Release v4.6.71

Relocate PR #2072 runtime tests from repo root into tests/unit/runtime and
tests/unit/agent; remove sys.path hacks and use pytest patterns.

.github/workflows/claude.yml

@@ -151,24 +151,40 @@ 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
+            });
+
+            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', (pr.data.mergeable_state || pr.data.merge_state_status || '').toUpperCase());
+
+            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 +196,19 @@ 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 +218,13 @@ jobs:
           allowed_bots: 'praisonai-triage-agent[bot]'
           claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
           github_token: ${{ steps.app-token.outputs.token }}
+          model: opus
           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 +253,9 @@ 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 +281,7 @@ 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/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/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/

CONTRIBUTING.md

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

README.md

@@ -770,11 +770,11 @@ agent.start('Write a movie script about a robot in Mars');
 
 ## ⚡ Performance
 
-PraisonAI is built for speed, with agent instantiation in 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

@@ -15,6 +15,7 @@ Methods:
 * <code title="class GuardrailResult">GuardrailResult.<a href="./src/praisonai-agents/praisonaiagents/guardrails/guardrail_result.py">from_tuple</a>(result: Tuple[bool, Any]) -> 'GuardrailResult'</code>
 * <code title="class HandoffConfig">HandoffConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">from_dict</a>(data: Dict[str, Any]) -> 'HandoffConfig'</code>
 * <code title="class HandoffConfig">HandoffConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class HandoffResult">HandoffResult.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">from_outcome</a>(outcome: AgentRunOutcome, target_agent: Optional[str] = None, source_agent: Optional[str] = None, handoff_depth: int = 0) -> 'HandoffResult'</code>
 * <code title="class TaskOutput">TaskOutput.<a href="./src/praisonai-agents/praisonaiagents/main.py">json</a>() -> Optional[str]</code>
 * <code title="class TaskOutput">TaskOutput.<a href="./src/praisonai-agents/praisonaiagents/main.py">to_dict</a>() -> dict</code>
 * <code title="class ToolResult">ToolResult.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">to_dict</a>() -> Dict[str, Any]</code>
@@ -58,6 +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>
@@ -75,7 +78,7 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_until</a>(prompt: str, criteria: str, threshold: float = 8.0, max_iterations: int = 5, mode: str = 'optimize', on_iteration: Optional[Callable[[Any], None]] = None, verbose: bool = False) -> 'EvaluationLoopResult'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_until_async</a>(prompt: str, criteria: str, threshold: float = 8.0, max_iterations: int = 5, mode: str = 'optimize', on_iteration: Optional[Callable[[Any], None]] = None, verbose: bool = False) -> 'EvaluationLoopResult'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">skill_manager</a>() -> Optional[Any]</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">store_memory</a>(content: str, memory_type: str = 'short_term', **kwargs: Any) -> None</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">store_memory</a>(content: str, memory_type: str = 'short_term', action: str = 'add', **kwargs: Any) -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">stream_emitter</a>() -> Optional[Any]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">stream_emitter</a>(value: Optional[Any]) -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">thinking_budget</a>() -> Optional[int]</code>
@@ -175,6 +178,7 @@ Methods:
 * <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_schema_roundtrip</a>() -> bool</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">__call__</a>(*args, **kwargs) -> Any</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">check_availability</a>() -> tuple[bool, str]</code>
+* <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">get_schema</a>() -> Dict[str, Any]</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">injected_params</a>() -> Dict[str, Any]</code>
 * <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">run</a>(**kwargs) -> Any</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">clear</a>() -> None</code>
@@ -182,16 +186,18 @@ Methods:
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">discover_single_file_plugins</a>() -> int</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get</a>(name: str) -> Optional[Union[BaseTool, Callable]]</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_all</a>() -> Dict[str, Union[BaseTool, Callable]]</code>
-* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_available_tools</a>(context: Optional[Dict[str, Any]] = None) -> List[Union[BaseTool, Callable]]</code>
+* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_tool_definitions</a>() -> List[Dict[str, Any]]</code>
+* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_trust_level</a>(name: str) -> Optional[str]</code>
+* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_available_tools</a>(context: Optional[Dict[str, Any]] = None, ttl_seconds: Optional[float] = None) -> List[Union[BaseTool, Callable]]</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_base_tools</a>() -> List[BaseTool]</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_tools</a>() -> List[str]</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_tools_with_allowed_filter</a>(context: Optional[Dict[str, Any]] = None) -> List[str]</code>
-* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None, overwrite: bool = False) -> None</code>
+* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None, overwrite: bool = False, trust_level: Optional[str] = None, dynamic_schema_overrides: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None) -> None</code>
 * <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">unregister</a>(name: str) -> bool</code>
 * <code title="class Tools">Tools.<a href="./src/praisonai-agents/praisonaiagents/tools/tools.py">internet_search</a>(*args, **kwargs)</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_registry</a>() -> ToolRegistry</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_tool</a>(name: str) -> Optional[Union[BaseTool, Callable]]</code>
-* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register_tool</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None) -> None</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register_tool</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None, trust_level: Optional[str] = None, dynamic_schema_overrides: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None) -> None</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">tool</a>(func: Optional[Callable] = None) -> Union[FunctionTool, Callable[[Callable], FunctionTool]]</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_tool</a>(tool: Any) -> bool</code>
 
@@ -236,6 +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>
@@ -353,7 +360,7 @@ Methods:
 * <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">to_tool_function</a>(source_agent: 'Agent') -> Callable</code>
 * <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">tool_description</a>() -> str</code>
 * <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">tool_name</a>() -> str</code>
-* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">handoff</a>(agent: 'Agent', tool_name_override: Optional[str] = None, tool_description_override: Optional[str] = None, on_handoff: Optional[Callable] = None, input_type: Optional[type] = None, input_filter: Optional[Callable[[HandoffInputData], HandoffInputData]] = None, config: Optional[HandoffConfig] = None, context_policy: Optional[str] = None, timeout_seconds: Optional[float] = None, max_concurrent: Optional[int] = None, detect_cycles: Optional[bool] = None, max_depth: Optional[int] = None) -> Handoff</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">handoff</a>(agent: 'Agent', tool_name_override: Optional[str] = None, tool_description_override: Optional[str] = None, on_handoff: Optional[Callable] = None, input_type: Optional[type] = None, input_filter: Optional[Callable[[HandoffInputData], HandoffInputData]] = None, config: Optional[HandoffConfig] = None, context_policy: Optional[str] = None, timeout_seconds: Optional[float] = None, max_concurrent: Optional[int] = None, detect_cycles: Optional[bool] = None, max_depth: Optional[int] = None, tool_policy_mode: Optional[Literal['intersect', 'passthrough']] = None, blocked_tools: Optional[List[str]] = None) -> Handoff</code>
 * <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">compress_history</a>(data: HandoffInputData) -> HandoffInputData</code>
 * <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">keep_last_n_messages</a>(n: int) -> Callable[[HandoffInputData], HandoffInputData]</code>
 * <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">remove_all_tools</a>(data: HandoffInputData) -> HandoffInputData</code>
@@ -511,10 +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>
@@ -523,6 +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
 
@@ -605,7 +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
 
@@ -626,6 +636,7 @@ Methods:
 * <code title="class AutonomyConfig">AutonomyConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/autonomy.py">effective_track_changes</a>() -> bool</code>
 * <code title="class AutonomyConfig">AutonomyConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/autonomy.py">from_dict</a>(data: Dict[str, Any]) -> 'AutonomyConfig'</code>
 * <code title="class CachingConfig">CachingConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
+* <code title="class ExecutionConfig">ExecutionConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">from_dict</a>(data: Dict[str, Any]) -> 'ExecutionConfig'</code>
 * <code title="class ExecutionConfig">ExecutionConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="class GuardrailConfig">GuardrailConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="class HooksConfig">HooksConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
@@ -693,7 +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:
@@ -718,12 +729,16 @@ Methods:
 * <code title="class AgentFlow">AgentFlow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">verbose</a>(value: bool)</code>
 * <code title="class AgentOSProtocol">AgentOSProtocol.<a href="./src/praisonai-agents/praisonaiagents/app/protocols.py">get_app</a>() -> Any</code>
 * <code title="class AgentOSProtocol">AgentOSProtocol.<a href="./src/praisonai-agents/praisonaiagents/app/protocols.py">serve</a>(host: Optional[str] = None, port: Optional[int] = None, reload: bool = False, **kwargs: Any) -> None</code>
+* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aannounce_completion</a>(agent_id: str, task_id: str, result: Any, success: bool = True, error: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None) -> None</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">add_task</a>(task)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aexecute_task</a>(task_id)</code>
+* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">announce_completion</a>(agent_id: str, task_id: str, result: Any, success: bool = True, error: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None) -> None</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">append_to_state</a>(key: str, value: Any, max_length: Optional[int] = None) -> List[Any]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_all_tasks</a>()</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_task</a>(task_id)</code>
+* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aspawn_sub_agent</a>(agent: Agent, task: Any, completion_callback: Optional[Callable[[SubAgentCompletionEvent], Any]] = None, metadata: Optional[Dict[str, Any]] = None) -> SpawnedSubAgent</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">astart</a>(content = None, return_dict = False, **kwargs)</code>
+* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">await_for_completions</a>(timeout: Optional[float] = None, agent_ids: Optional[List[str]] = None) -> List[SubAgentCompletionEvent]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clean_json_output</a>(output: str) -> str</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clear_state</a>() -> None</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">close</a>() -> None</code>
@@ -738,6 +753,7 @@ Methods:
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_tasks_status</a>()</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_detailed_token_report</a>() -> Dict[str, Any]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_plan_markdown</a>() -> str</code>
+* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_spawned_agents</a>() -> List[SpawnedSubAgent]</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_state</a>(key: str, default: Any = None) -> Any</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_details</a>(task_id)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_result</a>(task_id)</code>
@@ -754,10 +770,12 @@ Methods:
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_output_to_file</a>(task, task_output)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_session_state</a>(session_id: str, include_memory: bool = True) -> None</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">set_state</a>(key: str, value: Any) -> None</code>
+* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">spawn_sub_agent</a>(agent: Agent, task: Any, completion_callback: Optional[Callable[[SubAgentCompletionEvent], Any]] = None, metadata: Optional[Dict[str, Any]] = None) -> SpawnedSubAgent</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, output = None, **kwargs)</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">todo_list</a>()</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_plan_step_status</a>(step_id: str, status: str) -> bool</code>
 * <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_state</a>(updates: Dict) -> None</code>
+* <code title="class AgentTeam">AgentTeam.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">wait_for_completions</a>(timeout: Optional[float] = None, agent_ids: Optional[List[str]] = None) -> List[SubAgentCompletionEvent]</code>
 * <code title="class AutoApproveBackend">AutoApproveBackend.<a href="./src/praisonai-agents/praisonaiagents/approval/backends.py">request_approval</a>(request: ApprovalRequest) -> ApprovalDecision</code>
 * <code title="class AutoApproveBackend">AutoApproveBackend.<a href="./src/praisonai-agents/praisonaiagents/approval/backends.py">request_approval_sync</a>(request: ApprovalRequest) -> ApprovalDecision</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/embedding/embed.py">aembedding</a>(input: Union[str, List[str]], model: str = 'text-embedding-3-small', dimensions: Optional[int] = None, encoding_format: str = 'float', timeout: float = 600.0, api_key: Optional[str] = None, api_base: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs) -> EmbeddingResult</code>
@@ -777,11 +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>
@@ -822,6 +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>
@@ -834,6 +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>
@@ -864,17 +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>
@@ -902,6 +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>
@@ -975,6 +1008,9 @@ 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>
@@ -992,6 +1028,14 @@ 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>
@@ -1071,6 +1115,7 @@ 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>
@@ -1138,6 +1183,9 @@ 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>
@@ -1290,7 +1338,7 @@ 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>=4.6.52" \
+    "praisonai>=4.6.71" \
     "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>=4.6.52" \
+    "praisonai>=4.6.71" \
     "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>=4.6.52" \
+    "praisonai>=4.6.71" \
     "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/benchmark/benchmark_example.py

@@ -35,7 +35,7 @@ def run_quick_comparison():
         prompt="What is 2+2?",
         iterations=2,
         paths=["openai_sdk", "praisonai_agent"],
-        output="verbose"
+        verbose=True
     )
     
     # Print comparison table
@@ -56,7 +56,7 @@ def run_full_benchmark():
     report = handler.run_full_benchmark(
         prompt="Hi",
         iterations=3,
-        output="verbose"
+        verbose=True
     )
     
     # Print full report
@@ -78,7 +78,7 @@ def run_agent_benchmark():
         prompt="Explain Python in one sentence",
         iterations=3,
         paths=["openai_sdk", "praisonai_agent"],
-        output="verbose"
+        verbose=True
     )
     
     # Show timeline diagrams

examples/compare/cli_compare_advanced.py

@@ -22,7 +22,7 @@ custom_modes = {
 }
 
 # Create handler with verbose output
-handler = CompareHandler(output="verbose")
+handler = CompareHandler(verbose=True)
 
 # Example: Manually create comparison results for analysis
 # (useful for testing without API calls)

examples/compare/cli_compare_basic.py

@@ -22,7 +22,7 @@ modes = parse_modes("basic,tools,planning")
 print(f"\nParsed modes: {modes}")
 
 # Create compare handler
-handler = CompareHandler(output="verbose")
+handler = CompareHandler(verbose=True)
 
 # Compare modes (this will run actual agent calls)
 # Uncomment to run:

examples/consolidated_params/advanced_memory.py

@@ -15,7 +15,7 @@ agent = Agent(
 # With array [preset, overrides]
 agent_override = Agent(
     instructions="You are a helpful assistant.",
-    memory=["redis", {"user_id": "custom_user"}],
+    memory=MemoryConfig(backend="redis", user_id="custom_user"),
 )
 
 if __name__ == "__main__":

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/consolidated_params/basic_workflow.py

@@ -15,8 +15,20 @@ workflow = AgentFlow(
     ],
     output="verbose",
     planning=True,
+    variables={"topic": "AI agents"},
 )
 
 if __name__ == "__main__":
-    result = workflow.run(variables={"topic": "AI agents"})
+    import os
+    import sys
+
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Set OPENAI_API_KEY to run this example.")
+        sys.exit(0)
+    if sys.platform == "win32":
+        try:
+            sys.stdout.reconfigure(encoding="utf-8")
+        except Exception:
+            pass
+    result = workflow.run("AI agents")
     print(result)

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/eval/accuracy_example.py

@@ -24,7 +24,7 @@ if has_api_key:
         input_text="What is 2 + 2?",
         expected_output="4",
         num_iterations=3,  # Run 3 times for statistical significance
-        output="verbose"
+        verbose=True
     )
 
     # Run evaluation
@@ -42,7 +42,7 @@ else:
         input_text="What is 2 + 2?",
         expected_output="4",
         num_iterations=1,
-        output="verbose"
+        verbose=True
     )
     
     # Test with mock output

examples/eval/criteria_example.py

@@ -27,7 +27,7 @@ if has_api_key:
         scoring_type="numeric",  # Score 1-10
         threshold=7.0,           # Pass if score >= 7
         num_iterations=2,
-        output="verbose"
+        verbose=True
     )
 
     # Run evaluation
@@ -45,7 +45,7 @@ if has_api_key:
         agent=agent,
         input_text="Tell me a joke",
         scoring_type="binary",  # Pass or Fail
-        output="verbose"
+        verbose=True
     )
 
     binary_result = binary_evaluator.run(print_summary=True)

examples/eval/performance_example.py

@@ -26,7 +26,7 @@ if has_api_key:
         num_iterations=10,  # Run 10 benchmark iterations
         warmup_runs=2,      # 2 warmup runs before measurement
         track_memory=True,  # Track memory usage
-        output="verbose"
+        verbose=True
     )
 
     # Run evaluation

examples/eval/reliability_example.py

@@ -48,7 +48,7 @@ if has_api_key:
         input_text="Search for the weather in Paris and calculate 25 * 4",
         expected_tools=["search_web", "calculate"],  # Tools that should be called
         forbidden_tools=["delete_file"],  # Tools that should NOT be called
-        output="verbose"
+        verbose=True
     )
 
     # Run evaluation
@@ -70,7 +70,7 @@ evaluator = ReliabilityEvaluator(
     agent=mock_agent,
     expected_tools=["search_web", "calculate"],  # Tools that should be called
     forbidden_tools=["delete_file"],  # Tools that should NOT be called
-    output="verbose"
+    verbose=True
 )
 
 result2 = evaluator.evaluate_tool_calls(

examples/knowledge/compression_demo.py

@@ -184,7 +184,7 @@ Answer questions based ONLY on the provided knowledge context.
 When asked about codes, provide the EXACT code from the documents.
 Be concise but accurate.""",
             knowledge=[temp_dir],
-            user_id="compression_demo_user",
+            memory={"user_id": "compression_demo_user"},
             output="verbose",
         )
         

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/knowledge/hierarchical_summaries_demo.py

@@ -164,7 +164,7 @@ Answer questions based ONLY on the provided knowledge context.
 When asked about department codes, provide the EXACT code from the documents.
 Be specific about which department you're referring to.""",
             knowledge=[temp_dir],
-            user_id="hierarchy_demo_user",
+            memory={"user_id": "hierarchy_demo_user"},
             output="verbose",
         )
         

examples/knowledge/large_corpus_strategy_demo.py

@@ -124,7 +124,7 @@ def main():
             # Show strategy selection for different corpus sizes
             print(f"\nStrategy selection by corpus size:")
             for size in [100, 1000, 10000, 50000, 100000]:
-                strategy = select_strategy(corpus_tokens=size)
+                strategy = select_strategy(CorpusStats(file_count=max(1, size // 1000), total_tokens=size))
                 print(f"  {size:>6} tokens -> {strategy.value}")
                 
         except ImportError as e:
@@ -142,7 +142,7 @@ Answer questions based ONLY on the provided knowledge context.
 When asked about verification codes, provide the EXACT code from the documents.
 If information is not in the context, say 'Information not found in documents.'""",
             knowledge=[temp_dir],
-            user_id="strategy_demo_user",
+            memory={"user_id": "strategy_demo_user"},
             output="verbose",  # Use new consolidated param
         )
         

examples/knowledge/mem0_backend_example.py

@@ -65,7 +65,7 @@ def main():
             name="PolicyExpert",
             instructions="You are a company policy expert. Answer questions based ONLY on the provided knowledge. Quote exact values when asked about codes or numbers.",
             knowledge=[temp_dir],  # Add directory with documents
-            user_id="example_user",  # Required for mem0 backend
+            memory={"user_id": "example_user"},
         )
         
         print("\n1. Testing Agent.chat() with context-required question:")

examples/knowledge/scope_identifiers_example.py

@@ -50,7 +50,7 @@ def main():
             name="HR_Assistant_User1",
             instructions="You are an HR assistant.",
             knowledge=[temp_dir],
-            user_id="user_alice",  # Knowledge scoped to Alice
+            memory={"user_id": "user_alice"},
         )
         
         response = user1_agent.chat("What is the policy on remote work?")
@@ -80,7 +80,7 @@ def main():
             name="Personal_HR_Bot",
             instructions="You are a personal HR assistant.",
             knowledge=[temp_dir],
-            user_id="user_bob",
+            memory={"user_id": "user_bob"},
             agent_id="hr_bot_v2",
         )
         

examples/knowledge/scope_isolation_example.py

@@ -49,7 +49,7 @@ Only authorized personnel with code WRENCH-992 may access this project.
             name="AgentA_Ingester",
             instructions="You ingest and store knowledge.",
             knowledge=[temp_dir],
-            user_id="user_alice",  # Scoped to Alice
+            memory={"user_id": "user_alice"},
             output="silent",
         )
         
@@ -103,7 +103,7 @@ Only authorized personnel with code WRENCH-992 may access this project.
             name="AgentB_Querier",
             instructions="Answer questions based on the provided knowledge. If you don't have the information, say so.",
             knowledge=[temp_dir],
-            user_id="user_alice",  # Same scope as Agent A
+            memory={"user_id": "user_alice"},
             output="silent",
         )
         

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

@@ -50,10 +50,12 @@ print("=== Qdrant Knowledge Store Demo (Agent-First) ===")
 agent = Agent(
     name="KnowledgeAssistant",
     instructions="You are a helpful assistant with access to technical documentation.",
-    knowledge=[doc_path],
     knowledge={
-        "vector_store": "qdrant",
-        "url": "http://localhost:6333"
+        "sources": [doc_path],
+        "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/git_integration_example.py

@@ -8,7 +8,7 @@ Docs: https://docs.praison.ai/cli/git-integration
 from praisonai.cli.features import GitIntegrationHandler
 
 # Initialize
-handler = GitIntegrationHandler(output="verbose")
+handler = GitIntegrationHandler(verbose=True)
 git = handler.initialize(repo_path=".")
 
 # Check if it's a git repo

examples/python/cli/repo_map_example.py

@@ -8,7 +8,7 @@ Docs: https://docs.praison.ai/cli/repo-map
 from praisonai.cli.features import RepoMapHandler
 
 # Initialize with current directory
-handler = RepoMapHandler(output="verbose")
+handler = RepoMapHandler(verbose=True)
 repo_map = handler.initialize(root=".")
 
 # Get the repository map

examples/python/cli/sandbox_execution_example.py

@@ -9,7 +9,7 @@ from praisonai.cli.features import SandboxExecutorHandler
 from praisonai.cli.features.sandbox_executor import SandboxMode
 
 # Initialize with basic sandbox
-handler = SandboxExecutorHandler(output="verbose")
+handler = SandboxExecutorHandler(verbose=True)
 sandbox = handler.initialize(mode="basic")
 
 print(f"Sandbox mode: {handler.get_mode()}")

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/chat-with-pdf.py

@@ -25,8 +25,7 @@ pdf_agent = Agent(
     goal="Help users understand and extract information from PDF documents",
     backstory="You are an expert at reading, analyzing, and answering questions about PDF documents. You provide accurate, detailed answers based on the document content.",
     instructions="Read the provided PDF document and answer questions about its content. Be specific and cite relevant sections when possible.",
-    knowledge=["document.pdf"],  # Replace with your PDF file path
-    knowledge=config,
+    knowledge={**config, "sources": ["document.pdf"]},  # Replace with your PDF path
     reflection=True,
     
     

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

@@ -1,6 +1,9 @@
+from pathlib import Path
+
 from praisonaiagents import Agent, Task, AgentTeam
 import logging
 import os
+import sys
 
 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -17,14 +20,18 @@ config = {
     }
 }
 
+_sample_path = Path(__file__).with_name("sample_knowledge.txt")
+if not _sample_path.exists():
+    _sample_path.write_text("Mervin Praison is the creator of PraisonAI.\n", encoding="utf-8")
+
 # Create an agent with knowledge capabilities
 knowledge_agent = Agent(
     name="KnowledgeAgent",
     role="Information Specialist",
     goal="Store and retrieve knowledge efficiently",
     backstory="Expert in managing and utilizing stored knowledge",
-    knowledge=["sample.pdf"],
-    knowledge=config)
+    knowledge={**config, "sources": [str(_sample_path)]},
+)
 
 # Define a task for the agent
 knowledge_task = Task(
@@ -39,8 +46,16 @@ agents = AgentTeam(
     agents=[knowledge_agent],
     tasks=[knowledge_task],
     process="sequential",
-    user_id="user1"
 )
 
-# Start execution
-result = agents.start()
+if __name__ == "__main__":
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Set OPENAI_API_KEY to run this example.")
+        sys.exit(0)
+    if sys.platform == "win32":
+        try:
+            sys.stdout.reconfigure(encoding="utf-8")
+        except Exception:
+            pass
+    result = agents.start()
+    print(result)

examples/python/concepts/knowledge-reranker-example.py

@@ -138,8 +138,7 @@ def main():
     agent = Agent(
         name="AI Research Assistant",
         instructions="You are an AI research assistant. Use your knowledge base to answer questions about artificial intelligence topics.",
-        knowledge=sample_documents,  # Will use default knowledge config
-        knowledge=rerank_config,  # Use reranking config
+        knowledge={**rerank_config, "sources": sample_documents},
         llm="gpt-4o-mini"
     )
     

examples/python/concepts/rag-agents.py

@@ -32,8 +32,12 @@ rag_task = Task(
 agents = AgentTeam(
     agents=[rag_agent],
     tasks=[rag_task],
-    user_id="user1"
 )
 
-# Start Agents
-agents.start()
+if __name__ == "__main__":
+    import os
+    import sys
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Set OPENAI_API_KEY to run this example.")
+        sys.exit(0)
+    agents.start()

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

@@ -5,7 +5,7 @@ import os
 def main():
     # Initialize memory config
     memory_config = {
-        "provider": "rag",
+        "backend": "sqlite",
         "use_embedding": True,
         "storage": {
             "type": "sqlite",
@@ -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={
-            "provider": "rag",
             "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": {
@@ -60,8 +60,8 @@ query_task = Task(
 agents_system = AgentTeam(
     agents=[knowledge_agent],
     tasks=[build_task, query_task],
-    memory=True,
-    memory=memory_config, output="verbose"
+    memory=memory_config,
+    output="verbose",
 )
 
 print("Starting graph memory demonstration...")

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": {
@@ -61,8 +61,8 @@ search_task = Task(
 agents_system = AgentTeam(
     agents=[research_agent],
     tasks=[build_memory_task, search_task],
-    memory=True,
-    memory=memory_config, output="verbose"
+    memory=memory_config,
+    output="verbose",
 )
 
 print("Starting memory search optimization demonstration...")

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

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