Release v4.6.72

Trigger praison.ai CI via workflow_dispatch when install.sh or install.ps1 change on main.

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

@@ -186,7 +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).\n' +
+              'Phase 1: Review per AGENTS.md principles (protocol-driven, backward compatible, no perf impact, SDK value gate — no feature bloat).\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});
@@ -313,7 +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\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'
+              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'
             });
             console.log(`Claude triggered on PR #${pr}`);
 
@@ -417,7 +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\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'
+              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'
             });
             console.log(`Claude triggered on PR #${pr}`);
 
@@ -468,7 +468,7 @@ 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\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'
+              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'
             });
 
   # ================================================================

.github/workflows/claude.yml

@@ -151,24 +151,58 @@ jobs:
         id: check_fork
         uses: actions/github-script@v7
         with:
+          github-token: ${{ steps.app-token.outputs.token }}
           script: |
-            const isPR = context.eventName === 'pull_request' || (context.payload.issue && context.payload.issue.pull_request);
-            if (isPR) {
-              const prNumber = context.payload.pull_request ? context.payload.pull_request.number : context.payload.issue.number;
-              const pr = await github.rest.pulls.get({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                pull_number: prNumber
-              });
-              
-              core.setOutput('pr_branch', pr.data.head.ref);
-              
-              if (pr.data.head.repo && pr.data.head.repo.full_name !== context.repo.full_name) {
-                core.setOutput('is_fork', 'true');
-                core.setOutput('clone_url', pr.data.head.repo.clone_url);
-                core.setOutput('branch', pr.data.head.ref);
-                return;
-              }
+            const isPR = context.eventName === 'pull_request'
+              || context.eventName === 'pull_request_review'
+              || context.eventName === 'pull_request_review_comment'
+              || (context.payload.issue && context.payload.issue.pull_request);
+
+            if (!isPR) {
+              core.setOutput('is_pr', 'false');
+              core.setOutput('is_fork', 'false');
+              return;
+            }
+
+            const prNumber = context.payload.pull_request
+              ? context.payload.pull_request.number
+              : context.payload.issue.number;
+            const pr = await github.rest.pulls.get({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              pull_number: prNumber
+            });
+
+            let mergeState = '';
+            try {
+              const gql = await github.graphql(
+                `query($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}`;
+            if (pr.data.head.repo && pr.data.head.repo.full_name !== baseRepo) {
+              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');
 
@@ -180,17 +214,19 @@ jobs:
           token: ${{ steps.app-token.outputs.token }}
 
       - name: Fetch PR branch and Setup Remote
-        if: github.event.issue.pull_request
+        if: steps.check_fork.outputs.is_pr == 'true'
         env:
           PR_BRANCH: ${{ steps.check_fork.outputs.pr_branch }}
+          PR_NUMBER: ${{ steps.check_fork.outputs.pr_number }}
           IS_FORK: ${{ steps.check_fork.outputs.is_fork }}
         run: |
-          # Fetch PR head from base repo and put it into local branch
-          git fetch origin pull/${{ github.event.issue.number }}/head:"${PR_BRANCH}"
-          
+          git fetch origin "pull/${PR_NUMBER}/head:${PR_BRANCH}"
+          git checkout "${PR_BRANCH}"
+          git fetch origin main
+
           # 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)
+            git remote set-url origin "file://$(pwd)"
           fi
 
       - uses: anthropics/claude-code-action@beta
@@ -200,11 +236,13 @@ jobs:
           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' && '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' }}
+            ${{ 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' || (steps.check_fork.outputs.is_pr == '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' || '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}, merge_state={4}. 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.merge_state) || '' }}
             You are working on the PraisonAI SDK. Follow AGENTS.md strictly.
 
             STEP 0 — SETUP GIT IDENTITY & AUTH (GLOBAL — required for all repos):
@@ -233,15 +271,9 @@ 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.
 
-            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.
+            ${{ steps.check_fork.outputs.is_pr == 'true' && steps.check_fork.outputs.is_fork != 'true' && steps.check_fork.outputs.merge_state == 'DIRTY' && format('STEP 3 — RESOLVE MERGE CONFLICTS ON PR (branch: {0}, PR #{1}):\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.is_pr == 'true' && steps.check_fork.outputs.is_fork != 'true' && steps.check_fork.outputs.merge_state != 'DIRTY' && format('STEP 3 — IMPLEMENT ON EXISTING PR (branch: {0}, PR #{1}):\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) || '' }}
+            ${{ steps.check_fork.outputs.is_pr != 'true' && 'STEP 3 — IMPLEMENT (for changes in THIS repo — PraisonAI):\n- Create a fix branch: git checkout -b claude/issue-$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-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.
@@ -267,10 +299,7 @@ jobs:
             - For docs changes: verify files exist and are valid markdown/MDX
             - Ensure no regressions
 
-            STEP 5 — CREATE PR:
-            - Commit with descriptive message, push, and create PR using `gh pr create`
-            - For external repos, use `gh pr create -R MervinPraison/<REPO_NAME>`
-            CRITICAL: You MUST create the PR automatically using `gh pr create`. Do NOT just provide a link or say "manual push required".
+            ${{ 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' }}
             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

@@ -0,0 +1,52 @@
+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

@@ -22,8 +22,6 @@ 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

@@ -36,9 +36,6 @@ 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

@@ -37,9 +37,6 @@ 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

@@ -48,9 +48,6 @@ 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 --timeout=300
+        python -m pytest tests/integration/test_aiui_* -v
         
     - 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 --timeout=300
+          python -m pytest tests/integration/test_agentic_roundtrip.py -v
         fi
         if [ -f tests/test_feature_sdk_backends.py ]; then
-          python -m pytest tests/test_feature_sdk_backends.py -v --timeout=300
+          python -m pytest tests/test_feature_sdk_backends.py -v
         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 --timeout=600
+          python -m pytest tests/integration/test_aiui_host_agentic.py -v
         else
           echo "OPENAI_API_KEY not available, skipping agentic tests"
         fi

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

@@ -0,0 +1,164 @@
+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: $number) {
+                      mergeStateStatus
+                      isDraft
+                      headRef { repository { nameWithOwner } }
+                    }
+                  }
+                }
+              `;
+              for (let attempt = 0; attempt < 3; attempt++) {
+                const result = await github.graphql(query, { owner, repo, number: prNumber });
+                const prGql = result.repository.pullRequest;
+                const status = (prGql?.mergeStateStatus || '').toUpperCase();
+                if (status && status !== 'UNKNOWN') {
+                  return {
+                    status,
+                    isDraft: prGql.isDraft,
+                    headRepo: prGql.headRef?.repository?.nameWithOwner,
+                  };
+                }
+                if (attempt < 2) await sleep(10000);
+              }
+              const pr = await github.rest.pulls.get({ owner, repo, pull_number: prNumber });
+              return {
+                status: (pr.data.mergeable_state || '').toUpperCase(),
+                isDraft: pr.data.draft,
+                headRepo: pr.data.head.repo?.full_name,
+              };
+            }
+
+            function hasRecentAutoComment(comments) {
+              const cutoff = Date.now() - COOLDOWN_MS;
+              return comments.some((c) => {
+                if (!AUTO_ACTORS.includes(c.user.login)) return false;
+                const body = (c.body || '').toLowerCase();
+                if (!body.includes('@claude') || !body.includes('merge conflict')) return false;
+                return new Date(c.created_at).getTime() > cutoff;
+              });
+            }
+
+            async function processPR(prNumber) {
+              const { status, isDraft, headRepo } = await getMergeState(prNumber);
+              core.info(`PR #${prNumber}: mergeState=${status}, head=${headRepo}, draft=${isDraft}`);
+
+              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) return { skipped: true, reason: 'fork' };
+
+              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' };
+              }
+
+              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

@@ -26,11 +26,8 @@ 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
@@ -48,11 +45,8 @@ 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
@@ -449,11 +443,8 @@ 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

@@ -0,0 +1,31 @@
+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

@@ -37,10 +37,8 @@ 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

@@ -13,10 +13,8 @@ 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
@@ -81,10 +79,8 @@ 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

@@ -33,10 +33,8 @@ 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

@@ -45,10 +45,8 @@ 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

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

AGENTS.md

@@ -0,0 +1,10 @@
+# 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/

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 under 4μs. 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 around 14μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.
 
 | Performance Metric | PraisonAI |
 |--------------------|-----------|
-| Avg Instantiation Time | **3.77 μs** |
+| Avg Instantiation Time | **14 μs** |
 
 ---
 

api.md

@@ -59,6 +59,8 @@ 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>
@@ -240,6 +242,7 @@ 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>
@@ -515,10 +518,12 @@ 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>
@@ -527,6 +532,7 @@ 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
 
@@ -609,7 +615,7 @@ 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) -> 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, tool_call_id: str = None, run_id: str = None) -> str</code>
 
 # UI
 
@@ -698,7 +704,7 @@ Methods:
 
 Types:
 ```python
-from praisonaiagents import AgentAppConfig, AgentAppProtocol, AgentFlow, AgentManager, AgentOSConfig, AgentOSProtocol, AgentTeam, AutoApproveBackend, EmbeddingResult, aembedding, aembeddings, embedding, embeddings, get_dimensions
+from praisonaiagents import AgentAppConfig, AgentAppProtocol, AgentFlow, AgentManager, AgentOSConfig, AgentOSProtocol, AgentTeam, AutoApproveBackend, EmbeddingResult, RetryBackoffConfig, __version__, aembedding, aembeddings, embedding, embeddings, get_dimensions
 ```
 
 Methods:
@@ -789,11 +795,17 @@ 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>
@@ -834,6 +846,8 @@ 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>
@@ -846,6 +860,9 @@ 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>
@@ -876,17 +893,18 @@ 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 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 docker <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>
@@ -914,6 +932,9 @@ 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>
@@ -923,6 +944,11 @@ 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>
@@ -987,6 +1013,9 @@ 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>
@@ -1004,6 +1033,14 @@ 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>
@@ -1083,6 +1120,7 @@ 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>
@@ -1150,6 +1188,9 @@ 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>
@@ -1302,7 +1343,7 @@ 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, getRegistry, getTool, getToolsRegistry, get_registry, get_tool, registerTool, register_tool, resetToolsRegistry, validateTool, validate_tool } from "./tools/registry";
+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 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>=4.6.55" \
+    "praisonai>=4.6.72" \
     "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>=4.6.55" \
+    "praisonai>=4.6.72" \
     "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>=4.6.55" \
+    "praisonai>=4.6.72" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/approval/agent_approval.py

@@ -12,10 +12,15 @@ from praisonaiagents import Agent
 from praisonaiagents.approval import AgentApproval
 from praisonaiagents.tools.shell_tools import execute_command
 
-reviewer = AgentApproval(
+reviewer_agent = Agent(
+    name="CommandReviewer",
     instructions="Only approve read-only commands like 'ls' or 'cat'. Deny destructive commands.",
 )
 
+reviewer = AgentApproval(
+    approver_agent=reviewer_agent,
+)
+
 agent = Agent(
     name="DevOps",
     instructions="You are a DevOps assistant. Use shell tools when asked.",

examples/consolidated_params/advanced_workflow_full_features.py

@@ -2,7 +2,7 @@
 from praisonaiagents import Agent
 from praisonaiagents import AgentFlow, Task
 from praisonaiagents.workflows.workflow_configs import (
-    WorkflowOutputConfig, WorkflowPlanningConfig, WorkflowMemoryConfig,
+    WorkflowPlanningConfig, WorkflowMemoryConfig,
 )
 
 # Full-featured workflow with all consolidated params
@@ -32,7 +32,7 @@ workflow = AgentFlow(
         ),
     ],
     # Workflow-level consolidated params
-    output=WorkflowOutputConfig(output="verbose", stream=True),
+    output="verbose",
     planning=WorkflowPlanningConfig(enabled=True, reasoning=True),
     memory=WorkflowMemoryConfig(backend="file"),
     # Agent-like params

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

examples/cookbooks/Industry_Templates/README.md

@@ -0,0 +1,216 @@
+# 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

@@ -0,0 +1,38 @@
+"""
+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

@@ -0,0 +1,551 @@
+"""
+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

@@ -0,0 +1,372 @@
+"""
+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

@@ -0,0 +1,478 @@
+"""
+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

@@ -0,0 +1,298 @@
+"""
+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

@@ -0,0 +1,599 @@
+"""
+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 response.upper():
+        if "ZEBRA-71" in str(response or "").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 response.upper():
+        if "HAWK-88" in str(response or "").upper():
             print("✅ VERIFIED: Agent correctly retrieved the security code!\n")
         else:
             print("❌ WARNING: Agent did not find the code - check retrieval\n")

examples/managed-agents/persistence/cockroachdb_managed.py

@@ -17,8 +17,10 @@ from praisonaiagents import Agent
 agent = Agent(
     name="CockroachDB Agent",
     instructions="You are a helpful assistant.",
-    db={"database_url": os.getenv("COCKROACHDB_URL")},
-    session_id="crdb-demo-session",
+    memory={
+        "session_id": "crdb-demo-session",
+        "db": {"database_url": os.getenv("COCKROACHDB_URL")},
+    },
 )
 
 result = agent.start("What is distributed SQL? Explain in 2 sentences.")

examples/managed-agents/persistence/neon_managed.py

@@ -18,8 +18,10 @@ from praisonaiagents import Agent
 agent = Agent(
     name="Neon Agent",
     instructions="You are a helpful assistant.",
-    db={"database_url": os.getenv("NEON_DATABASE_URL")},
-    session_id="neon-demo-session",
+    memory={
+        "session_id": "neon-demo-session",
+        "db": {"database_url": os.getenv("NEON_DATABASE_URL")},
+    },
 )
 
 # Option 2: Direct URL

examples/managed-agents/persistence/supabase_direct_managed.py

@@ -20,8 +20,10 @@ from praisonaiagents import Agent
 agent = Agent(
     name="Supabase Agent",
     instructions="You are a helpful assistant.",
-    db={"database_url": os.getenv("SUPABASE_DATABASE_URL")},
-    session_id="supabase-demo-session",
+    memory={
+        "session_id": "supabase-demo-session",
+        "db": {"database_url": os.getenv("SUPABASE_DATABASE_URL")},
+    },
 )
 
 result = agent.start("What is Supabase? Explain in 2 sentences.")

examples/managed-agents/persistence/turso_managed.py

@@ -18,11 +18,13 @@ from praisonaiagents import Agent
 agent = Agent(
     name="Turso Agent",
     instructions="You are a helpful assistant.",
-    db={
-        "database_url": os.getenv("TURSO_DATABASE_URL"),
-        "auth_token": os.getenv("TURSO_AUTH_TOKEN"),
+    memory={
+        "session_id": "turso-demo-session",
+        "db": {
+            "database_url": os.getenv("TURSO_DATABASE_URL"),
+            "auth_token": os.getenv("TURSO_AUTH_TOKEN"),
+        },
     },
-    session_id="turso-demo-session",
 )
 
 result = agent.start("What is edge computing? Explain in 2 sentences.")

examples/middleware/configurable_model.py

@@ -10,8 +10,7 @@ from praisonaiagents import Agent
 agent = Agent(
     name="FlexBot",
     instructions="You are a helpful assistant.",
-    llm="gpt-4o-mini",
-    llm_config={"configurable": True}
+    llm={"model": "gpt-4o-mini", "configurable": True},
 )
 
 if __name__ == "__main__":

examples/middleware/injected_state.py

@@ -21,7 +21,7 @@ agent = Agent(
     name="ContextBot",
     instructions="You help show context information.",
     tools=[show_context],
-    session_id="my-session-123"
+    memory={"session_id": "my-session-123"},
 )
 
 if __name__ == "__main__":

examples/observability/multi_agent_tracing.py

@@ -19,6 +19,16 @@ 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",
@@ -37,13 +47,13 @@ with obs.trace("content-creation", metadata={"workflow": "research-write"}):
     
     # Research phase
     with obs.span("research-phase", kind=SpanKind.AGENT) as research_span:
-        research_span.attributes["agent"] = "Researcher"
+        set_span_attr(research_span, "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:
-        write_span.attributes["agent"] = "Writer"
-        write_span.attributes["input_length"] = len(str(research_result))
+        set_span_attr(write_span, "agent", "Writer")
+        set_span_attr(write_span, "input_length", len(str(research_result)))
         final_content = writer.chat(f"Write a blog post based on: {research_result}")
 
 print("Final Content:")

examples/persistence/knowledge_qdrant.py

@@ -52,8 +52,10 @@ agent = Agent(
     instructions="You are a helpful assistant with access to technical documentation.",
     knowledge={
         "sources": [doc_path],
-        "vector_store": "qdrant",
-        "url": "http://localhost:6333",
+        "vector_store": {
+            "provider": "qdrant",
+            "url": "http://localhost:6333",
+        },
     }
 )
 

examples/persistence/minimal_agent_db.py

@@ -13,7 +13,8 @@ Expected output:
     - Session can be resumed later with same session_id
 """
 
-from praisonaiagents import Agent, db
+from praisonaiagents import Agent
+from praisonaiagents.db import db
 
 # Create database connection (PostgreSQL + Redis)
 my_db = db(
@@ -25,10 +26,12 @@ my_db = db(
 agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant. Be concise.",
-    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"
+    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",
+    },
 )
 
 # Chat - messages are automatically persisted

examples/persistence/postgres_runs_traces.py

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

examples/persistence/redis_state.py

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

examples/persistence/session_resume.py

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

examples/persistence/simple_db_agent.py

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

examples/persistence/sqlite_local.py

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

examples/policy/basic_policy.py

@@ -57,8 +57,8 @@ def main():
     agent = Agent(
         name="SecureAgent",
         instructions="You are a file management assistant.",
-        policy=engine
     )
+    agent.policy = engine
     
     print("\n--- Agent with Policy Enforcement Created ---")
     print(f"Agent: {agent.name}")

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

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

examples/python/agents/code-agent.py

@@ -6,10 +6,18 @@ It includes self-reflection for improving code quality and multiple specialized
 """
 
 from praisonaiagents import Agent, Task, AgentTeam
-from praisonaiagents.tools import CodeInterpreter
 
-# Initialize code execution tool
-code_interpreter = 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}"
 
 # Custom code analysis tool
 def analyze_code_complexity(code: str) -> str:
@@ -32,9 +40,7 @@ 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.",
-    reflection=True,
-    
-    
+    output="silent",
 )
 
 code_reviewer = Agent(
@@ -52,7 +58,7 @@ 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=[code_interpreter]
+    tools=[run_python_code]
 )
 
 # Example tasks
@@ -87,7 +93,7 @@ def demonstrate_code_agent():
     workflow = AgentTeam(
         agents=[code_writer, code_reviewer, code_executor],
         tasks=[write_task, review_task, execute_task],
-        process="sequential", output="verbose"
+        process="sequential", output="silent"
     )
     
     # Run the workflow
@@ -106,10 +112,8 @@ general_code_agent = Agent(
 3. Explain code concepts
 4. Refactor code for better performance
 5. Add tests and documentation""",
-    tools=[code_interpreter, analyze_code_complexity],
-    reflection=True,
-    
-    
+    tools=[run_python_code, analyze_code_complexity],
+    output="silent",
 )
 
 if __name__ == "__main__":

examples/python/agents/math-agent.py

@@ -41,21 +41,25 @@ def calculate_statistics(numbers: list) -> str:
     """Calculate basic statistics for a list of numbers."""
     if not numbers:
         return "No numbers provided"
-    
-    n = len(numbers)
-    mean = sum(numbers) / n
-    sorted_nums = sorted(numbers)
+    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)
     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 numbers) / n
+    variance = sum((x - mean)**2 for x in nums) / n
     std_dev = math.sqrt(variance)
-    
+
     return f"""Statistics:
 - Count: {n}
 - Mean: {mean:.4f}
 - Median: {median:.4f}
 - Standard Deviation: {std_dev:.4f}
-- Min: {min(numbers)}
-- Max: {max(numbers)}"""
+- Min: {min(nums)}
+- Max: {max(nums)}"""
 
 # Create specialized math agents
 calculation_agent = Agent(
@@ -66,8 +70,7 @@ 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(
@@ -82,8 +85,7 @@ 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(
@@ -91,7 +93,8 @@ 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."
+    instructions="Explain mathematical concepts using simple language, provide examples, and check for understanding.",
+    output="silent",
 )
 
 # Example workflow for solving complex problems
@@ -126,7 +129,8 @@ 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="verbose"
+        process="sequential",
+        output="silent",
     )
     
     return workflow.start()
@@ -148,8 +152,7 @@ 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

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

examples/python/bot_run_control_example.py

@@ -0,0 +1,238 @@
+#!/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

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

examples/python/camera/camera-continuous.py

@@ -75,7 +75,7 @@ class ContinuousCameraMonitor:
             agents=[self.vision_agent],
             tasks=[task],
             process="sequential",
-            verbose=0  # Reduced verbosity for continuous monitoring
+            output="silent",  # Reduced verbosity for continuous monitoring
         )
         
         return agents.start()

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

@@ -148,10 +148,19 @@ def main():
             "scene_analysis": "🌍 SCENE ANALYSIS"
         }
         
-        for task_id, task_result in result["task_results"].items():
+        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"\n{agent_names.get(task_id, f'Task {task_id}')}:")
             print("-" * 40)
-            print(task_result.raw)
+            raw_value = getattr(task_result, "raw", None)
+            print(raw_value if raw_value is not None else str(task_result))
             
         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 = handler.get_stats()
+stats = manager.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.display_summary()
+handler.print_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",
-    handler=my_custom_handler,
-    kind=CommandKind.ACTION,
-    aliases=["mc"]
+    action=my_custom_handler,
+    kind=CommandKind.CUSTOM,
+    alt_names=["mc"]
 )
 
 handler.register(custom_cmd)

examples/python/concepts/repetitive-agents.py

@@ -16,7 +16,8 @@ agents = AgentTeam(
     agents=[agent],
     tasks=[task],
     process="workflow",
-    max_iter=30
+    execution={"max_iter": 30},
 )
 
-agents.start()
+if __name__ == "__main__":
+    agents.start()

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

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

examples/python/custom_tools/example_latency_tracking.py

@@ -61,7 +61,6 @@ def example_with_wrappers():
         role="Data Analyst",
         goal="Analyze data and provide insights",
         llm="gpt-4o-mini",
-        request_id="request_2"  # Unique request ID
     )
     
     # Operations are automatically tracked

examples/python/data/cot-agents.py

@@ -132,7 +132,9 @@ agents = AgentTeam(
     agents=[qa_generator, total_questions_evaluator, cot_generator, upload_to_huggingface],
     tasks=[generate_task, evaluate_total_questions_task, generate_cot_task, upload_to_huggingface_task],
     process="workflow",
-    max_iter=30, output="minimal"
+    execution={"max_iter": 30},
+    output="minimal",
 )
 
-agents.start()
+if __name__ == "__main__":
+    agents.start()

examples/python/failover_example.py

@@ -18,7 +18,7 @@ openai_profile = AuthProfile(
     provider="openai",
     api_key=os.environ.get("OPENAI_API_KEY", "sk-..."),
     priority=1,  # Highest priority
-    rate_limit=100,
+    rate_limit_rpm=100,
 )
 
 anthropic_profile = AuthProfile(
@@ -26,7 +26,7 @@ anthropic_profile = AuthProfile(
     provider="anthropic",
     api_key=os.environ.get("ANTHROPIC_API_KEY", "sk-ant-..."),
     priority=2,  # Second priority
-    rate_limit=50,
+    rate_limit_rpm=50,
 )
 
 groq_profile = AuthProfile(
@@ -34,7 +34,7 @@ groq_profile = AuthProfile(
     provider="groq",
     api_key=os.environ.get("GROQ_API_KEY", "gsk-..."),
     priority=3,  # Third priority (fast but limited)
-    rate_limit=30,
+    rate_limit_rpm=30,
 )
 
 # Configure failover behavior

examples/python/gateway_server_prototype.py

@@ -0,0 +1,201 @@
+"""
+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/memory_example.py

@@ -105,13 +105,13 @@ def main():
         agents=[researcher, retriever],
         tasks=[store_task, verify_task, query_task, query_both_task],  # Use same verbose level as memory
         memory={
-            "backend": "sqlite",
             "embedder": {
                 "provider": "openai",
                 "config": {
                     "model": "text-embedding-3-small"
                 }
-            }
+            },
+            "config": {"provider": "sqlite"},
         }
     )
 

examples/python/general/multimodal.py

@@ -49,10 +49,20 @@ agents = AgentTeam(
     process="sequential"
 )
 
-# Run all tasks
-result = agents.start()
+if __name__ == "__main__":
+    # Run all tasks
+    result = agents.start()
 
-# Print results
-for task_id, task_result in result["task_results"].items():
-    print(f"\nTask {task_id} Result:")
-    print(task_result.raw)
+    # 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))

examples/python/guardrails/production-guardrails-patterns.py

@@ -6,6 +6,7 @@ built-in validation and safety mechanisms for secure agent operations.
 """
 
 from praisonaiagents import Agent, Task, AgentTeam
+from praisonaiagents.config.feature_configs import GuardrailConfig
 from praisonaiagents.tools import internet_search
 
 print("=== Production Guardrails Patterns Example ===\n")
@@ -38,8 +39,8 @@ production_agent = Agent(
     goal="Generate safe, compliant content for production use",
     backstory="Production-ready agent with built-in safety and compliance checks",
     tools=[internet_search],
-    guardrails=content_guardrail,
-    max_retries=3)
+    guardrails=GuardrailConfig(validator=content_guardrail, max_retries=3),
+)
 
 # Create secure task with validation
 secure_task = Task(

examples/python/linear_agent_example.py

@@ -40,7 +40,7 @@ agent = Agent(
         "execute_command"
     ],
     memory=True,
-    web_search=True,
+    web=True,
     auto_approve_tools=True,
 )
 

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

@@ -14,30 +14,33 @@ agent = Agent(name="env-mgmt", backend=managed)
 # First call creates agent + environment + session
 agent.start("Say hello briefly")
 
-# Access the underlying Anthropic client for environment management
-client = managed._get_client()
-env_id = managed.environment_id
-print(f"Environment ID: {env_id}")
+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}")
 
-# 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}")
+    # 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().")
 
 print("\nEnvironment management complete.")

examples/python/mcp/mcp-resumability.py

@@ -15,13 +15,6 @@ 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
@@ -29,6 +22,12 @@ agent = Agent(
 # 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

@@ -15,13 +15,6 @@ 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
@@ -29,6 +22,12 @@ agent = Agent(
 # 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

@@ -16,34 +16,31 @@ 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

@@ -16,36 +16,33 @@ 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/memory/advanced-graph-memory-integration.py

@@ -12,8 +12,8 @@ print("=== Advanced Graph Memory Integration Example ===\n")
 
 # Configure simple graph memory
 memory_config = {
-    "provider": "mem0",
     "config": {
+        "provider": "mem0",
         "vector_store": {
             "provider": "chroma",
             "config": {

examples/python/memory/advanced-memory-search-optimization.py

@@ -12,8 +12,8 @@ print("=== Advanced Memory Search and Optimization Example ===\n")
 
 # Configure optimized memory settings
 memory_config = {
-    "provider": "mem0",
     "config": {
+        "provider": "mem0",
         "vector_store": {
             "provider": "chroma",
             "config": {

examples/python/memory/gemini-embedding-example.py

@@ -86,13 +86,13 @@ def main():
         agents=[researcher, retriever],
         tasks=[store_task, retrieve_task],
         memory={
-            "backend": "sqlite",
             "embedder": {
                 "provider": "gemini",
                 "config": {
                     "model": "text-embedding-004",
                 }
-            }
+            },
+            "config": {"provider": "sqlite"},
         }
     )
     

examples/python/models/deepseek/deepseek-rag-agents.py

@@ -30,7 +30,11 @@ config = {
 agent = Agent(
     name="Knowledge Agent",
     instructions="You answer questions based on the provided knowledge.",
-    knowledge={**config, "sources": ["kag-research-paper.pdf"]},
+    knowledge={
+        "sources": ["kag-research-paper.pdf"],
+        "vector_store": config["vector_store"],
+        "embedder": config["embedder"],
+    },
     memory={"user_id": "user1"},
     llm="deepseek-r1"
 )

examples/python/models/googlegemini/any-llm-agents-advanced.py

@@ -31,10 +31,9 @@ llm_config = {
 agent = Agent(
     instructions="You are a helpful Assistant specialized in scientific explanations. "
                 "Provide clear, accurate, and engaging responses.",
-    llm=llm_config,                  # Pass the detailed configuration
-    # Enable detailed output
-    markdown=True,                   # Format responses in markdown
-    reflection=True               # Enable self-reflection
+    llm=llm_config,
+    output={"markdown": True},
+    reflection=True,
 )
 
 # Test the agent

examples/python/mongodb/mongodb_memory_example.py

@@ -28,8 +28,8 @@ if not os.environ.get("OPENAI_API_KEY"):
 def main():
     # MongoDB memory configuration
     mongodb_memory_config = {
-        "provider": "mongodb",
         "config": {
+            "provider": "mongodb",
             "connection_string": "mongodb://localhost:27017/",  # Replace with your MongoDB connection string
             "database": "praisonai_memory",
             "use_vector_search": True,  # Enable Atlas Vector Search (requires MongoDB Atlas)

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 AgentFlow
+from praisonaiagents import Agent, AgentFlow, Task
 
 # Define workflow with output_file
 workflow_config = {
@@ -33,9 +33,30 @@ workflow_config = {
     ]
 }
 
-# Run workflow
-workflow = AgentFlow(config=workflow_config)
-result = workflow.run()
+# 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()
 
 print("✅ Workflow completed!")
 print("Output saved to: generated/tutorial.md")

examples/python/session/session_persistence_example.py

@@ -28,7 +28,7 @@ def example_basic_persistence():
     agent = Agent(
         name="Assistant",
         instructions="You are a helpful assistant. Be very brief.",
-        session_id=session_id,
+        memory={"session_id": session_id},
         output="minimal",
     )
     
@@ -59,7 +59,7 @@ def example_session_restoration():
     agent1 = Agent(
         name="MemoryBot",
         instructions="You are a helpful assistant. Be very brief.",
-        session_id=session_id,
+        memory={"session_id": session_id},
         output="minimal",
     )
     
@@ -72,7 +72,7 @@ def example_session_restoration():
     agent2 = Agent(
         name="MemoryBot",
         instructions="You are a helpful assistant. Be very brief.",
-        session_id=session_id,
+        memory={"session_id": session_id},
         output="minimal",
     )
     

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

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

examples/python/stateful/session-example.py

@@ -15,7 +15,7 @@ def main():
     # Create a session with persistent storage
     session = Session(
         session_id="demo_chat_001",
-        memory={"user_id": "demo_user"},
+        user_id="demo_user",
     )
     
     print(f"📝 Created session: {session}")

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

@@ -1,284 +1,55 @@
 """
 Production Telemetry Example
 
-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
+Demonstrates compatible telemetry setup with `enable_telemetry()` and
+`TelemetryCollector` patterns.
 """
 
+from contextlib import nullcontext
 from praisonaiagents import Agent, Task, AgentTeam
 from praisonaiagents.tools import duckduckgo
-from praisonaiagents.telemetry import enable_telemetry
-import time
-import random
-from datetime import datetime
+from praisonaiagents.telemetry import enable_telemetry, TelemetryCollector
 
-# Initialize telemetry for production monitoring
-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"
-        }
-    }
-)
+def main() -> None:
+    print("=" * 80)
+    print("PRODUCTION TELEMETRY SETUP")
+    print("=" * 80)
 
-# 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."
-)
+    # New API: no args
+    enable_telemetry()
+    collector = TelemetryCollector()
 
-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."
-)
+    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.",
+    )
 
-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
-        }
+    requests = [
+        "Customer asking about monthly bill charges",
+        "Customer experiencing connectivity issues",
     ]
-    
-    return customer_requests
 
-# Execute production workflow with comprehensive telemetry
-print("Starting production workflow with telemetry tracking...")
+    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))}")
 
-customer_requests = simulate_customer_requests()
+    print("\nTelemetry demonstration completed.")
 
-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)
 
-# 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")
+if __name__ == "__main__":
+    main()

examples/python/token-metrics/01_basic_token_tracking.py

@@ -14,7 +14,7 @@ def main():
         goal="Find information about a topic",
         backstory="You are an expert researcher who finds comprehensive information.",
         llm="gpt-4o-mini",  # Use a cost-effective model for examples
-        metrics=True  # 🎯 SIMPLIFIED: Just add this parameter!
+        output={"metrics": True}  # Token metrics via consolidated output config
     )
     
     # Create a simple task

examples/python/token-metrics/02_session_metrics.py

@@ -14,7 +14,7 @@ def main():
         goal="Analyze data and provide insights",
         backstory="You are an experienced data analyst with expertise in market research.",
         llm="gpt-4o-mini",
-        metrics=True  # 🎯 Enable metrics tracking
+        output={"metrics": True}
     )
     
     writer_agent = Agent(
@@ -23,7 +23,7 @@ def main():
         goal="Create well-structured content",
         backstory="You are a skilled technical writer who creates clear, engaging content.",
         llm="gpt-4o-mini",
-        metrics=True  # 🎯 Enable metrics tracking
+        output={"metrics": True}
     )
     
     # Create multiple tasks

examples/python/token-metrics/03_cost_estimation.py

@@ -14,7 +14,7 @@ def main():
         goal="Conduct comprehensive market research",
         backstory="You are a seasoned market researcher with 10+ years of experience.",
         llm="gpt-4o-mini",
-        metrics=True  # 🎯 Enable automatic metrics tracking
+        output={"metrics": True}
     )
     
     strategy_agent = Agent(
@@ -23,7 +23,7 @@ def main():
         goal="Develop actionable business strategies",
         backstory="You are a strategic consultant who translates research into business plans.",
         llm="gpt-4o-mini",
-        metrics=True  # 🎯 Enable automatic metrics tracking
+        output={"metrics": True}
     )
     
     # Create tasks that will generate substantial token usage

examples/python/tools/exa-tool/rag_examples/agentic_rag/rag_agent.py

@@ -22,7 +22,8 @@ rag_agent = Agent(
     
     You can use the internet_search function to search the web when needed.""",
     llm="gpt-4o",
-    markdown=True)
+    output={"markdown": True},
+)
 
 if __name__ == "__main__":
     print("🤖 Thai Recipe RAG Agent is ready!")

examples/python/tools/jira_agent_example.py

@@ -0,0 +1,115 @@
+#!/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:
+   - JIRA_EMAIL=your_email@example.com (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

@@ -5,11 +5,17 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
-os.environ['SERPER_API_KEY'] = os.getenv('SERPER_API_KEY')
+serper_api_key = os.getenv("SERPER_API_KEY")
+if serper_api_key is not None:
+    os.environ["SERPER_API_KEY"] = serper_api_key
 
-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()
+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()

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

@@ -2,6 +2,7 @@ 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
@@ -60,7 +61,10 @@ def analyze_code(code_source: str) -> CodeAnalysisReport:
     Analyze code from directory path or GitHub URL
     """
     # Ingest code content
-    summary, tree, content = ingest(code_source)
+    try:
+        summary, tree, content = ingest(code_source)
+    except Exception as exc:
+        raise RuntimeError(f"Failed to ingest code source: {exc}") from exc
     
     # Concatenate context into structured format
     context_text = f"""
@@ -86,10 +90,22 @@ def analyze_code(code_source: str) -> CodeAnalysisReport:
         tasks=[code_analysis_task]
     )
     
-    return agents.start(context_text)
+    result = agents.start(context_text)
+    if result is None:
+        raise RuntimeError("No analysis result returned by AgentTeam.start()")
+    return result
 
-if __name__ == "__main__":
+
+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
-    result = analyze_code(code_source)
-    print(result) 
+    try:
+        result = analyze_code(code_source)
+    except RuntimeError as exc:
+        print(f"Code analysis failed: {exc}")
+        sys.exit(1)
+    print(result) 
+
+
+if __name__ == "__main__":
+    main()

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

@@ -77,25 +77,44 @@ 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: {result.pydantic.overall_score}/100")
+print(f"\nOverall Score: {pydantic_result.overall_score}/100")
 print("\nKey Strengths:")
-for strength in result.pydantic.key_strengths:
+for strength in pydantic_result.key_strengths:
     print(f"- {strength}")
 
 print("\nSkill Analysis:")
 print("\nTechnical Skills:")
-for skill in result.pydantic.skill_analysis.technical_skills:
+for skill in pydantic_result.skill_analysis.technical_skills:
     print(f"- {skill}")
 
 print("\nExperience Highlights:")
-for achievement in result.pydantic.experience_analysis.key_achievements:
+for achievement in pydantic_result.experience_analysis.key_achievements:
     print(f"- {achievement}")
 
 print("\nRecommendations:")
-for recommendation in result.pydantic.recommendations:
+for recommendation in pydantic_result.recommendations:
     print(f"- {recommendation}")

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

@@ -100,6 +100,8 @@ 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

@@ -1,5 +1,11 @@
 from praisonaiagents import VideoAgent
+import os
 
-agent = VideoAgent(llm="azure/sora-2")
-video = agent.generate("Mountain landscape")
-print(video.id)
+
+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)

examples/python/workflows/task_callbacks.py

@@ -6,6 +6,7 @@ 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(
@@ -47,8 +48,10 @@ if __name__ == "__main__":
         agents=[researcher],
         tasks=[task1, task2],
         process="workflow",
-        on_task_start=on_start,
-        on_task_complete=on_complete,
+        hooks=MultiAgentHooksConfig(
+            on_task_start=on_start,
+            on_task_complete=on_complete,
+        ),
         variables={"global_var": "shared_value"}  # Global variables
     )
     

examples/python/workflows/workflow_branching.py

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