Release v4.5.13

api.md

@@ -33,6 +33,7 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">agent_id</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">analyze_prompt</a>(prompt: str) -> set</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">arun</a>(prompt: str, **kwargs)</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">as_tool</a>(description: Optional[str] = None, tool_name: Optional[str] = None) -> 'Handoff'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">astart</a>(prompt: str, **kwargs)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">auto_memory</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">auto_memory</a>(value)</code>
@@ -361,6 +362,7 @@ Methods:
 * <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">tool_description</a>() -> str</code>
 * <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">tool_name</a>() -> str</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">handoff</a>(agent: 'Agent', tool_name_override: Optional[str] = None, tool_description_override: Optional[str] = None, on_handoff: Optional[Callable] = None, input_type: Optional[type] = None, input_filter: Optional[Callable[[HandoffInputData], HandoffInputData]] = None, config: Optional[HandoffConfig] = None, context_policy: Optional[str] = None, timeout_seconds: Optional[float] = None, max_concurrent: Optional[int] = None, detect_cycles: Optional[bool] = None, max_depth: Optional[int] = None) -> Handoff</code>
+* <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">compress_history</a>(data: HandoffInputData) -> HandoffInputData</code>
 * <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">keep_last_n_messages</a>(n: int) -> Callable[[HandoffInputData], HandoffInputData]</code>
 * <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">remove_all_tools</a>(data: HandoffInputData) -> HandoffInputData</code>
 * <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">remove_system_messages</a>(data: HandoffInputData) -> HandoffInputData</code>
@@ -1016,6 +1018,11 @@ Methods:
 * <code title="cli">praisonai traces enable <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
 * <code title="cli">praisonai traces list <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
 * <code title="cli">praisonai traces status <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
+* <code title="cli">praisonai tracker batch <a href="./src/praisonai/praisonai/cli/commands/tracker.py">--help</a></code>
+* <code title="cli">praisonai tracker judge <a href="./src/praisonai/praisonai/cli/commands/tracker.py">--help</a></code>
+* <code title="cli">praisonai tracker run <a href="./src/praisonai/praisonai/cli/commands/tracker.py">--help</a></code>
+* <code title="cli">praisonai tracker tools <a href="./src/praisonai/praisonai/cli/commands/tracker.py">--help</a></code>
+* <code title="cli">praisonai tracker tracker-main <a href="./src/praisonai/praisonai/cli/commands/tracker.py">--help</a></code>
 * <code title="cli">praisonai train agents <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
 * <code title="cli">praisonai train apply <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
 * <code title="cli">praisonai train list <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.5.7" \
+    "praisonai>=4.5.13" \
     "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>=4.5.7" \
+    "praisonai>=4.5.13" \
     "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>=4.5.7" \
+    "praisonai>=4.5.13" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

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

@@ -1918,6 +1918,9 @@ Summary:"""
             self.autonomy_config = {}
             self._autonomy_trigger = None
             self._doom_loop_tracker = None
+            self._file_snapshot = None
+            self._snapshot_stack = []
+            self._redo_stack = []
             return
         
         self.autonomy_enabled = True
@@ -1942,6 +1945,9 @@ Summary:"""
             self.autonomy_config = {}
             self._autonomy_trigger = None
             self._doom_loop_tracker = None
+            self._file_snapshot = None
+            self._snapshot_stack = []
+            self._redo_stack = []
             return
         
         # Preserve ALL AutonomyConfig fields in the dict (G14 fix: no lossy extraction)
@@ -1954,6 +1960,8 @@ Summary:"""
             "observe": config.observe,
             "completion_promise": config.completion_promise,
             "clear_context": config.clear_context,
+            "track_changes": config.effective_track_changes,
+            "snapshot_dir": config.snapshot_dir,
         }
         # Also preserve any extra user-provided keys from dict input
         if isinstance(autonomy, dict):
@@ -1967,6 +1975,21 @@ Summary:"""
         self._autonomy_trigger = AutonomyTrigger()
         self._doom_loop_tracker = DoomLoopTracker(threshold=config.doom_loop_threshold)
         
+        # Initialize FileSnapshot for filesystem tracking (lazy import)
+        self._file_snapshot = None
+        self._snapshot_stack = []  # Stack of snapshot hashes for undo/redo
+        self._redo_stack = []  # Redo stack
+        if config.effective_track_changes:
+            try:
+                from ..snapshot import FileSnapshot
+                import os
+                self._file_snapshot = FileSnapshot(
+                    project_path=os.getcwd(),
+                    snapshot_dir=config.snapshot_dir,
+                )
+            except Exception as e:
+                logger.debug(f"FileSnapshot init failed (git may not be available): {e}")
+        
         # Wire ObservabilityHooks when observe=True (G-UNUSED-2 fix)
         if config.observe:
             from ..escalation.observability import ObservabilityHooks
@@ -1977,6 +2000,93 @@ Summary:"""
         # Wire level → approval bridge (G3 fix)
         self._bridge_autonomy_level(config.level)
     
+    # ================================================================
+    # Filesystem tracking convenience methods (powered by FileSnapshot)
+    # ================================================================
+    
+    def undo(self) -> bool:
+        """Undo the last set of file changes.
+        
+        Restores files to the state before the last autonomous iteration.
+        Requires ``autonomy=AutonomyConfig(track_changes=True)``.
+        
+        Returns:
+            True if undo was successful, False if nothing to undo.
+            
+        Example::
+        
+            agent = Agent(autonomy="full_auto")
+            result = agent.start("Refactor utils.py")
+            agent.undo()  # Restore original files
+        """
+        if self._file_snapshot is None or not self._snapshot_stack:
+            return False
+        try:
+            target_hash = self._snapshot_stack.pop()
+            # Get current hash before restore (for redo)
+            current_hash = self._file_snapshot.get_current_hash()
+            if current_hash:
+                self._redo_stack.append(current_hash)
+            self._file_snapshot.restore(target_hash)
+            return True
+        except Exception as e:
+            logger.debug(f"Undo failed: {e}")
+            return False
+    
+    def redo(self) -> bool:
+        """Redo a previously undone set of file changes.
+        
+        Re-applies file changes that were reverted by :meth:`undo`.
+        
+        Returns:
+            True if redo was successful, False if nothing to redo.
+        """
+        if self._file_snapshot is None or not self._redo_stack:
+            return False
+        try:
+            target_hash = self._redo_stack.pop()
+            current_hash = self._file_snapshot.get_current_hash()
+            if current_hash:
+                self._snapshot_stack.append(current_hash)
+            self._file_snapshot.restore(target_hash)
+            return True
+        except Exception as e:
+            logger.debug(f"Redo failed: {e}")
+            return False
+    
+    def diff(self, from_hash: Optional[str] = None):
+        """Get file diffs from autonomous execution.
+        
+        Returns a list of :class:`FileDiff` objects showing what files
+        were modified, with additions/deletions counts.
+        
+        Args:
+            from_hash: Base commit hash to diff from. If None, uses the
+                first snapshot (pre-autonomous state).
+        
+        Returns:
+            List of FileDiff objects, or empty list if tracking not enabled.
+            
+        Example::
+        
+            agent = Agent(autonomy="full_auto")
+            result = agent.start("Refactor utils.py")
+            for d in agent.diff():
+                print(f"{d.status}: {d.path} (+{d.additions}/-{d.deletions})")
+        """
+        if self._file_snapshot is None:
+            return []
+        try:
+            base = from_hash
+            if base is None and self._snapshot_stack:
+                base = self._snapshot_stack[0]
+            if base is None:
+                return []
+            return self._file_snapshot.diff(base)
+        except Exception as e:
+            logger.debug(f"Diff failed: {e}")
+            return []
+    
     def analyze_prompt(self, prompt: str) -> set:
         """Analyze prompt for autonomy signals.
         
@@ -2159,6 +2269,15 @@ Summary:"""
                 "Create agent with autonomy=True or autonomy={...}"
             )
         
+        # Take initial snapshot before autonomous execution starts
+        if self._file_snapshot is not None:
+            try:
+                snap_info = self._file_snapshot.track(message="pre-autonomous")
+                self._snapshot_stack.append(snap_info.commit_hash)
+                self._redo_stack.clear()
+            except Exception as e:
+                logger.debug(f"Pre-autonomous snapshot failed: {e}")
+        
         start_time = time_module.time()
         started_at = datetime.now(timezone.utc).isoformat().replace("+00:00", "Z")
         iterations = 0
@@ -5285,6 +5404,54 @@ Your Goal: {self.goal}"""
         """Get the current session ID."""
         return self._session_id
 
+    def as_tool(
+        self,
+        description: Optional[str] = None,
+        tool_name: Optional[str] = None,
+    ) -> 'Handoff':
+        """Convert this agent to a callable tool for use by other agents.
+        
+        Unlike handoffs which pass conversation context, as_tool() creates a tool
+        where the child agent receives only the generated input (no history).
+        The parent agent retains control and receives the result.
+        
+        This is useful for hierarchical agent composition where you want to
+        invoke a specialist agent as a subordinate tool.
+        
+        Args:
+            description: Tool description for the LLM (what this agent does)
+            tool_name: Custom tool name (default: invoke_<agent_name>)
+        
+        Returns:
+            Handoff configured as a tool with no context passed
+        
+        Example:
+            researcher = Agent(name="Researcher", instructions="Research topics")
+            coder = Agent(name="Coder", instructions="Write Python code")
+            
+            writer = Agent(
+                name="Writer",
+                tools=[
+                    researcher.as_tool("Research a topic and return findings"),
+                    coder.as_tool("Write Python code for a given task"),
+                ]
+            )
+            
+            result = writer.chat("Write an article about async Python")
+        """
+        from .handoff import Handoff, HandoffConfig, ContextPolicy
+        
+        # Generate default tool name
+        agent_name_snake = self.name.lower().replace(' ', '_').replace('-', '_')
+        default_tool_name = f"invoke_{agent_name_snake}"
+        
+        return Handoff(
+            agent=self,
+            tool_name_override=tool_name or default_tool_name,
+            tool_description_override=description or f"Invoke {self.name} to complete a subtask and return the result",
+            config=HandoffConfig(context_policy=ContextPolicy.NONE),
+        )
+
     def chat(self, prompt, temperature=1.0, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False, stream=None, task_name=None, task_description=None, task_id=None, config=None, force_retrieval=False, skip_retrieval=False, attachments=None, tool_choice=None):
         """
         Chat with the agent.
@@ -6288,10 +6455,37 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             **kwargs: Additional arguments passed to achat()
             
         Returns:
-            The agent's response as a string
+            The agent's response as a string, or AutonomyResult if autonomy enabled
+            
+        Note:
+            If autonomy=True was set on the agent, astart() automatically uses
+            the autonomous loop (run_autonomous_async) instead of single-turn chat.
         """
         import sys
         
+        # ─────────────────────────────────────────────────────────────────────
+        # UNIFIED AUTONOMY API: If autonomy is enabled, route to run_autonomous_async
+        # This allows: Agent(autonomy=True) + await agent.astart("Task") to just work!
+        # ─────────────────────────────────────────────────────────────────────
+        if self.autonomy_enabled:
+            # Extract autonomy-specific kwargs
+            timeout = kwargs.pop('timeout', None)
+            kwargs.pop('stream', None)  # Not used in autonomous mode
+            
+            # Get config values from autonomy_config
+            auto_config = self.autonomy_config or {}
+            max_iterations = auto_config.get('max_iterations', 20)
+            completion_promise = auto_config.get('completion_promise')
+            clear_context = auto_config.get('clear_context', False)
+            
+            return await self.run_autonomous_async(
+                prompt=prompt,
+                max_iterations=max_iterations,
+                timeout_seconds=timeout,
+                completion_promise=completion_promise,
+                clear_context=clear_context,
+            )
+        
         # Determine streaming behavior (same logic as start())
         stream_requested = kwargs.get('stream')
         if stream_requested is None:
@@ -6523,6 +6717,9 @@ Write the complete compiled report:"""
             Unlike .run() which is always silent (production use), .start()
             enables verbose output by default when in a TTY for beginner-friendly
             interactive use. Use .run() for programmatic/scripted usage.
+            
+            If autonomy=True was set on the agent, start() automatically uses
+            the autonomous loop (run_autonomous) instead of single-turn chat.
         """
         import sys
         
@@ -6535,6 +6732,28 @@ Write the complete compiled report:"""
             from praisonaiagents.utils.variables import substitute_variables
             prompt = substitute_variables(prompt, {})
         
+        # ─────────────────────────────────────────────────────────────────────
+        # UNIFIED AUTONOMY API: If autonomy is enabled, route to run_autonomous
+        # This allows: Agent(autonomy=True) + agent.start("Task") to just work!
+        # ─────────────────────────────────────────────────────────────────────
+        if self.autonomy_enabled:
+            # Extract autonomy-specific kwargs
+            timeout = kwargs.pop('timeout', None)
+            
+            # Get config values from autonomy_config
+            auto_config = self.autonomy_config or {}
+            max_iterations = auto_config.get('max_iterations', 20)
+            completion_promise = auto_config.get('completion_promise')
+            clear_context = auto_config.get('clear_context', False)
+            
+            return self.run_autonomous(
+                prompt=prompt,
+                max_iterations=max_iterations,
+                timeout_seconds=timeout,
+                completion_promise=completion_promise,
+                clear_context=clear_context,
+            )
+        
         # Load history from past sessions
         self._load_history_context()
         

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

@@ -77,6 +77,9 @@ _AUTONOMY_TO_ESCALATION_SIGNAL = {v: k for k, v in _ESCALATION_TO_AUTONOMY_SIGNA
 class AutonomyConfig:
     """Configuration for Agent autonomy features.
     
+    Autonomy is the safety center: doom loops, escalation, sandbox,
+    filesystem tracking, and verification hooks all live here.
+    
     Attributes:
         enabled: Whether autonomy is enabled
         level: Autonomy level (suggest, auto_edit, full_auto)
@@ -87,6 +90,24 @@ class AutonomyConfig:
         completion_promise: Optional string that signals completion when wrapped in <promise>TEXT</promise>
         clear_context: Whether to clear chat history between iterations
         verification_hooks: List of VerificationHook instances for output verification
+        track_changes: Whether to track filesystem changes via shadow git.
+            Defaults to True when level="full_auto", False otherwise.
+            When enabled, agent.undo()/redo()/diff() become available.
+        sandbox: Optional SandboxConfig for execution isolation.
+            When level="full_auto" and sandbox is None, subprocess sandbox is auto-enabled.
+        snapshot_dir: Directory for snapshot storage. Defaults to ~/.praisonai/snapshots.
+    
+    Usage::
+    
+        # Simple — full_auto auto-enables tracking + sandbox
+        Agent(autonomy="full_auto")
+        
+        # Advanced — explicit config
+        Agent(autonomy=AutonomyConfig(
+            level="full_auto",
+            track_changes=True,
+            sandbox=SandboxConfig.native(writable_paths=["./src"]),
+        ))
     """
     enabled: bool = True
     level: str = "suggest"
@@ -97,6 +118,10 @@ class AutonomyConfig:
     completion_promise: Optional[str] = None
     clear_context: bool = False
     verification_hooks: Optional[List[Any]] = None
+    # Safety features — autonomy owns the safety model
+    track_changes: Optional[bool] = None  # None = auto (True for full_auto)
+    sandbox: Optional[Any] = None  # SandboxConfig (lazy import to avoid circular)
+    snapshot_dir: Optional[str] = None  # Defaults to ~/.praisonai/snapshots
     
     def __post_init__(self):
         if self.level not in VALID_AUTONOMY_LEVELS:
@@ -104,6 +129,20 @@ class AutonomyConfig:
                 f"Invalid autonomy level: {self.level!r}. "
                 f"Must be one of {sorted(VALID_AUTONOMY_LEVELS)}"
             )
+        # Auto-enable track_changes for full_auto if not explicitly set
+        if self.track_changes is None:
+            self.track_changes = (self.level == "full_auto")
+        # Default snapshot directory
+        if self.snapshot_dir is None:
+            import os
+            self.snapshot_dir = os.path.join(
+                os.path.expanduser("~"), ".praisonai", "snapshots"
+            )
+    
+    @property
+    def effective_track_changes(self) -> bool:
+        """Whether track_changes is effectively enabled."""
+        return bool(self.track_changes)
     
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> "AutonomyConfig":
@@ -124,6 +163,9 @@ class AutonomyConfig:
             completion_promise=data.get("completion_promise"),
             clear_context=data.get("clear_context", False),
             verification_hooks=data.get("verification_hooks"),
+            track_changes=data.get("track_changes"),
+            sandbox=data.get("sandbox"),
+            snapshot_dir=data.get("snapshot_dir"),
         )
 
 

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

@@ -10,7 +10,7 @@ Unified Handoff System:
 - Replaces/absorbs Agent.delegate() and SubagentDelegator functionality
 """
 
-from typing import Optional, Any, Callable, Dict, List, TYPE_CHECKING
+from typing import Optional, Any, Callable, Dict, List, Union, TYPE_CHECKING
 from dataclasses import dataclass, field
 from enum import Enum
 import inspect
@@ -209,7 +209,7 @@ class Handoff:
         tool_description_override: Optional[str] = None,
         on_handoff: Optional[Callable] = None,
         input_type: Optional[type] = None,
-        input_filter: Optional[Callable[[HandoffInputData], HandoffInputData]] = None,
+        input_filter: Optional[Union[Callable[[HandoffInputData], HandoffInputData], List[Callable[[HandoffInputData], HandoffInputData]]]] = None,
         config: Optional[HandoffConfig] = None,
     ):
         """
@@ -221,7 +221,8 @@ class Handoff:
             tool_description_override: Custom tool description
             on_handoff: Callback function executed when handoff is invoked
             input_type: Type of input expected by the handoff (for structured data)
-            input_filter: Function to filter/transform input before passing to target agent
+            input_filter: Function or list of functions to filter/transform input.
+                When a list is provided, filters are applied in order (chaining).
             config: HandoffConfig for advanced settings (context policy, timeouts, etc.)
         """
         self.agent = agent
@@ -329,9 +330,14 @@ class Handoff:
             handoff_chain=_get_handoff_chain()[:],
         )
         
-        # Apply custom input filter if provided
+        # Apply custom input filter(s) if provided
+        # Supports both single filter and list of filters for chaining
         if self.input_filter:
-            handoff_data = self.input_filter(handoff_data)
+            if isinstance(self.input_filter, list):
+                for filter_fn in self.input_filter:
+                    handoff_data = filter_fn(handoff_data)
+            else:
+                handoff_data = self.input_filter(handoff_data)
         
         return handoff_data
     
@@ -790,6 +796,36 @@ class handoff_filters:
         
         data.messages = filtered_messages
         return data
+    
+    @staticmethod
+    def compress_history(data: HandoffInputData) -> HandoffInputData:
+        """Compress all messages into a single summary message.
+        
+        This filter concatenates all message contents into one user message,
+        reducing token usage while preserving context. Useful for handoffs
+        where the target agent needs context but not full conversation history.
+        
+        Example:
+            Handoff(agent=target, input_filter=handoff_filters.compress_history)
+        """
+        if not data.messages:
+            return data
+        
+        summary_parts = []
+        for msg in data.messages:
+            if isinstance(msg, dict):
+                role = msg.get('role', 'unknown')
+                content = msg.get('content', '')
+                if content:
+                    summary_parts.append(f"[{role}]: {content}")
+        
+        if summary_parts:
+            summary = "\n".join(summary_parts)
+            data.messages = [{"role": "user", "content": f"Previous conversation summary:\n{summary}"}]
+        else:
+            data.messages = []
+        
+        return data
 
 
 # Recommended prompt prefix for agents that use handoffs

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

@@ -1,15 +1,151 @@
 """
 Policy Configuration for PraisonAI Agents.
+
+Extends the policy engine with learning capabilities:
+- ``learn=True`` auto-persists "always allow" decisions
+- ``rules=`` inline rules for static policy
+- ``rules_file=`` path to persistent rules file
 """
 
+import json
+import logging
+import os
 from dataclasses import dataclass, field
-from typing import Optional, List
+from typing import Optional, List, Any, Dict
+
+logger = logging.getLogger(__name__)
+
+# Default rules file path (follows ~/.praisonai/ convention)
+DEFAULT_RULES_DIR = os.path.join(os.path.expanduser("~"), ".praisonai", "rules")
+DEFAULT_RULES_FILE = os.path.join(DEFAULT_RULES_DIR, "default.rules")
 
 
 @dataclass
 class PolicyConfig:
-    """Configuration for the policy engine."""
+    """Configuration for the policy engine.
+    
+    Follows PraisonAI's ``False/True/Config`` progressive-disclosure pattern:
+    
+    - ``policy=False`` — disabled (no policy checks)
+    - ``policy=True`` — enabled with defaults
+    - ``policy=PolicyConfig(...)`` — full control
+    
+    Attributes:
+        enabled: Whether policy evaluation is enabled.
+        default_action: Action when no rule matches ("allow" or "deny").
+        log_decisions: Whether to log policy decisions.
+        strict_mode: If True, deny when no policy matches.
+        learn: When True, auto-persist user "always allow" decisions to
+            ``rules_file``. Only writes when user explicitly approves.
+        rules: Inline list of rule dicts or PolicyRule objects for static
+            policy. Each dict should have at minimum ``resource`` and ``action``.
+        rules_file: Path to persistent rules file. Defaults to
+            ``~/.praisonai/rules/default.rules``.
+    
+    Usage::
+    
+        # Simple — learn from user decisions
+        Agent(policy=PolicyConfig(learn=True))
+        
+        # With inline rules
+        Agent(policy=PolicyConfig(
+            rules=[
+                {"resource": "tool:pip *", "action": "allow"},
+                {"resource": "tool:rm *", "action": "deny"},
+            ],
+        ))
+    """
     enabled: bool = True
     default_action: str = "allow"  # Default action when no policy matches
     log_decisions: bool = True
     strict_mode: bool = False  # If True, deny when no policy matches
+    # G-1: Learning and persistence
+    learn: bool = False
+    rules: Optional[List[Any]] = None  # List of rule dicts or PolicyRule objects
+    rules_file: Optional[str] = None  # Defaults to ~/.praisonai/rules/default.rules
+    
+    def __post_init__(self):
+        if self.rules_file is None:
+            self.rules_file = DEFAULT_RULES_FILE
+    
+    def load_learned_rules(self) -> List[Dict[str, str]]:
+        """Load previously learned rules from the rules file.
+        
+        Returns:
+            List of rule dicts with 'resource' and 'action' keys.
+        """
+        if not self.rules_file or not os.path.exists(self.rules_file):
+            return []
+        try:
+            with open(self.rules_file, "r") as f:
+                data = json.load(f)
+            return data.get("rules", [])
+        except (json.JSONDecodeError, OSError) as e:
+            logger.debug(f"Failed to load rules from {self.rules_file}: {e}")
+            return []
+    
+    def save_learned_rule(self, resource: str, action: str = "allow") -> bool:
+        """Persist a learned rule to the rules file.
+        
+        Uses atomic write (tmp + rename) for safety.
+        
+        Args:
+            resource: Resource pattern (e.g., "tool:pip install *")
+            action: Action to take ("allow" or "deny")
+            
+        Returns:
+            True if saved successfully.
+        """
+        if not self.rules_file:
+            return False
+        
+        try:
+            # Load existing rules
+            existing = self.load_learned_rules()
+            
+            # Check for duplicates
+            for rule in existing:
+                if rule.get("resource") == resource:
+                    return True  # Already exists
+            
+            # Add new rule
+            existing.append({"resource": resource, "action": action})
+            
+            # Ensure directory exists
+            rules_dir = os.path.dirname(self.rules_file)
+            os.makedirs(rules_dir, exist_ok=True)
+            
+            # Atomic write: tmp file + rename
+            tmp_file = self.rules_file + ".tmp"
+            with open(tmp_file, "w") as f:
+                json.dump({"rules": existing}, f, indent=2)
+            os.replace(tmp_file, self.rules_file)
+            
+            logger.debug(f"Saved learned rule: {resource} -> {action}")
+            return True
+        except OSError as e:
+            logger.debug(f"Failed to save rule: {e}")
+            return False
+    
+    def get_all_rules(self) -> List[Dict[str, str]]:
+        """Get all rules: inline + learned from file.
+        
+        Returns:
+            Combined list of rule dicts.
+        """
+        result = []
+        
+        # Add inline rules
+        if self.rules:
+            for rule in self.rules:
+                if isinstance(rule, dict):
+                    result.append(rule)
+                elif hasattr(rule, "to_dict"):
+                    result.append(rule.to_dict())
+        
+        # Add learned rules from file
+        if self.learn:
+            result.extend(self.load_learned_rules())
+        
+        return result
+

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

@@ -153,3 +153,55 @@ class SandboxConfig:
         return cls(
             sandbox_type="e2b",
         )
+    
+    @classmethod
+    def native(
+        cls,
+        writable_paths: List[str] = None,
+        network: bool = False,
+    ) -> "SandboxConfig":
+        """Create an OS-native sandbox configuration.
+        
+        Uses platform-specific sandboxing:
+        - macOS: Seatbelt (sandbox-exec)
+        - Linux: Landlock + bubblewrap
+        - Fallback: subprocess with restricted security policy
+        
+        Args:
+            writable_paths: Directories the agent may write to.
+                Defaults to current directory only.
+            network: Whether to allow network access. Defaults to False.
+            
+        Returns:
+            SandboxConfig with native sandbox type and restricted policy.
+            
+        Example::
+        
+            sandbox = SandboxConfig.native(
+                writable_paths=["./src", "./tests"],
+                network=False,
+            )
+        """
+        import os
+        
+        if writable_paths is None:
+            writable_paths = [os.getcwd()]
+        
+        # Resolve paths to absolute
+        resolved_paths = [os.path.abspath(p) for p in writable_paths]
+        
+        return cls(
+            sandbox_type="native",
+            working_dir=os.getcwd(),
+            security_policy=SecurityPolicy(
+                allow_network=network,
+                allow_file_write=True,
+                allow_subprocess=True,
+                allowed_paths=resolved_paths,
+            ),
+            metadata={
+                "writable_paths": resolved_paths,
+                "network": network,
+            },
+        )
+

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "1.5.7"
+version = "1.5.13"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/tests/unit/agent/test_autonomy_unified_api.py

@@ -0,0 +1,294 @@
+"""
+TDD tests for unified autonomy API.
+
+Goal: Agent(autonomy=True) + agent.start("Task") should automatically
+use the autonomous loop. No need to call run_autonomous() separately.
+
+Current (redundant):
+    agent = Agent(autonomy=True)
+    result = agent.run_autonomous("Task")  # Why two steps?
+
+Proposed (unified):
+    agent = Agent(autonomy=True)
+    result = agent.start("Task")  # Automatically uses autonomy loop!
+"""
+
+import pytest
+from unittest.mock import patch, MagicMock
+
+
+class TestUnifiedAutonomyAPI:
+    """Test that start() automatically uses autonomy when enabled."""
+    
+    def test_start_uses_autonomy_when_enabled(self):
+        """start() should route to run_autonomous when autonomy=True."""
+        from praisonaiagents import Agent
+        
+        with patch.object(Agent, 'run_autonomous') as mock_run_auto:
+            mock_run_auto.return_value = MagicMock(
+                success=True,
+                output="Task completed",
+                completion_reason="goal",
+                iterations=1,
+            )
+            
+            agent = Agent(
+                name="test",
+                instructions="Test agent",
+                autonomy=True,
+                llm="gpt-4o-mini",
+            )
+            
+            result = agent.start("Do something")
+            
+            # Should have called run_autonomous, not chat
+            mock_run_auto.assert_called_once()
+            call_kwargs = mock_run_auto.call_args[1]
+            assert call_kwargs.get("prompt") == "Do something"
+    
+    def test_start_uses_chat_when_autonomy_disabled(self):
+        """start() should use regular chat when autonomy is not enabled."""
+        from praisonaiagents import Agent
+        
+        with patch.object(Agent, 'chat') as mock_chat:
+            mock_chat.return_value = "Hello response"
+            
+            agent = Agent(
+                name="test",
+                instructions="Test agent",
+                autonomy=False,  # Disabled
+                llm="gpt-4o-mini",
+            )
+            
+            # Force non-streaming for test
+            result = agent.start("Hello", stream=False)
+            
+            # Should have called chat, not run_autonomous
+            mock_chat.assert_called()
+    
+    def test_start_returns_autonomy_result_when_autonomy_enabled(self):
+        """start() should return AutonomyResult when autonomy is enabled."""
+        from praisonaiagents import Agent
+        from praisonaiagents.agent.autonomy import AutonomyResult
+        
+        with patch.object(Agent, 'run_autonomous') as mock_run_auto:
+            mock_result = AutonomyResult(
+                success=True,
+                output="Done!",
+                completion_reason="goal",
+                iterations=2,
+            )
+            mock_run_auto.return_value = mock_result
+            
+            agent = Agent(
+                name="test",
+                instructions="Test agent",
+                autonomy=True,
+                llm="gpt-4o-mini",
+            )
+            
+            result = agent.start("Task")
+            
+            assert isinstance(result, AutonomyResult)
+            assert result.success is True
+            assert result.output == "Done!"
+    
+    def test_start_passes_autonomy_config_to_run_autonomous(self):
+        """start() should pass autonomy config values to run_autonomous."""
+        from praisonaiagents import Agent
+        
+        with patch.object(Agent, 'run_autonomous') as mock_run_auto:
+            mock_run_auto.return_value = MagicMock(success=True, output="OK")
+            
+            agent = Agent(
+                name="test",
+                instructions="Test agent",
+                autonomy={
+                    "max_iterations": 15,
+                    "completion_promise": "DONE",
+                    "clear_context": True,
+                },
+                llm="gpt-4o-mini",
+            )
+            
+            agent.start("Task")
+            
+            call_kwargs = mock_run_auto.call_args[1]
+            assert call_kwargs.get("max_iterations") == 15
+            assert call_kwargs.get("completion_promise") == "DONE"
+            assert call_kwargs.get("clear_context") is True
+    
+    def test_start_timeout_passed_to_run_autonomous(self):
+        """start(timeout=X) should pass timeout to run_autonomous."""
+        from praisonaiagents import Agent
+        
+        with patch.object(Agent, 'run_autonomous') as mock_run_auto:
+            mock_run_auto.return_value = MagicMock(success=True, output="OK")
+            
+            agent = Agent(
+                name="test",
+                instructions="Test agent",
+                autonomy=True,
+                llm="gpt-4o-mini",
+            )
+            
+            agent.start("Task", timeout=60.0)
+            
+            call_kwargs = mock_run_auto.call_args[1]
+            assert call_kwargs.get("timeout_seconds") == 60.0
+    
+    def test_default_agent_uses_single_turn_chat(self):
+        """Default agent (no autonomy) should use single-turn chat."""
+        from praisonaiagents import Agent
+        
+        with patch.object(Agent, 'chat') as mock_chat:
+            mock_chat.return_value = "Response"
+            
+            # No autonomy parameter = default = single-turn
+            agent = Agent(
+                name="test",
+                instructions="Test agent",
+                llm="gpt-4o-mini",
+            )
+            
+            assert agent.autonomy_enabled is False
+            
+            agent.start("Hello", stream=False)
+            mock_chat.assert_called()
+    
+    def test_autonomy_false_explicit_uses_single_turn(self):
+        """autonomy=False should use single-turn chat."""
+        from praisonaiagents import Agent
+        
+        with patch.object(Agent, 'chat') as mock_chat:
+            mock_chat.return_value = "Response"
+            
+            agent = Agent(
+                name="test",
+                instructions="Test agent",
+                autonomy=False,
+                llm="gpt-4o-mini",
+            )
+            
+            assert agent.autonomy_enabled is False
+            
+            agent.start("Hello", stream=False)
+            mock_chat.assert_called()
+
+
+class TestRunAutonomousStillWorks:
+    """Ensure run_autonomous() still works for explicit calls (backward compat)."""
+    
+    def test_run_autonomous_explicit_call_works(self):
+        """run_autonomous() should still work when called explicitly."""
+        from praisonaiagents import Agent
+        
+        with patch.object(Agent, 'chat') as mock_chat:
+            mock_chat.return_value = "Task completed successfully"
+            
+            agent = Agent(
+                name="test",
+                instructions="Test agent",
+                autonomy=True,
+                llm="gpt-4o-mini",
+            )
+            
+            result = agent.run_autonomous("Do something", max_iterations=2)
+            
+            assert result.success is True
+            assert result.iterations >= 1
+    
+    def test_run_autonomous_without_autonomy_enabled_raises(self):
+        """run_autonomous() should raise if autonomy not enabled."""
+        from praisonaiagents import Agent
+        
+        agent = Agent(
+            name="test",
+            instructions="Test agent",
+            autonomy=False,
+            llm="gpt-4o-mini",
+        )
+        
+        with pytest.raises(ValueError, match="Autonomy must be enabled"):
+            agent.run_autonomous("Task")
+
+
+class TestAsyncUnifiedAPI:
+    """Test async variant of unified API."""
+    
+    @pytest.mark.asyncio
+    async def test_astart_uses_autonomy_when_enabled(self):
+        """astart() should route to run_autonomous_async when autonomy=True."""
+        from praisonaiagents import Agent
+        
+        with patch.object(Agent, 'run_autonomous_async') as mock_run_auto:
+            mock_run_auto.return_value = MagicMock(
+                success=True,
+                output="Task completed",
+                completion_reason="goal",
+                iterations=1,
+            )
+            
+            agent = Agent(
+                name="test",
+                instructions="Test agent",
+                autonomy=True,
+                llm="gpt-4o-mini",
+            )
+            
+            result = await agent.astart("Do something")
+            
+            mock_run_auto.assert_called_once()
+    
+    @pytest.mark.asyncio
+    async def test_astart_uses_achat_when_autonomy_disabled(self):
+        """astart() should use achat when autonomy is not enabled."""
+        from praisonaiagents import Agent
+        
+        with patch.object(Agent, 'achat') as mock_achat:
+            mock_achat.return_value = "Hello response"
+            
+            agent = Agent(
+                name="test",
+                instructions="Test agent",
+                autonomy=False,
+                llm="gpt-4o-mini",
+            )
+            
+            result = await agent.astart("Hello")
+            
+            mock_achat.assert_called()
+
+
+class TestCLISimplification:
+    """Test that CLI can use simplified API."""
+    
+    def test_cli_can_use_start_for_loop(self):
+        """CLI loop command should be able to use agent.start() directly."""
+        from praisonaiagents import Agent
+        
+        with patch.object(Agent, 'run_autonomous') as mock_run_auto:
+            mock_run_auto.return_value = MagicMock(
+                success=True,
+                output="Done",
+                completion_reason="goal",
+                iterations=3,
+                duration_seconds=5.0,
+                started_at="2024-01-01T00:00:00Z",
+            )
+            
+            # This is what CLI should do - simple!
+            agent = Agent(
+                name="loop_agent",
+                instructions="Complete the task",
+                autonomy={
+                    "max_iterations": 10,
+                    "completion_promise": "DONE",
+                },
+                llm="gpt-4o-mini",
+            )
+            
+            result = agent.start("Build something")
+            
+            assert result.success is True
+            mock_run_auto.assert_called_once()

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

@@ -0,0 +1,187 @@
+"""
+Unit tests for Agent.as_tool() method.
+
+Tests:
+1. as_tool() returns a Handoff with ContextPolicy.NONE
+2. as_tool() generates correct default tool name
+3. as_tool() uses custom description and name when provided
+4. as_tool() can be used in tools list
+"""
+
+from unittest.mock import MagicMock
+
+
+class TestAgentAsTool:
+    """Tests for Agent.as_tool() method."""
+    
+    def test_as_tool_returns_handoff(self):
+        """Test that as_tool() returns a Handoff instance."""
+        from praisonaiagents.agent.handoff import Handoff, HandoffConfig, ContextPolicy
+        
+        # Create a mock agent
+        mock_agent = MagicMock()
+        mock_agent.name = "TestAgent"
+        mock_agent.role = "Test Role"
+        mock_agent.goal = "Test Goal"
+        
+        # Simulate as_tool behavior
+        agent_name_snake = mock_agent.name.lower().replace(' ', '_').replace('-', '_')
+        default_tool_name = f"invoke_{agent_name_snake}"
+        
+        result = Handoff(
+            agent=mock_agent,
+            tool_name_override=default_tool_name,
+            tool_description_override=f"Invoke {mock_agent.name} to complete a subtask and return the result",
+            config=HandoffConfig(context_policy=ContextPolicy.NONE),
+        )
+        
+        assert isinstance(result, Handoff)
+        assert result.config.context_policy == ContextPolicy.NONE
+        assert result.tool_name == "invoke_testagent"
+    
+    def test_as_tool_context_policy_none(self):
+        """Test that as_tool() uses ContextPolicy.NONE (no history passed)."""
+        from praisonaiagents.agent.handoff import Handoff, HandoffConfig, ContextPolicy
+        
+        mock_agent = MagicMock()
+        mock_agent.name = "Researcher"
+        
+        result = Handoff(
+            agent=mock_agent,
+            tool_name_override="invoke_researcher",
+            tool_description_override="Research topics",
+            config=HandoffConfig(context_policy=ContextPolicy.NONE),
+        )
+        
+        assert result.config.context_policy == ContextPolicy.NONE
+    
+    def test_as_tool_default_tool_name(self):
+        """Test that as_tool() generates correct default tool name."""
+        from praisonaiagents.agent.handoff import Handoff, HandoffConfig, ContextPolicy
+        
+        # Test various agent names
+        test_cases = [
+            ("Researcher", "invoke_researcher"),
+            ("Code Writer", "invoke_code_writer"),
+            ("data-analyst", "invoke_data_analyst"),
+            ("MyAgent123", "invoke_myagent123"),
+        ]
+        
+        for agent_name, expected_tool_name in test_cases:
+            mock_agent = MagicMock()
+            mock_agent.name = agent_name
+            
+            agent_name_snake = agent_name.lower().replace(' ', '_').replace('-', '_')
+            tool_name = f"invoke_{agent_name_snake}"
+            
+            result = Handoff(
+                agent=mock_agent,
+                tool_name_override=tool_name,
+                tool_description_override="Test",
+                config=HandoffConfig(context_policy=ContextPolicy.NONE),
+            )
+            
+            assert result.tool_name == expected_tool_name, f"Failed for {agent_name}"
+    
+    def test_as_tool_custom_description(self):
+        """Test that as_tool() uses custom description when provided."""
+        from praisonaiagents.agent.handoff import Handoff, HandoffConfig, ContextPolicy
+        
+        mock_agent = MagicMock()
+        mock_agent.name = "Coder"
+        
+        custom_description = "Write Python code for any task"
+        
+        result = Handoff(
+            agent=mock_agent,
+            tool_name_override="invoke_coder",
+            tool_description_override=custom_description,
+            config=HandoffConfig(context_policy=ContextPolicy.NONE),
+        )
+        
+        assert result.tool_description == custom_description
+    
+    def test_as_tool_custom_name(self):
+        """Test that as_tool() uses custom tool name when provided."""
+        from praisonaiagents.agent.handoff import Handoff, HandoffConfig, ContextPolicy
+        
+        mock_agent = MagicMock()
+        mock_agent.name = "Researcher"
+        
+        custom_name = "research_topic"
+        
+        result = Handoff(
+            agent=mock_agent,
+            tool_name_override=custom_name,
+            tool_description_override="Research topics",
+            config=HandoffConfig(context_policy=ContextPolicy.NONE),
+        )
+        
+        assert result.tool_name == custom_name
+
+
+class TestAgentAsToolIntegration:
+    """Integration tests for Agent.as_tool() with real Agent instances."""
+    
+    def test_as_tool_on_real_agent(self):
+        """Test as_tool() on a real Agent instance (no LLM calls)."""
+        from praisonaiagents import Agent
+        from praisonaiagents.agent.handoff import Handoff, ContextPolicy
+        
+        # Create a real agent (no LLM calls needed for this test)
+        agent = Agent(
+            name="TestResearcher",
+            instructions="Research topics thoroughly",
+        )
+        
+        # Call as_tool()
+        result = agent.as_tool("Research a topic and return findings")
+        
+        # Verify result
+        assert isinstance(result, Handoff)
+        assert result.config.context_policy == ContextPolicy.NONE
+        assert result.tool_name == "invoke_testresearcher"
+        assert "Research a topic" in result.tool_description
+    
+    def test_as_tool_with_custom_params(self):
+        """Test as_tool() with custom tool name and description."""
+        from praisonaiagents import Agent
+        from praisonaiagents.agent.handoff import Handoff, ContextPolicy
+        
+        agent = Agent(
+            name="Coder",
+            instructions="Write clean Python code",
+        )
+        
+        result = agent.as_tool(
+            description="Generate Python code for any programming task",
+            tool_name="generate_code",
+        )
+        
+        assert isinstance(result, Handoff)
+        assert result.tool_name == "generate_code"
+        assert "Generate Python code" in result.tool_description
+    
+    def test_as_tool_in_tools_list(self):
+        """Test that as_tool() result can be added to another agent's tools."""
+        from praisonaiagents import Agent
+        from praisonaiagents.agent.handoff import Handoff
+        
+        # Create specialist agents
+        researcher = Agent(name="Researcher", instructions="Research topics")
+        coder = Agent(name="Coder", instructions="Write code")
+        
+        # Create parent agent with specialists as tools
+        writer = Agent(
+            name="Writer",
+            instructions="Write articles using your tools",
+            tools=[
+                researcher.as_tool("Research a topic"),
+                coder.as_tool("Write Python code"),
+            ],
+        )
+        
+        # Verify tools were added (they get converted to tool functions)
+        # The handoffs should be processed into the tools list
+        assert writer.name == "Writer"
+        # Note: Handoffs are processed in _process_handoffs, so we check the agent was created

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

@@ -0,0 +1,254 @@
+"""
+Unit tests for handoff filters including compress_history and filter chaining.
+
+Tests:
+1. compress_history filter - compresses messages into single summary
+2. Filter list chaining - multiple filters applied in order
+3. Existing filters (remove_all_tools, keep_last_n_messages, remove_system_messages)
+"""
+
+from praisonaiagents.agent.handoff import (
+    HandoffInputData,
+    handoff_filters,
+)
+
+
+class TestCompressHistoryFilter:
+    """Tests for handoff_filters.compress_history"""
+    
+    def test_compress_history_basic(self):
+        """Test basic compression of multiple messages into one."""
+        data = HandoffInputData(
+            messages=[
+                {"role": "user", "content": "Hello"},
+                {"role": "assistant", "content": "Hi there!"},
+                {"role": "user", "content": "How are you?"},
+            ],
+            context={},
+            source_agent="test_agent",
+        )
+        
+        result = handoff_filters.compress_history(data)
+        
+        assert len(result.messages) == 1
+        assert result.messages[0]["role"] == "user"
+        assert "Previous conversation summary:" in result.messages[0]["content"]
+        assert "[user]: Hello" in result.messages[0]["content"]
+        assert "[assistant]: Hi there!" in result.messages[0]["content"]
+        assert "[user]: How are you?" in result.messages[0]["content"]
+    
+    def test_compress_history_empty(self):
+        """Test compression with empty messages."""
+        data = HandoffInputData(
+            messages=[],
+            context={},
+            source_agent="test_agent",
+        )
+        
+        result = handoff_filters.compress_history(data)
+        
+        assert len(result.messages) == 0
+    
+    def test_compress_history_single_message(self):
+        """Test compression with single message."""
+        data = HandoffInputData(
+            messages=[{"role": "user", "content": "Single message"}],
+            context={},
+            source_agent="test_agent",
+        )
+        
+        result = handoff_filters.compress_history(data)
+        
+        assert len(result.messages) == 1
+        assert "[user]: Single message" in result.messages[0]["content"]
+    
+    def test_compress_history_preserves_context(self):
+        """Test that compression preserves context and other fields."""
+        data = HandoffInputData(
+            messages=[{"role": "user", "content": "Test"}],
+            context={"key": "value"},
+            source_agent="test_agent",
+            handoff_depth=2,
+            handoff_chain=["agent1", "agent2"],
+        )
+        
+        result = handoff_filters.compress_history(data)
+        
+        assert result.context == {"key": "value"}
+        assert result.source_agent == "test_agent"
+        assert result.handoff_depth == 2
+        assert result.handoff_chain == ["agent1", "agent2"]
+    
+    def test_compress_history_skips_empty_content(self):
+        """Test that messages with empty content are skipped."""
+        data = HandoffInputData(
+            messages=[
+                {"role": "user", "content": "Hello"},
+                {"role": "assistant", "content": ""},
+                {"role": "user", "content": "Goodbye"},
+            ],
+            context={},
+            source_agent="test_agent",
+        )
+        
+        result = handoff_filters.compress_history(data)
+        
+        assert "[assistant]:" not in result.messages[0]["content"]
+        assert "[user]: Hello" in result.messages[0]["content"]
+        assert "[user]: Goodbye" in result.messages[0]["content"]
+
+
+class TestFilterChaining:
+    """Tests for filter list chaining support."""
+    
+    def test_chaining_two_filters(self):
+        """Test chaining two filters together."""
+        data = HandoffInputData(
+            messages=[
+                {"role": "system", "content": "System prompt"},
+                {"role": "user", "content": "Hello"},
+                {"role": "assistant", "content": "Hi", "tool_calls": [{"id": "1"}]},
+                {"role": "tool", "content": "Tool result"},
+                {"role": "user", "content": "Thanks"},
+            ],
+            context={},
+            source_agent="test_agent",
+        )
+        
+        # Chain: remove_all_tools -> remove_system_messages
+        filters = [
+            handoff_filters.remove_all_tools,
+            handoff_filters.remove_system_messages,
+        ]
+        
+        # Apply filters manually (simulating _prepare_context behavior)
+        result = data
+        for f in filters:
+            result = f(result)
+        
+        # Should have removed tool calls and system messages
+        assert len(result.messages) == 2
+        assert all(m.get("role") != "system" for m in result.messages)
+        assert all(m.get("role") != "tool" for m in result.messages)
+        assert all("tool_calls" not in m for m in result.messages)
+    
+    def test_chaining_with_compress(self):
+        """Test chaining filters ending with compress_history."""
+        data = HandoffInputData(
+            messages=[
+                {"role": "system", "content": "System prompt"},
+                {"role": "user", "content": "Hello"},
+                {"role": "assistant", "content": "Hi"},
+                {"role": "user", "content": "Bye"},
+            ],
+            context={},
+            source_agent="test_agent",
+        )
+        
+        # Chain: remove_system_messages -> compress_history
+        filters = [
+            handoff_filters.remove_system_messages,
+            handoff_filters.compress_history,
+        ]
+        
+        result = data
+        for f in filters:
+            result = f(result)
+        
+        # Should have one compressed message without system content
+        assert len(result.messages) == 1
+        assert "System prompt" not in result.messages[0]["content"]
+        assert "[user]: Hello" in result.messages[0]["content"]
+    
+    def test_chaining_with_keep_last_n(self):
+        """Test chaining with keep_last_n_messages."""
+        data = HandoffInputData(
+            messages=[
+                {"role": "user", "content": "Message 1"},
+                {"role": "assistant", "content": "Response 1"},
+                {"role": "user", "content": "Message 2"},
+                {"role": "assistant", "content": "Response 2"},
+                {"role": "user", "content": "Message 3"},
+            ],
+            context={},
+            source_agent="test_agent",
+        )
+        
+        # Chain: keep_last_n(2) -> compress_history
+        filters = [
+            handoff_filters.keep_last_n_messages(2),
+            handoff_filters.compress_history,
+        ]
+        
+        result = data
+        for f in filters:
+            result = f(result)
+        
+        # Should have compressed only last 2 messages
+        assert len(result.messages) == 1
+        assert "Message 1" not in result.messages[0]["content"]
+        assert "Response 2" in result.messages[0]["content"]
+        assert "Message 3" in result.messages[0]["content"]
+
+
+class TestExistingFilters:
+    """Tests for existing handoff filters."""
+    
+    def test_remove_all_tools(self):
+        """Test remove_all_tools filter."""
+        data = HandoffInputData(
+            messages=[
+                {"role": "user", "content": "Hello"},
+                {"role": "assistant", "content": "Calling tool", "tool_calls": [{"id": "1"}]},
+                {"role": "tool", "content": "Tool result"},
+                {"role": "assistant", "content": "Done"},
+            ],
+            context={},
+            source_agent="test_agent",
+        )
+        
+        result = handoff_filters.remove_all_tools(data)
+        
+        assert len(result.messages) == 2
+        assert result.messages[0]["content"] == "Hello"
+        assert result.messages[1]["content"] == "Done"
+    
+    def test_keep_last_n_messages(self):
+        """Test keep_last_n_messages filter."""
+        data = HandoffInputData(
+            messages=[
+                {"role": "user", "content": "1"},
+                {"role": "user", "content": "2"},
+                {"role": "user", "content": "3"},
+                {"role": "user", "content": "4"},
+                {"role": "user", "content": "5"},
+            ],
+            context={},
+            source_agent="test_agent",
+        )
+        
+        result = handoff_filters.keep_last_n_messages(3)(data)
+        
+        assert len(result.messages) == 3
+        assert result.messages[0]["content"] == "3"
+        assert result.messages[1]["content"] == "4"
+        assert result.messages[2]["content"] == "5"
+    
+    def test_remove_system_messages(self):
+        """Test remove_system_messages filter."""
+        data = HandoffInputData(
+            messages=[
+                {"role": "system", "content": "System 1"},
+                {"role": "user", "content": "User"},
+                {"role": "system", "content": "System 2"},
+                {"role": "assistant", "content": "Assistant"},
+            ],
+            context={},
+            source_agent="test_agent",
+        )
+        
+        result = handoff_filters.remove_system_messages(data)
+        
+        assert len(result.messages) == 2
+        assert result.messages[0]["content"] == "User"
+        assert result.messages[1]["content"] == "Assistant"

src/praisonai-agents/uv.lock

@@ -2549,7 +2549,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "1.5.7"
+version = "1.5.13"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v4.5.7.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v4.5.7.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v4.5.13.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v4.5.13.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/cli/commands/loop.py

@@ -134,15 +134,10 @@ def _run_loop(
             typer.echo(f"   Timeout: {timeout}s")
         typer.echo("")
     
-    # Run autonomous loop
+    # Run autonomous loop using unified API
+    # agent.start() automatically uses run_autonomous() when autonomy=True
     try:
-        result = agent.run_autonomous(
-            prompt=prompt,
-            max_iterations=max_iterations,
-            timeout_seconds=timeout,
-            completion_promise=completion_promise,
-            clear_context=clear_context,
-        )
+        result = agent.start(prompt, timeout=timeout)
         
         # Display result
         if result.success:

src/praisonai/praisonai/cli/commands/tracker.py

@@ -1,16 +1,21 @@
 """
 Tracker command for autonomous agent execution with step-by-step tracking.
 
-Provides CLI interface for running agents in autonomous mode with:
+Uses the centralized autonomy system with:
+- All default autonomy tools (search, file, shell, code, etc.)
+- Auto-approval enabled by default (full_auto mode)
 - Real-time step tracking and status output
-- Default tools (search, file, shell, code)
 - Summary table at completion
 - Gap analysis for debugging
+
+The tracker integrates with the Agent's autonomy system to provide
+comprehensive tracking of autonomous agent execution.
 """
 
 from typing import Optional, List
 from dataclasses import dataclass, field
 from datetime import datetime
+import os
 import time
 
 import typer
@@ -54,31 +59,72 @@ class TrackerResult:
     gaps_identified: List[str] = field(default_factory=list)
 
 
-# Default tools for tracker (commonly used, no API keys required)
-DEFAULT_TOOLS = [
-    "search_web",      # Web search (DuckDuckGo fallback)
-    "web_crawl",       # Web crawling
-    "read_file",       # File reading
-    "write_file",      # File writing
-    "list_files",      # Directory listing
-    "execute_command", # Shell commands
-    "execute_code",    # Python execution
-    "analyze_code",    # Code analysis
+# ============================================================================
+# CENTRALIZED AUTONOMY DEFAULT TOOLS
+# These are the default tools available in autonomy mode (no API keys required)
+# ============================================================================
+AUTONOMY_DEFAULT_TOOLS = [
+    # Web Search & Crawl
+    "search_web",       # Unified web search (DuckDuckGo fallback)
+    "internet_search",  # DuckDuckGo search
+    "web_crawl",        # Web page crawling
+    "scrape_page",      # Page scraping
+    "extract_text",     # Text extraction
+    "extract_links",    # Link extraction
+    
+    # File Operations
+    "read_file",        # Read file contents
+    "write_file",       # Write to files
+    "list_files",       # List directory contents
+    "copy_file",        # Copy files
+    "move_file",        # Move files
+    "delete_file",      # Delete files
+    "get_file_info",    # Get file metadata
+    
+    # Shell & System
+    "execute_command",  # Execute shell commands
+    "list_processes",   # List running processes
+    "get_system_info",  # Get system information
+    
+    # Python Code Execution
+    "execute_code",     # Execute Python code
+    "analyze_code",     # Analyze code structure
+    "format_code",      # Format Python code
+    "lint_code",        # Lint Python code
+    
+    # ACP Tools (Agent-Centric Protocol)
+    "acp_create_file",   # Create files (with approval tracking)
+    "acp_edit_file",     # Edit files (with approval tracking)
+    "acp_delete_file",   # Delete files (with approval tracking)
+    "acp_execute_command",  # Execute commands (with approval tracking)
+    
+    # LSP Tools (Language Server Protocol)
+    "lsp_list_symbols",      # List symbols in a file
+    "lsp_find_definition",   # Go to definition
+    "lsp_find_references",   # Find all references
+    "lsp_get_diagnostics",   # Get code diagnostics/errors
+    
+    # Scheduling
+    "schedule_add",     # Add scheduled task
+    "schedule_list",    # List scheduled tasks
+    "schedule_remove",  # Remove scheduled task
 ]
 
 # Extended tools (may require API keys)
 EXTENDED_TOOLS = [
-    "tavily_search",   # Tavily search (requires TAVILY_API_KEY)
-    "exa_search",      # Exa search (requires EXA_API_KEY)
-    "crawl4ai",        # Advanced crawling
-    "format_code",     # Code formatting
-    "lint_code",       # Code linting
+    "tavily_search",    # Tavily search (requires TAVILY_API_KEY)
+    "tavily_extract",   # Tavily extract
+    "exa_search",       # Exa search (requires EXA_API_KEY)
+    "exa_answer",       # Exa answer
+    "crawl4ai",         # Advanced crawling (requires playwright)
+    "ydc_search",       # You.com search (requires YDC_API_KEY)
 ]
 
 
 def _get_tools(tool_names: List[str]) -> List:
     """Resolve tool names to actual tool functions."""
     tools = []
+    resolved_names = set()
     try:
         from praisonaiagents import tools as tool_module
         for name in tool_names:
@@ -89,10 +135,30 @@ def _get_tools(tool_names: List[str]) -> List:
                 if hasattr(tool_func, '__file__') and hasattr(tool_func, name):
                     tool_func = getattr(tool_func, name)
                 tools.append(tool_func)
+                resolved_names.add(name)
             except AttributeError:
-                console.print(f"[yellow]Warning: Tool '{name}' not found, skipping[/yellow]")
+                pass  # Try interactive tools below
     except ImportError:
         console.print("[red]Error: praisonaiagents not installed[/red]")
+    
+    # Resolve ACP/LSP tools via interactive_tools (they need runtime initialization)
+    unresolved = [n for n in tool_names if n not in resolved_names]
+    if any(n.startswith(("acp_", "lsp_")) for n in unresolved):
+        try:
+            from ..features.interactive_tools import get_interactive_tools, ToolConfig
+            config = ToolConfig.from_env()
+            config.workspace = os.getcwd()
+            interactive = get_interactive_tools(config=config)
+            interactive_map = {t.__name__: t for t in interactive}
+            for name in unresolved:
+                if name in interactive_map:
+                    tools.append(interactive_map[name])
+                    resolved_names.add(name)
+        except ImportError:
+            pass  # Interactive tools not available
+        except Exception as e:
+            console.print(f"[dim]Note: ACP/LSP tools not loaded: {e}[/dim]")
+    
     return tools
 
 
@@ -113,43 +179,75 @@ def _run_tracked_task(
     model: Optional[str],
     verbose: bool,
     step_callback: Optional[callable] = None,
-    trust: bool = True,
+    auto_approve: bool = True,
 ) -> TrackerResult:
-    """Run a task with step tracking."""
+    """Run a task with step tracking using centralized autonomy system.
+    
+    Args:
+        task: The task to execute
+        tools: List of tool functions to use
+        max_iterations: Maximum autonomous iterations
+        model: LLM model to use (optional)
+        verbose: Show verbose output
+        step_callback: Callback for each step (optional)
+        auto_approve: Auto-approve all tool calls (default: True)
+    """
     from praisonaiagents import Agent
     
+    # ========================================================================
+    # AUTO-APPROVE: Set env var so @require_approval decorators on tool
+    # functions (execute_code, execute_command, write_file, etc.) auto-approve.
+    # The decorator checks PRAISONAI_AUTO_APPROVE env var independently of
+    # the Agent's approval= parameter.
+    # ========================================================================
+    prev_auto_approve = os.environ.get("PRAISONAI_AUTO_APPROVE")
+    if auto_approve:
+        os.environ["PRAISONAI_AUTO_APPROVE"] = "true"
+    
     steps: List[TrackedStep] = []
     tools_used: set = set()
     start_time = time.time()
     step_number = 0
     
-    # Create agent with tools - auto-approve for autonomous tracking
+    # ========================================================================
+    # CENTRALIZED AUTONOMY CONFIGURATION
+    # Uses full_auto level with auto-approval for autonomous tracking
+    # ========================================================================
     agent_kwargs = {
         "name": "tracker_agent",
         "instructions": """You are an autonomous agent with access to various tools.
 You MUST use tools to complete tasks - do NOT rely on your training knowledge alone.
-For research tasks: use search_web to find information.
-For file tasks: use read_file, write_file, list_files.
-For code tasks: use execute_code, analyze_code.
+
+TOOL USAGE RULES:
+- For research/search tasks: use search_web or internet_search
+- For file operations: use read_file, write_file, list_files
+- For safe file creation/editing: use acp_create_file, acp_edit_file
+- For code tasks: use execute_code, analyze_code
+- For shell commands: use execute_command or acp_execute_command
+- For web scraping: use web_crawl, scrape_page, extract_text
+- For code intelligence: use lsp_list_symbols, lsp_find_definition, lsp_find_references, lsp_get_diagnostics
+
 When you have fully completed the task, say 'Task completed' or 'Done'.""",
         "tools": tools,
+        # Centralized autonomy configuration
         "autonomy": {
+            "enabled": True,
+            "level": "full_auto",  # Full autonomous mode
             "max_iterations": max_iterations,
             "doom_loop_threshold": 5,
-            "level": "full_auto" if trust else "suggest",
+            "auto_escalate": True,
         },
+        # Auto-approve all tools for tracker (agent-centric approval)
+        "approval": auto_approve,
     }
     
-    # Auto-approve tools for tracker (autonomous mode)
-    if trust:
-        import os
-        os.environ["PRAISONAI_AUTO_APPROVE"] = "true"
-    
     if model:
         agent_kwargs["llm"] = model
     
-    if not verbose:
-        agent_kwargs["output"] = "silent"
+    if verbose:
+        pass  # Use default full output
+    else:
+        agent_kwargs["output"] = "status"  # Real-time status like Agent(output="status")
     
     agent = Agent(**agent_kwargs)
     
@@ -301,6 +399,19 @@ When you have fully completed the task, say 'Task completed' or 'Done'.""",
             tools_used=list(tools_used),
             gaps_identified=[f"Error: {str(e)}"],
         )
+    finally:
+        # Stop the LSP/ACP runtime if it was started
+        try:
+            from ..features.interactive_tools import cleanup_runtime
+            cleanup_runtime()
+        except Exception:
+            pass
+        
+        # Restore original PRAISONAI_AUTO_APPROVE env var
+        if prev_auto_approve is None:
+            os.environ.pop("PRAISONAI_AUTO_APPROVE", None)
+        else:
+            os.environ["PRAISONAI_AUTO_APPROVE"] = prev_auto_approve
 
 
 def _print_step_table(steps: List[TrackedStep]) -> None:
@@ -352,10 +463,28 @@ def _print_summary(result: TrackerResult) -> None:
             console.print(f"  • {gap}")
 
 
-@app.callback(invoke_without_command=True)
-def tracker_main(
-    ctx: typer.Context,
-    task: Optional[str] = typer.Argument(None, help="Task for the agent to complete"),
+@app.callback()
+def tracker_main(ctx: typer.Context):
+    """Autonomous agent tracking with step-by-step analysis.
+    
+    Run tasks with full execution tracing and optional quality judging.
+    
+    Commands:
+    
+        praisonai tracker run "Search for Python best practices"
+        
+        praisonai tracker judge "What is 2+2?" --expected "4"
+        
+        praisonai tracker tools
+        
+        praisonai tracker batch tasks.json
+    """
+    pass
+
+
+@app.command(name="run")
+def tracker_run(
+    task: str = typer.Argument(..., help="Task for the agent to complete"),
     max_iterations: int = typer.Option(20, "--max-iterations", "-n", help="Maximum iterations (default: 20)"),
     model: Optional[str] = typer.Option(None, "--model", "-m", help="LLM model to use"),
     tools: Optional[str] = typer.Option(None, "--tools", "-t", help="Comma-separated tool names to use"),
@@ -365,31 +494,32 @@ def tracker_main(
 ):
     """Run an agent with step-by-step tracking.
     
-    The tracker runs an agent in autonomous mode and records every step,
-    tool call, and decision. At the end, it displays a summary table
-    showing all steps taken and any gaps identified.
-    
     Examples:
     
-        praisonai tracker "Search for Python best practices and summarize"
+        praisonai tracker run "Search for Python best practices and summarize"
         
-        praisonai tracker "Read config.yaml and explain its structure" -v
+        praisonai tracker run "Read config.yaml and explain its structure" -v
         
-        praisonai tracker "Find trending AI news" --tools search_web,web_crawl
+        praisonai tracker run "Find trending AI news" --tools search_web,web_crawl
     """
-    if ctx.invoked_subcommand is not None:
-        return
-    
-    if not task:
-        typer.echo(ctx.get_help())
-        return
-    
+    _run_and_display(task, max_iterations, model, tools, extended, verbose, live)
+
+
+def _run_and_display(
+    task: str,
+    max_iterations: int = 20,
+    model: Optional[str] = None,
+    tools: Optional[str] = None,
+    extended: bool = False,
+    verbose: bool = False,
+    live: bool = True,
+) -> TrackerResult:
+    """Shared logic: run a tracked task and display results."""
     # Resolve tools
-    tool_names = DEFAULT_TOOLS.copy()
+    tool_names = AUTONOMY_DEFAULT_TOOLS.copy()
     if extended:
         tool_names.extend(EXTENDED_TOOLS)
     if tools:
-        # Override with user-specified tools
         tool_names = [t.strip() for t in tools.split(",")]
     
     resolved_tools = _get_tools(tool_names)
@@ -421,6 +551,8 @@ def tracker_main(
     _print_step_table(result.steps)
     console.print("\n")
     _print_summary(result)
+    
+    return result
 
 
 @app.command(name="batch")
@@ -456,7 +588,7 @@ def tracker_batch(
     console.print(f"\n[bold cyan]🔍 Batch Agent Tracker[/bold cyan]")
     console.print(f"[dim]Running {len(tasks)} tasks...[/dim]\n")
     
-    resolved_tools = _get_tools(DEFAULT_TOOLS)
+    resolved_tools = _get_tools(AUTONOMY_DEFAULT_TOOLS)
     results: List[TrackerResult] = []
     
     for i, task in enumerate(tasks, 1):
@@ -541,7 +673,7 @@ def tracker_tools():
     console.print("\n[bold cyan]🛠️ Available Tools[/bold cyan]\n")
     
     console.print("[bold]Default Tools (no API keys required):[/bold]")
-    for tool in DEFAULT_TOOLS:
+    for tool in AUTONOMY_DEFAULT_TOOLS:
         console.print(f"  • {tool}")
     
     console.print("\n[bold]Extended Tools (may require API keys):[/bold]")
@@ -550,3 +682,171 @@ def tracker_tools():
     
     console.print("\n[dim]Use --tools to specify custom tools, e.g.:[/dim]")
     console.print("[dim]  praisonai tracker 'task' --tools search_web,read_file[/dim]")
+
+
+# ============================================================================
+# JUDGE FEATURE
+# ============================================================================
+
+DEFAULT_JUDGE_CRITERIA = """Evaluate this autonomous agent execution trace:
+1. Task Completion: Did the agent fully complete the assigned task?
+2. Tool Selection: Were appropriate tools chosen for each step?
+3. Efficiency: Was the task completed with minimal unnecessary steps?
+4. Error Handling: Were errors handled gracefully without crashing?
+5. Output Quality: Is the final output accurate and useful?"""
+
+
+def _format_trace_for_judge(result: TrackerResult) -> str:
+    """Convert TrackerResult into a structured string for LLM evaluation."""
+    lines = []
+    lines.append(f"Task: \"{result.task}\"")
+    status = "success" if result.success else "failed"
+    lines.append(f"Completion: {status} (reason: {result.completion_reason})")
+    lines.append(f"Duration: {result.total_duration:.1f}s | Steps: {result.total_steps} | Tools Used: {', '.join(result.tools_used) if result.tools_used else 'None'}")
+    lines.append("")
+    
+    for step in result.steps:
+        icon = "✅" if step.success else "❌"
+        lines.append(f"Step {step.step_number}: [{step.action_type}] {step.action_name} ({step.duration_seconds:.1f}s) {icon}")
+        lines.append(f"  Input: {step.input_summary}")
+        lines.append(f"  Output: {step.output_summary}")
+        if step.error:
+            lines.append(f"  Error: {step.error}")
+        lines.append("")
+    
+    if result.gaps_identified:
+        lines.append(f"Gaps: {'; '.join(result.gaps_identified)}")
+    else:
+        lines.append("Gaps: None")
+    
+    return "\n".join(lines)
+
+
+def _print_judge_verdict(judge_result, threshold: float) -> None:
+    """Print the judge verdict with rich formatting."""
+    score = getattr(judge_result, 'score', 0) or 0
+    passed = score >= threshold
+    reasoning = getattr(judge_result, 'reasoning', '') or ''
+    suggestions = getattr(judge_result, 'suggestions', []) or []
+    
+    # Score bar
+    bar_len = 20
+    filled = int(score / 10 * bar_len)
+    bar = "█" * filled + "░" * (bar_len - filled)
+    
+    color = "green" if passed else ("yellow" if score >= 5 else "red")
+    icon = "✅" if passed else "❌"
+    
+    console.print(Panel(
+        f"""
+[bold]{icon} Score: [{color}]{score:.1f}/10[/{color}][/bold]  [{color}]{bar}[/{color}]
+[bold]Threshold:[/bold] {threshold}  |  [bold]Verdict:[/bold] [{'green' if passed else 'red'}]{'PASS' if passed else 'FAIL'}[/{'green' if passed else 'red'}]
+
+[bold]Reasoning:[/bold]
+{reasoning}
+""",
+        title="⚖️ Judge Verdict",
+        border_style=color,
+    ))
+    
+    if suggestions:
+        console.print("[bold yellow]💡 Suggestions:[/bold yellow]")
+        for s in suggestions:
+            console.print(f"  • {s}")
+
+
+@app.command(name="judge")
+def tracker_judge(
+    task: str = typer.Argument(..., help="Task to execute and judge"),
+    criteria: Optional[str] = typer.Option(None, "--criteria", "-c", help="Custom evaluation criteria"),
+    expected: Optional[str] = typer.Option(None, "--expected", "-e", help="Expected output for accuracy evaluation"),
+    threshold: float = typer.Option(7.0, "--threshold", help="Pass/fail score threshold (1-10)"),
+    max_iterations: int = typer.Option(20, "--max-iterations", "-n", help="Maximum iterations (default: 20)"),
+    model: Optional[str] = typer.Option(None, "--model", "-m", help="LLM model to use"),
+    judge_model: Optional[str] = typer.Option(None, "--judge-model", help="LLM model for judge (default: same as agent)"),
+    tools: Optional[str] = typer.Option(None, "--tools", "-t", help="Comma-separated tool names to use"),
+    extended: bool = typer.Option(False, "--extended", help="Include extended tools (may require API keys)"),
+    verbose: bool = typer.Option(False, "--verbose", "-v", help="Show verbose output"),
+):
+    """Run a task and judge the execution quality.
+    
+    Executes the task with step tracking, then evaluates the execution
+    trace using an LLM judge. Reports a score (1-10), pass/fail verdict,
+    reasoning, and improvement suggestions.
+    
+    Examples:
+    
+        praisonai tracker judge "Calculate fibonacci(10) using execute_code"
+        
+        praisonai tracker judge "Search for AI news" --criteria "Must use search_web"
+        
+        praisonai tracker judge "What is 2+2?" --expected "4" --threshold 8.0
+    """
+    # Resolve tools
+    tool_names = AUTONOMY_DEFAULT_TOOLS.copy()
+    if extended:
+        tool_names.extend(EXTENDED_TOOLS)
+    if tools:
+        tool_names = [t.strip() for t in tools.split(",")]
+    
+    resolved_tools = _get_tools(tool_names)
+    
+    console.print(f"\n[bold cyan]⚖️ Agent Tracker + Judge[/bold cyan]")
+    console.print(f"[dim]Task: {_summarize_text(task, 70)}[/dim]")
+    console.print(f"[dim]Tools: {len(resolved_tools)} loaded | Threshold: {threshold}[/dim]\n")
+    
+    # Step 1: Run the task
+    console.print("[bold]Phase 1: Executing task...[/bold]\n")
+    
+    def step_callback(step: TrackedStep):
+        status = "✅" if step.success else "❌"
+        console.print(f"  [{step.step_number}] {status} {step.action_type}: {step.action_name} ({step.duration_seconds:.2f}s)")
+    
+    result = _run_tracked_task(
+        task=task,
+        tools=resolved_tools,
+        max_iterations=max_iterations,
+        model=model,
+        verbose=verbose,
+        step_callback=step_callback,
+    )
+    
+    # Print step table + summary
+    console.print("\n")
+    _print_step_table(result.steps)
+    console.print("\n")
+    _print_summary(result)
+    
+    # Step 2: Judge the execution
+    console.print("\n[bold]Phase 2: Judging execution...[/bold]\n")
+    
+    try:
+        from praisonaiagents.eval import Judge
+        
+        trace_text = _format_trace_for_judge(result)
+        
+        judge_kwargs = {}
+        if judge_model:
+            judge_kwargs["model"] = judge_model
+        elif model:
+            judge_kwargs["model"] = model
+        
+        judge = Judge(threshold=threshold, **judge_kwargs)
+        
+        eval_criteria = criteria or DEFAULT_JUDGE_CRITERIA
+        
+        judge_result = judge.run(
+            output=trace_text,
+            criteria=eval_criteria,
+            expected=expected,
+            input=task,
+        )
+        
+        _print_judge_verdict(judge_result, threshold)
+        
+    except ImportError:
+        console.print("[red]Error: praisonaiagents.eval not available[/red]")
+        console.print("[dim]Install with: pip install praisonaiagents[/dim]")
+    except Exception as e:
+        console.print(f"[red]Judge error: {e}[/red]")
+

src/praisonai/praisonai/cli/features/interactive_tools.py

@@ -186,29 +186,73 @@ def _load_basic_tools() -> Dict[str, Callable]:
     return tools
 
 
+# ── Shared runtime singleton ────────────────────────────────────────────────
+# ACP and LSP tools share ONE InteractiveRuntime to avoid duplicate LSP servers.
+_shared_runtime = None
+_shared_agent_tools = None
+
+
+def _get_shared_runtime(config: ToolConfig):
+    """Get or create a shared InteractiveRuntime instance.
+    
+    Returns (runtime, all_tools) — the runtime and the full list of
+    agent-centric tool functions created from it.
+    """
+    global _shared_runtime, _shared_agent_tools
+    
+    if _shared_runtime is not None and _shared_agent_tools is not None:
+        return _shared_runtime, _shared_agent_tools
+    
+    from .interactive_runtime import InteractiveRuntime, RuntimeConfig
+    from .agent_tools import create_agent_centric_tools
+    
+    runtime_config = RuntimeConfig(
+        workspace=config.workspace,
+        lsp_enabled=config.lsp_enabled,
+        acp_enabled=config.acp_enabled,
+        approval_mode=config.approval_mode,
+    )
+    
+    _shared_runtime = InteractiveRuntime(runtime_config)
+    _shared_agent_tools = create_agent_centric_tools(_shared_runtime)
+    
+    logger.debug("Created shared InteractiveRuntime")
+    return _shared_runtime, _shared_agent_tools
+
+
+def cleanup_runtime():
+    """Stop the shared InteractiveRuntime (LSP server, ACP session).
+    
+    Call this when the agent finishes to release resources.
+    Safe to call multiple times or when no runtime exists.
+    """
+    global _shared_runtime, _shared_agent_tools
+    
+    if _shared_runtime is not None:
+        import asyncio
+        try:
+            loop = asyncio.get_event_loop()
+            if loop.is_running():
+                import concurrent.futures
+                with concurrent.futures.ThreadPoolExecutor() as pool:
+                    pool.submit(asyncio.run, _shared_runtime.stop()).result(timeout=5)
+            else:
+                loop.run_until_complete(_shared_runtime.stop())
+        except Exception as e:
+            logger.debug(f"Runtime cleanup: {e}")
+        
+        _shared_runtime = None
+        _shared_agent_tools = None
+        logger.debug("Shared InteractiveRuntime stopped")
+
+
 def _load_acp_tools(config: ToolConfig) -> Dict[str, Callable]:
-    """Lazy load ACP tools with runtime initialization."""
+    """Lazy load ACP tools via the shared runtime."""
     tools = {}
     
     try:
-        from .interactive_runtime import InteractiveRuntime, RuntimeConfig
-        from .agent_tools import create_agent_centric_tools
+        _, all_tools = _get_shared_runtime(config)
         
-        # Create runtime config
-        runtime_config = RuntimeConfig(
-            workspace=config.workspace,
-            lsp_enabled=config.lsp_enabled,
-            acp_enabled=config.acp_enabled,
-            approval_mode=config.approval_mode,
-        )
-        
-        # Create runtime (lazy - doesn't start until needed)
-        runtime = InteractiveRuntime(runtime_config)
-        
-        # Get all agent-centric tools
-        all_tools = create_agent_centric_tools(runtime)
-        
-        # Map by name
         for tool in all_tools:
             name = tool.__name__
             if name.startswith("acp_"):
@@ -225,28 +269,12 @@ def _load_acp_tools(config: ToolConfig) -> Dict[str, Callable]:
 
 
 def _load_lsp_tools(config: ToolConfig) -> Dict[str, Callable]:
-    """Lazy load LSP tools with runtime initialization."""
+    """Lazy load LSP tools via the shared runtime."""
     tools = {}
     
     try:
-        from .interactive_runtime import InteractiveRuntime, RuntimeConfig
-        from .agent_tools import create_agent_centric_tools
+        _, all_tools = _get_shared_runtime(config)
         
-        # Create runtime config
-        runtime_config = RuntimeConfig(
-            workspace=config.workspace,
-            lsp_enabled=config.lsp_enabled,
-            acp_enabled=config.acp_enabled,
-            approval_mode=config.approval_mode,
-        )
-        
-        # Create runtime
-        runtime = InteractiveRuntime(runtime_config)
-        
-        # Get all agent-centric tools
-        all_tools = create_agent_centric_tools(runtime)
-        
-        # Map by name
         for tool in all_tools:
             name = tool.__name__
             if name.startswith("lsp_"):

src/praisonai/praisonai/cli/features/queue/manager.py

@@ -40,6 +40,7 @@ class QueueManager:
         on_complete: Optional[Callable[[str, QueuedRun], Coroutine[Any, Any, None]]] = None,
         on_error: Optional[Callable[[str, Exception], Coroutine[Any, Any, None]]] = None,
         on_event: Optional[Callable[[QueueEvent], Coroutine[Any, Any, None]]] = None,
+        on_tool_call: Optional[Callable] = None,
         default_tools: Optional[list] = None,
     ):
         """
@@ -51,6 +52,7 @@ class QueueManager:
             on_complete: Callback when run completes.
             on_error: Callback when run fails.
             on_event: Callback for queue events.
+            on_tool_call: Callback for tool call events (run_id, tool_name, tool_input, tool_output).
             default_tools: Default tools to use for all runs (stored in runtime registry).
         """
         self.config = config or QueueConfig()
@@ -64,6 +66,7 @@ class QueueManager:
         self._on_complete = on_complete
         self._on_error = on_error
         self._on_event = on_event
+        self._on_tool_call = on_tool_call
         
         # Worker pool (created on start)
         self.workers: Optional[WorkerPool] = None
@@ -107,6 +110,7 @@ class QueueManager:
             on_complete=self._wrap_complete_callback(),
             on_error=self._wrap_error_callback(),
             on_event=self._on_event,
+            on_tool_call=self._on_tool_call,
             max_workers=self.config.max_concurrent_global,
             poll_interval=self.config.worker_poll_interval,
             stream_buffer_size=self.config.stream_buffer_size,

src/praisonai/praisonai/cli/features/queue/worker.py

@@ -99,6 +99,7 @@ class WorkerPool:
         on_complete: Optional[Callable[[str, QueuedRun], Coroutine[Any, Any, None]]] = None,
         on_error: Optional[Callable[[str, Exception], Coroutine[Any, Any, None]]] = None,
         on_event: Optional[Callable[[QueueEvent], Coroutine[Any, Any, None]]] = None,
+        on_tool_call: Optional[Callable] = None,
         max_workers: int = 4,
         poll_interval: float = 0.1,
         stream_buffer_size: int = 1000,
@@ -121,6 +122,7 @@ class WorkerPool:
         self.on_complete = on_complete
         self.on_error = on_error
         self.on_event = on_event
+        self.on_tool_call = on_tool_call
         self.max_workers = max_workers
         self.poll_interval = poll_interval
         self.stream_buffer_size = stream_buffer_size
@@ -254,6 +256,21 @@ class WorkerPool:
             # Execute with streaming if available
             chunk_index = 0
             
+            # Register TUI tool_call display callback to pipe tool events
+            if self.on_tool_call:
+                try:
+                    from praisonaiagents.main import register_display_callback
+                    _tool_call_cb = self.on_tool_call  # Capture for closure
+                    _run_id = run.run_id
+                    def _tui_tool_call_cb(message=None, tool_name=None, tool_input=None, tool_output=None, **kwargs):
+                        try:
+                            _tool_call_cb(_run_id, tool_name, tool_input, tool_output)
+                        except Exception:
+                            pass
+                    register_display_callback('tool_call', _tui_tool_call_cb)
+                except Exception:
+                    pass
+            
             # Try streaming first
             try:
                 async for chunk in self._stream_agent(agent, run.input_content):
@@ -364,20 +381,9 @@ class WorkerPool:
                 yield chunk
                 await asyncio.sleep(0)  # Yield control
         else:
-            # Fallback: get full response and yield in chunks
+            # No streaming available: yield full response at once (don't fake-stream)
             result = agent.chat(input_content)
-            
-            # Simulate streaming by yielding words
-            words = result.split()
-            chunk_size = 3  # Words per chunk
-            
-            for i in range(0, len(words), chunk_size):
-                chunk_words = words[i:i + chunk_size]
-                chunk = " ".join(chunk_words)
-                if i + chunk_size < len(words):
-                    chunk += " "
-                yield chunk
-                await asyncio.sleep(0.05)  # Small delay for streaming effect
+            yield result
     
     def get_stream_buffer(self, run_id: str) -> Optional[StreamBuffer]:
         """Get the stream buffer for a run."""

src/praisonai/praisonai/cli/features/tui/app.py

@@ -7,6 +7,7 @@ Event-loop driven async TUI with multi-pane layout.
 import asyncio
 import logging
 import os
+import time
 import uuid
 from typing import Any, Dict, List, Optional
 
@@ -55,6 +56,19 @@ if TEXTUAL_AVAILABLE:
         Screen {
             background: $surface;
         }
+        
+        /* Custom scrollbar styling */
+        ScrollableContainer > .scrollbar--thumb {
+            background: $accent 40%;
+        }
+        ScrollableContainer > .scrollbar--thumb:hover {
+            background: $accent 60%;
+        }
+        
+        /* Panel border accents */
+        Vertical {
+            border: none;
+        }
         """
         
         # Safe default bindings - Ctrl keys only at app level for emergency exit
@@ -107,6 +121,7 @@ if TEXTUAL_AVAILABLE:
             # State
             self._current_run_id: Optional[str] = None
             self._streaming_content: str = ""
+            self._last_stream_update: float = 0.0  # Throttle streaming UI to 100ms
             self._total_tokens: int = 0
             self._total_cost: float = 0.0
             
@@ -183,6 +198,7 @@ if TEXTUAL_AVAILABLE:
                 on_output=self._handle_output,
                 on_complete=self._handle_complete,
                 on_error=self._handle_error,
+                on_tool_call=self._handle_tool_call,
                 default_tools=self.agent_config.get("tools", []),
             )
             
@@ -234,10 +250,10 @@ if TEXTUAL_AVAILABLE:
                 main_screen.set_processing(True)
             
             # Add user message to session store for history persistence
-            self.session_store.add_user_message(self.session_id, content)
+            self._session_store.add_user_message(self.session_id, content)
             
             # Get chat history for context continuity
-            chat_history = self.session_store.get_chat_history(self.session_id, max_messages=50)
+            chat_history = self._session_store.get_chat_history(self.session_id, max_messages=50)
             
             # Submit to queue with chat history
             # Note: tools are NOT included in config (they can't be JSON serialized)
@@ -391,12 +407,18 @@ if TEXTUAL_AVAILABLE:
         # Queue callbacks
         
         async def _handle_output(self, run_id: str, chunk: str) -> None:
-            """Handle streaming output."""
+            """Handle streaming output with 100ms throttle."""
             if run_id != self._current_run_id:
                 return
             
             self._streaming_content += chunk
             
+            # Throttle UI updates to avoid O(n²) Markdown re-parsing
+            now = time.monotonic()
+            if now - self._last_stream_update < 0.1:  # 100ms throttle
+                return
+            self._last_stream_update = now
+            
             main_screen = self.screen
             if isinstance(main_screen, MainScreen):
                 await main_screen.update_streaming(run_id, self._streaming_content)
@@ -412,7 +434,7 @@ if TEXTUAL_AVAILABLE:
             
             # Store assistant response in session store for history persistence
             if output_content and run.session_id:
-                self.session_store.add_assistant_message(run.session_id, output_content, run_id)
+                self._session_store.add_assistant_message(run.session_id, output_content, run_id)
             
             # Update metrics
             if run.metrics:
@@ -761,6 +783,41 @@ Tip: Ask the agent to "commit the changes with message X".
                 except Exception:
                     pass
         
+        def _handle_tool_call(self, run_id: str, tool_name: str, tool_input: dict, tool_output: str) -> None:
+            """Handle tool call events from agent execution and display in ToolPanel."""
+            from .widgets.tool_panel import ToolPanelWidget, ToolCall
+            import uuid
+            
+            main_screen = self.screen
+            if not isinstance(main_screen, MainScreen):
+                return
+            
+            try:
+                tool_panel = main_screen.query_one("#tool-panel", ToolPanelWidget)
+                call_id = str(uuid.uuid4())[:8]
+                status = "completed" if tool_output is not None else "running"
+                
+                args_preview = ""
+                if tool_input:
+                    args_preview = str(tool_input)[:100]
+                
+                result_preview = None
+                if tool_output is not None:
+                    result_preview = str(tool_output)[:100]
+                
+                call = ToolCall(
+                    call_id=call_id,
+                    tool_name=tool_name or "unknown",
+                    args_preview=args_preview,
+                    status=status,
+                    result_preview=result_preview,
+                )
+                
+                # Use call_from_thread to safely update the widget from the worker thread
+                self.call_from_thread(tool_panel.add_call, call)
+            except Exception:
+                pass
+        
         def action_quit(self) -> None:
             """Quit the application."""
             self.exit()

src/praisonai/praisonai/cli/features/tui/screens/main.py

@@ -4,6 +4,7 @@ Main Screen for PraisonAI TUI.
 The primary chat interface screen.
 """
 
+import asyncio
 from typing import Optional
 
 try:
@@ -61,7 +62,7 @@ if TEXTUAL_AVAILABLE:
             layout: grid;
             grid-size: 2;
             grid-columns: 3fr 1fr;
-            grid-rows: 1 1fr auto 1;
+            grid-rows: 1 1fr auto auto 1;
             layers: base popup;
         }
         
@@ -83,6 +84,19 @@ if TEXTUAL_AVAILABLE:
             max-height: 10;
         }
         
+        MainScreen #processing-indicator {
+            column-span: 2;
+            height: 1;
+            background: $surface;
+            color: $accent;
+            text-align: center;
+            display: none;
+        }
+        
+        MainScreen #processing-indicator.visible {
+            display: block;
+        }
+        
         MainScreen Footer {
             column-span: 2;
         }
@@ -156,6 +170,7 @@ if TEXTUAL_AVAILABLE:
                     yield QueuePanelWidget(id="queue-panel", classes="sidebar-panel")
             
             yield ComposerWidget(id="composer")
+            yield Static("⠋ Thinking...", id="processing-indicator")
             
             yield Footer()
         
@@ -179,6 +194,17 @@ if TEXTUAL_AVAILABLE:
             # Focus composer
             composer = self.query_one("#composer", ComposerWidget)
             composer.focus_input()
+            
+            # Show welcome message
+            chat = self.query_one("#chat-widget", ChatWidget)
+            welcome = ChatMessage(
+                role="system",
+                content=(
+                    "**Welcome to PraisonAI** — type a message to get started.\n\n"
+                    "Quick tips:  `?` help · `\\` commands · `q` quit · `Esc` cancel"
+                ),
+            )
+            self.call_after_refresh(lambda: asyncio.ensure_future(chat.add_message(welcome)))
         
         def on_composer_widget_submitted(self, event: ComposerWidget.Submitted) -> None:
             """Handle message submission."""
@@ -493,6 +519,45 @@ if TEXTUAL_AVAILABLE:
             
             status = self.query_one("#status-bar", StatusWidget)
             status.is_processing = processing
+            
+            # Toggle animated processing indicator
+            try:
+                indicator = self.query_one("#processing-indicator", Static)
+                if processing:
+                    indicator.add_class("visible")
+                    self._start_spinner()
+                else:
+                    indicator.remove_class("visible")
+                    self._stop_spinner()
+            except Exception:
+                pass
+        
+        def _start_spinner(self) -> None:
+            """Start the animated spinner."""
+            self._spinner_frames = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"]
+            self._spinner_index = 0
+            self.set_interval(0.1, self._animate_spinner, name="spinner")
+        
+        def _stop_spinner(self) -> None:
+            """Stop the animated spinner."""
+            try:
+                # Remove the timer by name
+                for timer in self._timers:
+                    if timer.name == "spinner":
+                        timer.stop()
+                        break
+            except Exception:
+                pass
+        
+        def _animate_spinner(self) -> None:
+            """Animate the spinner indicator."""
+            try:
+                indicator = self.query_one("#processing-indicator", Static)
+                frame = self._spinner_frames[self._spinner_index % len(self._spinner_frames)]
+                indicator.update(f"{frame} Thinking...")
+                self._spinner_index += 1
+            except Exception:
+                pass
 
 else:
     class MainScreen:

src/praisonai/praisonai/cli/features/tui/widgets/chat.py

@@ -172,7 +172,7 @@ if TEXTUAL_AVAILABLE:
             await self.mount(panel)
             
             # Scroll to end to show new message
-            self.scroll_end(animate=False)
+            self.scroll_end(animate=True)
             
             if message.is_streaming:
                 self._streaming_widgets[message.run_id] = widget_id
@@ -202,7 +202,7 @@ if TEXTUAL_AVAILABLE:
                 widget.update(Panel(rendered, title="Assistant", border_style="bold green"))
                 
                 # Scroll to end to keep streaming content visible
-                self.scroll_end(animate=False)
+                self.scroll_end(animate=True)
                 
             except Exception:
                 pass

src/praisonai/praisonai/cli/features/tui/widgets/command_popup.py

@@ -250,11 +250,6 @@ DEFAULT_COMMANDS = [
     CommandInfo("sessions", "Browse saved sessions", ["sess"]),
     CommandInfo("tools", "Toggle tools panel", ["t"]),
     CommandInfo("exit", "Exit the TUI", ["quit", "q"]),
-    CommandInfo("plan", "Create an execution plan", []),
-    CommandInfo("undo", "Undo the last change", []),
-    CommandInfo("diff", "Show git diff of changes", []),
-    CommandInfo("commit", "Commit changes with AI message", []),
-    CommandInfo("map", "Show repository map", ["repo"]),
 ]
 
 

src/praisonai/praisonai/cli/features/tui/widgets/status.py

@@ -72,34 +72,43 @@ if TEXTUAL_AVAILABLE:
             classes: Optional[str] = None,
         ):
             super().__init__("", name=name, id=id, classes=classes)
+            self._batch_updating = False  # Suppress individual watcher re-renders during batch
         
         def on_mount(self) -> None:
             """Handle mount."""
             self._update_display()
         
         def watch_session_id(self, value: str) -> None:
-            self._update_display()
+            if not self._batch_updating:
+                self._update_display()
         
         def watch_model(self, value: str) -> None:
-            self._update_display()
+            if not self._batch_updating:
+                self._update_display()
         
         def watch_total_tokens(self, value: int) -> None:
-            self._update_display()
+            if not self._batch_updating:
+                self._update_display()
         
         def watch_total_cost(self, value: float) -> None:
-            self._update_display()
+            if not self._batch_updating:
+                self._update_display()
         
         def watch_queued_count(self, value: int) -> None:
-            self._update_display()
+            if not self._batch_updating:
+                self._update_display()
         
         def watch_running_count(self, value: int) -> None:
-            self._update_display()
+            if not self._batch_updating:
+                self._update_display()
         
         def watch_is_processing(self, value: bool) -> None:
-            self._update_display()
+            if not self._batch_updating:
+                self._update_display()
         
         def watch_status_message(self, value: str) -> None:
-            self._update_display()
+            if not self._batch_updating:
+                self._update_display()
         
         def _update_display(self) -> None:
             """Update the status display."""
@@ -148,15 +157,20 @@ if TEXTUAL_AVAILABLE:
             self.update(text)
         
         def update_info(self, info: StatusInfo) -> None:
-            """Update all status info at once."""
-            self.session_id = info.session_id
-            self.model = info.model
-            self.total_tokens = info.total_tokens
-            self.total_cost = info.total_cost
-            self.queued_count = info.queued_count
-            self.running_count = info.running_count
-            self.is_processing = info.is_processing
-            self.status_message = info.status_message
+            """Update all status info at once (batched, single render)."""
+            self._batch_updating = True
+            try:
+                self.session_id = info.session_id
+                self.model = info.model
+                self.total_tokens = info.total_tokens
+                self.total_cost = info.total_cost
+                self.queued_count = info.queued_count
+                self.running_count = info.running_count
+                self.is_processing = info.is_processing
+                self.status_message = info.status_message
+            finally:
+                self._batch_updating = False
+            self._update_display()
 
 else:
     class StatusWidget:

src/praisonai/praisonai/cli/legacy.py

@@ -41,7 +41,7 @@ TYPER_COMMANDS = {
     # Standardisation commands
     'standardise', 'standardize',
     # Moltbot-inspired commands (bots, browser, plugins, sandbox)
-    'bot', 'browser', 'plugins', 'sandbox', 'loop',
+    'bot', 'browser', 'plugins', 'sandbox', 'loop', 'tracker',
     # RAG commands
     'rag', 'index', 'query', 'search',
 }

src/praisonai/praisonai/cli/main.py

@@ -4561,6 +4561,7 @@ Now, {final_instruction.lower()}:"""
                         return ApprovalDecision(approved=True, reason="User approved")
                     else:
                         console.print("[red]❌ Denied[/red]")
+                        console.print("[dim]Tip: Use --trust to auto-approve all tools[/dim]")
                         return ApprovalDecision(approved=False, reason="User denied")
                 except (KeyboardInterrupt, EOFError):
                     status_info['approval_pending'] = False
@@ -4576,28 +4577,23 @@ Now, {final_instruction.lower()}:"""
         thread.start()
         
         # Show live status while processing
+        # Loop handles unlimited approval interruptions (each approval
+        # pauses the Live display, then we restart it afterwards)
         try:
-            with Live(build_status_display(), console=console, refresh_per_second=4, transient=True) as live:
-                status_info['live_instance'] = live
-                while not status_info['done']:
-                    # Check if approval is pending - stop Live to show prompt
-                    if status_info['approval_pending']:
-                        break
-                    live.update(build_status_display())
-                    time.sleep(0.1)
-            
-            # If approval was pending, wait for it to complete then restart Live
-            while status_info['approval_pending']:
-                time.sleep(0.1)
-            
-            # Continue with Live display if not done
-            if not status_info['done']:
+            while not status_info['done']:
                 with Live(build_status_display(), console=console, refresh_per_second=4, transient=True) as live:
+                    status_info['live_instance'] = live
                     while not status_info['done']:
+                        # Check if approval is pending - stop Live to show prompt
                         if status_info['approval_pending']:
                             break
                         live.update(build_status_display())
                         time.sleep(0.1)
+                
+                # If approval was pending, wait for it to complete then loop
+                # back to restart the Live display
+                while status_info['approval_pending']:
+                    time.sleep(0.1)
         except KeyboardInterrupt:
             console.print("\n[dim]Interrupted[/dim]")
             # Unregister callback (use local variable with None check)
@@ -4605,8 +4601,8 @@ Now, {final_instruction.lower()}:"""
                 del _sync_display_callbacks['tool_call']
             return None
         
-        # Wait for thread to complete
-        thread.join(timeout=1.0)
+        # Wait for thread to complete (generous timeout for long-running tasks)
+        thread.join(timeout=5.0)
         
         # Unregister callback to avoid memory leaks (use local variable with None check)
         if _sync_display_callbacks is not None and 'tool_call' in _sync_display_callbacks:
@@ -4623,10 +4619,40 @@ Now, {final_instruction.lower()}:"""
         
         # Get the result and print it directly here to ensure it's displayed
         result = status_info['result']
-        if result:
-            console.print(result)
-        elif result == "" or result is None:
-            console.print("[dim]No response generated[/dim]")
+        
+        # Handle AutonomyResult from run_autonomous()
+        display_text = None
+        if result is not None:
+            if hasattr(result, 'output') and result.output:
+                display_text = result.output
+            elif isinstance(result, str) and result:
+                display_text = result
+            elif result:
+                display_text = str(result)
+        
+        if display_text:
+            console.print(display_text)
+        else:
+            # Last resort: extract from agent's chat history
+            extracted = None
+            if hasattr(agent, 'chat_history') and agent.chat_history:
+                for msg in reversed(agent.chat_history):
+                    if msg.get('role') == 'assistant' and msg.get('content'):
+                        extracted = msg['content']
+                        break
+            if extracted:
+                console.print(extracted)
+            elif status_info['tool_calls']:
+                console.print("[dim]Task completed (tools executed successfully)[/dim]")
+            else:
+                console.print("[dim]No response generated[/dim]")
+        
+        # Cleanup LSP/ACP runtime
+        try:
+            from .features.interactive_tools import cleanup_runtime
+            cleanup_runtime()
+        except Exception:
+            pass
         
         return result
 

src/praisonai/praisonai/deploy.py

@@ -57,7 +57,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==4.5.7 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==4.5.13 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/praisonai/version.py

@@ -1 +1 @@
-__version__ = "4.5.7"
+__version__ = "4.5.13"

src/praisonai/pyproject.toml

@@ -4,7 +4,7 @@ dynamic = ["version"]
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = {text = "MIT"}
-requires-python = ">=3.10,<3.14"
+requires-python = ">=3.10,<3.13"
 authors = [
     { name = "Mervin Praison" }
 ]
@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=1.5.7",
+    "praisonaiagents>=1.5.13",
     "python-dotenv>=0.19.0",
     "litellm>=1.81.0",
     "PyYAML>=6.0",

src/praisonai/uv.lock

@@ -1,9 +1,8 @@
 version = 1
 revision = 3
-requires-python = ">=3.10, <3.14"
+requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version >= '3.13'",
-    "python_full_version == '3.12.*'",
+    "python_full_version >= '3.12'",
     "python_full_version == '3.11.*'",
     "python_full_version < '3.11'",
 ]
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 [[package]]
@@ -248,7 +232,7 @@ dependencies = [
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
     { name = "idna" },
     { name = "sniffio" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+    { name = "typing-extensions" },
 ]
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