Release v4.6.62

- Revised agent instantiation time in README and benchmark results to reflect updated performance metrics.
- Added model specification in GitHub workflow configuration.
- Incremented version number in package.json for PraisonAI TypeScript framework.
- Enhanced tool approval handling in agent loop and updated utility tools for shell metacharacter validation.
- Added tests to block allowlist bypass and metacharacter chaining for commands.

.github/workflows/claude.yml

@@ -200,6 +200,7 @@ 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"
 

.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/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"

AGENTS.md

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

README.md

@@ -770,11 +770,11 @@ agent.start('Write a movie script about a robot in Mars');
 
 ## ⚡ Performance
 
-PraisonAI is built for speed, with agent instantiation in under 4μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.
+PraisonAI is built for speed, with agent instantiation in around 14μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.
 
 | Performance Metric | PraisonAI |
 |--------------------|-----------|
-| Avg Instantiation Time | **3.77 μs** |
+| Avg Instantiation Time | **14 μs** |
 
 ---
 

api.md

@@ -59,6 +59,8 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">handoff_to</a>(target_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None, config: Optional['HandoffConfig'] = None) -> 'HandoffResult'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">handoff_to_async</a>(target_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None, config: Optional['HandoffConfig'] = None) -> 'HandoffResult'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">is_closed</a>() -> bool</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">llm_instance</a>()</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">llm_instance</a>(value)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">llm_model</a>() -> Optional[str]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">output_style</a>() -> Optional[str]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">output_style</a>(value: Optional[str]) -> None</code>
@@ -240,6 +242,7 @@ from praisonaiagents import Memory, MemoryBackend, MemoryConfig
 
 Methods:
 
+* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">build_cache_optimized_context</a>(task_descr: str, user_id: Optional[str] = None, additional: str = '', max_items: int = 3, include_cache_boundary: bool = True, include_in_output: Optional[bool] = None) -> Dict[str, str]</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">build_context_for_task</a>(task_descr: str, user_id: Optional[str] = None, additional: str = '', max_items: int = 3, include_in_output: Optional[bool] = None) -> str</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">calculate_quality_metrics</a>(output: str, expected_output: str, llm: Optional[str] = None, custom_prompt: Optional[str] = None) -> Dict[str, float]</code>
 * <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">close_connections</a>()</code>
@@ -698,7 +701,7 @@ 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, __version__, aembedding, aembeddings, embedding, embeddings, get_dimensions
 ```
 
 Methods:
@@ -914,6 +917,9 @@ 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>
@@ -1302,7 +1308,7 @@ export { // Python parity additions
 export { // Subagent Tool (agent-as-tool pattern)
   SubagentTool, BaseTool, FunctionTool, ToolRegistry, ToolResult, ToolValidationError, createDelegator, createSubagentTool, createSubagentTools, createTool, getRegistry, getTool, registerTool, tool, validateTool } from "./tools";
 export { airweaveSearch, bedrockBrowserClick, bedrockBrowserFill, bedrockBrowserNavigate, bedrockCodeInterpreter, codeExecution, codeMode, createCustomTool, exaSearch, firecrawlCrawl, firecrawlScrape, parallelSearch, perplexitySearch, registerCustomTool, registerLocalTool, registerNpmTool, superagentGuard, superagentRedact, superagentVerify, tavilyCrawl, tavilyExtract, tavilySearch, valyuBioSearch, valyuCompanyResearch, valyuEconomicsSearch, valyuFinanceSearch, valyuPaperSearch, valyuPatentSearch, valyuSecSearch, valyuWebSearch } from "./tools/builtins";
-export { BudgetExceededError, MissingDependencyError, MissingEnvVarError, ToolsRegistry, composeMiddleware, createLoggingMiddleware, createRateLimitMiddleware, createRedactionMiddleware, createRetryMiddleware, createTimeoutMiddleware, createToolsRegistry, createTracingMiddleware, createValidationMiddleware, getRegistry, getTool, getToolsRegistry, get_registry, get_tool, registerTool, register_tool, resetToolsRegistry, validateTool, validate_tool } from "./tools/registry";
+export { BudgetExceededError, MissingDependencyError, MissingEnvVarError, ToolsRegistry, composeMiddleware, createLoggingMiddleware, createRateLimitMiddleware, createRedactionMiddleware, createRetryMiddleware, createTimeoutMiddleware, createToolsRegistry, createTracingMiddleware, createValidationMiddleware, getToolsRegistry, get_registry, get_tool, register_tool, resetToolsRegistry, validate_tool } from "./tools/registry";
 export type { InstallHints, PraisonTool, RedactionHooks, RegisteredTool, ToolCapabilities, ToolExecutionContext, ToolExecutionResult, ToolFactory, ToolHooks, ToolInstallStatus, ToolLimits, ToolLogger, ToolMetadata, ToolMiddleware, ToolParameterProperty, ToolParameterSchema } from "./tools/registry";
 export { registerBuiltinTools, tools } from "./tools/tools";
 export { // Classes

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.55" \
+    "praisonai>=4.6.62" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.55" \
+    "praisonai>=4.6.62" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.55" \
+    "praisonai>=4.6.62" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/consolidated_params/advanced_workflow_full_features.py

@@ -32,7 +32,7 @@ workflow = AgentFlow(
         ),
     ],
     # Workflow-level consolidated params
-    output=WorkflowOutputConfig(output="verbose", stream=True),
+    output=WorkflowOutputConfig(verbose=True, stream=True),
     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=WebConfig(search=True, fetch=False, search_provider="duckduckgo"),
 )
 
 if __name__ == "__main__":

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/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/workflows/workflow_branching.py

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

examples/python/workflows/workflow_early_stop.py

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

examples/python/workflows/workflow_loops.py

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

examples/python/workflows/workflow_mixed_steps.py

@@ -53,7 +53,7 @@ workflow = AgentFlow(
         # Step 4: Function - formats output
         format_output
     ],
-    default_llm="gpt-4o-mini"  # Used for action-based steps
+    llm="gpt-4o-mini"  # Used for action-based steps
 )
 
 if __name__ == "__main__":

examples/python/workflows/workflow_parallel.py

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

examples/python/workflows/workflow_routing.py

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

src/praisonai-agents/benchmarks/BENCHMARK_RESULTS.md

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

src/praisonai-agents/benchmarks/INSTANTIATION_RCA.md

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

src/praisonai-agents/benchmarks/param_impact_benchmark.py

@@ -120,8 +120,8 @@ def run_benchmark(iterations: int = 100) -> List[ParamResult]:
         ('backstory', 'I am an AI assistant.', 'backstory="..."'),
         
         # LLM configuration
-        ('llm', 'gpt-4o-mini', 'llm="gpt-4o-mini"'),
-        ('llm', 'openai/gpt-4o-mini', 'llm="openai/gpt-4o-mini" (LiteLLM)'),
+        ('model', 'gpt-4o-mini', 'model="gpt-4o-mini"'),
+        ('model', 'openai/gpt-4o-mini', 'model="openai/gpt-4o-mini" (LiteLLM)'),
         
         # Tools
         ('tools', [sample_tool], 'tools=[sample_tool]'),
@@ -298,7 +298,7 @@ def save_results(results: List[ParamResult], filename: str = 'PARAM_IMPACT_RESUL
         f.write('### Best Practices\n\n')
         f.write('1. Use `output="silent"` for production workloads\n')
         f.write('2. Only enable `context=True` when needed for long conversations\n')
-        f.write('3. Use `llm="gpt-4o-mini"` (OpenAI SDK) for fastest instantiation\n')
+        f.write('3. Use `model="gpt-4o-mini"` for fastest instantiation\n')
         f.write('4. Avoid `memory=True` unless persistent memory is required\n')
         
         f.write('\n## How to Reproduce\n\n')

src/praisonai-agents/benchmarks/performance_benchmark.py

@@ -185,7 +185,7 @@ def instantiate_praisonai_agent():
     return PraisonAgent(
         name="Test Agent",
         instructions="Be concise, reply with one sentence.",
-        llm="gpt-4o-mini",
+        model="gpt-4o-mini",
         tools=tools,
         output="silent"
     )
@@ -196,7 +196,7 @@ def instantiate_praisonai_litellm_agent():
     return PraisonAgent(
         name="Test Agent",
         instructions="Be concise, reply with one sentence.",
-        llm="openai/gpt-4o-mini",
+        model="openai/gpt-4o-mini",
         tools=tools,
         output="silent"
     )

src/praisonai-agents/benchmarks/simple_benchmark.py

@@ -14,6 +14,7 @@ import argparse
 
 
 ITERATIONS = 100
+WARMUP = 10
 
 
 def sample_tool(city: Literal['nyc', 'sf']):
@@ -23,8 +24,10 @@ def sample_tool(city: Literal['nyc', 'sf']):
     return 'sunny'
 
 
-def measure_instantiation(create_fn, iterations=ITERATIONS):
+def measure_instantiation(create_fn, iterations=ITERATIONS, warmup=WARMUP):
     """Measure average instantiation time in microseconds."""
+    for _ in range(warmup):
+        create_fn()
     times = []
     for _ in range(iterations):
         start = time.perf_counter()
@@ -48,11 +51,11 @@ def run_benchmark():
     from praisonaiagents import Agent as PraisonAgent
     
     results['PraisonAI'] = measure_instantiation(
-        lambda: PraisonAgent(name='Test', llm='gpt-4o-mini', output="silent")
+        lambda: PraisonAgent(name='Test', model='gpt-4o-mini', output="silent")
     )
     
     results['PraisonAI (LiteLLM)'] = measure_instantiation(
-        lambda: PraisonAgent(name='Test', llm='openai/gpt-4o-mini', output="silent")
+        lambda: PraisonAgent(name='Test', model='openai/gpt-4o-mini', output="silent")
     )
     
     # Other frameworks for comparison

src/praisonai-agents/praisonaiagents/__init__.py

@@ -114,6 +114,9 @@ def _get_lazy_cache():
 # ============================================================================
 
 _LAZY_IMPORTS = {
+    # Version information (lazy loaded to avoid file I/O on import)
+    '__version__': ('praisonaiagents._version', '__version__'),
+    
     # Tools (moved from eager imports for lazy loading)
     'Tools': ('praisonaiagents.tools.tools', 'Tools'),
     'BaseTool': ('praisonaiagents.tools.base', 'BaseTool'),
@@ -775,6 +778,9 @@ def warmup(include_litellm: bool = False, include_openai: bool = True) -> dict:
 # ============================================================================
 
 __all__ = [
+    # Version information
+    '__version__',
+    
     # Core classes - the essentials
     'Agent',
     'AgentTeam',  # Primary class for multi-agent coordination (v1.0+)

src/praisonai-agents/praisonaiagents/_version.py

@@ -0,0 +1,51 @@
+"""
+Version utilities for praisonaiagents package.
+
+This module provides version information by reading from pyproject.toml
+to avoid duplication and ensure the single source of truth.
+"""
+
+import re
+from pathlib import Path
+
+
+def get_version() -> str:
+    """
+    Get the version string from pyproject.toml.
+    
+    Returns:
+        str: Version string (e.g., "1.6.52")
+        
+    Raises:
+        RuntimeError: If version cannot be found or parsed
+    """
+    try:
+        # Get the package root (praisonaiagents directory)
+        package_root = Path(__file__).parent
+        # Go up to src/praisonai-agents directory
+        pyproject_path = package_root.parent / "pyproject.toml"
+        
+        if not pyproject_path.exists():
+            raise RuntimeError(f"pyproject.toml not found at {pyproject_path}")
+            
+        content = pyproject_path.read_text()
+        
+        # Look for version = "X.Y.Z" pattern
+        match = re.search(r'version\s*=\s*["\']([^"\']+)["\']', content)
+        if not match:
+            raise RuntimeError("Version not found in pyproject.toml")
+            
+        return match.group(1)
+        
+    except Exception as e:
+        # Fallback to "unknown" to avoid breaking imports
+        import warnings
+        warnings.warn(
+            f"Failed to read version from pyproject.toml: {e}. Using 'unknown'.",
+            RuntimeWarning
+        )
+        return "unknown"
+
+
+# Cache the version to avoid reading the file multiple times
+__version__ = get_version()

src/praisonai-agents/praisonaiagents/agent/agent.py

@@ -313,6 +313,38 @@ class Agent(SteeringMixin, SandboxMixin, UnifiedExecutionMixin, ToolExecutionMix
                     cls._default_model_checked = True
         return cls._default_model
     
+    def _ensure_llm_instance(self):
+        """Lazy-create LLM instance from deferred init params (avoids import at Agent())."""
+        if self._llm_instance is not None:
+            return self._llm_instance
+        if self._llm_init_params:
+            try:
+                from ..llm.llm import LLM
+                self._llm_instance = LLM(**self._llm_init_params)
+            except ImportError as e:
+                raise ImportError(
+                    "LLM features requested but dependencies not installed. "
+                    "Please install with: pip install \"praisonaiagents[llm]\""
+                ) from e
+        return self._llm_instance
+
+    @property
+    def llm_instance(self):
+        return self._ensure_llm_instance()
+
+    @llm_instance.setter
+    def llm_instance(self, value):
+        self._llm_instance = value
+        if value is not None:
+            self._llm_init_params = None
+
+    def _ensure_loop_guard(self):
+        """Lazy-create loop guard on first tool execution."""
+        if self._loop_guard is None:
+            from ..escalation.loop_guard import LoopGuard, LoopGuardConfig
+            self._loop_guard = LoopGuard(LoopGuardConfig(enabled=True))
+        return self._loop_guard
+    
     @classmethod
     def _configure_logging(cls):
         """Configure logging settings once for all agent instances."""
@@ -718,16 +750,16 @@ class Agent(SteeringMixin, SandboxMixin, UnifiedExecutionMixin, ToolExecutionMix
         # Precedence: Explicit params > Config file > Built-in defaults
         # Only applies when param is None (not explicitly set)
         # ============================================================
-        from ..config.loader import apply_config_defaults, get_default
+        from ..config.loader import apply_config_defaults, get_default, _defaults_has_any_values
         
         # Apply config defaults for LLM if not explicitly set
-        if llm is None and model is None:
+        if llm is None and model is None and _defaults_has_any_values():
             config_model = get_default("model")
             if config_model:
                 llm = config_model
         
         # Apply config defaults for base_url if not explicitly set
-        if base_url is None:
+        if base_url is None and _defaults_has_any_values():
             config_base_url = get_default("base_url")
             if config_base_url:
                 base_url = config_base_url
@@ -1478,10 +1510,8 @@ class Agent(SteeringMixin, SandboxMixin, UnifiedExecutionMixin, ToolExecutionMix
         # Initialize autonomy features (agent-centric escalation/doom-loop)
         self._init_autonomy(autonomy, verification_hooks=verification_hooks)
         
-        # Initialize loop guard for all execution modes (not just autonomous)
-        # Provides idempotency-aware guardrails for tool execution loops
-        from ..escalation.loop_guard import LoopGuard, LoopGuardConfig
-        self._loop_guard = LoopGuard(LoopGuardConfig(enabled=True))
+        # Initialize loop guard lazily on first tool execution (zero init overhead)
+        self._loop_guard = None
 
         # If instructions are provided, use them to set role, goal, and backstory
         if instructions:
@@ -1527,6 +1557,8 @@ class Agent(SteeringMixin, SandboxMixin, UnifiedExecutionMixin, ToolExecutionMix
         self.interrupt_controller = interrupt_controller
         # Check for model name in environment variable if not provided
         self._using_custom_llm = False
+        self._llm_instance = None
+        self._llm_init_params = None
         # Flag to track if final result has been displayed to prevent duplicates
         self._final_display_shown = False
         
@@ -1565,99 +1597,67 @@ class Agent(SteeringMixin, SandboxMixin, UnifiedExecutionMixin, ToolExecutionMix
         self.base_url = base_url
         self.api_key = api_key
 
-        # If base_url is provided, always create a custom LLM instance
+        # If base_url is provided, defer custom LLM instance creation
         if base_url:
-            try:
-                from ..llm.llm import LLM
-                # Handle different llm parameter types with base_url
-                if isinstance(llm, dict):
-                    # Merge base_url and api_key into the dict
-                    llm_config = llm.copy()
-                    llm_config['base_url'] = base_url
-                    if api_key:
-                        llm_config['api_key'] = api_key
-                    if auth:
-                        llm_config['auth'] = auth
-                    llm_config['metrics'] = metrics
-                    llm_config['max_iter'] = max_iter
-                    self.llm_instance = LLM(**llm_config)
-                    self.llm = llm.get('model', Agent._get_default_model())
-                else:
-                    # Create LLM with model string and base_url (cached for performance)
-                    model_name = llm or Agent._get_default_model()
-                    self.llm_instance = LLM(
-                        model=model_name,
-                        base_url=base_url,
-                        api_key=api_key,
-                        auth=auth,
-                        metrics=metrics,
-                        max_iter=max_iter,
-                        web_search=web_search,
-                        web_fetch=web_fetch,
-                        prompt_caching=prompt_caching,
-                        claude_memory=claude_memory
-                    )
-                    self.llm = model_name
-                self._using_custom_llm = True
-            except ImportError as e:
-                raise ImportError(
-                    "LLM features requested but dependencies not installed. "
-                    "Please install with: pip install \"praisonaiagents[llm]\""
-                ) from e
+            if isinstance(llm, dict):
+                llm_config = llm.copy()
+                llm_config['base_url'] = base_url
+                if api_key:
+                    llm_config['api_key'] = api_key
+                if auth:
+                    llm_config['auth'] = auth
+                llm_config['metrics'] = metrics
+                llm_config['max_iter'] = max_iter
+                self._llm_init_params = llm_config
+                self.llm = llm.get('model', Agent._get_default_model())
+            else:
+                model_name = llm or Agent._get_default_model()
+                self._llm_init_params = {
+                    'model': model_name,
+                    'base_url': base_url,
+                    'api_key': api_key,
+                    'auth': auth,
+                    'metrics': metrics,
+                    'max_iter': max_iter,
+                    'web_search': web_search,
+                    'web_fetch': web_fetch,
+                    'prompt_caching': prompt_caching,
+                    'claude_memory': claude_memory,
+                }
+                self.llm = model_name
+            self._using_custom_llm = True
         # If the user passes a dictionary (for advanced configuration)
         elif isinstance(llm, dict) and "model" in llm:
-            try:
-                from ..llm.llm import LLM
-                # Add api_key if provided and not in dict
-                if api_key and 'api_key' not in llm:
-                    llm = llm.copy()
-                    llm['api_key'] = api_key
-                # Add auth if provided and not in dict
-                if auth and 'auth' not in llm:
-                    llm = llm.copy()
-                    llm['auth'] = auth
-                # Add metrics parameter
-                llm = llm.copy()
-                llm['metrics'] = metrics
-                llm['max_iter'] = max_iter
-                self.llm_instance = LLM(**llm)  # Pass all dict items as kwargs
-                self._using_custom_llm = True
-                self.llm = llm.get('model', Agent._get_default_model())
-            except ImportError as e:
-                raise ImportError(
-                    "LLM features requested but dependencies not installed. "
-                    "Please install with: pip install \"praisonaiagents[llm]\""
-                ) from e
-        # If the user passes a string with a slash (provider/model)
+            llm_config = llm.copy()
+            if api_key and 'api_key' not in llm_config:
+                llm_config['api_key'] = api_key
+            if auth and 'auth' not in llm_config:
+                llm_config['auth'] = auth
+            llm_config['metrics'] = metrics
+            llm_config['max_iter'] = max_iter
+            self._llm_init_params = llm_config
+            self._using_custom_llm = True
+            self.llm = llm_config.get('model', Agent._get_default_model())
+        # If the user passes a string with a slash (provider/model) — defer LiteLLM import
         elif isinstance(llm, str) and "/" in llm:
-            try:
-                from ..llm.llm import LLM
-                # Pass the entire string so LiteLLM can parse provider/model
-                llm_params = {'model': llm}
-                if api_key:
-                    llm_params['api_key'] = api_key
-                if auth:
-                    llm_params['auth'] = auth
-                llm_params['metrics'] = metrics
-                llm_params['web_search'] = web_search
-                llm_params['web_fetch'] = web_fetch
-                llm_params['prompt_caching'] = prompt_caching
-                llm_params['claude_memory'] = claude_memory
-                llm_params['max_iter'] = max_iter
-                self.llm_instance = LLM(**llm_params)
-                self._using_custom_llm = True
-                self.llm = llm
-                
-                # Ensure tools are properly accessible when using custom LLM
-                if tools:
-                    logging.debug(f"Tools passed to Agent with custom LLM: {tools}")
-                    # Store the tools for later use
-                    self.tools = tools
-            except ImportError as e:
-                raise ImportError(
-                    "LLM features requested but dependencies not installed. "
-                    "Please install with: pip install \"praisonaiagents[llm]\""
-                ) from e
+            llm_params = {'model': llm}
+            if api_key:
+                llm_params['api_key'] = api_key
+            if auth:
+                llm_params['auth'] = auth
+            llm_params['metrics'] = metrics
+            llm_params['web_search'] = web_search
+            llm_params['web_fetch'] = web_fetch
+            llm_params['prompt_caching'] = prompt_caching
+            llm_params['claude_memory'] = claude_memory
+            llm_params['max_iter'] = max_iter
+            self._llm_init_params = llm_params
+            self._using_custom_llm = True
+            self.llm = llm
+            
+            if tools:
+                logging.debug(f"Tools passed to Agent with custom LLM: {tools}")
+                self.tools = tools
         # Otherwise, fall back to OpenAI environment/name (cached for performance)
         else:
             self.llm = llm or Agent._get_default_model()

src/praisonai-agents/praisonaiagents/agent/chat_mixin.py

@@ -87,12 +87,41 @@ Your Goal: {self.goal}"""
         
         # Add memory context if memory is enabled
         if self._memory_instance:
-            memory_context = self.get_memory_context()
-            if memory_context:
-                system_prompt += f"\n\n## Memory (Information you remember about the user)\n{memory_context}"
-                # Display memory info to user if verbose
-                if self.verbose:
-                    self._display_memory_info()
+            # Use cache-optimized context if model supports prompt caching and cache boundary is requested
+            use_cache_boundaries = hasattr(self, '_model_supports_prompt_caching') and self._model_supports_prompt_caching()
+            if use_cache_boundaries and hasattr(self._memory_instance, 'build_cache_optimized_context'):
+                try:
+                    # Get the goal/task description for context building
+                    task_desc = getattr(self, 'goal', '') or 'general assistance'
+                    cache_result = self._memory_instance.build_cache_optimized_context(
+                        task_descr=task_desc,
+                        include_cache_boundary=True
+                    )
+                    memory_context = cache_result.get('stable_prefix', '')
+                    cache_boundary = cache_result.get('cache_boundary', '')
+                    if memory_context:
+                        system_prompt += f"\n\n## Memory (Information you remember about the user)\n{memory_context}"
+                        if cache_boundary:
+                            system_prompt += cache_boundary
+                        # Display memory info to user if verbose
+                        if self.verbose:
+                            self._display_memory_info()
+                except (AttributeError, KeyError, TypeError) as e:
+                    # Fall back to standard memory context if cache optimization fails
+                    memory_context = self.get_memory_context()
+                    if memory_context:
+                        system_prompt += f"\n\n## Memory (Information you remember about the user)\n{memory_context}"
+                        # Display memory info to user if verbose
+                        if self.verbose:
+                            self._display_memory_info()
+            else:
+                # Standard memory context without cache boundaries
+                memory_context = self.get_memory_context()
+                if memory_context:
+                    system_prompt += f"\n\n## Memory (Information you remember about the user)\n{memory_context}"
+                    # Display memory info to user if verbose
+                    if self.verbose:
+                        self._display_memory_info()
             
             # Add learn context if learn is enabled (auto-inject when memory="learn")
             learn_context = self.get_learn_context()
@@ -452,6 +481,17 @@ Your Goal: {self.goal}"""
             else:
                 cleaned_tools.append(tool)
         
+        # Sort tools deterministically for cache stability
+        def sort_formatted_tools(tools_list):
+            """Sort already-formatted tool schemas by function name."""
+            def sort_key(tool):
+                if isinstance(tool, dict) and tool.get('type') == 'function':
+                    return str(tool.get('function', {}).get('name') or '')
+                return ''
+            return sorted(tools_list, key=sort_key)
+        
+        cleaned_tools = sort_formatted_tools(cleaned_tools)
+        
         # Cache the formatted tools with LRU eviction, including tool search metadata
         self._cache_put(
             self._formatted_tools_cache,

src/praisonai-agents/praisonaiagents/agent/tool_execution.py

@@ -231,9 +231,10 @@ class ToolExecutionMixin:
                     arguments.update(res.output.modified_data)
 
             # Loop guard check - prevent tool execution loops with graduated response
-            if hasattr(self, '_loop_guard') and self._loop_guard:
+            if hasattr(self, '_ensure_loop_guard'):
+                loop_guard = self._ensure_loop_guard()
                 from ..escalation.loop_guard import GuardAction
-                decision = self._loop_guard.check(function_name, arguments, is_pre_execution=True)
+                decision = loop_guard.check(function_name, arguments, is_pre_execution=True)
                 
                 if decision.action == GuardAction.WARN:
                     # Inject warning into tool result so LLM sees guidance
@@ -394,11 +395,12 @@ class ToolExecutionMixin:
                     self._doom_loop_tracker.mark_progress(f"tool:{function_name}")
             
             # Record tool execution in loop guard
-            if hasattr(self, '_loop_guard') and self._loop_guard:
+            if hasattr(self, '_ensure_loop_guard'):
+                loop_guard = self._ensure_loop_guard()
                 is_success = result is not None and not (isinstance(result, dict) and result.get('error'))
-                self._loop_guard.record(function_name, arguments, is_success)
+                loop_guard.record(function_name, arguments, is_success)
                 # Handle warning injection for WARN decisions
-                decision = self._loop_guard.check(function_name, arguments, is_pre_execution=False) 
+                decision = loop_guard.check(function_name, arguments, is_pre_execution=False) 
                 if decision.action.value == "warn":
                     if isinstance(result, str):
                         result = f"{result}\n\n[loop-guard] {decision.message}"

src/praisonai-agents/praisonaiagents/agent/unified_execution_mixin.py

@@ -78,8 +78,8 @@ class UnifiedExecutionMixin:
         _trace_emitter.agent_start(self.name, {"role": self.role, "goal": self.goal})
         
         # Reset loop guard for new chat turn (always-on protection)
-        if hasattr(self, '_loop_guard') and self._loop_guard:
-            self._loop_guard.reset_turn()
+        if hasattr(self, '_ensure_loop_guard'):
+            self._ensure_loop_guard().reset_turn()
         
         try:
             # CLI Backend routing - delegate entire turn if configured

src/praisonai-agents/praisonaiagents/agents/agents.py

@@ -40,6 +40,21 @@ class TaskStatus(Enum):
 # Set up logger
 logger = get_logger(__name__)
 
+
+def _launch_auth_token() -> Optional[str]:
+    return os.environ.get("PRAISONAI_LAUNCH_AUTH_TOKEN")
+
+
+def _authorise_launch_request(request) -> bool:
+    token = _launch_auth_token()
+    if not token:
+        return True
+    auth = request.headers.get("Authorization", "")
+    if auth.startswith("Bearer ") and auth[7:] == token:
+        return True
+    return request.headers.get("X-Auth-Token") == token
+
+
 # Agent server registry for thread-safe server management
 
 
@@ -245,7 +260,20 @@ Expected Output: {task.expected_output}."""
     # Add memory context if available
     if task.memory:
         try:
-            memory_context = task.memory.build_context_for_task(task.description)
+            # Use cache-optimized context if available for better prompt caching
+            if hasattr(task.memory, 'build_cache_optimized_context'):
+                try:
+                    cache_result = task.memory.build_cache_optimized_context(
+                        task_descr=task.description,
+                        include_cache_boundary=False  # Don't include boundary for agent task context
+                    )
+                    memory_context = cache_result.get('stable_prefix', '')
+                except Exception as e:
+                    # Fall back to standard context building
+                    logger.debug(f"Cache-optimized context failed for task '{task.description or task.id}', falling back to standard: {e}")
+                    memory_context = task.memory.build_context_for_task(task.description)
+            else:
+                memory_context = task.memory.build_context_for_task(task.description)
             if memory_context:
                 # Log detailed memory context for debugging
                 logger.debug(f"Memory context for task '{task.description}': {memory_context}")
@@ -652,7 +680,7 @@ class AgentTeam(SpawnAnnounceProtocol):
             _stream = _output_config.stream
         else:
             _verbose = 0
-            _stream = True
+            _stream = False
         
         # ─────────────────────────────────────────────────────────────────────
         # Resolve EXECUTION param using canonical resolver
@@ -1993,6 +2021,8 @@ class AgentTeam(SpawnAnnounceProtocol):
             # Define the endpoint handler
             @app.post(path)
             async def handle_query(request: Request, query_data: Optional[AgentQuery] = None):
+                if not _authorise_launch_request(request):
+                    raise HTTPException(status_code=401, detail="Unauthorized")
                 # Handle both direct JSON with query field and form data
                 if query_data is None:
                     try:
@@ -2071,7 +2101,9 @@ class AgentTeam(SpawnAnnounceProtocol):
             
             # Add GET endpoint to list available agents
             @app.get(f"{path}/list")
-            async def list_agents():
+            async def list_agents(request: Request):
+                if not _authorise_launch_request(request):
+                    raise HTTPException(status_code=401, detail="Unauthorized")
                 return {
                     "agents": [
                         {"name": agent.display_name, "id": agent.display_name.lower().replace(' ', '_')}
@@ -2086,6 +2118,8 @@ class AgentTeam(SpawnAnnounceProtocol):
                 # Create a closure to capture the agent instance
                 def create_agent_handler(agent):
                     async def handle_single_agent(request: Request):
+                        if not _authorise_launch_request(request):
+                            raise HTTPException(status_code=401, detail="Unauthorized")
                         try:
                             request_data = await request.json()
                             query = request_data.get("query", "")

src/praisonai-agents/praisonaiagents/app/config.py

@@ -53,3 +53,4 @@ class AgentAppConfig:
     workers: int = 1
     timeout: int = 60
     metadata: Dict[str, Any] = field(default_factory=dict)
+    api_key: Optional[str] = None

src/praisonai-agents/praisonaiagents/approval/registry.py

@@ -194,9 +194,13 @@ class ApprovalRegistry:
 
     def is_yaml_approved(self, tool_name: str) -> bool:
         try:
-            return tool_name in self._yaml_approved_tools.get()
+            if tool_name not in self._yaml_approved_tools.get():
+                return False
         except LookupError:
             return False
+        if self.get_risk_level(tool_name) == "critical":
+            return False
+        return True
 
     # ── Env-var check ────────────────────────────────────────────────────
 

src/praisonai-agents/praisonaiagents/bots/config.py

@@ -91,6 +91,16 @@ class BotConfig:
     # When set, stale sessions older than this are auto-reaped.
     session_ttl: int = 0
     
+    # In-flight run control settings for mid-run message handling
+    # busy_mode: Policy for handling messages during active runs
+    #   - "queue": Queue message for next turn (default)
+    #   - "interrupt": Cancel current run and start new one  
+    #   - "steer": Inject message into current run via steering
+    busy_mode: str = "queue"
+    
+    # Template for busy acknowledgment messages (use {action} placeholder)
+    busy_ack: str = "⏳ {action} — will be considered next"
+    
     # Workspace settings for file operation containment and security
     workspace_dir: Optional[str] = None  # default: ~/.praisonai/workspaces/<scope>/<session_key>
     workspace_access: str = "rw"  # "rw" (read-write) | "ro" (read-only) | "none" (copy-on-write sandbox)
@@ -136,6 +146,8 @@ class BotConfig:
             "ack_emoji": self.ack_emoji,
             "done_emoji": self.done_emoji,
             "session_ttl": self.session_ttl,
+            "busy_mode": self.busy_mode,
+            "busy_ack": self.busy_ack,
             "workspace_dir": self.workspace_dir,
             "workspace_access": self.workspace_access,
             "workspace_scope": self.workspace_scope,

src/praisonai-agents/praisonaiagents/compaction/__init__.py

@@ -33,6 +33,10 @@ __all__ = [
     "CompactionStrategy",
     # Results
     "CompactionResult",
+    # Protocols
+    "ToolResultPrunerProtocol",
+    "MessageFormatterProtocol", 
+    "SummaryBuilderProtocol",
 ]
 
 
@@ -54,4 +58,16 @@ def __getattr__(name: str):
         from .result import CompactionResult
         return CompactionResult
     
+    if name == "ToolResultPrunerProtocol":
+        from .protocols import ToolResultPrunerProtocol
+        return ToolResultPrunerProtocol
+    
+    if name == "MessageFormatterProtocol":
+        from .protocols import MessageFormatterProtocol
+        return MessageFormatterProtocol
+    
+    if name == "SummaryBuilderProtocol":
+        from .protocols import SummaryBuilderProtocol
+        return SummaryBuilderProtocol
+    
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/compaction/compactor.py

@@ -5,12 +5,13 @@ Manages context window by compacting messages when needed.
 """
 
 import re
-from typing import List, Dict, Any, Optional, Callable, Awaitable
+from typing import List, Dict, Any, Optional, Callable, Awaitable, Tuple
 import asyncio
 
 from .config import CompactionConfig, COMPACTION_PREFIX, SUMMARY_TEMPLATE
 from .strategy import CompactionStrategy
 from .result import CompactionResult
+from .protocols import ToolResultPrunerProtocol, MessageFormatterProtocol, SummaryBuilderProtocol
 
 
 class ContextCompactor:
@@ -34,7 +35,10 @@ class ContextCompactor:
         preserve_system: bool = True,
         preserve_recent: int = 5,
         config: Optional[CompactionConfig] = None,
-        llm_summarize_fn: Optional[Callable[[str], Awaitable[str]]] = None
+        llm_summarize_fn: Optional[Callable[[str], Awaitable[str]]] = None,
+        tool_pruner: Optional[ToolResultPrunerProtocol] = None,
+        message_formatter: Optional[MessageFormatterProtocol] = None,
+        summary_builder: Optional[SummaryBuilderProtocol] = None
     ):
         """
         Initialize the compactor.
@@ -45,30 +49,43 @@ class ContextCompactor:
             strategy: Compaction strategy to use
             preserve_system: Keep system messages
             preserve_recent: Number of recent messages to preserve
-<<<<<<< HEAD
-            config: Optional CompactionConfig to override defaults
+            config: Optional CompactionConfig for advanced settings
             llm_summarize_fn: Async function to call LLM for summarization
+            tool_pruner: Protocol implementation for tool result pruning
+            message_formatter: Protocol implementation for message formatting
+            summary_builder: Protocol implementation for summary building
         """
-        # Initialize config first
-        self.config = config or CompactionConfig()
+        # Use provided config or create default
+        self.config = config or CompactionConfig(
+            max_tokens=max_tokens,
+            target_tokens=target_tokens or int(max_tokens * 0.75),
+            preserve_system=preserve_system,
+            preserve_recent=preserve_recent
+        )
         
-        # Use config values if config provided, otherwise use constructor args
-        if config is not None:
-            self.max_tokens = config.max_tokens
-            self.target_tokens = config.target_tokens
-            self.preserve_system = config.preserve_system
-            self.preserve_recent = config.preserve_recent
-        else:
-            self.max_tokens = max_tokens
-            self.target_tokens = target_tokens or int(max_tokens * 0.75)
-            self.preserve_system = preserve_system
-            self.preserve_recent = preserve_recent
-            
+        # Set instance attributes from config to ensure consistency
+        self.max_tokens = self.config.max_tokens
+        self.target_tokens = self.config.target_tokens
         self.strategy = strategy
+        self.preserve_system = self.config.preserve_system
+        self.preserve_recent = self.config.preserve_recent
         self.llm_summarize_fn = llm_summarize_fn
         
-        # Track previous summaries for iterative update
+        # Protocol implementations (defaults to None - no heavy implementations in core)
+        self.tool_pruner = tool_pruner
+        self.message_formatter = message_formatter  
+        self.summary_builder = summary_builder
+        
+        # Anti-thrashing state tracking
+        self._last_savings_pct: float = 100.0  # Start high to allow first compaction
+        self._low_savings_streak: int = 0
+        
+        # Iterative summary state
         self._previous_summary: Optional[str] = None
+        self._previous_summary_global_idx: int = 0
+        
+        # Reset state tracking for each compact() call
+        self._used_previous_summary: bool = False
     
     def estimate_tokens(self, text: str) -> int:
         """Estimate token count for text."""
@@ -96,12 +113,26 @@ class ContextCompactor:
         return sum(self.count_message_tokens(m) for m in messages)
     
     def needs_compaction(self, messages: List[Dict[str, Any]]) -> bool:
-        """Check if messages need compaction."""
-        return self.count_total_tokens(messages) > self.max_tokens
+        """
+        Check if messages need compaction with anti-thrashing protection.
+        
+        Returns False if:
+        - Token count is below threshold
+        - We've had too many consecutive low-savings attempts
+        """
+        if self.count_total_tokens(messages) <= self.max_tokens:
+            return False
+            
+        # Anti-thrashing: skip if we've had too many low-savings attempts
+        if self._low_savings_streak >= self.config.max_consecutive_low_savings:
+            return False
+            
+        return True
     
     def compact(
         self,
-        messages: List[Dict[str, Any]]
+        messages: List[Dict[str, Any]],
+        focus_topic: str = ""
     ) -> tuple[List[Dict[str, Any]], CompactionResult]:
         """
         Compact messages to fit within token limit (synchronous version).
@@ -111,31 +142,49 @@ class ContextCompactor:
         
         Args:
             messages: List of messages to compact
+            focus_topic: Optional topic to focus on during summarization
             
         Returns:
             Tuple of (compacted messages, result)
         """
+        # Reset state for this compaction call
+        self._used_previous_summary = False
+        
         original_tokens = self.count_total_tokens(messages)
         
         if original_tokens <= self.max_tokens:
-            return messages, CompactionResult(
+            result = CompactionResult(
                 original_tokens=original_tokens,
                 compacted_tokens=original_tokens,
                 messages_removed=0,
                 messages_kept=len(messages),
                 strategy_used=self.strategy
             )
+            result.calculate_savings_pct()
+            return messages, result
+        
+        # Apply tool result deduplication pre-pass if enabled
+        processed_messages = messages
+        tool_results_pruned = 0
+        if self.config.tool_prune_before_summarise and self.tool_pruner:
+            processed_messages, tool_results_pruned = self.tool_pruner.prune(
+                messages, 
+                self.config.max_tool_result_size
+            )
+        
+        # Skip early exit - proceed with full compaction strategy
+        # Anti-thrashing check will be applied after strategy runs
         
         if self.strategy == CompactionStrategy.TRUNCATE:
-            compacted = self._truncate(messages)
+            compacted = self._truncate(processed_messages)
         elif self.strategy == CompactionStrategy.SLIDING:
-            compacted = self._sliding_window(messages)
+            compacted = self._sliding_window(processed_messages)
         elif self.strategy == CompactionStrategy.SUMMARIZE:
-            compacted = self._summarize(messages)
+            compacted = self._summarize(processed_messages)
         elif self.strategy == CompactionStrategy.SMART:
-            compacted = self._smart_compact(messages)
+            compacted = self._smart_compact(processed_messages)
         elif self.strategy == CompactionStrategy.PRUNE:
-            compacted = self._prune(messages)
+            compacted = self._prune(processed_messages)
         elif self.strategy == CompactionStrategy.LLM_SUMMARIZE:
             if self.llm_summarize_fn:
                 # For sync calls with LLM function, we need to run async
@@ -144,17 +193,17 @@ class ContextCompactor:
                     try:
                         loop = asyncio.get_running_loop()
                         # If in async context, fallback to naive summarization
-                        compacted = self._summarize(messages)
+                        compacted = self._summarize(processed_messages)
                     except RuntimeError:
                         # No running loop, safe to create one
-                        compacted = asyncio.run(self._llm_summarize_async(messages))
+                        compacted = asyncio.run(self._llm_summarize_async(processed_messages, focus_topic))
                 except Exception:
                     # Fallback to naive summarization if async fails
-                    compacted = self._summarize(messages)
+                    compacted = self._summarize(processed_messages)
             else:
-                compacted = self._llm_summarize(messages)
+                compacted = self._llm_summarize(processed_messages, focus_topic)
         else:
-            compacted = self._truncate(messages)
+            compacted = self._truncate(processed_messages)
         
         compacted_tokens = self.count_total_tokens(compacted)
         
@@ -163,49 +212,75 @@ class ContextCompactor:
             compacted_tokens=compacted_tokens,
             messages_removed=len(messages) - len(compacted),
             messages_kept=len(compacted),
-            strategy_used=self.strategy
+            strategy_used=self.strategy,
+            tool_results_pruned=tool_results_pruned,
+            previous_summary_reused=getattr(self, '_used_previous_summary', False)
         )
+        result.calculate_savings_pct()
+        
+        # Update anti-thrashing tracking based on actual results
+        self._last_savings_pct = result.savings_pct
+        if result.savings_pct >= self.config.min_savings_pct:
+            self._low_savings_streak = 0
+        else:
+            self._low_savings_streak += 1
         
         return compacted, result
 
     async def compact_async(
         self,
-        messages: List[Dict[str, Any]]
+        messages: List[Dict[str, Any]],
+        focus_topic: str = ""
     ) -> tuple[List[Dict[str, Any]], CompactionResult]:
         """
         Compact messages to fit within token limit (asynchronous version).
         
         Args:
             messages: List of messages to compact
+            focus_topic: Optional topic to focus on during summarization
             
         Returns:
             Tuple of (compacted messages, result)
         """
+        # Reset state for this compaction call
+        self._used_previous_summary = False
+        
         original_tokens = self.count_total_tokens(messages)
         
         if original_tokens <= self.max_tokens:
-            return messages, CompactionResult(
+            result = CompactionResult(
                 original_tokens=original_tokens,
                 compacted_tokens=original_tokens,
                 messages_removed=0,
                 messages_kept=len(messages),
                 strategy_used=self.strategy
             )
+            result.calculate_savings_pct()
+            return messages, result
+        
+        # Apply tool result deduplication pre-pass if enabled
+        processed_messages = messages
+        tool_results_pruned = 0
+        if self.config.tool_prune_before_summarise and self.tool_pruner:
+            processed_messages, tool_results_pruned = self.tool_pruner.prune(
+                messages, 
+                self.config.max_tool_result_size
+            )
         
         if self.strategy == CompactionStrategy.TRUNCATE:
-            compacted = self._truncate(messages)
+            compacted = self._truncate(processed_messages)
         elif self.strategy == CompactionStrategy.SLIDING:
-            compacted = self._sliding_window(messages)
+            compacted = self._sliding_window(processed_messages)
         elif self.strategy == CompactionStrategy.SUMMARIZE:
-            compacted = self._summarize(messages)
+            compacted = self._summarize(processed_messages)
         elif self.strategy == CompactionStrategy.SMART:
-            compacted = self._smart_compact(messages)
+            compacted = self._smart_compact(processed_messages)
         elif self.strategy == CompactionStrategy.PRUNE:
-            compacted = self._prune(messages)
+            compacted = self._prune(processed_messages)
         elif self.strategy == CompactionStrategy.LLM_SUMMARIZE:
-            compacted = await self._llm_summarize_async(messages)
+            compacted = await self._llm_summarize_async(processed_messages, focus_topic)
         else:
-            compacted = self._truncate(messages)
+            compacted = self._truncate(processed_messages)
         
         compacted_tokens = self.count_total_tokens(compacted)
         
@@ -214,11 +289,35 @@ class ContextCompactor:
             compacted_tokens=compacted_tokens,
             messages_removed=len(messages) - len(compacted),
             messages_kept=len(compacted),
-            strategy_used=self.strategy
+            strategy_used=self.strategy,
+            tool_results_pruned=tool_results_pruned,
+            previous_summary_reused=getattr(self, '_used_previous_summary', False)
         )
+        result.calculate_savings_pct()
+        
+        # Update anti-thrashing tracking based on actual results
+        self._last_savings_pct = result.savings_pct
+        if result.savings_pct >= self.config.min_savings_pct:
+            self._low_savings_streak = 0
+        else:
+            self._low_savings_streak += 1
         
         return compacted, result
     
+    def _prune_tool_results(self, messages: List[Dict[str, Any]]) -> Tuple[List[Dict[str, Any]], int]:
+        """
+        Delegate tool result pruning to injected protocol implementation.
+        
+        If no tool pruner is provided, returns messages unchanged.
+        This maintains backward compatibility while enforcing protocol-driven design.
+        
+        Returns:
+            Tuple of (processed messages, number of tool results pruned)
+        """
+        if self.tool_pruner:
+            return self.tool_pruner.prune(messages, self.config.max_tool_result_size)
+        return messages, 0
+    
     def _truncate(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
         """Truncate oldest messages."""
         result = []
@@ -360,72 +459,19 @@ class ContextCompactor:
         result.extend(recent)
         return result
     
-    def _llm_summarize(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    def _llm_summarize(self, messages: List[Dict[str, Any]], focus_topic: str = "") -> List[Dict[str, Any]]:
         """
-        Use LLM to summarize older messages with anti-injection framing.
+        Use LLM to summarize older messages with iterative support.
         
-        Note: This implementation uses structured templates and anti-injection prefixes
-        to prevent the model from treating summarized content as active instructions.
-        """
-        result = []
+        Supports iterative summarization - if we have a previous summary,
+        we only summarize the new messages since that summary.
+        Also includes anti-injection framing when configured.
         
-        # Keep system messages (excluding previous compacted summaries)
-        system_msgs = [
-            m for m in messages 
-            if m.get("role") == "system" and not m.get("_compacted")
-        ]
-        other_msgs = [m for m in messages if m.get("role") != "system"]
-        
-        result.extend(system_msgs)
-        
-        # Keep recent messages
-        recent = other_msgs[-self.preserve_recent:]
-        older = other_msgs[:-self.preserve_recent] if len(other_msgs) > self.preserve_recent else []
-        
-        if older:
-            if self.config.structured_template:
-                structured = self._build_structured_summary(older)
-            else:
-                # Fallback to simple summary
-                summary_parts = []
-                for msg in older:
-                    role = msg.get("role", "unknown")
-                    content = msg.get("content", "")
-                    if isinstance(content, str) and content:
-                        summary_parts.append(f"[{role}]: {content[:150]}...")
-                structured = "\n".join(summary_parts[:10])
-            
-            if structured:
-                # Apply anti-injection prefix
-                prefixed = f"{self.config.compaction_prefix}\n\n{structured}"
-                
-                # Handle iterative update if enabled
-                if self.config.iterative_update and self._previous_summary:
-                    structured = self._merge_summaries(self._previous_summary, structured)
-                    prefixed = f"{self.config.compaction_prefix}\n\n{structured}"
-                
-                # Store for next iteration
-                if self.config.iterative_update:
-                    self._previous_summary = structured
-                
-                result.append({
-                    "role": "system",
-                    "content": prefixed,
-                    "_compacted": True,
-                    "_original_count": len(older),
-                    "_anti_injection": True,
-                })
-        
-        result.extend(recent)
-        return result
-
-    async def _llm_summarize_async(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
-        """
-        Use LLM to intelligently summarize older messages.
-        
-        This method invokes the agent's LLM to create a meaningful summary
-        that preserves key facts, identifiers, and the user's intent.
+        Args:
+            messages: List of messages to compact
+            focus_topic: Optional topic to focus on during summarization
         """
+        self._used_previous_summary = False
         result = []
         
         # Keep system messages
@@ -436,33 +482,169 @@ class ContextCompactor:
         
         # Keep recent messages
         recent = other_msgs[-self.preserve_recent:]
-        older = other_msgs[:-self.preserve_recent] if len(other_msgs) > self.preserve_recent else []
+        
+        # Determine what to summarize
+        total_original_messages = len(messages)
+        if (self.config.enable_iterative_summary and 
+            self._previous_summary and 
+            total_original_messages > self._previous_summary_global_idx):
+            # Iterative: summarize only new messages since previous summary
+            # Calculate how many old messages to summarize based on global position
+            messages_since_summary = total_original_messages - self._previous_summary_global_idx
+            new_older_messages = max(0, messages_since_summary - self.preserve_recent)
+            if new_older_messages > 0:
+                to_summarize = other_msgs[-messages_since_summary:-self.preserve_recent]
+            else:
+                to_summarize = []
+            self._used_previous_summary = True
+        else:
+            # Fresh summary: summarize all older messages
+            to_summarize = other_msgs[:-self.preserve_recent] if len(other_msgs) > self.preserve_recent else []
+            self._used_previous_summary = False
+        
+        if to_summarize or self._previous_summary:
+            # Build summary content
+            summary_parts = []
+            
+            # Include previous summary if doing iterative summarization
+            if self._used_previous_summary and self._previous_summary:
+                summary_parts.append(f"[Previous Summary]: {self._previous_summary}")
+                summary_parts.append("[New Activity]:")
+            
+            # Add new messages to summarize
+            for msg in to_summarize:
+                role = msg.get("role", "unknown")
+                content = msg.get("content", "")
+                if isinstance(content, str) and content:
+                    # Extract key information with focus consideration
+                    if focus_topic and focus_topic.lower() in content.lower():
+                        # Prioritize content related to focus topic
+                        summary_parts.append(f"[{role}] *FOCUS*: {content[:200]}...")
+                    else:
+                        summary_parts.append(f"[{role}]: {content[:150]}...")
+            
+            if summary_parts:
+                focus_hint = f"\nFocus on: {focus_topic}" if focus_topic else ""
+                
+                if self._used_previous_summary:
+                    summary = (
+                        f"[Iterative conversation summary - update with new activity{focus_hint}]\n"
+                        + "\n".join(summary_parts[:15])
+                    )
+                else:
+                    summary = (
+                        f"[Compacted conversation history - summarize key points{focus_hint}]\n"
+                        + "\n".join(summary_parts[:10])
+                    )
+                
+                # Apply anti-injection prefix if configured
+                if hasattr(self.config, 'compaction_prefix') and self.config.compaction_prefix:
+                    summary = f"{self.config.compaction_prefix}\n\n{summary}"
+                
+                summary_msg = {
+                    "role": "system",
+                    "content": summary,
+                    "_compacted": True,
+                    "_original_count": len(to_summarize),
+                    "_iterative": self._used_previous_summary,
+                    "_focus_topic": focus_topic
+                }
+                result.append(summary_msg)
+                
+                # Update tracking for future iterative summarization
+                self._previous_summary = summary
+                self._previous_summary_global_idx = len(messages)
+        
+        result.extend(recent)
+        return result
+
+    async def _llm_summarize_async(self, messages: List[Dict[str, Any]], focus_topic: str = "") -> List[Dict[str, Any]]:
+        """
+        Use LLM to intelligently summarize older messages.
+        
+        This method invokes the agent's LLM to create a meaningful summary
+        that preserves key facts, identifiers, and the user's intent.
+        
+        Args:
+            messages: List of messages to compact
+            focus_topic: Optional topic to focus on during summarization
+        """
+        self._used_previous_summary = False
+        result = []
+        
+        # Keep system messages
+        system_msgs = [m for m in messages if m.get("role") == "system"]
+        other_msgs = [m for m in messages if m.get("role") != "system"]
+        
+        result.extend(system_msgs)
+        
+        # Keep recent messages
+        recent = other_msgs[-self.preserve_recent:]
+        
+        # Determine what to summarize based on iterative settings
+        total_original_messages = len(messages)
+        if (self.config.enable_iterative_summary and 
+            self._previous_summary and 
+            total_original_messages > self._previous_summary_global_idx):
+            # Iterative: summarize only new messages since previous summary
+            messages_since_summary = total_original_messages - self._previous_summary_global_idx
+            new_older_messages = max(0, messages_since_summary - self.preserve_recent)
+            if new_older_messages > 0:
+                older = other_msgs[-messages_since_summary:-self.preserve_recent]
+            else:
+                older = []
+            self._used_previous_summary = True
+        else:
+            # Fresh summary: summarize all older messages
+            older = other_msgs[:-self.preserve_recent] if len(other_msgs) > self.preserve_recent else []
+            self._used_previous_summary = False
         
         if older and self.llm_summarize_fn:
             try:
-                # Format messages for summarization
-                history_text = self._format_messages_for_summary(older)
+                # Format messages for summarization using protocol
+                if self.message_formatter:
+                    history_text = self.message_formatter.format_for_summary(older)
+                else:
+                    # Fallback to basic formatting if no protocol implementation
+                    history_text = str(older)
                 
                 # Create summarization prompt that preserves important information
+                focus_hint = f" Focus especially on: {focus_topic}." if focus_topic else ""
+                iterative_hint = ""
+                if self._used_previous_summary and self._previous_summary:
+                    iterative_hint = f"\n\n[Previous Summary]: {self._previous_summary}\n[New Activity to Add]:"
+                
                 prompt = (
                     "Summarise the following conversation history. Preserve verbatim: "
                     "all file paths, IDs, hashes, URLs, task references, error messages, "
                     "tool outputs, and the user's requests. Be concise but complete. "
-                    "Focus on facts and actions taken, not general conversation.\n\n"
-                    f"{history_text}"
+                    f"Focus on facts and actions taken, not general conversation.{focus_hint}"
+                    f"{iterative_hint}\n\n{history_text}"
                 )
                 
                 # Call the LLM for summarization
                 summary = await self.llm_summarize_fn(prompt)
                 
                 # Add the LLM-generated summary as a system message
+                summary_content = summary
+                if self._used_previous_summary:
+                    summary_content = f"[Iterative conversation summary]\n{summary}"
+                else:
+                    summary_content = f"[Previous conversation summary]\n{summary}"
+                
                 result.append({
                     "role": "system",
-                    "content": f"[Previous conversation summary]\n{summary}",
+                    "content": summary_content,
                     "_compacted": True,
                     "_original_count": len(older),
                     "_llm_generated": True,
+                    "_iterative": self._used_previous_summary,
+                    "_focus_topic": focus_topic
                 })
+                
+                # Update tracking for future iterative summarization
+                self._previous_summary = summary
+                self._previous_summary_global_idx = len(messages)
             except Exception as e:
                 # Fallback to naive summarization if LLM call fails
                 import logging
@@ -506,133 +688,41 @@ class ContextCompactor:
                     "content": summary,
                     "_compacted": True,
                     "_original_count": len(older),
+                    "_fallback": True,
                 })
         
         result.extend(recent)
         return result
 
     def _format_messages_for_summary(self, messages: List[Dict[str, Any]]) -> str:
-        """Format messages for LLM summarization."""
-        formatted = []
-        for i, msg in enumerate(messages):
-            role = msg.get("role", "unknown")
-            content = msg.get("content", "")
-            
-            # Handle tool calls
-            if msg.get("tool_calls"):
-                tool_calls = msg.get("tool_calls", [])
-                tool_names = [tc.get("function", {}).get("name", "unknown") for tc in tool_calls]
-                formatted.append(f"{i+1}. {role}: Called tools: {', '.join(tool_names)}")
-            
-            # Handle content
-            if isinstance(content, str) and content.strip():
-                # Truncate very long content but preserve structure
-                if len(content) > 1000:
-                    content = content[:800] + "...[truncated]..." + content[-200:]
-                formatted.append(f"{i+1}. {role}: {content}")
-            elif isinstance(content, list):
-                # Handle multi-part content
-                parts = []
-                for part in content[:3]:  # Limit to first 3 parts
-                    if isinstance(part, dict):
-                        text = part.get("text", "")
-                        if text:
-                            parts.append(text[:200] + "..." if len(text) > 200 else text)
-                if parts:
-                    formatted.append(f"{i+1}. {role}: {' | '.join(parts)}")
-            
-            # Handle tool results with IDs
-            if msg.get("tool_call_id"):
-                tool_id = msg.get("tool_call_id", "")
-                formatted.append(f"{i+1}. {role} (tool {tool_id}): {str(content)[:500]}...")
+        """
+        Delegate message formatting to protocol implementation.
         
-        return "\n".join(formatted)
+        If no formatter is provided, returns basic string representation.
+        """
+        if self.message_formatter:
+            return self.message_formatter.format_for_summary(messages)
+        return str(messages)
     
     def _build_structured_summary(self, messages: List[Dict[str, Any]]) -> str:
         """
-        Build a structured summary using the configured template.
+        Delegate structured summary building to protocol implementation.
         
-        Args:
-            messages: Messages to summarize
-            
-        Returns:
-            Structured summary string
+        If no summary builder is provided, returns basic summary.
         """
-        # Extract information from messages
-        active_task = "No specific task identified"
-        completed = []
-        in_progress = []
-        pending = []
-        files = set()
-        remaining = []
-        
-        for msg in messages:
-            role = msg.get("role", "")
-            content = msg.get("content", "")
-            
-            if not isinstance(content, str):
-                continue
-                
-            content_lower = content.lower()
-            
-            # Extract file paths
-            file_matches = re.findall(r'[\w/\.\-]+\.[a-zA-Z]{1,4}', content)
-            files.update(file_matches[:5])  # Limit to 5 files
-            
-            # Categorize content based on keywords and role
-            content_snippet = content[:200] + ("..." if len(content) > 200 else "")
-            
-            if role == "user":
-                if any(word in content_lower for word in ["please", "can you", "help"]) or re.search(r'\bdo\b', content_lower):
-                    active_task = content_snippet
-                elif "?" in content:
-                    pending.append(content_snippet)
-                    
-            elif role == "assistant":
-                if any(word in content_lower for word in ["completed", "done", "finished"]):
-                    completed.append(content_snippet)
-                elif any(word in content_lower for word in ["working", "processing", "analyzing"]):
-                    in_progress.append(content_snippet)
-                elif any(word in content_lower for word in ["will", "plan to", "next"]):
-                    remaining.append(content_snippet)
-                    
-            elif role == "tool":
-                # Tool results are generally completed actions
-                tool_name = msg.get("name", "unknown")
-                completed.append(f"Tool {tool_name}: {content_snippet}")
-        
-        # Format using template
-        return SUMMARY_TEMPLATE.format(
-            active_task=active_task,
-            completed="\n".join(f"- {item}" for item in completed[:3]) or "None identified",
-            in_progress="\n".join(f"- {item}" for item in in_progress[:3]) or "None identified",
-            pending="\n".join(f"- {item}" for item in pending[:3]) or "None identified",
-            files=", ".join(list(files)[:5]) or "None mentioned",
-            remaining="\n".join(f"- {item}" for item in remaining[:3]) or "None identified"
-        )
+        if self.summary_builder:
+            return self.summary_builder.build_structured_summary(messages)
+        return f"Summary of {len(messages)} messages"
     
     def _merge_summaries(self, previous: str, current: str) -> str:
         """
-        Merge previous summary with current one for iterative updates.
+        Delegate summary merging to protocol implementation.
         
-        Args:
-            previous: Previous summary content
-            current: Current summary content
-            
-        Returns:
-            Merged summary
+        If no summary builder is provided, returns current summary only.
         """
-        # Simple merge strategy: prioritize current but preserve unique info from previous
-        if not previous:
-            return current
-            
-        # Preserve context but avoid excessive growth
-        MAX_PREVIOUS_LENGTH = 500
-        if len(previous) > MAX_PREVIOUS_LENGTH:
-            truncated_previous = previous[:MAX_PREVIOUS_LENGTH]
-            return f"{current}\n\n[Previous context]: {truncated_previous}..."
-        else:
-            return f"{current}\n\n[Previous context]: {previous}"
+        if self.summary_builder:
+            return self.summary_builder.merge_summaries(previous, current)
+        return current
     
     def get_stats(self, messages: List[Dict[str, Any]]) -> Dict[str, Any]:
         """Get statistics about messages."""

src/praisonai-agents/praisonaiagents/compaction/config.py

@@ -50,3 +50,28 @@ class CompactionConfig:
     compaction_prefix: str = COMPACTION_PREFIX  # Override for custom framing
     structured_template: bool = True  # Emit Active Task / Remaining Work sections
     iterative_update: bool = True  # Merge previous summary on re-compaction
+    
+    # Anti-thrashing protection
+    min_savings_pct: float = 10.0  # Skip if projected saving < 10% (0-100 scale)
+    max_consecutive_low_savings: int = 2  # Abort after N low-savings attempts
+    
+    # Tool result optimization
+    tool_prune_before_summarise: bool = True  # Deduplicate tool results before summarization
+    max_tool_result_size: int = 500  # Maximum size for tool results before pruning
+    
+    # Iterative summarization
+    enable_iterative_summary: bool = True  # Build on previous summaries instead of starting from scratch
+    
+    def __post_init__(self):
+        """Validate and normalize configuration values."""
+        # Normalize min_savings_pct to 0-100 scale if provided as ratio
+        if self.min_savings_pct < 1.0:
+            self.min_savings_pct *= 100.0
+        
+        # Validate ranges
+        if not 0.0 <= self.min_savings_pct <= 100.0:
+            raise ValueError("min_savings_pct must be between 0 and 100")
+        if self.max_consecutive_low_savings < 0:
+            raise ValueError("max_consecutive_low_savings must be >= 0")
+        if self.max_tool_result_size <= 0:
+            raise ValueError("max_tool_result_size must be > 0")

src/praisonai-agents/praisonaiagents/compaction/protocols.py

@@ -0,0 +1,73 @@
+"""
+Compaction Protocols for PraisonAI Agents.
+
+Protocol-driven interfaces for context compaction functionality.
+"""
+
+from typing import Protocol, List, Dict, Any, Tuple, Optional
+
+
+class ToolResultPrunerProtocol(Protocol):
+    """Protocol for pruning and deduplicating tool results."""
+    
+    def prune(
+        self,
+        messages: List[Dict[str, Any]],
+        max_tool_result_size: int = 500
+    ) -> Tuple[List[Dict[str, Any]], int]:
+        """
+        Prune and deduplicate tool results to reduce token usage.
+        
+        Args:
+            messages: List of messages to process
+            max_tool_result_size: Maximum size for tool results before truncation
+            
+        Returns:
+            Tuple of (processed messages, number of tool results pruned)
+        """
+        ...
+
+
+class MessageFormatterProtocol(Protocol):
+    """Protocol for formatting messages for summarization."""
+    
+    def format_for_summary(self, messages: List[Dict[str, Any]]) -> str:
+        """
+        Format messages for LLM summarization.
+        
+        Args:
+            messages: Messages to format
+            
+        Returns:
+            Formatted string suitable for summarization
+        """
+        ...
+
+
+class SummaryBuilderProtocol(Protocol):
+    """Protocol for building structured summaries."""
+    
+    def build_structured_summary(self, messages: List[Dict[str, Any]]) -> str:
+        """
+        Build a structured summary from messages.
+        
+        Args:
+            messages: Messages to summarize
+            
+        Returns:
+            Structured summary string
+        """
+        ...
+    
+    def merge_summaries(self, previous: str, current: str) -> str:
+        """
+        Merge previous summary with current one for iterative updates.
+        
+        Args:
+            previous: Previous summary content
+            current: Current summary content
+            
+        Returns:
+            Merged summary
+        """
+        ...

src/praisonai-agents/praisonaiagents/compaction/result.py

@@ -18,6 +18,16 @@ class CompactionResult:
     strategy_used: CompactionStrategy
     summary: str = ""
     
+    # Anti-thrashing and optimization info
+    savings_pct: float = field(default=0.0, init=False)  # Percentage of tokens saved (computed)
+    tool_results_pruned: int = 0  # Number of tool results deduplicated/pruned
+    previous_summary_reused: bool = False  # Whether iterative summary was used
+    was_skipped_due_to_low_savings: bool = False  # Whether compaction was skipped due to anti-thrashing
+    
+    def __post_init__(self):
+        """Initialize computed fields after construction."""
+        self.calculate_savings_pct()
+    
     @property
     def tokens_saved(self) -> int:
         """Get number of tokens saved."""
@@ -30,6 +40,13 @@ class CompactionResult:
             return 0.0
         return self.compacted_tokens / self.original_tokens
     
+    def calculate_savings_pct(self) -> None:
+        """Calculate and set the savings percentage."""
+        if self.original_tokens > 0:
+            self.savings_pct = (self.tokens_saved / self.original_tokens) * 100.0
+        else:
+            self.savings_pct = 0.0
+    
     @property
     def was_compacted(self) -> bool:
         """Check if compaction occurred."""
@@ -45,5 +62,9 @@ class CompactionResult:
             "messages_kept": self.messages_kept,
             "strategy_used": self.strategy_used.value,
             "compression_ratio": self.compression_ratio,
+            "savings_pct": self.savings_pct,
+            "tool_results_pruned": self.tool_results_pruned,
+            "previous_summary_reused": self.previous_summary_reused,
+            "was_skipped_due_to_low_savings": self.was_skipped_due_to_low_savings,
             "summary": self.summary
         }

src/praisonai-agents/praisonaiagents/config/feature_configs.py

@@ -774,29 +774,39 @@ class ExecutionConfig:
                 self.context_compaction
             )
         
-        # Emit deprecation warning for default behavior change
-        # Only warn if context_compaction was not explicitly set (i.e., using the default False)
-        import warnings
-        import inspect
-        frame = inspect.currentframe()
-        try:
-            # Check if this is being called from user code (not internal)
-            caller_frame = frame.f_back
-            if (caller_frame and 
-                not any(path_part in caller_frame.f_code.co_filename 
-                       for path_part in ['praisonaiagents', 'test_', '__pycache__'])):
-                # This is being called from user code
-                if self.context_compaction is False:
-                    warnings.warn(
-                        "ExecutionConfig.context_compaction will default to True in the next "
-                        "release for proactive context overflow protection. To disable, explicitly "
-                        "set context_compaction=False. To use the new default early, set "
-                        "context_compaction=True.",
-                        DeprecationWarning,
-                        stacklevel=3
-                    )
-        finally:
-            del frame
+        # Emit deprecation warning once per process for default behavior change.
+        # Walk the stack once — dataclass __init__ uses caller filename "<string>".
+        if self.context_compaction is False:
+            if getattr(ExecutionConfig, '_context_compaction_internal', False):
+                return
+            if getattr(ExecutionConfig, '_context_compaction_warned', False):
+                return
+            import warnings
+            import inspect
+            _INTERNAL = ('praisonaiagents', 'test_', '__pycache__')
+            frame = inspect.currentframe()
+            try:
+                caller = frame.f_back if frame else None
+                is_internal = False
+                while caller is not None:
+                    if any(p in caller.f_code.co_filename for p in _INTERNAL):
+                        is_internal = True
+                        break
+                    caller = caller.f_back
+                if is_internal:
+                    ExecutionConfig._context_compaction_internal = True
+                    return
+                warnings.warn(
+                    "ExecutionConfig.context_compaction will default to True in the next "
+                    "release for proactive context overflow protection. To disable, explicitly "
+                    "set context_compaction=False. To use the new default early, set "
+                    "context_compaction=True.",
+                    DeprecationWarning,
+                    stacklevel=3,
+                )
+                ExecutionConfig._context_compaction_warned = True
+            finally:
+                del frame
 
     def to_dict(self) -> Dict[str, Any]:
         """Convert to dictionary."""
@@ -1089,8 +1099,8 @@ class MultiAgentOutputConfig:
     # Verbosity level (0=silent, 1=minimal, 2+=verbose)
     verbose: int = 0
     
-    # Streaming output
-    stream: bool = True
+    # Streaming output - False by default for sync multi-agent compatibility
+    stream: bool = False
     
     def to_dict(self) -> Dict[str, Any]:
         """Convert to dictionary."""

src/praisonai-agents/praisonaiagents/config/loader.py

@@ -83,6 +83,7 @@ class ConfigValidationError(Exception):
 # Global config cache (lazy loaded)
 _config_cache: Optional[Dict[str, Any]] = None
 _config_loaded: bool = False
+_defaults_has_values: Optional[bool] = None
 
 
 def _default_project_plugins_dir():
@@ -418,11 +419,27 @@ def get_enabled_plugins() -> Optional[List[str]]:
     return None  # All plugins enabled
 
 
+def _defaults_has_any_values() -> bool:
+    """Return True if config file defines any defaults (cached after first check)."""
+    global _defaults_has_values
+    if _defaults_has_values is None:
+        defaults = get_defaults_config()
+        _defaults_has_values = any(
+            getattr(defaults, key, None) is not None
+            for key in (
+                "model", "base_url", "memory", "knowledge", "planning",
+                "reflection", "web", "output", "execution", "caching", "autonomy",
+            )
+        )
+    return _defaults_has_values
+
+
 def clear_config_cache() -> None:
     """Clear the config cache (for testing)."""
-    global _config_cache, _config_loaded
+    global _config_cache, _config_loaded, _defaults_has_values
     _config_cache = None
     _config_loaded = False
+    _defaults_has_values = None
 
 
 def validate_config(config: Dict[str, Any], raise_on_error: bool = False) -> List[str]:
@@ -603,6 +620,8 @@ def apply_config_defaults(
     if explicit_value is not None:
         # If True and we have config defaults, merge them
         if explicit_value is True:
+            if not _defaults_has_any_values():
+                return explicit_value
             config_defaults = get_default(param_name)
             if config_defaults and isinstance(config_defaults, dict):
                 if config_class:
@@ -612,6 +631,10 @@ def apply_config_defaults(
                 return config_defaults
         return explicit_value
     
+    # Fast path: no config defaults file values — skip lookups
+    if not _defaults_has_any_values():
+        return None
+    
     # Check if config has defaults for this param
     config_defaults = get_default(param_name)
     if config_defaults is None:

src/praisonai-agents/praisonaiagents/config/presets.py

@@ -322,8 +322,8 @@ CACHING_PRESETS: Dict[str, Dict[str, Any]] = {
 # =============================================================================
 
 MULTI_AGENT_OUTPUT_PRESETS: Dict[str, Dict[str, Any]] = {
-    "verbose": {"verbose": 2, "stream": True},
-    "minimal": {"verbose": 1, "stream": True},
+    "verbose": {"verbose": 2, "stream": False},
+    "minimal": {"verbose": 1, "stream": False},
     "silent": {"verbose": 0, "stream": False},
 }
 

src/praisonai-agents/praisonaiagents/llm/llm.py

@@ -24,6 +24,7 @@ import xml.etree.ElementTree as ET
 from ..errors import AgentErrorKind, FailoverDecision, IdleTimeoutBreaker
 # Gap 2: Tool call execution imports
 from ..tools.call_executor import ToolCall, create_tool_call_executor
+from ..tools.schema import annotation_to_json_schema, get_parameter_requirements
 # Display functions - lazy loaded to avoid importing rich at startup
 # These are only needed when output=verbose
 _display_module = None
@@ -5886,34 +5887,74 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             function_name = function_or_name
             logging.debug(f"Attempting to generate tool definition for: {function_name}")
             
-            # First try to get the tool definition if it exists
-            tool_def_name = f"{function_name}_definition"
-            tool_def = globals().get(tool_def_name)
-            logging.debug(f"Looking for {tool_def_name} in globals: {tool_def is not None}")
-            
-            if not tool_def:
-                import __main__
-                tool_def = getattr(__main__, tool_def_name, None)
-                logging.debug(f"Looking for {tool_def_name} in __main__: {tool_def is not None}")
-            
-            if tool_def:
-                logging.debug(f"Found tool definition: {tool_def}")
-                return tool_def
-
-            # Try to find the function
-            func = globals().get(function_name)
-            logging.debug(f"Looking for {function_name} in globals: {func is not None}")
-            
-            if not func:
-                import __main__
-                func = getattr(__main__, function_name, None)
-                logging.debug(f"Looking for {function_name} in __main__: {func is not None}")
-            
-            if not func or not callable(func):
-                logging.debug(f"Function {function_name} not found or not callable")
-                return None
+            # First try to get from the tool registry (preferred method)
+            try:
+                from ..tools.registry import get_registry
+                registry = get_registry()
+                tool = registry.get(function_name)
+                if tool:
+                    logging.debug(f"Found tool in registry: {function_name}")
+                    # If it's a BaseTool, get its schema
+                    if hasattr(tool, 'get_schema'):
+                        tool_def = tool.get_schema()
+                        # Apply same normalization as the callable path
+                        if (
+                            isinstance(tool_def, dict)
+                            and isinstance(tool_def.get("function"), dict)
+                            and isinstance(tool_def["function"].get("parameters"), dict)
+                        ):
+                            tool_def = tool_def.copy()
+                            tool_def["function"] = tool_def["function"].copy()
+                            tool_def["function"]["parameters"] = self._fix_array_schemas(
+                                tool_def["function"]["parameters"]
+                            )
+                        return tool_def
+                    # If it's a callable, use it as func
+                    if callable(tool):
+                        func = tool
+                    else:
+                        logging.debug(f"Tool {function_name} in registry is not callable")
+                        return None
+                else:
+                    logging.debug(f"Tool {function_name} not found in registry, falling back to globals/__main__")
+                    # Fall back to globals and __main__ for backward compatibility
+                    tool_def_name = f"{function_name}_definition"
+                    tool_def = globals().get(tool_def_name)
+                    if not tool_def:
+                        import __main__
+                        tool_def = getattr(__main__, tool_def_name, None)
+                    if tool_def:
+                        return tool_def
+                    
+                    func = globals().get(function_name)
+                    if not func:
+                        import __main__
+                        func = getattr(__main__, function_name, None)
+                    if not func or not callable(func):
+                        logging.debug(f"Function {function_name} not found or not callable")
+                        return None
+            except ImportError:
+                logging.debug("Tool registry not available, falling back to globals/__main__")
+                # Fall back to globals and __main__ when registry unavailable
+                tool_def_name = f"{function_name}_definition"
+                tool_def = globals().get(tool_def_name)
+                if not tool_def:
+                    import __main__
+                    tool_def = getattr(__main__, tool_def_name, None)
+                if tool_def:
+                    return tool_def
+                
+                func = globals().get(function_name)
+                if not func:
+                    import __main__
+                    func = getattr(__main__, function_name, None)
+                if not func or not callable(func):
+                    logging.debug(f"Function {function_name} not found or not callable")
+                    return None
 
         import inspect
+        from typing import get_type_hints
+        
         # Handle Langchain and CrewAI tools
         if inspect.isclass(func) and hasattr(func, 'run') and not hasattr(func, '_run'):
             original_func = func
@@ -5961,26 +6002,26 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         
         logging.debug(f"Parameter descriptions: {param_descriptions}")
 
+        # Get type hints for proper schema generation
+        try:
+            hints = get_type_hints(func) if getattr(func, "__annotations__", None) else {}
+        except (NameError, TypeError, AttributeError):
+            hints = getattr(func, "__annotations__", {}) or {}
+
         for name, param in parameters_list:
-            param_type = "string"  # Default type
-            if param.annotation != inspect.Parameter.empty:
-                if param.annotation == int:
-                    param_type = "integer"
-                elif param.annotation == float:
-                    param_type = "number"
-                elif param.annotation == bool:
-                    param_type = "boolean"
-                elif param.annotation == list:
-                    param_type = "array"
-                elif param.annotation == dict:
-                    param_type = "object"
+            # Get type annotation
+            param_type = hints.get(name, param.annotation if param.annotation != inspect.Parameter.empty else str)
             
-            parameters["properties"][name] = {
-                "type": param_type,
-                "description": param_descriptions.get(name, "Parameter description not available")
-            }
+            # Use new schema utility for proper type handling
+            prop_schema = annotation_to_json_schema(param_type)
             
-            if param.default == inspect.Parameter.empty:
+            # Add description from docstring
+            prop_schema["description"] = param_descriptions.get(name, "Parameter description not available")
+            
+            parameters["properties"][name] = prop_schema
+            
+            # Check if required using improved logic
+            if get_parameter_requirements(sig, name):
                 parameters["required"].append(name)
         
         logging.debug(f"Generated parameters: {parameters}")

src/praisonai-agents/praisonaiagents/mcp/mcp_security.py

@@ -246,3 +246,74 @@ class SecurityConfig:
     def get_bind_address(self) -> str:
         """Get recommended bind address based on config."""
         return "127.0.0.1" if self.bind_localhost_only else "0.0.0.0"
+
+
+def load_sse_security_from_env():
+    """Load optional SSE security settings from environment."""
+    import os
+
+    token = os.environ.get("MCP_SSE_AUTH_TOKEN") or os.environ.get("MCP_AUTH_TOKEN")
+    origins_raw = os.environ.get("MCP_SSE_ALLOWED_ORIGINS")
+    if not token and not origins_raw:
+        return None, None
+
+    origins = (
+        [origin.strip() for origin in origins_raw.split(",") if origin.strip()]
+        if origins_raw
+        else None
+    )
+    config = SecurityConfig(
+        require_auth=bool(token),
+        validate_origin=origins is not None,
+        allowed_origins=origins or SecurityConfig().allowed_origins,
+    )
+    return config, token
+
+
+def build_sse_security_app(app, security: Optional["SecurityConfig"] = None):
+    """Wrap a Starlette app with SSE security middleware when configured."""
+    config = security
+    token = None
+    if config is None:
+        config, token = load_sse_security_from_env()
+    elif config.require_auth:
+        import os
+
+        token = os.environ.get("MCP_SSE_AUTH_TOKEN") or os.environ.get("MCP_AUTH_TOKEN")
+    if config is None or (not config.require_auth and not config.validate_origin):
+        return app
+
+    class _SSESecurityMiddleware:
+        def __init__(self, inner, sec: SecurityConfig, expected_token: Optional[str]):
+            self._inner = inner
+            self._sec = sec
+            self._token = expected_token
+
+        async def __call__(self, scope, receive, send):
+            if scope.get("type") != "http":
+                await self._inner(scope, receive, send)
+                return
+
+            headers = {k.decode().lower(): v.decode() for k, v in scope.get("headers", [])}
+
+            if self._sec.require_auth and self._token:
+                auth = headers.get("authorization", "")
+                if not auth.startswith("Bearer ") or auth[7:] != self._token:
+                    from starlette.responses import JSONResponse
+
+                    response = JSONResponse({"error": "Unauthorized"}, status_code=401)
+                    await response(scope, receive, send)
+                    return
+
+            if self._sec.validate_origin:
+                origin = headers.get("origin") or None
+                if not self._sec.is_origin_allowed(origin):
+                    from starlette.responses import JSONResponse
+
+                    response = JSONResponse({"error": "Origin not allowed"}, status_code=403)
+                    await response(scope, receive, send)
+                    return
+
+            await self._inner(scope, receive, send)
+
+    return _SSESecurityMiddleware(app, config, token)

src/praisonai-agents/praisonaiagents/mcp/mcp_server.py

@@ -242,12 +242,18 @@ class ToolsMCPServer:
         
         mcp.run()
     
-    def run_sse(self, host: str = "0.0.0.0", port: int = 8080) -> None:
+    def run_sse(
+        self,
+        host: str = "0.0.0.0",
+        port: int = 8080,
+        security: "Optional[SecurityConfig]" = None,
+    ) -> None:
         """Run the MCP server using SSE transport.
         
         Args:
             host: Host to bind to
             port: Port to listen on
+            security: Optional security config; when unset, reads MCP_SSE_* env vars
         """
         try:
             import uvicorn
@@ -260,6 +266,8 @@ class ToolsMCPServer:
                 f"SSE dependencies not available: {e}. "
                 "Install with: pip install uvicorn starlette"
             )
+
+        from .mcp_security import SecurityConfig, build_sse_security_app
         
         mcp = self.get_fastmcp()
         
@@ -286,7 +294,8 @@ class ToolsMCPServer:
                 Mount(messages_path, app=sse_transport.handle_post_message),
             ]
         )
-        
+
+        app = build_sse_security_app(app, security)
         print(f"🚀 Starting MCP server '{self.name}' with SSE transport")
         print(f"📡 SSE endpoint: http://{host}:{port}{sse_path}")
         print(f"🛠️  Available tools: {', '.join(self.get_tool_names())}")

src/praisonai-agents/praisonaiagents/memory/file_memory.py

@@ -115,6 +115,16 @@ class FileMemory:
         "importance_threshold": 0.7,   # Min importance for long-term
         "auto_promote": True,          # Auto-promote important short-term to long-term
     }
+
+    @staticmethod
+    def _sanitise_user_id(user_id: str) -> str:
+        """Reject path traversal in user_id before using it as a directory name."""
+        if not user_id or not isinstance(user_id, str):
+            return "default"
+        if ".." in user_id or "/" in user_id or "\\" in user_id:
+            raise ValueError("user_id must not contain path separators or parent references")
+        safe = user_id.strip()
+        return safe or "default"
     
     def __init__(
         self,
@@ -132,7 +142,7 @@ class FileMemory:
             config: Configuration overrides
             verbose: Verbosity level (0=quiet, 1=info, 2+=debug)
         """
-        self.user_id = user_id
+        self.user_id = self._sanitise_user_id(user_id)
         self.verbose = verbose
         
         # Set up paths

src/praisonai-agents/praisonaiagents/memory/memory.py

@@ -29,6 +29,11 @@ logger = get_logger(__name__, extra_data={"subsystem": "memory"})
 TRACE_LEVEL = 5
 logging.addLevelName(TRACE_LEVEL, 'TRACE')
 
+# Cache boundary constants for prompt prefix caching optimization
+# NOTE: This is a placeholder - current implementation does not provide
+# functional prompt caching as it requires structured API metadata
+CACHE_BOUNDARY = ""
+
 
 
 class Memory(StorageMixin, SearchMixin, MemoryCoreMixin):
@@ -1704,6 +1709,41 @@ class Memory(StorageMixin, SearchMixin, MemoryCoreMixin):
         entities = self.search_entity(q, limit=max_items)
         user_mem = self.search_user_memory(user_id, q, limit=max_items) if user_id else []
 
+        # Apply stable sorting to ensure deterministic order for prompt caching
+        # Sort by timestamp descending, then by content hash for stable ordering
+        def _sort_memory_results(results: List[Any]) -> List[Any]:
+            """Stable sort so identical query sets produce identical context strings."""
+            import hashlib
+            if not results:
+                return results
+            
+            def sort_key(r):
+                if not isinstance(r, dict):
+                    return (0.0, hashlib.sha256(str(r).encode()).hexdigest())
+                
+                # Prefer created_at over timestamp for better determinism
+                timestamp = r.get("created_at") or r.get("timestamp") or 0
+                if isinstance(timestamp, str):
+                    try:
+                        from datetime import datetime
+                        timestamp = datetime.fromisoformat(timestamp.replace("Z", "+00:00")).timestamp()
+                    except ValueError:
+                        timestamp = 0.0
+                timestamp = -(float(timestamp))  # Negative for descending order
+                
+                # Use full content hash for true deterministic ordering
+                content = r.get("text", "") if isinstance(r, dict) else str(r)
+                content_hash = hashlib.sha256(content.encode()).hexdigest()
+                return (timestamp, content_hash)
+            
+            return sorted(results, key=sort_key)
+
+        # Apply stable sorting to all memory results
+        short_term = _sort_memory_results(short_term)
+        long_term = _sort_memory_results(long_term)
+        entities = _sort_memory_results(entities)
+        user_mem = _sort_memory_results(user_mem)
+
         # Add sections in order of priority
         add_section("Short-term Memory Context", short_term)
         add_section("Long-term Memory Context", long_term)
@@ -1713,6 +1753,51 @@ class Memory(StorageMixin, SearchMixin, MemoryCoreMixin):
 
         return "\n".join(lines) if lines else ""
 
+    def build_cache_optimized_context(
+        self,
+        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]:
+        """
+        Build context with cache boundary markers for prompt prefix caching optimization.
+        
+        Returns a dictionary with 'stable_prefix' and 'cache_boundary' keys.
+        The stable prefix contains deterministically ordered content that should
+        remain constant between turns for effective prompt caching.
+        
+        Args:
+            task_descr: Task description for memory search
+            user_id: Optional user ID for personalized memory
+            additional: Additional context to include in search
+            max_items: Maximum items per memory category
+            include_cache_boundary: Whether to include cache boundary marker
+            include_in_output: Whether to include memory content in output
+            
+        Returns:
+            Dict with 'stable_prefix' and 'cache_boundary' keys
+        """
+        # Preserve original behavior: include_in_output=None means debug-level-only
+        # Don't change the default behavior for backward compatibility
+            
+        # Build the stable context using existing method
+        stable_context = self.build_context_for_task(
+            task_descr=task_descr,
+            user_id=user_id,
+            additional=additional,
+            max_items=max_items,
+            include_in_output=include_in_output
+        )
+        
+        result = {"stable_prefix": stable_context}
+        
+        result["cache_boundary"] = CACHE_BOUNDARY if include_cache_boundary else ""
+            
+        return result
+
     # -------------------------------------------------------------------------
     #                      Master Reset (Everything)
     # -------------------------------------------------------------------------

src/praisonai-agents/praisonaiagents/server/server.py

@@ -117,6 +117,16 @@ class AgentServer:
         self._server_thread: Optional[threading.Thread] = None
         self._app = None
         self._server = None
+
+    def _authorise_request(self, request) -> bool:
+        """Verify bearer token when auth_token is configured."""
+        token = self.config.auth_token
+        if not token:
+            return True
+        auth = request.headers.get("Authorization", "")
+        if auth.startswith("Bearer ") and auth[7:] == token:
+            return True
+        return request.headers.get("X-Auth-Token") == token
     
     def _create_app(self):
         """Create the ASGI application."""
@@ -137,6 +147,8 @@ class AgentServer:
             })
         
         async def events(request):
+            if not self._authorise_request(request):
+                return JSONResponse({"error": "Unauthorized"}, status_code=401)
             client_id = str(uuid.uuid4())
             client = SSEClient(client_id)
             self._clients[client_id] = client
@@ -162,6 +174,8 @@ class AgentServer:
             )
         
         async def publish(request):
+            if not self._authorise_request(request):
+                return JSONResponse({"error": "Unauthorized"}, status_code=401)
             try:
                 data = await request.json()
                 event_type = data.get("type", "message")

src/praisonai-agents/praisonaiagents/session.py

@@ -14,6 +14,10 @@ import time
 import threading
 from typing import Any, Dict, List, Optional, TYPE_CHECKING
 
+from .utils.logger import get_logger
+
+logger = get_logger(__name__)
+
 if TYPE_CHECKING:
     from .memory.memory import Memory
     from .knowledge.knowledge import Knowledge
@@ -504,6 +508,20 @@ class Session:
         Returns:
             Formatted context string
         """
+        # Use cache-optimized context if available for better prompt caching
+        if hasattr(self.memory, 'build_cache_optimized_context'):
+            try:
+                cache_result = self.memory.build_cache_optimized_context(
+                    task_descr=query,
+                    user_id=self.user_id,
+                    max_items=max_items,
+                    include_cache_boundary=False  # Don't include boundary for session context
+                )
+                return cache_result.get('stable_prefix', '')
+            except Exception as e:
+                # Fall back to standard context building
+                logger.debug(f"Cache-optimized context failed for session '{self.id or self.user_id}', falling back to standard: {e}")
+        
         return self.memory.build_context_for_task(
             task_descr=query,
             user_id=self.user_id,

src/praisonai-agents/praisonaiagents/session/api.py

@@ -14,6 +14,8 @@ import json
 import logging
 from typing import Any, Dict, List, Optional, TYPE_CHECKING
 
+logger = logging.getLogger(__name__)
+
 if TYPE_CHECKING:
     from ..memory.memory import Memory
     from ..knowledge.knowledge import Knowledge
@@ -477,6 +479,20 @@ class Session:
         Returns:
             Formatted context string
         """
+        # Use cache-optimized context if available for better prompt caching
+        if hasattr(self.memory, 'build_cache_optimized_context'):
+            try:
+                cache_result = self.memory.build_cache_optimized_context(
+                    task_descr=query,
+                    user_id=self.user_id,
+                    max_items=max_items,
+                    include_cache_boundary=False  # Don't include boundary for session context
+                )
+                return cache_result.get('stable_prefix', '')
+            except Exception as e:
+                # Fall back to standard context building
+                logger.debug(f"Cache-optimized context failed for session '{self.session_id}', falling back to standard: {e}")
+        
         return self.memory.build_context_for_task(
             task_descr=query,
             user_id=self.user_id,

src/praisonai-agents/praisonaiagents/task/task.py

@@ -740,10 +740,27 @@ class Task:
                 # Build context for next tasks
                 if self.next_tasks:
                     logger.info(f"Task {self.id}: Building context for next tasks...")
-                    context = self.memory.build_context_for_task(
-                        task_descr=task_output.raw,
-                        max_items=5
-                    )
+                    # Use cache-optimized context if available for better prompt caching
+                    if hasattr(self.memory, 'build_cache_optimized_context'):
+                        try:
+                            cache_result = self.memory.build_cache_optimized_context(
+                                task_descr=task_output.raw,
+                                max_items=5,
+                                include_cache_boundary=False  # Don't include boundary for task context
+                            )
+                            context = cache_result.get('stable_prefix', '')
+                        except Exception as e:
+                            # Fall back to standard context building
+                            logger.debug(f"Cache-optimized context failed for task '{self.id}', falling back to standard: {e}")
+                            context = self.memory.build_context_for_task(
+                                task_descr=task_output.raw,
+                                max_items=5
+                            )
+                    else:
+                        context = self.memory.build_context_for_task(
+                            task_descr=task_output.raw,
+                            max_items=5
+                        )
                     logger.info(f"Task {self.id}: Built context for next tasks: {len(context)} items")
 
                 logger.info(f"Task {self.id}: Memory operations complete")

src/praisonai-agents/praisonaiagents/tools/ast_grep_tool.py

@@ -31,6 +31,8 @@ import logging
 from praisonaiagents._logging import get_logger
 from typing import Optional, List
 
+from ..approval import require_approval
+
 logger = get_logger(__name__)
 
 # Availability cache for performance (checked once per process)
@@ -146,6 +148,7 @@ def ast_grep_search(
         logger.exception("Unexpected error in ast_grep_search")
         return f"Error: {e}"
 
+@require_approval(risk_level="high")
 def ast_grep_rewrite(
     pattern: str,
     replacement: str,

src/praisonai-agents/praisonaiagents/tools/base.py

@@ -17,9 +17,12 @@ Usage:
 from abc import ABC, abstractmethod
 from typing import Any, Callable, Dict, List, Optional, Type, get_type_hints
 import inspect
+import json
 import logging
 import copy
 
+from .schema import annotation_to_json_schema, get_parameter_requirements
+
 
 class ToolValidationError(Exception):
     """Raised when a tool fails validation."""
@@ -129,18 +132,17 @@ class BaseTool(ABC):
                 
                 # Get type hint
                 param_type = hints.get(param_name, Any)
-                json_type = self._python_type_to_json(param_type)
                 
-                # Build property schema
-                prop_schema = {"type": json_type}
+                # Use new schema utility for proper type handling
+                prop_schema = annotation_to_json_schema(param_type)
                 
                 # Add description from docstring if available
                 # (Could parse docstring for param descriptions)
                 
                 schema["properties"][param_name] = prop_schema
                 
-                # Check if required (no default value)
-                if param.default is inspect.Parameter.empty:
+                # Check if required using improved logic
+                if get_parameter_requirements(sig, param_name):
                     schema["required"].append(param_name)
         except Exception as e:
             logging.debug(f"Could not generate schema for {self.name}: {e}")
@@ -149,28 +151,15 @@ class BaseTool(ABC):
     
     @staticmethod
     def _python_type_to_json(python_type: Type) -> str:
-        """Convert Python type to JSON Schema type."""
-        type_map = {
-            str: "string",
-            int: "integer",
-            float: "number",
-            bool: "boolean",
-            list: "array",
-            dict: "object",
-            type(None): "null"
-        }
+        """Convert Python type to JSON Schema type.
         
-        # Handle Optional, Union, etc.
-        origin = getattr(python_type, '__origin__', None)
-        if origin is not None:
-            # For List[X], return "array"
-            if origin is list:
-                return "array"
-            # For Dict[X, Y], return "object"
-            if origin is dict:
-                return "object"
-        
-        return type_map.get(python_type, "string")
+        DEPRECATED: Use annotation_to_json_schema() from schema.py instead.
+        This method is kept for backward compatibility but will be removed.
+        """
+        # Legacy fallback - delegate to new schema utility and extract type
+        from .schema import annotation_to_json_schema
+        schema = annotation_to_json_schema(python_type)
+        return schema.get("type", "string")
     
     @abstractmethod
     def run(self, **kwargs) -> Any:
@@ -442,5 +431,49 @@ def validate_tool_schema_consistency(tools: List[Any]) -> bool:
     return True
 
 
+def get_sorted_tool_schemas(tools: List[Any]) -> List[Dict[str, Any]]:
+    """Get tool schemas sorted by function name for deterministic ordering.
+    
+    This ensures tool schemas are always ordered consistently for prompt caching optimization.
+    
+    Args:
+        tools: List of tool objects (BaseTool instances, callables, etc.)
+        
+    Returns:
+        List of tool schemas sorted alphabetically by function name
+        
+    Raises:
+        ToolValidationError: If validation fails
+    """
+    if not tools:
+        return []
+        
+    # First validate all tools (reuse existing validation logic)
+    validate_tool_schema_consistency(tools)
+    
+    from .decorator import get_tool_schema
+    schemas = []
+    
+    for tool in tools:
+        # Get schema from different tool types
+        if isinstance(tool, BaseTool):
+            schema = tool.get_schema()
+        elif hasattr(tool, 'get_schema') and callable(getattr(tool, 'get_schema')):
+            schema = tool.get_schema()
+        elif callable(tool):
+            schema = get_tool_schema(tool)
+        else:
+            continue  # Skip invalid tools (validation already happened)
+            
+        if schema:
+            schemas.append(schema)
+    
+    # Sort schemas by function name for deterministic ordering
+    def sort_key(schema):
+        return schema.get("function", {}).get("name", "")
+    
+    return sorted(schemas, key=sort_key)
+
+
 # For backward compatibility - tools can also just be functions
 # The @tool decorator (in decorator.py) wraps functions into BaseTool instances

src/praisonai-agents/praisonaiagents/tools/decorator.py

@@ -31,6 +31,7 @@ import copy
 from typing import Any, Callable, Dict, Optional, Union, get_type_hints
 
 from .base import BaseTool
+from .schema import annotation_to_json_schema, get_parameter_requirements
 
 # Lazy load injected module functions to reduce import time
 _injected_module = None
@@ -132,12 +133,13 @@ class FunctionTool(BaseTool):
                 if is_injected_type(param_type):
                     continue
                 
-                json_type = BaseTool._python_type_to_json(param_type)
+                # Use new schema utility for proper type handling
+                prop_schema = annotation_to_json_schema(param_type)
                 
-                schema["properties"][param_name] = {"type": json_type}
+                schema["properties"][param_name] = prop_schema
                 
-                # Check if required (no default value)
-                if param.default is inspect.Parameter.empty:
+                # Check if required using improved logic  
+                if get_parameter_requirements(sig, param_name):
                     schema["required"].append(param_name)
         except Exception as e:
             logging.debug(f"Could not generate schema for {func.__name__}: {e}")
@@ -342,11 +344,14 @@ def _schema_from_function(func: Callable) -> Dict[str, Any]:
                 continue
             
             param_type = hints.get(param_name, Any)
-            json_type = BaseTool._python_type_to_json(param_type)
             
-            properties[param_name] = {"type": json_type}
+            # Use new schema utility for proper type handling
+            prop_schema = annotation_to_json_schema(param_type)
             
-            if param.default is inspect.Parameter.empty:
+            properties[param_name] = prop_schema
+            
+            # Check if required using improved logic
+            if get_parameter_requirements(sig, param_name):
                 required.append(param_name)
     except Exception as e:
         logging.debug(f"Could not generate schema for {name}: {e}")

src/praisonai-agents/praisonaiagents/tools/email_tools.py

@@ -532,6 +532,14 @@ def _smtp_reply_email(message_id: str, body: str) -> str:
         logger.error(f"Failed to reply to {message_id}: {e}")
         return f"Failed to reply: {e}"
 
+def _imap_sanitize(value: str) -> str:
+    """Strip characters that break IMAP quoted-string search criteria."""
+    cleaned = value.replace('"', "").replace("\\", "")
+    if any(c in cleaned for c in "\r\n\x00"):
+        raise ValueError("Invalid IMAP search value")
+    return cleaned
+
+
 def _smtp_search_emails(
     query: Optional[str] = None,
     from_addr: Optional[str] = None,
@@ -544,11 +552,11 @@ def _smtp_search_emails(
         # Build IMAP search criteria
         criteria = []
         if from_addr:
-            criteria.append(f'FROM "{from_addr}"')
+            criteria.append(f'FROM "{_imap_sanitize(from_addr)}"')
         if subject:
-            criteria.append(f'SUBJECT "{subject}"')
+            criteria.append(f'SUBJECT "{_imap_sanitize(subject)}"')
         if query:
-            criteria.append(f'TEXT "{query}"')
+            criteria.append(f'TEXT "{_imap_sanitize(query)}"')
         if not criteria:
             criteria.append("ALL")
         search_str = " ".join(criteria)

src/praisonai-agents/praisonaiagents/tools/mentions.py

@@ -165,12 +165,17 @@ class MentionsParser:
     def _process_file_mention(self, file_path: str) -> Optional[str]:
         """Process @file:path mention."""
         try:
-            # Resolve path relative to workspace
-            full_path = self.workspace_path / file_path
-            if not full_path.exists():
-                # Try as absolute path
-                full_path = Path(file_path)
-            
+            if ".." in file_path:
+                self._log(f"Rejected path traversal in mention: {file_path}", logging.WARNING)
+                return f"# File: {file_path}\n[Path not allowed]"
+
+            # Resolve only within workspace (no arbitrary absolute paths)
+            full_path = (self.workspace_path / file_path).resolve()
+            workspace_root = self.workspace_path.resolve()
+            if not str(full_path).startswith(str(workspace_root) + os.sep) and full_path != workspace_root:
+                self._log(f"File outside workspace: {file_path}", logging.WARNING)
+                return f"# File: {file_path}\n[Path outside workspace]"
+
             if not full_path.exists():
                 self._log(f"File not found: {file_path}", logging.WARNING)
                 return f"# File: {file_path}\n[File not found]"

src/praisonai-agents/praisonaiagents/tools/python_tools.py

@@ -57,6 +57,7 @@ _SANDBOX_BLOCKED_CALLS = frozenset({
     'exec', 'eval', 'compile', '__import__',
     'open', 'input', 'breakpoint',
     'setattr', 'delattr', 'dir', 'vars',
+    'format', 'format_map',
 })
 
 

src/praisonai-agents/praisonaiagents/tools/schema.py

@@ -0,0 +1,152 @@
+"""JSON Schema generation utilities for tool parameters.
+
+This module provides a shared utility for converting Python type annotations
+to JSON Schema format. It handles complex types like Optional, Union, Literal,
+and Enum that were previously collapsed to simple "string" types.
+
+Usage:
+    from praisonaiagents.tools.schema import annotation_to_json_schema
+    
+    schema = annotation_to_json_schema(Optional[int])
+    # {"anyOf": [{"type": "integer"}, {"type": "null"}]}
+    
+    schema = annotation_to_json_schema(Literal["fast", "deep"])
+    # {"type": "string", "enum": ["fast", "deep"]}
+"""
+
+import enum
+import inspect
+from typing import Any, Union, Literal, get_origin, get_args
+
+
+def annotation_to_json_schema(annotation: Any) -> dict:
+    """Convert Python type annotation to JSON Schema format.
+    
+    Handles:
+    - Basic types (str, int, float, bool)
+    - Optional[T] -> {"anyOf": [T schema, {"type": "null"}]}
+    - Union[T1, T2] -> {"anyOf": [T1 schema, T2 schema]}
+    - Literal["a", "b"] -> {"type": "string", "enum": ["a", "b"]}
+    - Enum subclasses -> {"type": "string", "enum": [e.value for e in MyEnum]}
+    - List[T] -> {"type": "array", "items": T schema}
+    - Dict[K, V] -> {"type": "object", "additionalProperties": V schema}
+    
+    Args:
+        annotation: Python type annotation to convert
+        
+    Returns:
+        dict: JSON Schema representation
+    """
+    # Handle None type
+    if annotation is type(None):
+        return {"type": "null"}
+    
+    # Get typing origin and args
+    origin = get_origin(annotation)
+    args = get_args(annotation)
+    
+    # Handle Union types (including Optional[T] which is Union[T, None])
+    if origin is Union:
+        schemas = [annotation_to_json_schema(arg) for arg in args]
+        
+        # Check if this is Optional[T] (Union[T, None])
+        null_schemas = [s for s in schemas if s == {"type": "null"}]
+        non_null_schemas = [s for s in schemas if s != {"type": "null"}]
+        
+        if null_schemas and len(non_null_schemas) == 1:
+            # This is Optional[T] - simplified format
+            return {"anyOf": [non_null_schemas[0], {"type": "null"}]}
+        else:
+            # General Union type
+            return {"anyOf": schemas}
+    
+    # Handle Literal types
+    if origin is Literal or (hasattr(annotation, '__origin__') and 
+                           str(annotation.__origin__).endswith('Literal')):
+        # Infer type from literal values if all are the same type
+        values = list(args)
+        if values:
+            types_seen = set(type(v) for v in values)
+            if len(types_seen) == 1:
+                single_type = next(iter(types_seen))
+                type_map = {str: "string", int: "integer", float: "number", bool: "boolean"}
+                if single_type in type_map:
+                    return {"type": type_map[single_type], "enum": values}
+        # Mixed types or non-primitive - use enum without type constraint
+        return {"enum": values}
+    
+    # Handle Enum subclasses
+    if isinstance(annotation, type) and issubclass(annotation, enum.Enum):
+        # Infer type from enum values if all are the same type
+        values = [e.value for e in annotation]
+        if values:
+            types_seen = set(type(v) for v in values)
+            if len(types_seen) == 1:
+                single_type = next(iter(types_seen))
+                type_map = {str: "string", int: "integer", float: "number", bool: "boolean"}
+                if single_type in type_map:
+                    return {"type": type_map[single_type], "enum": values}
+        # Mixed types or non-primitive - use enum without type constraint
+        return {"enum": values}
+    
+    # Handle List types
+    if origin is list:
+        if args:
+            items_schema = annotation_to_json_schema(args[0])
+            return {"type": "array", "items": items_schema}
+        else:
+            return {"type": "array"}
+    
+    # Handle Dict types
+    if origin is dict:
+        if len(args) >= 2:
+            value_schema = annotation_to_json_schema(args[1])
+            return {"type": "object", "additionalProperties": value_schema}
+        else:
+            return {"type": "object"}
+    
+    # Handle basic types
+    type_map = {
+        str: "string",
+        int: "integer", 
+        float: "number",
+        bool: "boolean",
+        list: "array",
+        dict: "object",
+        type(None): "null"
+    }
+    
+    return {"type": type_map.get(annotation, "string")}
+
+
+def get_parameter_requirements(sig: inspect.Signature, param_name: str) -> bool:
+    """Determine if a parameter is required based on its signature.
+    
+    A parameter is required if it has no default value, unless it's Optional.
+    
+    Args:
+        sig: Function signature
+        param_name: Parameter name to check
+        
+    Returns:
+        bool: True if parameter is required
+    """
+    param = sig.parameters.get(param_name)
+    if not param:
+        return False
+    
+    # Parameter with default is not required
+    if param.default is not inspect.Parameter.empty:
+        return False
+    
+    # Check if the type annotation indicates optional
+    if param.annotation is not inspect.Parameter.empty:
+        origin = get_origin(param.annotation)
+        if origin is Union:
+            args = get_args(param.annotation)
+            # Optional[T] is Union[T, None]
+            if type(None) in args:
+                return False
+    
+    # No default and not optional = required
+    return True

src/praisonai-agents/praisonaiagents/tools/searxng_tools.py

@@ -37,9 +37,14 @@ def searxng_search(
     
     try:
         import requests
+        from praisonaiagents.tools.url_safety import validate_searxng_url
         
         # Default URL for local SearxNG instance
         url = searxng_url or "http://localhost:32768/search"
+        if validate_searxng_url(url) is None:
+            error_msg = "SearxNG URL blocked by SSRF policy"
+            logging.error(error_msg)
+            return [{"error": error_msg}]
         
         params = {
             'q': query, 

src/praisonai-agents/praisonaiagents/tools/spider_tools.py

@@ -58,9 +58,11 @@ def _host_is_blocked(hostname: str) -> bool:
         pass
 
     try:
-        return _ip_blocked(ipaddress.ip_address(socket.inet_aton(host)))
-    except OSError:
-        pass
+        for info in socket.getaddrinfo(host, None):
+            if _ip_blocked(ipaddress.ip_address(info[4][0])):
+                return True
+    except socket.gaierror:
+        return True
 
     return False
 
@@ -169,12 +171,31 @@ class SpiderTools:
                 return {"error": error_msg}
             from bs4 import BeautifulSoup
 
-            # Make request
-            response = session.get(
-                url,
-                timeout=timeout,
-                verify=verify_ssl
-            )
+            # Make request with manual redirect validation (SSRF-safe)
+            current_url = url
+            redirect_statuses = {301, 302, 303, 307, 308}
+            max_redirects = 5
+            response = None
+            for _ in range(max_redirects + 1):
+                if not self._validate_url(current_url):
+                    return {"error": f"Invalid or blocked redirect target: {current_url}"}
+                response = session.get(
+                    current_url,
+                    timeout=timeout,
+                    verify=verify_ssl,
+                    allow_redirects=False,
+                )
+                if response.status_code not in redirect_statuses:
+                    break
+                location = response.headers.get("Location")
+                if not location:
+                    break
+                current_url = urljoin(current_url, location)
+            else:
+                return {"error": "Too many redirects"}
+
+            if response is None:
+                return {"error": "Request failed"}
             response.raise_for_status()
             
             # Parse HTML
@@ -186,7 +207,7 @@ class SpiderTools:
             
             # Initialize result
             result = {
-                'url': url,
+                'url': current_url,
                 'status_code': response.status_code,
                 'encoding': response.encoding,
                 'headers': dict(response.headers),

src/praisonai-agents/praisonaiagents/tools/url_safety.py

@@ -0,0 +1,55 @@
+"""Shared URL safety checks for tool HTTP requests (SSRF mitigation)."""
+
+from __future__ import annotations
+
+import ipaddress
+import os
+import socket
+import urllib.parse
+from typing import Optional, Set
+
+
+def _allowlist_hosts() -> Set[str]:
+    raw = os.environ.get("SEARXNG_URL_ALLOWLIST", "")
+    hosts = {h.strip().lower() for h in raw.split(",") if h.strip()}
+    hosts.update({"localhost", "127.0.0.1", "::1"})
+    return hosts
+
+
+def is_safe_http_url(url: str, *, allow_local: Optional[bool] = None) -> bool:
+    """Return True when *url* is safe for server-side HTTP requests."""
+    try:
+        parsed = urllib.parse.urlparse(url)
+        if parsed.scheme not in ("http", "https"):
+            return False
+        hostname = parsed.hostname
+        if not hostname:
+            return False
+        if hostname.lower() in _allowlist_hosts():
+            return True
+        if allow_local is None:
+            allow_local = os.environ.get("ALLOW_LOCAL_CRAWL") == "true"
+        if allow_local:
+            return True
+        for info in socket.getaddrinfo(hostname, None):
+            ip = ipaddress.ip_address(info[4][0])
+            if (
+                ip.is_loopback
+                or ip.is_private
+                or ip.is_link_local
+                or ip.is_multicast
+                or ip.is_unspecified
+            ):
+                return False
+        return True
+    except (socket.gaierror, ValueError, OSError):
+        return False
+
+
+def validate_searxng_url(url: str) -> Optional[str]:
+    """Return normalised URL or None if blocked."""
+    if not url:
+        return None
+    if not is_safe_http_url(url):
+        return None
+    return url

src/praisonai-agents/praisonaiagents/tools/web_crawl_tools.py

@@ -130,17 +130,64 @@ def _crawl_with_crawl4ai(urls: List[str]) -> List[Dict[str, Any]]:
     except RuntimeError:
         return asyncio.run(_crawl_async())
 
+def _is_safe_crawl_url(url: str) -> bool:
+    """Return True when a URL passes SSRF checks for crawling."""
+    import urllib.parse
+    import socket
+    import ipaddress
+
+    try:
+        parsed = urllib.parse.urlparse(url)
+        if parsed.scheme not in ("http", "https"):
+            return False
+        hostname = parsed.hostname
+        if not hostname:
+            return False
+        if os.environ.get("ALLOW_LOCAL_CRAWL") == "true":
+            return True
+        for info in socket.getaddrinfo(hostname, None):
+            ip = ipaddress.ip_address(info[4][0])
+            if (
+                ip.is_loopback
+                or ip.is_private
+                or ip.is_link_local
+                or ip.is_multicast
+                or ip.is_unspecified
+            ):
+                return False
+        return True
+    except (socket.gaierror, ValueError, OSError):
+        return False
+
+
 def _crawl_with_httpx(urls: List[str]) -> List[Dict[str, Any]]:
     """Crawl URLs using basic HTTP fetch with httpx or urllib."""
+    import urllib.parse
     results = []
-    
+    redirect_statuses = {301, 302, 303, 307, 308}
+    max_redirects = 5
+
     for url in urls:
         try:
             # Try httpx first
             try:
                 import httpx
-                with httpx.Client(follow_redirects=True, timeout=30.0) as client:
-                    response = client.get(url)
+                current = url
+                response = None
+                with httpx.Client(follow_redirects=False, timeout=30.0) as client:
+                    for _ in range(max_redirects + 1):
+                        if not _is_safe_crawl_url(current):
+                            raise ValueError("Redirect target failed SSRF validation")
+                        response = client.get(current)
+                        if response.status_code not in redirect_statuses:
+                            break
+                        location = response.headers.get("Location")
+                        if not location:
+                            break
+                        current = urllib.parse.urljoin(current, location)
+                    else:
+                        raise ValueError("Too many redirects")
+                if response is not None:
                     response.raise_for_status()
                     content = response.text
             except ImportError:
@@ -210,37 +257,12 @@ def web_crawl(
         raw_url_list = [urls] if single_url else urls
 
     # Validate URLs to prevent SSRF and Local File Read
-    import urllib.parse
-    import socket
-    import ipaddress
-    
     url_list = []
     for u in raw_url_list:
-        try:
-            parsed = urllib.parse.urlparse(u)
-            if parsed.scheme not in ('http', 'https'):
-                logger.warning(f"Rejected non-http/https URL: {u}")
-                continue
-            hostname = parsed.hostname
-            if not hostname:
-                continue
-                
-            # Allow opting out of SSRF protection for advanced use cases
-            if os.environ.get("ALLOW_LOCAL_CRAWL") != "true":
-                try:
-                    ip_str = socket.gethostbyname(hostname)
-                    ip = ipaddress.ip_address(ip_str)
-                    if ip.is_loopback or ip.is_private or ip.is_link_local or ip.is_multicast or ip.is_unspecified:
-                        logger.warning(f"Rejected SSRF or private IP attempt: {u}")
-                        continue
-                except socket.gaierror:
-                    logger.warning(f"Could not resolve hostname for: {u}")
-                    continue
-                
+        if _is_safe_crawl_url(u):
             url_list.append(u)
-        except Exception as e:
-            logger.warning(f"URL validation failed for {u}: {e}")
-            continue
+        else:
+            logger.warning(f"Rejected unsafe crawl URL: {u}")
             
     if not url_list:
         return {"error": "No valid or safe URLs provided. Local and non-http(s) URLs are blocked for security."}

src/praisonai-agents/praisonaiagents/tools/web_search.py

@@ -235,8 +235,11 @@ def _search_duckduckgo(query: str, max_results: int = 5) -> List[Dict[str, Any]]
 def _search_searxng(query: str, max_results: int = 5, searxng_url: Optional[str] = None) -> List[Dict[str, Any]]:
     """Search using SearxNG."""
     import requests
+    from praisonaiagents.tools.url_safety import validate_searxng_url
     
     url = searxng_url or os.environ.get("SEARXNG_URL", "http://localhost:32768/search")
+    if validate_searxng_url(url) is None:
+        raise ValueError("SearxNG URL blocked by SSRF policy")
     
     params = {
         'q': query,

src/praisonai-agents/praisonaiagents/utils/deprecation.py

@@ -12,6 +12,9 @@ import inspect
 
 __all__ = ['deprecated', 'DeprecationConfig', 'check_expired_deprecations']
 
+# Warn once per process for param deprecations (avoids hot-path overhead in Agent.__init__)
+_warned_params: set[str] = set()
+
 class DeprecationConfig:
     """Configuration for deprecation behavior and version management."""
     
@@ -182,6 +185,10 @@ def warn_deprecated_param(
     
     message = "".join(msg_parts)
     
+    warn_key = f"{param_name}:{since}"
+    if warn_key in _warned_params:
+        return
+    _warned_params.add(warn_key)
     warnings.warn(message, category=DeprecationWarning, stacklevel=stacklevel)
 
 

src/praisonai-agents/praisonaiagents/workflows/workflows.py

@@ -1020,10 +1020,11 @@ class AgentFlow:
         # Update workflow status
         self.status = "running"
         
-        # Set YAML-approved tools in approval context (for auto-approval of dangerous tools)
+        # Set YAML-approved tools only when caller allows dangerous tools
         _approval_token = None
         approve_tools = getattr(self, 'approve_tools', [])
-        if approve_tools:
+        allow_dangerous = getattr(self, 'allow_dangerous_tools', False)
+        if approve_tools and allow_dangerous:
             from ..approval import set_yaml_approved_tools
             _approval_token = set_yaml_approved_tools(approve_tools)
         
@@ -2952,22 +2953,36 @@ CONCISE SUMMARY:"""
                     "variables": all_variables
                 }
             
-            # Load tools from the recipe's tools.py if present
+            # Load tools from the recipe's tools.py if present (opt-in only)
             tool_registry = {}
             tools_py = recipe_path / "tools.py"
             if tools_py.exists():
                 try:
-                    import importlib.util
-                    spec = importlib.util.spec_from_file_location("recipe_tools", tools_py)
-                    recipe_module = importlib.util.module_from_spec(spec)
-                    spec.loader.exec_module(recipe_module)
-                    
-                    # Extract callable tools
-                    for name in dir(recipe_module):
-                        if not name.startswith("_"):
-                            obj = getattr(recipe_module, name)
-                            if callable(obj):
-                                tool_registry[name] = obj
+                    import os
+                    _local_ok = os.environ.get("PRAISONAI_ALLOW_LOCAL_TOOLS", "").strip().lower() in (
+                        "1", "true", "yes", "on"
+                    )
+                    _template_ok = os.environ.get("PRAISONAI_ALLOW_TEMPLATE_TOOLS", "").strip().lower() in (
+                        "1", "true", "yes", "on"
+                    )
+                    if _local_ok or _template_ok:
+                        import importlib.util
+                        spec = importlib.util.spec_from_file_location("recipe_tools", tools_py)
+                        recipe_module = importlib.util.module_from_spec(spec)
+                        spec.loader.exec_module(recipe_module)
+                    else:
+                        logger.warning(
+                            "Skipping included recipe tools.py: set PRAISONAI_ALLOW_LOCAL_TOOLS "
+                            "or PRAISONAI_ALLOW_TEMPLATE_TOOLS to enable."
+                        )
+                        recipe_module = None
+                    if recipe_module is not None:
+                        # Extract callable tools
+                        for name in dir(recipe_module):
+                            if not name.startswith("_"):
+                                obj = getattr(recipe_module, name)
+                                if callable(obj):
+                                    tool_registry[name] = obj
                 except Exception as e:
                     logger.warning(f"Failed to load tools from {tools_py}: {e}")
             

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "1.6.55"
+version = "1.6.62"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/pytest.ini

@@ -5,8 +5,7 @@ testpaths = tests
 python_files = test_*.py
 python_classes = Test*
 python_functions = test_*
-addopts = -v --tb=short --timeout=60
-timeout_method = thread
+addopts = -v --tb=short
 filterwarnings =
     ignore::DeprecationWarning
     ignore::PendingDeprecationWarning

src/praisonai-agents/tests/integration/test_execution_config_max_iter.py

@@ -18,6 +18,7 @@ class TestExecutionConfigMaxIter:
     def setup_method(self):
         """Set up test fixtures."""
         self.test_llm_config = {
+            "model": "gpt-4o-mini",
             "api_base": "http://test",
             "api_key": "test-key",
             "provider": "custom"
@@ -33,21 +34,13 @@ class TestExecutionConfigMaxIter:
         config = ExecutionConfig(max_iter=15)
         assert config.max_iter == 15
     
-    @patch('praisonaiagents.llm.llm.LLM._chat_completion')
-    def test_agent_with_execution_config_max_iter(self, mock_chat):
+    def test_agent_with_execution_config_max_iter(self):
         """Test that Agent passes ExecutionConfig.max_iter to LLM."""
-        # Mock the LLM response to avoid actual API calls
-        mock_response = Mock()
-        mock_response.choices = [Mock()]
-        mock_response.choices[0].message.content = "Hello, I'm a test response!"
-        mock_response.choices[0].message.tool_calls = None
-        mock_chat.return_value = mock_response
-        
-        # Create agent with custom max_iter
         execution_config = ExecutionConfig(max_iter=15)
         
-        with patch('praisonaiagents.agent.agent.LLM') as MockLLM:
+        with patch('praisonaiagents.llm.llm.LLM') as MockLLM:
             mock_llm_instance = Mock()
+            mock_llm_instance.get_response.return_value = "Hello, I'm a test response!"
             MockLLM.return_value = mock_llm_instance
             
             agent = Agent(
@@ -57,6 +50,9 @@ class TestExecutionConfigMaxIter:
                 execution=execution_config
             )
             
+            # LLM is deferred until first access
+            _ = agent.llm_instance
+            
             # Verify LLM was instantiated with max_iter from ExecutionConfig
             MockLLM.assert_called()
             call_args = MockLLM.call_args
@@ -72,7 +68,7 @@ class TestExecutionConfigMaxIter:
     def test_llm_constructor_accepts_max_iter(self):
         """Test that LLM constructor accepts max_iter parameter."""
         llm = LLM(
-            api_base="http://test",
+            model="gpt-4o-mini",
             api_key="test-key",
             max_iter=25
         )
@@ -82,7 +78,7 @@ class TestExecutionConfigMaxIter:
     def test_llm_constructor_default_max_iter(self):
         """Test LLM constructor default max_iter value."""
         llm = LLM(
-            api_base="http://test", 
+            model="gpt-4o-mini",
             api_key="test-key"
         )
         
@@ -90,7 +86,7 @@ class TestExecutionConfigMaxIter:
         assert hasattr(llm, 'max_iter')
         assert llm.max_iter == 20  # Default aligned with ExecutionConfig
     
-    @patch('praisonaiagents.agent.agent.LLM')
+    @patch('praisonaiagents.llm.llm.LLM')
     def test_agent_without_execution_config_uses_llm_default(self, MockLLM):
         """Test that Agent without ExecutionConfig doesn't override LLM max_iter."""
         mock_llm_instance = Mock()
@@ -102,6 +98,8 @@ class TestExecutionConfigMaxIter:
             llm=self.test_llm_config
         )
         
+        _ = agent.llm_instance
+        
         # Verify LLM was called without max_iter override
         MockLLM.assert_called()
         call_args = MockLLM.call_args
@@ -121,7 +119,7 @@ class TestExecutionConfigMaxIter:
             # Skip if presets not implemented yet
             pytest.skip("ExecutionConfig presets not implemented yet")
     
-    @patch('praisonaiagents.agent.agent.LLM')
+    @patch('praisonaiagents.llm.llm.LLM')
     def test_different_llm_init_paths_respect_max_iter(self, MockLLM):
         """Test that all LLM initialization paths in Agent respect max_iter."""
         mock_llm_instance = Mock()
@@ -129,15 +127,15 @@ class TestExecutionConfigMaxIter:
         execution_config = ExecutionConfig(max_iter=30)
         
         test_cases = [
-            # Dict config
-            {"provider": "openai", "model": "gpt-4o", "api_key": "test"},
-            # String config
-            "gpt-4o",
-            # With base_url
-            {"base_url": "http://test", "model": "test-model", "api_key": "test"}
+            # Dict config — deferred LLM
+            ({"provider": "openai", "model": "gpt-4o", "api_key": "test"}, True),
+            # Plain model string — OpenAI path, no LLM() at init
+            ("gpt-4o", False),
+            # Dict with base_url — deferred LLM
+            ({"base_url": "http://test", "model": "test-model", "api_key": "test"}, True),
         ]
         
-        for llm_config in test_cases:
+        for llm_config, expects_custom_llm in test_cases:
             MockLLM.reset_mock()
             
             agent = Agent(
@@ -147,11 +145,17 @@ class TestExecutionConfigMaxIter:
                 execution=execution_config
             )
             
-            # Verify LLM was called with max_iter
-            MockLLM.assert_called()
-            call_args = MockLLM.call_args
-            assert 'max_iter' in call_args.kwargs
-            assert call_args.kwargs['max_iter'] == 30
+            if expects_custom_llm:
+                assert agent._llm_init_params is not None
+                assert agent._llm_init_params.get('max_iter') == 30
+                _ = agent.llm_instance
+                MockLLM.assert_called()
+                call_args = MockLLM.call_args
+                assert call_args.kwargs.get('max_iter') == 30
+            else:
+                assert agent._llm_init_params is None
+                assert not agent._using_custom_llm
+                assert agent.max_iter == 30
     
     def test_max_iter_bounds_validation(self):
         """Test validation of max_iter bounds."""
@@ -184,7 +188,7 @@ class TestExecutionConfigMaxIter:
         autonomy_config = AutonomyConfig()
         
         # This should not conflict
-        with patch('praisonaiagents.agent.agent.LLM') as MockLLM:
+        with patch('praisonaiagents.llm.llm.LLM') as MockLLM:
             mock_llm_instance = Mock()
             MockLLM.return_value = mock_llm_instance
             
@@ -196,6 +200,8 @@ class TestExecutionConfigMaxIter:
                 autonomy=autonomy_config
             )
             
+            _ = agent.llm_instance
+            
             # Verify both configs can coexist and max_iter is propagated
             MockLLM.assert_called()
             call_args = MockLLM.call_args

src/praisonai-agents/tests/unit/agents/test_multiagent_streaming_fix.py

@@ -0,0 +1,45 @@
+"""
+Test script to reproduce and verify fix for multi-agent streaming issue #1882
+"""
+import os
+
+import pytest
+
+# Set minimal OpenAI API key for testing (should fail gracefully)
+os.environ.setdefault("OPENAI_API_KEY", "sk-test-key-for-reproduction")
+
+
+def test_config_defaults():
+    """Test that the config default is now False"""
+    from praisonaiagents.config.feature_configs import MultiAgentOutputConfig
+
+    config = MultiAgentOutputConfig()
+    assert config.stream is False
+
+
+@pytest.mark.live
+def test_single_agent():
+    """Test single agent (should work with fallback)"""
+    from praisonaiagents import Agent
+
+    agent = Agent(instructions="Reply with exactly the requested text")
+    result = agent.start("Reply with exactly: OK")
+    assert result is not None
+
+
+@pytest.mark.live
+def test_multi_agent():
+    """Test multi-agent (should work after fix)"""
+    from praisonaiagents import Agent, Agents
+
+    research_agent = Agent(instructions="Research about AI")
+    summarise_agent = Agent(instructions="Summarise research agent's findings")
+    agents = Agents(agents=[research_agent, summarise_agent])
+
+    try:
+        result = agents.start("What is Python?")
+        assert result is not None
+    except Exception as exc:
+        if "Streaming is not supported in sync OpenAIAdapter" in str(exc):
+            pytest.fail("Streaming error still exists — fix did not work")
+        raise

src/praisonai-agents/tests/unit/context/test_cache_integration.py

@@ -0,0 +1,182 @@
+#!/usr/bin/env python3
+"""
+Test script to validate cache optimization integration.
+"""
+
+import sys
+import os
+
+# Add the source path to Python path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src', 'praisonai-agents'))
+
+def test_imports():
+    """Test that all imports work correctly."""
+    try:
+        from praisonaiagents.tools.base import get_sorted_tool_schemas
+        from praisonaiagents.memory.memory import Memory, CACHE_BOUNDARY
+        print("✓ Imports successful")
+        return True
+    except ImportError as e:
+        print(f"✗ Import failed: {e}")
+        return False
+
+def test_tool_sorting():
+    """Test tool schema sorting functionality (internal sorting)."""
+    try:
+        # Test the sorting function directly
+        def sort_formatted_tools(tools_list):
+            """Sort already-formatted tool schemas by function name."""
+            def sort_key(tool):
+                if isinstance(tool, dict) and tool.get('type') == 'function':
+                    return str(tool.get('function', {}).get('name') or '')
+                return ''
+            return sorted(tools_list, key=sort_key)
+        
+        # Test with formatted tool schemas
+        test_tools = [
+            {"type": "function", "function": {"name": "zebra_tool", "description": "Z tool"}},
+            {"type": "function", "function": {"name": "alpha_tool", "description": "A tool"}},
+            {"type": "function", "function": {"name": "beta_tool", "description": "B tool"}},
+        ]
+        
+        sorted_tools = sort_formatted_tools(test_tools)
+        tool_names = [tool["function"]["name"] for tool in sorted_tools]
+        
+        expected_order = ["alpha_tool", "beta_tool", "zebra_tool"]
+        if tool_names == expected_order:
+            print("✓ Tool sorting works correctly")
+            return True
+        else:
+            print(f"✗ Tool sorting failed. Got: {tool_names}, Expected: {expected_order}")
+            return False
+    except Exception as e:
+        print(f"✗ Tool sorting test failed: {e}")
+        return False
+
+def test_cache_optimized_context():
+    """Test cache-optimized context building API structure."""
+    try:
+        from praisonaiagents.memory.memory import Memory
+        
+        # Create a basic memory instance for API testing
+        config = {}
+        memory = Memory(config)
+        
+        # Mock the underlying methods to avoid database dependencies in unit test
+        memory.build_context_for_task = lambda task_descr, **kwargs: "test context"
+        
+        # Test cache-optimized context building
+        result = memory.build_cache_optimized_context(
+            task_descr="test task",
+            include_cache_boundary=True
+        )
+        
+        if isinstance(result, dict) and 'stable_prefix' in result and 'cache_boundary' in result:
+            # Verify the cache boundary is now empty (our fix)
+            if result['cache_boundary'] == "":
+                print("✓ Cache-optimized context building works (cache boundary properly empty)")
+                return True
+            else:
+                print(f"✗ Cache boundary should be empty but got: '{result['cache_boundary']}'")
+                return False
+        else:
+            print(f"✗ Cache-optimized context building failed. Got: {result}")
+            return False
+    except Exception as e:
+        print(f"✗ Cache-optimized context test failed: {e}")
+        return False
+
+def test_agent_integration():
+    """Test agent integration with cache optimization."""
+    try:
+        from praisonaiagents.agent.chat_mixin import ChatMixin
+        
+        # Mock the necessary methods for testing
+        class TestAgent(ChatMixin):
+            def __init__(self):
+                self.tools = []
+                self._formatted_tools_cache = {}
+                self._system_prompt_cache = {}
+                self._memory_instance = None
+                self.use_system_prompt = True
+                self.role = "test"
+                self.goal = "test"
+                self.backstory = "test"
+                self._rules_manager_initialized = False
+                self._rules_manager = None
+                self._skills = None
+                self._skills_dirs = None
+                self.verbose = False
+                
+            def _cache_get(self, cache, key):
+                return cache.get(key)
+                
+            def _cache_put(self, cache, key, value):
+                cache[key] = value
+                
+            def _get_tools_cache_key(self, tools):
+                return str(tools)
+                
+            def get_memory_context(self):
+                return ""
+                
+            def get_learn_context(self):
+                return ""
+                
+            def get_skills_prompt(self):
+                return ""
+        
+        agent = TestAgent()
+        
+        # Test tool formatting with sorting - pass already formatted tools
+        test_tools = [
+            {"type": "function", "function": {"name": "zebra_tool", "description": "Z tool"}},
+            {"type": "function", "function": {"name": "alpha_tool", "description": "A tool"}},
+        ]
+        
+        formatted_tools = agent._format_tools_for_completion(test_tools)
+        if len(formatted_tools) == 2:
+            tool_names = [tool["function"]["name"] for tool in formatted_tools]
+            if tool_names == ["alpha_tool", "zebra_tool"]:
+                print("✓ Agent tool integration works correctly")
+                return True
+            else:
+                print(f"✗ Agent tools not sorted correctly. Got: {tool_names}")
+                return False
+        else:
+            print(f"✗ Agent tool formatting failed. Got {len(formatted_tools)} tools")
+            return False
+            
+    except Exception as e:
+        print(f"✗ Agent integration test failed: {e}")
+        return False
+
+def main():
+    """Run all tests."""
+    print("Testing cache optimization integration...")
+    print()
+    
+    tests = [
+        test_imports,
+        test_tool_sorting,
+        test_cache_optimized_context,
+        test_agent_integration
+    ]
+    
+    results = []
+    for test in tests:
+        results.append(test())
+        
+    print()
+    passed = sum(results)
+    total = len(results)
+    
+    if passed == total:
+        print(f"🎉 All {total} tests passed!")
+        return 0
+    else:
+        print(f"💥 {total - passed} out of {total} tests failed!")
+        return 1
+
+if __name__ == "__main__":
+    sys.exit(main())

src/praisonai-agents/tests/unit/context/test_cache_optimization.py

@@ -0,0 +1,147 @@
+#!/usr/bin/env python3
+"""
+Test script for context assembly cache optimization (Issue #1808).
+
+This test verifies that:
+1. Memory search results are sorted deterministically
+2. Tool schemas are sorted by function name
+3. Cache boundary markers are properly included
+4. Multiple runs produce identical context strings for caching
+"""
+
+def test_memory_stable_sorting():
+    """Test that memory context is assembled deterministically."""
+    print("Testing memory stable sorting...")
+    
+    from praisonaiagents.memory.memory import Memory
+    
+    # Create test data that simulates memory search results
+    test_results = [
+        {"text": "First memory", "timestamp": 1000, "id": "1"},
+        {"text": "Second memory", "timestamp": 2000, "id": "2"},  
+        {"text": "Third memory", "timestamp": 1500, "id": "3"}
+    ]
+    
+    # Create a memory instance and stub the search methods to return controlled data
+    memory = Memory(config={"provider": "none"})
+    
+    # Mock the search methods to return our test data
+    original_search_short = memory.search_short_term
+    original_search_long = memory.search_long_term
+    original_search_entity = memory.search_entity
+    original_search_user = memory.search_user_memory
+    
+    try:
+        memory.search_short_term = lambda q, limit=3: test_results.copy()
+        memory.search_long_term = lambda q, limit=3: []
+        memory.search_entity = lambda q, limit=3: []
+        memory.search_user_memory = lambda user_id, q, limit=3: []
+        
+        # Test multiple runs produce identical context strings
+        context1 = memory.build_context_for_task("test task", include_in_output=True)
+        context2 = memory.build_context_for_task("test task", include_in_output=True)
+        
+        assert context1 == context2, "Memory context should be deterministic"
+        
+        # Verify the order is correct in the actual context (newest first)
+        lines = context1.split('\n')
+        second_idx = next((i for i, line in enumerate(lines) if "Second memory" in line), -1)
+        third_idx = next((i for i, line in enumerate(lines) if "Third memory" in line), -1) 
+        first_idx = next((i for i, line in enumerate(lines) if "First memory" in line), -1)
+        
+        assert second_idx < third_idx < first_idx, "Memory should be ordered newest first"
+        print("✓ Memory context is deterministic and correctly ordered")
+        
+    finally:
+        # Restore original methods
+        memory.search_short_term = original_search_short
+        memory.search_long_term = original_search_long
+        memory.search_entity = original_search_entity
+        memory.search_user_memory = original_search_user
+    
+    # Test cache boundary constants are available
+    from praisonaiagents.memory.memory import CACHE_BOUNDARY
+    assert CACHE_BOUNDARY is not None, "CACHE_BOUNDARY should be defined"
+    print("✓ Cache boundary constants are available")
+
+def test_tool_schema_sorting():
+    """Test that tool schemas are sorted deterministically."""
+    print("Testing tool schema sorting...")
+    
+    from praisonaiagents.tools.base import get_sorted_tool_schemas
+    from praisonaiagents.tools.decorator import tool
+    
+    # Create test tools with different names
+    @tool
+    def zebra_tool():
+        """A tool starting with Z."""
+        return "zebra"
+    
+    @tool  
+    def apple_tool():
+        """A tool starting with A."""
+        return "apple"
+        
+    @tool
+    def middle_tool():
+        """A tool in the middle."""
+        return "middle"
+    
+    tools = [zebra_tool, apple_tool, middle_tool]
+    
+    # Get sorted schemas multiple times
+    schemas1 = get_sorted_tool_schemas(tools)
+    schemas2 = get_sorted_tool_schemas(tools)
+    
+    # Results should be identical
+    assert schemas1 == schemas2, "Tool schemas should be deterministic"
+    
+    # Check they are sorted by name
+    names = [schema["function"]["name"] for schema in schemas1]
+    expected_names = ["apple_tool", "middle_tool", "zebra_tool"]
+    assert names == expected_names, f"Tools should be sorted alphabetically, got {names}"
+    print("✓ Tool schemas are sorted deterministically")
+
+def test_cache_boundary_markers():
+    """Test that cache boundary markers work correctly."""
+    print("Testing cache boundary markers...")
+    
+    from praisonaiagents.memory.memory import Memory, CACHE_BOUNDARY
+    
+    # Test that the boundary constant is properly defined
+    assert "CACHE_BOUNDARY" in CACHE_BOUNDARY
+    assert len(CACHE_BOUNDARY) > 0
+    print("✓ Cache boundary constant is defined")
+    
+    # Test the cache-optimized context method actually works
+    memory = Memory(config={"provider": "none"})
+    
+    # Mock search methods to return empty results for simpler test
+    memory.search_short_term = lambda q, limit=3: []
+    memory.search_long_term = lambda q, limit=3: []
+    memory.search_entity = lambda q, limit=3: []
+    memory.search_user_memory = lambda user_id, q, limit=3: []
+    
+    # Test with cache boundary enabled
+    result = memory.build_cache_optimized_context("test task", include_cache_boundary=True, include_in_output=True)
+    assert "stable_prefix" in result, "Result should have stable_prefix"
+    assert "cache_boundary" in result, "Result should have cache_boundary"
+    assert result["cache_boundary"] == CACHE_BOUNDARY, "Cache boundary should match constant"
+    
+    # Test with cache boundary disabled
+    result2 = memory.build_cache_optimized_context("test task", include_cache_boundary=False, include_in_output=True)
+    assert result2["cache_boundary"] == "", "Cache boundary should be empty when disabled"
+    
+    print("✓ Cache optimization method works correctly")
+
+if __name__ == "__main__":
+    import sys
+
+    try:
+        test_memory_stable_sorting()
+        test_tool_schema_sorting()
+        test_cache_boundary_markers()
+        print("\n🎉 All tests passed! Context assembly cache optimization is working correctly.")
+    except Exception as e:
+        print(f"\n❌ Test failed: {e}")
+        sys.exit(1)

src/praisonai-agents/tests/unit/test_agent_instantiation_lazy.py

@@ -0,0 +1,75 @@
+"""Unit tests for Agent instantiation performance optimisations (lazy LLM, LoopGuard)."""
+
+from __future__ import annotations
+
+import warnings
+from unittest.mock import Mock, patch
+
+import pytest
+
+from praisonaiagents import Agent
+from praisonaiagents.config.feature_configs import ExecutionConfig
+from praisonaiagents.config.loader import clear_config_cache
+
+
+class TestLazyInstantiation:
+    def test_agent_init_no_deprecation_warning(self):
+        with warnings.catch_warnings(record=True) as caught:
+            warnings.simplefilter("always")
+            Agent(name="T", output="silent")
+            Agent(name="T", model="gpt-4o-mini", output="silent")
+        deprecations = [w for w in caught if issubclass(w.category, DeprecationWarning)]
+        assert not deprecations
+
+    def test_litellm_model_deferred_at_init(self):
+        agent = Agent(name="T", model="openai/gpt-4o-mini", output="silent")
+        assert agent._llm_instance is None
+        assert agent._llm_init_params["model"] == "openai/gpt-4o-mini"
+        assert agent._using_custom_llm
+
+    def test_loop_guard_deferred_at_init(self):
+        agent = Agent(name="T", output="silent")
+        assert agent._loop_guard is None
+        guard = agent._ensure_loop_guard()
+        assert guard is not None
+        assert agent._ensure_loop_guard() is guard
+
+    def test_llm_instance_materializes_once(self):
+        with patch("praisonaiagents.llm.llm.LLM") as MockLLM:
+            MockLLM.return_value = Mock()
+            agent = Agent(name="T", model="openai/gpt-4o-mini", output="silent")
+            first = agent.llm_instance
+            second = agent.llm_instance
+            assert first is second
+            MockLLM.assert_called_once()
+
+    def test_dict_llm_stores_max_iter_in_deferred_params(self):
+        agent = Agent(
+            name="T",
+            llm={"model": "gpt-4o-mini", "api_key": "x"},
+            execution=ExecutionConfig(max_iter=12),
+            output="silent",
+        )
+        assert agent._llm_init_params["max_iter"] == 12
+
+    def test_plain_model_string_no_custom_llm(self):
+        agent = Agent(name="T", model="gpt-4o-mini", output="silent")
+        assert not agent._using_custom_llm
+        assert agent._llm_init_params is None
+
+    def test_config_defaults_fast_path_when_empty(self, tmp_path, monkeypatch):
+        monkeypatch.chdir(tmp_path)
+        clear_config_cache()
+        agent = Agent(name="T", output="silent")
+        assert agent.name == "T"
+
+    def test_execution_config_internal_from_tests_is_suppressed(self):
+        """Test/SDK paths suppress context_compaction spam during Agent init."""
+        ExecutionConfig._context_compaction_warned = False  # noqa: SLF001
+        ExecutionConfig._context_compaction_internal = False  # noqa: SLF001
+        with warnings.catch_warnings(record=True) as caught:
+            warnings.simplefilter("always")
+            ExecutionConfig()
+        compaction = [w for w in caught if "context_compaction" in str(w.message)]
+        assert not compaction
+        assert ExecutionConfig._context_compaction_internal is True  # noqa: SLF001

src/praisonai-agents/tests/unit/test_security_audit_fixes.py

@@ -0,0 +1,64 @@
+"""Smoke tests for security audit hardening."""
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+
+
+def test_workflow_include_skips_tools_without_opt_in(tmp_path, monkeypatch):
+    monkeypatch.delenv("PRAISONAI_ALLOW_LOCAL_TOOLS", raising=False)
+    monkeypatch.delenv("PRAISONAI_ALLOW_TEMPLATE_TOOLS", raising=False)
+    monkeypatch.chdir(tmp_path)
+
+    child = tmp_path / "child_recipe"
+    child.mkdir()
+    marker = tmp_path / "marker.txt"
+    (child / "tools.py").write_text(f'from pathlib import Path\nPath({str(marker)!r}).write_text("x")\n')
+    (child / "workflow.yaml").write_text("name: child\nsteps: []\n")
+
+    from praisonaiagents.workflows.workflows import Workflow, include
+
+    Workflow(steps=[include("child_recipe")]).run(input="", llm="dummy/local", stream=False)
+    assert not marker.exists()
+
+
+def test_web_crawl_rejects_redirect_to_loopback():
+    pytest.importorskip("httpx")
+    from praisonaiagents.tools.web_crawl_tools import _is_safe_crawl_url
+
+    assert _is_safe_crawl_url("http://127.0.0.1/secret") is False
+    assert _is_safe_crawl_url("http://example.com/") is True
+
+
+def test_spider_blocks_rebound_hostname():
+    from praisonaiagents.tools.spider_tools import _host_is_blocked
+
+    with patch("socket.getaddrinfo", return_value=[(2, 1, 6, "", ("127.0.0.1", 0))]):
+        assert _host_is_blocked("rebound.example") is True
+
+
+def test_file_memory_rejects_traversal_user_id():
+    from praisonaiagents.memory.file_memory import FileMemory
+
+    with pytest.raises(ValueError):
+        FileMemory(user_id="../escape")
+
+
+def test_ast_grep_rewrite_requires_approval():
+    from praisonaiagents.tools import ast_grep_tool  # noqa: F401
+    from praisonaiagents.approval import is_approval_required
+
+    assert is_approval_required("ast_grep_rewrite") is True
+
+
+def test_agent_server_enforces_auth_token():
+    from praisonaiagents.server.server import AgentServer, ServerConfig
+
+    server = AgentServer(config=ServerConfig(auth_token="secret-token"))
+    request = type("Req", (), {"headers": {"Authorization": "Bearer wrong"}})()
+    assert server._authorise_request(request) is False
+    request_ok = type("Req", (), {"headers": {"Authorization": "Bearer secret-token"}})()
+    assert server._authorise_request(request_ok) is True

src/praisonai-agents/tests/unit/test_security_hardening.py

@@ -0,0 +1,103 @@
+"""Unit tests for security advisory hardening."""
+from __future__ import annotations
+
+import pytest
+
+
+def test_webhook_fail_closed_without_secret(monkeypatch):
+    from praisonai.bots.webhook_security import webhooks_require_verification
+
+    monkeypatch.delenv("PRAISONAI_INSECURE_WEBHOOKS", raising=False)
+    assert webhooks_require_verification() is True
+
+
+def test_webhook_insecure_override(monkeypatch):
+    from praisonai.bots.webhook_security import webhooks_require_verification
+
+    monkeypatch.setenv("PRAISONAI_INSECURE_WEBHOOKS", "true")
+    assert webhooks_require_verification() is False
+
+
+def test_jobs_agent_root_rejects_escape(tmp_path, monkeypatch):
+    from praisonai.jobs.path_validation import validate_agent_file_path
+
+    root = tmp_path / "agents"
+    root.mkdir()
+    outside = tmp_path / "outside.yaml"
+    outside.write_text("agents: []")
+    monkeypatch.setenv("PRAISONAI_JOBS_AGENT_ROOT", str(root))
+
+    with pytest.raises(ValueError, match="PRAISONAI_JOBS_AGENT_ROOT"):
+        validate_agent_file_path(str(outside))
+
+
+def test_jobs_agent_root_allows_inside(tmp_path, monkeypatch):
+    from praisonai.jobs.path_validation import validate_agent_file_path
+
+    root = tmp_path / "agents"
+    root.mkdir()
+    inside = root / "team.yaml"
+    inside.write_text("agents: []")
+    monkeypatch.setenv("PRAISONAI_JOBS_AGENT_ROOT", str(root))
+
+    assert validate_agent_file_path(str(inside)) == str(inside)
+
+
+def test_yaml_approve_blocks_critical_tools():
+    from praisonaiagents.approval.registry import ApprovalRegistry
+
+    registry = ApprovalRegistry()
+    token = registry.set_yaml_approved_tools(["execute_code", "search_web"])
+    try:
+        assert registry.is_yaml_approved("search_web") is True
+        assert registry.is_yaml_approved("execute_code") is False
+    finally:
+        registry.reset_yaml_approved_tools(token)
+
+
+def test_sandbox_blocks_format():
+    from praisonaiagents.tools.python_tools import _validate_code_ast
+
+    assert _validate_code_ast('"{0.__class__}".format(42)') is not None
+    assert _validate_code_ast('"".format_map({})') is not None
+
+
+def test_sse_security_middleware_rejects_bad_origin(monkeypatch):
+    import asyncio
+
+    from starlette.applications import Starlette
+    from starlette.responses import PlainTextResponse
+    from starlette.routing import Route
+
+    from praisonaiagents.mcp.mcp_security import SecurityConfig, build_sse_security_app
+
+    monkeypatch.delenv("MCP_SSE_ALLOWED_ORIGINS", raising=False)
+    monkeypatch.delenv("MCP_SSE_AUTH_TOKEN", raising=False)
+    monkeypatch.delenv("MCP_AUTH_TOKEN", raising=False)
+
+    app = Starlette(routes=[Route("/", lambda r: PlainTextResponse("ok"))])
+    config = SecurityConfig(validate_origin=True, allowed_origins=["localhost"])
+    app = build_sse_security_app(app, config)
+
+    async def _run(origin: str):
+        scope = {
+            "type": "http",
+            "method": "GET",
+            "path": "/",
+            "headers": [(b"origin", origin.encode())],
+        }
+        status_code = 500
+
+        async def receive():
+            return {"type": "http.request", "body": b"", "more_body": False}
+
+        async def send(message):
+            nonlocal status_code
+            if message["type"] == "http.response.start":
+                status_code = message["status"]
+
+        await app(scope, receive, send)
+        return status_code
+
+    assert asyncio.run(_run("https://evil.com")) == 403
+    assert asyncio.run(_run("http://localhost:8080")) == 200

src/praisonai-agents/tests/unit/test_security_hardening_round2.py

@@ -0,0 +1,48 @@
+"""Tests for remaining security hardening (round 2)."""
+
+import pytest
+
+
+def test_launch_auth_optional_by_default():
+    from praisonaiagents.agents.agents import _authorise_launch_request
+
+    class Req:
+        headers = {}
+
+    assert _authorise_launch_request(Req()) is True
+
+
+def test_launch_auth_enforced_when_token_set(monkeypatch):
+    from praisonaiagents.agents.agents import _authorise_launch_request
+
+    monkeypatch.setenv("PRAISONAI_LAUNCH_AUTH_TOKEN", "secret")
+
+    class Bad:
+        headers = {}
+
+    class Good:
+        headers = {"Authorization": "Bearer secret"}
+
+    assert _authorise_launch_request(Bad()) is False
+    assert _authorise_launch_request(Good()) is True
+
+    monkeypatch.delenv("PRAISONAI_LAUNCH_AUTH_TOKEN", raising=False)
+
+
+def test_mentions_reject_outside_workspace(tmp_path):
+    from praisonaiagents.tools.mentions import MentionsParser
+
+    outside = tmp_path / "secret.txt"
+    outside.write_text("secret", encoding="utf-8")
+    ws = tmp_path / "ws"
+    ws.mkdir()
+    parser = MentionsParser(workspace_path=str(ws))
+    context, _ = parser.process(f"@file:{outside}")
+    assert "outside workspace" in context.lower() or "not allowed" in context.lower()
+
+
+def test_imap_sanitize_rejects_injection():
+    from praisonaiagents.tools.email_tools import _imap_sanitize
+
+    with pytest.raises(ValueError):
+        _imap_sanitize('test"\r\nBAD')

src/praisonai-agents/tests/unit/test_security_outstanding_ghsas.py

@@ -0,0 +1,33 @@
+"""Tests for outstanding GHSA security fixes in praisonaiagents."""
+
+from __future__ import annotations
+
+import pytest
+
+
+def test_searxng_url_blocks_private_ip():
+    from praisonaiagents.tools.url_safety import validate_searxng_url
+
+    assert validate_searxng_url("http://10.0.0.1/search") is None
+
+
+def test_workflow_yaml_approve_gated_without_allow_dangerous():
+    """GHSA-7qw2: approve list must not auto-approve when allow_dangerous_tools=False."""
+    from praisonaiagents import Agent, Workflow
+    from praisonaiagents.approval import get_approval_registry
+
+    agent = Agent(name="a", instructions="test")
+    wf = Workflow(steps=[agent])
+    wf.approve_tools = ["execute_command"]
+    wf.allow_dangerous_tools = False
+
+    registry = get_approval_registry()
+    before = set(registry._yaml_approved_tools.get() or ())
+
+    try:
+        wf.run("hello", verbose=False)
+    except Exception:
+        pass
+
+    after = set(registry._yaml_approved_tools.get() or ())
+    assert after == before

src/praisonai-agents/uv.lock

@@ -3125,7 +3125,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "1.6.55"
+version = "1.6.62"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai-platform/praisonai_platform/api/deps.py

@@ -95,6 +95,25 @@ async def require_workspace_owner(
     )
 
 
+async def require_delete_permission(
+    workspace_id: str,
+    user: AuthIdentity,
+    session: AsyncSession,
+    *,
+    resource_owner_id: Optional[str] = None,
+) -> None:
+    """Allow delete when user is admin/owner or owns the resource."""
+    member_svc = MemberService(session)
+    if await member_svc.has_role(workspace_id, user.id, "admin"):
+        return
+    if resource_owner_id and resource_owner_id == user.id:
+        return
+    raise HTTPException(
+        status_code=status.HTTP_403_FORBIDDEN,
+        detail="Insufficient permission to delete this resource",
+    )
+
+
 def ensure_resource_in_workspace(
     resource_workspace_id: str | None,
     workspace_id: str,

src/praisonai-platform/praisonai_platform/api/routes/agents.py

@@ -9,7 +9,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from praisonaiagents.auth import AuthIdentity
 
-from ..deps import get_db, require_workspace_member
+from ..deps import get_db, require_delete_permission, require_workspace_member
 from ..schemas import AgentCreate, AgentResponse, AgentUpdate
 from ...services.agent_service import AgentService
 
@@ -96,6 +96,12 @@ async def delete_agent(
     session: AsyncSession = Depends(get_db),
 ):
     svc = AgentService(session)
+    agent = await svc.get(agent_id, workspace_id=workspace_id)
+    if agent is None:
+        raise HTTPException(status_code=404, detail="Agent not found")
+    await require_delete_permission(
+        workspace_id, user, session, resource_owner_id=agent.owner_id
+    )
     deleted = await svc.delete(agent_id, workspace_id=workspace_id)
     if not deleted:
         raise HTTPException(status_code=404, detail="Agent not found")

src/praisonai-platform/praisonai_platform/api/routes/dependencies.py

@@ -9,7 +9,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from praisonaiagents.auth import AuthIdentity
 
-from ..deps import get_db, require_issue_in_workspace, require_workspace_member
+from ..deps import get_db, require_delete_permission, require_issue_in_workspace, require_workspace_member
 from ...services.dependency_service import DependencyService
 from ..schemas import DependencyCreate, DependencyResponse
 
@@ -58,12 +58,21 @@ async def delete_dependency(
     session: AsyncSession = Depends(get_db),
 ):
     await require_issue_in_workspace(workspace_id, issue_id, session)
+    from ...services.issue_service import IssueService
+
+    issue_svc = IssueService(session)
+    issue = await issue_svc.get(issue_id, workspace_id=workspace_id)
+    if issue is None:
+        raise HTTPException(status_code=404, detail="Issue not found")
     svc = DependencyService(session)
     dep = await svc.get(dep_id)
     if dep is None:
         raise HTTPException(status_code=404, detail="Dependency not found")
     if dep.issue_id != issue_id and dep.depends_on_issue_id != issue_id:
         raise HTTPException(status_code=404, detail="Dependency not found")
+    await require_delete_permission(
+        workspace_id, user, session, resource_owner_id=issue.creator_id
+    )
     deleted = await svc.delete(dep_id)
     if not deleted:
         raise HTTPException(status_code=404, detail="Dependency not found")

src/praisonai-platform/praisonai_platform/api/routes/issues.py

@@ -9,7 +9,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from praisonaiagents.auth import AuthIdentity
 
-from ..deps import ensure_resource_in_workspace, get_db, require_workspace_member
+from ..deps import ensure_resource_in_workspace, get_db, require_delete_permission, require_workspace_member
 from ..schemas import (
     CommentCreate,
     CommentResponse,
@@ -133,6 +133,12 @@ async def delete_issue(
     session: AsyncSession = Depends(get_db),
 ):
     svc = IssueService(session)
+    issue = await svc.get(issue_id, workspace_id=workspace_id)
+    if issue is None:
+        raise HTTPException(status_code=404, detail="Issue not found")
+    await require_delete_permission(
+        workspace_id, user, session, resource_owner_id=issue.creator_id
+    )
     deleted = await svc.delete(issue_id, workspace_id=workspace_id)
     if not deleted:
         raise HTTPException(status_code=404, detail="Issue not found")

src/praisonai-platform/praisonai_platform/api/routes/labels.py

@@ -12,6 +12,7 @@ from praisonaiagents.auth import AuthIdentity
 from ..deps import (
     ensure_resource_in_workspace,
     get_db,
+    require_delete_permission,
     require_issue_in_workspace,
     require_workspace_member,
 )
@@ -75,6 +76,7 @@ async def delete_label(
     if label is None:
         raise HTTPException(status_code=404, detail="Label not found")
     ensure_resource_in_workspace(label.workspace_id, workspace_id, label="Label")
+    await require_delete_permission(workspace_id, user, session)
     deleted = await svc.delete(label_id)
     if not deleted:
         raise HTTPException(status_code=404, detail="Label not found")

src/praisonai-platform/praisonai_platform/api/routes/projects.py

@@ -9,7 +9,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from praisonaiagents.auth import AuthIdentity
 
-from ..deps import get_db, require_workspace_member
+from ..deps import get_db, require_delete_permission, require_workspace_member
 from ..schemas import ProjectCreate, ProjectResponse, ProjectUpdate
 from ...services.project_service import ProjectService
 
@@ -93,6 +93,12 @@ async def delete_project(
     session: AsyncSession = Depends(get_db),
 ):
     svc = ProjectService(session)
+    project = await svc.get(project_id, workspace_id=workspace_id)
+    if project is None:
+        raise HTTPException(status_code=404, detail="Project not found")
+    await require_delete_permission(
+        workspace_id, user, session, resource_owner_id=project.lead_id
+    )
     deleted = await svc.delete(project_id, workspace_id=workspace_id)
     if not deleted:
         raise HTTPException(status_code=404, detail="Project not found")

src/praisonai-platform/praisonai_platform/services/auth_service.py

@@ -19,40 +19,14 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from praisonaiagents.auth import AuthIdentity
 
 from ..db.models import Member, User
+from .jwt_secret import resolve_jwt_secret
 
 _pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
 
-_DEFAULT_SECRET = "dev-secret-change-me"
-JWT_SECRET = os.environ.get("PLATFORM_JWT_SECRET")
+JWT_SECRET = resolve_jwt_secret()
 JWT_ALGORITHM = "HS256"
 JWT_TTL_SECONDS = int(os.environ.get("PLATFORM_JWT_TTL", str(30 * 24 * 3600)))
 
-if JWT_SECRET is None or JWT_SECRET == _DEFAULT_SECRET:
-    # Determine if we are in an explicit dev environment.
-    # Only trust PLATFORM_ENV when it is explicitly set — an unset value
-    # must NOT silently fall back to "dev" because production deployments
-    # that forget the variable would run with a well-known signing key.
-    _explicit_env = os.environ.get("PLATFORM_ENV")
-    if _explicit_env == "dev":
-        # Developer explicitly opted in to insecure defaults — allow it.
-        JWT_SECRET = _DEFAULT_SECRET
-    else:
-        # Either production or environment not specified.
-        # Auto-generate an ephemeral secret so the service can start, but
-        # warn loudly because tokens will not survive a restart.
-        import secrets as _secrets
-        import warnings as _warnings
-
-        JWT_SECRET = _secrets.token_urlsafe(32)
-        _warnings.warn(
-            "PLATFORM_JWT_SECRET is not set. Auto-generated an ephemeral "
-            "signing key — JWT tokens will be invalidated on restart. "
-            "Set PLATFORM_JWT_SECRET to a stable random value for production "
-            "use, or set PLATFORM_ENV=dev to use the development default.",
-            stacklevel=2,
-        )
-
-# Type-narrow: at this point JWT_SECRET is always a non-None str.
 assert JWT_SECRET is not None  # noqa: S105
 
 

src/praisonai-platform/praisonai_platform/services/jwt_secret.py

@@ -0,0 +1,51 @@
+"""JWT secret resolution for praisonai-platform (testable, fail-closed in production)."""
+
+from __future__ import annotations
+
+import os
+import secrets
+import warnings
+from typing import Optional
+
+_DEFAULT_SECRET = "dev-secret-change-me"
+
+
+def resolve_jwt_secret(
+    *,
+    env: Optional[dict[str, str]] = None,
+) -> str:
+    """Resolve PLATFORM_JWT_SECRET with production guards."""
+    env = env if env is not None else os.environ
+    explicit_env = env.get("PLATFORM_ENV")
+    raw_secret = env.get("PLATFORM_JWT_SECRET")
+
+    if explicit_env == "production":
+        if not raw_secret or raw_secret == _DEFAULT_SECRET:
+            raise RuntimeError(
+                "PLATFORM_JWT_SECRET must be set to a stable random value when "
+                "PLATFORM_ENV=production"
+            )
+        return raw_secret
+
+    if raw_secret == _DEFAULT_SECRET:
+        if explicit_env == "dev":
+            return _DEFAULT_SECRET
+        raise RuntimeError(
+            "PLATFORM_JWT_SECRET cannot be the development default unless "
+            "PLATFORM_ENV=dev"
+        )
+
+    if raw_secret:
+        return raw_secret
+
+    if explicit_env == "dev":
+        return _DEFAULT_SECRET
+
+    ephemeral = secrets.token_urlsafe(32)
+    warnings.warn(
+        "PLATFORM_JWT_SECRET is not set. Auto-generated an ephemeral signing key — "
+        "JWT tokens will be invalidated on restart. Set PLATFORM_JWT_SECRET for "
+        "production, or PLATFORM_ENV=dev for development defaults.",
+        stacklevel=2,
+    )
+    return ephemeral

src/praisonai-platform/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonai-platform"
-version = "0.1.4"
+version = "0.1.5"
 description = "Platform layer for PraisonAI — workspace, auth, issues, projects"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-platform/tests/test_delete_ownership.py

@@ -0,0 +1,55 @@
+"""DELETE ownership checks for platform resources."""
+
+from __future__ import annotations
+
+import pytest
+
+from praisonai_platform.services.auth_service import AuthService
+from praisonai_platform.services.issue_service import IssueService
+from praisonai_platform.services.member_service import MemberService
+from praisonai_platform.services.workspace_service import WorkspaceService
+from praisonaiagents.auth import AuthIdentity
+
+from praisonai_platform.api.deps import require_delete_permission
+
+
+@pytest.mark.asyncio
+async def test_member_cannot_delete_others_issue(session):
+    auth = AuthService(session)
+    owner, _ = await auth.register("[email protected]", "pass")
+    other_user, _ = await auth.register("[email protected]", "pass")
+    ws = await WorkspaceService(session).create("Team", owner.id)
+    await MemberService(session).add(ws.id, other_user.id, "member")
+
+    issue = await IssueService(session).create(
+        workspace_id=ws.id,
+        title="private",
+        creator_id=owner.id,
+    )
+
+    other = AuthIdentity(id=other_user.id, email=other_user.email, name=other_user.name)
+    with pytest.raises(Exception) as exc:
+        await require_delete_permission(
+            ws.id, other, session, resource_owner_id=issue.creator_id
+        )
+    assert exc.value.status_code == 403
+
+
+@pytest.mark.asyncio
+async def test_admin_can_delete_any_issue(session):
+    auth = AuthService(session)
+    owner, _ = await auth.register("[email protected]", "pass")
+    admin_user, _ = await auth.register("[email protected]", "pass")
+    ws = await WorkspaceService(session).create("AdminTeam", owner.id)
+    await MemberService(session).add(ws.id, admin_user.id, "admin")
+
+    issue = await IssueService(session).create(
+        workspace_id=ws.id,
+        title="shared",
+        creator_id=owner.id,
+    )
+
+    admin = AuthIdentity(id=admin_user.id, email=admin_user.email, name=admin_user.name)
+    await require_delete_permission(
+        ws.id, admin, session, resource_owner_id=issue.creator_id
+    )

src/praisonai-platform/tests/unit/test_jwt_secret.py

@@ -0,0 +1,43 @@
+"""Tests for platform JWT secret resolution (GHSA-cwj8, GHSA-f38v)."""
+
+from __future__ import annotations
+
+import importlib.util
+from pathlib import Path
+
+import pytest
+
+# Direct import to avoid pulling auth_service deps at collection time
+_jwt_path = Path(__file__).resolve().parents[2] / "praisonai_platform" / "services" / "jwt_secret.py"
+_spec = importlib.util.spec_from_file_location("jwt_secret", _jwt_path)
+_mod = importlib.util.module_from_spec(_spec)
+assert _spec and _spec.loader
+_spec.loader.exec_module(_mod)
+resolve_jwt_secret = _mod.resolve_jwt_secret
+
+
+def test_jwt_secret_rejects_dev_default_in_prod():
+    with pytest.raises(RuntimeError):
+        resolve_jwt_secret(env={
+            "PLATFORM_ENV": "production",
+            "PLATFORM_JWT_SECRET": "dev-secret-change-me",
+        })
+
+
+def test_jwt_secret_allows_dev_default_when_dev_env():
+    secret = resolve_jwt_secret(env={
+        "PLATFORM_ENV": "dev",
+        "PLATFORM_JWT_SECRET": "dev-secret-change-me",
+    })
+    assert secret == "dev-secret-change-me"
+
+
+def test_jwt_secret_rejects_explicit_dev_secret_without_dev_env():
+    with pytest.raises(RuntimeError):
+        resolve_jwt_secret(env={"PLATFORM_JWT_SECRET": "dev-secret-change-me"})
+
+
+def test_jwt_secret_ephemeral_when_unset():
+    secret = resolve_jwt_secret(env={})
+    assert secret != "dev-secret-change-me"
+    assert len(secret) > 20

src/praisonai-rust/FEATURE_PARITY_TRACKER.json

@@ -1,14 +1,14 @@
 {
   "version": "1.5.87",
-  "lastUpdated": "2026-06-12",
+  "lastUpdated": "2026-06-17",
   "generatedBy": "praisonai._dev.parity.generator",
   "sourceOfTruth": "Python SDK (praisonaiagents)",
   "status": "PARITY_ACHIEVED",
   "summary": {
-    "pythonCoreFeatures": 359,
+    "pythonCoreFeatures": 360,
     "rustFeatures": 667,
-    "gapCount": 80,
-    "parityPercentage": 185.8
+    "gapCount": 81,
+    "parityPercentage": 185.3
   },
   "pythonCoreSDK": {
     "exports": [
@@ -275,6 +275,7 @@
       "WebSearchProvider",
       "Workflow",
       "WorkflowContext",
+      "__version__",
       "aembed",
       "aembedding",
       "aembeddings",
@@ -4374,6 +4375,15 @@
         "status": "DONE",
         "category": "config"
       },
+      {
+        "feature": "__version__",
+        "python": true,
+        "rust": false,
+        "priority": "P3",
+        "effort": "low",
+        "status": "TODO",
+        "category": "other"
+      },
       {
         "feature": "aembed",
         "python": true,

src/praisonai-rust/PARITY.md

@@ -1,16 +1,16 @@
 # Rust Feature Parity Tracker
 
-> **Python Features:** 359 | **Rust Features:** 667 | **Parity:** 79.4%
+> **Python Features:** 360 | **Rust Features:** 667 | **Parity:** 79.2%
 
 ## Summary
 
 | Metric | Count |
 |--------|-------|
-| Python Core Features | 359 |
+| Python Core Features | 360 |
 | Rust Features | 667 |
-| **Actual Gap Count** | **74** |
+| **Actual Gap Count** | **75** |
 | Language Limitations (N/A) | 5 |
-| **Parity** | **79.4%** |
+| **Parity** | **79.2%** |
 
 ## Implemented Features
 
@@ -751,6 +751,7 @@ These Python features cannot be directly implemented in Rust due to reserved key
 - ❌ `ToolValidationError`
 - ❌ `ToolsetRegistry`
 - ❌ `ToolsetSpec`
+- ❌ `__version__`
 - ❌ `configure_structured_logging`
 - ❌ `get_default_policy`
 - ❌ `get_logger`

src/praisonai-ts/FEATURE_PARITY_TRACKER.json

@@ -1,17 +1,17 @@
 {
   "version": "1.5.87",
-  "lastUpdated": "2026-06-12",
+  "lastUpdated": "2026-06-17",
   "generatedBy": "praisonai._dev.parity.generator",
   "sourceOfTruth": "Python SDK (praisonaiagents)",
   "summary": {
-    "pythonCoreFeatures": 359,
+    "pythonCoreFeatures": 360,
     "pythonWrapperFeatures": 99,
     "typescriptFeatures": 1192,
-    "gapCount": 67,
+    "gapCount": 68,
     "priorityP0": 2,
     "priorityP1": 0,
     "priorityP2": 0,
-    "priorityP3": 65
+    "priorityP3": 66
   },
   "pythonCoreSDK": {
     "path": "/home/runner/work/PraisonAI/PraisonAI/src/praisonai-agents/praisonaiagents",
@@ -279,6 +279,7 @@
       "WebSearchProvider",
       "Workflow",
       "WorkflowContext",
+      "__version__",
       "aembed",
       "aembedding",
       "aembeddings",
@@ -655,6 +656,7 @@
         "ToolsetRegistry",
         "ToolsetSpec",
         "ValidationError",
+        "__version__",
         "config",
         "configure_structured_logging",
         "embedding",
@@ -5491,6 +5493,15 @@
         "status": "DONE",
         "category": "config"
       },
+      {
+        "feature": "__version__",
+        "python": true,
+        "typescript": false,
+        "priority": "P3",
+        "effort": "low",
+        "status": "TODO",
+        "category": "other"
+      },
       {
         "feature": "aembed",
         "python": true,

src/praisonai-ts/PARITY.md

@@ -1,20 +1,20 @@
 # Feature Parity Tracker
 
-> **Version:** 1.5.87 | **Last Updated:** 2026-06-12
+> **Version:** 1.5.87 | **Last Updated:** 2026-06-17
 > **Source of Truth:** Python SDK (praisonaiagents)
 
 ## Summary
 
 | Metric | Count |
 |--------|-------|
-| Python Core Features | 359 |
+| Python Core Features | 360 |
 | Python Wrapper Features | 99 |
 | TypeScript Features | 1192 |
-| **Gap Count** | **67** |
+| **Gap Count** | **68** |
 | P0 (Critical) | 2 |
 | P1 (High) | 0 |
 | P2 (Medium) | 0 |
-| P3 (Low) | 65 |
+| P3 (Low) | 66 |
 
 ## Gap Matrix
 
@@ -148,7 +148,7 @@
 | `enable\_telemetry` | ✅ | ✅ | low | ✅ DONE |
 | `get\_telemetry` | ✅ | ✅ | low | ✅ DONE |
 
-### P3_Advanced (179 done, 65 todo)
+### P3_Advanced (179 done, 66 todo)
 
 | Feature | Python | TypeScript | Effort | Status |
 |---------|--------|------------|--------|--------|
@@ -205,6 +205,7 @@
 | `ToolsetRegistry` | ✅ | ❌ | high | ⏳ TODO |
 | `ToolsetSpec` | ✅ | ❌ | high | ⏳ TODO |
 | `ValidationError` | ✅ | ❌ | low | ⏳ TODO |
+| `\_\_version\_\_` | ✅ | ❌ | low | ⏳ TODO |
 | `configure\_structured\_logging` | ✅ | ❌ | low | ⏳ TODO |
 | `get\_default\_policy` | ✅ | ❌ | low | ⏳ TODO |
 | `get\_logger` | ✅ | ❌ | low | ⏳ TODO |
@@ -555,7 +556,7 @@ from praisonaiagents import Memory
 </details>
 
 <details>
-<summary><strong>other</strong> (70 exports)</summary>
+<summary><strong>other</strong> (71 exports)</summary>
 
 ```python
 from praisonaiagents import AGGRESSIVE_POLICY, AgentMessageEvent, AgentRunOutcome, Agents, AsyncLearnProtocol, AutonomyConfig, BALANCED_POLICY, BudgetExceededError, CONSERVATIVE_POLICY, CliBackendConfig...

src/praisonai-ts/package.json

@@ -1,6 +1,6 @@
 {
   "name": "praisonai",
-  "version": "1.7.1",
+  "version": "1.7.2",
   "description": "PraisonAI TypeScript AI Agents Framework - Node.js, npm, and Javascript AI Agents Framework",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",

src/praisonai-ts/src/ai/agent-loop.ts

@@ -135,6 +135,10 @@ export class AgentLoop {
       ...config,
     };
 
+    if (this.config.onToolCall && this.config.tools) {
+      this.config.tools = this.wrapToolsWithApproval(this.config.tools);
+    }
+
     // Add system message if provided
     if (config.system) {
       this.messages.push({ role: 'system', content: config.system });
@@ -218,16 +222,9 @@ export class AgentLoop {
 
     // Handle tool calls
     if (step.toolCalls.length > 0) {
-      // Check for approval if callback provided
-      if (this.config.onToolCall) {
-        for (const toolCall of step.toolCalls) {
-          const approved = await this.config.onToolCall(toolCall);
-          if (!approved) {
-            this.complete = true;
-            step.finishReason = 'tool_rejected';
-            break;
-          }
-        }
+      // Rejection is handled in wrapped tool execute before execution
+      if (step.finishReason === 'tool_rejected') {
+        this.complete = true;
       }
 
       // Add tool call message
@@ -306,6 +303,35 @@ export class AgentLoop {
     };
   }
 
+  /**
+   * Wrap tools so onToolCall approval runs before execution.
+   */
+  private wrapToolsWithApproval(tools: Record<string, AgentTool>): Record<string, AgentTool> {
+    const onToolCall = this.config.onToolCall;
+    if (!onToolCall) {
+      return tools;
+    }
+
+    const wrapped: Record<string, AgentTool> = {};
+    for (const [name, tool] of Object.entries(tools)) {
+      wrapped[name] = {
+        ...tool,
+        execute: async (args: any) => {
+          const approved = await onToolCall({
+            toolCallId: `pending-${name}`,
+            toolName: name,
+            args,
+          });
+          if (!approved) {
+            throw new Error(`Tool call rejected: ${name}`);
+          }
+          return tool.execute(args);
+        },
+      };
+    }
+    return wrapped;
+  }
+
   /**
    * Check if the loop should stop.
    */

src/praisonai-ts/src/cli/features/sandbox-executor.ts

@@ -67,6 +67,33 @@ export const DEFAULT_BLOCKED_PATHS = [
   '/proc'
 ];
 
+/** Shell metacharacters that enable command chaining or substitution */
+export const SHELL_METACHAR_PATTERN = /[;|&`><]|\$\([^)]*\)|\$\{/;
+
+/** Commands that typically require network access */
+export const NETWORK_COMMANDS = [
+  'curl', 'wget', 'nc', 'netcat', 'ssh', 'scp', 'sftp', 'ftp', 'telnet',
+  'ping', 'nslookup', 'dig', 'host', 'traceroute', 'tracepath', 'nmap',
+  'socat', 'openssl', 'node', 'python', 'python3', 'ruby', 'perl',
+];
+
+/** Environment variables commonly used for network/proxy configuration */
+const NETWORK_ENV_VARS = [
+  'http_proxy', 'https_proxy', 'HTTP_PROXY', 'HTTPS_PROXY',
+  'ALL_PROXY', 'all_proxy', 'NO_PROXY', 'no_proxy',
+  'FTP_PROXY', 'ftp_proxy', 'SOCKS_PROXY', 'socks_proxy',
+];
+
+export function containsShellMetacharacters(command: string): boolean {
+  return SHELL_METACHAR_PATTERN.test(command);
+}
+
+function parseCommandParts(command: string): { cmd: string; args: string[] } {
+  const parts = command.trim().match(/(?:[^\s"']+|"[^"]*"|'[^']*')+/g) ?? [];
+  const unquoted = parts.map((part) => part.replace(/^["']|["']$/g, ''));
+  return { cmd: unquoted[0] ?? '', args: unquoted.slice(1) };
+}
+
 /**
  * Command validator
  */
@@ -74,27 +101,43 @@ export class CommandValidator {
   private blockedCommands: string[];
   private blockedPaths: string[];
   private allowedCommands?: string[];
+  private networkIsolated: boolean;
 
   constructor(config: Partial<SandboxConfig> = {}) {
     this.blockedCommands = config.blockedCommands || DEFAULT_BLOCKED_COMMANDS;
     this.blockedPaths = config.blockedPaths || DEFAULT_BLOCKED_PATHS;
     this.allowedCommands = config.allowedCommands;
+    this.networkIsolated = config.mode === 'network-isolated';
   }
 
   /**
    * Validate a command
    */
   validate(command: string): { valid: boolean; reason?: string } {
-    const normalized = command.toLowerCase().trim();
+    const trimmed = command.trim();
+    if (!trimmed) {
+      return { valid: false, reason: 'Empty command' };
+    }
+
+    if (containsShellMetacharacters(trimmed)) {
+      return { valid: false, reason: 'Shell metacharacters are not allowed' };
+    }
+
+    const { cmd } = parseCommandParts(trimmed);
+    const normalized = trimmed.toLowerCase();
 
     // Check allowlist first if specified
     if (this.allowedCommands) {
-      const baseCmd = normalized.split(/\s+/)[0];
-      if (!this.allowedCommands.includes(baseCmd)) {
-        return { valid: false, reason: `Command '${baseCmd}' not in allowlist` };
+      if (!this.allowedCommands.includes(cmd.toLowerCase())) {
+        return { valid: false, reason: `Command '${cmd}' not in allowlist` };
       }
     }
 
+    // network-isolated: reject network-oriented commands (proxy env alone is insufficient)
+    if (this.networkIsolated && NETWORK_COMMANDS.includes(cmd.toLowerCase())) {
+      return { valid: false, reason: `Network command '${cmd}' blocked in network-isolated mode` };
+    }
+
     // Check blocked commands
     for (const blocked of this.blockedCommands) {
       if (normalized.includes(blocked.toLowerCase())) {
@@ -195,20 +238,29 @@ export class SandboxExecutor {
   }
 
   /**
-   * Spawn the command
+   * Spawn the command without shell when allowlisted or in strict mode
    */
   private async spawn(command: string): Promise<Omit<ExecutionResult, 'duration'>> {
     const { spawn } = await import('child_process');
+    const env = this.buildEnv();
+    const useDirectSpawn = Boolean(this.config.allowedCommands) || this.config.mode === 'strict';
+    const { cmd, args } = parseCommandParts(command);
 
     return new Promise((resolve) => {
-      const env = this.buildEnv();
-      
-      const proc = spawn('sh', ['-c', command], {
-        cwd: this.config.cwd,
-        env,
-        timeout: this.config.timeout,
-        stdio: ['pipe', 'pipe', 'pipe']
-      });
+      const proc = useDirectSpawn
+        ? spawn(cmd, args, {
+            cwd: this.config.cwd,
+            env,
+            timeout: this.config.timeout,
+            shell: false,
+            stdio: ['pipe', 'pipe', 'pipe'],
+          })
+        : spawn('sh', ['-c', command], {
+            cwd: this.config.cwd,
+            env,
+            timeout: this.config.timeout,
+            stdio: ['pipe', 'pipe', 'pipe'],
+          });
 
       let stdout = '';
       let stderr = '';
@@ -275,7 +327,11 @@ export class SandboxExecutor {
         };
 
       case 'network-isolated':
-        // No network access (requires additional OS-level setup)
+        // Proxy env vars alone do not block network; also reject network commands in validator.
+        // Strip common proxy/network env vars as a defence-in-depth measure.
+        for (const key of NETWORK_ENV_VARS) {
+          delete baseEnv[key];
+        }
         return {
           ...baseEnv,
           http_proxy: 'http://localhost:0',
@@ -283,7 +339,7 @@ export class SandboxExecutor {
           HTTP_PROXY: 'http://localhost:0',
           HTTPS_PROXY: 'http://localhost:0',
           no_proxy: '',
-          NO_PROXY: ''
+          NO_PROXY: '',
         };
 
       case 'basic':
@@ -315,6 +371,7 @@ export class SandboxExecutor {
    */
   setMode(mode: SandboxMode): void {
     this.config.mode = mode;
+    this.validator = new CommandValidator(this.config);
   }
 }
 

src/praisonai-ts/src/index.ts

@@ -91,7 +91,7 @@ export * from './tools/mcpSse';
 
 // AI SDK Tools Registry
 export { tools, registerBuiltinTools } from './tools/tools';
-export { getToolsRegistry, createToolsRegistry, resetToolsRegistry, ToolsRegistry, get_registry, getRegistry, get_tool, getTool, register_tool, registerTool, validate_tool, validateTool } from './tools/registry';
+export { getToolsRegistry, createToolsRegistry, resetToolsRegistry, ToolsRegistry, get_registry, get_tool, register_tool, validate_tool } from './tools/registry';
 export type {
   ToolExecutionContext, ToolLimits, RedactionHooks, ToolLogger,
   ToolCapabilities, InstallHints, ToolMetadata, ToolExecutionResult,

src/praisonai-ts/src/mcp/security.ts

@@ -205,6 +205,39 @@ export class MCPSecurity {
         return Math.max(0, config.requests - state.count);
     }
 
+    /**
+     * Validate credentials for a given auth method (fail closed by default)
+     */
+    private async validateCredentials(
+        auth: { method: AuthMethod; validate?: (token: string) => Promise<boolean> },
+        token: string
+    ): Promise<boolean> {
+        switch (auth.method) {
+            case 'none':
+                return true;
+
+            case 'api-key':
+            case 'bearer':
+                if (auth.validate) {
+                    return auth.validate(token);
+                }
+                if (this.apiKeys.size === 0) {
+                    return false;
+                }
+                return this.validateApiKey(token);
+
+            case 'basic':
+            case 'oauth':
+                if (!auth.validate) {
+                    return false;
+                }
+                return auth.validate(token);
+
+            default:
+                return false;
+        }
+    }
+
     /**
      * Match policy against request
      */
@@ -237,21 +270,18 @@ export class MCPSecurity {
                 return { allowed: true };
 
             case 'authenticate':
-                if (!policy.auth) return { allowed: true };
+                if (!policy.auth) {
+                    return { allowed: false, reason: 'Authentication policy misconfigured' };
+                }
 
                 const token = request.headers ? this.extractToken(request.headers) : null;
-                if (!token) {
+                if (!token || !token.trim()) {
                     return { allowed: false, reason: 'Authentication required' };
                 }
 
-                if (policy.auth.method === 'api-key' || policy.auth.method === 'bearer') {
-                    const valid = policy.auth.validate
-                        ? await policy.auth.validate(token)
-                        : this.validateApiKey(token);
-
-                    if (!valid) {
-                        return { allowed: false, reason: 'Invalid credentials' };
-                    }
+                const valid = await this.validateCredentials(policy.auth, token);
+                if (!valid) {
+                    return { allowed: false, reason: 'Invalid credentials' };
                 }
 
                 return { allowed: true, context: { authenticated: true } };

src/praisonai-ts/src/mcp/server.ts

@@ -73,6 +73,8 @@ export interface MCPServerConfig {
     port?: number | null;
     /** Enable logging */
     logging?: boolean;
+    /** Optional bearer token for HTTP transport (or set PRAISONAI_MCP_AUTH_TOKEN) */
+    authToken?: string;
 }
 
 /**
@@ -108,6 +110,7 @@ export class MCPServer {
     private logging: boolean;
     private running: boolean = false;
     private httpServer: any = null;
+    private authToken?: string;
 
     constructor(config: MCPServerConfig = {}) {
         this.id = randomUUID();
@@ -117,6 +120,7 @@ export class MCPServer {
         this.resources = new Map();
         this.prompts = new Map();
         this.logging = config.logging ?? false;
+        this.authToken = config.authToken ?? process.env.PRAISONAI_MCP_AUTH_TOKEN ?? undefined;
 
         // Register initial tools
         if (config.tools) {
@@ -397,6 +401,29 @@ export class MCPServer {
         });
     }
 
+    /**
+     * Verify HTTP bearer token when authToken is configured
+     */
+    private verifyHttpAuth(req: import('http').IncomingMessage): boolean {
+        if (!this.authToken) {
+            return true;
+        }
+
+        const authHeader = req.headers.authorization ?? req.headers['Authorization'];
+        if (typeof authHeader === 'string') {
+            if (authHeader.startsWith('Bearer ') && authHeader.slice(7) === this.authToken) {
+                return true;
+            }
+        }
+
+        const xAuth = req.headers['x-auth-token'] ?? req.headers['X-Auth-Token'];
+        if (typeof xAuth === 'string' && xAuth === this.authToken) {
+            return true;
+        }
+
+        return false;
+    }
+
     /**
      * Start HTTP transport
      */
@@ -408,6 +435,12 @@ export class MCPServer {
 
         this.httpServer = http.createServer(async (req, res) => {
             if (req.method === 'POST') {
+                if (!this.verifyHttpAuth(req)) {
+                    res.writeHead(401, { 'Content-Type': 'application/json' });
+                    res.end(JSON.stringify({ error: 'Unauthorized' }));
+                    return;
+                }
+
                 let body = '';
                 req.on('data', chunk => body += chunk);
                 req.on('end', async () => {

src/praisonai-ts/src/os/agentos.ts

@@ -201,6 +201,29 @@ export class AgentOS implements AgentOSProtocol {
             });
         }
 
+        // Optional API key middleware (except /health)
+        if (this.config.apiKey) {
+            app.use((req: any, res: any, next: any) => {
+                if (req.path === '/health') {
+                    return next();
+                }
+
+                const authHeader = req.headers.authorization;
+                if (typeof authHeader === 'string' && authHeader.startsWith('Bearer ')) {
+                    if (authHeader.slice(7) === this.config.apiKey) {
+                        return next();
+                    }
+                }
+
+                const xAuth = req.headers['x-auth-token'] ?? req.headers['X-Auth-Token'];
+                if (typeof xAuth === 'string' && xAuth === this.config.apiKey) {
+                    return next();
+                }
+
+                return res.status(401).json({ error: 'Unauthorized' });
+            });
+        }
+
         // Register routes
         this._registerRoutes(app);