Release v2.3.74

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>=2.3.73" \
+    "praisonai>=2.3.74" \
     "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>=2.3.73" \
+    "praisonai>=2.3.74" \
     "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>=2.3.73" \
+    "praisonai>=2.3.74" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/eval/accuracy_example.py

@@ -5,30 +5,65 @@ This example demonstrates how to evaluate agent output accuracy
 by comparing against expected outputs using LLM-as-judge.
 """
 
+import os
 from praisonaiagents import Agent
 from praisonaiagents.eval import AccuracyEvaluator
 
-# Create a simple math tutor agent
-agent = Agent(
-    instructions="You are a math tutor. Answer math questions concisely with just the number."
-)
+# Check if we have an API key
+has_api_key = os.getenv("OPENAI_API_KEY") is not None
 
-# Create accuracy evaluator
-evaluator = AccuracyEvaluator(
-    agent=agent,
-    input_text="What is 2 + 2?",
-    expected_output="4",
-    num_iterations=3,  # Run 3 times for statistical significance
-    verbose=True
-)
+if has_api_key:
+    # Create a simple math tutor agent
+    agent = Agent(
+        instructions="You are a math tutor. Answer math questions concisely with just the number."
+    )
 
-# Run evaluation
-result = evaluator.run(print_summary=True)
+    # Create accuracy evaluator
+    evaluator = AccuracyEvaluator(
+        agent=agent,
+        input_text="What is 2 + 2?",
+        expected_output="4",
+        num_iterations=3,  # Run 3 times for statistical significance
+        verbose=True
+    )
 
-# Check results
-print(f"\nAverage Score: {result.avg_score}/10")
-print(f"Passed: {result.passed}")
+    # Run evaluation
+    result = evaluator.run(print_summary=True)
+
+    # Check results
+    print(f"\nAverage Score: {result.avg_score}/10")
+    print(f"Passed: {result.passed}")
+else:
+    print("⚠️  No OPENAI_API_KEY found. Using mock evaluation...")
+    
+    # Create evaluator without agent for mock testing
+    evaluator = AccuracyEvaluator(
+        func=lambda x: "4",  # Mock function
+        input_text="What is 2 + 2?",
+        expected_output="4",
+        num_iterations=1,
+        verbose=True
+    )
+    
+    # Test with mock output
+    try:
+        result = evaluator.run(print_summary=True)
+        print(f"\nMock Average Score: {result.avg_score}/10")
+        print(f"Mock Passed: {result.passed}")
+    except Exception as e:
+        print(f"Mock evaluation failed: {e}")
+        print("This is expected without an API key")
 
 # You can also evaluate pre-generated outputs
+print("\n--- Testing pre-generated output evaluation ---")
 output = "The answer is 4"
-result2 = evaluator.evaluate_output(output, print_summary=True)
+
+if has_api_key:
+    try:
+        result2 = evaluator.evaluate_output(output, print_summary=True)
+        print(f"Pre-generated output score: {result2.avg_score}/10")
+    except Exception as e:
+        print(f"Pre-generated evaluation failed: {e}")
+else:
+    print("⚠️  Skipping pre-generated evaluation (no API key)")
+    print("To run full evaluation, set OPENAI_API_KEY environment variable")

examples/eval/batch_example.py

@@ -5,9 +5,13 @@ This example demonstrates how to run batch evaluations
 from a JSON test file.
 """
 
+import os
 import json
 import tempfile
-from praisonaiagents.eval import AccuracyEvaluator, CriteriaEvaluator
+from praisonaiagents.eval import AccuracyEvaluator
+
+# Check if we have an API key
+has_api_key = os.getenv("OPENAI_API_KEY") is not None
 
 # Create a test file with multiple test cases
 test_cases = [
@@ -33,40 +37,69 @@ with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
 print(f"Test file created: {test_file}")
 print(f"Running {len(test_cases)} test cases...")
 
-# You can use the CLI for batch evaluation:
-# praisonai eval batch --agent agents.yaml --test-file tests.json --batch-type accuracy
+if has_api_key:
+    # You can use the CLI for batch evaluation:
+    # praisonai eval batch --agent agents.yaml --test-file tests.json --batch-type accuracy
 
-# Or programmatically:
-from praisonaiagents import Agent
+    # Or programmatically:
+    from praisonaiagents import Agent
 
-agent = Agent(
-    instructions="You are a helpful assistant. Answer questions concisely."
-)
-
-results = []
-for i, test_case in enumerate(test_cases):
-    print(f"\nTest {i + 1}: {test_case['input']}")
-    
-    evaluator = AccuracyEvaluator(
-        agent=agent,
-        input_text=test_case["input"],
-        expected_output=test_case["expected"],
-        num_iterations=1
+    agent = Agent(
+        instructions="You are a helpful assistant. Answer questions concisely."
     )
-    
-    result = evaluator.run()
-    results.append({
-        "input": test_case["input"],
-        "expected": test_case["expected"],
-        "score": result.avg_score,
-        "passed": result.passed
-    })
-    
-    print(f"  Score: {result.avg_score}/10")
-    print(f"  Passed: {result.passed}")
 
-# Summary
-passed = sum(1 for r in results if r["passed"])
-print(f"\n{'='*50}")
-print(f"Summary: {passed}/{len(results)} tests passed")
-print(f"Pass Rate: {passed/len(results):.1%}")
+    results = []
+    for i, test_case in enumerate(test_cases):
+        print(f"\nTest {i + 1}: {test_case['input']}")
+        
+        evaluator = AccuracyEvaluator(
+            agent=agent,
+            input_text=test_case["input"],
+            expected_output=test_case["expected"],
+            num_iterations=1
+        )
+        
+        result = evaluator.run()
+        results.append({
+            "input": test_case["input"],
+            "expected": test_case["expected"],
+            "score": result.avg_score,
+            "passed": result.passed
+        })
+        
+        print(f"  Score: {result.avg_score}/10")
+        print(f"  Passed: {result.passed}")
+
+    # Summary
+    passed = sum(1 for r in results if r["passed"])
+    print(f"\n{'='*50}")
+    print(f"Summary: {passed}/{len(results)} tests passed")
+    print(f"Pass Rate: {passed/len(results):.1%}")
+else:
+    print("⚠️  No OPENAI_API_KEY found. Running mock batch evaluation...")
+    
+    # Mock evaluation without API key
+    results = []
+    for i, test_case in enumerate(test_cases):
+        print(f"\nTest {i + 1}: {test_case['input']}")
+        
+        # Mock result (all would fail without API key)
+        mock_score = 0.0
+        mock_passed = False
+        
+        results.append({
+            "input": test_case["input"],
+            "expected": test_case["expected"],
+            "score": mock_score,
+            "passed": mock_passed
+        })
+        
+        print(f"  Score: {mock_score}/10 (mock)")
+        print(f"  Passed: {mock_passed} (expected without API key)")
+
+    # Summary
+    passed = sum(1 for r in results if r["passed"])
+    print(f"\n{'='*50}")
+    print(f"Summary: {passed}/{len(results)} tests passed (mock evaluation)")
+    print(f"Pass Rate: {passed/len(results):.1%}")
+    print("\nTo run real batch evaluation, set OPENAI_API_KEY environment variable")

examples/eval/criteria_example.py

@@ -5,58 +5,78 @@ This example demonstrates how to evaluate agent outputs
 against custom criteria using LLM-as-judge.
 """
 
+import os
 from praisonaiagents import Agent
 from praisonaiagents.eval import CriteriaEvaluator
 
-# Create a customer service agent
-agent = Agent(
-    instructions="You are a friendly customer service agent. Be helpful and empathetic."
-)
+# Check if we have an API key
+has_api_key = os.getenv("OPENAI_API_KEY") is not None
 
-# Create criteria evaluator with numeric scoring
-evaluator = CriteriaEvaluator(
-    criteria="Response is helpful, empathetic, and provides a clear solution",
-    agent=agent,
-    input_text="I'm frustrated because my order hasn't arrived yet.",
-    scoring_type="numeric",  # Score 1-10
-    threshold=7.0,           # Pass if score >= 7
-    num_iterations=2,
-    verbose=True
-)
+if has_api_key:
+    print("--- Testing Criteria Evaluation with Agent ---")
+    # Create a customer service agent
+    agent = Agent(
+        instructions="You are a friendly customer service agent. Be helpful and empathetic."
+    )
 
-# Run evaluation
-result = evaluator.run(print_summary=True)
+    # Create criteria evaluator with numeric scoring
+    evaluator = CriteriaEvaluator(
+        criteria="Response is helpful, empathetic, and provides a clear solution",
+        agent=agent,
+        input_text="I'm frustrated because my order hasn't arrived yet.",
+        scoring_type="numeric",  # Score 1-10
+        threshold=7.0,           # Pass if score >= 7
+        num_iterations=2,
+        verbose=True
+    )
 
-print(f"\nAverage Score: {result.avg_score}/10")
-print(f"Pass Rate: {result.pass_rate:.1%}")
-print(f"Passed: {result.passed}")
+    # Run evaluation
+    result = evaluator.run(print_summary=True)
 
-# Binary scoring example (pass/fail)
-binary_evaluator = CriteriaEvaluator(
-    criteria="Response does not contain any offensive language",
-    agent=agent,
-    input_text="Tell me a joke",
-    scoring_type="binary",  # Pass or Fail
-    verbose=True
-)
+    print("\nNumeric Scoring Results:")
+    print(f"  Average Score: {result.avg_score}/10")
+    print(f"  Pass Rate: {result.pass_rate:.1%}")
+    print(f"  Passed: {result.passed}")
 
-binary_result = binary_evaluator.run(print_summary=True)
+    # Binary scoring example (pass/fail)
+    print("\n--- Testing Binary Scoring ---")
+    binary_evaluator = CriteriaEvaluator(
+        criteria="Response does not contain any offensive language",
+        agent=agent,
+        input_text="Tell me a joke",
+        scoring_type="binary",  # Pass or Fail
+        verbose=True
+    )
 
-# Evaluate pre-generated output
-output = "I understand your frustration. Let me check on your order right away."
-result2 = evaluator.evaluate_output(output, print_summary=True)
+    binary_result = binary_evaluator.run(print_summary=True)
+    
+    # With on_fail callback
+    print("\n--- Testing Failure Callback ---")
+    def handle_failure(score):
+        print(f"ALERT: Evaluation failed with score {score.score}")
+        print(f"Reasoning: {score.reasoning}")
 
-# With on_fail callback
-def handle_failure(score):
-    print(f"ALERT: Evaluation failed with score {score.score}")
-    print(f"Reasoning: {score.reasoning}")
+    callback_evaluator = CriteriaEvaluator(
+        criteria="Response is professional and helpful",
+        agent=agent,
+        input_text="Help me",
+        on_fail=handle_failure,
+        threshold=8.0
+    )
 
-callback_evaluator = CriteriaEvaluator(
-    criteria="Response is professional and helpful",
-    agent=agent,
-    input_text="Help me",
-    on_fail=handle_failure,
-    threshold=8.0
-)
+    callback_evaluator.run()
+else:
+    print("⚠️  No OPENAI_API_KEY found. Skipping agent-based criteria evaluation...")
 
-callback_evaluator.run()
+# Test pre-generated output evaluation (doesn't need agent)
+print("\n--- Testing Pre-generated Output Evaluation ---")
+if has_api_key:
+    output = "I understand your frustration. Let me check on your order right away."
+    try:
+        result2 = evaluator.evaluate_output(output, print_summary=True)
+        print(f"Pre-generated output score: {result2.avg_score}/10")
+    except Exception as e:
+        print(f"Pre-generated evaluation failed: {e}")
+else:
+    print("⚠️  Skipping pre-generated evaluation (no API key)")
+    print("To run full evaluation, set OPENAI_API_KEY environment variable")

examples/eval/performance_example.py

@@ -5,35 +5,43 @@ This example demonstrates how to benchmark agent performance
 by measuring runtime and memory usage across multiple iterations.
 """
 
+import os
 from praisonaiagents import Agent
 from praisonaiagents.eval import PerformanceEvaluator
 
-# Create a simple agent
-agent = Agent(
-    instructions="You are a helpful assistant. Answer questions briefly."
-)
+# Check if we have an API key
+has_api_key = os.getenv("OPENAI_API_KEY") is not None
 
-# Create performance evaluator
-evaluator = PerformanceEvaluator(
-    agent=agent,
-    input_text="What is the capital of France?",
-    num_iterations=10,  # Run 10 benchmark iterations
-    warmup_runs=2,      # 2 warmup runs before measurement
-    track_memory=True,  # Track memory usage
-    verbose=True
-)
+if has_api_key:
+    print("--- Testing Agent Performance ---")
+    # Create a simple agent
+    agent = Agent(
+        instructions="You are a helpful assistant. Answer questions briefly."
+    )
 
-# Run evaluation
-result = evaluator.run(print_summary=True)
+    # Create performance evaluator
+    evaluator = PerformanceEvaluator(
+        agent=agent,
+        input_text="What is the capital of France?",
+        num_iterations=10,  # Run 10 benchmark iterations
+        warmup_runs=2,      # 2 warmup runs before measurement
+        track_memory=True,  # Track memory usage
+        verbose=True
+    )
 
-# Access detailed metrics
-print(f"\nDetailed Results:")
-print(f"  Average Time: {result.avg_run_time:.4f}s")
-print(f"  Min Time: {result.min_run_time:.4f}s")
-print(f"  Max Time: {result.max_run_time:.4f}s")
-print(f"  Median Time: {result.median_run_time:.4f}s")
-print(f"  P95 Time: {result.p95_run_time:.4f}s")
-print(f"  Avg Memory: {result.avg_memory:.2f} MB")
+    # Run evaluation
+    result = evaluator.run(print_summary=True)
+
+    # Access detailed metrics
+    print("\nAgent Performance Results:")
+    print(f"  Average Time: {result.avg_run_time:.4f}s")
+    print(f"  Min Time: {result.min_run_time:.4f}s")
+    print(f"  Max Time: {result.max_run_time:.4f}s")
+    print(f"  Median Time: {result.median_run_time:.4f}s")
+    print(f"  P95 Time: {result.p95_run_time:.4f}s")
+    print(f"  Avg Memory: {result.avg_memory:.2f} MB")
+else:
+    print("⚠️  No OPENAI_API_KEY found. Skipping agent performance test...")
 
 # You can also benchmark any function
 def my_function():

examples/eval/reliability_example.py

@@ -5,6 +5,7 @@ This example demonstrates how to verify that agents call
 the expected tools during execution.
 """
 
+import os
 from praisonaiagents import Agent
 from praisonaiagents.eval import ReliabilityEvaluator
 
@@ -19,30 +20,53 @@ def calculate(expression: str) -> str:
     return str(eval(expression))
 
 
-# Create an agent with tools
-agent = Agent(
-    instructions="You are a helpful assistant with access to search and calculator tools.",
-    tools=[search_web, calculate]
-)
+# Check if we have an API key
+has_api_key = os.getenv("OPENAI_API_KEY") is not None
 
-# Create reliability evaluator
+if has_api_key:
+    print("--- Testing Agent Tool Call Reliability ---")
+    # Create an agent with tools
+    agent = Agent(
+        instructions="You are a helpful assistant with access to search and calculator tools.",
+        tools=[search_web, calculate]
+    )
+
+    # Create reliability evaluator
+    evaluator = ReliabilityEvaluator(
+        agent=agent,
+        input_text="Search for the weather in Paris and calculate 25 * 4",
+        expected_tools=["search_web", "calculate"],  # Tools that should be called
+        forbidden_tools=["delete_file"],  # Tools that should NOT be called
+        verbose=True
+    )
+
+    # Run evaluation
+    result = evaluator.run(print_summary=True)
+
+    # Check results
+    print("\nAgent Reliability Results:")
+    print(f"  Passed: {result.passed}")
+    print(f"  Pass Rate: {result.pass_rate:.1%}")
+else:
+    print("⚠️  No OPENAI_API_KEY found. Skipping agent reliability test...")
+    print("Agent would fail to call tools without API key (expected behavior)")
+
+# You can also evaluate pre-recorded tool calls (doesn't need API key)
+print("\n--- Testing Pre-recorded Tool Call Evaluation ---")
+# Create a mock agent for tool call evaluation
+mock_agent = Agent(instructions="Mock agent")
 evaluator = ReliabilityEvaluator(
-    agent=agent,
-    input_text="Search for the weather in Paris and calculate 25 * 4",
+    agent=mock_agent,
     expected_tools=["search_web", "calculate"],  # Tools that should be called
     forbidden_tools=["delete_file"],  # Tools that should NOT be called
     verbose=True
 )
 
-# Run evaluation
-result = evaluator.run(print_summary=True)
-
-# Check results
-print(f"\nPassed: {result.passed}")
-print(f"Pass Rate: {result.pass_rate:.1%}")
-
-# You can also evaluate pre-recorded tool calls
 result2 = evaluator.evaluate_tool_calls(
     actual_tools=["search_web", "calculate"],
     print_summary=True
 )
+
+print("\nPre-recorded Tool Call Results:")
+print(f"  Passed: {result2.passed}")
+print(f"  Pass Rate: {result2.pass_rate:.1%}")

src/praisonai-agents/praisonaiagents/__init__.py

@@ -3,7 +3,7 @@ Praison AI Agents - A package for hierarchical AI agent task execution
 """
 
 # Apply warning patch BEFORE any imports to intercept warnings at the source
-from . import _warning_patch
+from . import _warning_patch  # noqa: F401
 
 # Import centralized logging configuration FIRST
 from . import _logging
@@ -302,6 +302,23 @@ __all__ = [
     'ExpandResult'
 ]
 
+# Add workflow exports
+__all__.extend([
+    'Workflow',
+    'WorkflowStep', 
+    'WorkflowContext',
+    'StepResult',
+    'Route',
+    'Parallel',
+    'Loop',
+    'Repeat',
+    'route',
+    'parallel',
+    'loop',
+    'repeat',
+    'Pipeline'
+])
+
 # Add MCP to __all__ if available
 if _mcp_available:
     __all__.append('MCP')
@@ -318,6 +335,14 @@ __all__.extend([
     'LineRange'
 ])
 
+# Add Agent Skills exports (lazy loaded)
+__all__.extend([
+    'SkillManager',
+    'SkillProperties',
+    'SkillMetadata',
+    'SkillLoader'
+])
+
 # AG-UI support (optional, lazy loaded)
 try:
     from praisonaiagents.ui.agui import AGUI

src/praisonai-agents/praisonaiagents/_warning_patch.py

@@ -8,6 +8,7 @@ import functools
 import sys
 
 # Apply aggressive warning filters first
+warnings.filterwarnings("ignore", message=".*There is no current event loop.*", category=DeprecationWarning)
 warnings.filterwarnings("ignore", message=".*Pydantic serializer warnings.*")
 warnings.filterwarnings("ignore", message=".*PydanticSerializationUnexpectedValue.*")
 warnings.filterwarnings("ignore", message=".*Expected 9 fields but got.*")
@@ -31,7 +32,8 @@ SUPPRESSED_PATTERNS = [
     "Expected `StreamingChoices` but got `Choices`",
     "serialized value may not be as expected",
     "Mixing V1 models and V2 models",
-    "Please upgrade `Settings` to V2"
+    "Please upgrade `Settings` to V2",
+    "There is no current event loop"
 ]
 
 @functools.wraps(_original_warn)
@@ -43,7 +45,7 @@ def _patched_warn(message, category=None, stacklevel=1, source=None):
         if pattern in msg_str:
             return
     
-    if category == UserWarning and "pydantic" in msg_str.lower():
+    if category is UserWarning and "pydantic" in msg_str.lower():
         return
     
     _original_warn(message, category, stacklevel, source)
@@ -57,7 +59,7 @@ def _patched_warn_explicit(message, category, filename, lineno, module=None, reg
         if pattern in msg_str:
             return
     
-    if category == UserWarning and "pydantic" in msg_str.lower():
+    if category is UserWarning and "pydantic" in msg_str.lower():
         return
     
     if module and "pydantic" in str(module):

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.1.22"
+version = "0.1.23"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.1.22"
+version = "0.1.23"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.73.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.73.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.74.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.74.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/cli/features/skills.py

@@ -253,18 +253,11 @@ def add_skills_parser(subparsers) -> None:
     Args:
         subparsers: Subparsers object from argparse
     """
-    skills_parser = subparsers.add_parser(
-        'skills',
-        help='Manage Agent Skills'
-    )
-    
-    skills_subparsers = skills_parser.add_subparsers(
-        dest='skills_command',
-        help='Skills commands'
-    )
+    # subparsers is already the skills subparsers object
+    # We just need to add the subcommands to it
     
     # list command
-    list_parser = skills_subparsers.add_parser(
+    list_parser = subparsers.add_parser(
         'list',
         help='List available skills'
     )
@@ -275,7 +268,7 @@ def add_skills_parser(subparsers) -> None:
     )
     
     # validate command
-    validate_parser = skills_subparsers.add_parser(
+    validate_parser = subparsers.add_parser(
         'validate',
         help='Validate a skill directory'
     )
@@ -286,7 +279,7 @@ def add_skills_parser(subparsers) -> None:
     )
     
     # create command
-    create_parser = skills_subparsers.add_parser(
+    create_parser = subparsers.add_parser(
         'create',
         help='Create a new skill from template'
     )
@@ -306,7 +299,7 @@ def add_skills_parser(subparsers) -> None:
     )
     
     # prompt command
-    prompt_parser = skills_subparsers.add_parser(
+    prompt_parser = subparsers.add_parser(
         'prompt',
         help='Generate prompt XML for skills'
     )

src/praisonai/praisonai/cli/main.py

@@ -613,7 +613,7 @@ class PraisonAI:
             return default_args
         
         # Define special commands
-        special_commands = ['chat', 'code', 'call', 'realtime', 'train', 'ui', 'context', 'research', 'memory', 'rules', 'workflow', 'hooks', 'knowledge', 'session', 'tools', 'todo', 'docs', 'mcp', 'commit', 'serve', 'schedule']
+        special_commands = ['chat', 'code', 'call', 'realtime', 'train', 'ui', 'context', 'research', 'memory', 'rules', 'workflow', 'hooks', 'knowledge', 'session', 'tools', 'todo', 'docs', 'mcp', 'commit', 'serve', 'schedule', 'skills']
         
         parser = argparse.ArgumentParser(prog="praisonai", description="praisonAI command-line interface")
         parser.add_argument("--framework", choices=["crewai", "autogen", "praisonai"], help="Specify the framework")
@@ -814,7 +814,7 @@ class PraisonAI:
                 except ModuleNotFoundError as e:
                     missing_module = str(e).split("'")[1]
                     print(f"[red]ERROR: Missing dependency {missing_module}. Install with:[/red]")
-                    print(f"\npip install \"praisonai[chat]\"\n")
+                    print("\npip install \"praisonai[chat]\"\n")
                     sys.exit(1)
                 sys.exit(0)
 
@@ -1042,6 +1042,23 @@ class PraisonAI:
                 self.handle_commit_command(unknown_args)
                 sys.exit(0)
 
+            elif args.command == 'skills':
+                if not PRAISONAI_AVAILABLE:
+                    print("[red]ERROR: PraisonAI Agents is not installed. Install with:[/red]")
+                    print("\npip install praisonaiagents\n")
+                    sys.exit(1)
+                
+                from .features.skills import handle_skills_command, add_skills_parser
+                
+                # Create a parser for skills command
+                skills_parser = argparse.ArgumentParser(prog="praisonai skills")
+                skills_subparsers = skills_parser.add_subparsers(dest='skills_command', help='Skills commands')
+                add_skills_parser(skills_subparsers)
+                skills_args = skills_parser.parse_args(unknown_args)
+                
+                exit_code = handle_skills_command(skills_args)
+                sys.exit(exit_code)
+
         # Only check framework availability for agent-related operations
         if not args.command and (args.init or args.auto or args.framework):
             if not CREWAI_AVAILABLE and not AUTOGEN_AVAILABLE and not PRAISONAI_AVAILABLE:

src/praisonai/praisonai/deploy.py

@@ -57,7 +57,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.3.73 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.3.74 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/praisonai/version.py

@@ -1 +1 @@
-__version__ = "2.3.73"
+__version__ = "2.3.74"

src/praisonai/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.1.22",
+    "praisonaiagents>=0.1.23",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",

src/praisonai/uv.lock

@@ -4197,7 +4197,7 @@ requires-dist = [
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.22" },
     { name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.0.22" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.22" },
-    { name = "praisonaiagents", specifier = ">=0.1.22" },
+    { name = "praisonaiagents", specifier = ">=0.1.23" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = "==0.2.29" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4252,7 +4252,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.1.22"
+version = "0.1.23"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -4261,9 +4261,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/c5/fd/ddc0a0dc7d3d67d49a6e941aa7fbd1fce9c88d27c91f872679b694a3f75a/praisonaiagents-0.1.22.tar.gz", hash = "sha256:673dd4ffc5cc4910642e85da50eb238ae532a179e377fc6d7f0b98a1f8656a54", size = 677941, upload-time = "2025-12-22T15:46:13.8Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/79/5e/563a931f0d79f6c64fa8f891ad6eb88cacbbd3f26af5efe52c07a31f11b4/praisonaiagents-0.1.23.tar.gz", hash = "sha256:7a840172892c67feb68e04167db827017d5df8a0e496eb91e309d0e272db60c9", size = 678076, upload-time = "2025-12-22T17:00:28.27Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/47/e0/0008cd7b6c8739abfb754986cec83d10a2e3030deffa39dcc19f7689aa3c/praisonaiagents-0.1.22-py3-none-any.whl", hash = "sha256:9a938116fc9312f12e739b5a9a5a5971cd545082b01512b43af4f30d4d5fec87", size = 616650, upload-time = "2025-12-22T15:46:11.677Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/b3/bf02847078595ab1cf109dfd3c7282eaece21f7b879fe96a3ae0d23e8571/praisonaiagents-0.1.23-py3-none-any.whl", hash = "sha256:a455c8fd04fdbd7dd7bf9755c24f19c9c496c404ef60a4fdfc4d4a51decb2e60", size = 616785, upload-time = "2025-12-22T17:00:26.55Z" },
 ]
 
 [[package]]