
-result = workflow.start()
+# Run workflow
+workflow = AgentFlow(config=workflow_config)
+result = workflow.run()
 
 print("✅ Workflow completed!")
 print("Output saved to: generated/tutorial.md")

examples/python/stateful/advanced-state-management-patterns.py

@@ -69,10 +69,9 @@ agents_system = AgentTeam(
     memory=True, output="verbose"
 )
 
-if __name__ == "__main__":
-    print("Starting state management demonstration...")
-    result = agents_system.start()
+print("Starting state management demonstration...")
+result = agents_system.start()
 
-    print(f"\nState Management Result: {str(result)[:200]}...")
-    print("\n✅ State management patterns complete!")
-    print("Agent demonstrated persistent state tracking across multiple tasks.")
+print(f"\nState Management Result: {result[:200]}...")
+print("\n✅ State management patterns complete!")
+print("Agent demonstrated persistent state tracking across multiple tasks.")

examples/python/stateful/memory-quality-example.py

@@ -179,7 +179,7 @@ def main():
     
     context = memory.build_context_for_task(
         task_descr="Explain AI alignment challenges",
-        memory={"user_id": "quality_demo_user"},
+        user_id="quality_demo_user",
         max_items=3
     )
     

examples/python/telemetry/production-telemetry-example.py

@@ -1,55 +1,284 @@
 """
 Production Telemetry Example
 
-Demonstrates compatible telemetry setup with `enable_telemetry()` and
-`TelemetryCollector` patterns.
+This example demonstrates comprehensive telemetry and monitoring setup for
+production AI agent systems, including metrics collection, performance tracking,
+and observability.
+
+Features demonstrated:
+- OpenTelemetry integration for distributed tracing
+- Custom metrics collection and monitoring
+- Performance tracking and analytics
+- Error tracking and alerting
+- Usage analytics and reporting
 """
 
-from contextlib import nullcontext
 from praisonaiagents import Agent, Task, AgentTeam
 from praisonaiagents.tools import duckduckgo
-from praisonaiagents.telemetry import enable_telemetry, TelemetryCollector
+from praisonaiagents.telemetry import enable_telemetry
+import time
+import random
+from datetime import datetime
 
+# Initialize telemetry for production monitoring
+print("="*80)
+print("PRODUCTION TELEMETRY SETUP")
+print("="*80)
 
-def main() -> None:
-    print("=" * 80)
-    print("PRODUCTION TELEMETRY SETUP")
-    print("=" * 80)
+# Configure telemetry (in production, you'd use actual telemetry backends)
+telemetry = enable_telemetry(
+    backend="opentelemetry",
+    config={
+        "service_name": "praisonai_production_agents",
+        "service_version": "1.0.0",
+        "environment": "production",
+        "export_console": True,  # For demo purposes
+        "export_file": True,     # Export to file for analysis
+        "sample_rate": 1.0,      # 100% sampling for demo
+        "attributes": {
+            "deployment": "cloud",
+            "region": "us-east-1",
+            "cluster": "main"
+        }
+    }
+)
 
-    # New API: no args
-    enable_telemetry()
-    collector = TelemetryCollector()
+# Create agents for production workflow with telemetry
+customer_service_agent = Agent(
+    name="CustomerServiceAgent",
+    role="Customer Service Representative", 
+    goal="Provide excellent customer service with fast response times",
+    backstory="You are a customer service representative focused on resolving customer issues quickly and effectively.",
+    tools=[duckduckgo],
+    instructions="Provide helpful, accurate customer service responses. Research solutions when needed."
+)
 
-    customer_service_agent = Agent(
-        name="CustomerServiceAgent",
-        role="Customer Service Representative",
-        goal="Provide excellent customer service with fast response times",
-        backstory="Customer service expert focused on quick issue resolution.",
-        tools=[duckduckgo],
-        instructions="Provide accurate customer-service responses.",
-    )
+technical_support_agent = Agent(
+    name="TechnicalSupportAgent",
+    role="Technical Support Specialist",
+    goal="Resolve technical issues with detailed solutions",
+    backstory="You are a technical support specialist who provides detailed technical solutions and troubleshooting.",
+    instructions="Provide detailed technical solutions with step-by-step instructions."
+)
 
-    requests = [
-        "Customer asking about monthly bill charges",
-        "Customer experiencing connectivity issues",
+quality_assurance_agent = Agent(
+    name="QualityAssuranceAgent", 
+    role="Quality Assurance Specialist",
+    goal="Ensure all customer interactions meet quality standards",
+    backstory="You review and assess the quality of customer service interactions to ensure high standards.",
+    instructions="Review customer service interactions and provide quality scores with detailed feedback."
+)
+
+# Production workflow simulation with telemetry tracking
+def simulate_customer_requests():
+    """Simulate various customer requests for telemetry demonstration"""
+    
+    customer_requests = [
+        {
+            "type": "billing_inquiry",
+            "complexity": "low",
+            "description": "Customer asking about their monthly bill charges",
+            "expected_duration": 30
+        },
+        {
+            "type": "technical_issue", 
+            "complexity": "high",
+            "description": "Customer experiencing connectivity issues with the service",
+            "expected_duration": 120
+        },
+        {
+            "type": "account_setup",
+            "complexity": "medium", 
+            "description": "New customer needs help setting up their account",
+            "expected_duration": 60
+        },
+        {
+            "type": "feature_question",
+            "complexity": "low",
+            "description": "Customer asking about product features and capabilities",
+            "expected_duration": 45
+        }
     ]
+    
+    return customer_requests
 
-    for idx, description in enumerate(requests, start=1):
-        trace_ctx = getattr(collector, "trace", None)
-        trace_cm = trace_ctx(f"customer_request_{idx}") if callable(trace_ctx) else nullcontext()
-        with trace_cm:
-            task = Task(
-                name=f"customer_request_{idx}",
-                description=description,
-                expected_output="Professional customer service response with solution",
-                agent=customer_service_agent,
-            )
-            agents = AgentTeam(agents=[customer_service_agent], tasks=[task], output="minimal")
-            result = agents.start()
-            print(f"✅ Request {idx} completed, response length={len(str(result))}")
+# Execute production workflow with comprehensive telemetry
+print("Starting production workflow with telemetry tracking...")
 
-    print("\nTelemetry demonstration completed.")
+customer_requests = simulate_customer_requests()
 
+for i, request in enumerate(customer_requests):
+    print(f"\n{'='*60}")
+    print(f"PROCESSING CUSTOMER REQUEST {i+1}: {request['type'].upper()}")
+    print(f"Complexity: {request['complexity']} | Expected Duration: {request['expected_duration']}s")
+    print(f"{'='*60}")
+    
+    # Start telemetry trace for this customer interaction
+    with telemetry.trace_operation(f"customer_request_{request['type']}") as trace:
+        
+        # Add custom attributes to the trace
+        trace.set_attributes({
+            "request.type": request['type'],
+            "request.complexity": request['complexity'],
+            "request.expected_duration": request['expected_duration'],
+            "customer.id": f"customer_{random.randint(1000, 9999)}",
+            "agent.assigned": "CustomerServiceAgent"
+        })
+        
+        start_time = time.time()
+        
+        # Create customer service task with telemetry
+        customer_task = Task(
+            name=f"customer_request_{i+1}",
+            description=request['description'],
+            expected_output="Professional customer service response with solution",
+            agent=customer_service_agent
+        )
+        
+        # Execute with telemetry tracking
+        try:
+            # Customer service response
+            with telemetry.trace_agent_execution("CustomerServiceAgent") as agent_trace:
+                agents = AgentTeam(
+                    agents=[customer_service_agent],
+                    tasks=[customer_task],
+                    output="minimal"  # Reduce verbosity for telemetry demo
+                )
+                
+                cs_result = agents.start()
+                agent_trace.set_attributes({
+                    "agent.response_length": len(str(cs_result)),
+                    "agent.tools_used": len(customer_service_agent.tools) if customer_service_agent.tools else 0
+                })
+            
+            # Technical support (if needed for high complexity)
+            if request['complexity'] == 'high':
+                with telemetry.trace_agent_execution("TechnicalSupportAgent") as tech_trace:
+                    tech_task = Task(
+                        name=f"technical_support_{i+1}",
+                        description=f"Provide detailed technical support for: {request['description']}",
+                        expected_output="Detailed technical solution with troubleshooting steps",
+                        agent=technical_support_agent,
+                        context=[customer_task]
+                    )
+                    
+                    tech_agents = AgentTeam(
+                        agents=[technical_support_agent],
+                        tasks=[tech_task], output="minimal"
+                    )
+                    
+                    tech_result = tech_agents.start()
+                    tech_trace.set_attributes({
+                        "agent.response_length": len(str(tech_result)),
+                        "agent.escalation": True
+                    })
+            
+            # Quality assurance check
+            with telemetry.trace_agent_execution("QualityAssuranceAgent") as qa_trace:
+                qa_task = Task(
+                    name=f"quality_check_{i+1}",
+                    description=f"Review the quality of customer service provided for: {request['description']}",
+                    expected_output="Quality assessment score and feedback",
+                    agent=quality_assurance_agent
+                )
+                
+                qa_agents = AgentTeam(
+                    agents=[quality_assurance_agent],
+                    tasks=[qa_task], output="minimal"
+                )
+                
+                qa_result = qa_agents.start()
+                
+                # Extract quality score (simulated)
+                quality_score = random.uniform(0.8, 1.0)  # Simulated score
+                qa_trace.set_attributes({
+                    "qa.score": quality_score,
+                    "qa.passed": quality_score >= 0.85
+                })
+            
+            # Calculate performance metrics
+            end_time = time.time()
+            duration = end_time - start_time
+            
+            # Record custom metrics
+            telemetry.record_metric("request_duration", duration, {
+                "request_type": request['type'],
+                "complexity": request['complexity']
+            })
+            
+            telemetry.record_metric("request_success", 1, {
+                "request_type": request['type']
+            })
+            
+            # Set final trace attributes
+            trace.set_attributes({
+                "request.duration_seconds": duration,
+                "request.status": "completed",
+                "request.quality_score": quality_score,
+                "request.escalated": request['complexity'] == 'high'
+            })
+            
+            print(f"✅ Request completed successfully in {duration:.2f}s (Quality: {quality_score:.2f})")
+            
+        except Exception as e:
+            # Record error metrics
+            telemetry.record_metric("request_error", 1, {
+                "request_type": request['type'],
+                "error_type": type(e).__name__
+            })
+            
+            trace.set_attributes({
+                "request.status": "error",
+                "request.error": str(e)
+            })
+            
+            print(f"❌ Request failed: {str(e)}")
+    
+    # Add some delay between requests
+    time.sleep(2)
 
-if __name__ == "__main__":
-    main()
+# Generate telemetry summary
+print(f"\n{'='*80}")
+print("TELEMETRY SUMMARY AND ANALYTICS")
+print(f"{'='*80}")
+
+# Simulated telemetry analytics (in production, this would come from your telemetry backend)
+print("📊 Performance Metrics:")
+print("- Total Requests Processed: 4")
+print("- Average Response Time: 3.2s")
+print("- Success Rate: 100%")
+print("- Quality Score Average: 0.91")
+print("- High Complexity Requests: 25%")
+
+print("\n📈 Usage Analytics:")
+print("- Most Common Request Type: technical_issue")
+print("- Peak Performance Window: All requests within SLA")
+print("- Agent Utilization: CustomerService (100%), TechnicalSupport (25%), QA (100%)")
+
+print("\n🔍 Observability Features Demonstrated:")
+print("- Distributed tracing across agent interactions")
+print("- Custom metrics for business KPIs")
+print("- Performance monitoring and SLA tracking")
+print("- Error tracking and alerting")
+print("- Quality assurance metrics")
+print("- User journey tracking")
+print("- Resource utilization monitoring")
+
+print("\n⚙️ Production Monitoring Setup:")
+print("- OpenTelemetry integration for standardized observability")
+print("- Custom metrics for business-specific KPIs") 
+print("- Trace correlation across multi-agent workflows")
+print("- Performance benchmarking against expected durations")
+print("- Quality scoring and automated validation")
+print("- Error categorization and alerting")
+
+print(f"\n{'='*80}")
+print("TELEMETRY DEMONSTRATION COMPLETED")
+print(f"{'='*80}")
+print("This example demonstrated comprehensive production telemetry including:")
+print("- Request tracing and performance monitoring")
+print("- Custom business metrics collection")
+print("- Quality assurance tracking")
+print("- Error handling and alerting")
+print("- Multi-agent workflow observability")
+print("- Production-ready monitoring patterns")

examples/python/tools/jira_agent_example.py

@@ -1,115 +0,0 @@
-#!/usr/bin/env python3
-"""
-JIRA Agent Watch Example
-
-This example demonstrates how to use the JIRA watch tools with PraisonAI agents.
-The agent can monitor JIRA issues and projects for changes, search for issues,
-and get detailed issue information.
-
-Setup:
-1. Install JIRA library: pip install jira
-2. Set environment variables:
-   - [email protected] (for cloud JIRA)
-   - JIRA_API_TOKEN=your_api_token
-   - Or use JIRA_USERNAME + JIRA_API_TOKEN for server JIRA
-
-Usage:
-    python jira_agent_example.py
-"""
-
-import os
-from praisonaiagents import Agent
-from praisonaiagents.tools import jira_tools
-
-
-def main():
-    """Demonstrate JIRA agent watch capabilities."""
-    
-    # Check if JIRA credentials are available
-    jira_email = os.getenv('JIRA_EMAIL')
-    jira_token = os.getenv('JIRA_API_TOKEN')
-    jira_username = os.getenv('JIRA_USERNAME')
-    
-    if not jira_token:
-        print("❌ JIRA_API_TOKEN environment variable not set")
-        print("Please set your JIRA API token:")
-        print("export JIRA_API_TOKEN='your_token_here'")
-        return
-        
-    if not (jira_email or jira_username):
-        print("❌ JIRA credentials incomplete")
-        print("Please set either:")
-        print("- JIRA_EMAIL (for cloud JIRA)")
-        print("- JIRA_USERNAME (for server JIRA)")
-        return
-
-    # Create agent with JIRA tools
-    agent = Agent(
-        name="JIRA Monitor Agent",
-        instructions="""
-        You are a JIRA monitoring agent. You can:
-        
-        1. Watch specific JIRA issues for changes
-        2. Monitor JIRA projects for new issues and updates  
-        3. Search for issues using JQL queries
-        4. Get detailed information about specific issues
-        
-        When using JIRA tools, always provide the full JIRA URL 
-        (e.g., https://yourcompany.atlassian.net).
-        
-        Be helpful and provide clear summaries of JIRA activity.
-        """,
-        tools=jira_tools(),  # Add all JIRA tools
-        llm="gpt-4o-mini"
-    )
-    
-    print("🎯 JIRA Agent Watch Example")
-    print("=" * 50)
-    print()
-    print("Available JIRA tools:")
-    print("- jira_watch_issue: Monitor a specific issue for changes")
-    print("- jira_watch_project: Monitor a project for new/updated issues")
-    print("- jira_get_issue_info: Get detailed info about an issue")  
-    print("- jira_search_issues: Search issues using JQL")
-    print()
-    
-    # Example interactions
-    example_prompts = [
-        "Get information about JIRA issue DEMO-1 from https://praisonai.atlassian.net",
-        "Search for open issues in project DEMO from https://praisonai.atlassian.net using JQL: 'project = DEMO AND status = Open'",
-        "Watch JIRA issue DEMO-1 from https://praisonai.atlassian.net for 2 minutes (check every 60 seconds, max 2 checks)",
-    ]
-    
-    print("📝 Example prompts you can try:")
-    for i, prompt in enumerate(example_prompts, 1):
-        print(f"{i}. {prompt}")
-    print()
-    
-    # Interactive mode
-    print("💬 Interactive JIRA Agent (type 'quit' to exit)")
-    print("-" * 50)
-    
-    while True:
-        try:
-            user_input = input("\n🧑 You: ").strip()
-            
-            if user_input.lower() in ['quit', 'exit', 'q']:
-                print("👋 Goodbye!")
-                break
-                
-            if not user_input:
-                continue
-                
-            print("🤖 Agent: ", end="", flush=True)
-            response = agent.start(user_input)
-            print(response)
-            
-        except KeyboardInterrupt:
-            print("\n👋 Goodbye!")
-            break
-        except Exception as e:
-            print(f"❌ Error: {e}")
-
-
-if __name__ == "__main__":
-    main()

examples/python/tools/langchain/google-serper-search.py

@@ -5,17 +5,11 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
-serper_api_key = os.getenv("SERPER_API_KEY")
-if serper_api_key is not None:
-    os.environ["SERPER_API_KEY"] = serper_api_key
+os.environ['SERPER_API_KEY'] = os.getenv('SERPER_API_KEY')
 
+search = GoogleSerperAPIWrapper()
 
-if __name__ == "__main__":
-    if not serper_api_key:
-        print("SERPER_API_KEY is not set. Skipping Google Serper example.")
-    else:
-        search = GoogleSerperAPIWrapper()
-        data_agent = Agent(instructions="Suggest me top 5 most visited websites for Dosa Recipe", tools=[search])
-        editor_agent = Agent(instructions="List out the websites with their url and a short description")
-        agents = AgentTeam(agents=[data_agent, editor_agent])
-        agents.start()
+data_agent = Agent(instructions="Suggest me top 5 most visited websites for Dosa Recipe", tools=[search])
+editor_agent = Agent(instructions="List out the websites with their url and a short description")
+agents = AgentTeam(agents=[data_agent, editor_agent])
+agents.start()

examples/python/toolsets_example.py

@@ -1,227 +0,0 @@
-"""
-Named Toolsets Example
-
-This example demonstrates how to use named toolsets to organize and reuse 
-groups of tools across agents and workflows.
-
-Toolsets provide:
-- Organized tool collections (web, research, development, etc.)
-- Progressive disclosure (safe → research → development)
-- Composable design (toolsets can include other toolsets)
-- YAML configuration support
-"""
-
-import os
-from praisonaiagents import Agent, Task, AgentTeam
-from praisonaiagents.toolsets import (
-    list_toolsets, 
-    resolve_toolset,
-    register_toolset
-)
-
-# Set up environment (replace with your actual API key)
-os.environ.setdefault("OPENAI_API_KEY", "your-openai-api-key-here")
-
-
-def demo_basic_toolsets():
-    """Demonstrate basic toolset functionality."""
-    print("=== Basic Toolsets Demo ===")
-    
-    # List available toolsets
-    available_toolsets = list_toolsets()
-    print(f"Available toolsets: {available_toolsets}")
-    
-    # Show what tools are in each prebuilt toolset
-    for toolset_name in ["safe", "web", "research"]:
-        tools = resolve_toolset(toolset_name)
-        print(f"\n{toolset_name.upper()} toolset contains: {tools}")
-
-
-def demo_agent_with_toolsets():
-    """Demonstrate agents using toolsets."""
-    print("\n=== Agent with Toolsets Demo ===")
-    
-    # Create a research agent with research toolset
-    researcher = Agent(
-        name="researcher",
-        role="Research Specialist",
-        goal="Research topics thoroughly using web tools",
-        instructions="You are a research specialist with access to web search and file tools.",
-        toolsets=["research"],  # includes web tools + file tools
-        llm="gpt-4o-mini"
-    )
-    
-    print(f"Research agent has {len(researcher.tools)} tools available")
-    print(f"Tools: {[getattr(t, '__name__', str(t)) for t in researcher.tools]}")
-    
-    # Create a safe agent with minimal tools
-    safe_agent = Agent(
-        name="safe_assistant",
-        role="Safe Assistant", 
-        goal="Help users safely without risky operations",
-        instructions="You are a helpful assistant with access to only safe, read-only tools.",
-        toolsets=["safe"],
-        llm="gpt-4o-mini"
-    )
-    
-    print(f"\nSafe agent has {len(safe_agent.tools)} tools available")
-    print(f"Safe tools: {[getattr(t, '__name__', str(t)) for t in safe_agent.tools]}")
-
-
-def demo_custom_toolsets():
-    """Demonstrate creating and using custom toolsets."""
-    print("\n=== Custom Toolsets Demo ===")
-    
-    # Register a custom toolset for data analysis
-    register_toolset(
-        "data_analysis",
-        tools=["read_file", "write_file"],
-        includes=["safe"],  # Include safe tools too
-        description="Tools for data analysis workflows"
-    )
-    
-    # Create an agent with the custom toolset
-    analyst = Agent(
-        name="data_analyst",
-        role="Data Analyst",
-        goal="Analyze data files and generate reports",
-        instructions="You specialize in data analysis. Read files, process data, and write reports.",
-        toolsets=["data_analysis"],
-        llm="gpt-4o-mini"
-    )
-    
-    print(f"Data analyst has {len(analyst.tools)} tools available")
-    print(f"Analysis tools: {[getattr(t, '__name__', str(t)) for t in analyst.tools]}")
-
-
-def demo_mixed_tools_and_toolsets():
-    """Demonstrate mixing explicit tools with toolsets."""
-    print("\n=== Mixed Tools and Toolsets Demo ===")
-    
-    # Create agent with both explicit tools and toolsets
-    hybrid_agent = Agent(
-        name="hybrid_assistant",
-        role="Hybrid Assistant",
-        goal="Assist with both research and custom tasks",
-        instructions="You can research topics and perform custom analysis tasks.",
-        tools=["write_file"],  # Explicit tool
-        toolsets=["research"],  # Toolset with web + file tools
-        llm="gpt-4o-mini"
-    )
-    
-    print(f"Hybrid agent has {len(hybrid_agent.tools)} tools available")
-    tool_names = [getattr(t, '__name__', str(t)) for t in hybrid_agent.tools]
-    print(f"Hybrid tools: {tool_names}")
-    
-    # Note: write_file might appear twice (once explicit, once from toolset)
-    # The agent handles deduplication automatically
-
-
-def demo_multi_agent_with_toolsets():
-    """Demonstrate multi-agent workflows with different toolsets."""
-    print("\n=== Multi-Agent with Toolsets Demo ===")
-    
-    # Create agents with different toolsets for different roles
-    researcher = Agent(
-        name="researcher",
-        role="Research Specialist",
-        goal="Research topics and gather information",
-        toolsets=["research"],
-        llm="gpt-4o-mini"
-    )
-    
-    writer = Agent(
-        name="writer",
-        role="Content Writer",  
-        goal="Write content based on research",
-        toolsets=["safe"],  # Safe tools for writing
-        llm="gpt-4o-mini"
-    )
-    
-    # Create tasks for each agent
-    research_task = Task(
-        name="research_task",
-        description="Research the benefits of renewable energy",
-        expected_output="A summary of key benefits of renewable energy",
-        agent=researcher
-    )
-    
-    writing_task = Task(
-        name="writing_task", 
-        description="Write a blog post about renewable energy based on the research",
-        expected_output="A well-written blog post about renewable energy benefits",
-        agent=writer
-    )
-    
-    # Create team with different toolsets per agent
-    team = AgentTeam(
-        agents=[researcher, writer],
-        tasks=[research_task, writing_task],
-        process="sequential"
-    )
-    
-    print(f"Team created with {len(team.agents)} agents using different toolsets")
-    print(f"Research agent tools: {len(researcher.tools)}")
-    print(f"Writer agent tools: {len(writer.tools)}")
-
-
-def demo_yaml_config():
-    """Show how toolsets work in YAML configuration."""
-    print("\n=== YAML Configuration Example ===")
-    
-    yaml_config = """
-# agents.yaml - Example using toolsets
-agents:
-  researcher:
-    role: Research Specialist
-    goal: Research topics thoroughly
-    toolsets:
-      - research  # Includes web search, file tools
-    llm: gpt-4o-mini
-    
-  analyst:
-    role: Data Analyst
-    goal: Analyze research data
-    tools:
-      - write_file  # Explicit tool
-    toolsets:
-      - safe  # Safe toolset
-    llm: gpt-4o-mini
-
-tasks:
-  research_task:
-    description: Research renewable energy trends
-    agent: researcher
-    expected_output: Research summary
-    
-  analysis_task:
-    description: Analyze the research data
-    agent: analyst
-    expected_output: Analysis report
-"""
-    
-    print("Example YAML configuration with toolsets:")
-    print(yaml_config)
-    print("Use: praisonai run 'Research renewable energy' --toolset research")
-    print("Or:  praisonai chat --toolset web,files")
-
-
-if __name__ == "__main__":
-    print("🛠️  PraisonAI Named Toolsets Example")
-    print("=" * 50)
-    
-    # Run all demos
-    demo_basic_toolsets()
-    demo_agent_with_toolsets()
-    demo_custom_toolsets()
-    demo_mixed_tools_and_toolsets()
-    demo_multi_agent_with_toolsets()
-    demo_yaml_config()
-    
-    print("\n✅ Toolsets example completed!")
-    print("\n💡 Key Benefits:")
-    print("   • Organized tool collections")
-    print("   • Progressive disclosure (safe → research → development)")
-    print("   • Reusable across agents and workflows") 
-    print("   • YAML configuration support")
-    print("   • Composable design with includes")

examples/python/usecases/analysis/code-analysis-agents.py

@@ -2,7 +2,6 @@ from praisonaiagents import Agent, Task, AgentTeam
 from pydantic import BaseModel
 from typing import List, Dict
 from gitingest import ingest
-import sys
 
 class CodeMetrics(BaseModel):
     category: str
@@ -61,10 +60,7 @@ def analyze_code(code_source: str) -> CodeAnalysisReport:
     Analyze code from directory path or GitHub URL
     """
     # Ingest code content
-    try:
-        summary, tree, content = ingest(code_source)
-    except Exception as exc:
-        raise RuntimeError(f"Failed to ingest code source: {exc}") from exc
+    summary, tree, content = ingest(code_source)
     
     # Concatenate context into structured format
     context_text = f"""
@@ -90,22 +86,10 @@ def analyze_code(code_source: str) -> CodeAnalysisReport:
         tasks=[code_analysis_task]
     )
     
-    result = agents.start(context_text)
-    if result is None:
-        raise RuntimeError("No analysis result returned by AgentTeam.start()")
-    return result
-
-
-def main() -> None:
-    # Example usage
-    code_source = "https://github.com/openai/openai-python/tree/main/src/openai/cli/_api/chat"  # GitHub URL or local directory
-    try:
-        result = analyze_code(code_source)
-    except RuntimeError as exc:
-        print(f"Code analysis failed: {exc}")
-        sys.exit(1)
-    print(result) 
-
+    return agents.start(context_text)
 
 if __name__ == "__main__":
-    main()
+    # Example usage
+    code_source = "https://github.com/openai/openai-python/tree/main/src/openai/cli/_api/chat"  # GitHub URL or local directory
+    result = analyze_code(code_source)
+    print(result) 

examples/python/usecases/analysis/cv-analysis.py

@@ -77,44 +77,25 @@ agents = AgentTeam(
     process="sequential", output="verbose"
 )
 
-
-def _extract_pydantic_result(result_obj):
-    """Extract pydantic payload from AgentTeam.start() output shapes."""
-    if hasattr(result_obj, "pydantic"):
-        return result_obj.pydantic
-    if isinstance(result_obj, dict):
-        task_results = result_obj.get("task_results")
-        if isinstance(task_results, dict):
-            values = list(task_results.values())
-            if values and hasattr(values[-1], "pydantic"):
-                return values[-1].pydantic
-        if isinstance(task_results, list) and task_results and hasattr(task_results[-1], "pydantic"):
-            return task_results[-1].pydantic
-    return None
-
-
 # Start the analysis
 result = agents.start()
-pydantic_result = _extract_pydantic_result(result)
-if pydantic_result is None:
-    raise ValueError("Could not extract structured CVAnalysisReport from workflow result")
 
 # Access the structured results
 print("\nCV Analysis Results:")
-print(f"\nOverall Score: {pydantic_result.overall_score}/100")
+print(f"\nOverall Score: {result.pydantic.overall_score}/100")
 print("\nKey Strengths:")
-for strength in pydantic_result.key_strengths:
+for strength in result.pydantic.key_strengths:
     print(f"- {strength}")
 
 print("\nSkill Analysis:")
 print("\nTechnical Skills:")
-for skill in pydantic_result.skill_analysis.technical_skills:
+for skill in result.pydantic.skill_analysis.technical_skills:
     print(f"- {skill}")
 
 print("\nExperience Highlights:")
-for achievement in pydantic_result.experience_analysis.key_achievements:
+for achievement in result.pydantic.experience_analysis.key_achievements:
     print(f"- {achievement}")
 
 print("\nRecommendations:")
-for recommendation in pydantic_result.recommendations:
+for recommendation in result.pydantic.recommendations:
     print(f"- {recommendation}")

examples/python/usecases/analysis/hackathon-judge-agent.py

@@ -100,8 +100,6 @@ def evaluate_project(video_path: str) -> ProjectEvaluation:
             evaluation_data = response
             
         print(f"Debug - Parsed evaluation_data: {evaluation_data}")
-        if not evaluation_data or not isinstance(evaluation_data, dict):
-            raise ValueError("No structured evaluation data returned by agent")
         
         # Create and return ProjectEvaluation instance
         return ProjectEvaluation(

examples/python/video/video_azure.py

@@ -1,11 +1,5 @@
 from praisonaiagents import VideoAgent
-import os
 
-
-if __name__ == "__main__":
-    if not os.getenv("AZURE_API_BASE"):
-        print("AZURE_API_BASE is not configured. Skipping Azure video example.")
-    else:
-        agent = VideoAgent(llm="azure/sora-2")
-        video = agent.generate("Mountain landscape")
-        print(video.id)
+agent = VideoAgent(llm="azure/sora-2")
+video = agent.generate("Mountain landscape")
+print(video.id)

examples/python/workflows/simple_workflow.py

@@ -5,7 +5,7 @@ Demonstrates using Agent instances directly as workflow steps.
 Each agent processes the input and passes output to the next agent.
 """
 
-from praisonaiagents import Agent, AgentFlow
+from praisonaiagents import Agent, Workflow
 
 # Create agents with specific roles
 researcher = Agent(

examples/python/workflows/task_callbacks.py

@@ -6,7 +6,6 @@ with Agents for workflow monitoring and logging.
 """
 
 from praisonaiagents import Agent, Task, AgentTeam
-from praisonaiagents.config.feature_configs import MultiAgentHooksConfig
 
 # Create an agent
 researcher = Agent(
@@ -48,10 +47,8 @@ if __name__ == "__main__":
         agents=[researcher],
         tasks=[task1, task2],
         process="workflow",
-        hooks=MultiAgentHooksConfig(
-            on_task_start=on_start,
-            on_task_complete=on_complete,
-        ),
+        on_task_start=on_start,
+        on_task_complete=on_complete,
         variables={"global_var": "shared_value"}  # Global variables
     )
     

examples/python/workflows/workflow_branching.py

@@ -6,6 +6,7 @@ to different next steps based on output content.
 """
 
 from praisonaiagents import AgentFlow, Task
+from praisonaiagents import AgentFlowManager
 
 # Create a workflow with branching
 workflow = AgentFlow(
@@ -35,9 +36,16 @@ workflow = AgentFlow(
 )
 
 if __name__ == "__main__":
-    result = workflow.run("", llm="gpt-4o-mini", verbose=True)
+    # Create manager and register workflow
+    manager = WorkflowManager()
+    manager.workflows["Decision Workflow"] = workflow
+    
+    # Execute - will branch to success_handler since 42 is positive
+    result = manager.execute(
+        "Decision Workflow",
+        default_llm="gpt-4o-mini"
+    )
+    
     print("Workflow completed!")
-    for step in result.get("steps", []):
-        name = step.get("step", step.get("name", "unknown"))
-        status = step.get("status", "completed")
-        print(f"  {name}: {status}")
+    for step_result in result["results"]:
+        print(f"  {step_result['step']}: {step_result['status']}")

examples/python/workflows/workflow_conditional.py

@@ -54,13 +54,11 @@ workflow = AgentFlow(
         analyzer,  # Always runs
         Task(
             name="tech_review",
-            description="Review technical aspects when technical keywords are detected.",
             agent=tech_reviewer,
             should_run=is_technical  # Only runs for technical content
         ),
         Task(
             name="creative_enhance",
-            description="Enhance narrative quality when creative keywords are detected.",
             agent=creative_enhancer,
             should_run=needs_creative  # Only runs for creative content
         ),

examples/python/workflows/workflow_early_stop.py

@@ -6,8 +6,9 @@ functions that return StepResult with stop_workflow=True.
 """
 
 from praisonaiagents import (
-    AgentFlow, Task, WorkflowContext, StepResult
+    Workflow, Task, WorkflowContext, StepResult
 )
+from praisonaiagents import AgentFlowManager
 
 # Custom validator that can stop the workflow
 def validate_data(context: WorkflowContext) -> StepResult:
@@ -49,13 +50,20 @@ workflow = AgentFlow(
 )
 
 if __name__ == "__main__":
+    manager = WorkflowManager()
+    manager.workflows["Data Processing"] = workflow
+    
+    # Test with invalid data (will stop early)
     print("=== Testing with invalid data ===")
-    result = workflow.run("", llm="gpt-4o-mini", verbose=True)
-    for step in result.get("steps", []):
-        print(f"  {step.get('step', 'step')}: {step.get('output', step.get('status'))}")
-
+    result = manager.execute("Data Processing", default_llm="gpt-4o-mini")
+    
+    for step_result in result["results"]:
+        print(f"  {step_result['step']}: {step_result['output']}")
+    
+    # Test with valid data (will complete)
     print("\n=== Testing with valid data ===")
     workflow.variables["data"] = {"value": 42}
-    result = workflow.run("", llm="gpt-4o-mini", verbose=True)
-    for step in result.get("steps", []):
-        print(f"  {step.get('step', 'step')}: {step.get('status', 'completed')}")
+    result = manager.execute("Data Processing", default_llm="gpt-4o-mini")
+    
+    for step_result in result["results"]:
+        print(f"  {step_result['step']}: {step_result['status']}")

examples/python/workflows/workflow_loops.py

@@ -6,6 +6,7 @@ Each item is processed individually with the loop variable.
 """
 
 from praisonaiagents import AgentFlow, Task
+from praisonaiagents import AgentFlowManager
 
 # Create a workflow with loop
 workflow = AgentFlow(
@@ -29,12 +30,18 @@ workflow = AgentFlow(
 )
 
 if __name__ == "__main__":
-    result = workflow.run("", llm="gpt-4o-mini", verbose=True)
+    # Create manager and register workflow
+    manager = WorkflowManager()
+    manager.workflows["Process Items"] = workflow
+    
+    # Execute - will process each fruit in the list
+    result = manager.execute(
+        "Process Items",
+        default_llm="gpt-4o-mini"
+    )
+    
     print("Workflow completed!")
-    for step in result.get("steps", []):
-        name = step.get("step", step.get("name", "unknown"))
-        status = step.get("status", "completed")
-        print(f"  {name}: {status}")
-        out = step.get("output")
-        if isinstance(out, list):
-            print(f"    Loop processed {len(out)} items")
+    for step_result in result["results"]:
+        print(f"  {step_result['step']}: {step_result['status']}")
+        if isinstance(step_result.get("output"), list):
+            print(f"    Loop processed {len(step_result['output'])} items")

examples/python/workflows/workflow_parallel.py

@@ -5,7 +5,7 @@ Demonstrates running multiple agents concurrently and
 combining their results with an aggregator agent.
 """
 
-from praisonaiagents import Agent, AgentFlow
+from praisonaiagents import Agent, Workflow
 from praisonaiagents.workflows import parallel
 
 # Create parallel research agents

examples/python/workflows/workflow_robustness.py

@@ -42,7 +42,6 @@ def main():
     
     # Create tasks with robustness features
     research_task = Task(
-        name="research",
         description="Research the latest trends in AI agents for 2025",
         expected_output="A summary of key AI agent trends",
         agent=researcher,
@@ -52,7 +51,6 @@ def main():
     
     # Optional enrichment step - workflow continues even if this fails
     enrich_task = Task(
-        name="enrich",
         description="Enrich the research with real-world examples",
         expected_output="Research with added examples",
         agent=enricher,
@@ -62,7 +60,6 @@ def main():
     )
     
     write_task = Task(
-        name="write",
         description="Write a brief summary based on the research",
         expected_output="A well-written summary paragraph",
         agent=writer,

examples/python/workflows/workflow_routing.py

@@ -5,7 +5,7 @@ Demonstrates decision-based routing where an agent classifier
 routes to specialized handler agents based on the request type.
 """
 
-from praisonaiagents import Agent, AgentFlow
+from praisonaiagents import Agent, Workflow
 from praisonaiagents.workflows import route
 
 # Create classifier agent

examples/storage/storage_backends_example.py

@@ -190,7 +190,7 @@ def example_run_history_with_backend():
         
         # Use with RunHistory
         history = RunHistory(backend=backend)
-        runs = history.list_runs()
+        runs = history.list()
         
         print(f"RunHistory created with SQLite backend, {len(runs)} runs")
         print("Recipe runs will be stored in database instead of files")

examples/terminal_bench/test_integration.py

@@ -257,7 +257,7 @@ if __name__ == "__main__":
     # Check dependencies
     try:
         import praisonaiagents
-        print(f"✅ PraisonAI version: {getattr(praisonaiagents, '__version__', None)}")
+        print(f"✅ PraisonAI version: {praisonaiagents.__version__}")
     except ImportError:
         print("❌ PraisonAI not installed: pip install praisonaiagents")
         sys.exit(1)

examples/web/00_agent_web_basic.py

@@ -12,7 +12,7 @@ assert os.getenv("OPENAI_API_KEY"), "OPENAI_API_KEY must be set"
 # Enable web search and fetch
 agent = Agent(
     instructions="You are a research assistant with web access.",
-    web="search_only",
+    web=True,
 )
 
 # Disable web (default)

examples/web/01_agent_web_config.py

@@ -4,7 +4,7 @@ WebConfig Example
 Demonstrates using WebConfig for fine-grained control.
 """
 import os
-from praisonaiagents import Agent
+from praisonaiagents import Agent, WebConfig
 
 # Ensure API key is set from environment
 assert os.getenv("OPENAI_API_KEY"), "OPENAI_API_KEY must be set"
@@ -12,13 +12,21 @@ assert os.getenv("OPENAI_API_KEY"), "OPENAI_API_KEY must be set"
 # Custom web configuration
 agent = Agent(
     instructions="You are a research assistant.",
-    web="search_only",
+    web=WebConfig(
+        search=True,
+        fetch=True,
+        search_provider="duckduckgo",
+        max_results=5,
+    ),
 )
 
 # Search only (no fetch)
 agent_search_only = Agent(
     instructions="You are a research assistant.",
-    web="search_only",
+    web=WebConfig(
+        search=True,
+        fetch=False,
+    ),
 )
 
 if __name__ == "__main__":

examples/yaml/message_steering_example.yaml

@@ -1,47 +0,0 @@
-framework: praisonai
-topic: AI Research with Message Steering
-
-roles:
-  researcher:
-    role: AI Research Analyst
-    goal: Conduct comprehensive AI research with real-time guidance
-    backstory: You are an expert AI researcher who can receive and incorporate real-time guidance during your work
-    message_steering: true  # Enable real-time message steering
-    tools:
-      - internet_search
-      - write_file
-    tasks:
-      research_task:
-        description: |
-          Research the latest developments in AI and write a comprehensive report.
-          You may receive additional guidance during execution via message steering.
-        expected_output: |
-          A well-structured research report covering recent AI developments,
-          incorporating any real-time guidance received during execution.
-
-  writer:
-    role: Technical Writer
-    goal: Write clear, engaging content based on research
-    backstory: You are a skilled technical writer who can adapt content based on real-time feedback
-    message_steering: true  # Enable steering for dynamic content adjustment
-    tools:
-      - read_file
-      - write_file
-    tasks:
-      writing_task:
-        description: |
-          Take the research findings and create an accessible article for a general audience.
-          Adjust style and focus based on any steering messages received.
-        expected_output: |
-          A polished article that transforms technical research into accessible content,
-          refined based on real-time steering guidance.
-
-dependencies:
-  - writing_task: [research_task]
-
-# Usage Instructions:
-# 1. Run: praisonai --agent-file message_steering_example.yaml
-# 2. While running, use steering API to send guidance:
-#    agent.steer("Focus more on practical applications")
-#    agent.steer("Keep it under 500 words", priority=15)
-#    agent.steer("Add more examples", priority=10)

examples/yaml/toolsets_workflow.yaml

@@ -1,148 +0,0 @@
-# Named Toolsets YAML Example
-# This example demonstrates using toolsets in YAML configuration
-
-# Global configuration
-config:
-  llm: gpt-4o-mini
-  memory: true
-
-# Define agents with different toolsets
-agents:
-  # Research agent with research toolset (includes web tools)
-  researcher:
-    role: Senior Research Analyst
-    goal: Research topics thoroughly using web search and analysis tools
-    instructions: |
-      You are a senior research analyst with access to web search and file tools.
-      Research topics comprehensively and provide detailed findings.
-    toolsets:
-      - research  # Includes: web search, file tools, basic analysis
-    
-  # Writer agent with safe toolset for writing tasks
-  writer:
-    role: Content Writer
-    goal: Create engaging content based on research
-    instructions: |
-      You are a skilled content writer. Transform research into clear,
-      engaging content for the target audience.
-    toolsets:
-      - safe  # Safe, read-only tools for writing
-    
-  # Developer agent with development toolset
-  developer:
-    role: Software Developer
-    goal: Create and modify code based on requirements
-    instructions: |
-      You are a software developer with access to development tools.
-      Write, test, and debug code according to specifications.
-    toolsets:
-      - development  # Includes: code tools, file operations, testing
-      
-  # Data analyst with mixed tools and toolsets
-  analyst:
-    role: Data Analyst  
-    goal: Analyze data and generate insights
-    instructions: |
-      You are a data analyst. Process data files and generate
-      actionable insights and visualizations.
-    tools:
-      - read_file  # Explicit tool
-      - write_file  # Explicit tool
-    toolsets:
-      - safe  # Safe toolset for analysis
-      
-  # Security-focused agent with minimal toolset
-  security_auditor:
-    role: Security Auditor
-    goal: Review and audit for security issues
-    instructions: |
-      You are a security auditor with minimal tool access.
-      Focus on reading and analyzing without making changes.
-    toolsets:
-      - safe  # Minimal, safe tools only
-
-# Define tasks for the workflow
-tasks:
-  # Research task using research toolset
-  research_phase:
-    description: |
-      Research the latest trends in AI agent frameworks.
-      Focus on:
-      - Popular frameworks and their features
-      - Industry adoption patterns
-      - Security considerations
-      - Performance benchmarks
-    expected_output: |
-      A comprehensive research report covering AI agent frameworks,
-      including trends, adoption, security, and performance data.
-    agent: researcher
-    
-  # Content creation using safe toolset
-  content_creation:
-    description: |
-      Create a technical blog post based on the research findings.
-      The post should be:
-      - Well-structured with clear sections
-      - Accessible to technical and non-technical audiences  
-      - Include actionable insights
-      - 1000-1500 words
-    expected_output: |
-      A polished blog post about AI agent frameworks suitable
-      for publication on a technical blog.
-    agent: writer
-    
-  # Code development using development toolset
-  code_examples:
-    description: |
-      Create code examples demonstrating key concepts from the research.
-      Include:
-      - Simple "hello world" style examples
-      - Intermediate use cases
-      - Best practices examples
-      - Proper documentation
-    expected_output: |
-      A collection of well-documented code examples that illustrate
-      the concepts discussed in the research and blog post.
-    agent: developer
-    
-  # Data analysis using mixed tools/toolsets
-  trend_analysis:
-    description: |
-      Analyze the research data to identify trends and patterns.
-      Create:
-      - Summary statistics
-      - Trend analysis
-      - Recommendations based on findings
-    expected_output: |
-      A data analysis report with trends, patterns, and
-      actionable recommendations for AI framework adoption.
-    agent: analyst
-    
-  # Security review using minimal toolset
-  security_review:
-    description: |
-      Review all outputs for security considerations.
-      Check for:
-      - Potential security vulnerabilities in code examples
-      - Security best practices in recommendations
-      - Privacy considerations in data handling
-    expected_output: |
-      A security review report identifying any security concerns
-      and recommendations for secure implementation.
-    agent: security_auditor
-
-# Workflow process (sequential execution)
-process: sequential
-
-# Optional: Define workflow dependencies
-# tasks:
-#   research_phase:
-#     dependencies: []
-#   content_creation: 
-#     dependencies: [research_phase]
-#   code_examples:
-#     dependencies: [research_phase]
-#   trend_analysis:
-#     dependencies: [research_phase]
-#   security_review:
-#     dependencies: [content_creation, code_examples, trend_analysis]

src/praisonai-agents/.agent/workflows/e2e-analysis-issue-pr-merge.md

@@ -539,7 +539,7 @@ Repeat Phases 12 → 13 → 14 until the mergeability gate in Phase 13 is fully
 
 ### When the PR has merge conflicts
 
-If `mergeStateStatus=DIRTY`, the **Auto Merge Conflict → Claude** workflow (`.github/workflows/merge-conflict-claude.yml`) posts `@claude` automatically on push to `main` and on PR sync. You can still comment manually:
+If `mergeStateStatus=DIRTY`, ask Claude to rebase rather than resolving conflicts yourself on non-trivial logic:
 
 ```bash
 gh pr comment $PR --repo $REPO --body "@claude this PR has merge conflicts with \`main\` (PR #<X> was merged, touching the same regions). Please rebase onto latest \`main\`, re-run \`pytest <paths>\`, and force-push."

src/praisonai-agents/.agent/workflows/install-deploy.md

@@ -755,20 +755,6 @@ Developer Push ─────────────────────
 
 **Result**: `https://praison.ai/install.sh` is automatically updated **only when the source files change**!
 
-### Auto-deploy when install scripts change (PraisonAI → praison.ai)
-
-Pushing `install.sh` or `install.ps1` to PraisonAI **`main`** triggers [`.github/workflows/deploy-install-scripts.yml`](/Users/praison/praisonai-package/.github/workflows/deploy-install-scripts.yml), which dispatches the existing praison.ai **`CI`** workflow (`workflow_dispatch` on `master`). No manual website push or `make upgrade` is required.
-
-```mermaid
-flowchart LR
-  pushMain["PraisonAI push main\ninstall.sh|install.ps1"] --> deployWF["deploy-install-scripts.yml"]
-  deployWF --> dispatch["gh workflow run CI"]
-  dispatch --> siteCI["praison.ai azure-pipeline.yaml"]
-  siteCI --> live["https://praison.ai/install.sh"]
-```
-
-Manual re-deploy: `gh workflow run "Deploy install scripts to praison.ai" --repo MervinPraison/PraisonAI`
-
 ### How install.sh is Served
 
 The `web/install.sh` file is copied into the Docker image at `/var/www/html/web/install.sh` and served by nginx:
@@ -811,20 +797,20 @@ git push origin master
 
 **Task 2: Update install.sh and deploy**
 ```bash
-# 1. Update install.sh / install.ps1
+# 1. Update install.sh
 vim /Users/praison/praisonai-package/src/praisonai/scripts/install.sh
 
 # 2. Test install.sh
-bash /Users/praison/praisonai-package/src/praisonai/scripts/test-install-smoke.sh
+bash /Users/praison/praisonai-package/src/praisonai/scripts/install.sh --dry-run
 
-# 3. Commit and push to PraisonAI main — auto-triggers praison.ai deploy
-git add src/praisonai/scripts/install.sh src/praisonai/scripts/install.ps1
+# 3. Commit and push
+git add /Users/praison/praisonai-package/src/praisonai/scripts/install.sh
 git commit -m "fix: update install script"
 git push origin main
-# deploy-install-scripts.yml → praison.ai CI → https://praison.ai/install.sh
 
-# Manual re-deploy (optional):
-gh workflow run "Deploy install scripts to praison.ai" --repo MervinPraison/PraisonAI
+# 4. Deploy website (if needed)
+cd /Users/praison/Sites/localhost/praisonai
+make upgrade
 ```
 
 **Task 3: Rollback deployment**

src/praisonai-agents/.agent/workflows/pr-review.md

@@ -257,21 +257,6 @@ find $(python -c "import site; print(site.getsitepackages()[0])") -name "<suspic
 
 ---
 
-## Fork PRs — contributor checklist and Claude push
-
-For PRs from external forks (`head.repo != MervinPraison/PraisonAI`), ask the contributor:
-
-1. **Allow edits from maintainers** on the PR (right sidebar).
-2. **Keep fork and branch** until the PR is merged or closed.
-
-Optional: private fork → add maintainer as collaborator; fork with Actions → only enable “Allow edits and access to secrets by maintainers” if they accept the risk.
-
-When `maintainerCanModify` is true, `claude.yml` pushes via `GH_TOKEN` to the fork remote and `merge-conflict-claude.yml` may auto-trigger `@claude` for `DIRTY` PRs (after FINAL architecture review).
-
-Manual unblock: `gh pr checkout <N> && git fetch origin main && git rebase origin/main && git push --force-with-lease`
-
----
-
 ## Quick Reference — Common Commands
 
 | Action | Command |

src/praisonai-agents/AGENTS.md

@@ -378,7 +378,7 @@ Agent parameters are consolidated into Config objects following the pattern:
 
 | Config Object | Replaces (Deprecated) |
 |--------------|----------------------|
-| `execution=ExecutionConfig(code_execution=True, code_mode="safe", rate_limiter=obj, context_compaction=False)` | `allow_code_execution`, `code_execution_mode`, `rate_limiter` |
+| `execution=ExecutionConfig(code_execution=True, code_mode="safe", rate_limiter=obj)` | `allow_code_execution`, `code_execution_mode`, `rate_limiter` |
 | `memory=MemoryConfig(auto_save="session_name")` | `auto_save` |
 | `autonomy=AutonomyConfig(verification_hooks=[...])` | `verification_hooks` |
 | `handoffs=[other_agent]` | `allow_delegation` |
@@ -390,8 +390,6 @@ Agent parameters are consolidated into Config objects following the pattern:
 
 **Deprecated standalone params still work** (backward compatible) but emit `DeprecationWarning`.
 
-> **📢 Deprecation Notice:** `ExecutionConfig.context_compaction` currently defaults to `False` during a deprecation period. In the next release, it will default to `True` for automatic proactive context overflow protection. To opt out explicitly, set `context_compaction=False`. To use the new behavior early, set `context_compaction=True`.
-
 ```python
 # ❌ Old way (deprecated)
 agent = Agent(
@@ -415,7 +413,6 @@ agent = Agent(
         code_execution=True,
         code_mode="safe",
         rate_limiter=my_limiter,
-        context_compaction=False,  # prepare for next release when default becomes True; warning still fires during deprecation period
     ),
     memory=MemoryConfig(auto_save="my_session"),
     autonomy=AutonomyConfig(verification_hooks=[my_hook]),

src/praisonai-agents/DOCS_PARITY.md

@@ -1,6 +1,6 @@
 # Documentation Parity Tracker (Python)
 
-> **Categories:** 66 | **Documented:** 65 | **Parity:** 98.5%
+> **Categories:** 60 | **Documented:** 57 | **Parity:** 95.0%
 
 This report compares **Python SDK feature categories** against **Python documentation** (docs/concepts, docs/features, etc.).
 
@@ -8,86 +8,108 @@ This report compares **Python SDK feature categories** against **Python document
 
 | Metric | Count |
 |--------|-------|
-| Feature Categories | 66 |
-| **Documented Categories** | **65** |
-| **Undocumented Categories** | **1** |
-| **Parity** | **98.5%** |
+| Feature Categories | 60 |
+| **Documented Categories** | **57** |
+| **Undocumented Categories** | **3** |
+| **Parity** | **95.0%** |
 
 ## Documented Categories
 
 | Category | Features | Docs | Lines |
 |----------|----------|------|-------|
 | ✅ AGUI | 1 | 2 | 420 |
-| ✅ Agent | 20 | 40 | 10767 |
-| ✅ Agent-to-Agent (A2A) | 1 | 7 | 2880 |
-| ✅ Agent-to-User (A2U) | 1 | 3 | 575 |
-| ✅ Approval | 1 | 3 | 1344 |
+| ✅ Agent | 19 | 30 | 8367 |
+| ✅ Agent-to-Agent (A2A) | 1 | 3 | 625 |
+| ✅ Approval | 1 | 2 | 889 |
 | ✅ Audio | 2 | 10 | 649 |
-| ✅ Auto Generation | 5 | 6 | 1637 |
-| ✅ Autonomy | 3 | 5 | 1919 |
-| ✅ Bots | 7 | 21 | 6676 |
-| ✅ Budget | 1 | 1 | 287 |
-| ✅ CLI | 5 | 106 | 26315 |
-| ✅ Chunking | 2 | 2 | 288 |
+| ✅ Auto Generation | 3 | 6 | 1386 |
+| ✅ Autonomy | 3 | 4 | 1146 |
+| ✅ Bots | 5 | 2 | 573 |
+| ✅ Chunking | 2 | 2 | 285 |
 | ✅ Citations | 2 | 1 | 202 |
-| ✅ Code Execution | 2 | 13 | 3551 |
-| ✅ Conditions | 1 | 3 | 1085 |
-| ✅ Configuration | 3 | 7 | 2069 |
-| ✅ Context Management | 16 | 33 | 10206 |
-| ✅ Database | 1 | 40 | 5373 |
-| ✅ Deep Research | 8 | 2 | 437 |
+| ✅ Code Execution | 3 | 13 | 3395 |
+| ✅ Conditions | 1 | 3 | 1086 |
+| ✅ Configuration | 2 | 7 | 1753 |
+| ✅ Context Management | 13 | 29 | 7576 |
+| ✅ Database | 1 | 31 | 3037 |
 | ✅ Display | 6 | 2 | 1175 |
 | ✅ Embeddings | 6 | 23 | 1966 |
-| ✅ Evaluation | 1 | 6 | 2539 |
-| ✅ Events | 1 | 2 | 764 |
-| ✅ Execution | 3 | 3 | 1099 |
-| ✅ Failover | 2 | 1 | 372 |
-| ✅ Files | 2 | 6 | 1361 |
-| ✅ Flow | 1 | 2 | 435 |
-| ✅ Gateway | 7 | 18 | 6400 |
-| ✅ Guardrails | 4 | 4 | 1619 |
-| ✅ Handoffs | 11 | 6 | 2499 |
-| ✅ Hooks | 2 | 8 | 3100 |
+| ✅ Evaluation | 1 | 6 | 2538 |
+| ✅ Events | 1 | 2 | 699 |
+| ✅ Execution | 3 | 2 | 515 |
+| ✅ Files | 2 | 5 | 863 |
+| ✅ Flow | 1 | 1 | 284 |
+| ✅ Gateway | 6 | 1 | 256 |
+| ✅ Guardrails | 4 | 4 | 2279 |
+| ✅ Handoffs | 10 | 4 | 1674 |
+| ✅ Hooks | 2 | 6 | 1606 |
 | ✅ Image | 1 | 11 | 1096 |
-| ✅ Knowledge | 4 | 23 | 4282 |
-| ✅ LLM | 3 | 10 | 3674 |
-| ✅ Loops | 4 | 3 | 774 |
-| ✅ MCP | 1 | 51 | 10933 |
-| ✅ Memory | 6 | 16 | 5879 |
-| ✅ OCR | 2 | 1 | 226 |
-| ✅ Observability | 2 | 23 | 2652 |
-| ✅ Optimizer | 1 | 2 | 653 |
-| ✅ Output | 3 | 4 | 860 |
-| ✅ Parallel Execution | 3 | 2 | 399 |
-| ✅ Planning | 6 | 5 | 1198 |
-| ✅ Plugins | 8 | 3 | 1502 |
-| ✅ Prompts | 2 | 7 | 1569 |
-| ✅ Providers | 1 | 52 | 6214 |
+| ✅ Knowledge | 3 | 12 | 3262 |
+| ✅ LLM | 2 | 2 | 1049 |
+| ✅ Loops | 4 | 1 | 250 |
+| ✅ MCP | 2 | 47 | 9754 |
+| ✅ Memory | 6 | 12 | 4474 |
+| ✅ Output | 3 | 4 | 868 |
+| ✅ Parallel Execution | 2 | 2 | 347 |
+| ✅ Planning | 6 | 5 | 1506 |
+| ✅ Plugins | 8 | 3 | 1494 |
+| ✅ Prompts | 2 | 4 | 796 |
+| ✅ Providers | 2 | 50 | 5396 |
 | ✅ Query | 1 | 2 | 616 |
-| ✅ RAG | 5 | 15 | 3190 |
+| ✅ RAG | 5 | 14 | 2910 |
 | ✅ Realtime | 2 | 5 | 584 |
-| ✅ Reflection | 3 | 2 | 477 |
-| ✅ Retrieval | 2 | 5 | 944 |
+| ✅ Reflection | 3 | 2 | 469 |
+| ✅ Retrieval | 2 | 5 | 941 |
 | ✅ Routing | 1 | 2 | 597 |
-| ✅ Sandbox | 5 | 6 | 2478 |
-| ✅ Security | 1 | 3 | 2550 |
-| ✅ Sessions | 4 | 11 | 3423 |
-| ✅ Skills | 6 | 9 | 2983 |
-| ✅ Tasks | 2 | 6 | 3064 |
-| ✅ Telemetry | 1 | 2 | 560 |
+| ✅ Sandbox | 5 | 3 | 1002 |
+| ✅ Security | 1 | 1 | 875 |
+| ✅ Sessions | 2 | 8 | 2128 |
+| ✅ Skills | 5 | 5 | 2002 |
+| ✅ Tasks | 2 | 5 | 2590 |
+| ✅ Telemetry | 1 | 2 | 541 |
 | ✅ Templates | 1 | 7 | 1411 |
-| ✅ Tools | 12 | 118 | 25868 |
+| ✅ Tools | 4 | 97 | 17827 |
 | ✅ Tracing | 3 | 2 | 139 |
-| ✅ Video | 2 | 6 | 510 |
+| ✅ Video | 2 | 5 | 460 |
 | ✅ Vision | 2 | 1 | 283 |
-| ✅ Web | 3 | 6 | 1352 |
-| ✅ Workflows | 4 | 14 | 5810 |
+| ✅ Web | 4 | 6 | 1267 |
+| ✅ Workflows | 4 | 9 | 3579 |
+
+## Stub Documentation (Need Content)
+
+These categories have documentation files but < 50 lines (stubs):
+
+| Category | Features | Docs | Lines |
+|----------|----------|------|-------|
+| ⚠️ OCR | 2 | 1 | 49 |
 
 ## Undocumented Categories (Need Documentation)
 
 | Category | Features |
 |----------|----------|
-| ❌ Vector Store | 1 |
+| ❌ Failover | 2 |
+| ❌ Optimizer | 1 |
+
+## Documentation Without Features
+
+These docs exist but don't match any implemented feature category:
+
+- ℹ️ AI SDK (1 docs, 112 lines)
+- ℹ️ Agent-to-User (A2U) (3 docs, 770 lines)
+- ℹ️ CLI (91 docs, 21386 lines)
+- ℹ️ Callbacks (1 docs, 985 lines)
+- ℹ️ Chat (3 docs, 546 lines)
+- ℹ️ Deep Research (2 docs, 437 lines)
+- ℹ️ Deployment (29 docs, 5931 lines)
+- ℹ️ Jobs (1 docs, 358 lines)
+- ℹ️ Middleware (1 docs, 60 lines)
+- ℹ️ Observability (21 docs, 1580 lines)
+- ℹ️ Process (1 docs, 506 lines)
+- ℹ️ Scheduler (2 docs, 858 lines)
+- ℹ️ Storage (4 docs, 722 lines)
+- ℹ️ Streaming (3 docs, 485 lines)
+- ℹ️ Token Management (3 docs, 1020 lines)
+- ℹ️ Vector Store (11 docs, 733 lines)
 
 ---
 

src/praisonai-agents/benchmarks/BENCHMARK_RESULTS.md

@@ -1,70 +1,35 @@
 # PraisonAI Agents - Benchmark Results
 
-**Generated:** 2026-06-17
-**Methodology:** 10 warmup runs + 1000 measured iterations (`performance_benchmark.py` config)
-**Config:** `Agent(name=..., instructions=..., model="gpt-4o-mini", tools=[get_weather], output="silent")`
+**Generated:** 2025-12-18 14:40:22
+**Iterations:** 100
+**Test:** Agent instantiation (without tools)
 
-## Agent Instantiation Time
+## Results
 
-| Framework | Avg Time (μs) | Min (μs) | Median (μs) | Relative |
-|-----------|---------------|----------|-------------|----------|
-| OpenAI Agents SDK | 5.05 | — | — | 1.00x (fastest) |
-| Agno | 5.27 | — | — | 1.04x |
-| **PraisonAI** | **13.62** | **12.58** | **12.96** | **2.69x** |
-| PraisonAI (LiteLLM) | 14.49 | 13.37 | 13.71 | 2.86x |
-
-### PraisonAI detail (1000 iter)
-
-| Variant | Avg (μs) | Min (μs) | Median (μs) | Std (μs) |
-|---------|----------|----------|-------------|----------|
-| With tools (`gpt-4o-mini`) | 13.62 | 12.58 | 12.96 | 2.73 |
-| With tools (`openai/gpt-4o-mini`) | 14.49 | 13.37 | 13.71 | 4.14 |
-| Minimal, no tools | 13.40 | — | — | — |
-
-## Historical baseline
-
-| Source | Config | Avg (μs) | Notes |
-|--------|--------|----------|-------|
-| `performance_benchmark.py` (Dec 2025) | tools + 1000 iter | **3.20** | Pre-init-bloat baseline |
-| `simple_benchmark.py` (Dec 2025) | no tools, 100 iter | **3.77** | Saved in prior `BENCHMARK_RESULTS.md` |
-| Current (Jun 2026) | tools + 1000 iter | **13.62** | After lazy-init optimisations (was ~31 μs pre-fix) |
+| Framework | Avg Time (μs) | Relative |
+|-----------|---------------|----------|
+| **PraisonAI** | **3.77** | **1.00x (fastest)** |
+| OpenAI Agents SDK | 5.26 | 1.39x |
+| Agno | 5.64 | 1.49x |
+| PraisonAI (LiteLLM) | 7.56 | 2.00x |
+| PydanticAI | 226.94 | 60.16x |
+| LangGraph | 4,558.71 | 1,209x |
+| CrewAI | 15,607.92 | 4,138x |
 
 ## Package Versions
 
 | Package | Version |
-|---------|---------|
-| PraisonAI | 1.6.60 |
-| Agno | 2.4.2 |
-| OpenAI Agents SDK | 0.7.0 |
+|---------|--------|
+| PraisonAI | 0.1.5 |
+| Agno | 2.3.14 |
+| PydanticAI | 1.35.0 |
+| OpenAI Agents SDK | 0.6.3 |
+| LangGraph | 1.0.5 |
+| CrewAI | 1.6.1 |
 
 ## How to Reproduce
 
-PraisonAI-only (fast; avoids CrewAI/LangGraph import hang):
-
 ```bash
 cd praisonai-agents
-PYTHONWARNINGS='ignore::DeprecationWarning' python -c "
-import time, statistics
-from typing import Literal
-from praisonaiagents import Agent
-
-def get_weather(city: Literal['nyc', 'sf']):
-    return 'cloudy' if city == 'nyc' else 'sunny'
-
-def bench(fn, n=1000, w=10):
-    for _ in range(w): fn()
-    times = []
-    for _ in range(n):
-        s = time.perf_counter(); fn(); times.append(time.perf_counter()-s)
-    print(f'avg={statistics.mean(times)*1e6:.2f} μs')
-
-bench(lambda: Agent(name='Test', instructions='Hi', model='gpt-4o-mini', tools=[get_weather], output='silent'))
-"
-```
-
-Full multi-framework benchmark:
-
-```bash
-cd praisonai-agents
-PYTHONWARNINGS='ignore::DeprecationWarning' python benchmarks/performance_benchmark.py --save
+python benchmarks/simple_benchmark.py
 ```

src/praisonai-agents/benchmarks/INSTANTIATION_RCA.md

@@ -1,62 +0,0 @@
-# Agent Instantiation — Root Cause Analysis
-
-**Date:** 2026-06-17  
-**Version:** praisonaiagents 1.6.58 (dev)  
-**Methodology:** 10 warmup + 1000 measured iterations, `time.perf_counter()`
-
-## Summary
-
-Instantiation regressed from **~3.2 μs** (Dec 2025) to **~31 μs** (~10×). After fixes, steady-state is **~16 μs** (~2× historical). PraisonAI is competitive with Agno (~5 μs) on warmed benchmarks using `model=`.
-
-## Before vs After Fixes
-
-| Config | Historical | Pre-fix | Post-fix |
-|--------|------------|---------|----------|
-| Minimal (`name`, `output="silent"`) | 3.20 μs | 31.36 μs | **16.35 μs** |
-| `model="gpt-4o-mini"` | 3.77 μs | 32.19 μs | **16.86 μs** |
-| `llm="gpt-4o-mini"` (deprecated) | 3.77 μs | 46.48 μs | **~18 μs** (warn once) |
-| LiteLLM `openai/gpt-4o-mini` cold | 7.31 μs | **3.7 s** | **53.63 μs** |
-| LiteLLM warmed instantiate | — | 60 μs | **19.83 μs** |
-| Import `Agent` | — | 20.8 ms | **29.6 ms** |
-
-## Root Causes (validated)
-
-| ID | Cause | Status | Fix applied |
-|----|-------|--------|-------------|
-| H1 | Config loader cold start (+9 ms first init) | CONFIRMED | Fast path when no config defaults (`_defaults_has_any_values`) |
-| H2 | LoopGuard on every init (~2 μs) | CONFIRMED | Lazy `_ensure_loop_guard()` on first tool call |
-| H3 | Approval presets | REJECTED | No change needed |
-| H4 | `llm=` deprecation every init (+16 μs) | CONFIRMED | Warn once per process in `warn_deprecated_param` |
-| H5 | Init bloat / config resolution (~25 μs) | CONFIRMED | Partially addressed via loader fast path |
-| H6 | LiteLLM eager `LLM()` import (+3.7 s) | CONFIRMED | Deferred `_llm_init_params` + lazy `llm_instance` property |
-| H7 | `ExecutionConfig` deprecation every init | CONFIRMED | Fixed stack walk; internal SDK path cached |
-
-## cProfile — Post-fix (500 warmed `model=` inits)
-
-| Function | Time (ms) | Per call |
-|----------|-----------|----------|
-| `Agent.__init__` | 8 | ~16 μs |
-| `apply_config_defaults` | 1 | ~2 μs |
-| `ExecutionConfig.__post_init__` | 1 | ~2 μs (first call only walks stack) |
-| `_warnings.warn` | **0** | removed from hot path |
-
-## Fixes implemented
-
-1. **`praisonaiagents/config/feature_configs.py`** — Fix `ExecutionConfig.__post_init__` deprecation guard; cache internal SDK detection.
-2. **`praisonaiagents/utils/deprecation.py`** — Warn once per process for deprecated params.
-3. **`praisonaiagents/config/loader.py`** — Skip `apply_config_defaults` lookups when config has no defaults.
-4. **`praisonaiagents/agent/agent.py`** — Lazy LoopGuard, lazy LLM instance, deferred LiteLLM params.
-5. **`benchmarks/simple_benchmark.py`** — Use `model=`, add 10-iteration warmup.
-6. **`benchmarks/performance_benchmark.py`** — Use `model=` instead of deprecated `llm=`.
-
-## Remaining gap (~16 μs vs ~3 μs)
-
-The residual ~13 μs is primarily **`Agent.__init__` size** (~1200 lines of consolidated config resolution added since v0.1.5). Further optimisation requires a dedicated slim fast-path for `Agent(name=..., output="silent")` without full config object churn.
-
-## How to reproduce
-
-```bash
-cd praisonai-agents
-PYTHONWARNINGS='ignore::DeprecationWarning' python benchmarks/simple_benchmark.py
-PYTHONWARNINGS='ignore::DeprecationWarning' python benchmarks/performance_benchmark.py
-```