Release 0.2.0

[email protected]
[email protected]
[email protected]

Generated by cargo-workspaces

.github/workflows/update-parity-tracker.yml

@@ -0,0 +1,58 @@
+name: Update Feature Parity Tracker
+
+on:
+  push:
+    branches: [ "main" ]
+    paths:
+      - 'src/praisonai-agents/praisonaiagents/**/*.py'
+      - 'src/praisonai-ts/src/**/*.ts'
+      - 'src/praisonai-rust/src/**/*.rs'
+      - '.github/workflows/update-parity-tracker.yml'
+
+permissions:
+  contents: write
+
+jobs:
+  update-parity-tracker:
+    runs-on: ubuntu-latest
+    
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+      with:
+        ref: main
+        persist-credentials: true
+        fetch-depth: 0
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.11'
+
+    - name: Generate parity trackers
+      run: |
+        cd $GITHUB_WORKSPACE
+        python3 src/praisonai/scripts/generate_parity_tracker.py
+        
+    - name: Check for changes
+      id: check_changes
+      run: |
+        if git diff --quiet src/praisonai-ts/FEATURE_PARITY_TRACKER.json src/praisonai-ts/PARITY.md src/praisonai-rust/FEATURE_PARITY_TRACKER.json src/praisonai-rust/PARITY.md 2>/dev/null; then
+          echo "changes=false" >> $GITHUB_OUTPUT
+          echo "No changes to parity trackers"
+        else
+          echo "changes=true" >> $GITHUB_OUTPUT
+          echo "Parity trackers have been updated"
+          git diff src/praisonai-ts/FEATURE_PARITY_TRACKER.json | head -50
+        fi
+
+    - name: Commit and push changes
+      if: steps.check_changes.outputs.changes == 'true'
+      uses: stefanzweifel/git-auto-commit-action@v5
+      with:
+        commit_message: "docs: auto-update feature parity trackers [skip ci]"
+        file_pattern: 'src/praisonai-ts/FEATURE_PARITY_TRACKER.json src/praisonai-ts/PARITY.md src/praisonai-rust/FEATURE_PARITY_TRACKER.json src/praisonai-rust/PARITY.md'
+        commit_user_name: "github-actions[bot]"
+        commit_user_email: "41898282+github-actions[bot]@users.noreply.github.com"
+        commit_author: "github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>"
+

.gitignore

@@ -84,4 +84,11 @@ issue_details.json
 .mcpregistry_*
 
 *.log
-.internal
+.internal
+.praisonai
+
+# Rust / Cargo
+target/
+Cargo.lock
+**/*.rs.bk
+*.pdb

api.md

@@ -58,6 +58,7 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">generate_task</a>() -> 'Task'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_available_tools</a>() -> List[Any]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_history_size</a>() -> int</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_learn_context</a>() -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_memory_context</a>(query: Optional[str] = None) -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_recommended_stage</a>(prompt: str) -> str</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_rules_context</a>(file_path: Optional[str] = None, include_manual: Optional[List[str]] = None) -> str</code>
@@ -81,6 +82,8 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run</a>(prompt: str, **kwargs)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_autonomous</a>(prompt: str, max_iterations: Optional[int] = None, timeout_seconds: Optional[float] = None, completion_promise: Optional[str] = None, clear_context: bool = False)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_autonomous_async</a>(prompt: str, max_iterations: Optional[int] = None, timeout_seconds: Optional[float] = None, completion_promise: Optional[str] = None, clear_context: bool = False)</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_until</a>(prompt: str, criteria: str, threshold: float = 8.0, max_iterations: int = 5, mode: str = 'optimize', on_iteration: Optional[Callable[[Any], None]] = None, verbose: bool = False) -> 'EvaluationLoopResult'</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_until_async</a>(prompt: str, criteria: str, threshold: float = 8.0, max_iterations: int = 5, mode: str = 'optimize', on_iteration: Optional[Callable[[Any], None]] = None, verbose: bool = False) -> 'EvaluationLoopResult'</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">session_id</a>() -> Optional[str]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">skill_manager</a>()</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">start</a>(prompt: str = None, **kwargs)</code>
@@ -208,10 +211,15 @@ from praisonaiagents import Loop, Parallel, Pipeline, Repeat, Route, Task, Workf
 
 Methods:
 
+* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">depends_on</a>()</code>
+* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">depends_on</a>(value)</code>
+* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">evaluate_when</a>(context: Dict[str, Any]) -> bool</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">execute_callback</a>(task_output: TaskOutput) -> None</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">execute_callback_sync</a>(task_output: TaskOutput) -> None</code>
+* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">get_next_task</a>(context: Dict[str, Any]) -> Optional[str]</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">initialize_memory</a>()</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">store_in_memory</a>(content: str, agent_name: str = None, task_id: str = None)</code>
+* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">loop</a>(step: Any = None, steps: Optional[List[Any]] = None, over: Optional[str] = None, from_csv: Optional[str] = None, from_file: Optional[str] = None, var_name: str = 'item', parallel: bool = False, max_workers: Optional[int] = None, output_variable: Optional[str] = None) -> Loop</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">parallel</a>(steps: List) -> Parallel</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">repeat</a>(step: Any, until: Optional[Callable[[WorkflowContext], bool]] = None, max_iterations: int = 10) -> Repeat</code>
@@ -568,7 +576,7 @@ Methods:
 
 Types:
 ```python
-from praisonaiagents import ContextManager, ManagerConfig
+from praisonaiagents import ContextManager
 ```
 
 Methods:
@@ -586,20 +594,16 @@ Methods:
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">reset</a>() -> None</code>
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">set_tool_budget</a>(tool_name: str, max_tokens: int, protected: bool = False) -> None</code>
 * <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">truncate_tool_output</a>(tool_name: str, output: str) -> str</code>
-* <code title="class ManagerConfig">ManagerConfig.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">from_env</a>() -> 'ManagerConfig'</code>
-* <code title="class ManagerConfig">ManagerConfig.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">merge</a>(**overrides) -> 'ManagerConfig'</code>
-* <code title="class ManagerConfig">ManagerConfig.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">to_dict</a>() -> Dict[str, Any]</code>
 
 # Config
 
 Types:
 ```python
-from praisonaiagents import AutonomyConfig, AutonomyLevel, CachingConfig, ExecutionConfig, ExecutionPreset, GuardrailConfig, HooksConfig, KnowledgeConfig, MemoryConfig, MultiAgentExecutionConfig, MultiAgentHooksConfig, MultiAgentMemoryConfig, MultiAgentOutputConfig, MultiAgentPlanningConfig, OutputConfig, OutputPreset, PlanningConfig, ReflectionConfig, SkillsConfig, TemplateConfig, WebConfig, WebSearchProvider
+from praisonaiagents import AutonomyLevel, CachingConfig, ExecutionConfig, ExecutionPreset, GuardrailConfig, HooksConfig, KnowledgeConfig, MemoryConfig, MultiAgentExecutionConfig, MultiAgentHooksConfig, MultiAgentMemoryConfig, MultiAgentOutputConfig, MultiAgentPlanningConfig, OutputConfig, OutputPreset, PlanningConfig, ReflectionConfig, SkillsConfig, TemplateConfig, WebConfig, WebSearchProvider
 ```
 
 Methods:
 
-* <code title="class AutonomyConfig">AutonomyConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="class CachingConfig">CachingConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="class ExecutionConfig">ExecutionConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
 * <code title="class GuardrailConfig">GuardrailConfig.<a href="./src/praisonai-agents/praisonaiagents/config/feature_configs.py">to_dict</a>() -> Dict[str, Any]</code>
@@ -1001,6 +1005,7 @@ Methods:
 * <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 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>
 * <code title="cli">praisonai train llm <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
 * <code title="cli">praisonai train show <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>=4.2.3" \
+    "praisonai>=4.4.4" \
     "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>=4.2.3" \
+    "praisonai>=4.4.4" \
     "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>=4.2.3" \
+    "praisonai>=4.4.4" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/python/eval/README.md

@@ -0,0 +1,43 @@
+# EvaluationLoop Examples
+
+Examples demonstrating iterative evaluation and improvement patterns.
+
+## Quick Start
+
+```bash
+pip install praisonaiagents
+export OPENAI_API_KEY=your-key
+```
+
+## Examples
+
+| File | Description |
+|------|-------------|
+| `basic_evaluation_loop.py` | Simple loop: Run → Judge → Improve |
+| `multi_step_task_loop.py` | Sequential tasks with context passing |
+| `recipe_optimization_loop.py` | CLI-based recipe optimization |
+
+## Pattern
+
+```
+┌─────────┐     ┌─────────┐     ┌──────────┐
+│  Agent  │ ──► │  Judge  │ ──► │ Feedback │
+│  runs   │     │ scores  │     │ improves │
+└─────────┘     └─────────┘     └──────────┘
+     ▲                               │
+     └───────────────────────────────┘
+           (repeat until 8.0+)
+```
+
+## Run Examples
+
+```bash
+# Basic loop
+python basic_evaluation_loop.py
+
+# Multi-step with context
+python multi_step_task_loop.py
+
+# CLI recipe loop
+python recipe_optimization_loop.py "research AI trends"
+```

examples/python/eval/basic_evaluation_loop.py

@@ -0,0 +1,73 @@
+"""
+Basic EvaluationLoop Example
+
+Demonstrates the iterative evaluation loop pattern:
+Run agent → Judge output → Improve → Repeat until threshold met.
+
+Usage:
+    pip install praisonaiagents
+    export OPENAI_API_KEY=your-key
+    python basic_evaluation_loop.py
+"""
+from praisonaiagents import Agent
+from praisonaiagents.eval import EvaluationLoop
+
+
+def on_iteration(result):
+    """Callback showing progress after each iteration."""
+    status = "✅" if result.score >= 8.0 else "⚠️" if result.score >= 6.0 else "❌"
+    print(f"{status} Iteration {result.iteration}: {result.score:.1f}/10")
+    if result.findings:
+        print(f"   → {result.findings[0][:60]}...")
+
+
+def main():
+    # Create an agent
+    agent = Agent(
+        name="writer",
+        instructions="You are a technical content writer. Write clear, accurate explanations."
+    )
+
+    # Run evaluation loop
+    loop = EvaluationLoop(
+        agent=agent,
+        criteria="""
+        Evaluate the response on:
+        1. CLARITY: Is the explanation easy to understand?
+        2. ACCURACY: Is the technical content correct?
+        3. COMPLETENESS: Does it cover the key aspects?
+        4. EXAMPLES: Does it include practical examples?
+        
+        Score 1-10 based on all criteria.
+        """,
+        threshold=8.0,
+        max_iterations=5,
+        on_iteration=on_iteration,
+        verbose=False,
+    )
+
+    print("╔════════════════════════════════════════════════════════════╗")
+    print("║            Basic EvaluationLoop Example                     ║")
+    print("║  Pattern: Run → Judge → Improve → Repeat                   ║")
+    print("║  Target: 8.0/10 score                                       ║")
+    print("╚════════════════════════════════════════════════════════════╝\n")
+
+    result = loop.run("Explain how API rate limiting works and why it matters")
+
+    print("\n" + "─" * 60)
+    print("RESULTS:")
+    print("─" * 60)
+    print(f"Success: {result.success}")
+    print(f"Final Score: {result.final_score:.1f}/10")
+    print(f"Iterations: {result.num_iterations}")
+    print(f"Score History: {[f'{s:.1f}' for s in result.score_history]}")
+    print(f"Duration: {result.total_duration_seconds:.2f}s")
+
+    print("\n" + "─" * 60)
+    print("FINAL OUTPUT (first 500 chars):")
+    print("─" * 60)
+    print(result.final_output[:500] + "..." if len(result.final_output) > 500 else result.final_output)
+
+
+if __name__ == "__main__":
+    main()

examples/python/eval/multi_step_task_loop.py

@@ -0,0 +1,144 @@
+"""
+Multi-Step Task Loop Example
+
+Demonstrates evaluating sequential task steps where each step can
+reference output from previous steps.
+
+Pattern:
+1. Research → Evaluate → Improve
+2. Analyze (using research) → Evaluate → Improve  
+3. Report (using analysis) → Evaluate → Improve
+
+Usage:
+    pip install praisonaiagents
+    export OPENAI_API_KEY=your-key
+    python multi_step_task_loop.py
+"""
+from praisonaiagents import Agent
+from praisonaiagents.eval import EvaluationLoop
+
+
+def on_iteration(result):
+    """Show iteration progress."""
+    status = "✅" if result.score >= 8.0 else "⚠️" if result.score >= 6.0 else "❌"
+    print(f"  {status} Iteration {result.iteration}: {result.score:.1f}/10")
+
+
+def run_step(step_name: str, agent: Agent, task: str, criteria: str, context: str = "") -> dict:
+    """Run a single step through an evaluation loop."""
+    print(f"\n{'─'*60}")
+    print(f"STEP: {step_name}")
+    print(f"{'─'*60}")
+    
+    # Include context from previous step if provided
+    full_task = task
+    if context:
+        full_task = f"{task}\n\nContext from previous step:\n{context[:1000]}"
+    
+    loop = EvaluationLoop(
+        agent=agent,
+        criteria=criteria,
+        threshold=8.0,
+        max_iterations=3,
+        on_iteration=on_iteration,
+        verbose=False,
+    )
+    
+    result = loop.run(full_task)
+    
+    print(f"  → Final: {result.final_score:.1f}/10 ({result.num_iterations} iterations)")
+    
+    return {
+        "name": step_name,
+        "output": result.final_output,
+        "score": result.final_score,
+        "success": result.success,
+        "iterations": result.num_iterations,
+    }
+
+
+def main():
+    print("╔════════════════════════════════════════════════════════════╗")
+    print("║         Multi-Step Task Loop Example                       ║")
+    print("║  Pattern: Step1 → Step2 → Step3 (with context passing)    ║")
+    print("║  Each step: Run → Judge → Improve until 8.0+              ║")
+    print("╚════════════════════════════════════════════════════════════╝")
+
+    # Create specialized agents
+    researcher = Agent(
+        name="researcher",
+        instructions="You are a research specialist. Find and summarize key information."
+    )
+    
+    analyst = Agent(
+        name="analyst", 
+        instructions="You are an analyst. Identify patterns, insights, and implications."
+    )
+    
+    writer = Agent(
+        name="writer",
+        instructions="You are a report writer. Create clear, actionable summaries."
+    )
+
+    # Define task steps
+    steps = [
+        {
+            "name": "Research",
+            "agent": researcher,
+            "task": "Research the top 3 trends in AI for 2024-2025. Provide brief descriptions.",
+            "criteria": "Covers 3 distinct trends with accurate descriptions.",
+        },
+        {
+            "name": "Analysis",
+            "agent": analyst,
+            "task": "Analyze the implications of these AI trends for software development.",
+            "criteria": "Identifies actionable insights and potential challenges.",
+        },
+        {
+            "name": "Report",
+            "agent": writer,
+            "task": "Create a brief executive summary (3-4 bullet points) for a CTO.",
+            "criteria": "Concise, actionable, appropriate for executive audience.",
+        },
+    ]
+
+    # Run all steps with context passing
+    results = []
+    context = ""
+    
+    for step in steps:
+        result = run_step(
+            step_name=step["name"],
+            agent=step["agent"],
+            task=step["task"],
+            criteria=step["criteria"],
+            context=context,
+        )
+        results.append(result)
+        context = result["output"]  # Pass output as context to next step
+
+    # Summary
+    print("\n" + "═"*60)
+    print("SUMMARY")
+    print("═"*60)
+    
+    total_iterations = sum(r["iterations"] for r in results)
+    avg_score = sum(r["score"] for r in results) / len(results)
+    all_success = all(r["success"] for r in results)
+    
+    for r in results:
+        status = "✅" if r["success"] else "❌"
+        print(f"  {status} {r['name']}: {r['score']:.1f}/10 ({r['iterations']} iterations)")
+    
+    print(f"\n  Total Iterations: {total_iterations}")
+    print(f"  Average Score: {avg_score:.1f}/10")
+    print(f"  All Steps Passed: {'Yes' if all_success else 'No'}")
+
+    print("\n" + "─"*60)
+    print("FINAL REPORT OUTPUT:")
+    print("─"*60)
+    print(results[-1]["output"][:600])
+
+
+if __name__ == "__main__":
+    main()

examples/python/eval/recipe_optimization_loop.py

@@ -0,0 +1,183 @@
+"""
+Recipe Optimization Loop (CLI-based)
+
+Demonstrates the 5-cycle recipe improvement pattern using CLI commands:
+    Create (once) → Run → Judge → Optimize (loop 5x)
+
+Usage:
+    pip install praisonai
+    export OPENAI_API_KEY=your-key
+    export TAVILY_API_KEY=your-key
+    python recipe_optimization_loop.py "research AI trends in healthcare"
+"""
+import subprocess
+import sys
+import os
+import json
+from pathlib import Path
+
+
+def run_cli(cmd: str, cwd: str = None) -> tuple[int, str, str]:
+    """Run a CLI command and return (exit_code, stdout, stderr)."""
+    result = subprocess.run(
+        cmd,
+        shell=True,
+        capture_output=True,
+        text=True,
+        cwd=cwd,
+    )
+    return result.returncode, result.stdout, result.stderr
+
+
+def create_recipe(goal: str, work_dir: str) -> str:
+    """Create a recipe once. Returns recipe name."""
+    print(f"\n{'═'*60}")
+    print("STEP 1: CREATE RECIPE (once)")
+    print(f"{'═'*60}")
+    print(f"Goal: {goal}")
+    
+    cmd = f'praisonai recipe create "{goal}" --no-optimize'
+    code, stdout, stderr = run_cli(cmd, cwd=work_dir)
+    
+    if code != 0:
+        print(f"❌ Failed to create recipe: {stderr}")
+        return None
+    
+    # Extract recipe name from output
+    for line in stdout.split('\n'):
+        if 'Created recipe:' in line:
+            recipe_name = line.split('Created recipe:')[1].strip()
+            print(f"✅ Created: {recipe_name}")
+            return recipe_name
+    
+    print(f"✅ Recipe created")
+    return None
+
+
+def run_recipe(recipe_name: str, trace_name: str, work_dir: str) -> tuple[bool, str]:
+    """Run the recipe and save trace. Returns (success, output)."""
+    cmd = f'praisonai recipe run {recipe_name} --save --name {trace_name}'
+    code, stdout, stderr = run_cli(cmd, cwd=work_dir)
+    
+    success = code == 0 and 'status: completed' in stdout.lower()
+    return success, stdout
+
+
+def judge_recipe(trace_name: str, work_dir: str) -> tuple[float, str]:
+    """Judge the trace. Returns (score, feedback)."""
+    cmd = f'praisonai recipe judge {trace_name}'
+    code, stdout, stderr = run_cli(cmd, cwd=work_dir)
+    
+    # Extract score from output
+    score = 0.0
+    for line in stdout.split('\n'):
+        if 'Overall Score:' in line:
+            try:
+                score = float(line.split(':')[1].strip().split('/')[0])
+            except:
+                pass
+    
+    return score, stdout
+
+
+def optimize_recipe(recipe_name: str, iterations: int, work_dir: str) -> tuple[float, str]:
+    """Optimize the recipe. Returns (final_score, output)."""
+    cmd = f'praisonai recipe optimize {recipe_name} --iterations {iterations}'
+    code, stdout, stderr = run_cli(cmd, cwd=work_dir)
+    
+    # Extract final score
+    score = 0.0
+    for line in stdout.split('\n'):
+        if 'Final score:' in line or 'score:' in line.lower():
+            try:
+                score = float(line.split(':')[1].strip().split('/')[0])
+            except:
+                pass
+    
+    return score, stdout
+
+
+def run_optimization_loop(goal: str, num_cycles: int = 5, threshold: float = 8.0):
+    """Run the full Create → Run → Judge → Optimize loop."""
+    
+    print("╔════════════════════════════════════════════════════════════╗")
+    print("║       Recipe Optimization Loop (CLI-based)                 ║")
+    print("║  Pattern: Create (once) → Run → Judge → Optimize (loop)   ║")
+    print(f"║  Cycles: {num_cycles} | Threshold: {threshold}/10                         ║")
+    print("╚════════════════════════════════════════════════════════════╝")
+    
+    # Create work directory
+    work_dir = Path("/tmp/recipe-optimization-loop")
+    work_dir.mkdir(parents=True, exist_ok=True)
+    os.chdir(work_dir)
+    
+    # Step 1: Create recipe (once)
+    recipe_name = create_recipe(goal, str(work_dir))
+    if not recipe_name:
+        print("Failed to create recipe. Exiting.")
+        return
+    
+    # Step 2-N: Run → Judge → Optimize loop
+    results = []
+    
+    for cycle in range(1, num_cycles + 1):
+        print(f"\n{'─'*60}")
+        print(f"CYCLE {cycle}/{num_cycles}")
+        print(f"{'─'*60}")
+        
+        trace_name = f"cycle-{cycle}"
+        
+        # Run
+        print(f"  📝 Running recipe...")
+        success, run_output = run_recipe(recipe_name, trace_name, str(work_dir))
+        run_status = "✅" if success else "⚠️"
+        print(f"  {run_status} Run complete")
+        
+        # Judge
+        print(f"  ⚖️ Judging output...")
+        score, judge_output = judge_recipe(trace_name, str(work_dir))
+        score_status = "✅" if score >= threshold else "⚠️" if score >= 6.0 else "❌"
+        print(f"  {score_status} Score: {score}/10")
+        
+        # Check if threshold met
+        if score >= threshold:
+            print(f"  🎉 Threshold {threshold}/10 reached!")
+            results.append({"cycle": cycle, "score": score, "status": "passed"})
+            break
+        
+        # Optimize
+        print(f"  🔧 Optimizing recipe...")
+        opt_score, opt_output = optimize_recipe(recipe_name, 1, str(work_dir))
+        print(f"  → Optimized score: {opt_score}/10")
+        
+        results.append({
+            "cycle": cycle,
+            "initial_score": score,
+            "optimized_score": opt_score,
+            "status": "optimized" if opt_score > score else "no_change"
+        })
+    
+    # Summary
+    print(f"\n{'═'*60}")
+    print("SUMMARY")
+    print(f"{'═'*60}")
+    
+    for r in results:
+        if "initial_score" in r:
+            print(f"  Cycle {r['cycle']}: {r['initial_score']} → {r['optimized_score']}/10")
+        else:
+            print(f"  Cycle {r['cycle']}: {r['score']}/10 ✅ PASSED")
+    
+    final_score = results[-1].get("optimized_score", results[-1].get("score", 0))
+    success = final_score >= threshold
+    
+    print(f"\n  Final Score: {final_score}/10")
+    print(f"  Success: {'Yes ✅' if success else 'No ❌'}")
+    print(f"  Recipe: {work_dir}/{recipe_name}")
+    
+    return results
+
+
+if __name__ == "__main__":
+    goal = sys.argv[1] if len(sys.argv) > 1 else "research latest AI trends and create a summary report"
+    run_optimization_loop(goal, num_cycles=5, threshold=8.0)

src/praisonai-agents/.gitignore

@@ -89,4 +89,6 @@ graph.py
 chroma_db/
 .qodo
 *_COMPARISON_ANALYSIS.md
-*_REPORT.md
+*_REPORT.md
+
+.praisonai

src/praisonai-agents/.praisonai/memory/default/config.json

@@ -0,0 +1,9 @@
+{
+  "short_term_limit": 100,
+  "long_term_limit": 1000,
+  "episodic_retention_days": 30,
+  "enable_summarization": false,
+  "summarization_threshold": 50,
+  "importance_threshold": 0.7,
+  "auto_promote": true
+}

src/praisonai-agents/.praisonai/summaries/_var_folders_0b_t9c4hwvx6lg6jk58gw8w_dp80000gn_T_tmp6_tn0h1t_hierarchy.json

@@ -0,0 +1,21 @@
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+      "created_at": "2026-02-02T17:47:19.241224",
+      "metadata": {
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+        "root_file_count": 0
+      }
+    }
+  }
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src/praisonai-agents/.praisonai/summaries/_var_folders_0b_t9c4hwvx6lg6jk58gw8w_dp80000gn_T_tmp7wxr57wm_hierarchy.json

@@ -0,0 +1,35 @@
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+      "level": 1,
+      "summary": "# Application code",
+      "children": [],
+      "token_count": 5,
+      "created_at": "2026-02-02T17:47:19.243833",
+      "metadata": {
+        "filename": "app.py",
+        "relative_path": "src/app.py"
+      }
+    },
+    "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmp7wxr57wm/src": {
+      "path": "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmp7wxr57wm/src",
+      "level": 2,
+      "summary": "Folder contains 1 files: # Application code",
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+        "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmp7wxr57wm/src/app.py"
+      ],
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+      "created_at": "2026-02-02T17:47:19.243852",
+      "metadata": {
+        "folder_name": "src",
+        "file_count": 1
+      }
+    }
+  }
+}

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@@ -0,0 +1,21 @@
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+      "level": 3,
+      "summary": "Project with 0 folders and 0 root files: ",
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+      "created_at": "2026-02-02T17:49:37.074201",
+      "metadata": {
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+        "root_file_count": 0
+      }
+    }
+  }
+}

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@@ -0,0 +1,33 @@
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+      "level": 1,
+      "summary": "def helper(): return True",
+      "children": [],
+      "token_count": 7,
+      "created_at": "2026-02-02T17:47:19.242582",
+      "metadata": {
+        "filename": "utils.py",
+        "relative_path": "utils.py"
+      }
+    },
+    "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmp9nw2kfoj/main.py": {
+      "path": "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmp9nw2kfoj/main.py",
+      "level": 1,
+      "summary": "def main(): print('Hello')",
+      "children": [],
+      "token_count": 7,
+      "created_at": "2026-02-02T17:47:19.242630",
+      "metadata": {
+        "filename": "main.py",
+        "relative_path": "main.py"
+      }
+    }
+  }
+}

src/praisonai-agents/.praisonai/summaries/_var_folders_0b_t9c4hwvx6lg6jk58gw8w_dp80000gn_T_tmp_dbottar_hierarchy.json

@@ -0,0 +1,35 @@
+{
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+  "total_files": 1,
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+  "levels": 2,
+  "built_at": "2026-02-02T17:49:37.077857",
+  "nodes": {
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+      "path": "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmp_dbottar/src/app.py",
+      "level": 1,
+      "summary": "# Application code",
+      "children": [],
+      "token_count": 5,
+      "created_at": "2026-02-02T17:49:37.077985",
+      "metadata": {
+        "filename": "app.py",
+        "relative_path": "src/app.py"
+      }
+    },
+    "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmp_dbottar/src": {
+      "path": "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmp_dbottar/src",
+      "level": 2,
+      "summary": "Folder contains 1 files: # Application code",
+      "children": [
+        "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmp_dbottar/src/app.py"
+      ],
+      "token_count": 11,
+      "created_at": "2026-02-02T17:49:37.078011",
+      "metadata": {
+        "folder_name": "src",
+        "file_count": 1
+      }
+    }
+  }
+}

src/praisonai-agents/.praisonai/summaries/_var_folders_0b_t9c4hwvx6lg6jk58gw8w_dp80000gn_T_tmpp5k48vv4_hierarchy.json

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+      "level": 1,
+      "summary": "def helper(): return True",
+      "children": [],
+      "token_count": 7,
+      "created_at": "2026-02-02T17:49:37.076036",
+      "metadata": {
+        "filename": "utils.py",
+        "relative_path": "utils.py"
+      }
+    },
+    "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmpp5k48vv4/main.py": {
+      "path": "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmpp5k48vv4/main.py",
+      "level": 1,
+      "summary": "def main(): print('Hello')",
+      "children": [],
+      "token_count": 7,
+      "created_at": "2026-02-02T17:49:37.076227",
+      "metadata": {
+        "filename": "main.py",
+        "relative_path": "main.py"
+      }
+    }
+  }
+}

src/praisonai-agents/.praisonai/summaries/_var_folders_0b_t9c4hwvx6lg6jk58gw8w_dp80000gn_T_tmpwwb01831_hierarchy.json

@@ -0,0 +1,33 @@
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+  "total_files": 2,
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+  "nodes": {
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+      "summary": "JavaScript web development",
+      "children": [],
+      "token_count": 7,
+      "created_at": "2026-02-02T17:49:37.079612",
+      "metadata": {
+        "filename": "javascript.js",
+        "relative_path": "javascript.js"
+      }
+    },
+    "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmpwwb01831/python.py": {
+      "path": "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmpwwb01831/python.py",
+      "level": 1,
+      "summary": "Python programming language code",
+      "children": [],
+      "token_count": 9,
+      "created_at": "2026-02-02T17:49:37.079658",
+      "metadata": {
+        "filename": "python.py",
+        "relative_path": "python.py"
+      }
+    }
+  }
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src/praisonai-agents/.praisonai/summaries/_var_folders_0b_t9c4hwvx6lg6jk58gw8w_dp80000gn_T_tmpwzdvf3_7_hierarchy.json

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+      "level": 1,
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+      "created_at": "2026-02-02T17:47:19.245344",
+      "metadata": {
+        "filename": "javascript.js",
+        "relative_path": "javascript.js"
+      }
+    },
+    "/var/folders/0b/t9c4hwvx6lg6jk58gw8w_dp80000gn/T/tmpwzdvf3_7/python.py": {
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+      "level": 1,
+      "summary": "Python programming language code",
+      "children": [],
+      "token_count": 9,
+      "created_at": "2026-02-02T17:47:19.245395",
+      "metadata": {
+        "filename": "python.py",
+        "relative_path": "python.py"
+      }
+    }
+  }
+}

src/praisonai-agents/examples/smoke_test_condition_syntax.py

@@ -0,0 +1,177 @@
+#!/usr/bin/env python3
+"""
+Smoke Test: Condition Syntax Comparison
+
+This script tests both condition syntaxes WITHOUT LLM calls (deterministic):
+1. AgentFlow with when() string expressions
+2. AgentTeam with Task.condition dict routing
+
+Run: python examples/smoke_test_condition_syntax.py
+"""
+from praisonaiagents import AgentFlow, Task
+from praisonaiagents.workflows import when
+
+print("=" * 60)
+print("SMOKE TEST: Condition Syntax Comparison")
+print("=" * 60)
+
+# ============================================================
+# TEST 1: AgentFlow with when() - String Expression Syntax
+# ============================================================
+print("\n" + "=" * 60)
+print("TEST 1: AgentFlow with when() - String Expression")
+print("=" * 60)
+
+def high_score_handler(ctx):
+    print("  ✅ HIGH SCORE PATH: Score was >= 50")
+    return {"output": "High score processed"}
+
+def low_score_handler(ctx):
+    print("  ❌ LOW SCORE PATH: Score was < 50")
+    return {"output": "Low score processed"}
+
+# AgentFlow uses STRING condition: "{{score}} >= 50"
+flow = AgentFlow(
+    name="Score-Based Flow",
+    steps=[
+        when(
+            condition="{{score}} >= 50",  # STRING expression
+            then_steps=[high_score_handler],
+            else_steps=[low_score_handler]
+        )
+    ],
+    variables={"score": 75}  # Pre-set score for deterministic test
+)
+
+print("\nAgentFlow Syntax:")
+print('  when(condition="{{score}} >= 50", then_steps=[...], else_steps=[...])')
+print("  Variables: {'score': 75}")
+print("\nRunning AgentFlow...")
+
+try:
+    result = flow.start("Process the score")
+    print("\n✅ AgentFlow completed successfully")
+except Exception as e:
+    print(f"\n❌ AgentFlow failed: {e}")
+
+# ============================================================
+# TEST 2: AgentTeam with Task.condition - Dict Routing Syntax
+# ============================================================
+print("\n" + "=" * 60)
+print("TEST 2: AgentTeam with Task.condition - Dict Routing")
+print("=" * 60)
+
+# Task uses DICT condition: {"approved": ["publish"], "rejected": ["edit"]}
+# NOTE: This is just showing the syntax - no LLM call needed
+review_task = Task(
+    name="review",
+    description="Review the content and decide: approved or rejected",
+    task_type="decision",
+    is_start=True,
+    condition={  # DICT routing
+        "approved": ["publish"],
+        "rejected": ["edit"]
+    }
+)
+
+print("\nAgentTeam/Task Syntax:")
+print('  Task(condition={"approved": ["publish"], "rejected": ["edit"]})')
+print("  task_type='decision' triggers LLM to output a decision value")
+print("\nTask condition structure:")
+print(f"  review_task.condition = {review_task.condition}")
+print(f"  review_task.task_type = '{review_task.task_type}'")
+print("\n✅ Task condition syntax validated (no LLM call needed)")
+
+# ============================================================
+# TEST 3: NEW UNIFIED SYNTAX - Task.when (same as AgentFlow!)
+# ============================================================
+print("\n" + "=" * 60)
+print("TEST 3: NEW UNIFIED SYNTAX - Task.when (same as AgentFlow!)")
+print("=" * 60)
+
+# Task now supports the same string expression syntax as AgentFlow!
+unified_task = Task(
+    name="score_check",
+    description="Check if score passes threshold",
+    when="{{score}} > 80",  # SAME syntax as AgentFlow!
+    then_task="approve",
+    else_task="reject"
+)
+
+print("\nNEW Unified Task Syntax:")
+print('  Task(when="{{score}} > 80", then_task="approve", else_task="reject")')
+print("\nTask structure:")
+print(f"  unified_task.when = '{unified_task.when}'")
+print(f"  unified_task.then_task = '{unified_task.then_task}'")
+print(f"  unified_task.else_task = '{unified_task.else_task}'")
+
+# Test evaluate_when method
+print("\nTesting evaluate_when():")
+result_high = unified_task.evaluate_when({"score": 90})
+result_low = unified_task.evaluate_when({"score": 70})
+print(f"  evaluate_when({{'score': 90}}) = {result_high}")
+print(f"  evaluate_when({{'score': 70}}) = {result_low}")
+
+# Test get_next_task method
+print("\nTesting get_next_task():")
+next_high = unified_task.get_next_task({"score": 90})
+next_low = unified_task.get_next_task({"score": 70})
+print(f"  get_next_task({{'score': 90}}) = '{next_high}'")
+print(f"  get_next_task({{'score': 70}}) = '{next_low}'")
+
+print("\n✅ NEW unified Task.when syntax works!")
+
+# ============================================================
+# SUMMARY: Syntax Differences
+# ============================================================
+print("\n" + "=" * 60)
+print("SUMMARY: Syntax Differences (CONFUSING FOR USERS)")
+print("=" * 60)
+
+print("""
+┌─────────────────────────────────────────────────────────────────┐
+│                    CONDITION SYNTAX COMPARISON                   │
+├─────────────────────────────────────────────────────────────────┤
+│                                                                  │
+│  AGENTFLOW (Deterministic Pipelines)                            │
+│  ─────────────────────────────────────                          │
+│  Syntax: STRING expression with {{variable}} placeholders       │
+│                                                                  │
+│    when(                                                        │
+│        condition="{{score}} >= 50",  ← STRING                   │
+│        then_steps=[approve],                                    │
+│        else_steps=[reject]                                      │
+│    )                                                            │
+│                                                                  │
+│  Supported: >, <, >=, <=, ==, !=, in, contains                 │
+│                                                                  │
+├─────────────────────────────────────────────────────────────────┤
+│                                                                  │
+│  AGENTTEAM (Multi-Agent Task Graphs)                            │
+│  ─────────────────────────────────────                          │
+│  Syntax: DICT mapping decision values to next task names        │
+│                                                                  │
+│    Task(                                                        │
+│        condition={                    ← DICT                    │
+│            "approved": ["publish"],                             │
+│            "rejected": ["edit"]                                 │
+│        },                                                       │
+│        task_type="decision"                                     │
+│    )                                                            │
+│                                                                  │
+│  LLM outputs a decision value, Process routes by key lookup    │
+│                                                                  │
+├─────────────────────────────────────────────────────────────────┤
+│                                                                  │
+│  🚨 CONFUSION POINTS FOR BEGINNERS:                             │
+│                                                                  │
+│  1. Same word "condition" means DIFFERENT things                │
+│  2. AgentFlow: evaluates expression → boolean                   │
+│  3. AgentTeam: routes by key lookup → next task                 │
+│  4. No unified syntax for simple use cases                      │
+│  5. Task also has should_run (callable) - 3rd way!              │
+│                                                                  │
+└─────────────────────────────────────────────────────────────────┘
+""")
+
+print("\n✅ Smoke test completed!")

src/praisonai-agents/examples/streaming_reasoning_example.py

@@ -0,0 +1,175 @@
+"""
+Streaming with Reasoning Content Example
+
+This example demonstrates how to handle streaming events with reasoning content
+from models like o1, o1-mini, and deepseek-reasoner.
+
+The is_reasoning flag allows you to distinguish between:
+- Reasoning/thinking content (is_reasoning=True)
+- Final response content (is_reasoning=False)
+"""
+
+from praisonaiagents import Agent
+from praisonaiagents.streaming import StreamEvent, StreamEventType, StreamMetrics
+
+
+def create_styled_callback():
+    """Create a callback that styles reasoning content differently."""
+    
+    def callback(event: StreamEvent):
+        if event.type == StreamEventType.DELTA_TEXT:
+            if event.is_reasoning:
+                # Dim gray for reasoning content
+                print(f"\033[90m{event.content}\033[0m", end="", flush=True)
+            else:
+                # Normal for response content
+                print(event.content, end="", flush=True)
+        elif event.type == StreamEventType.STREAM_END:
+            print()  # Newline at end
+    
+    return callback
+
+
+def create_collecting_callback():
+    """Create a callback that collects reasoning and response separately."""
+    reasoning_parts = []
+    response_parts = []
+    
+    def callback(event: StreamEvent):
+        if event.type == StreamEventType.DELTA_TEXT:
+            if event.is_reasoning:
+                reasoning_parts.append(event.content)
+            else:
+                response_parts.append(event.content)
+    
+    def get_results():
+        return {
+            "reasoning": "".join(reasoning_parts),
+            "response": "".join(response_parts)
+        }
+    
+    return callback, get_results
+
+
+def example_basic_streaming():
+    """Basic streaming with reasoning detection."""
+    print("=" * 60)
+    print("Example 1: Basic Streaming with Reasoning Detection")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="thinker",
+        instructions="Think step by step before answering.",
+        llm="gpt-4o-mini"  # Change to o1-mini for actual reasoning
+    )
+    
+    # Add styled callback
+    agent.stream_emitter.add_callback(create_styled_callback())
+    
+    # Enable metrics
+    agent.stream_emitter.enable_metrics()
+    
+    # Run with streaming
+    response = agent.start("What is 15 * 23?", stream=True)
+    
+    # Show metrics
+    metrics = agent.stream_emitter.get_metrics()
+    if metrics:
+        print(f"\n📊 {metrics.format_summary()}")
+    
+    return response
+
+
+def example_collect_separately():
+    """Collect reasoning and response content separately."""
+    print("\n" + "=" * 60)
+    print("Example 2: Collect Reasoning and Response Separately")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="analyzer",
+        instructions="Analyze the problem carefully.",
+        llm="gpt-4o-mini"
+    )
+    
+    # Create collecting callback
+    callback, get_results = create_collecting_callback()
+    agent.stream_emitter.add_callback(callback)
+    
+    # Run with streaming
+    response = agent.start("Explain why the sky is blue.", stream=True)
+    
+    # Get separated results
+    results = get_results()
+    
+    print(f"\n📝 Reasoning length: {len(results['reasoning'])} chars")
+    print(f"📝 Response length: {len(results['response'])} chars")
+    
+    return response
+
+
+def example_with_metrics():
+    """Track streaming performance metrics."""
+    print("\n" + "=" * 60)
+    print("Example 3: Streaming with Performance Metrics")
+    print("=" * 60)
+    
+    metrics = StreamMetrics()
+    
+    def metrics_callback(event: StreamEvent):
+        metrics.update_from_event(event)
+        
+        if event.type == StreamEventType.FIRST_TOKEN:
+            print("⚡ First token received!")
+        elif event.type == StreamEventType.DELTA_TEXT:
+            print(event.content, end="", flush=True)
+        elif event.type == StreamEventType.STREAM_END:
+            print()
+    
+    agent = Agent(name="assistant", llm="gpt-4o-mini")
+    agent.stream_emitter.add_callback(metrics_callback)
+    
+    response = agent.start("Write a haiku about coding.", stream=True)
+    
+    print("\n📊 Performance Metrics:")
+    print(f"   TTFT: {metrics.ttft * 1000:.0f}ms")
+    print(f"   Stream Duration: {metrics.stream_duration * 1000:.0f}ms")
+    print(f"   Total Time: {metrics.total_time * 1000:.0f}ms")
+    print(f"   Tokens: {metrics.token_count}")
+    print(f"   Speed: {metrics.tokens_per_second:.1f} tokens/sec")
+    
+    return response
+
+
+def example_multi_agent_context():
+    """Show agent context in multi-agent scenarios."""
+    print("\n" + "=" * 60)
+    print("Example 4: Multi-Agent Context")
+    print("=" * 60)
+    
+    def context_callback(event: StreamEvent):
+        if event.type == StreamEventType.DELTA_TEXT:
+            prefix = f"[{event.agent_id}] " if event.agent_id else ""
+            print(f"{prefix}{event.content}", end="", flush=True)
+        elif event.type == StreamEventType.STREAM_END:
+            print()
+    
+    # Note: agent_id is populated when using multi-agent workflows
+    agent = Agent(name="helper", llm="gpt-4o-mini")
+    agent.stream_emitter.add_callback(context_callback)
+    
+    response = agent.start("Say hello!", stream=True)
+    
+    return response
+
+
+if __name__ == "__main__":
+    print("🚀 PraisonAI Streaming with Reasoning Examples\n")
+    
+    # Run examples
+    example_basic_streaming()
+    example_collect_separately()
+    example_with_metrics()
+    example_multi_agent_context()
+    
+    print("\n✅ All examples completed!")

src/praisonai-agents/praisonaiagents/__init__.py

@@ -150,10 +150,19 @@ _LAZY_IMPORTS = {
     'Parallel': ('praisonaiagents.workflows', 'Parallel'),
     'Loop': ('praisonaiagents.workflows', 'Loop'),
     'Repeat': ('praisonaiagents.workflows', 'Repeat'),
+    'If': ('praisonaiagents.workflows', 'If'),
     'route': ('praisonaiagents.workflows', 'route'),
     'parallel': ('praisonaiagents.workflows', 'parallel'),
     'loop': ('praisonaiagents.workflows', 'loop'),
     'repeat': ('praisonaiagents.workflows', 'repeat'),
+    'when': ('praisonaiagents.workflows', 'when'),
+    
+    # Conditions (Protocol-driven condition evaluation)
+    'ConditionProtocol': ('praisonaiagents.conditions.protocols', 'ConditionProtocol'),
+    'RoutingConditionProtocol': ('praisonaiagents.conditions.protocols', 'RoutingConditionProtocol'),
+    'ExpressionCondition': ('praisonaiagents.conditions.evaluator', 'ExpressionCondition'),
+    'DictCondition': ('praisonaiagents.conditions.evaluator', 'DictCondition'),
+    'evaluate_condition': ('praisonaiagents.conditions.evaluator', 'evaluate_condition'),
     
     # Handoff
     'Handoff': ('praisonaiagents.agent.handoff', 'Handoff'),
@@ -421,6 +430,46 @@ _LAZY_IMPORTS = {
     'ProviderStatus': ('praisonaiagents.llm.failover', 'ProviderStatus'),
     'FailoverConfig': ('praisonaiagents.llm.failover', 'FailoverConfig'),
     'FailoverManager': ('praisonaiagents.llm.failover', 'FailoverManager'),
+    
+    # Plugins - Core classes
+    'PluginManager': ('praisonaiagents.plugins', 'PluginManager'),
+    'Plugin': ('praisonaiagents.plugins', 'Plugin'),
+    'PluginInfo': ('praisonaiagents.plugins', 'PluginInfo'),
+    'PluginHook': ('praisonaiagents.plugins', 'PluginHook'),
+    'FunctionPlugin': ('praisonaiagents.plugins', 'FunctionPlugin'),
+    'get_plugin_manager': ('praisonaiagents.plugins', 'get_plugin_manager'),
+    
+    # Plugins - Protocols
+    'PluginProtocol': ('praisonaiagents.plugins', 'PluginProtocol'),
+    'ToolPluginProtocol': ('praisonaiagents.plugins', 'ToolPluginProtocol'),
+    'HookPluginProtocol': ('praisonaiagents.plugins', 'HookPluginProtocol'),
+    'AgentPluginProtocol': ('praisonaiagents.plugins', 'AgentPluginProtocol'),
+    'LLMPluginProtocol': ('praisonaiagents.plugins', 'LLMPluginProtocol'),
+    
+    # Plugins - Single-file plugin support
+    'PluginMetadata': ('praisonaiagents.plugins', 'PluginMetadata'),
+    'PluginParseError': ('praisonaiagents.plugins', 'PluginParseError'),
+    'parse_plugin_header': ('praisonaiagents.plugins', 'parse_plugin_header'),
+    'parse_plugin_header_from_file': ('praisonaiagents.plugins', 'parse_plugin_header_from_file'),
+    'discover_plugins': ('praisonaiagents.plugins', 'discover_plugins'),
+    'load_plugin': ('praisonaiagents.plugins', 'load_plugin'),
+    'discover_and_load_plugins': ('praisonaiagents.plugins', 'discover_and_load_plugins'),
+    'get_default_plugin_dirs': ('praisonaiagents.plugins', 'get_default_plugin_dirs'),
+    'get_plugin_template': ('praisonaiagents.plugins', 'get_plugin_template'),
+    'ensure_plugin_dir': ('praisonaiagents.plugins', 'ensure_plugin_dir'),
+    
+    # Config loader - for config-driven defaults
+    'get_config': ('praisonaiagents.config.loader', 'get_config'),
+    'get_default': ('praisonaiagents.config.loader', 'get_default'),
+    'get_plugins_config': ('praisonaiagents.config.loader', 'get_plugins_config'),
+    'get_defaults_config': ('praisonaiagents.config.loader', 'get_defaults_config'),
+    'apply_config_defaults': ('praisonaiagents.config.loader', 'apply_config_defaults'),
+    'validate_config': ('praisonaiagents.config.loader', 'validate_config'),
+    'get_config_path': ('praisonaiagents.config.loader', 'get_config_path'),
+    'ConfigValidationError': ('praisonaiagents.config.loader', 'ConfigValidationError'),
+    'PraisonConfig': ('praisonaiagents.config.loader', 'PraisonConfig'),
+    'PluginsConfig': ('praisonaiagents.config.loader', 'PluginsConfig'),
+    'DefaultsConfig': ('praisonaiagents.config.loader', 'DefaultsConfig'),
 }
 
 

src/praisonai-agents/praisonaiagents/_config.py

@@ -9,6 +9,10 @@ Environment Variables:
     PRAISONAI_TELEMETRY_ENABLED: Enable telemetry (default: false, opt-in)
     PRAISONAI_TELEMETRY_DISABLED: Disable telemetry (takes precedence)
     DO_NOT_TRACK: Standard opt-out flag (takes precedence)
+    PRAISONAI_PLUGINS: Enable plugins (default: false)
+        - "true" or "1": Enable all discovered plugins
+        - "logging,metrics": Enable specific plugins (comma-separated)
+        - "false" or "0": Disable plugins
 """
 import os
 from typing import Optional
@@ -77,6 +81,38 @@ AUTO_INSTRUMENT = _str_to_bool(
 )
 
 
+# Plugins: Enable background plugins (hooks, metrics, logging)
+# Note: Tools and guardrails work WITHOUT this flag - they are explicit
+def _get_plugins_enabled() -> bool:
+    """Determine if plugins should be auto-enabled based on env var."""
+    env_value = os.environ.get('PRAISONAI_PLUGINS', '').lower()
+    if not env_value:
+        return False
+    if env_value in ('true', '1', 'yes', 'on'):
+        return True
+    if env_value in ('false', '0', 'no', 'off'):
+        return False
+    # Treat as comma-separated list of plugin names (implies enabled)
+    return True
+
+
+def _get_plugins_list() -> list:
+    """Get list of specific plugins to enable from env var.
+    
+    Returns:
+        List of plugin names, or empty list if all plugins enabled.
+    """
+    env_value = os.environ.get('PRAISONAI_PLUGINS', '').lower()
+    if not env_value or env_value in ('true', '1', 'yes', 'on', 'false', '0', 'no', 'off'):
+        return []
+    # Parse comma-separated list
+    return [p.strip() for p in env_value.split(',') if p.strip()]
+
+
+PLUGINS_ENABLED = _get_plugins_enabled()
+PLUGINS_LIST = _get_plugins_list()
+
+
 def missing_dependency_error(
     package_name: str,
     extra_name: Optional[str] = None,

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

@@ -150,6 +150,7 @@ if TYPE_CHECKING:
     from ..task.task import Task
     from .handoff import Handoff, HandoffConfig, HandoffResult
     from ..rag.models import RAGResult, ContextPack
+    from ..eval.results import EvaluationLoopResult
 
 class Agent:
     # Class-level counter for generating unique display names for nameless agents
@@ -540,6 +541,49 @@ class Agent:
             Agent._configure_logging()
             Agent._logging_configured = True
 
+        # ============================================================
+        # CONFIG-DRIVEN DEFAULTS (apply before parameter resolution)
+        # Precedence: Explicit params > Config file > Built-in defaults
+        # Only applies when param is None (not explicitly set)
+        # ============================================================
+        from ..config.loader import apply_config_defaults, get_default
+        
+        # Apply config defaults for LLM if not explicitly set
+        if llm is None and model is None:
+            config_model = get_default("model")
+            if config_model:
+                llm = config_model
+        
+        # Apply config defaults for base_url if not explicitly set
+        if base_url is None:
+            config_base_url = get_default("base_url")
+            if config_base_url:
+                base_url = config_base_url
+        
+        # Apply config defaults for feature params (memory, knowledge, etc.)
+        # These use apply_config_defaults which handles enabled=True/False logic
+        if memory is None:
+            memory = apply_config_defaults("memory", memory, MemoryConfig)
+        if knowledge is None:
+            knowledge = apply_config_defaults("knowledge", knowledge, KnowledgeConfig)
+        if planning is False:  # Only override if explicitly False (default)
+            config_planning = apply_config_defaults("planning", None, PlanningConfig)
+            if config_planning:
+                planning = config_planning
+        if reflection is None:
+            reflection = apply_config_defaults("reflection", reflection, ReflectionConfig)
+        if web is None:
+            web = apply_config_defaults("web", web, WebConfig)
+        if output is None:
+            output = apply_config_defaults("output", output, OutputConfig)
+        if execution is None:
+            execution = apply_config_defaults("execution", execution, ExecutionConfig)
+        if caching is None:
+            caching = apply_config_defaults("caching", caching, CachingConfig)
+        if autonomy is None:
+            # AutonomyConfig is in agent/autonomy.py - use dict for config defaults
+            autonomy = apply_config_defaults("autonomy", autonomy, None)
+
         # ============================================================
         # CONSOLIDATED PARAMS EXTRACTION (agent-centric API)
         # Uses unified resolver: Instance > Config > Array > String > Bool > Default
@@ -839,7 +883,10 @@ class Agent:
                 backend = _memory_config.backend
                 if hasattr(backend, 'value'):
                     backend = backend.value
-                if backend == "file":
+                # If learn is enabled, pass as dict to trigger full Memory class
+                if _memory_config.learn:
+                    memory = _memory_config.to_dict()
+                elif backend == "file":
                     memory = True
                 elif _memory_config.config:
                     memory = _memory_config.config
@@ -851,6 +898,24 @@ class Agent:
         elif memory is False:
             memory = None
         
+        # ─────────────────────────────────────────────────────────────────────
+        # Resolve HISTORY from MemoryConfig - FAST PATH
+        # History is enabled via memory="history" preset or MemoryConfig(history=True)
+        # ─────────────────────────────────────────────────────────────────────
+        _history_enabled = False
+        _history_limit = 10
+        _history_session_id = None
+        
+        # Check if memory config has history settings
+        if _memory_config is not None and isinstance(_memory_config, MemoryConfig):
+            if _memory_config.history:
+                _history_enabled = True
+                _history_limit = _memory_config.history_limit
+        
+        # Use auto_save session if no explicit session and history is enabled
+        if _history_enabled and _history_session_id is None and auto_save:
+            _history_session_id = auto_save
+        
         # ─────────────────────────────────────────────────────────────────────
         # Resolve KNOWLEDGE param - FAST PATH
         # ─────────────────────────────────────────────────────────────────────
@@ -1085,9 +1150,19 @@ class Agent:
         self.__stream_emitter = None  # Will be initialized on first access
         self._hooks_registry_param = hooks  # Store for lazy init
         
-        # Handle model= alias for llm= (NO warnings - both are valid)
-        if llm is None and model is not None:
-            llm = model  # model= is an alias for llm=
+        # Handle llm= deprecation: model= is the preferred parameter name
+        # llm= still works but shows deprecation warning
+        if llm is not None and model is None:
+            import warnings
+            warnings.warn(
+                "Parameter 'llm' is deprecated, use 'model' instead. "
+                "Example: Agent(model='gpt-4o-mini') instead of Agent(llm='gpt-4o-mini')",
+                DeprecationWarning,
+                stacklevel=2
+            )
+        # model= is the preferred parameter (no warning)
+        if model is not None:
+            llm = model  # model= takes precedence
         
         # Store rate limiter (optional, zero overhead when None)
         self._rate_limiter = rate_limiter
@@ -1343,6 +1418,11 @@ Your Goal: {self.goal}
         # Used when session_id is provided but no DB adapter
         self._session_store = None
         self._session_store_initialized = False
+        
+        # History injection settings (for auto-injecting session history into context)
+        self._history_enabled = _history_enabled
+        self._history_limit = _history_limit
+        self._history_session_id = _history_session_id
 
         # Agent-centric feature instances (lazy loaded for zero performance impact)
         self._auto_memory = auto_memory
@@ -2288,6 +2368,99 @@ Summary:"""
         )
         return handoff_obj.execute_programmatic(self, prompt, context)
     
+    def run_until(
+        self,
+        prompt: str,
+        criteria: str,
+        threshold: float = 8.0,
+        max_iterations: int = 5,
+        mode: str = "optimize",
+        on_iteration: Optional[Callable[[Any], None]] = None,
+        verbose: bool = False,
+    ) -> "EvaluationLoopResult":
+        """
+        Run agent iteratively until output meets quality criteria.
+        
+        This method implements the "Ralph Loop" pattern: run agent → judge output
+        → improve based on feedback → repeat until threshold met.
+        
+        Args:
+            prompt: The prompt to send to the agent
+            criteria: Evaluation criteria for the Judge (e.g., "Response is thorough")
+            threshold: Score threshold for success (default: 8.0, scale 1-10)
+            max_iterations: Maximum iterations before stopping (default: 5)
+            mode: "optimize" (stop on success) or "review" (run all iterations)
+            on_iteration: Optional callback called after each iteration
+            verbose: Enable verbose logging
+            
+        Returns:
+            EvaluationLoopResult with iteration history and final score
+            
+        Example:
+            ```python
+            agent = Agent(name="analyzer", instructions="Analyze systems")
+            result = agent.run_until(
+                "Analyze the auth flow",
+                criteria="Analysis is thorough and actionable",
+                threshold=8.0,
+            )
+            print(result.final_score)  # 8.5
+            print(result.success)      # True
+            ```
+        """
+        from ..eval.loop import EvaluationLoop
+        
+        loop = EvaluationLoop(
+            agent=self,
+            criteria=criteria,
+            threshold=threshold,
+            max_iterations=max_iterations,
+            mode=mode,
+            on_iteration=on_iteration,
+            verbose=verbose,
+        )
+        return loop.run(prompt)
+    
+    async def run_until_async(
+        self,
+        prompt: str,
+        criteria: str,
+        threshold: float = 8.0,
+        max_iterations: int = 5,
+        mode: str = "optimize",
+        on_iteration: Optional[Callable[[Any], None]] = None,
+        verbose: bool = False,
+    ) -> "EvaluationLoopResult":
+        """
+        Async version of run_until().
+        
+        Run agent iteratively until output meets quality criteria.
+        
+        Args:
+            prompt: The prompt to send to the agent
+            criteria: Evaluation criteria for the Judge
+            threshold: Score threshold for success (default: 8.0)
+            max_iterations: Maximum iterations before stopping (default: 5)
+            mode: "optimize" (stop on success) or "review" (run all iterations)
+            on_iteration: Optional callback called after each iteration
+            verbose: Enable verbose logging
+            
+        Returns:
+            EvaluationLoopResult with iteration history and final score
+        """
+        from ..eval.loop import EvaluationLoop
+        
+        loop = EvaluationLoop(
+            agent=self,
+            criteria=criteria,
+            threshold=threshold,
+            max_iterations=max_iterations,
+            mode=mode,
+            on_iteration=on_iteration,
+            verbose=verbose,
+        )
+        return await loop.run_async(prompt)
+    
     async def handoff_to_async(
         self,
         target_agent: 'Agent',
@@ -2494,8 +2667,8 @@ Summary:"""
         Initialize RulesManager for persistent rules/instructions.
         
         Automatically discovers rules from:
-        - ~/.praison/rules/ (global)
-        - .praison/rules/ (workspace)
+        - ~/.praisonai/rules/ (global)
+        - .praisonai/rules/ (workspace)
         - Subdirectory rules
         
         NOTE: This is called lazily via the rules_manager property for performance.
@@ -2585,8 +2758,19 @@ Summary:"""
                 self._memory_instance = FileMemory(user_id=mem_user_id)
         elif isinstance(memory, dict):
             # Configuration dict
-            provider = memory.get("provider", "file")
-            if provider == "file":
+            provider = memory.get("provider", memory.get("backend", "file"))
+            learn_enabled = memory.get("learn", False)
+            
+            # Use full Memory class if learn is enabled (requires LearnManager)
+            if learn_enabled:
+                try:
+                    from ..memory.memory import Memory
+                    self._memory_instance = Memory(memory)
+                except ImportError:
+                    logging.warning("Memory with learn requires additional dependencies. Falling back to FileMemory (learn disabled).")
+                    from ..memory.file_memory import FileMemory
+                    self._memory_instance = FileMemory(user_id=memory.get("user_id", mem_user_id))
+            elif provider == "file":
                 from ..memory.file_memory import FileMemory
                 self._memory_instance = FileMemory(
                     user_id=memory.get("user_id", mem_user_id),
@@ -2622,6 +2806,24 @@ Summary:"""
         
         return ""
     
+    def get_learn_context(self) -> str:
+        """
+        Get learning context for injection into system prompt.
+        
+        Returns learned preferences, insights, and patterns when memory="learn"
+        is enabled. Returns empty string when learn is not enabled (zero overhead).
+        
+        Returns:
+            Formatted learning context string, or empty string if learn not enabled
+        """
+        if not self._memory_instance:
+            return ""
+        
+        if hasattr(self._memory_instance, 'get_learn_context'):
+            return self._memory_instance.get_learn_context()
+        
+        return ""
+    
     def store_memory(self, content: str, memory_type: str = "short_term", **kwargs):
         """
         Store content in memory.
@@ -3262,6 +3464,11 @@ Your Goal: {self.goal}"""
                 # Display memory info to user if verbose
                 if self.verbose:
                     self._display_memory_info()
+            
+            # Add learn context if learn is enabled (auto-inject when memory="learn")
+            learn_context = self.get_learn_context()
+            if learn_context:
+                system_prompt += f"\n\n## Learned Context (Patterns and insights from past interactions)\n{learn_context}"
         
         # Add skills prompt if skills are configured
         if self._skills or self._skills_dirs:
@@ -3432,7 +3639,19 @@ Your Goal: {self.goal}"""
             if system_prompt:
                 messages.append({"role": "system", "content": system_prompt})
             
-            # Add chat history
+            # Inject session history if enabled (from persistent storage)
+            if self._history_enabled and self._session_store is not None:
+                try:
+                    session_history = self._session_store.get_chat_history(
+                        self._history_session_id,
+                        max_messages=self._history_limit
+                    )
+                    if session_history:
+                        messages.extend(session_history)
+                except Exception as e:
+                    logging.debug(f"Failed to load session history: {e}")
+            
+            # Add in-memory chat history (current conversation)
             if self.chat_history:
                 messages.extend(self.chat_history)
             

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

@@ -162,7 +162,7 @@ class ContextAgent(Agent):
         
         # Enhanced logging and output management
         self.debug_mode = os.getenv('LOGLEVEL', '').lower() == 'debug'
-        self.output_dir = Path(".praison/prp")  # Save in .praison/prp folder
+        self.output_dir = Path(".praisonai/prp")  # Save in .praisonai/prp folder
         self.setup_output_directories()  # Create directories first
         self.setup_logging()              # Then setup logging
         
@@ -1136,7 +1136,7 @@ detailed, actionable implementation steps."""
             filtered = []
             exclude_patterns = [
                 '__pycache__', '.pytest_cache', '.git', 'node_modules', 
-                '.venv', 'venv', 'dist', 'build', '.praison', '.chroma_db',
+                '.venv', 'venv', 'dist', 'build', '.praisonai', '.chroma_db',
                 '.ruff_cache', '.mypy_cache', 'context_engineering_output'
             ]
             
@@ -2263,7 +2263,7 @@ This PRP provides:
             )
             print(f"🐛 Debug mode: Comprehensive session report + individual files + final PRP saved")
         else:
-            # Non-debug mode: Save ONLY the final comprehensive PRP directly in .praison/prp
+            # Non-debug mode: Save ONLY the final comprehensive PRP directly in .praisonai/prp
             # Use clean filename: goal name + timestamp
             final_output_file = self.output_dir / f"{safe_goal}_prp_{timestamp}.md"
             final_output_file.parent.mkdir(parents=True, exist_ok=True)

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

@@ -513,9 +513,9 @@ class AgentTeam:
                         "use_embedding": True,
                         "storage": {
                             "type": "sqlite",
-                            "path": "./.praison/memory.db"
+                            "path": "./.praisonai/memory.db"
                         },
-                        "rag_db_path": "./.praison/chroma_db"
+                        "rag_db_path": "./.praisonai/chroma_db"
                     }
                 if _embedder:
                     if isinstance(_embedder, dict):
@@ -2345,7 +2345,7 @@ Context:
                 context=context if context else None,
                 # Inherit from original task if available
                 memory=original_task.memory if original_task else None,
-                callback=original_task.callback if original_task else None,
+                on_task_complete=original_task.callback if original_task else None,
                 guardrails=original_task.guardrail if original_task else None,
                 max_retries=original_task.max_retries if original_task else 3,
                 output_json=original_task.output_json if original_task else None,

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

@@ -141,8 +141,19 @@ class SubagentDelegator:
         self._total_count: int = 0
         self._task_counter: int = 0
         
-        # Semaphore for concurrency control
-        self._semaphore = asyncio.Semaphore(self.config.max_concurrent_subagents)
+        # Note: asyncio.Semaphore() is created lazily via _get_semaphore() to support
+        # Python 3.9 where Semaphore() requires an event loop at creation time.
+        self.__semaphore: Optional[asyncio.Semaphore] = None
+    
+    def _get_semaphore(self) -> asyncio.Semaphore:
+        """Get the semaphore, creating it lazily if needed.
+        
+        This deferred creation is required for Python 3.9 compatibility
+        where asyncio.Semaphore() calls get_event_loop() at creation time.
+        """
+        if self.__semaphore is None:
+            self.__semaphore = asyncio.Semaphore(self.config.max_concurrent_subagents)
+        return self.__semaphore
     
     def get_available_agents(self) -> List[str]:
         """Get list of available agent names for delegation."""
@@ -204,7 +215,7 @@ class SubagentDelegator:
         self._total_count += 1
         
         # Execute with concurrency control
-        async with self._semaphore:
+        async with self._get_semaphore():
             return await self._execute_task(task)
     
     async def delegate_parallel(

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

@@ -51,10 +51,17 @@ class BackgroundRunner:
         """
         self.config = config or BackgroundConfig()
         self._tasks: Dict[str, BackgroundTask] = {}
-        self._semaphore = asyncio.Semaphore(self.config.max_concurrent_tasks)
+        # Lazily create asyncio.Semaphore() to avoid Python 3.9 event loop issues
+        self._semaphore: Optional[asyncio.Semaphore] = None
         self._running = False
         self._cleanup_task: Optional[asyncio.Task] = None
     
+    def _get_semaphore(self) -> asyncio.Semaphore:
+        """Get or create the semaphore lazily (Python 3.9 compatible)."""
+        if self._semaphore is None:
+            self._semaphore = asyncio.Semaphore(self.config.max_concurrent_tasks)
+        return self._semaphore
+    
     @property
     def tasks(self) -> List[BackgroundTask]:
         """Get all tasks."""
@@ -109,7 +116,7 @@ class BackgroundRunner:
         
         # Create the execution coroutine
         async def execute():
-            async with self._semaphore:
+            async with self._get_semaphore():
                 task.start()
                 logger.info(f"Background task started: {task.name} ({task.id})")
                 

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

@@ -48,7 +48,16 @@ class BackgroundTask:
     _cancel_event: Optional[asyncio.Event] = field(default=None, repr=False)
     
     def __post_init__(self):
-        self._cancel_event = asyncio.Event()
+        # Lazily create asyncio.Event() to avoid Python 3.9 event loop issues
+        self._cancel_event = None
+        # Simple flag for sync cancellation check (doesn't require event loop)
+        self._cancelled = False
+    
+    def _get_cancel_event(self) -> asyncio.Event:
+        """Get or create the cancel event lazily (Python 3.9 compatible)."""
+        if self._cancel_event is None:
+            self._cancel_event = asyncio.Event()
+        return self._cancel_event
     
     @property
     def is_running(self) -> bool:
@@ -93,7 +102,10 @@ class BackgroundTask:
     
     def cancel(self):
         """Request task cancellation."""
-        if self._cancel_event:
+        # Set simple flag (works without event loop)
+        self._cancelled = True
+        # Also set the event if it exists (for async waiters)
+        if self._cancel_event is not None:
             self._cancel_event.set()
         self.status = TaskStatus.CANCELLED
         self.completed_at = datetime.now()
@@ -135,6 +147,10 @@ class BackgroundTask:
     
     def should_cancel(self) -> bool:
         """Check if cancellation was requested."""
+        # Check simple flag first (works without event loop)
+        if self._cancelled:
+            return True
+        # Also check event if it exists
         return self._cancel_event is not None and self._cancel_event.is_set()
     
     def to_dict(self) -> Dict[str, Any]:

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

@@ -20,7 +20,7 @@ Usage:
     # Create checkpoint service
     service = CheckpointService(
         workspace_dir="/path/to/project",
-        storage_dir="~/.praison/checkpoints"
+        storage_dir="~/.praisonai/checkpoints"
     )
     
     # Initialize shadow git

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

@@ -19,6 +19,14 @@ from .types import (
 
 logger = logging.getLogger(__name__)
 
+
+def _parse_iso_timestamp(timestamp: str) -> datetime:
+    """Parse ISO format timestamp, handling 'Z' suffix for Python 3.9 compatibility."""
+    # Python 3.9's fromisoformat() doesn't support 'Z' suffix
+    if timestamp.endswith('Z'):
+        timestamp = timestamp[:-1] + '+00:00'
+    return datetime.fromisoformat(timestamp)
+
 # Protected paths that should never be checkpointed
 PROTECTED_PATHS = [
     os.path.expanduser("~"),
@@ -43,7 +51,7 @@ class CheckpointService:
     Example:
         service = CheckpointService(
             workspace_dir="/path/to/project",
-            storage_dir="~/.praison/checkpoints"
+            storage_dir="~/.praisonai/checkpoints"
         )
         
         await service.initialize()
@@ -275,7 +283,7 @@ class CheckpointService:
                 id=commit_hash,
                 short_id=commit_hash[:8],
                 message=message,
-                timestamp=datetime.fromisoformat(timestamp)
+                timestamp=_parse_iso_timestamp(timestamp)
             )
             
             self._checkpoints.append(checkpoint)
@@ -321,7 +329,7 @@ class CheckpointService:
                 id=checkpoint_id,
                 short_id=checkpoint_id[:8],
                 message=message,
-                timestamp=datetime.fromisoformat(timestamp)
+                timestamp=_parse_iso_timestamp(timestamp)
             )
             
             self._emit(CheckpointEvent.CHECKPOINT_RESTORED, checkpoint)
@@ -450,7 +458,7 @@ class CheckpointService:
                         id=parts[0],
                         short_id=parts[0][:8],
                         message=parts[1],
-                        timestamp=datetime.fromisoformat(parts[2])
+                        timestamp=_parse_iso_timestamp(parts[2])
                     ))
             
             return checkpoints

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

@@ -11,6 +11,15 @@ from typing import Optional, Dict, Any, List
 from datetime import datetime
 from pathlib import Path
 
+from ..paths import get_checkpoints_dir
+
+
+def _parse_iso_timestamp(timestamp: str) -> datetime:
+    """Parse ISO format timestamp, handling 'Z' suffix for Python 3.9 compatibility."""
+    if timestamp.endswith('Z'):
+        timestamp = timestamp[:-1] + '+00:00'
+    return datetime.fromisoformat(timestamp)
+
 
 class CheckpointEvent(str, Enum):
     """Events emitted by the checkpoint service."""
@@ -30,7 +39,7 @@ class CheckpointConfig:
     max_checkpoints: int = 100  # Maximum checkpoints to keep
     exclude_patterns: List[str] = field(default_factory=lambda: [
         ".git",
-        ".praison",
+        ".praisonai",
         "__pycache__",
         "*.pyc",
         ".env",
@@ -46,8 +55,8 @@ class CheckpointConfig:
         if self.storage_dir:
             self.storage_dir = os.path.expanduser(self.storage_dir)
         else:
-            # Default storage in user's home directory
-            self.storage_dir = os.path.expanduser("~/.praison/checkpoints")
+            # Default storage in user's home directory (uses centralized paths - DRY)
+            self.storage_dir = str(get_checkpoints_dir())
     
     def get_checkpoint_dir(self) -> str:
         """Get the checkpoint directory for this workspace."""
@@ -77,7 +86,7 @@ class Checkpoint:
             id=commit_hash,
             short_id=commit_hash[:8],
             message=message,
-            timestamp=datetime.fromisoformat(timestamp) if isinstance(timestamp, str) else timestamp
+            timestamp=_parse_iso_timestamp(timestamp) if isinstance(timestamp, str) else timestamp
         )
     
     def to_dict(self) -> Dict[str, Any]:

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

@@ -0,0 +1,25 @@
+"""
+Conditions Module - Unified condition evaluation for workflows.
+
+This module provides a protocol-driven approach to condition evaluation,
+enabling DRY condition handling across AgentFlow and AgentTeam.
+
+Exports:
+    - ConditionProtocol: Protocol interface for condition implementations
+    - ExpressionCondition: String-based conditions like "{{score}} > 80"
+    - DictCondition: Dict-based routing conditions
+    - evaluate_condition: Standalone function for condition evaluation
+"""
+from .protocols import ConditionProtocol
+from .evaluator import (
+    ExpressionCondition,
+    DictCondition,
+    evaluate_condition,
+)
+
+__all__ = [
+    'ConditionProtocol',
+    'ExpressionCondition',
+    'DictCondition',
+    'evaluate_condition',
+]

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

@@ -0,0 +1,284 @@
+"""
+Condition Evaluator - Shared condition evaluation logic.
+
+This module provides the shared condition evaluation logic used by both
+AgentFlow (string-based conditions) and AgentTeam (dict-based routing).
+
+The implementation is extracted from workflows.py _evaluate_condition()
+to enable DRY condition handling across the codebase.
+"""
+import re
+import logging
+from typing import Dict, Any, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+class ExpressionCondition:
+    """
+    String-based condition evaluator for expressions like "{{score}} > 80".
+    
+    Supports:
+        - Numeric comparison: "{{var}} > 80", "{{var}} >= 50", "{{var}} < 10"
+        - String equality: "{{var}} == approved", "{{var}} != rejected"
+        - Contains check: "error in {{message}}", "{{status}} contains success"
+        - Boolean: "{{flag}}" (truthy check)
+        - Nested property: "{{item.score}} >= 60"
+    
+    Example:
+        ```python
+        cond = ExpressionCondition("{{score}} > 80")
+        result = cond.evaluate({"score": 90})  # True
+        ```
+    """
+    
+    def __init__(self, expression: str):
+        """
+        Initialize with a condition expression.
+        
+        Args:
+            expression: Condition string with {{var}} placeholders.
+        """
+        self.expression = expression
+    
+    def evaluate(self, context: Dict[str, Any]) -> bool:
+        """
+        Evaluate the condition against the given context.
+        
+        Args:
+            context: Dictionary containing variables for evaluation.
+                     Special key 'previous_output' is also substituted.
+            
+        Returns:
+            Boolean result of condition evaluation.
+            Returns False on evaluation errors (fail-safe).
+        """
+        return evaluate_condition(
+            self.expression,
+            context,
+            previous_output=context.get('previous_output')
+        )
+    
+    def __repr__(self) -> str:
+        return f"ExpressionCondition({self.expression!r})"
+
+
+class DictCondition:
+    """
+    Dict-based condition evaluator for routing decisions.
+    
+    Used by AgentTeam for task routing based on decision keys.
+    
+    Example:
+        ```python
+        cond = DictCondition(
+            {"approved": ["publish"], "rejected": ["revise"]},
+            key="decision"
+        )
+        result = cond.evaluate({"decision": "approved"})  # True
+        targets = cond.get_target({"decision": "approved"})  # ["publish"]
+        ```
+    """
+    
+    def __init__(self, routes: Dict[str, List[str]], key: str = "decision"):
+        """
+        Initialize with routing configuration.
+        
+        Args:
+            routes: Dict mapping decision values to target task lists.
+            key: Context key to read decision value from.
+        """
+        self.routes = routes
+        self.key = key
+    
+    def evaluate(self, context: Dict[str, Any]) -> bool:
+        """
+        Check if the context contains a valid routing decision.
+        
+        Args:
+            context: Dictionary containing the decision key.
+            
+        Returns:
+            True if decision value matches a route key.
+        """
+        decision = context.get(self.key, "")
+        if isinstance(decision, str):
+            decision = decision.lower()
+        return decision in self.routes
+    
+    def get_target(self, context: Dict[str, Any]) -> List[str]:
+        """
+        Get target tasks based on the decision value.
+        
+        Args:
+            context: Dictionary containing the decision key.
+            
+        Returns:
+            List of target task names, or empty list if no match.
+        """
+        decision = context.get(self.key, "")
+        if isinstance(decision, str):
+            decision = decision.lower()
+        targets = self.routes.get(decision, [])
+        if isinstance(targets, str):
+            return [targets]
+        return targets if targets else []
+    
+    def __repr__(self) -> str:
+        return f"DictCondition(routes={self.routes!r}, key={self.key!r})"
+
+
+def evaluate_condition(
+    condition: str,
+    variables: Dict[str, Any],
+    previous_output: Optional[str] = None
+) -> bool:
+    """
+    Evaluate a condition expression with variable substitution.
+    
+    This is the shared condition evaluation function used by both
+    AgentFlow and AgentTeam. It supports various condition formats.
+    
+    Supported formats:
+        - Numeric comparison: "{{var}} > 80", "{{var}} >= 50", "{{var}} < 10"
+        - String equality: "{{var}} == approved", "{{var}} != rejected"
+        - Contains check: "error in {{message}}", "{{status}} contains success"
+        - Boolean: "{{flag}}" (truthy check)
+        - Nested property: "{{item.score}} >= 60"
+    
+    Args:
+        condition: Condition expression with {{var}} placeholders.
+        variables: Dictionary containing variables for substitution.
+        previous_output: Optional output from previous step (substitutes {{previous_output}}).
+        
+    Returns:
+        Boolean result of condition evaluation.
+        Returns False on evaluation errors (fail-safe).
+    
+    Example:
+        ```python
+        result = evaluate_condition("{{score}} > 80", {"score": 90})  # True
+        result = evaluate_condition("{{status}} == approved", {"status": "approved"})  # True
+        result = evaluate_condition("error in {{message}}", {"message": "An error occurred"})  # True
+        ```
+    """
+    # Substitute variables in condition
+    substituted = condition
+    
+    # Handle nested property access like {{item.score}}
+    def get_nested_value(var_path: str, vars_dict: Dict[str, Any]) -> Any:
+        parts = var_path.split('.')
+        value = vars_dict
+        for part in parts:
+            if isinstance(value, dict):
+                value = value.get(part)
+            else:
+                return None
+        return value
+    
+    # First substitute {{previous_output}} if provided (before general variable substitution)
+    if previous_output is not None:
+        substituted = substituted.replace("{{previous_output}}", str(previous_output))
+    
+    # Find all {{var}} patterns and substitute
+    pattern = r'\{\{([^}]+)\}\}'
+    matches = re.findall(pattern, substituted)  # Use substituted, not condition
+    
+    for var_name in matches:
+        # Skip previous_output as it's already handled
+        if var_name == 'previous_output':
+            continue
+            
+        if '.' in var_name:
+            # Nested property access
+            value = get_nested_value(var_name, variables)
+        else:
+            value = variables.get(var_name)
+        
+        if value is None:
+            value = ""
+        
+        # Replace the placeholder with the value
+        placeholder = f"{{{{{var_name}}}}}"
+        if isinstance(value, (int, float)):
+            substituted = substituted.replace(placeholder, str(value))
+        elif isinstance(value, bool):
+            substituted = substituted.replace(placeholder, str(value).lower())
+        else:
+            substituted = substituted.replace(placeholder, str(value))
+    
+    # Now evaluate the substituted condition
+    try:
+        # Handle different condition formats
+        
+        # Check if we have unsubstituted empty values in comparisons
+        # If a variable was missing, it becomes empty string which can cause issues
+        if ' > ' in substituted or ' < ' in substituted or ' >= ' in substituted or ' <= ' in substituted:
+            # Check for empty left side in comparison (missing variable)
+            if substituted.strip().startswith('>') or substituted.strip().startswith('<'):
+                return False
+            if substituted.strip().startswith('='):
+                return False
+        
+        # Numeric comparisons: "90 > 80", "50 >= 50", "10 < 20", etc.
+        numeric_pattern = r'^(-?\d+(?:\.\d+)?)\s*(>|>=|<|<=|==|!=)\s*(-?\d+(?:\.\d+)?)$'
+        numeric_match = re.match(numeric_pattern, substituted.strip())
+        if numeric_match:
+            left = float(numeric_match.group(1))
+            op = numeric_match.group(2)
+            right = float(numeric_match.group(3))
+            
+            if op == '>':
+                return left > right
+            if op == '>=':
+                return left >= right
+            if op == '<':
+                return left < right
+            if op == '<=':
+                return left <= right
+            if op == '==':
+                return left == right
+            if op == '!=':
+                return left != right
+        
+        # String equality: "approved == approved", "status != rejected"
+        string_eq_pattern = r'^(.+?)\s*(==|!=)\s*(.+)$'
+        string_match = re.match(string_eq_pattern, substituted.strip())
+        if string_match:
+            left = string_match.group(1).strip()
+            op = string_match.group(2)
+            right = string_match.group(3).strip()
+            
+            if op == '==':
+                return left == right
+            if op == '!=':
+                return left != right
+        
+        # Contains check: "error in some message", "status contains success"
+        if ' in ' in substituted:
+            parts = substituted.split(' in ', 1)
+            if len(parts) == 2:
+                needle = parts[0].strip()
+                haystack = parts[1].strip()
+                return needle.lower() in haystack.lower()
+        
+        if ' contains ' in substituted:
+            parts = substituted.split(' contains ', 1)
+            if len(parts) == 2:
+                haystack = parts[0].strip()
+                needle = parts[1].strip()
+                return needle.lower() in haystack.lower()
+        
+        # Boolean check: truthy evaluation
+        # Handle "true", "false", "True", "False"
+        if substituted.strip().lower() == 'true':
+            return True
+        if substituted.strip().lower() == 'false':
+            return False
+        
+        # Non-empty string is truthy
+        return bool(substituted.strip())
+        
+    except Exception as e:
+        logger.warning(f"Condition evaluation failed for '{condition}': {e}")
+        return False

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

@@ -0,0 +1,92 @@
+"""
+Condition Protocol Definitions.
+
+Provides Protocol interfaces that define the minimal contract for condition implementations.
+This enables:
+- Unified condition evaluation across AgentFlow and AgentTeam
+- Custom condition implementations
+- Type checking with static analyzers
+- Mocking conditions in tests
+
+These protocols are lightweight and have zero performance impact.
+"""
+from typing import Protocol, runtime_checkable, Dict, Any, List, Optional
+
+
+@runtime_checkable
+class ConditionProtocol(Protocol):
+    """
+    Minimal Protocol for condition implementations.
+    
+    This defines the essential interface that any condition must provide.
+    It enables unified condition evaluation across AgentFlow (string-based)
+    and AgentTeam (dict-based) systems.
+    
+    Example:
+        ```python
+        # Create a custom condition
+        class ScoreCondition:
+            def __init__(self, threshold: int):
+                self.threshold = threshold
+            
+            def evaluate(self, context: Dict[str, Any]) -> bool:
+                return context.get("score", 0) > self.threshold
+        
+        # Use in workflows
+        cond: ConditionProtocol = ScoreCondition(80)
+        result = cond.evaluate({"score": 90})  # True
+        ```
+    """
+    
+    def evaluate(self, context: Dict[str, Any]) -> bool:
+        """
+        Evaluate the condition against the given context.
+        
+        Args:
+            context: Dictionary containing variables for evaluation.
+                     May include workflow variables, previous outputs, etc.
+            
+        Returns:
+            Boolean result of condition evaluation.
+            Returns False on evaluation errors (fail-safe).
+        """
+        ...
+
+
+@runtime_checkable
+class RoutingConditionProtocol(ConditionProtocol, Protocol):
+    """
+    Extended Protocol for conditions that support routing to targets.
+    
+    This extends ConditionProtocol with the ability to return target
+    tasks/steps based on the condition evaluation. Used primarily
+    by AgentTeam for task routing.
+    
+    Example:
+        ```python
+        class ApprovalCondition:
+            def __init__(self, routes: Dict[str, List[str]]):
+                self.routes = routes
+            
+            def evaluate(self, context: Dict[str, Any]) -> bool:
+                decision = context.get("decision", "")
+                return decision in self.routes
+            
+            def get_target(self, context: Dict[str, Any]) -> List[str]:
+                decision = context.get("decision", "")
+                return self.routes.get(decision, [])
+        ```
+    """
+    
+    def get_target(self, context: Dict[str, Any]) -> List[str]:
+        """
+        Get the target tasks/steps based on condition evaluation.
+        
+        Args:
+            context: Dictionary containing variables for evaluation.
+            
+        Returns:
+            List of target task/step names to route to.
+            Returns empty list if no match found.
+        """
+        ...

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

@@ -33,6 +33,9 @@ from dataclasses import dataclass, field
 from typing import Dict, List, Any, Optional, Callable, Tuple, Union
 from enum import Enum
 
+# Import AutonomyConfig from canonical location (no circular dep)
+from ..agent.autonomy import AutonomyConfig
+
 
 class MemoryBackend(str, Enum):
     """Memory storage backends."""
@@ -165,6 +168,10 @@ class MemoryConfig:
     # Continuous learning configuration
     learn: Optional[Union[bool, LearnConfig]] = None
     
+    # History injection (auto-inject session history into context)
+    history: bool = False
+    history_limit: int = 10
+    
     def to_dict(self) -> Dict[str, Any]:
         """Convert to dictionary."""
         learn_dict = None
@@ -172,6 +179,9 @@ class MemoryConfig:
             learn_dict = LearnConfig().to_dict()
         elif isinstance(self.learn, LearnConfig):
             learn_dict = self.learn.to_dict()
+        elif isinstance(self.learn, dict):
+            # Pass through dict directly (user provided learn config as dict)
+            learn_dict = self.learn
         
         return {
             "backend": self.backend.value if isinstance(self.backend, MemoryBackend) else self.backend,
@@ -181,6 +191,8 @@ class MemoryConfig:
             "claude_memory": self.claude_memory,
             "config": self.config,
             "learn": learn_dict,
+            "history": self.history,
+            "history_limit": self.history_limit,
         }
 
 
@@ -743,7 +755,7 @@ class SkillsConfig:
         # With config
         Agent(skills=SkillsConfig(
             paths=["./my-skill"],
-            dirs=["~/.praison/skills/"],
+            dirs=["~/.praisonai/skills/"],
             auto_discover=True,
         ))
     """
@@ -944,54 +956,6 @@ class AutonomyLevel(str, Enum):
     AUTO_EDIT = "auto_edit"
     FULL_AUTO = "full_auto"
 
-
-@dataclass
-class AutonomyConfig:
-    """
-    Configuration for agent autonomy behavior.
-    
-    Controls escalation, doom-loop detection, and approval policies.
-    
-    Usage:
-        # Simple enable
-        Agent(autonomy=True)
-        
-        # With config
-        Agent(autonomy=AutonomyConfig(
-            level="auto_edit",
-            escalation_enabled=True,
-            doom_loop_detection=True,
-            max_consecutive_failures=3,
-        ))
-    """
-    # Autonomy level
-    level: Union[str, AutonomyLevel] = AutonomyLevel.SUGGEST
-    
-    # Escalation pipeline
-    escalation_enabled: bool = True
-    escalation_threshold: int = 3
-    
-    # Doom loop detection
-    doom_loop_detection: bool = True
-    max_consecutive_failures: int = 3
-    
-    # Approval policies
-    require_approval_for_writes: bool = True
-    require_approval_for_shell: bool = True
-    
-    def to_dict(self) -> Dict[str, Any]:
-        """Convert to dictionary."""
-        return {
-            "level": self.level.value if isinstance(self.level, AutonomyLevel) else self.level,
-            "escalation_enabled": self.escalation_enabled,
-            "escalation_threshold": self.escalation_threshold,
-            "doom_loop_detection": self.doom_loop_detection,
-            "max_consecutive_failures": self.max_consecutive_failures,
-            "require_approval_for_writes": self.require_approval_for_writes,
-            "require_approval_for_shell": self.require_approval_for_shell,
-        }
-
-
 # Type aliases for Union types used in Agent.__init__
 MemoryParam = Union[bool, MemoryConfig, Any]  # Any = MemoryManager instance
 KnowledgeParam = Union[bool, List[str], KnowledgeConfig, Any]  # Any = KnowledgeBase instance
@@ -1005,7 +969,7 @@ TemplateParam = TemplateConfig
 CachingParam = Union[bool, CachingConfig]
 HooksParam = Union[List[Any], HooksConfig]
 SkillsParam = Union[List[str], SkillsConfig]
-AutonomyParam = Union[bool, Dict[str, Any], AutonomyConfig]
+AutonomyParam = Union[bool, Dict[str, Any], "AutonomyConfig"]
 
 
 # =============================================================================

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

@@ -0,0 +1,609 @@
+"""
+Configuration Loader for PraisonAI Agents.
+
+Loads configuration from multiple sources with precedence:
+1. Explicit parameters (highest)
+2. Environment variables
+3. Config file (.praisonai/config.toml or praisonai.toml)
+4. Defaults (lowest)
+
+Zero Performance Impact:
+- Config is loaded lazily on first access
+- No file I/O at import time
+- Cached after first load
+
+Usage:
+    from praisonaiagents.config.loader import get_config, get_default
+    
+    # Get entire config
+    config = get_config()
+    
+    # Get specific default with fallback
+    model = get_default("model", "gpt-4o-mini")
+    memory_config = get_default("memory", {})
+    
+    # Validate config file
+    errors = validate_config(config.to_dict())
+"""
+
+import os
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
+from dataclasses import dataclass, field
+
+
+# ============================================================================
+# CONFIG VALIDATION SCHEMA
+# ============================================================================
+# Defines valid keys and types for config file validation
+
+VALID_PLUGINS_KEYS = {
+    "enabled": (bool, list, str),  # bool, list of plugin names, or "true"/"false"
+    "auto_discover": (bool,),
+    "directories": (list,),
+}
+
+VALID_DEFAULTS_KEYS = {
+    # LLM settings
+    "model": (str,),
+    "base_url": (str,),
+    "api_key": (str,),
+    # Feature flags
+    "allow_delegation": (bool,),
+    "allow_code_execution": (bool,),
+    "code_execution_mode": (str,),
+    # Feature configs (nested dicts with 'enabled' key)
+    "memory": (dict, bool),
+    "knowledge": (dict, bool),
+    "planning": (dict, bool),
+    "reflection": (dict, bool),
+    "guardrails": (dict, bool),
+    "web": (dict, bool),
+    "output": (dict, str),  # dict or preset string
+    "execution": (dict, str),  # dict or preset string
+    "caching": (dict, bool),
+    "autonomy": (dict, bool),
+    "skills": (dict, list),
+    "context": (dict, bool),
+    "hooks": (dict, list),
+    "templates": (dict,),
+}
+
+VALID_ROOT_KEYS = {"plugins", "defaults"}
+
+
+class ConfigValidationError(Exception):
+    """Raised when config file has validation errors."""
+    
+    def __init__(self, errors: List[str]):
+        self.errors = errors
+        message = "Config validation failed:\n" + "\n".join(f"  - {e}" for e in errors)
+        super().__init__(message)
+
+# Global config cache (lazy loaded)
+_config_cache: Optional[Dict[str, Any]] = None
+_config_loaded: bool = False
+
+
+@dataclass
+class PluginsConfig:
+    """Configuration for the plugin system."""
+    enabled: Union[bool, List[str]] = False  # True, False, or list of plugin names
+    auto_discover: bool = True
+    directories: List[str] = field(default_factory=lambda: [
+        "./.praisonai/plugins/",
+        "~/.praisonai/plugins/"
+    ])
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "enabled": self.enabled,
+            "auto_discover": self.auto_discover,
+            "directories": self.directories,
+        }
+
+
+@dataclass  
+class DefaultsConfig:
+    """Default configuration for Agent parameters."""
+    # LLM defaults
+    model: Optional[str] = None
+    base_url: Optional[str] = None
+    
+    # Feature flags
+    allow_delegation: bool = False
+    allow_code_execution: bool = False
+    code_execution_mode: str = "safe"
+    
+    # Feature configs (nested dicts)
+    memory: Optional[Dict[str, Any]] = None
+    knowledge: Optional[Dict[str, Any]] = None
+    planning: Optional[Dict[str, Any]] = None
+    reflection: Optional[Dict[str, Any]] = None
+    web: Optional[Dict[str, Any]] = None
+    output: Optional[Dict[str, Any]] = None
+    execution: Optional[Dict[str, Any]] = None
+    caching: Optional[Dict[str, Any]] = None
+    autonomy: Optional[Dict[str, Any]] = None
+    skills: Optional[Dict[str, Any]] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "model": self.model,
+            "base_url": self.base_url,
+            "allow_delegation": self.allow_delegation,
+            "allow_code_execution": self.allow_code_execution,
+            "code_execution_mode": self.code_execution_mode,
+            "memory": self.memory,
+            "knowledge": self.knowledge,
+            "planning": self.planning,
+            "reflection": self.reflection,
+            "web": self.web,
+            "output": self.output,
+            "execution": self.execution,
+            "caching": self.caching,
+            "autonomy": self.autonomy,
+            "skills": self.skills,
+        }
+
+
+@dataclass
+class PraisonConfig:
+    """Root configuration for PraisonAI Agents."""
+    plugins: PluginsConfig = field(default_factory=PluginsConfig)
+    defaults: DefaultsConfig = field(default_factory=DefaultsConfig)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "plugins": self.plugins.to_dict(),
+            "defaults": self.defaults.to_dict(),
+        }
+
+
+def _find_config_file() -> Optional[Path]:
+    """Find config file in standard locations.
+    
+    Search order:
+    1. .praisonai/config.toml (project-local)
+    2. praisonai.toml (project root)
+    3. ~/.praisonai/config.toml (user global)
+    
+    Returns:
+        Path to config file if found, None otherwise
+    """
+    # Project-local locations
+    cwd = Path.cwd()
+    local_paths = [
+        cwd / ".praisonai" / "config.toml",
+        cwd / "praisonai.toml",
+    ]
+    
+    for path in local_paths:
+        if path.exists():
+            return path
+    
+    # User global location
+    home = Path.home()
+    global_path = home / ".praisonai" / "config.toml"
+    if global_path.exists():
+        return global_path
+    
+    return None
+
+
+def _parse_toml(path: Path) -> Dict[str, Any]:
+    """Parse TOML file with fallback for Python < 3.11.
+    
+    Args:
+        path: Path to TOML file
+        
+    Returns:
+        Parsed config dict
+    """
+    try:
+        # Python 3.11+ has built-in tomllib
+        import tomllib
+        with open(path, "rb") as f:
+            return tomllib.load(f)
+    except ImportError:
+        # Fallback for Python < 3.11
+        try:
+            import tomli
+            with open(path, "rb") as f:
+                return tomli.load(f)
+        except ImportError:
+            # No TOML parser available - return empty config
+            import logging
+            logging.debug(
+                "TOML parser not available. Install tomli for Python < 3.11: "
+                "pip install tomli"
+            )
+            return {}
+
+
+def _load_config() -> Dict[str, Any]:
+    """Load configuration from file.
+    
+    Returns:
+        Config dict (empty if no config file found)
+    """
+    config_path = _find_config_file()
+    if config_path is None:
+        return {}
+    
+    try:
+        return _parse_toml(config_path)
+    except Exception as e:
+        import logging
+        logging.warning(f"Failed to load config from {config_path}: {e}")
+        return {}
+
+
+def _dict_to_plugins_config(data: Dict[str, Any]) -> PluginsConfig:
+    """Convert dict to PluginsConfig."""
+    return PluginsConfig(
+        enabled=data.get("enabled", False),
+        auto_discover=data.get("auto_discover", True),
+        directories=data.get("directories", [
+            "./.praisonai/plugins/",
+            "~/.praisonai/plugins/"
+        ]),
+    )
+
+
+def _dict_to_defaults_config(data: Dict[str, Any]) -> DefaultsConfig:
+    """Convert dict to DefaultsConfig."""
+    return DefaultsConfig(
+        model=data.get("model"),
+        base_url=data.get("base_url"),
+        allow_delegation=data.get("allow_delegation", False),
+        allow_code_execution=data.get("allow_code_execution", False),
+        code_execution_mode=data.get("code_execution_mode", "safe"),
+        memory=data.get("memory"),
+        knowledge=data.get("knowledge"),
+        planning=data.get("planning"),
+        reflection=data.get("reflection"),
+        web=data.get("web"),
+        output=data.get("output"),
+        execution=data.get("execution"),
+        caching=data.get("caching"),
+        autonomy=data.get("autonomy"),
+        skills=data.get("skills"),
+    )
+
+
+def _dict_to_praison_config(data: Dict[str, Any]) -> PraisonConfig:
+    """Convert dict to PraisonConfig."""
+    plugins_data = data.get("plugins", {})
+    defaults_data = data.get("defaults", {})
+    
+    return PraisonConfig(
+        plugins=_dict_to_plugins_config(plugins_data),
+        defaults=_dict_to_defaults_config(defaults_data),
+    )
+
+
+def get_config(reload: bool = False) -> PraisonConfig:
+    """Get the global configuration.
+    
+    Loads config lazily on first access and caches it.
+    
+    Args:
+        reload: Force reload from file
+        
+    Returns:
+        PraisonConfig instance
+    """
+    global _config_cache, _config_loaded
+    
+    if not _config_loaded or reload:
+        raw_config = _load_config()
+        _config_cache = _dict_to_praison_config(raw_config)
+        _config_loaded = True
+    
+    return _config_cache
+
+
+def get_plugins_config() -> PluginsConfig:
+    """Get plugins configuration.
+    
+    Returns:
+        PluginsConfig instance
+    """
+    return get_config().plugins
+
+
+def get_defaults_config() -> DefaultsConfig:
+    """Get defaults configuration.
+    
+    Returns:
+        DefaultsConfig instance
+    """
+    return get_config().defaults
+
+
+def get_default(key: str, fallback: Any = None) -> Any:
+    """Get a specific default value.
+    
+    Args:
+        key: Config key (e.g., "model", "memory", "memory.backend")
+        fallback: Value to return if key not found
+        
+    Returns:
+        Config value or fallback
+    """
+    defaults = get_defaults_config()
+    
+    # Handle nested keys like "memory.backend"
+    if "." in key:
+        parts = key.split(".")
+        value = getattr(defaults, parts[0], None)
+        if value is None:
+            return fallback
+        for part in parts[1:]:
+            if isinstance(value, dict):
+                value = value.get(part)
+            else:
+                return fallback
+            if value is None:
+                return fallback
+        return value
+    
+    # Simple key
+    value = getattr(defaults, key, None)
+    return value if value is not None else fallback
+
+
+def is_plugins_enabled() -> bool:
+    """Check if plugins are enabled via config or env var.
+    
+    Returns:
+        True if plugins should be enabled
+    """
+    # Check env var first (highest precedence)
+    env_value = os.environ.get("PRAISONAI_PLUGINS", "").lower()
+    if env_value:
+        if env_value in ("true", "1", "yes", "on"):
+            return True
+        if env_value in ("false", "0", "no", "off"):
+            return False
+        # Treat as comma-separated list of plugin names
+        return True
+    
+    # Check config file
+    plugins_config = get_plugins_config()
+    if isinstance(plugins_config.enabled, bool):
+        return plugins_config.enabled
+    if isinstance(plugins_config.enabled, list):
+        return len(plugins_config.enabled) > 0
+    
+    return False
+
+
+def get_enabled_plugins() -> Optional[List[str]]:
+    """Get list of enabled plugins (if specific list provided).
+    
+    Returns:
+        List of plugin names, or None if all plugins enabled
+    """
+    # Check env var first
+    env_value = os.environ.get("PRAISONAI_PLUGINS", "").lower()
+    if env_value and env_value not in ("true", "1", "yes", "on", "false", "0", "no", "off"):
+        # Treat as comma-separated list
+        return [p.strip() for p in env_value.split(",") if p.strip()]
+    
+    # Check config file
+    plugins_config = get_plugins_config()
+    if isinstance(plugins_config.enabled, list):
+        return plugins_config.enabled
+    
+    return None  # All plugins enabled
+
+
+def clear_config_cache() -> None:
+    """Clear the config cache (for testing)."""
+    global _config_cache, _config_loaded
+    _config_cache = None
+    _config_loaded = False
+
+
+def validate_config(config: Dict[str, Any], raise_on_error: bool = False) -> List[str]:
+    """Validate config file structure and types.
+    
+    Args:
+        config: Raw config dict (from TOML parsing)
+        raise_on_error: If True, raise ConfigValidationError on errors
+        
+    Returns:
+        List of validation error messages (empty if valid)
+        
+    Example:
+        config = {"plugins": {"enabled": True}, "defaults": {"model": "gpt-4o"}}
+        errors = validate_config(config)
+        if errors:
+            print("Config errors:", errors)
+    """
+    errors = []
+    
+    # Check root keys
+    for key in config.keys():
+        if key not in VALID_ROOT_KEYS:
+            similar = _suggest_similar_key(key, VALID_ROOT_KEYS)
+            if similar:
+                errors.append(f"Unknown root key '{key}'. Did you mean '{similar}'?")
+            else:
+                errors.append(f"Unknown root key '{key}'. Valid keys: {', '.join(sorted(VALID_ROOT_KEYS))}")
+    
+    # Validate [plugins] section
+    if "plugins" in config:
+        plugins = config["plugins"]
+        if not isinstance(plugins, dict):
+            errors.append(f"[plugins] must be a table, got {type(plugins).__name__}")
+        else:
+            for key, value in plugins.items():
+                if key not in VALID_PLUGINS_KEYS:
+                    similar = _suggest_similar_key(key, VALID_PLUGINS_KEYS.keys())
+                    if similar:
+                        errors.append(f"[plugins] Unknown key '{key}'. Did you mean '{similar}'?")
+                    else:
+                        errors.append(f"[plugins] Unknown key '{key}'. Valid keys: {', '.join(sorted(VALID_PLUGINS_KEYS.keys()))}")
+                else:
+                    valid_types = VALID_PLUGINS_KEYS[key]
+                    if not isinstance(value, valid_types):
+                        errors.append(f"[plugins.{key}] Expected {_format_types(valid_types)}, got {type(value).__name__}")
+    
+    # Validate [defaults] section
+    if "defaults" in config:
+        defaults = config["defaults"]
+        if not isinstance(defaults, dict):
+            errors.append(f"[defaults] must be a table, got {type(defaults).__name__}")
+        else:
+            for key, value in defaults.items():
+                if key not in VALID_DEFAULTS_KEYS:
+                    similar = _suggest_similar_key(key, VALID_DEFAULTS_KEYS.keys())
+                    if similar:
+                        errors.append(f"[defaults] Unknown key '{key}'. Did you mean '{similar}'?")
+                    else:
+                        errors.append(f"[defaults] Unknown key '{key}'. Valid keys: {', '.join(sorted(VALID_DEFAULTS_KEYS.keys()))}")
+                else:
+                    valid_types = VALID_DEFAULTS_KEYS[key]
+                    if not isinstance(value, valid_types):
+                        errors.append(f"[defaults.{key}] Expected {_format_types(valid_types)}, got {type(value).__name__}")
+                    # Validate nested feature configs
+                    if isinstance(value, dict) and key in ("memory", "knowledge", "planning", "reflection", 
+                                                           "guardrails", "web", "output", "execution", 
+                                                           "caching", "autonomy", "context"):
+                        nested_errors = _validate_feature_config(key, value)
+                        errors.extend(nested_errors)
+    
+    if raise_on_error and errors:
+        raise ConfigValidationError(errors)
+    
+    return errors
+
+
+def _suggest_similar_key(key: str, valid_keys) -> Optional[str]:
+    """Suggest a similar valid key using simple string matching."""
+    key_lower = key.lower()
+    for valid in valid_keys:
+        # Exact match after lowercasing
+        if valid.lower() == key_lower:
+            return valid
+        # Prefix match
+        if valid.lower().startswith(key_lower) or key_lower.startswith(valid.lower()):
+            return valid
+        # Substring match
+        if key_lower in valid.lower() or valid.lower() in key_lower:
+            return valid
+    return None
+
+
+def _format_types(types: tuple) -> str:
+    """Format type tuple for error messages."""
+    type_names = []
+    for t in types:
+        if t is bool:
+            type_names.append("bool")
+        elif t is str:
+            type_names.append("str")
+        elif t is dict:
+            type_names.append("table")
+        elif t is list:
+            type_names.append("array")
+        else:
+            type_names.append(t.__name__)
+    return " or ".join(type_names)
+
+
+def _validate_feature_config(feature: str, config: Dict[str, Any]) -> List[str]:
+    """Validate a nested feature config (memory, knowledge, etc.)."""
+    errors = []
+    
+    # Common valid keys for feature configs
+    common_keys = {"enabled", "backend", "provider", "preset", "verbose"}
+    
+    # Feature-specific valid keys
+    feature_keys = {
+        "memory": {"enabled", "backend", "provider", "use_long_term", "use_short_term", 
+                   "user_id", "session_id", "db_path", "collection_name"},
+        "knowledge": {"enabled", "sources", "provider", "chunk_size", "chunk_overlap"},
+        "planning": {"enabled", "max_steps", "reasoning"},
+        "reflection": {"enabled", "max_reflect", "min_reflect", "llm"},
+        "guardrails": {"enabled", "max_retries", "validator"},
+        "web": {"enabled", "search", "fetch", "provider"},
+        "output": {"enabled", "verbose", "markdown", "stream", "metrics", "preset"},
+        "execution": {"enabled", "max_iter", "max_rpm", "max_execution_time", "preset"},
+        "caching": {"enabled", "prompt_caching", "ttl"},
+        "autonomy": {"enabled", "max_steps", "approval_required"},
+        "context": {"enabled", "max_tokens", "strategy"},
+    }
+    
+    valid_keys = feature_keys.get(feature, common_keys)
+    
+    for key in config.keys():
+        if key not in valid_keys:
+            similar = _suggest_similar_key(key, valid_keys)
+            if similar:
+                errors.append(f"[defaults.{feature}.{key}] Unknown key. Did you mean '{similar}'?")
+            # Don't error on unknown nested keys - allow flexibility
+    
+    return errors
+
+
+def get_config_path() -> Optional[Path]:
+    """Get the path to the config file if it exists.
+    
+    Returns:
+        Path to config file, or None if not found
+    """
+    return _find_config_file()
+
+
+# Convenience function to merge config defaults with explicit params
+def apply_config_defaults(
+    param_name: str,
+    explicit_value: Any,
+    config_class: Optional[type] = None,
+) -> Any:
+    """Apply config defaults to a parameter if not explicitly set.
+    
+    Args:
+        param_name: Name of the parameter (e.g., "memory", "knowledge")
+        explicit_value: Value explicitly passed by user (None if not set)
+        config_class: Optional config class to instantiate
+        
+    Returns:
+        Resolved value with config defaults applied
+        
+    Example:
+        # User passes memory=True, config has memory.backend="postgres"
+        # Result: MemoryConfig with backend="postgres"
+        
+        memory = apply_config_defaults("memory", True, MemoryConfig)
+    """
+    # If user explicitly passed a value, respect it
+    if explicit_value is not None:
+        # If True and we have config defaults, merge them
+        if explicit_value is True:
+            config_defaults = get_default(param_name)
+            if config_defaults and isinstance(config_defaults, dict):
+                if config_class:
+                    # Check if enabled key exists and is True
+                    if config_defaults.get("enabled", True):
+                        return config_class(**{k: v for k, v in config_defaults.items() if k != "enabled"})
+                return config_defaults
+        return explicit_value
+    
+    # Check if config has defaults for this param
+    config_defaults = get_default(param_name)
+    if config_defaults is None:
+        return None
+    
+    # If config has enabled=True, return the config
+    if isinstance(config_defaults, dict):
+        if config_defaults.get("enabled", False):
+            if config_class:
+                return config_class(**{k: v for k, v in config_defaults.items() if k != "enabled"})
+            return True
+    
+    return None

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

@@ -26,6 +26,10 @@ MEMORY_PRESETS: Dict[str, Dict[str, Any]] = {
     "mongodb": {"backend": "mongodb"},
     # Feature presets
     "learn": {"backend": "file", "learn": True},  # Enable Agent Learn with file backend
+    # History presets (auto-inject session history into context)
+    "history": {"backend": "file", "history": True, "history_limit": 10},
+    "session": {"backend": "file", "history": True, "history_limit": 10},  # Alias
+    "chat": {"backend": "file", "history": True, "history_limit": 20},  # Conversational
 }
 
 MEMORY_URL_SCHEMES: Dict[str, str] = {

src/praisonai-agents/praisonaiagents/config/sample_config.toml

@@ -0,0 +1,156 @@
+# PraisonAI Agents Configuration File
+# Copy this file to .praisonai/config.toml in your project directory
+# or to ~/.praisonai/config.toml for global settings
+
+# ============================================================================
+# PLUGINS CONFIGURATION
+# ============================================================================
+# Enable background plugins (hooks, metrics, logging)
+# Note: Tools and guardrails work WITHOUT enabling plugins
+
+[plugins]
+# Enable all plugins: true
+# Enable specific plugins: ["logging", "metrics"]
+# Disable: false
+enabled = false
+
+# Auto-discover plugins from default directories
+auto_discover = true
+
+# Plugin directories to scan
+directories = [
+    "./.praisonai/plugins/",
+    "~/.praisonai/plugins/"
+]
+
+# ============================================================================
+# DEFAULT SETTINGS
+# ============================================================================
+# These defaults are used when you pass True to a feature parameter
+# Example: Agent(memory=True) will use [defaults.memory] settings
+
+[defaults]
+# Default LLM model
+model = "gpt-4o-mini"
+
+# Custom LLM endpoint (optional)
+# base_url = "http://localhost:11434/v1"
+
+# Feature flags
+allow_delegation = false
+allow_code_execution = false
+code_execution_mode = "safe"
+
+# ============================================================================
+# MEMORY DEFAULTS
+# ============================================================================
+# Used when: Agent(memory=True)
+
+[defaults.memory]
+enabled = false
+backend = "file"  # file, sqlite, redis, postgres, mongodb
+# user_id = ""    # Auto-generated if empty
+auto_memory = false
+history = false
+history_limit = 10
+
+# Learning configuration
+[defaults.memory.learn]
+enabled = false
+persona = true
+insights = true
+patterns = false
+
+# ============================================================================
+# KNOWLEDGE DEFAULTS
+# ============================================================================
+# Used when: Agent(knowledge=True)
+
+[defaults.knowledge]
+enabled = false
+embedder = "openai"
+chunking_strategy = "semantic"
+chunk_size = 1000
+chunk_overlap = 200
+retrieval_k = 5
+retrieval_threshold = 0.0
+rerank = false
+
+# ============================================================================
+# PLANNING DEFAULTS
+# ============================================================================
+# Used when: Agent(planning=True)
+
+[defaults.planning]
+enabled = false
+reasoning = false
+auto_approve = false
+read_only = false
+
+# ============================================================================
+# REFLECTION DEFAULTS
+# ============================================================================
+# Used when: Agent(reflection=True)
+
+[defaults.reflection]
+enabled = false
+min_iterations = 1
+max_iterations = 3
+
+# ============================================================================
+# WEB DEFAULTS
+# ============================================================================
+# Used when: Agent(web=True)
+
+[defaults.web]
+enabled = false
+search = true
+fetch = true
+search_provider = "duckduckgo"
+max_results = 5
+
+# ============================================================================
+# OUTPUT DEFAULTS
+# ============================================================================
+# Used when: Agent(output=True) or default output mode
+
+[defaults.output]
+preset = "silent"  # silent, actions, verbose, json, stream, status
+verbose = false
+markdown = false
+stream = false
+metrics = false
+
+# ============================================================================
+# EXECUTION DEFAULTS
+# ============================================================================
+# Used when: Agent(execution=True) or default execution limits
+
+[defaults.execution]
+max_iter = 20
+max_retry_limit = 2
+# max_rpm = null
+# max_execution_time = null
+
+# ============================================================================
+# CACHING DEFAULTS
+# ============================================================================
+# Used when: Agent(caching=True)
+
+[defaults.caching]
+enabled = true
+prompt_caching = false
+
+# ============================================================================
+# AUTONOMY DEFAULTS
+# ============================================================================
+# Used when: Agent(autonomy=True)
+
+[defaults.autonomy]
+level = "suggest"  # suggest, auto_edit, full_auto
+escalation_enabled = true
+escalation_threshold = 3
+doom_loop_detection = true
+max_consecutive_failures = 3
+require_approval_for_writes = true
+require_approval_for_shell = true

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

@@ -181,7 +181,7 @@ class ArtifactStoreProtocol(Protocol):
     Example implementation in wrapper:
         class FileSystemArtifactStore:
             def store(self, content, metadata):
-                # Write to ~/.praison/runs/{run_id}/artifacts/
+                # Write to ~/.praisonai/runs/{run_id}/artifacts/
                 ...
     """
     

src/praisonai-agents/praisonaiagents/context/fast/search_backends.py

@@ -323,6 +323,7 @@ class SmartBackend:
         # Check if ripgrep is available
         rg = self._get_ripgrep()
         if not rg.is_available():
+            self._use_ripgrep = False
             return False
         
         # Count files if not already counted for this path

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

@@ -24,7 +24,8 @@ from pathlib import Path
 from enum import Enum
 
 from .models import (
-    ContextLedger, BudgetAllocation, OptimizerStrategy, OptimizationResult
+    ContextLedger, BudgetAllocation, OptimizerStrategy, OptimizationResult,
+    ContextConfig as ManagerConfig  # Deprecated alias for backward compatibility
 )
 from .tokens import estimate_tokens_heuristic, estimate_messages_tokens, estimate_message_tokens
 from .budgeter import ContextBudgeter
@@ -288,180 +289,6 @@ class SnapshotHookData:
             "budget": self.budget.to_dict() if self.budget else None,
         }
 
-
-@dataclass
-class ManagerConfig:
-    """
-    Complete configuration for ContextManager.
-    
-    Consolidates all context management settings with proper precedence.
-    """
-    # Auto-compaction
-    auto_compact: bool = True
-    compact_threshold: float = 0.8
-    strategy: OptimizerStrategy = OptimizerStrategy.SMART
-    
-    # Compression benefit check
-    compression_min_gain_pct: float = 5.0
-    compression_max_attempts: int = 3
-    
-    # Budget
-    output_reserve: int = 8000
-    history_ratio: float = 0.6
-    default_tool_output_max: int = 10000
-    
-    # Per-tool budgets
-    tool_budgets: Dict[str, PerToolBudget] = field(default_factory=dict)
-    protected_tools: List[str] = field(default_factory=list)
-    
-    # LLM-powered summarization
-    llm_summarize: bool = False  # Enable LLM-powered summarization
-    
-    # Smart tool output summarization
-    smart_tool_summarize: bool = True  # Summarize large tool outputs using LLM before truncating
-    tool_summarize_limits: Dict[str, int] = field(default_factory=dict)  # Per-tool min_chars_to_summarize
-    
-    # Estimation
-    estimation_mode: EstimationMode = EstimationMode.HEURISTIC
-    log_estimation_mismatch: bool = False
-    mismatch_threshold_pct: float = 15.0
-    
-    # Monitoring
-    monitor_enabled: bool = False
-    monitor_path: str = "./context.txt"
-    monitor_format: Literal["human", "json"] = "human"
-    monitor_frequency: Literal["turn", "tool_call", "manual", "overflow"] = "turn"
-    monitor_write_mode: Literal["sync", "async"] = "sync"
-    redact_sensitive: bool = True
-    snapshot_timing: Literal["pre_optimization", "post_optimization", "both"] = "post_optimization"
-    
-    # Path validation
-    allow_absolute_paths: bool = False
-    
-    # Pruning
-    prune_after_tokens: int = 40000
-    keep_recent_turns: int = 5
-    
-    # Source tracking
-    source: str = "defaults"  # defaults, env, config_file, cli
-    
-    def to_dict(self) -> Dict[str, Any]:
-        result = {
-            "auto_compact": self.auto_compact,
-            "compact_threshold": self.compact_threshold,
-            "strategy": self.strategy.value,
-            "compression_min_gain_pct": self.compression_min_gain_pct,
-            "compression_max_attempts": self.compression_max_attempts,
-            "output_reserve": self.output_reserve,
-            "history_ratio": self.history_ratio,
-            "default_tool_output_max": self.default_tool_output_max,
-            "tool_budgets": {k: v.to_dict() for k, v in self.tool_budgets.items()},
-            "protected_tools": self.protected_tools,
-            "estimation_mode": self.estimation_mode.value,
-            "log_estimation_mismatch": self.log_estimation_mismatch,
-            "mismatch_threshold_pct": self.mismatch_threshold_pct,
-            "monitor_enabled": self.monitor_enabled,
-            "monitor_path": self.monitor_path,
-            "monitor_format": self.monitor_format,
-            "monitor_frequency": self.monitor_frequency,
-            "monitor_write_mode": self.monitor_write_mode,
-            "redact_sensitive": self.redact_sensitive,
-            "snapshot_timing": self.snapshot_timing,
-            "allow_absolute_paths": self.allow_absolute_paths,
-            "prune_after_tokens": self.prune_after_tokens,
-            "keep_recent_turns": self.keep_recent_turns,
-            "llm_summarize": self.llm_summarize,
-            "smart_tool_summarize": self.smart_tool_summarize,
-            "tool_summarize_limits": self.tool_summarize_limits,
-            "source": self.source,
-        }
-        return result
-    
-    @classmethod
-    def from_env(cls) -> "ManagerConfig":
-        """Load config from environment variables."""
-        import os
-        
-        def get_bool(key: str, default: bool) -> bool:
-            val = os.getenv(key, str(default)).lower()
-            return val in ("true", "1", "yes", "on")
-        
-        def get_float(key: str, default: float) -> float:
-            try:
-                return float(os.getenv(key, str(default)))
-            except ValueError:
-                return default
-        
-        def get_int(key: str, default: int) -> int:
-            try:
-                return int(os.getenv(key, str(default)))
-            except ValueError:
-                return default
-        
-        strategy_str = os.getenv("PRAISONAI_CONTEXT_STRATEGY", "smart")
-        try:
-            strategy = OptimizerStrategy(strategy_str)
-        except ValueError:
-            strategy = OptimizerStrategy.SMART
-        
-        estimation_str = os.getenv("PRAISONAI_CONTEXT_ESTIMATION_MODE", "heuristic")
-        try:
-            estimation_mode = EstimationMode(estimation_str)
-        except ValueError:
-            estimation_mode = EstimationMode.HEURISTIC
-        
-        return cls(
-            auto_compact=get_bool("PRAISONAI_CONTEXT_AUTO_COMPACT", True),
-            compact_threshold=get_float("PRAISONAI_CONTEXT_THRESHOLD", 0.8),
-            strategy=strategy,
-            compression_min_gain_pct=get_float("PRAISONAI_CONTEXT_COMPRESSION_MIN_GAIN", 5.0),
-            compression_max_attempts=get_int("PRAISONAI_CONTEXT_COMPRESSION_MAX_ATTEMPTS", 3),
-            output_reserve=get_int("PRAISONAI_CONTEXT_OUTPUT_RESERVE", 8000),
-            default_tool_output_max=get_int("PRAISONAI_CONTEXT_TOOL_OUTPUT_MAX", 10000),
-            estimation_mode=estimation_mode,
-            log_estimation_mismatch=get_bool("PRAISONAI_CONTEXT_LOG_MISMATCH", False),
-            monitor_enabled=get_bool("PRAISONAI_CONTEXT_MONITOR", False),
-            monitor_path=os.getenv("PRAISONAI_CONTEXT_MONITOR_PATH", "./context.txt"),
-            monitor_format=os.getenv("PRAISONAI_CONTEXT_MONITOR_FORMAT", "human"),
-            monitor_frequency=os.getenv("PRAISONAI_CONTEXT_MONITOR_FREQUENCY", "turn"),
-            monitor_write_mode=os.getenv("PRAISONAI_CONTEXT_MONITOR_WRITE_MODE", "sync"),
-            redact_sensitive=get_bool("PRAISONAI_CONTEXT_REDACT", True),
-            source="env",
-        )
-    
-    def merge(self, **overrides) -> "ManagerConfig":
-        """Create new config with overrides applied."""
-        current = self.to_dict()
-        
-        for key, value in overrides.items():
-            if value is not None and key in current:
-                current[key] = value
-        
-        # Handle enum conversions
-        if isinstance(current.get("strategy"), str):
-            try:
-                current["strategy"] = OptimizerStrategy(current["strategy"])
-            except ValueError:
-                current["strategy"] = OptimizerStrategy.SMART
-        
-        if isinstance(current.get("estimation_mode"), str):
-            try:
-                current["estimation_mode"] = EstimationMode(current["estimation_mode"])
-            except ValueError:
-                current["estimation_mode"] = EstimationMode.HEURISTIC
-        
-        # Reconstruct tool budgets
-        tool_budgets = {}
-        for name, budget_dict in current.get("tool_budgets", {}).items():
-            if isinstance(budget_dict, dict):
-                tool_budgets[name] = PerToolBudget(**budget_dict)
-            elif isinstance(budget_dict, PerToolBudget):
-                tool_budgets[name] = budget_dict
-        current["tool_budgets"] = tool_budgets
-        
-        return ManagerConfig(**current)
-
-
 class ContextManager:
     """
     Unified facade for context management.

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

@@ -272,6 +272,7 @@ class ContextConfig:
     output_reserve: int = 8000
     history_ratio: float = 0.6  # 60% of usable for history
     tool_output_max: int = 10000  # Max tokens per tool output
+    default_tool_output_max: int = 10000  # Alias for tool_output_max (ManagerConfig compat)
     
     # Pruning
     prune_after_tokens: int = 40000
@@ -308,6 +309,38 @@ class ContextConfig:
     ])
     aggregate_max_tokens: int = 4000   # Max tokens for aggregated context
     
+    # -------------------------------------------------------------------------
+    # Internal fields (from ManagerConfig consolidation)
+    # These are typically set by the system, not users directly
+    # -------------------------------------------------------------------------
+    
+    # Compression benefit check
+    compression_min_gain_pct: float = 5.0
+    compression_max_attempts: int = 3
+    
+    # Per-tool budgets (advanced)
+    tool_budgets: Dict[str, Any] = field(default_factory=dict)  # tool_name -> PerToolBudget
+    tool_summarize_limits: Dict[str, int] = field(default_factory=dict)  # Per-tool min_chars
+    
+    # Estimation modes
+    estimation_mode: str = "heuristic"  # heuristic, accurate, validated
+    log_estimation_mismatch: bool = False
+    mismatch_threshold_pct: float = 15.0
+    
+    # Extended monitoring options
+    monitor_enabled: bool = False  # Enable monitoring
+    monitor_path: str = "./context.txt"  # Path for monitoring output
+    monitor_format: str = "human"  # human, json
+    monitor_frequency: str = "turn"  # turn, tool_call, manual, overflow
+    monitor_write_mode: str = "sync"  # sync, async
+    redact_sensitive: bool = True
+    snapshot_timing: str = "post_optimization"  # pre_optimization, post_optimization, both
+    allow_absolute_paths: bool = False
+    
+    # Source tracking
+    source: str = "defaults"  # defaults, env, config_file, cli
+
+    
     def to_dict(self) -> Dict[str, Any]:
         """Convert to dictionary."""
         result = asdict(self)
@@ -338,6 +371,73 @@ class ContextConfig:
             output_reserve=8000,
             history_ratio=0.6,
         )
+    
+    @classmethod
+    def from_env(cls) -> "ContextConfig":
+        """Load config from environment variables.
+        
+        Reads PRAISONAI_CONTEXT_* environment variables.
+        """
+        import os
+        
+        def get_bool(key: str, default: bool) -> bool:
+            val = os.getenv(key, str(default)).lower()
+            return val in ("true", "1", "yes", "on")
+        
+        def get_float(key: str, default: float) -> float:
+            try:
+                return float(os.getenv(key, str(default)))
+            except ValueError:
+                return default
+        
+        def get_int(key: str, default: int) -> int:
+            try:
+                return int(os.getenv(key, str(default)))
+            except ValueError:
+                return default
+        
+        strategy_str = os.getenv("PRAISONAI_CONTEXT_STRATEGY", "smart")
+        try:
+            strategy = OptimizerStrategy(strategy_str)
+        except ValueError:
+            strategy = OptimizerStrategy.SMART
+        
+        return cls(
+            auto_compact=get_bool("PRAISONAI_CONTEXT_AUTO_COMPACT", True),
+            compact_threshold=get_float("PRAISONAI_CONTEXT_THRESHOLD", 0.8),
+            strategy=strategy,
+            compression_min_gain_pct=get_float("PRAISONAI_CONTEXT_COMPRESSION_MIN_GAIN", 5.0),
+            compression_max_attempts=get_int("PRAISONAI_CONTEXT_COMPRESSION_MAX_ATTEMPTS", 3),
+            output_reserve=get_int("PRAISONAI_CONTEXT_OUTPUT_RESERVE", 8000),
+            tool_output_max=get_int("PRAISONAI_CONTEXT_TOOL_OUTPUT_MAX", 10000),
+            default_tool_output_max=get_int("PRAISONAI_CONTEXT_TOOL_OUTPUT_MAX", 10000),
+            log_estimation_mismatch=get_bool("PRAISONAI_CONTEXT_LOG_MISMATCH", False),
+            monitor_enabled=get_bool("PRAISONAI_CONTEXT_MONITOR", False),
+            monitor_path=os.getenv("PRAISONAI_CONTEXT_MONITOR_PATH", "./context.txt"),
+            monitor_format=os.getenv("PRAISONAI_CONTEXT_MONITOR_FORMAT", "human"),
+            monitor_frequency=os.getenv("PRAISONAI_CONTEXT_MONITOR_FREQUENCY", "turn"),
+            monitor_write_mode=os.getenv("PRAISONAI_CONTEXT_MONITOR_WRITE_MODE", "sync"),
+            redact_sensitive=get_bool("PRAISONAI_CONTEXT_REDACT", True),
+            source="env",
+        )
+    
+    def merge(self, **overrides) -> "ContextConfig":
+        """Create new config with overrides applied."""
+        current = self.to_dict()
+        
+        for key, value in overrides.items():
+            if value is not None and key in current:
+                current[key] = value
+        
+        # Handle enum conversions
+        if isinstance(current.get("strategy"), str):
+            try:
+                current["strategy"] = OptimizerStrategy(current["strategy"])
+            except ValueError:
+                current["strategy"] = OptimizerStrategy.SMART
+        
+        return ContextConfig(**{k: v for k, v in current.items() 
+                               if k in ContextConfig.__dataclass_fields__})
 
 
 @runtime_checkable

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

@@ -76,6 +76,13 @@ __all__ = [
     "count_tokens",
     "needs_chunking",
     "get_recommended_chunk_size",
+    # EvaluationLoop (iterative improvement)
+    "EvaluationLoop",
+    "EvaluationLoopConfig",
+    "IterationResult",
+    "EvaluationLoopResult",
+    "EvaluationLoopProtocol",
+    "EvaluationLoopResultProtocol",
 ]
 
 _LAZY_IMPORTS = {
@@ -134,6 +141,13 @@ _LAZY_IMPORTS = {
     "count_tokens": ("tokens", "count_tokens"),
     "needs_chunking": ("tokens", "needs_chunking"),
     "get_recommended_chunk_size": ("tokens", "get_recommended_chunk_size"),
+    # EvaluationLoop (iterative improvement)
+    "EvaluationLoop": ("loop", "EvaluationLoop"),
+    "EvaluationLoopConfig": ("loop", "EvaluationLoopConfig"),
+    "IterationResult": ("results", "IterationResult"),
+    "EvaluationLoopResult": ("results", "EvaluationLoopResult"),
+    "EvaluationLoopProtocol": ("protocols", "EvaluationLoopProtocol"),
+    "EvaluationLoopResultProtocol": ("protocols", "EvaluationLoopResultProtocol"),
 }
 
 

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

@@ -0,0 +1,276 @@
+"""
+EvaluationLoop - Iterative evaluation and improvement loop for agents.
+
+This module provides the EvaluationLoop class which implements the "Ralph Loop"
+pattern: run agent → judge output → improve → repeat until threshold met.
+
+Example:
+    from praisonaiagents import Agent
+    from praisonaiagents.eval import EvaluationLoop
+    
+    agent = Agent(name="analyzer", instructions="Analyze systems")
+    loop = EvaluationLoop(agent=agent, criteria="Analysis is thorough")
+    result = loop.run("Analyze the auth flow")
+    
+    print(result.final_score)  # 8.5
+    print(result.success)      # True
+"""
+
+import time
+import logging
+from typing import TYPE_CHECKING, Optional, Callable, Any, List
+from dataclasses import dataclass
+
+if TYPE_CHECKING:
+    from ..agent.agent import Agent
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class EvaluationLoopConfig:
+    """Configuration for EvaluationLoop."""
+    criteria: str = ""
+    threshold: float = 8.0
+    max_iterations: int = 5
+    mode: str = "optimize"
+    model: str = "gpt-4o-mini"
+    verbose: bool = False
+
+
+class EvaluationLoop:
+    """
+    Iterative evaluation loop that runs an agent, judges output, and improves.
+    
+    The loop continues until either:
+    - The score meets or exceeds the threshold (success)
+    - Max iterations is reached (failure in optimize mode)
+    - All iterations complete (review mode always runs all)
+    
+    Args:
+        agent: The Agent instance to evaluate
+        criteria: Evaluation criteria for the Judge
+        threshold: Score threshold for success (default: 8.0)
+        max_iterations: Maximum number of iterations (default: 5)
+        mode: "optimize" (stop on success) or "review" (run all iterations)
+        judge: Optional custom Judge instance
+        on_iteration: Optional callback called after each iteration
+        verbose: Enable verbose logging
+        model: LLM model for Judge (default: gpt-4o-mini)
+        
+    Example:
+        loop = EvaluationLoop(
+            agent=my_agent,
+            criteria="Response is helpful and accurate",
+            threshold=8.0,
+            max_iterations=5,
+            on_iteration=lambda r: print(f"Score: {r.score}")
+        )
+        result = loop.run("Analyze the codebase")
+    """
+    
+    def __init__(
+        self,
+        agent: "Agent",
+        criteria: str,
+        threshold: float = 8.0,
+        max_iterations: int = 5,
+        mode: str = "optimize",
+        judge: Optional[Any] = None,
+        on_iteration: Optional[Callable[[Any], None]] = None,
+        verbose: bool = False,
+        model: str = "gpt-4o-mini",
+    ):
+        self.agent = agent
+        self.criteria = criteria
+        self.threshold = threshold
+        self.max_iterations = max_iterations
+        self.mode = mode
+        self._judge = judge
+        self.on_iteration = on_iteration
+        self.verbose = verbose
+        self.model = model
+        
+        if mode not in ("optimize", "review"):
+            raise ValueError(f"mode must be 'optimize' or 'review', got '{mode}'")
+    
+    @property
+    def judge(self):
+        """Lazy-load Judge to avoid import-time overhead."""
+        if self._judge is None:
+            from .judge import Judge
+            self._judge = Judge(
+                criteria=self.criteria,
+                model=self.model,
+            )
+        return self._judge
+    
+    def _get_agent_output(self, prompt: str, iteration: int, feedback: str = "") -> str:
+        """Get output from agent, optionally including feedback from previous iteration."""
+        if iteration == 1 or not feedback:
+            return str(self.agent.chat(prompt))
+        
+        improved_prompt = f"{prompt}\n\nPrevious feedback to address:\n{feedback}"
+        return str(self.agent.chat(improved_prompt))
+    
+    async def _get_agent_output_async(self, prompt: str, iteration: int, feedback: str = "") -> str:
+        """Async version of _get_agent_output."""
+        if iteration == 1 or not feedback:
+            if hasattr(self.agent, 'chat_async'):
+                return str(await self.agent.chat_async(prompt))
+            return str(self.agent.chat(prompt))
+        
+        improved_prompt = f"{prompt}\n\nPrevious feedback to address:\n{feedback}"
+        if hasattr(self.agent, 'chat_async'):
+            return str(await self.agent.chat_async(improved_prompt))
+        return str(self.agent.chat(improved_prompt))
+    
+    def run(self, prompt: str) -> "EvaluationLoopResult":
+        """
+        Run the evaluation loop synchronously.
+        
+        Args:
+            prompt: The prompt to send to the agent
+            
+        Returns:
+            EvaluationLoopResult with iteration results
+        """
+        from .results import IterationResult, EvaluationLoopResult
+        
+        start_time = time.time()
+        iterations: List[IterationResult] = []
+        feedback = ""
+        
+        for i in range(1, self.max_iterations + 1):
+            if self.verbose:
+                logger.info(f"EvaluationLoop iteration {i}/{self.max_iterations}")
+            
+            output = self._get_agent_output(prompt, i, feedback)
+            
+            judge_result = self.judge.run(
+                output=output,
+                criteria=self.criteria,
+            )
+            
+            findings = getattr(judge_result, 'suggestions', []) or []
+            
+            iteration_result = IterationResult(
+                iteration=i,
+                output=output,
+                score=judge_result.score,
+                reasoning=judge_result.reasoning,
+                findings=findings,
+            )
+            iterations.append(iteration_result)
+            
+            if self.on_iteration:
+                try:
+                    self.on_iteration(iteration_result)
+                except Exception as e:
+                    logger.warning(f"on_iteration callback error: {e}")
+            
+            if self.mode == "optimize" and judge_result.score >= self.threshold:
+                if self.verbose:
+                    logger.info(f"Threshold met at iteration {i}: {judge_result.score} >= {self.threshold}")
+                break
+            
+            feedback = judge_result.reasoning
+            if findings:
+                feedback += "\nSuggestions:\n" + "\n".join(f"- {s}" for s in findings)
+        
+        total_duration = time.time() - start_time
+        success = iterations[-1].score >= self.threshold if iterations else False
+        
+        result = EvaluationLoopResult(
+            iterations=iterations,
+            success=success,
+            total_duration_seconds=total_duration,
+            threshold=self.threshold,
+            mode=self.mode,
+        )
+        
+        if self.verbose:
+            result.print_summary()
+        
+        return result
+    
+    async def run_async(self, prompt: str) -> "EvaluationLoopResult":
+        """
+        Run the evaluation loop asynchronously.
+        
+        Args:
+            prompt: The prompt to send to the agent
+            
+        Returns:
+            EvaluationLoopResult with iteration results
+        """
+        from .results import IterationResult, EvaluationLoopResult
+        
+        start_time = time.time()
+        iterations: List[IterationResult] = []
+        feedback = ""
+        
+        for i in range(1, self.max_iterations + 1):
+            if self.verbose:
+                logger.info(f"EvaluationLoop iteration {i}/{self.max_iterations}")
+            
+            output = await self._get_agent_output_async(prompt, i, feedback)
+            
+            if hasattr(self.judge, 'run_async'):
+                judge_result = await self.judge.run_async(
+                    output=output,
+                    criteria=self.criteria,
+                )
+            else:
+                judge_result = self.judge.run(
+                    output=output,
+                    criteria=self.criteria,
+                )
+            
+            findings = getattr(judge_result, 'suggestions', []) or []
+            
+            iteration_result = IterationResult(
+                iteration=i,
+                output=output,
+                score=judge_result.score,
+                reasoning=judge_result.reasoning,
+                findings=findings,
+            )
+            iterations.append(iteration_result)
+            
+            if self.on_iteration:
+                try:
+                    self.on_iteration(iteration_result)
+                except Exception as e:
+                    logger.warning(f"on_iteration callback error: {e}")
+            
+            if self.mode == "optimize" and judge_result.score >= self.threshold:
+                if self.verbose:
+                    logger.info(f"Threshold met at iteration {i}: {judge_result.score} >= {self.threshold}")
+                break
+            
+            feedback = judge_result.reasoning
+            if findings:
+                feedback += "\nSuggestions:\n" + "\n".join(f"- {s}" for s in findings)
+        
+        total_duration = time.time() - start_time
+        success = iterations[-1].score >= self.threshold if iterations else False
+        
+        result = EvaluationLoopResult(
+            iterations=iterations,
+            success=success,
+            total_duration_seconds=total_duration,
+            threshold=self.threshold,
+            mode=self.mode,
+        )
+        
+        if self.verbose:
+            result.print_summary()
+        
+        return result
+
+
+__all__ = [
+    'EvaluationLoop',
+    'EvaluationLoopConfig',
+]

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

@@ -243,4 +243,58 @@ __all__ = [
     'OptimizationRuleProtocol',
     'JudgeProtocol',
     'JudgeResultProtocol',
+    'EvaluationLoopProtocol',
+    'EvaluationLoopResultProtocol',
 ]
+
+
+class EvaluationLoopResultProtocol(Protocol):
+    """Protocol for evaluation loop results."""
+    
+    @property
+    def success(self) -> bool:
+        """Whether the loop achieved the threshold."""
+        ...
+    
+    @property
+    def final_score(self) -> float:
+        """Get the final iteration's score."""
+        ...
+    
+    @property
+    def score_history(self) -> List[float]:
+        """Get list of all scores across iterations."""
+        ...
+    
+    @property
+    def final_output(self) -> str:
+        """Get the final iteration's output."""
+        ...
+
+
+class EvaluationLoopProtocol(Protocol):
+    """Protocol for evaluation loops that iteratively improve agent outputs."""
+    
+    def run(self, prompt: str) -> EvaluationLoopResultProtocol:
+        """
+        Run the evaluation loop synchronously.
+        
+        Args:
+            prompt: The prompt to send to the agent
+            
+        Returns:
+            EvaluationLoopResultProtocol with iteration results
+        """
+        ...
+    
+    async def run_async(self, prompt: str) -> EvaluationLoopResultProtocol:
+        """
+        Run the evaluation loop asynchronously.
+        
+        Args:
+            prompt: The prompt to send to the agent
+            
+        Returns:
+            EvaluationLoopResultProtocol with iteration results
+        """
+        ...

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

@@ -547,3 +547,189 @@ class CriteriaResult:
             print(f"  Average Score: {self.avg_score:.2f}")
             print(f"  Pass Rate: {self.pass_rate:.1%}")
             print(f"  Status: {'PASSED' if self.passed else 'FAILED'}")
+
+
+@dataclass
+class IterationResult:
+    """
+    Result from a single iteration of an evaluation loop.
+    
+    Attributes:
+        iteration: The iteration number (1-indexed)
+        output: The agent's output for this iteration
+        score: Quality score (1-10)
+        reasoning: Explanation for the score
+        findings: List of findings/suggestions from this iteration
+        timestamp: When this iteration completed
+        metadata: Additional metadata
+    """
+    iteration: int
+    output: str
+    score: float
+    reasoning: str
+    findings: List[str] = field(default_factory=list)
+    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    @property
+    def passed(self) -> bool:
+        """Check if this iteration passed (score >= 7.0)."""
+        return self.score >= 7.0
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert result to dictionary."""
+        return {
+            "iteration": self.iteration,
+            "output": self.output,
+            "score": self.score,
+            "reasoning": self.reasoning,
+            "findings": self.findings,
+            "timestamp": self.timestamp,
+            "passed": self.passed,
+            "metadata": self.metadata,
+        }
+
+
+@dataclass
+class EvaluationLoopResult:
+    """
+    Result from running an evaluation loop.
+    
+    Attributes:
+        iterations: List of IterationResult from each iteration
+        success: Whether the loop achieved the threshold
+        total_duration_seconds: Total time taken
+        threshold: The score threshold used
+        mode: The mode used ('optimize' or 'review')
+        metadata: Additional metadata
+    """
+    iterations: List[IterationResult] = field(default_factory=list)
+    success: bool = False
+    total_duration_seconds: float = 0.0
+    threshold: float = 8.0
+    mode: str = "optimize"
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    @property
+    def final_score(self) -> float:
+        """Get the final iteration's score."""
+        if not self.iterations:
+            return 0.0
+        return self.iterations[-1].score
+    
+    @property
+    def score_history(self) -> List[float]:
+        """Get list of all scores across iterations."""
+        return [it.score for it in self.iterations]
+    
+    @property
+    def final_output(self) -> str:
+        """Get the final iteration's output."""
+        if not self.iterations:
+            return ""
+        return self.iterations[-1].output
+    
+    @property
+    def accumulated_findings(self) -> List[str]:
+        """Get all findings accumulated across iterations."""
+        findings = []
+        for it in self.iterations:
+            findings.extend(it.findings)
+        return findings
+    
+    @property
+    def num_iterations(self) -> int:
+        """Get the number of iterations completed."""
+        return len(self.iterations)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert result to dictionary."""
+        return {
+            "success": self.success,
+            "final_score": self.final_score,
+            "score_history": self.score_history,
+            "final_output": self.final_output,
+            "accumulated_findings": self.accumulated_findings,
+            "num_iterations": self.num_iterations,
+            "total_duration_seconds": self.total_duration_seconds,
+            "threshold": self.threshold,
+            "mode": self.mode,
+            "iterations": [it.to_dict() for it in self.iterations],
+            "metadata": self.metadata,
+        }
+    
+    def to_json(self) -> str:
+        """Convert result to JSON string."""
+        return json.dumps(self.to_dict(), indent=2)
+    
+    @property
+    def final_report(self) -> str:
+        """Generate a markdown report of the evaluation loop."""
+        lines = [
+            "# Evaluation Loop Report",
+            "",
+            f"**Status**: {'✅ Success' if self.success else '❌ Failed'}",
+            f"**Final Score**: {self.final_score:.1f}/10",
+            f"**Threshold**: {self.threshold}/10",
+            f"**Iterations**: {self.num_iterations}",
+            f"**Duration**: {self.total_duration_seconds:.2f}s",
+            f"**Mode**: {self.mode}",
+            "",
+            "## Score History",
+            "",
+        ]
+        
+        for i, score in enumerate(self.score_history, 1):
+            status = "✅" if score >= self.threshold else "⬜"
+            lines.append(f"- Iteration {i}: {score:.1f}/10 {status}")
+        
+        lines.append("")
+        lines.append("## Iteration Details")
+        lines.append("")
+        
+        for it in self.iterations:
+            lines.append(f"### Iteration {it.iteration}")
+            lines.append(f"**Score**: {it.score:.1f}/10")
+            lines.append(f"**Reasoning**: {it.reasoning}")
+            if it.findings:
+                lines.append("**Findings**:")
+                for f in it.findings:
+                    lines.append(f"  - {f}")
+            lines.append("")
+        
+        if self.accumulated_findings:
+            lines.append("## All Findings")
+            lines.append("")
+            for f in self.accumulated_findings:
+                lines.append(f"- {f}")
+        
+        return "\n".join(lines)
+    
+    def print_summary(self) -> None:
+        """Print a summary of the evaluation loop results."""
+        try:
+            from rich.console import Console
+            from rich.table import Table
+            
+            console = Console()
+            
+            table = Table(title="Evaluation Loop Summary")
+            table.add_column("Metric", style="cyan")
+            table.add_column("Value", style="green" if self.success else "red")
+            
+            table.add_row("Status", "✅ SUCCESS" if self.success else "❌ FAILED")
+            table.add_row("Final Score", f"{self.final_score:.1f}/10")
+            table.add_row("Threshold", f"{self.threshold}/10")
+            table.add_row("Iterations", str(self.num_iterations))
+            table.add_row("Duration", f"{self.total_duration_seconds:.2f}s")
+            table.add_row("Mode", self.mode)
+            table.add_row("Score History", " → ".join(f"{s:.1f}" for s in self.score_history))
+            
+            console.print(table)
+        except ImportError:
+            print("Evaluation Loop Summary")
+            print(f"  Status: {'SUCCESS' if self.success else 'FAILED'}")
+            print(f"  Final Score: {self.final_score:.1f}/10")
+            print(f"  Threshold: {self.threshold}/10")
+            print(f"  Iterations: {self.num_iterations}")
+            print(f"  Duration: {self.total_duration_seconds:.2f}s")

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

@@ -11,7 +11,15 @@ from typing import Optional, Dict, Any, List, Callable, Union, Literal
 
 
 class HookEvent(str, Enum):
-    """Event names for the hook system."""
+    """Event names for the hook system.
+    
+    This enum is also aliased as PluginHook for backward compatibility.
+    All plugin lifecycle events are included here for DRY compliance.
+    """
+    # Plugin/System lifecycle
+    ON_INIT = "on_init"
+    ON_SHUTDOWN = "on_shutdown"
+    
     # Tool lifecycle
     BEFORE_TOOL = "before_tool"
     AFTER_TOOL = "after_tool"
@@ -33,6 +41,8 @@ class HookEvent(str, Enum):
     ON_RETRY = "on_retry"
     
     # Message lifecycle (for bot/channel integrations)
+    BEFORE_MESSAGE = "before_message"
+    AFTER_MESSAGE = "after_message"
     MESSAGE_RECEIVED = "message_received"
     MESSAGE_SENDING = "message_sending"
     MESSAGE_SENT = "message_sent"
@@ -48,6 +58,11 @@ class HookEvent(str, Enum):
     # Tool result persistence (for modifying tool results before storage)
     TOOL_RESULT_PERSIST = "tool_result_persist"
     
+    # Permission/Config/Auth hooks
+    ON_PERMISSION_ASK = "on_permission_ask"
+    ON_CONFIG = "on_config"
+    ON_AUTH = "on_auth"
+    
     # Claude Code parity events
     USER_PROMPT_SUBMIT = "user_prompt_submit"  # When user submits a prompt
     NOTIFICATION = "notification"              # When notification is sent

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

@@ -416,7 +416,7 @@ class Knowledge:
     def config(self):
         # Generate unique collection name for each instance (only if not provided in config)
         default_collection = f"test_{int(time.time())}_{str(uuid.uuid4())[:8]}"
-        persist_dir = ".praison"
+        persist_dir = ".praisonai"
 
         # Create persistent client config
         base_config = {
@@ -862,7 +862,7 @@ class Knowledge:
         result = IndexResult()
         
         # Initialize file tracker for incremental indexing
-        state_dir = os.path.join(os.path.dirname(path) if os.path.isfile(path) else path, ".praison")
+        state_dir = os.path.join(os.path.dirname(path) if os.path.isfile(path) else path, ".praisonai")
         os.makedirs(state_dir, exist_ok=True)
         state_file = os.path.join(state_dir, ".index_state.json")
         

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

@@ -1681,6 +1681,23 @@ Now provide your final answer using this result. Summarize the information natur
                         response_text = resp["choices"][0]["message"]["content"]
                         final_response = resp
                         
+                        # Emit StreamEvent for reasoning content if callback provided
+                        if _emit and reasoning_content:
+                            stream_callback(StreamEvent(
+                                type=StreamEventType.DELTA_TEXT,
+                                timestamp=time.perf_counter(),
+                                content=reasoning_content,
+                                is_reasoning=True
+                            ))
+                        # Emit StreamEvent for response content if callback provided
+                        if _emit and response_text:
+                            stream_callback(StreamEvent(
+                                type=StreamEventType.DELTA_TEXT,
+                                timestamp=time.perf_counter(),
+                                content=response_text,
+                                is_reasoning=False
+                            ))
+                        
                         # Track token usage
                         if self.metrics:
                             self._track_token_usage(final_response, self.model)

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

@@ -764,6 +764,14 @@ class OpenAIClient:
                             if rc:
                                 reasoning_content += rc
                                 live.update(display_fn(f"{full_response_text}\n[Reasoning: {reasoning_content}]", start_time))
+                                # Emit reasoning content as StreamEvent with is_reasoning=True
+                                if _emit:
+                                    stream_callback(StreamEvent(
+                                        type=StreamEventType.DELTA_TEXT,
+                                        timestamp=time.perf_counter(),
+                                        content=rc,
+                                        is_reasoning=True
+                                    ))
                 
                 # Clear the last generating display with a blank line
                 console.print()
@@ -978,6 +986,14 @@ class OpenAIClient:
                             if rc:
                                 reasoning_content += rc
                                 live.update(display_fn(f"{full_response_text}\n[Reasoning: {reasoning_content}]", start_time))
+                                # Emit reasoning content as StreamEvent with is_reasoning=True
+                                if _emit:
+                                    stream_callback(StreamEvent(
+                                        type=StreamEventType.DELTA_TEXT,
+                                        timestamp=time.perf_counter(),
+                                        content=rc,
+                                        is_reasoning=True
+                                    ))
                 
                 # Clear the last generating display with a blank line
                 console.print()
@@ -1543,6 +1559,8 @@ class OpenAIClient:
         reasoning_steps: bool = False,
         verbose: bool = True,
         max_iterations: int = 10,
+        stream_callback: Optional[Callable] = None,
+        emit_events: bool = False,
         **kwargs
     ):
         """
@@ -1560,6 +1578,8 @@ class OpenAIClient:
             reasoning_steps: Whether to show reasoning
             verbose: Whether to show verbose output
             max_iterations: Maximum tool calling iterations
+            stream_callback: Optional callback for StreamEvent emission
+            emit_events: Whether to emit StreamEvents via callback
             **kwargs: Additional API parameters
             
         Yields:
@@ -1568,8 +1588,19 @@ class OpenAIClient:
         # Format tools for OpenAI API
         formatted_tools = self.format_tools(tools)
         
+        # Setup StreamEvent emission if enabled
+        _emit = emit_events and stream_callback is not None
+        if _emit:
+            from ..streaming.events import StreamEvent, StreamEventType
+            stream_callback(StreamEvent(
+                type=StreamEventType.REQUEST_START,
+                timestamp=time.perf_counter(),
+                metadata={"model": model, "provider": "openai"}
+            ))
+        
         # Continue tool execution loop until no more tool calls are needed
         iteration_count = 0
+        _first_token_emitted = False
         
         while iteration_count < max_iterations:
             try:
@@ -1594,6 +1625,20 @@ class OpenAIClient:
                         content = chunk.choices[0].delta.content
                         full_response_text += content
                         yield content
+                        # Emit StreamEvent for text delta
+                        if _emit:
+                            if not _first_token_emitted:
+                                stream_callback(StreamEvent(
+                                    type=StreamEventType.FIRST_TOKEN,
+                                    timestamp=time.perf_counter()
+                                ))
+                                _first_token_emitted = True
+                            stream_callback(StreamEvent(
+                                type=StreamEventType.DELTA_TEXT,
+                                timestamp=time.perf_counter(),
+                                content=content,
+                                is_reasoning=False
+                            ))
                     
                     # Handle reasoning content if enabled
                     if reasoning_steps and chunk.choices and hasattr(chunk.choices[0].delta, "reasoning_content"):
@@ -1601,6 +1646,14 @@ class OpenAIClient:
                         if rc:
                             reasoning_content += rc
                             yield f"[Reasoning: {rc}]"
+                            # Emit StreamEvent for reasoning content
+                            if _emit:
+                                stream_callback(StreamEvent(
+                                    type=StreamEventType.DELTA_TEXT,
+                                    timestamp=time.perf_counter(),
+                                    content=rc,
+                                    is_reasoning=True
+                                ))
                 
                 # Process the complete response to check for tool calls
                 final_response = process_stream_chunks(chunks)

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

@@ -86,7 +86,8 @@ class RateLimiter:
         else:
             self._tokens = float('inf')
         self._last_update = time.monotonic()
-        self._lock = asyncio.Lock()
+        # Lazily create asyncio.Lock() to avoid Python 3.9 event loop issues
+        self._lock = None
 
         # Token-based limiting
         if self.tokens_per_minute is not None:
@@ -95,7 +96,8 @@ class RateLimiter:
         else:
             self._api_tokens = float('inf')
         self._api_tokens_last_update = time.monotonic()
-        self._api_tokens_lock = asyncio.Lock()
+        # Lazily create asyncio.Lock() to avoid Python 3.9 event loop issues
+        self._api_tokens_lock = None
 
         # Default implementations (can be overridden for testing)
         if self._get_time is None:
@@ -105,6 +107,18 @@ class RateLimiter:
         if self._async_sleep is None:
             self._async_sleep = asyncio.sleep
 
+    def _get_lock(self) -> asyncio.Lock:
+        """Get or create the request lock lazily (Python 3.9 compatible)."""
+        if self._lock is None:
+            self._lock = asyncio.Lock()
+        return self._lock
+
+    def _get_api_tokens_lock(self) -> asyncio.Lock:
+        """Get or create the API tokens lock lazily (Python 3.9 compatible)."""
+        if self._api_tokens_lock is None:
+            self._api_tokens_lock = asyncio.Lock()
+        return self._api_tokens_lock
+
     @property
     def _rate(self) -> float:
         """Requests per second."""
@@ -203,7 +217,7 @@ class RateLimiter:
         if self.requests_per_minute is None:
             return
 
-        async with self._lock:
+        async with self._get_lock():
             self._refill()
 
             wait = self._wait_time()
@@ -228,7 +242,7 @@ class RateLimiter:
         if self.tokens_per_minute is None:
             return
 
-        async with self._api_tokens_lock:
+        async with self._get_api_tokens_lock():
             self._refill_api_tokens()
 
             wait = self._wait_time_for_tokens(num_tokens)

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

@@ -25,11 +25,10 @@ def get_default_auth_filepath() -> str:
     Returns:
         Path to mcp-auth.json in user's praison config directory
     """
-    # Use ~/.praison/ directory for config
-    home = Path.home()
-    praison_dir = home / ".praison"
-    praison_dir.mkdir(parents=True, exist_ok=True)
-    return str(praison_dir / "mcp-auth.json")
+    # Use centralized paths (DRY)
+    from ..paths import get_mcp_auth_path, ensure_dir, get_data_dir
+    ensure_dir(get_data_dir())
+    return str(get_mcp_auth_path())
 
 
 class MCPAuthStorage:
@@ -64,7 +63,7 @@ class MCPAuthStorage:
         Initialize auth storage.
         
         Args:
-            filepath: Path to auth storage file. Defaults to ~/.praison/mcp-auth.json
+            filepath: Path to auth storage file. Defaults to ~/.praisonai/mcp-auth.json
         """
         self.filepath = filepath or get_default_auth_filepath()
         self._ensure_directory()

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

@@ -7,14 +7,14 @@ Provides reusable documentation context support similar to:
 - Project documentation for AI context
 
 Features:
-- Auto-discovery of docs from .praison/docs/
+- Auto-discovery of docs from .praisonai/docs/
 - Support for markdown, text, and code files
 - @doc mentions for including specific docs
 - Priority ordering for docs
 - Character limits for context window management
 
 Storage Structure:
-    .praison/docs/
+    .praisonai/docs/
     ├── project-overview.md      # Project overview
     ├── architecture.md          # Architecture decisions
     ├── api-reference.md         # API documentation
@@ -74,13 +74,13 @@ class DocsManager:
     Manages documentation context for AI agents.
     
     Provides:
-    - Auto-discovery of docs from .praison/docs/
-    - Global docs from ~/.praison/docs/
+    - Auto-discovery of docs from .praisonai/docs/
+    - Global docs from ~/.praisonai/docs/
     - @doc mentions for including specific docs
     - Priority-based ordering
     """
     
-    DOCS_DIR_NAME = ".praison/docs"
+    DOCS_DIR_NAME = ".praisonai/docs"
     SUPPORTED_EXTENSIONS = [".md", ".txt", ".rst"]
     
     # Maximum characters per doc (context window management)
@@ -97,11 +97,11 @@ class DocsManager:
         
         Args:
             workspace_path: Path to workspace/project root
-            global_docs_path: Path to global docs (default: ~/.praison/docs)
+            global_docs_path: Path to global docs (default: ~/.praisonai/docs)
             verbose: Verbosity level
         """
         self.workspace_path = Path(workspace_path) if workspace_path else Path.cwd()
-        self.global_docs_path = Path(global_docs_path) if global_docs_path else Path.home() / ".praison" / "docs"
+        self.global_docs_path = Path(global_docs_path) if global_docs_path else Path.home() / ".praisonai" / "docs"
         self.verbose = verbose
         
         self._docs: Dict[str, Doc] = {}

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

@@ -6,7 +6,7 @@ Provides short-term, long-term, entity, and episodic memory
 without requiring any external packages.
 
 Storage Structure:
-    .praison/memory/{user_id}/
+    .praisonai/memory/{user_id}/
     ├── config.json           # Memory configuration
     ├── short_term.json       # Rolling buffer (recent context)
     ├── long_term.json        # Persistent facts/knowledge
@@ -129,7 +129,7 @@ class FileMemory:
         
         Args:
             user_id: Unique identifier for the user/session
-            base_path: Base directory for memory storage (default: .praison/memory)
+            base_path: Base directory for memory storage (default: .praisonai/memory)
             config: Configuration overrides
             verbose: Verbosity level (0=quiet, 1=info, 2+=debug)
         """
@@ -140,7 +140,7 @@ class FileMemory:
         if base_path:
             self.base_path = Path(base_path)
         else:
-            self.base_path = Path(".praison/memory")
+            self.base_path = Path(".praisonai/memory")
         
         self.user_path = self.base_path / user_id
         self.episodic_path = self.user_path / "episodic"

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

@@ -6,12 +6,12 @@ allowing custom actions before/after agent operations.
 
 Features:
 - Pre/post hooks for read, write, command, prompt operations
-- JSON configuration (.praison/hooks.json)
+- JSON configuration (.praisonai/hooks.json)
 - Script execution with timeout
 - Exit code handling
 - Lazy loading for performance
 
-Configuration (.praison/hooks.json):
+Configuration (.praisonai/hooks.json):
     {
         "hooks": {
             "pre_write_code": "./scripts/lint.sh",
@@ -85,7 +85,7 @@ class HooksManager:
     """
     Manages hook discovery, loading, and execution.
     
-    Hooks are configured in .praison/hooks.json and can execute
+    Hooks are configured in .praisonai/hooks.json and can execute
     scripts before/after agent operations.
     
     Example:
@@ -103,7 +103,7 @@ class HooksManager:
         ```
     """
     
-    CONFIG_FILE = ".praison/hooks.json"
+    CONFIG_FILE = ".praisonai/hooks.json"
     
     def __init__(
         self,
@@ -384,7 +384,7 @@ class HooksManager:
             timeout: Global timeout in seconds
             enabled: Whether hooks are enabled
         """
-        config_dir = self.workspace_path / ".praison"
+        config_dir = self.workspace_path / ".praisonai"
         config_dir.mkdir(parents=True, exist_ok=True)
         
         config = {

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

@@ -220,10 +220,11 @@ class LearnManager:
         """
         Generate context suitable for injection into system prompt.
         
-        Returns a formatted string with relevant learnings.
+        Returns a formatted string with relevant learnings from all enabled stores.
         """
         parts = []
         
+        # 1. Persona - User preferences and profile
         if "persona" in self._stores:
             personas = self.get_persona_context(limit=5)
             if personas:
@@ -231,6 +232,7 @@ class LearnManager:
                 for p in personas:
                     parts.append(f"- {p}")
         
+        # 2. Insights - Observations and learnings
         if "insights" in self._stores:
             insights = self.get_insights_context(limit=5)
             if insights:
@@ -238,4 +240,38 @@ class LearnManager:
                 for i in insights:
                     parts.append(f"- {i}")
         
+        # 3. Patterns - Reusable knowledge patterns
+        if "patterns" in self._stores:
+            entries = self._stores["patterns"].list_all(limit=3)
+            if entries:
+                parts.append("\nKnown Patterns:")
+                for e in entries:
+                    parts.append(f"- {e.content}")
+        
+        # 4. Decisions - Past decision records (for consistency)
+        if "decisions" in self._stores:
+            entries = self._stores["decisions"].list_all(limit=3)
+            if entries:
+                parts.append("\nPast Decisions:")
+                for e in entries:
+                    parts.append(f"- {e.content}")
+        
+        # 5. Feedback - User feedback signals
+        if "feedback" in self._stores:
+            entries = self._stores["feedback"].list_all(limit=3)
+            if entries:
+                parts.append("\nUser Feedback:")
+                for e in entries:
+                    parts.append(f"- {e.content}")
+        
+        # 6. Improvements - Self-improvement proposals
+        if "improvements" in self._stores:
+            entries = self._stores["improvements"].list_all(limit=3)
+            if entries:
+                parts.append("\nImprovement Areas:")
+                for e in entries:
+                    parts.append(f"- {e.content}")
+        
+        # Note: threads are session-specific and not included in system prompt
+        
         return "\n".join(parts) if parts else ""

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

@@ -88,9 +88,10 @@ class BaseStore(ABC):
         pass
     
     def _default_path(self) -> str:
-        """Default storage path."""
-        base = Path.home() / ".praison" / "learn" / self.scope / self.user_id
-        base.mkdir(parents=True, exist_ok=True)
+        """Default storage path (uses centralized paths - DRY)."""
+        from ...paths import get_learn_dir, ensure_dir
+        base = get_learn_dir() / self.scope / self.user_id
+        ensure_dir(base)
         return str(base / f"{self.store_name}.json")
     
     def _load(self) -> None:

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

@@ -6,13 +6,13 @@ Provides JSON-based MCP server configuration support similar to:
 - Windsurf MCP configuration
 
 Features:
-- Auto-discovery of MCP configs from .praison/mcp/
+- Auto-discovery of MCP configs from .praisonai/mcp/
 - JSON config files for MCP servers
 - Environment variable support
 - Easy server management
 
 Storage Structure:
-    .praison/mcp/
+    .praisonai/mcp/
     ├── filesystem.json      # Filesystem MCP server
     ├── brave-search.json    # Brave search MCP server
     └── postgres.json        # PostgreSQL MCP server
@@ -108,12 +108,12 @@ class MCPConfigManager:
     Manages MCP server configurations for AI agents.
     
     Provides:
-    - Auto-discovery of MCP configs from .praison/mcp/
-    - Global configs from ~/.praison/mcp/
+    - Auto-discovery of MCP configs from .praisonai/mcp/
+    - Global configs from ~/.praisonai/mcp/
     - Easy server management
     """
     
-    MCP_DIR_NAME = ".praison/mcp"
+    MCP_DIR_NAME = ".praisonai/mcp"
     SUPPORTED_EXTENSIONS = [".json", ".mcpjson"]
     
     def __init__(
@@ -127,11 +127,11 @@ class MCPConfigManager:
         
         Args:
             workspace_path: Path to workspace/project root
-            global_mcp_path: Path to global MCP configs (default: ~/.praison/mcp)
+            global_mcp_path: Path to global MCP configs (default: ~/.praisonai/mcp)
             verbose: Verbosity level
         """
         self.workspace_path = Path(workspace_path) if workspace_path else Path.cwd()
-        self.global_mcp_path = Path(global_mcp_path) if global_mcp_path else Path.home() / ".praison" / "mcp"
+        self.global_mcp_path = Path(global_mcp_path) if global_mcp_path else Path.home() / ".praisonai" / "mcp"
         self.verbose = verbose
         
         self._configs: Dict[str, MCPConfig] = {}

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

@@ -226,15 +226,15 @@ class Memory:
         self.embedding_dimensions = self._get_embedding_dimensions(self.embedding_model)
         self._log_verbose(f"Using embedding dimensions: {self.embedding_dimensions}")
 
-        # Create .praison directory if it doesn't exist
-        os.makedirs(".praison", exist_ok=True)
+        # Create .praisonai directory if it doesn't exist
+        os.makedirs(".praisonai", exist_ok=True)
 
         # Short-term DB
-        self.short_db = self.cfg.get("short_db", ".praison/short_term.db")
+        self.short_db = self.cfg.get("short_db", ".praisonai/short_term.db")
         self._init_stm()
 
         # Long-term DB
-        self.long_db = self.cfg.get("long_db", ".praison/long_term.db")
+        self.long_db = self.cfg.get("long_db", ".praisonai/long_term.db")
         self._init_ltm()
 
         # Conditionally init Mem0, MongoDB, or local RAG

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

@@ -18,7 +18,7 @@ Features:
 - Symlink support for shared rules
 
 Storage Structure:
-    .praison/rules/
+    .praisonai/rules/
     ├── global.md           # Always applied (global)
     ├── python.md           # Glob: **/*.py
     ├── testing.md          # Glob: **/*.test.*
@@ -99,9 +99,9 @@ class RulesManager:
     Manages rules/instructions for AI agents.
     
     Supports multiple rule sources:
-    - Global rules (~/.praison/rules/)
-    - Workspace rules (.praison/rules/)
-    - Subdirectory rules (subdir/.praison/rules/)
+    - Global rules (~/.praisonai/rules/)
+    - Workspace rules (.praisonai/rules/)
+    - Subdirectory rules (subdir/.praisonai/rules/)
     - Root instruction files (CLAUDE.md, AGENTS.md, GEMINI.md, .cursorrules, .windsurfrules)
     
     Example:
@@ -122,7 +122,7 @@ class RulesManager:
         ```
     """
     
-    RULES_DIR_NAME = ".praison/rules"
+    RULES_DIR_NAME = ".praisonai/rules"
     SUPPORTED_EXTENSIONS = [".md", ".mdc", ".txt"]
     
     # Root-level instruction files (like Claude, Codex, Gemini, Cursor, Windsurf)
@@ -161,11 +161,11 @@ class RulesManager:
         
         Args:
             workspace_path: Path to workspace/project root
-            global_rules_path: Path to global rules (default: ~/.praison/rules)
+            global_rules_path: Path to global rules (default: ~/.praisonai/rules)
             verbose: Verbosity level
         """
         self.workspace_path = Path(workspace_path) if workspace_path else Path.cwd()
-        self.global_rules_path = Path(global_rules_path) if global_rules_path else Path.home() / ".praison" / "rules"
+        self.global_rules_path = Path(global_rules_path) if global_rules_path else Path.home() / ".praisonai" / "rules"
         self.verbose = verbose
         
         self._rules: Dict[str, Rule] = {}
@@ -457,7 +457,7 @@ class RulesManager:
             if rule:
                 self._rules[f"root:{rule.name}"] = rule
         
-        # 4. Load workspace rules from .praison/rules/
+        # 4. Load workspace rules from .praisonai/rules/
         workspace_rules_dir = self.workspace_path / self.RULES_DIR_NAME.replace("/", os.sep)
         workspace_rules = self._discover_rules_in_dir(workspace_rules_dir)
         for rule in workspace_rules:
@@ -485,7 +485,7 @@ class RulesManager:
         stop_at = git_root if git_root else self.workspace_path
         current = Path.cwd()
         while current != stop_at and current != current.parent:
-            # Check .praison/rules/
+            # Check .praisonai/rules/
             subdir_rules_dir = current / self.RULES_DIR_NAME.replace("/", os.sep)
             subdir_rules = self._discover_rules_in_dir(subdir_rules_dir)
             for rule in subdir_rules:

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

@@ -6,14 +6,14 @@ allowing users to define and execute complex task sequences.
 
 Features:
 - YAML/Markdown workflow definitions
-- Auto-discovery from .praison/workflows/
+- Auto-discovery from .praisonai/workflows/
 - Step-by-step execution with context passing
 - Variable substitution
 - Conditional steps
 - Lazy loading for performance
 
 Storage Structure:
-    .praison/workflows/
+    .praisonai/workflows/
     ├── deploy.md           # Deployment workflow
     ├── test.md             # Testing workflow
     └── review.md           # Code review workflow
@@ -731,7 +731,7 @@ class WorkflowManager:
     Workflows are defined in markdown files with YAML frontmatter
     and step definitions.
     
-    Example workflow file (.praison/workflows/deploy.md):
+    Example workflow file (.praisonai/workflows/deploy.md):
         ```markdown
         ---
         name: Deploy to Production
@@ -782,7 +782,7 @@ class WorkflowManager:
         ```
     """
     
-    WORKFLOWS_DIR = ".praison/workflows"
+    WORKFLOWS_DIR = ".praisonai/workflows"
     SUPPORTED_EXTENSIONS = [".md", ".yaml", ".yml"]
     
     def __init__(
@@ -1857,7 +1857,7 @@ class WorkflowManager:
     
     def _get_checkpoints_dir(self) -> Path:
         """Get the checkpoints directory path."""
-        checkpoints_dir = self.workspace_path / ".praison" / "checkpoints"
+        checkpoints_dir = self.workspace_path / ".praisonai" / "checkpoints"
         checkpoints_dir.mkdir(parents=True, exist_ok=True)
         return checkpoints_dir
     

src/praisonai-agents/praisonaiagents/paths.py

@@ -0,0 +1,365 @@
+"""
+Centralized Path Utilities for PraisonAI Agents.
+
+All persistent data uses ~/.praisonai/ by default.
+Override with PRAISONAI_HOME environment variable.
+
+This module provides a single source of truth for all data storage paths,
+eliminating hardcoded paths throughout the codebase (DRY principle).
+
+Usage:
+    from praisonaiagents.paths import get_data_dir, get_sessions_dir
+    
+    # Get user data directory
+    data_dir = get_data_dir()  # ~/.praisonai/
+    
+    # Get specific subdirectories
+    sessions_dir = get_sessions_dir()  # ~/.praisonai/sessions/
+    
+    # Override with environment variable
+    # export PRAISONAI_HOME=/custom/path
+    # data_dir = get_data_dir()  # /custom/path/
+
+Backward Compatibility:
+    If ~/.praisonai/ doesn't exist but ~/.praison/ does, the legacy
+    path will be used with a deprecation warning. Run 'praisonai migrate-data'
+    to migrate to the new location.
+"""
+
+import os
+import warnings
+from pathlib import Path
+from typing import Dict, Optional, Union
+
+# Environment variable for override
+ENV_VAR = "PRAISONAI_HOME"
+
+# Default directory name (branded)
+DEFAULT_DIR_NAME = ".praisonai"
+
+# Legacy directory name (for backward compat)
+LEGACY_DIR_NAME = ".praison"
+
+# Cache for data dir to avoid repeated filesystem checks
+_data_dir_cache: Optional[Path] = None
+
+
+def _clear_cache() -> None:
+    """Clear the data dir cache. Used for testing."""
+    global _data_dir_cache
+    _data_dir_cache = None
+
+
+def get_data_dir() -> Path:
+    """
+    Get PraisonAI data directory.
+    
+    Priority:
+    1. PRAISONAI_HOME env var
+    2. ~/.praisonai/ (default)
+    3. ~/.praison/ (legacy fallback with warning)
+    
+    Returns:
+        Path to data directory
+    
+    Example:
+        >>> from praisonaiagents.paths import get_data_dir
+        >>> data_dir = get_data_dir()
+        >>> print(data_dir)
+        /home/user/.praisonai
+    """
+    global _data_dir_cache
+    
+    # Check env var first (always takes precedence, no caching)
+    env_path = os.environ.get(ENV_VAR)
+    if env_path:
+        return Path(env_path).expanduser()
+    
+    # Return cached value if available
+    if _data_dir_cache is not None:
+        return _data_dir_cache
+    
+    home = Path.home()
+    
+    # Check new location first
+    new_path = home / DEFAULT_DIR_NAME
+    if new_path.exists():
+        _data_dir_cache = new_path
+        return new_path
+    
+    # Check legacy location (backward compat)
+    legacy_path = home / LEGACY_DIR_NAME
+    if legacy_path.exists():
+        warnings.warn(
+            f"Using legacy data directory {legacy_path}. "
+            f"Run 'praisonai migrate-data' to migrate to {new_path}.",
+            DeprecationWarning,
+            stacklevel=2
+        )
+        _data_dir_cache = legacy_path
+        return legacy_path
+    
+    # Default to new location (will be created when needed)
+    _data_dir_cache = new_path
+    return new_path
+
+
+def get_sessions_dir() -> Path:
+    """
+    Get sessions directory.
+    
+    Returns:
+        Path to ~/.praisonai/sessions/
+    """
+    return get_data_dir() / "sessions"
+
+
+def get_skills_dir() -> Path:
+    """
+    Get user skills directory.
+    
+    Returns:
+        Path to ~/.praisonai/skills/
+    """
+    return get_data_dir() / "skills"
+
+
+def get_plugins_dir() -> Path:
+    """
+    Get user plugins directory.
+    
+    Returns:
+        Path to ~/.praisonai/plugins/
+    """
+    return get_data_dir() / "plugins"
+
+
+def get_mcp_dir() -> Path:
+    """
+    Get MCP config directory.
+    
+    Returns:
+        Path to ~/.praisonai/mcp/
+    """
+    return get_data_dir() / "mcp"
+
+
+def get_docs_dir() -> Path:
+    """
+    Get docs directory.
+    
+    Returns:
+        Path to ~/.praisonai/docs/
+    """
+    return get_data_dir() / "docs"
+
+
+def get_rules_dir() -> Path:
+    """
+    Get rules directory.
+    
+    Returns:
+        Path to ~/.praisonai/rules/
+    """
+    return get_data_dir() / "rules"
+
+
+def get_permissions_dir() -> Path:
+    """
+    Get permissions directory.
+    
+    Returns:
+        Path to ~/.praisonai/permissions/
+    """
+    return get_data_dir() / "permissions"
+
+
+def get_storage_dir() -> Path:
+    """
+    Get generic storage directory.
+    
+    Returns:
+        Path to ~/.praisonai/storage/
+    """
+    return get_data_dir() / "storage"
+
+
+def get_checkpoints_dir() -> Path:
+    """
+    Get checkpoints directory.
+    
+    Returns:
+        Path to ~/.praisonai/checkpoints/
+    """
+    return get_data_dir() / "checkpoints"
+
+
+def get_snapshots_dir() -> Path:
+    """
+    Get snapshots directory.
+    
+    Returns:
+        Path to ~/.praisonai/snapshots/
+    """
+    return get_data_dir() / "snapshots"
+
+
+def get_learn_dir() -> Path:
+    """
+    Get learn directory for learning stores.
+    
+    Returns:
+        Path to ~/.praisonai/learn/
+    """
+    return get_data_dir() / "learn"
+
+
+def get_cache_dir() -> Path:
+    """
+    Get cache directory (disposable data).
+    
+    Returns:
+        Path to ~/.praisonai/cache/
+    """
+    return get_data_dir() / "cache"
+
+
+def get_mcp_auth_path() -> Path:
+    """
+    Get path to MCP auth storage file.
+    
+    Returns:
+        Path to ~/.praisonai/mcp-auth.json
+    """
+    return get_data_dir() / "mcp-auth.json"
+
+
+def get_memory_dir() -> Path:
+    """
+    Get memory directory for short/long term databases.
+    
+    Returns:
+        Path to ~/.praisonai/memory/
+    """
+    return get_data_dir() / "memory"
+
+
+def get_workflows_dir() -> Path:
+    """
+    Get workflows directory.
+    
+    Returns:
+        Path to ~/.praisonai/workflows/
+    """
+    return get_data_dir() / "workflows"
+
+
+def get_summaries_dir() -> Path:
+    """
+    Get summaries directory for RAG.
+    
+    Returns:
+        Path to ~/.praisonai/summaries/
+    """
+    return get_data_dir() / "summaries"
+
+
+def get_prp_dir() -> Path:
+    """
+    Get PRP (Prompt Response Pair) output directory.
+    
+    Returns:
+        Path to ~/.praisonai/prp/
+    """
+    return get_data_dir() / "prp"
+
+
+def get_runs_dir() -> Path:
+    """
+    Get runs directory for artifacts.
+    
+    Returns:
+        Path to ~/.praisonai/runs/
+    """
+    return get_data_dir() / "runs"
+
+
+def get_project_data_dir(project_path: Optional[Union[str, Path]] = None) -> Path:
+    """
+    Get project-level data directory.
+    
+    Args:
+        project_path: Project root (defaults to cwd)
+        
+    Returns:
+        Path to .praisonai/ in project
+    
+    Example:
+        >>> from praisonaiagents.paths import get_project_data_dir
+        >>> project_dir = get_project_data_dir("/path/to/project")
+        >>> print(project_dir)
+        /path/to/project/.praisonai
+    """
+    if project_path is None:
+        base = Path.cwd()
+    elif isinstance(project_path, str):
+        base = Path(project_path)
+    else:
+        base = project_path
+    return base / DEFAULT_DIR_NAME
+
+
+def ensure_dir(path: Union[str, Path]) -> Path:
+    """
+    Ensure a directory exists, creating it if necessary.
+    
+    Args:
+        path: Directory path to ensure exists
+        
+    Returns:
+        Path object for the directory
+    
+    Example:
+        >>> from praisonaiagents.paths import ensure_dir, get_sessions_dir
+        >>> sessions = ensure_dir(get_sessions_dir())
+    """
+    if isinstance(path, str):
+        path = Path(path)
+    path.mkdir(parents=True, exist_ok=True)
+    return path
+
+
+def get_all_paths() -> Dict[str, Path]:
+    """
+    Get all PraisonAI data paths.
+    
+    Returns:
+        Dictionary mapping path names to Path objects
+    
+    Example:
+        >>> from praisonaiagents.paths import get_all_paths
+        >>> paths = get_all_paths()
+        >>> for name, path in paths.items():
+        ...     print(f"{name}: {path}")
+    """
+    return {
+        "data_dir": get_data_dir(),
+        "sessions": get_sessions_dir(),
+        "skills": get_skills_dir(),
+        "plugins": get_plugins_dir(),
+        "mcp": get_mcp_dir(),
+        "docs": get_docs_dir(),
+        "rules": get_rules_dir(),
+        "permissions": get_permissions_dir(),
+        "storage": get_storage_dir(),
+        "checkpoints": get_checkpoints_dir(),
+        "snapshots": get_snapshots_dir(),
+        "learn": get_learn_dir(),
+        "cache": get_cache_dir(),
+        "mcp_auth": get_mcp_auth_path(),
+        "memory": get_memory_dir(),
+        "workflows": get_workflows_dir(),
+        "summaries": get_summaries_dir(),
+        "prp": get_prp_dir(),
+        "runs": get_runs_dir(),
+    }

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

@@ -14,10 +14,12 @@ from typing import Any, Callable, Dict, List, Optional
 from .rules import PermissionRule, PermissionAction, PermissionResult, PersistentApproval
 from .doom_loop import DoomLoopDetector
 
+from ..paths import get_permissions_dir
+
 logger = logging.getLogger(__name__)
 
-# Default storage path
-DEFAULT_PERMISSIONS_DIR = os.path.expanduser("~/.praison/permissions")
+# Default storage path (uses centralized paths - DRY)
+DEFAULT_PERMISSIONS_DIR = str(get_permissions_dir())
 
 
 class PermissionManager:

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

@@ -36,16 +36,21 @@ class PlanStorage:
         todos_dir: Directory for todo files
     """
     
-    DEFAULT_BASE_PATH = ".praison"
+    DEFAULT_BASE_PATH = ".praisonai"  # Updated to use branded path
     
     def __init__(self, base_path: Optional[str] = None):
         """
         Initialize PlanStorage.
         
         Args:
-            base_path: Base directory for storage. Defaults to .praison/
+            base_path: Base directory for storage. Defaults to .praisonai/
         """
-        self.base_path = base_path or self.DEFAULT_BASE_PATH
+        # Use centralized paths for project-level data (DRY)
+        if base_path is None:
+            from ..paths import get_project_data_dir
+            self.base_path = str(get_project_data_dir())
+        else:
+            self.base_path = base_path
         self.plans_dir = os.path.join(self.base_path, "plans")
         self.todos_dir = os.path.join(self.base_path, "todos")
         

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

@@ -37,10 +37,16 @@ Usage:
 """
 
 __all__ = [
+    # Easy enable API
+    "enable",
+    "disable",
+    "list_plugins",
+    "is_enabled",
     # Core
     "PluginManager",
     "Plugin",
     "PluginHook",
+    "PluginType",
     "PluginInfo",
     "FunctionPlugin",
     "get_plugin_manager",
@@ -64,6 +70,170 @@ __all__ = [
 ]
 
 
+# ============================================================================
+# EASY ENABLE API
+# ============================================================================
+# These functions provide a simple way to enable/disable plugins globally.
+# Zero performance impact when not called - plugins only load on enable().
+# ============================================================================
+
+# Global state for plugin system (lazy initialized)
+_plugins_enabled: bool = False
+_enabled_plugin_names: list = None  # None = all, list = specific
+
+
+def enable(plugins: list = None) -> None:
+    """Enable the plugin system.
+    
+    Discovers and enables plugins from default directories.
+    This is the main entry point for using plugins.
+    
+    Args:
+        plugins: Optional list of plugin names to enable.
+                 If None, enables all discovered plugins.
+    
+    Examples:
+        # Enable all discovered plugins
+        from praisonaiagents import plugins
+        plugins.enable()
+        
+        # Enable specific plugins only
+        plugins.enable(["logging", "metrics"])
+    
+    Note:
+        - Tools and guardrails work WITHOUT calling enable()
+        - Only background plugins (hooks, metrics, logging) need enable()
+        - Can also be enabled via PRAISONAI_PLUGINS env var
+        - Can also be enabled via .praisonai/config.toml
+    """
+    global _plugins_enabled, _enabled_plugin_names
+    
+    _plugins_enabled = True
+    _enabled_plugin_names = plugins  # None = all, list = specific
+    
+    # Get plugin manager and auto-discover
+    from .manager import get_plugin_manager
+    manager = get_plugin_manager()
+    
+    # Auto-discover plugins from default directories
+    manager.auto_discover_plugins()
+    
+    # Enable specific plugins or all
+    if plugins is not None:
+        # Enable only specified plugins
+        for name in plugins:
+            manager.enable(name)
+    else:
+        # Enable all discovered plugins
+        for plugin_info in manager.list_plugins():
+            manager.enable(plugin_info.get("name", ""))
+    
+    import logging
+    logging.debug(f"Plugins enabled: {plugins if plugins else 'all'}")
+
+
+def disable(plugins: list = None) -> None:
+    """Disable plugins.
+    
+    Args:
+        plugins: Optional list of plugin names to disable.
+                 If None, disables all plugins.
+    
+    Examples:
+        # Disable all plugins
+        plugins.disable()
+        
+        # Disable specific plugins
+        plugins.disable(["logging"])
+    """
+    global _plugins_enabled, _enabled_plugin_names
+    
+    from .manager import get_plugin_manager
+    manager = get_plugin_manager()
+    
+    if plugins is not None:
+        # Disable specific plugins
+        for name in plugins:
+            manager.disable(name)
+    else:
+        # Disable all plugins
+        _plugins_enabled = False
+        _enabled_plugin_names = None
+        for plugin_info in manager.list_plugins():
+            manager.disable(plugin_info.get("name", ""))
+
+
+def list_plugins() -> list:
+    """List all discovered plugins.
+    
+    Returns:
+        List of plugin info dicts with name, version, enabled status.
+    
+    Example:
+        from praisonaiagents import plugins
+        all_plugins = plugins.list_plugins()
+        for p in all_plugins:
+            print(f"{p['name']} v{p['version']} - {'enabled' if p['enabled'] else 'disabled'}")
+    """
+    from .manager import get_plugin_manager
+    manager = get_plugin_manager()
+    
+    # Auto-discover if not already done
+    if not manager._plugins and not manager._single_file_plugins:
+        manager.auto_discover_plugins()
+    
+    # Combine registered plugins and single-file plugins
+    result = []
+    
+    # Add registered plugins (Plugin class instances)
+    for info in manager.list_plugins():
+        if hasattr(info, 'name'):
+            # PluginInfo object
+            result.append({
+                "name": info.name,
+                "version": getattr(info, 'version', '1.0.0'),
+                "description": getattr(info, 'description', ''),
+                "enabled": manager.is_enabled(info.name),
+                "type": "registered",
+            })
+        elif isinstance(info, dict):
+            # Already a dict
+            info["enabled"] = manager.is_enabled(info.get("name", ""))
+            info["type"] = "registered"
+            result.append(info)
+    
+    # Add single-file plugins
+    for name, meta in manager._single_file_plugins.items():
+        result.append({
+            "name": name,
+            "version": meta.get("version", "1.0.0"),
+            "description": meta.get("description", ""),
+            "enabled": manager.is_enabled(name),
+            "type": "single_file",
+        })
+    
+    return result
+
+
+def is_enabled(name: str = None) -> bool:
+    """Check if plugins are enabled.
+    
+    Args:
+        name: Optional plugin name to check. If None, checks if system is enabled.
+    
+    Returns:
+        True if enabled, False otherwise.
+    """
+    global _plugins_enabled
+    
+    if name is None:
+        return _plugins_enabled
+    
+    from .manager import get_plugin_manager
+    manager = get_plugin_manager()
+    return manager.is_enabled(name)
+
+
 def __getattr__(name: str):
     """Lazy load module components."""
     # Core classes
@@ -91,6 +261,10 @@ def __getattr__(name: str):
         from .plugin import FunctionPlugin
         return FunctionPlugin
     
+    if name == "PluginType":
+        from .plugin import PluginType
+        return PluginType
+    
     # Protocols
     if name == "PluginProtocol":
         from .protocols import PluginProtocol

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

@@ -5,8 +5,8 @@ Discovers and loads plugins from directories.
 Plugins are simple Python files with WordPress-style docstring headers.
 
 Default plugin directories (in precedence order):
-1. Project: ./.praison/plugins/
-2. User: ~/.praison/plugins/
+1. Project: ./.praisonai/plugins/
+2. User: ~/.praisonai/plugins/
 
 Usage:
     from praisonaiagents.plugins.discovery import discover_plugins, load_plugin
@@ -25,6 +25,7 @@ from pathlib import Path
 from typing import Any, Dict, List, Optional
 
 from .parser import parse_plugin_header_from_file, PluginParseError
+from ..paths import get_plugins_dir, get_project_data_dir
 
 logger = logging.getLogger(__name__)
 
@@ -32,25 +33,26 @@ logger = logging.getLogger(__name__)
 def get_default_plugin_dirs() -> List[Path]:
     """Get default plugin directory locations.
     
+    Uses centralized paths.py for consistent path management.
     Returns directories in precedence order (high to low):
-    1. Project: ./.praison/plugins/
-    2. User: ~/.praison/plugins/
+    1. Project: ./.praisonai/plugins/
+    2. User: ~/.praisonai/plugins/
     
     Returns:
         List of existing plugin directories
     """
     dirs = []
-    cwd = Path.cwd()
     
-    # Project-level directory
-    project_dir = cwd / ".praison" / "plugins"
-    if project_dir.exists() and project_dir.is_dir():
-        dirs.append(project_dir)
+    # Project-level directory (use centralized path)
+    project_data_dir = get_project_data_dir()
+    project_plugins = project_data_dir / "plugins"
+    if project_plugins.exists() and project_plugins.is_dir():
+        dirs.append(project_plugins)
     
-    # User-level directory
-    user_dir = Path.home() / ".praison" / "plugins"
-    if user_dir.exists() and user_dir.is_dir():
-        dirs.append(user_dir)
+    # User-level directory (use centralized path)
+    user_plugins = get_plugins_dir()
+    if user_plugins.exists() and user_plugins.is_dir():
+        dirs.append(user_plugins)
     
     return dirs
 
@@ -253,12 +255,13 @@ def unload_plugin(module_name: str) -> bool:
 def ensure_plugin_dir() -> Path:
     """Ensure the user plugin directory exists.
     
-    Creates ~/.praison/plugins/ if it doesn't exist.
+    Creates ~/.praisonai/plugins/ if it doesn't exist.
+    Uses centralized paths.py for consistent path management.
     
     Returns:
         Path to the user plugin directory
     """
-    user_dir = Path.home() / ".praison" / "plugins"
+    user_dir = get_plugins_dir()
     user_dir.mkdir(parents=True, exist_ok=True)
     return user_dir
 

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

@@ -2,14 +2,16 @@
 Plugin Manager for PraisonAI Agents.
 
 Provides dynamic plugin discovery, loading, and lifecycle management.
+Thread-safe and async-safe for multi-agent environments.
 """
 
+import asyncio
 import importlib.util
 import logging
-import os
 import sys
+import threading
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Type
+from typing import Any, Dict, List, Optional
 
 from .plugin import Plugin, PluginHook, PluginInfo, FunctionPlugin
 
@@ -20,6 +22,9 @@ class PluginManager:
     """
     Manages plugin discovery, loading, and execution.
     
+    Thread-safe for concurrent access in multi-agent environments.
+    Supports both sync and async hook execution.
+    
     Example:
         manager = PluginManager()
         
@@ -29,14 +34,18 @@ class PluginManager:
         # Register a plugin directly
         manager.register(MyPlugin())
         
-        # Execute hooks
+        # Execute hooks (sync)
         args = manager.execute_hook(PluginHook.BEFORE_TOOL, "bash", {"cmd": "ls"})
+        
+        # Execute hooks (async)
+        args = await manager.async_execute_hook(PluginHook.BEFORE_TOOL, "bash", {"cmd": "ls"})
     """
     
     def __init__(self):
         self._plugins: Dict[str, Plugin] = {}
         self._enabled: Dict[str, bool] = {}
         self._single_file_plugins: Dict[str, Dict[str, Any]] = {}  # WordPress-style plugins
+        self._lock = threading.RLock()  # Thread safety for multi-agent environments
     
     def register(self, plugin: Plugin) -> bool:
         """
@@ -266,6 +275,33 @@ class PluginManager:
         
         return result
     
+    async def async_execute_hook(
+        self,
+        hook: PluginHook,
+        *args,
+        **kwargs
+    ) -> Any:
+        """
+        Execute a hook across all enabled plugins asynchronously.
+        
+        Async-safe for use in async agent contexts.
+        
+        Args:
+            hook: The hook to execute
+            *args: Arguments to pass to hook
+            **kwargs: Keyword arguments to pass to hook
+            
+        Returns:
+            The result (may be modified by plugins)
+        """
+        # Use the sync version in an executor to avoid blocking
+        # This is safe because execute_hook is thread-safe with _lock
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(
+            None,
+            lambda: self.execute_hook(hook, *args, **kwargs)
+        )
+    
     def get_all_tools(self) -> List[Dict[str, Any]]:
         """Get all tools from all enabled plugins."""
         tools = []
@@ -364,8 +400,8 @@ class PluginManager:
         Auto-discover and load plugins from default directories.
         
         Scans:
-        - ./.praison/plugins/ (project-level)
-        - ~/.praison/plugins/ (user-level)
+        - ./.praisonai/plugins/ (project-level)
+        - ~/.praisonai/plugins/ (user-level)
         
         Returns:
             Number of plugins loaded

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

@@ -2,6 +2,7 @@
 Plugin definitions for PraisonAI Agents.
 
 Provides base plugin class and hook definitions.
+PluginHook is now an alias for HookEvent (DRY compliance).
 """
 
 import logging
@@ -10,68 +11,26 @@ from dataclasses import dataclass, field
 from enum import Enum
 from typing import Any, Callable, Dict, List, Optional
 
+# Import HookEvent at module level for alias (DRY)
+from ..hooks.types import HookEvent
+
 logger = logging.getLogger(__name__)
 
+# PluginHook is now an alias for HookEvent - single source of truth
+PluginHook = HookEvent
 
-class PluginHook(str, Enum):
-    """Available plugin hooks."""
+
+class PluginType(str, Enum):
+    """Types of plugins that can be registered.
     
-    # Lifecycle hooks
-    ON_INIT = "on_init"
-    ON_SHUTDOWN = "on_shutdown"
-    
-    # Agent hooks
-    BEFORE_AGENT = "before_agent"
-    AFTER_AGENT = "after_agent"
-    
-    # Tool hooks
-    BEFORE_TOOL = "before_tool"
-    AFTER_TOOL = "after_tool"
-    
-    # Message hooks (for bot/channel integrations)
-    BEFORE_MESSAGE = "before_message"
-    AFTER_MESSAGE = "after_message"
-    MESSAGE_RECEIVED = "message_received"
-    MESSAGE_SENDING = "message_sending"
-    MESSAGE_SENT = "message_sent"
-    
-    # LLM hooks
-    BEFORE_LLM = "before_llm"
-    AFTER_LLM = "after_llm"
-    
-    # Session hooks
-    SESSION_START = "session_start"
-    SESSION_END = "session_end"
-    
-    # Gateway hooks
-    GATEWAY_START = "gateway_start"
-    GATEWAY_STOP = "gateway_stop"
-    
-    # Compaction hooks (memory management)
-    BEFORE_COMPACTION = "before_compaction"
-    AFTER_COMPACTION = "after_compaction"
-    
-    # Tool result persistence (for modifying tool results before storage)
-    TOOL_RESULT_PERSIST = "tool_result_persist"
-    
-    # Permission hooks
-    ON_PERMISSION_ASK = "on_permission_ask"
-    
-    # Config hooks
-    ON_CONFIG = "on_config"
-    
-    # Auth hooks
-    ON_AUTH = "on_auth"
-    
-    # Error hooks
-    ON_ERROR = "on_error"
-    ON_RETRY = "on_retry"
-    
-    # Claude Code parity hooks
-    USER_PROMPT_SUBMIT = "user_prompt_submit"  # When user submits a prompt
-    NOTIFICATION = "notification"              # When notification is sent
-    SUBAGENT_STOP = "subagent_stop"           # When subagent completes
-    SETUP = "setup"                           # On initialization/maintenance
+    Used to categorize plugins and enable type-specific discovery.
+    """
+    TOOL = "tool"           # Provides tools for agents
+    HOOK = "hook"           # Intercepts lifecycle events
+    SKILL = "skill"         # Provides agent capabilities (SKILL.md)
+    POLICY = "policy"       # Provides execution rules
+    GUARDRAIL = "guardrail" # Provides validation rules
+    INTEGRATION = "integration"  # External tool integrations
 
 
 @dataclass

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

@@ -18,21 +18,42 @@ class Process:
     DEFAULT_RETRY_LIMIT = 3  # Predefined retry limit in a common place
     VALIDATION_FAILURE_DECISIONS = ["invalid", "retry", "failed", "error", "unsuccessful", "fail", "errors", "reject", "rejected", "incomplete"]  # Decision strings that trigger validation feedback
 
-    def __init__(self, tasks: Dict[str, Task], agents: List[Agent], manager_llm: Optional[str] = None, verbose: bool = False, max_iter: int = 10):
+    def __init__(
+        self,
+        tasks: Dict[str, Task],
+        agents: List[Agent],
+        manager_llm: Optional[str] = None,
+        verbose: bool = False,
+        max_iter: int = 10,
+        output: Optional[str] = None,
+    ):
         logging.debug(f"=== Initializing Process ===")
         logging.debug(f"Number of tasks: {len(tasks)}")
         logging.debug(f"Number of agents: {len(agents)}")
         logging.debug(f"Manager LLM: {manager_llm}")
-        logging.debug(f"Verbose mode: {verbose}")
         logging.debug(f"Max iterations: {max_iter}")
 
         self.tasks = tasks
         self.agents = agents
         self.manager_llm = manager_llm
-        self.verbose = verbose
         self.max_iter = max_iter
         self.task_retry_counter: Dict[str, int] = {} # Initialize retry counter
         self.workflow_finished = False # ADDED: Workflow finished flag
+        
+        # Resolve verbose from output= param (takes precedence) or legacy verbose= param
+        if output is not None:
+            # output= takes precedence over verbose=
+            from ..config.presets import OUTPUT_PRESETS
+            preset = OUTPUT_PRESETS.get(output, {})
+            self._verbose = preset.get("verbose", False)
+        else:
+            # Backward compat: use legacy verbose= param
+            self._verbose = verbose
+        
+        # Keep self.verbose as alias for backward compat
+        self.verbose = self._verbose
+        
+        logging.debug(f"Verbose mode: {self._verbose}")
 
     def _create_llm_instance(self):
         """Create and return a configured LLM instance for manager tasks."""
@@ -95,7 +116,7 @@ class Process:
                             agent=loop_task.agent,
                             name=f"{loop_task.name}_{task_count}" if loop_task.name else task_desc,
                             expected_output=getattr(loop_task, 'expected_output', None),
-                            callback=loop_task.callback,  # Inherit callback from parent loop task
+                            on_task_complete=loop_task.callback,  # Inherit callback from parent loop task
                             is_start=(task_count == 1),
                             task_type="task"
                         )
@@ -121,7 +142,7 @@ class Process:
                             agent=loop_task.agent,
                             name=f"{loop_task.name}_{i+1}" if loop_task.name else line.strip(),
                             expected_output=getattr(loop_task, 'expected_output', None),
-                            callback=loop_task.callback,  # Inherit callback from parent loop task
+                            on_task_complete=loop_task.callback,  # Inherit callback from parent loop task
                             is_start=(i == 0),
                             task_type="task"
                         )
@@ -915,7 +936,7 @@ Provide a JSON with the structure:
                                 agent=start_task.agent,
                                 name=f"{start_task.name}_{task_count}" if start_task.name else task_desc,
                                 expected_output=getattr(start_task, 'expected_output', None),
-                                callback=start_task.callback,  # Inherit callback from parent loop task
+                                on_task_complete=start_task.callback,  # Inherit callback from parent loop task
                                 is_start=(task_count == 1),
                                 task_type="decision",  # Change to decision type
                                 next_tasks=inherited_next_tasks,  # Inherit parent's next tasks
@@ -949,7 +970,7 @@ Provide a JSON with the structure:
                                 agent=start_task.agent,
                                 name=f"{start_task.name}_{i+1}" if start_task.name else line.strip(),
                                 expected_output=getattr(start_task, 'expected_output', None),
-                                callback=start_task.callback,  # Inherit callback from parent loop task
+                                on_task_complete=start_task.callback,  # Inherit callback from parent loop task
                                 is_start=(i == 0),
                                 task_type="task",
                                 condition={
@@ -1033,7 +1054,7 @@ Tasks by type:
                                             agent=current_task.agent,
                                             name=f"{current_task.name}_{i+1}" if current_task.name else task_desc,
                                             expected_output=getattr(current_task, 'expected_output', None),
-                                            callback=current_task.callback,  # Inherit callback from parent loop task
+                                            on_task_complete=current_task.callback,  # Inherit callback from parent loop task
                                             is_start=(i == 0),
                                             task_type="task",
                                             condition={
@@ -1058,7 +1079,7 @@ Tasks by type:
                                         agent=current_task.agent,
                                         name=f"{current_task.name}_{i+1}" if current_task.name else line.strip(),
                                         expected_output=getattr(current_task, 'expected_output', None),
-                                        callback=current_task.callback,  # Inherit callback from parent loop task
+                                        on_task_complete=current_task.callback,  # Inherit callback from parent loop task
                                         is_start=(i == 0),
                                         task_type="task",
                                         condition={

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

@@ -92,7 +92,7 @@ class RetrievalConfig:
     vector_store_provider: str = "chroma"
     vector_store_config: Dict[str, Any] = field(default_factory=dict)
     collection_name: Optional[str] = None
-    persist_path: str = ".praison"
+    persist_path: str = ".praisonai"
     
     # Embedding configuration
     embedder_provider: Optional[str] = None
@@ -233,7 +233,7 @@ class RetrievalConfig:
             vector_store_provider=data.get("vector_store_provider", "chroma"),
             vector_store_config=data.get("vector_store_config", {}),
             collection_name=data.get("collection_name"),
-            persist_path=data.get("persist_path", ".praison"),
+            persist_path=data.get("persist_path", ".praisonai"),
             embedder_provider=data.get("embedder_provider"),
             embedder_model=data.get("embedder_model"),
             auto_keywords=auto_keywords,

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

@@ -99,7 +99,7 @@ class HierarchicalSummarizer:
     def __init__(
         self,
         llm=None,
-        persist_path: str = ".praison/summaries",
+        persist_path: str = ".praisonai/summaries",
         verbose: bool = False,
     ):
         """

src/praisonai-agents/praisonaiagents/session.py

@@ -83,7 +83,7 @@ class Session:
             default_memory_config = {
                 "provider": "rag",
                 "use_embedding": False,  # Disable embeddings to avoid OpenAI API key requirement
-                "rag_db_path": f".praison/sessions/{self.session_id}/chroma_db"
+                "rag_db_path": f".praisonai/sessions/{self.session_id}/chroma_db"
             }
             if memory_config:
                 default_memory_config.update(memory_config)
@@ -94,7 +94,7 @@ class Session:
             self.knowledge_config = default_knowledge_config
 
             # Create session directory
-            os.makedirs(f".praison/sessions/{self.session_id}", exist_ok=True)
+            os.makedirs(f".praisonai/sessions/{self.session_id}", exist_ok=True)
 
             # Initialize components lazily
             self._memory = None

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

@@ -22,7 +22,7 @@ Usage:
     )
     agent.start("What did I say before?")  # Remembers!
 
-Default storage: ~/.praison/sessions/{session_id}.json
+Default storage: ~/.praisonai/sessions/{session_id}.json
 """
 
 from typing import TYPE_CHECKING

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

@@ -86,7 +86,7 @@ class Session:
             default_memory_config = {
                 "provider": "rag",
                 "use_embedding": False,
-                "rag_db_path": f".praison/sessions/{self.session_id}/chroma_db"
+                "rag_db_path": f".praisonai/sessions/{self.session_id}/chroma_db"
             }
             if memory_config:
                 default_memory_config.update(memory_config)
@@ -95,7 +95,7 @@ class Session:
             default_knowledge_config = knowledge_config or {}
             self.knowledge_config = default_knowledge_config
 
-            os.makedirs(f".praison/sessions/{self.session_id}", exist_ok=True)
+            os.makedirs(f".praisonai/sessions/{self.session_id}", exist_ok=True)
 
             self._memory = None
             self._knowledge = None

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

@@ -17,10 +17,12 @@ from dataclasses import dataclass, field
 from datetime import datetime, timezone
 from typing import Any, Dict, List, Optional
 
+from ..paths import get_sessions_dir
+
 logger = logging.getLogger(__name__)
 
-# Default session directory
-DEFAULT_SESSION_DIR = os.path.expanduser("~/.praison/sessions")
+# Default session directory (uses centralized paths - DRY)
+DEFAULT_SESSION_DIR = str(get_sessions_dir())
 
 # Default limits
 DEFAULT_MAX_MESSAGES = 100
@@ -211,7 +213,7 @@ class DefaultSessionStore:
         Initialize session store.
         
         Args:
-            session_dir: Directory for session files. Defaults to ~/.praison/sessions/
+            session_dir: Directory for session files. Defaults to ~/.praisonai/sessions/
             max_messages: Maximum messages to keep per session.
             lock_timeout: Timeout for file lock acquisition.
         """

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

@@ -5,14 +5,16 @@ from typing import List, Optional
 
 from .parser import find_skill_md, read_properties
 from .models import SkillProperties
+from ..paths import get_skills_dir, get_project_data_dir
 
 
 def get_default_skill_dirs() -> List[Path]:
     """Get default skill directory locations.
 
+    Uses centralized paths.py for consistent path management.
     Returns directories in precedence order (high to low):
-    1. Project: ./.praison/skills/ or ./.claude/skills/
-    2. User: ~/.praison/skills/
+    1. Project: ./.praisonai/skills/ or ./.claude/skills/
+    2. User: ~/.praisonai/skills/
     3. System: /etc/praison/skills/ (admin-managed)
 
     Returns:
@@ -21,19 +23,21 @@ def get_default_skill_dirs() -> List[Path]:
     dirs = []
     cwd = Path.cwd()
 
-    # Project-level directories
-    project_dirs = [
-        cwd / ".praison" / "skills",
-        cwd / ".claude" / "skills",
-    ]
-    for d in project_dirs:
-        if d.exists() and d.is_dir():
-            dirs.append(d)
+    # Project-level directories (use centralized path)
+    project_data_dir = get_project_data_dir()
+    project_skills = project_data_dir / "skills"
+    if project_skills.exists() and project_skills.is_dir():
+        dirs.append(project_skills)
+    
+    # Also check .claude/skills for compatibility
+    claude_skills = cwd / ".claude" / "skills"
+    if claude_skills.exists() and claude_skills.is_dir():
+        dirs.append(claude_skills)
 
-    # User-level directory
-    user_dir = Path.home() / ".praison" / "skills"
-    if user_dir.exists() and user_dir.is_dir():
-        dirs.append(user_dir)
+    # User-level directory (use centralized path)
+    user_skills = get_skills_dir()
+    if user_skills.exists() and user_skills.is_dir():
+        dirs.append(user_skills)
 
     # System-level directory (Unix-like systems)
     system_dir = Path("/etc/praison/skills")

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

@@ -19,7 +19,7 @@ class SkillManager:
         manager = SkillManager()
 
         # Discover skills from directories
-        manager.discover(["./skills", "~/.praison/skills"])
+        manager.discover(["./skills", "~/.praisonai/skills"])
 
         # Get XML for system prompt
         prompt_xml = manager.to_prompt()

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

@@ -14,10 +14,12 @@ import uuid
 from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional
 
+from ..paths import get_snapshots_dir
+
 logger = logging.getLogger(__name__)
 
-# Default snapshot directory
-DEFAULT_SNAPSHOT_DIR = os.path.expanduser("~/.praison/snapshots")
+# Default snapshot directory (uses centralized paths - DRY)
+DEFAULT_SNAPSHOT_DIR = str(get_snapshots_dir())
 
 
 @dataclass

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

@@ -19,8 +19,13 @@ import logging
 from pathlib import Path
 from typing import Any, Dict, List, Optional
 
+from ..paths import get_storage_dir
+
 logger = logging.getLogger(__name__)
 
+# Default storage directory (uses centralized paths - DRY)
+DEFAULT_STORAGE_DIR = str(get_storage_dir())
+
 
 class FileBackend:
     """
@@ -31,7 +36,7 @@ class FileBackend:
     
     Example:
         ```python
-        backend = FileBackend(storage_dir="~/.praison/data")
+        backend = FileBackend(storage_dir="~/.praisonai/storage")
         backend.save("session_123", {"messages": []})
         data = backend.load("session_123")
         ```
@@ -39,7 +44,7 @@ class FileBackend:
     
     def __init__(
         self,
-        storage_dir: str = "~/.praison/storage",
+        storage_dir: str = None,
         suffix: str = ".json",
         pretty: bool = True,
     ):
@@ -51,7 +56,7 @@ class FileBackend:
             suffix: File suffix (default: .json)
             pretty: Use pretty-printed JSON
         """
-        self.storage_dir = Path(os.path.expanduser(storage_dir))
+        self.storage_dir = Path(os.path.expanduser(storage_dir or DEFAULT_STORAGE_DIR))
         self.suffix = suffix
         self.pretty = pretty
         self._lock = threading.Lock()
@@ -162,7 +167,7 @@ class SQLiteBackend:
     
     Example:
         ```python
-        backend = SQLiteBackend(db_path="~/.praison/data.db")
+        backend = SQLiteBackend(db_path="~/.praisonai/data.db")
         backend.save("session_123", {"messages": []})
         data = backend.load("session_123")
         ```
@@ -170,7 +175,7 @@ class SQLiteBackend:
     
     def __init__(
         self,
-        db_path: str = "~/.praison/storage.db",
+        db_path: str = "~/.praisonai/storage.db",
         table_name: str = "praison_storage",
         auto_create: bool = True,
     ):
@@ -468,10 +473,10 @@ def get_backend(
     Example:
         ```python
         # File backend (default)
-        backend = get_backend("file", storage_dir="~/.praison/data")
+        backend = get_backend("file", storage_dir="~/.praisonai/data")
         
         # SQLite backend
-        backend = get_backend("sqlite", db_path="~/.praison/data.db")
+        backend = get_backend("sqlite", db_path="~/.praisonai/data.db")
         
         # Redis backend
         backend = get_backend("redis", url="redis://localhost:6379")

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

@@ -43,6 +43,10 @@ class StreamEvent:
         tool_call: Tool call data for DELTA_TOOL_CALL events
         metadata: Additional event-specific metadata
         error: Error message for ERROR events
+        is_reasoning: Whether this content is reasoning/thinking (default False)
+        agent_id: Optional agent identifier for multi-agent scenarios
+        session_id: Optional session identifier for tracking
+        run_id: Optional run identifier for correlation
     """
     type: StreamEventType
     timestamp: float = field(default_factory=time.perf_counter)
@@ -50,6 +54,10 @@ class StreamEvent:
     tool_call: Optional[Dict[str, Any]] = None
     metadata: Optional[Dict[str, Any]] = None
     error: Optional[str] = None
+    is_reasoning: bool = False
+    agent_id: Optional[str] = None
+    session_id: Optional[str] = None
+    run_id: Optional[str] = None
     
     def __repr__(self) -> str:
         parts = [f"StreamEvent({self.type.value}"]

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

@@ -47,12 +47,14 @@ class Task:
         name: Optional[str] = None,
         tools: Optional[List[Any]] = None,
         context: Optional[List[Union[str, List, 'Task']]] = None,
+        depends_on: Optional[List[Union[str, List, 'Task']]] = None,  # Alias for context (clearer semantics)
         async_execution: Optional[bool] = False,
         config: Optional[Dict[str, Any]] = None,
         output_file: Optional[str] = None,
         output_json: Optional[Type[BaseModel]] = None,
         output_pydantic: Optional[Type[BaseModel]] = None,
         callback: Optional[Union[Callable[[TaskOutput], Any], Callable[[TaskOutput], Coroutine[Any, Any, Any]]]] = None,
+        on_task_complete: Optional[Union[Callable[[TaskOutput], Any], Callable[[TaskOutput], Coroutine[Any, Any, Any]]]] = None,
         status: str = "not started",
         result: Optional[TaskOutput] = None,
         create_directory: Optional[bool] = False,
@@ -79,6 +81,7 @@ class Task:
         # ============================================================
         skip_on_failure: bool = False,  # Allow workflow to continue if this task fails
         retry_delay: float = 0.0,  # Seconds between retries (supports exponential backoff)
+        on_error: str = "stop",  # Flow control: "stop", "continue", or "retry"
         # ============================================================
         # NEW PARAMS FROM WORKFLOWSTEP (Phase 4 Consolidation)
         # ============================================================
@@ -93,8 +96,22 @@ class Task:
         loop_var: str = "item",
         # Consolidated config objects (from Task)
         execution: Optional[Any] = None,
-        routing: Optional[Any] = None,
+        routing: Optional[Dict[str, List[str]]] = None,  # Renamed from condition for clarity
         output_config: Optional[Any] = None,
+        # ============================================================
+        # UNIFIED OUTPUT PARAMETER (consolidates all output configs)
+        # ============================================================
+        # Single output param that handles: file path (str), Pydantic model, TaskOutputConfig
+        output: Optional[Any] = None,
+        # ============================================================
+        # UNIFIED CONDITION SYNTAX (same as AgentFlow)
+        # ============================================================
+        # String-based condition expression (e.g., "{{score}} > 80")
+        when: Optional[str] = None,
+        # Task to route to when condition is True
+        then_task: Optional[str] = None,
+        # Task to route to when condition is False
+        else_task: Optional[str] = None,
         # Feature configs (from Task)
         autonomy: Optional[Any] = None,
         knowledge: Optional[Any] = None,
@@ -136,13 +153,36 @@ class Task:
         self.expected_output = expected_output if expected_output is not None else "Complete the task successfully"
         self.agent = agent
         self.tools = tools if tools else []
-        self.context = context if context else []
+        # Handle depends_on as alias for context (depends_on takes precedence)
+        if depends_on is not None:
+            self.context = depends_on
+        else:
+            self.context = context if context else []
+        # Store depends_on as property alias for context
+        self._depends_on = self.context
         self.async_execution = async_execution
         self.config = config if config else {}
         self.output_file = output_file
         self.output_json = output_json
         self.output_pydantic = output_pydantic
-        self.callback = callback
+        # Handle callback/on_task_complete: on_task_complete is canonical, callback is deprecated
+        if callback is not None and on_task_complete is None:
+            import warnings
+            warnings.warn(
+                "Parameter 'callback' is deprecated, use 'on_task_complete' instead. "
+                "Example: Task(on_task_complete=my_fn) instead of Task(callback=my_fn)",
+                DeprecationWarning,
+                stacklevel=2
+            )
+            self.callback = callback
+        elif callback is not None and on_task_complete is not None:
+            raise ValueError(
+                "Cannot specify both 'callback' and 'on_task_complete'. "
+                "Use 'on_task_complete' only (callback is deprecated)."
+            )
+        else:
+            # on_task_complete takes precedence (or both are None)
+            self.callback = on_task_complete
         self.status = status
         self.result = result
         self.create_directory = create_directory
@@ -156,7 +196,17 @@ class Task:
         self.quality_check = quality_check
         self.rerun = rerun # Assigning the rerun parameter
         self.retain_full_context = retain_full_context
-        self.guardrail = guardrail if guardrail is not None else guardrails  # guardrails is alias
+        # Handle guardrail/guardrails: guardrails (plural) is canonical, guardrail (singular) is deprecated
+        if guardrail is not None and guardrails is None:
+            import warnings
+            warnings.warn(
+                "Parameter 'guardrail' is deprecated, use 'guardrails' instead. "
+                "Example: Task(guardrails=my_fn) instead of Task(guardrail=my_fn)",
+                DeprecationWarning,
+                stacklevel=2
+            )
+        # guardrails takes precedence over guardrail
+        self.guardrail = guardrails if guardrails is not None else guardrail
         self.max_retries = max_retries
         self.retry_count = retry_count
         self._guardrail_fn = None
@@ -182,8 +232,90 @@ class Task:
         self.loop_var = loop_var
         # Consolidated config objects (from Task)
         self.execution = execution
-        self.routing = routing
+        # ============================================================
+        # RESOLVE EXECUTION CONFIG FIELDS TO TASK PROPERTIES
+        # Precedence: Direct params (if non-default) > Config object > Defaults
+        # We check if direct param equals its default to determine if user set it
+        # ============================================================
+        if execution is not None:
+            # Resolve async_exec -> async_execution
+            # Only apply config if direct param was NOT explicitly set (is default False)
+            if hasattr(execution, 'async_exec'):
+                # async_execution default is False, so if it's False, use config value
+                if not async_execution:  # direct param is False (default)
+                    self.async_execution = execution.async_exec
+            # Resolve quality_check
+            # quality_check default is True, so if it's True, use config value
+            if hasattr(execution, 'quality_check'):
+                if quality_check:  # direct param is True (default)
+                    self.quality_check = execution.quality_check
+            # Resolve max_retries
+            # max_retries default is 3, so if it's 3, use config value
+            if hasattr(execution, 'max_retries'):
+                if max_retries == 3:  # direct param is 3 (default)
+                    self.max_retries = execution.max_retries
+            # Resolve rerun
+            # rerun default is False, so if it's False, use config value
+            if hasattr(execution, 'rerun'):
+                if not rerun:  # direct param is False (default)
+                    self.rerun = execution.rerun
+            # Resolve on_error (config takes precedence for backward compat)
+            if hasattr(execution, 'on_error'):
+                self.on_error = execution.on_error
+            else:
+                self.on_error = on_error
+        else:
+            self.on_error = on_error
+        # Handle routing parameter - use routing if provided, else fall back to condition
+        if routing is not None:
+            self.condition = routing
+        self.routing = self.condition  # Alias for backward compat
         self.output_config = output_config
+        # ============================================================
+        # UNIFIED OUTPUT PARAMETER RESOLUTION
+        # ============================================================
+        # Resolve unified 'output' param (consolidates output_file, output_json, output_pydantic, output_config)
+        # Priority: unified 'output' param > individual params (for backward compat)
+        if output is not None:
+            if isinstance(output, str):
+                # String = file path
+                self.output_file = output
+            elif hasattr(output, 'model_fields') or hasattr(output, '__fields__'):
+                # Pydantic model class (v2 or v1)
+                if isinstance(output, type):
+                    # It's a class, use for output_json
+                    self.output_json = output
+                else:
+                    # It's an instance, use for output_pydantic
+                    self.output_pydantic = type(output)
+            elif hasattr(output, 'file') or hasattr(output, 'variable'):
+                # TaskOutputConfig or similar config object
+                if hasattr(output, 'file') and output.file:
+                    self.output_file = output.file
+                if hasattr(output, 'json_model') and output.json_model:
+                    self.output_json = output.json_model
+                if hasattr(output, 'pydantic_model') and output.pydantic_model:
+                    self.output_pydantic = output.pydantic_model
+                if hasattr(output, 'variable') and output.variable:
+                    self.output_variable = output.variable
+            elif isinstance(output, dict):
+                # Dict config
+                if 'file' in output:
+                    self.output_file = output['file']
+                if 'json_model' in output or 'json' in output:
+                    self.output_json = output.get('json_model') or output.get('json')
+                if 'pydantic_model' in output or 'pydantic' in output:
+                    self.output_pydantic = output.get('pydantic_model') or output.get('pydantic')
+                if 'variable' in output:
+                    self.output_variable = output.get('variable')
+        # Store the original output param for reference
+        self.output = output
+        # ============================================================
+        # UNIFIED CONDITION SYNTAX (same as AgentFlow)
+        # ============================================================
+        self.when = when
+        self.then_task = then_task
+        self.else_task = else_task
         # Feature configs (from Task)
         self.autonomy = autonomy
         self.knowledge = knowledge
@@ -193,7 +325,11 @@ class Task:
         self.hooks = hooks
         self.caching = caching
         # Output variable name - for storing output in workflow variables
-        self.output_variable = output_variable
+        # Only set from param if explicitly provided (unified output may have set it)
+        if output_variable is not None:
+            self.output_variable = output_variable
+        elif not hasattr(self, 'output_variable'):
+            self.output_variable = None
 
         # Set logger level based on config verbose level
         verbose = self.config.get("verbose", 0)
@@ -258,6 +394,16 @@ class Task:
         # Initialize guardrail
         self._setup_guardrail()
 
+    @property
+    def depends_on(self):
+        """Alias for context - returns the list of dependent tasks."""
+        return self.context
+    
+    @depends_on.setter
+    def depends_on(self, value):
+        """Alias for context - sets the list of dependent tasks."""
+        self.context = value
+
     def _setup_guardrail(self):
         """Setup the guardrail function based on the provided guardrail parameter."""
         if self.guardrail is None:
@@ -316,6 +462,40 @@ class Task:
     def __str__(self):
         return f"Task(name='{self.name if self.name else 'None'}', description='{self.description}', agent='{self.agent.name if self.agent else 'None'}', status='{self.status}')"
 
+    def to_dict(self) -> Dict[str, Any]:
+        """Serialize the Task to a dictionary.
+        
+        Returns:
+            Dictionary representation of the Task.
+        """
+        return {
+            "id": self.id,
+            "name": self.name,
+            "description": self.description,
+            "action": self.action,
+            "expected_output": self.expected_output,
+            "status": self.status,
+            "task_type": self.task_type,
+            "tools": self.tools,
+            "context": [str(c) if hasattr(c, '__str__') else c for c in self.context] if self.context else [],
+            "next_tasks": self.next_tasks,
+            "condition": self.condition,
+            "routing": self.routing,
+            "is_start": self.is_start,
+            "max_retries": self.max_retries,
+            "async_execution": self.async_execution,
+            "output_file": self.output_file,
+            "output_variable": self.output_variable,
+            "retain_full_context": self.retain_full_context,
+            "loop_over": self.loop_over,
+            "loop_var": self.loop_var,
+            "when": self.when,
+            "then_task": self.then_task,
+            "else_task": self.else_task,
+            "agent_config": self.agent_config,
+            "variables": self.variables,
+        }
+
     def initialize_memory(self):
         """Initialize memory if config exists but memory doesn't"""
         if not self.memory and self.config.get('memory_config'):
@@ -632,4 +812,51 @@ Context:
             if asyncio.iscoroutinefunction(self.callback):
                 await self.callback(task_output)
             else:
-                self.callback(task_output)
+                self.callback(task_output)
+
+    def evaluate_when(self, context: Dict[str, Any]) -> bool:
+        """
+        Evaluate the 'when' condition against the given context.
+        
+        Uses the shared evaluate_condition function from the conditions module
+        for DRY compliance with AgentFlow condition evaluation.
+        
+        Args:
+            context: Dictionary containing variables for evaluation.
+                     May include workflow variables, previous outputs, etc.
+            
+        Returns:
+            True if condition is met or no condition is set.
+            False if condition is not met.
+        """
+        if self.when is None:
+            return True
+        
+        from ..conditions.evaluator import evaluate_condition
+        return evaluate_condition(
+            self.when,
+            context,
+            previous_output=context.get('previous_output')
+        )
+    
+    def get_next_task(self, context: Dict[str, Any]) -> Optional[str]:
+        """
+        Get the next task name based on the 'when' condition evaluation.
+        
+        This provides a simple routing mechanism for Task objects,
+        similar to AgentFlow's when() function.
+        
+        Args:
+            context: Dictionary containing variables for evaluation.
+            
+        Returns:
+            then_task if condition is True, else_task if False.
+            None if no routing is configured.
+        """
+        if self.when is None and self.then_task is None and self.else_task is None:
+            return None
+        
+        if self.evaluate_when(context):
+            return self.then_task
+        else:
+            return self.else_task

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

@@ -43,8 +43,8 @@ def internet_search(query: str, max_results: int = 5, retries: int = MAX_RETRIES
             results = []
             ddgs = DDGS()
             
-            # Try text search
-            search_results = list(ddgs.text(keywords=query, max_results=max_results))
+            # Try text search (use positional 'query' for v8+ compatibility)
+            search_results = list(ddgs.text(query, max_results=max_results))
             
             for result in search_results:
                 results.append({

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

@@ -4,7 +4,7 @@ Mentions Parser for PraisonAI Agents.
 Provides @mention syntax support similar to Cursor IDE:
 - @file:path/to/file.py - Include file content
 - @web:query - Search the web
-- @doc:name - Include doc from .praison/docs/
+- @doc:name - Include doc from .praisonai/docs/
 - @rule:name - Include specific rule
 
 Usage:
@@ -30,7 +30,7 @@ class MentionsParser:
     Supports:
     - @file:path - Include file content
     - @web:query - Web search (requires web search tool)
-    - @doc:name - Include doc from .praison/docs/
+    - @doc:name - Include doc from .praisonai/docs/
     - @rule:name - Include specific rule
     - @url:https://... - Fetch URL content
     """

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

@@ -213,8 +213,8 @@ class ToolRegistry:
         """Discover and load tools from single-file plugins.
         
         Scans default plugin directories for WordPress-style plugins:
-        - ./.praison/plugins/ (project-level)
-        - ~/.praison/plugins/ (user-level)
+        - ./.praisonai/plugins/ (project-level)
+        - ~/.praisonai/plugins/ (user-level)
         
         Returns:
             Number of plugins loaded
@@ -333,8 +333,8 @@ def discover_plugins() -> int:
     """Discover and load tools from single-file plugins.
     
     Scans default plugin directories for WordPress-style plugins:
-    - ./.praison/plugins/ (project-level)
-    - ~/.praison/plugins/ (user-level)
+    - ./.praisonai/plugins/ (project-level)
+    - ~/.praisonai/plugins/ (user-level)
     
     Returns:
         Number of plugins loaded

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

@@ -226,7 +226,8 @@ def _search_duckduckgo(query: str, max_results: int = 5) -> List[Dict[str, Any]]
         try:
             results = []
             ddgs = DDGS()
-            search_results = list(ddgs.text(keywords=query, max_results=max_results))
+            # Use positional 'query' (v8+) instead of keywords= (deprecated)
+            search_results = list(ddgs.text(query, max_results=max_results))
             
             for result in search_results:
                 results.append({

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

@@ -6,14 +6,14 @@ allowing users to define and execute complex task sequences.
 
 Features:
 - YAML/Markdown workflow definitions
-- Auto-discovery from .praison/workflows/
+- Auto-discovery from .praisonai/workflows/
 - Step-by-step execution with context passing
 - Variable substitution
 - Conditional steps
 - Lazy loading for performance
 
 Storage Structure:
-    .praison/workflows/
+    .praisonai/workflows/
     ├── deploy.md           # Deployment workflow
     ├── test.md             # Testing workflow
     └── review.md           # Code review workflow
@@ -2696,6 +2696,9 @@ CONCISE SUMMARY:"""
     ) -> bool:
         """Evaluate a condition expression with variable substitution.
         
+        This method delegates to the shared evaluate_condition() function
+        from praisonaiagents.conditions for DRY condition handling.
+        
         Supported formats:
             - Numeric comparison: "{{var}} > 80", "{{var}} >= 50", "{{var}} < 10"
             - String equality: "{{var}} == approved", "{{var}} != rejected"
@@ -2711,115 +2714,8 @@ CONCISE SUMMARY:"""
         Returns:
             Boolean result of condition evaluation
         """
-        import re
-        
-        # Substitute variables in condition
-        substituted = condition
-        
-        # Handle nested property access like {{item.score}}
-        def get_nested_value(var_path: str, vars_dict: Dict[str, Any]) -> Any:
-            parts = var_path.split('.')
-            value = vars_dict
-            for part in parts:
-                if isinstance(value, dict):
-                    value = value.get(part)
-                else:
-                    return None
-            return value
-        
-        # Find all {{var}} patterns and substitute
-        pattern = r'\{\{([^}]+)\}\}'
-        matches = re.findall(pattern, condition)
-        
-        for var_name in matches:
-            if '.' in var_name:
-                # Nested property access
-                value = get_nested_value(var_name, variables)
-            else:
-                value = variables.get(var_name)
-            
-            if value is None:
-                value = ""
-            
-            # Replace the placeholder with the value
-            placeholder = f"{{{{{var_name}}}}}"
-            if isinstance(value, (int, float)):
-                substituted = substituted.replace(placeholder, str(value))
-            elif isinstance(value, bool):
-                substituted = substituted.replace(placeholder, str(value).lower())
-            else:
-                substituted = substituted.replace(placeholder, str(value))
-        
-        # Also substitute {{previous_output}}
-        if previous_output:
-            substituted = substituted.replace("{{previous_output}}", str(previous_output))
-        
-        # Now evaluate the substituted condition
-        try:
-            # Handle different condition formats
-            
-            # Numeric comparisons: "90 > 80", "50 >= 50", "10 < 20", etc.
-            numeric_pattern = r'^(-?\d+(?:\.\d+)?)\s*(>|>=|<|<=|==|!=)\s*(-?\d+(?:\.\d+)?)$'
-            numeric_match = re.match(numeric_pattern, substituted.strip())
-            if numeric_match:
-                left = float(numeric_match.group(1))
-                op = numeric_match.group(2)
-                right = float(numeric_match.group(3))
-                
-                if op == '>':
-                    return left > right
-                if op == '>=':
-                    return left >= right
-                if op == '<':
-                    return left < right
-                if op == '<=':
-                    return left <= right
-                if op == '==':
-                    return left == right
-                if op == '!=':
-                    return left != right
-            
-            # String equality: "approved == approved", "status != rejected"
-            string_eq_pattern = r'^(.+?)\s*(==|!=)\s*(.+)$'
-            string_match = re.match(string_eq_pattern, substituted.strip())
-            if string_match:
-                left = string_match.group(1).strip()
-                op = string_match.group(2)
-                right = string_match.group(3).strip()
-                
-                if op == '==':
-                    return left == right
-                if op == '!=':
-                    return left != right
-            
-            # Contains check: "error in some message", "status contains success"
-            if ' in ' in substituted:
-                parts = substituted.split(' in ', 1)
-                if len(parts) == 2:
-                    needle = parts[0].strip()
-                    haystack = parts[1].strip()
-                    return needle.lower() in haystack.lower()
-            
-            if ' contains ' in substituted:
-                parts = substituted.split(' contains ', 1)
-                if len(parts) == 2:
-                    haystack = parts[0].strip()
-                    needle = parts[1].strip()
-                    return needle.lower() in haystack.lower()
-            
-            # Boolean check: truthy evaluation
-            # Handle "true", "false", "True", "False"
-            if substituted.strip().lower() == 'true':
-                return True
-            if substituted.strip().lower() == 'false':
-                return False
-            
-            # Non-empty string is truthy
-            return bool(substituted.strip())
-            
-        except Exception as e:
-            logger.warning(f"Condition evaluation failed for '{condition}': {e}")
-            return False
+        from praisonaiagents.conditions.evaluator import evaluate_condition
+        return evaluate_condition(condition, variables, previous_output)
     
     def _execute_include(
         self,
@@ -3008,7 +2904,7 @@ class WorkflowManager:
     Workflows are defined in markdown files with YAML frontmatter
     and step definitions.
     
-    Example workflow file (.praison/workflows/deploy.md):
+    Example workflow file (.praisonai/workflows/deploy.md):
         ```markdown
         ---
         name: Deploy to Production
@@ -3059,7 +2955,7 @@ class WorkflowManager:
         ```
     """
     
-    WORKFLOWS_DIR = ".praison/workflows"
+    WORKFLOWS_DIR = ".praisonai/workflows"
     SUPPORTED_EXTENSIONS = [".md", ".yaml", ".yml"]
     
     def __init__(
@@ -3502,13 +3398,17 @@ class WorkflowManager:
         
         context_parts = []
         
-        if step.context_from:
+        # Use getattr with defaults for workflow-specific attributes that may not exist on Task
+        context_from = getattr(step, 'context_from', None)
+        retain_full_context = getattr(step, 'retain_full_context', False)
+        
+        if context_from:
             # Include only specified steps
-            for step_name in step.context_from:
+            for step_name in context_from:
                 for prev_result in results:
                     if prev_result["step"] == step_name and prev_result.get("output"):
                         context_parts.append(f"## {step_name} Output:\n{prev_result['output']}")
-        elif step.retain_full_context:
+        elif retain_full_context:
             # Include all previous outputs
             for prev_result in results:
                 if prev_result.get("output") and prev_result.get("status") == "success":
@@ -3867,18 +3767,21 @@ class WorkflowManager:
             
             # Handle branching
             next_step_idx = None
-            if step.branch_condition and step_result["output"]:
+            # Use getattr with defaults for workflow-specific attributes that may not exist on Task
+            branch_condition = getattr(step, 'branch_condition', None)
+            next_steps = getattr(step, 'next_steps', None)
+            if branch_condition and step_result["output"]:
                 # Evaluate branch condition based on output
                 output_lower = str(step_result["output"]).lower()
-                for branch_key, branch_targets in step.branch_condition.items():
+                for branch_key, branch_targets in branch_condition.items():
                     if branch_key.lower() in output_lower or output_lower.startswith(branch_key.lower()):
                         if branch_targets and branch_targets[0] in step_lookup:
                             next_step_idx, _ = step_lookup[branch_targets[0]]
                             break
-            elif step.next_steps:
+            elif next_steps:
                 # Use explicit next_steps
-                if step.next_steps[0] in step_lookup:
-                    next_step_idx, _ = step_lookup[step.next_steps[0]]
+                if next_steps[0] in step_lookup:
+                    next_step_idx, _ = step_lookup[next_steps[0]]
             
             # Move to next step
             if next_step_idx is not None:
@@ -4343,7 +4246,7 @@ class WorkflowManager:
     
     def _get_checkpoints_dir(self) -> Path:
         """Get the checkpoints directory path."""
-        checkpoints_dir = self.workspace_path / ".praison" / "checkpoints"
+        checkpoints_dir = self.workspace_path / ".praisonai" / "checkpoints"
         checkpoints_dir.mkdir(parents=True, exist_ok=True)
         return checkpoints_dir
     

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "1.2.3"
+version = "1.4.4"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"
@@ -57,6 +57,13 @@ api = [
     "uvicorn>=0.34.0"
 ]
 
+# AgentOS dependencies (alias for api + auth for deployment)
+os = [
+    "fastapi>=0.115.0",
+    "uvicorn>=0.34.0",
+    "PyJWT>=2.8.0"
+]
+
 # Telemetry dependencies
 telemetry = [
     "posthog>=3.0.0"

src/praisonai-agents/tests/example_callback.py

@@ -1,6 +1,7 @@
 from praisonaiagents import (
     Agent, 
     Task, 
+    AgentTeam,
     Agents, 
     error_logs, 
     register_display_callback,
@@ -147,7 +148,7 @@ def main():
         markdown=True
     )
 
-    # Create tasks with callbacks
+    # Create tasks with on_task_complete callbacks
     task1 = Task(
         name="research_task",
         description="""Analyze 2024's AI advancements. 
@@ -155,7 +156,7 @@ def main():
         expected_output="""A detailed report on 2024 AI advancements""",
         agent=researcher,
         tools=[internet_search_tool],
-        callback=task_callback
+        on_task_complete=task_callback
     )
 
     task2 = Task(
@@ -166,7 +167,7 @@ def main():
         expected_output="A blog post of at least 4 paragraphs",
         agent=writer,
         context=[task1],
-        callback=task_callback,
+        on_task_complete=task_callback,
         tools=[]
     )
 
@@ -175,7 +176,7 @@ def main():
         description="""Create a json object with a title of "My Task" and content of "My content".""",
         expected_output="""JSON output with title and content""",
         agent=researcher,
-        callback=task_callback
+        on_task_complete=task_callback
     )
 
     task4 = Task(
@@ -186,7 +187,7 @@ def main():
         context=[task2],
         output_file='test.txt',
         create_directory=True,
-        callback=task_callback
+        on_task_complete=task_callback
     )
 
     # Create and run agents manager

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

@@ -6,12 +6,13 @@ Requires OPENAI_API_KEY environment variable.
 """
 
 import os
-import sys
+import pytest
 
-# Skip if no API key
-if not os.environ.get("OPENAI_API_KEY"):
-    print("Skipping integration tests - OPENAI_API_KEY not set")
-    sys.exit(0)
+# Skip entire module if no API key
+pytestmark = pytest.mark.skipif(
+    not os.environ.get("OPENAI_API_KEY"),
+    reason="OPENAI_API_KEY not set"
+)
 
 
 def test_guardrail_string_preset():

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

@@ -0,0 +1,256 @@
+"""
+Integration tests comparing Task.condition vs AgentFlow when() syntax.
+
+This test file validates both condition syntaxes work correctly and
+documents the differences for analysis.
+"""
+import pytest
+
+
+class TestAgentFlowConditionSyntax:
+    """Test AgentFlow's when()/if_() string-based condition syntax."""
+    
+    def test_agentflow_numeric_condition(self):
+        """AgentFlow uses string expressions: '{{score}} > 80'"""
+        from praisonaiagents import AgentFlow
+        from praisonaiagents.workflows import if_
+        
+        results = []
+        
+        def approve(ctx):
+            results.append("approved")
+            return {"output": "approved"}
+        
+        def reject(ctx):
+            results.append("rejected")
+            return {"output": "rejected"}
+        
+        # Test passing condition
+        flow = AgentFlow(
+            steps=[
+                if_(
+                    condition="{{score}} > 80",
+                    then_steps=[approve],
+                    else_steps=[reject]
+                )
+            ],
+            variables={"score": 90}
+        )
+        flow.start("test")
+        assert "approved" in results
+        
+        # Test failing condition
+        results.clear()
+        flow2 = AgentFlow(
+            steps=[
+                if_(
+                    condition="{{score}} > 80",
+                    then_steps=[approve],
+                    else_steps=[reject]
+                )
+            ],
+            variables={"score": 70}
+        )
+        flow2.start("test")
+        assert "rejected" in results
+    
+    def test_agentflow_string_condition(self):
+        """AgentFlow string equality: '{{status}} == approved'"""
+        from praisonaiagents import AgentFlow
+        from praisonaiagents.workflows import when
+        
+        results = []
+        
+        def process_approved(ctx):
+            results.append("processed_approved")
+            return {"output": "done"}
+        
+        def process_rejected(ctx):
+            results.append("processed_rejected")
+            return {"output": "done"}
+        
+        flow = AgentFlow(
+            steps=[
+                when(
+                    condition="{{status}} == approved",
+                    then_steps=[process_approved],
+                    else_steps=[process_rejected]
+                )
+            ],
+            variables={"status": "approved"}
+        )
+        flow.start("test")
+        assert "processed_approved" in results
+
+
+class TestTaskConditionSyntax:
+    """Test Task's Dict-based condition syntax for routing."""
+    
+    def test_task_dict_condition_routing(self):
+        """Task uses Dict[str, List[str]]: {'approved': ['next_task']}"""
+        from praisonaiagents import Task
+        
+        # Task condition is a dict mapping decision values to next task names
+        task = Task(
+            name="review_task",
+            description="Review and decide",
+            task_type="decision",
+            condition={
+                "approved": ["publish_task"],
+                "rejected": ["revise_task"]
+            }
+        )
+        
+        # Verify condition structure
+        assert isinstance(task.condition, dict)
+        assert "approved" in task.condition
+        assert task.condition["approved"] == ["publish_task"]
+        assert task.condition["rejected"] == ["revise_task"]
+    
+    def test_task_should_run_callable(self):
+        """Task also supports should_run callable for conditional execution."""
+        from praisonaiagents import Task
+        
+        def check_condition(ctx):
+            return ctx.get("score", 0) > 80
+        
+        task = Task(
+            name="conditional_task",
+            description="Only run if score > 80",
+            should_run=check_condition
+        )
+        
+        assert task.should_run is not None
+        assert callable(task.should_run)
+        # Test the callable
+        assert task.should_run({"score": 90}) is True
+        assert task.should_run({"score": 70}) is False
+
+
+class TestSyntaxComparison:
+    """Compare the two syntaxes side-by-side."""
+    
+    def test_document_syntax_differences(self):
+        """Document the syntax differences for analysis."""
+        
+        # ============================================================
+        # AGENTFLOW SYNTAX (Deterministic Pipelines)
+        # ============================================================
+        # Uses: String expressions with {{variable}} placeholders
+        # Location: workflows/workflows.py - when(), if_(), If class
+        # Evaluation: evaluate_condition() in conditions/evaluator.py
+        # 
+        # Example:
+        #   when(
+        #       condition="{{score}} > 80",  # STRING expression
+        #       then_steps=[approve],
+        #       else_steps=[reject]
+        #   )
+        #
+        # Supported formats:
+        #   - Numeric: "{{var}} > 80", "{{var}} >= 50"
+        #   - String: "{{var}} == approved"
+        #   - Contains: "error in {{message}}"
+        #   - Boolean: "{{flag}}"
+        #   - Nested: "{{item.score}} >= 60"
+        
+        agentflow_condition = "{{score}} > 80"  # STRING
+        
+        # ============================================================
+        # TASK/AGENTTEAM SYNTAX (Multi-Agent Task Graphs)
+        # ============================================================
+        # Uses: Dict[str, List[str]] mapping decision values to task names
+        # Location: task/task.py - Task.condition parameter
+        # Evaluation: process/process.py - matches decision_str to dict keys
+        #
+        # Example:
+        #   Task(
+        #       condition={
+        #           "approved": ["publish_task"],  # DICT routing
+        #           "rejected": ["revise_task"]
+        #       }
+        #   )
+        #
+        # How it works:
+        #   1. Task with task_type="decision" returns a decision value
+        #   2. Process looks up decision value in condition dict
+        #   3. Routes to the corresponding next task(s)
+        
+        task_condition = {"approved": ["publish_task"], "rejected": ["revise_task"]}  # DICT
+        
+        # ============================================================
+        # KEY DIFFERENCES (CONFUSING FOR USERS)
+        # ============================================================
+        #
+        # | Aspect          | AgentFlow          | Task/AgentTeam       |
+        # |-----------------|--------------------|-----------------------|
+        # | Condition Type  | STRING expression  | DICT routing map      |
+        # | Evaluation      | Expression parsing | Key lookup            |
+        # | Purpose         | Branch on value    | Route to next task    |
+        # | Variables       | {{var}} syntax     | decision_str from LLM |
+        # | Flexibility     | Any comparison     | Exact key match       |
+        #
+        # CONFUSION POINTS:
+        # 1. Same word "condition" means different things
+        # 2. AgentFlow: condition="{{x}} > 5" (evaluates expression)
+        # 3. Task: condition={"yes": ["task_a"]} (routes by key)
+        # 4. No unified syntax for beginners
+        
+        assert isinstance(agentflow_condition, str)
+        assert isinstance(task_condition, dict)
+        
+        # This test documents the confusion - both use "condition" but differently
+
+
+class TestRealWorldScenarios:
+    """Test real-world scenarios with both syntaxes."""
+    
+    def test_agentflow_score_based_routing(self):
+        """Real scenario: Route based on numeric score."""
+        from praisonaiagents import AgentFlow
+        from praisonaiagents.workflows import when
+        
+        execution_log = []
+        
+        def high_score_handler(ctx):
+            execution_log.append("high_score")
+            return {"output": "Premium service"}
+        
+        def low_score_handler(ctx):
+            execution_log.append("low_score")
+            return {"output": "Standard service"}
+        
+        # AgentFlow: Uses string expression
+        flow = AgentFlow(
+            steps=[
+                when(
+                    condition="{{customer_score}} >= 80",
+                    then_steps=[high_score_handler],
+                    else_steps=[low_score_handler]
+                )
+            ],
+            variables={"customer_score": 85}
+        )
+        
+        result = flow.start("Assign service tier")
+        assert "high_score" in execution_log
+    
+    def test_task_decision_routing(self):
+        """Real scenario: Task decision routes to next task."""
+        from praisonaiagents import Task
+        
+        # Task: Uses dict for routing
+        review_task = Task(
+            name="content_review",
+            description="Review content and decide: approved or rejected",
+            task_type="decision",
+            condition={
+                "approved": ["publish_content"],
+                "rejected": ["request_revision"]
+            }
+        )
+        
+        # The condition dict maps LLM decision output to next task names
+        assert review_task.task_type == "decision"
+        assert "approved" in review_task.condition
+        assert "rejected" in review_task.condition

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

@@ -172,15 +172,20 @@ class TestWorkflowDictParams:
         assert workflow.memory is not None
     
     def test_workflowstep_context_dict(self):
-        """Task accepts context as dict."""
+        """Task accepts context as dict (via TaskContextConfig)."""
         from praisonaiagents.workflows.workflows import Task
+        from praisonaiagents.workflows.workflow_configs import TaskContextConfig
         
         step = Task(
             name="step1",
-            context={"from_steps": ["step0"], "retain_full": True},
+            action="do something",
+            context=TaskContextConfig(from_steps=["step0"], retain_full=True),
         )
-        assert step.context_from == ["step0"]
-        assert step.retain_full_context is True
+        # context stores the TaskContextConfig object
+        assert hasattr(step.context, 'from_steps')
+        assert step.context.from_steps == ["step0"]
+        # retain_full_context is a separate param on Task
+        assert step.context.retain_full is True
     
     def test_workflowstep_output_dict(self):
         """Task accepts output as dict."""
@@ -188,31 +193,35 @@ class TestWorkflowDictParams:
         
         step = Task(
             name="step1",
+            action="do something",
             output={"file": "output.txt", "variable": "result"},
         )
         assert step.output_file == "output.txt"
         assert step.output_variable == "result"
     
     def test_workflowstep_execution_dict(self):
-        """Task accepts execution as dict."""
+        """Task accepts execution as dict (params passed directly)."""
         from praisonaiagents.workflows.workflows import Task
         
         step = Task(
             name="step1",
-            execution={"max_retries": 5, "quality_check": True},
+            action="do something",
+            max_retries=5,
+            quality_check=True,
         )
         assert step.max_retries == 5
         assert step.quality_check is True
     
     def test_workflowstep_routing_dict(self):
-        """Task accepts routing as dict."""
+        """Task accepts routing as dict (via next_tasks param)."""
         from praisonaiagents.workflows.workflows import Task
         
         step = Task(
             name="step1",
-            routing={"next_steps": ["step2", "step3"]},
+            action="do something",
+            next_tasks=["step2", "step3"],
         )
-        assert step.next_steps == ["step2", "step3"]
+        assert step.next_tasks == ["step2", "step3"]
 
 
 # =============================================================================
@@ -297,9 +306,11 @@ class TestBackwardCompatibility:
         
         step = Task(
             name="step1",
+            action="do something",
             context=["step0", "step_prev"],
         )
-        assert step.context_from == ["step0", "step_prev"]
+        # context stores the list directly
+        assert step.context == ["step0", "step_prev"]
 
 
 # =============================================================================

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

@@ -0,0 +1,275 @@
+"""Integration tests for EvaluationLoop with mocked LLM."""
+
+import pytest
+from unittest.mock import MagicMock, patch
+
+
+class TestEvaluationLoopIntegration:
+    """Integration tests for EvaluationLoop."""
+    
+    def test_evaluation_loop_with_agent_and_judge(self):
+        """Test full integration with Agent and Judge (mocked LLM)."""
+        from praisonaiagents import Agent
+        from praisonaiagents.eval import EvaluationLoop, EvaluationLoopResult
+        
+        with patch('praisonaiagents.agent.agent.Agent.chat') as mock_chat, \
+             patch('litellm.completion') as mock_completion:
+            
+            mock_chat.side_effect = [
+                "First attempt at analysis",
+                "Improved analysis with more detail",
+                "Final comprehensive analysis with all requirements",
+            ]
+            
+            mock_response = MagicMock()
+            mock_response.choices = [MagicMock()]
+            mock_response.choices[0].message.content = """
+SCORE: 9
+REASONING: Excellent comprehensive analysis
+SUGGESTIONS: None needed
+"""
+            mock_completion.return_value = mock_response
+            
+            agent = Agent(name="test_agent", instructions="Analyze systems")
+            
+            loop = EvaluationLoop(
+                agent=agent,
+                criteria="Analysis is thorough",
+                threshold=8.0,
+                max_iterations=3,
+            )
+            
+            result = loop.run("Analyze the auth flow")
+            
+            assert isinstance(result, EvaluationLoopResult)
+            assert result.final_score == 9.0
+            assert result.success is True
+    
+    def test_agent_run_until_method(self):
+        """Test Agent.run_until() convenience method."""
+        from praisonaiagents import Agent
+        from praisonaiagents.eval.results import EvaluationLoopResult
+        
+        with patch('praisonaiagents.agent.agent.Agent.chat') as mock_chat, \
+             patch('litellm.completion') as mock_completion:
+            
+            mock_chat.return_value = "Good analysis output"
+            
+            mock_response = MagicMock()
+            mock_response.choices = [MagicMock()]
+            mock_response.choices[0].message.content = """
+SCORE: 8.5
+REASONING: Good quality analysis
+SUGGESTIONS: Consider edge cases
+"""
+            mock_completion.return_value = mock_response
+            
+            agent = Agent(name="test_agent", instructions="Analyze systems")
+            
+            result = agent.run_until(
+                "Analyze the codebase",
+                criteria="Analysis is thorough",
+                threshold=8.0,
+            )
+            
+            assert isinstance(result, EvaluationLoopResult)
+            assert result.success is True
+            assert result.final_score == 8.5
+    
+    def test_evaluation_loop_iteration_callback(self):
+        """Test that iteration callback is called correctly."""
+        from praisonaiagents import Agent
+        from praisonaiagents.eval import EvaluationLoop
+        
+        with patch('praisonaiagents.agent.agent.Agent.chat') as mock_chat, \
+             patch('litellm.completion') as mock_completion:
+            
+            mock_chat.return_value = "Output"
+            
+            mock_response = MagicMock()
+            mock_response.choices = [MagicMock()]
+            mock_response.choices[0].message.content = "SCORE: 9\nREASONING: Good"
+            mock_completion.return_value = mock_response
+            
+            callback_results = []
+            def on_iteration(result):
+                callback_results.append(result)
+            
+            agent = Agent(name="test", instructions="Test")
+            loop = EvaluationLoop(
+                agent=agent,
+                criteria="Be good",
+                threshold=8.0,
+                on_iteration=on_iteration,
+            )
+            
+            loop.run("Test prompt")
+            
+            assert len(callback_results) == 1
+            assert callback_results[0].score == 9.0
+    
+    def test_evaluation_loop_multiple_iterations(self):
+        """Test loop runs multiple iterations when score is below threshold."""
+        from praisonaiagents import Agent
+        from praisonaiagents.eval import EvaluationLoop
+        
+        with patch('praisonaiagents.agent.agent.Agent.chat') as mock_chat, \
+             patch('litellm.completion') as mock_completion:
+            
+            mock_chat.side_effect = ["Output 1", "Output 2", "Output 3"]
+            
+            responses = [
+                "SCORE: 5\nREASONING: Needs work\nSUGGESTIONS: Improve detail",
+                "SCORE: 7\nREASONING: Better\nSUGGESTIONS: Add examples",
+                "SCORE: 9\nREASONING: Excellent\nSUGGESTIONS: None",
+            ]
+            
+            mock_response = MagicMock()
+            mock_response.choices = [MagicMock()]
+            mock_response.choices[0].message.content = responses[0]
+            
+            call_count = [0]
+            def side_effect(*args, **kwargs):
+                idx = min(call_count[0], len(responses) - 1)
+                mock_response.choices[0].message.content = responses[idx]
+                call_count[0] += 1
+                return mock_response
+            
+            mock_completion.side_effect = side_effect
+            
+            agent = Agent(name="test", instructions="Test")
+            loop = EvaluationLoop(
+                agent=agent,
+                criteria="Be thorough",
+                threshold=8.0,
+                max_iterations=5,
+            )
+            
+            result = loop.run("Test prompt")
+            
+            assert result.success is True
+            assert len(result.iterations) == 3
+            assert result.score_history == [5.0, 7.0, 9.0]
+    
+    def test_evaluation_loop_max_iterations_reached(self):
+        """Test loop stops at max iterations even if threshold not met."""
+        from praisonaiagents import Agent
+        from praisonaiagents.eval import EvaluationLoop
+        
+        with patch('praisonaiagents.agent.agent.Agent.chat') as mock_chat, \
+             patch('litellm.completion') as mock_completion:
+            
+            mock_chat.return_value = "Output"
+            
+            mock_response = MagicMock()
+            mock_response.choices = [MagicMock()]
+            mock_response.choices[0].message.content = "SCORE: 5\nREASONING: Poor"
+            mock_completion.return_value = mock_response
+            
+            agent = Agent(name="test", instructions="Test")
+            loop = EvaluationLoop(
+                agent=agent,
+                criteria="Be thorough",
+                threshold=8.0,
+                max_iterations=3,
+            )
+            
+            result = loop.run("Test prompt")
+            
+            assert result.success is False
+            assert len(result.iterations) == 3
+            assert result.final_score == 5.0
+    
+    def test_evaluation_loop_review_mode(self):
+        """Test review mode runs all iterations regardless of score."""
+        from praisonaiagents import Agent
+        from praisonaiagents.eval import EvaluationLoop
+        
+        with patch('praisonaiagents.agent.agent.Agent.chat') as mock_chat, \
+             patch('litellm.completion') as mock_completion:
+            
+            mock_chat.return_value = "Output"
+            
+            mock_response = MagicMock()
+            mock_response.choices = [MagicMock()]
+            mock_response.choices[0].message.content = "SCORE: 10\nREASONING: Perfect"
+            mock_completion.return_value = mock_response
+            
+            agent = Agent(name="test", instructions="Test")
+            loop = EvaluationLoop(
+                agent=agent,
+                criteria="Be thorough",
+                threshold=8.0,
+                max_iterations=3,
+                mode="review",
+            )
+            
+            result = loop.run("Test prompt")
+            
+            assert result.success is True
+            assert len(result.iterations) == 3
+
+
+class TestEvaluationLoopResultSerialization:
+    """Tests for result serialization."""
+    
+    def test_result_to_json(self):
+        """Test EvaluationLoopResult.to_json()."""
+        from praisonaiagents.eval.results import IterationResult, EvaluationLoopResult
+        import json
+        
+        iterations = [
+            IterationResult(iteration=1, output="Out1", score=7.0, reasoning="OK"),
+            IterationResult(iteration=2, output="Out2", score=9.0, reasoning="Good"),
+        ]
+        
+        result = EvaluationLoopResult(
+            iterations=iterations,
+            success=True,
+            total_duration_seconds=5.0,
+        )
+        
+        json_str = result.to_json()
+        parsed = json.loads(json_str)
+        
+        assert parsed["success"] is True
+        assert parsed["final_score"] == 9.0
+        assert parsed["score_history"] == [7.0, 9.0]
+        assert len(parsed["iterations"]) == 2
+    
+    def test_result_final_report(self):
+        """Test EvaluationLoopResult.final_report property."""
+        from praisonaiagents.eval.results import IterationResult, EvaluationLoopResult
+        
+        iterations = [
+            IterationResult(
+                iteration=1, 
+                output="First output", 
+                score=6.0, 
+                reasoning="Needs improvement",
+                findings=["Add more detail"]
+            ),
+            IterationResult(
+                iteration=2, 
+                output="Better output", 
+                score=8.5, 
+                reasoning="Good quality",
+                findings=[]
+            ),
+        ]
+        
+        result = EvaluationLoopResult(
+            iterations=iterations,
+            success=True,
+            total_duration_seconds=3.5,
+            threshold=8.0,
+        )
+        
+        report = result.final_report
+        
+        assert "# Evaluation Loop Report" in report
+        assert "Success" in report or "✅" in report
+        assert "8.5" in report
+        assert "Iteration 1" in report
+        assert "Iteration 2" in report
+        assert "Add more detail" in report

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

@@ -15,15 +15,18 @@ import time
 
 # Skip entire module if MCP package is not installed
 try:
+    import mcp
     from praisonaiagents.mcp import MCP
     from praisonaiagents import Agent
     MCP_AVAILABLE = True
 except ImportError:
     MCP_AVAILABLE = False
+    MCP = None
+    Agent = None
 
 
 @pytest.mark.integration
-@pytest.mark.skipif(not MCP_AVAILABLE, reason="MCP package not installed")
+@pytest.mark.skipif(not MCP_AVAILABLE, reason="MCP package not installed. Install with: pip install praisonaiagents[mcp]")
 class TestMultiAgentMCP:
     """Integration tests for multiple agents with MCP."""
     

src/praisonai-agents/tests/test_mcp_http_stream.py

@@ -11,6 +11,9 @@ Tests the HTTPStreamTransport implementation including:
 
 import pytest
 
+# Skip all tests in this module if MCP is not installed
+pytest.importorskip("mcp", reason="MCP module not installed. Install with: pip install praisonaiagents[mcp]")
+
 
 class TestHTTPStreamTransportInit:
     """Test HTTPStreamTransport initialization."""

src/praisonai-agents/tests/test_mcp_server.py

@@ -6,6 +6,9 @@ These tests define the expected behavior of ToolsMCPServer class.
 import pytest
 from unittest.mock import patch
 
+# Skip all tests in this module if MCP is not installed
+pytest.importorskip("mcp", reason="MCP module not installed. Install with: pip install praisonaiagents[mcp]")
+
 
 class TestToolsMCPServerInit:
     """Test ToolsMCPServer initialization."""

src/praisonai-agents/tests/test_mcp_websocket.py

@@ -262,6 +262,14 @@ class TestWebSocketKeepalive:
     
     def test_ping_interval_configurable(self):
         """Test that ping interval is configurable."""
+        import asyncio
+        # Create event loop for asyncio.Queue() used in __init__
+        try:
+            loop = asyncio.get_event_loop()
+        except RuntimeError:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+        
         from praisonaiagents.mcp.mcp_websocket import WebSocketTransport
         
         transport = WebSocketTransport(

src/praisonai-agents/tests/test_memory_advanced.py

@@ -339,7 +339,7 @@ class TestRulesManager:
     @pytest.fixture
     def rules_dir(self, tmp_path):
         """Create a temp rules directory."""
-        rules_path = tmp_path / ".praison" / "rules"
+        rules_path = tmp_path / ".praisonai" / "rules"
         rules_path.mkdir(parents=True)
         return rules_path
     
@@ -363,7 +363,7 @@ class TestRulesManager:
         assert "**/*.py" in rule.globs
         
         # Verify file was created
-        rule_file = tmp_path / ".praison" / "rules" / "python_style.md"
+        rule_file = tmp_path / ".praisonai" / "rules" / "python_style.md"
         assert rule_file.exists()
     
     def test_load_rule_with_frontmatter(self, rules_dir, tmp_path):
@@ -378,10 +378,10 @@ priority: 10
 # Test Rule
 - Rule content here
 """
-        (rules_dir / "test_rule.md").write_text(rule_content)
+        (rules_dir / "unique_frontmatter_test.md").write_text(rule_content)
         
         manager = RulesManager(workspace_path=str(tmp_path))
-        rule = manager.get_rule_by_name("test_rule")
+        rule = manager.get_rule_by_name("unique_frontmatter_test")
         
         assert rule is not None
         assert rule.description == "Test rule"

src/praisonai-agents/tests/test_memory_features.py

@@ -252,7 +252,7 @@ class TestAiDecisionActivation(unittest.TestCase):
     def test_ai_decision_rule_loaded(self):
         """Test that ai_decision rules are loaded."""
         with tempfile.TemporaryDirectory() as tmpdir:
-            rules_dir = Path(tmpdir) / ".praison" / "rules"
+            rules_dir = Path(tmpdir) / ".praisonai" / "rules"
             rules_dir.mkdir(parents=True)
             
             rule_content = """---
@@ -273,7 +273,7 @@ activation: ai_decision
     def test_evaluate_ai_decision_without_llm(self):
         """Test ai_decision evaluation without LLM (defaults to True)."""
         with tempfile.TemporaryDirectory() as tmpdir:
-            rules_dir = Path(tmpdir) / ".praison" / "rules"
+            rules_dir = Path(tmpdir) / ".praisonai" / "rules"
             rules_dir.mkdir(parents=True)
             
             rule_content = """---
@@ -365,7 +365,7 @@ class TestWorkflows(unittest.TestCase):
         from praisonaiagents.memory import WorkflowManager
         
         with tempfile.TemporaryDirectory() as tmpdir:
-            workflows_dir = Path(tmpdir) / ".praison" / "workflows"
+            workflows_dir = Path(tmpdir) / ".praisonai" / "workflows"
             workflows_dir.mkdir(parents=True)
             
             workflow_content = """---
@@ -401,7 +401,7 @@ Build application
         from praisonaiagents.memory import WorkflowManager
         
         with tempfile.TemporaryDirectory() as tmpdir:
-            workflows_dir = Path(tmpdir) / ".praison" / "workflows"
+            workflows_dir = Path(tmpdir) / ".praisonai" / "workflows"
             workflows_dir.mkdir(parents=True)
             
             workflow_content = """---
@@ -462,7 +462,7 @@ class TestHooks(unittest.TestCase):
         from praisonaiagents.memory import HooksManager
         
         with tempfile.TemporaryDirectory() as tmpdir:
-            config_dir = Path(tmpdir) / ".praison"
+            config_dir = Path(tmpdir) / ".praisonai"
             config_dir.mkdir()
             
             config = {
@@ -526,7 +526,7 @@ class TestHooks(unittest.TestCase):
                 timeout=60
             )
             
-            config_path = Path(tmpdir) / ".praison" / "hooks.json"
+            config_path = Path(tmpdir) / ".praisonai" / "hooks.json"
             self.assertTrue(config_path.exists())
             
             config = json.loads(config_path.read_text())