fix: update broken Full Flow docs link in praisonai package README

- Fix duplicate broken link in src/praisonai/README.md
- Change from /docs/concepts/flow (404) to /docs/concepts/agentflow (200)
- Ensures PyPI package readers get working documentation link
- Addresses issue identified by Qodo code review

Co-authored-by: Mervin Praison <[email protected]>

api.md

@@ -911,18 +911,6 @@ Methods:
 * <code title="cli">praisonai hooks add <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
 * <code title="cli">praisonai hooks list <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
 * <code title="cli">praisonai hooks remove <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
-* <code title="cli">praisonai kanban block <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban boards <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban comment <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban complete <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban create <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban dispatch <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban link <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban list <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban move <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban reclaim <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban show <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
-* <code title="cli">praisonai kanban unblock <a href="./src/praisonai/praisonai/cli/commands/kanban.py">--help</a></code>
 * <code title="cli">praisonai knowledge add <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
 * <code title="cli">praisonai knowledge index <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
 * <code title="cli">praisonai knowledge list <a href="./src/praisonai/praisonai/cli/commands/knowledge.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>=4.6.52" \
+    "praisonai>=4.6.48" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.52" \
+    "praisonai>=4.6.48" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=4.6.52" \
+    "praisonai>=4.6.48" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/benchmark/benchmark_example.py

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

examples/compare/cli_compare_advanced.py

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

examples/compare/cli_compare_basic.py

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

examples/consolidated_params/advanced_memory.py

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

examples/consolidated_params/advanced_workflow_full_features.py

@@ -2,7 +2,7 @@
 from praisonaiagents import Agent
 from praisonaiagents import AgentFlow, Task
 from praisonaiagents.workflows.workflow_configs import (
-    WorkflowPlanningConfig, WorkflowMemoryConfig,
+    WorkflowOutputConfig, WorkflowPlanningConfig, WorkflowMemoryConfig,
 )
 
 # Full-featured workflow with all consolidated params
@@ -32,7 +32,7 @@ workflow = AgentFlow(
         ),
     ],
     # Workflow-level consolidated params
-    output="verbose",
+    output=WorkflowOutputConfig(output="verbose", stream=True),
     planning=WorkflowPlanningConfig(enabled=True, reasoning=True),
     memory=WorkflowMemoryConfig(backend="file"),
     # Agent-like params

examples/consolidated_params/basic_workflow.py

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

examples/eval/accuracy_example.py

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

examples/eval/criteria_example.py

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

examples/eval/performance_example.py

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

examples/eval/reliability_example.py

@@ -16,19 +16,8 @@ def search_web(query: str) -> str:
 
 
 def calculate(expression: str) -> str:
-    """Calculate a math expression safely."""
-    import ast, operator
-    _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-            ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-            ast.Mod: operator.mod, ast.Pow: operator.pow,
-            ast.USub: operator.neg, ast.UAdd: operator.pos}
-    def _ev(n):
-        if isinstance(n, ast.Expression): return _ev(n.body)
-        if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-        if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-        if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-        raise ValueError(f"Unsupported: {ast.dump(n)}")
-    return str(_ev(ast.parse(expression, mode="eval")))
+    """Calculate a math expression."""
+    return str(eval(expression))
 
 
 # Check if we have an API key
@@ -48,7 +37,7 @@ if has_api_key:
         input_text="Search for the weather in Paris and calculate 25 * 4",
         expected_tools=["search_web", "calculate"],  # Tools that should be called
         forbidden_tools=["delete_file"],  # Tools that should NOT be called
-        verbose=True
+        output="verbose"
     )
 
     # Run evaluation
@@ -70,7 +59,7 @@ evaluator = ReliabilityEvaluator(
     agent=mock_agent,
     expected_tools=["search_web", "calculate"],  # Tools that should be called
     forbidden_tools=["delete_file"],  # Tools that should NOT be called
-    verbose=True
+    output="verbose"
 )
 
 result2 = evaluator.evaluate_tool_calls(

examples/knowledge/compression_demo.py

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

examples/knowledge/hierarchical_summaries_demo.py

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

examples/knowledge/large_corpus_strategy_demo.py

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

examples/knowledge/mem0_backend_example.py

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

examples/knowledge/scope_identifiers_example.py

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

examples/knowledge/scope_isolation_example.py

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

examples/managed-agents/persistence/cockroachdb_managed.py

@@ -17,10 +17,8 @@ from praisonaiagents import Agent
 agent = Agent(
     name="CockroachDB Agent",
     instructions="You are a helpful assistant.",
-    memory={
-        "session_id": "crdb-demo-session",
-        "db": {"database_url": os.getenv("COCKROACHDB_URL")},
-    },
+    db={"database_url": os.getenv("COCKROACHDB_URL")},
+    session_id="crdb-demo-session",
 )
 
 result = agent.start("What is distributed SQL? Explain in 2 sentences.")

examples/managed-agents/persistence/neon_managed.py

@@ -18,10 +18,8 @@ from praisonaiagents import Agent
 agent = Agent(
     name="Neon Agent",
     instructions="You are a helpful assistant.",
-    memory={
-        "session_id": "neon-demo-session",
-        "db": {"database_url": os.getenv("NEON_DATABASE_URL")},
-    },
+    db={"database_url": os.getenv("NEON_DATABASE_URL")},
+    session_id="neon-demo-session",
 )
 
 # Option 2: Direct URL

examples/managed-agents/persistence/supabase_direct_managed.py

@@ -20,10 +20,8 @@ from praisonaiagents import Agent
 agent = Agent(
     name="Supabase Agent",
     instructions="You are a helpful assistant.",
-    memory={
-        "session_id": "supabase-demo-session",
-        "db": {"database_url": os.getenv("SUPABASE_DATABASE_URL")},
-    },
+    db={"database_url": os.getenv("SUPABASE_DATABASE_URL")},
+    session_id="supabase-demo-session",
 )
 
 result = agent.start("What is Supabase? Explain in 2 sentences.")

examples/managed-agents/persistence/turso_managed.py

@@ -18,13 +18,11 @@ from praisonaiagents import Agent
 agent = Agent(
     name="Turso Agent",
     instructions="You are a helpful assistant.",
-    memory={
-        "session_id": "turso-demo-session",
-        "db": {
-            "database_url": os.getenv("TURSO_DATABASE_URL"),
-            "auth_token": os.getenv("TURSO_AUTH_TOKEN"),
-        },
+    db={
+        "database_url": os.getenv("TURSO_DATABASE_URL"),
+        "auth_token": os.getenv("TURSO_AUTH_TOKEN"),
     },
+    session_id="turso-demo-session",
 )
 
 result = agent.start("What is edge computing? Explain in 2 sentences.")

examples/mcp/multi_server.py

@@ -19,43 +19,20 @@ import os
 
 def calculate(expression: str) -> str:
     """
-    A simple calculator tool that evaluates mathematical expressions safely.
-
+    A simple calculator tool that evaluates mathematical expressions.
+    
     Args:
         expression: A mathematical expression to evaluate (e.g., "2 + 2")
-
+    
     Returns:
         The result of the calculation
     """
-    import ast
-    import operator
-
-    _OPS = {
-        ast.Add: operator.add,
-        ast.Sub: operator.sub,
-        ast.Mult: operator.mul,
-        ast.Div: operator.truediv,
-        ast.FloorDiv: operator.floordiv,
-        ast.Mod: operator.mod,
-        ast.Pow: operator.pow,
-        ast.USub: operator.neg,
-        ast.UAdd: operator.pos,
-    }
-
-    def _safe_eval(node):
-        if isinstance(node, ast.Expression):
-            return _safe_eval(node.body)
-        if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)):
-            return node.value
-        if isinstance(node, ast.UnaryOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_safe_eval(node.operand))
-        if isinstance(node, ast.BinOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_safe_eval(node.left), _safe_eval(node.right))
-        raise ValueError(f"Unsupported expression: {ast.dump(node)}")
-
     try:
-        tree = ast.parse(expression, mode="eval")
-        result = _safe_eval(tree)
+        # Safe evaluation of mathematical expressions
+        allowed_chars = set("0123456789+-*/.() ")
+        if not all(c in allowed_chars for c in expression):
+            return "Error: Invalid characters in expression"
+        result = eval(expression)
         return f"Result: {result}"
     except Exception as e:
         return f"Error: {str(e)}"

examples/mcp_server/custom_tools_server.py

@@ -24,18 +24,12 @@ def main():
     def calculate(expression: str) -> str:
         """Safely evaluate a mathematical expression."""
         try:
-            import ast, operator
-            _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                    ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                    ast.Mod: operator.mod, ast.Pow: operator.pow,
-                    ast.USub: operator.neg, ast.UAdd: operator.pos}
-            def _ev(n):
-                if isinstance(n, ast.Expression): return _ev(n.body)
-                if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-                if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-                if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-                raise ValueError(f"Unsupported: {ast.dump(n)}")
-            return f"Result: {_ev(ast.parse(expression, mode='eval'))}"
+            # Only allow safe math operations
+            allowed = set("0123456789+-*/.(). ")
+            if not all(c in allowed for c in expression):
+                return "Error: Invalid characters in expression"
+            result = eval(expression)  # Safe due to character filtering
+            return f"Result: {result}"
         except Exception as e:
             return f"Error: {e}"
     

examples/middleware/configurable_model.py

@@ -10,7 +10,8 @@ from praisonaiagents import Agent
 agent = Agent(
     name="FlexBot",
     instructions="You are a helpful assistant.",
-    llm={"model": "gpt-4o-mini", "configurable": True},
+    llm="gpt-4o-mini",
+    llm_config={"configurable": True}
 )
 
 if __name__ == "__main__":

examples/middleware/injected_state.py

@@ -21,7 +21,7 @@ agent = Agent(
     name="ContextBot",
     instructions="You help show context information.",
     tools=[show_context],
-    memory={"session_id": "my-session-123"},
+    session_id="my-session-123"
 )
 
 if __name__ == "__main__":

examples/persistence/knowledge_qdrant.py

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

examples/policy/basic_policy.py

@@ -57,8 +57,8 @@ def main():
     agent = Agent(
         name="SecureAgent",
         instructions="You are a file management assistant.",
+        policy=engine
     )
-    agent.policy = engine
     
     print("\n--- Agent with Policy Enforcement Created ---")
     print(f"Agent: {agent.name}")

examples/python/a2a/a2a-server.py

@@ -21,36 +21,9 @@ def search_web(query: str) -> str:
     return f"Search results for: {query}"
 
 def calculate(expression: str) -> str:
-    """Calculate a mathematical expression safely."""
-    import ast
-    import operator
-
-    _OPS = {
-        ast.Add: operator.add,
-        ast.Sub: operator.sub,
-        ast.Mult: operator.mul,
-        ast.Div: operator.truediv,
-        ast.FloorDiv: operator.floordiv,
-        ast.Mod: operator.mod,
-        ast.Pow: operator.pow,
-        ast.USub: operator.neg,
-        ast.UAdd: operator.pos,
-    }
-
-    def _eval(node):
-        if isinstance(node, ast.Expression):
-            return _eval(node.body)
-        if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)):
-            return node.value
-        if isinstance(node, ast.UnaryOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_eval(node.operand))
-        if isinstance(node, ast.BinOp) and type(node.op) in _OPS:
-            return _OPS[type(node.op)](_eval(node.left), _eval(node.right))
-        raise ValueError(f"Unsupported expression: {ast.dump(node)}")
-
+    """Calculate a mathematical expression."""
     try:
-        tree = ast.parse(expression, mode="eval")
-        return f"Result: {_eval(tree)}"
+        return f"Result: {eval(expression)}"
     except Exception:
         return "Invalid expression"
 

examples/python/camera/camera-continuous.py

@@ -75,7 +75,7 @@ class ContinuousCameraMonitor:
             agents=[self.vision_agent],
             tasks=[task],
             process="sequential",
-            output="silent",  # Reduced verbosity for continuous monitoring
+            verbose=0  # Reduced verbosity for continuous monitoring
         )
         
         return agents.start()

examples/python/cli/git_integration_example.py

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

examples/python/cli/repo_map_example.py

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

examples/python/cli/sandbox_execution_example.py

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

examples/python/concepts/chat-with-pdf.py

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

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

@@ -1,8 +1,6 @@
-import tempfile
-import os
-import sys
-import logging
 from praisonaiagents import Agent, Task, AgentTeam
+import logging
+import os
 
 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -19,20 +17,14 @@ config = {
     }
 }
 
-# Create sample file in system temp directory
-_sample_path = os.path.join(tempfile.gettempdir(), "sample_knowledge.txt")
-if not os.path.exists(_sample_path):
-    with open(_sample_path, "w", encoding="utf-8") as f:
-        f.write("Mervin Praison is the creator of PraisonAI.\n")
-
 # Create an agent with knowledge capabilities
 knowledge_agent = Agent(
     name="KnowledgeAgent",
     role="Information Specialist",
     goal="Store and retrieve knowledge efficiently",
     backstory="Expert in managing and utilizing stored knowledge",
-    knowledge={**config, "sources": [_sample_path]},
-)
+    knowledge=["sample.pdf"],
+    knowledge=config)
 
 # Define a task for the agent
 knowledge_task = Task(
@@ -47,16 +39,8 @@ agents = AgentTeam(
     agents=[knowledge_agent],
     tasks=[knowledge_task],
     process="sequential",
+    user_id="user1"
 )
 
-if __name__ == "__main__":
-    if not os.environ.get("OPENAI_API_KEY"):
-        print("Set OPENAI_API_KEY to run this example.")
-        sys.exit(0)
-    if sys.platform == "win32":
-        try:
-            sys.stdout.reconfigure(encoding="utf-8")
-        except Exception:
-            pass
-    result = agents.start()
-    print(result)
+# Start execution
+result = agents.start()

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

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

examples/python/concepts/rag-agents.py

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

examples/python/concepts/repetitive-agents.py

@@ -16,7 +16,7 @@ agents = AgentTeam(
     agents=[agent],
     tasks=[task],
     process="workflow",
-    execution={"max_iter": 30},
+    max_iter=30
 )
 
 agents.start()

examples/python/custom_tools/example_latency_tracking.py

@@ -61,6 +61,7 @@ def example_with_wrappers():
         role="Data Analyst",
         goal="Analyze data and provide insights",
         llm="gpt-4o-mini",
+        request_id="request_2"  # Unique request ID
     )
     
     # Operations are automatically tracked

examples/python/custom_tools/tools_with_latency.py

@@ -49,18 +49,7 @@ def calculate_with_tracking(expression: str) -> str:
     time.sleep(0.05)
     
     try:
-        import ast, operator
-        _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                ast.Mod: operator.mod, ast.Pow: operator.pow,
-                ast.USub: operator.neg, ast.UAdd: operator.pos}
-        def _ev(n):
-            if isinstance(n, ast.Expression): return _ev(n.body)
-            if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-            if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-            if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-            raise ValueError(f"Unsupported: {ast.dump(n)}")
-        result = _ev(ast.parse(expression, mode="eval"))
+        result = eval(expression)
         return f"Result: {result}"
     except Exception as e:
         return f"Error: {str(e)}"

examples/python/data/cot-agents.py

@@ -132,8 +132,7 @@ agents = AgentTeam(
     agents=[qa_generator, total_questions_evaluator, cot_generator, upload_to_huggingface],
     tasks=[generate_task, evaluate_total_questions_task, generate_cot_task, upload_to_huggingface_task],
     process="workflow",
-    execution={"max_iter": 30},
-    output="minimal",
+    max_iter=30, output="minimal"
 )
 
 agents.start()

examples/python/failover_example.py

@@ -18,7 +18,7 @@ openai_profile = AuthProfile(
     provider="openai",
     api_key=os.environ.get("OPENAI_API_KEY", "sk-..."),
     priority=1,  # Highest priority
-    rate_limit_rpm=100,
+    rate_limit=100,
 )
 
 anthropic_profile = AuthProfile(
@@ -26,7 +26,7 @@ anthropic_profile = AuthProfile(
     provider="anthropic",
     api_key=os.environ.get("ANTHROPIC_API_KEY", "sk-ant-..."),
     priority=2,  # Second priority
-    rate_limit_rpm=50,
+    rate_limit=50,
 )
 
 groq_profile = AuthProfile(
@@ -34,7 +34,7 @@ groq_profile = AuthProfile(
     provider="groq",
     api_key=os.environ.get("GROQ_API_KEY", "gsk-..."),
     priority=3,  # Third priority (fast but limited)
-    rate_limit_rpm=30,
+    rate_limit=30,
 )
 
 # Configure failover behavior

examples/python/general/memory_example.py

@@ -5,7 +5,7 @@ import os
 def main():
     # Initialize memory config
     memory_config = {
-        "backend": "sqlite",
+        "provider": "rag",
         "use_embedding": True,
         "storage": {
             "type": "sqlite",
@@ -105,13 +105,13 @@ def main():
         agents=[researcher, retriever],
         tasks=[store_task, verify_task, query_task, query_both_task],  # Use same verbose level as memory
         memory={
+            "provider": "rag",
             "embedder": {
                 "provider": "openai",
                 "config": {
                     "model": "text-embedding-3-small"
                 }
-            },
-            "config": {"provider": "sqlite"},
+            }
         }
     )
 

examples/python/guardrails/production-guardrails-patterns.py

@@ -6,7 +6,6 @@ built-in validation and safety mechanisms for secure agent operations.
 """
 
 from praisonaiagents import Agent, Task, AgentTeam
-from praisonaiagents.config.feature_configs import GuardrailConfig
 from praisonaiagents.tools import internet_search
 
 print("=== Production Guardrails Patterns Example ===\n")
@@ -39,8 +38,8 @@ production_agent = Agent(
     goal="Generate safe, compliant content for production use",
     backstory="Production-ready agent with built-in safety and compliance checks",
     tools=[internet_search],
-    guardrails=GuardrailConfig(validator=content_guardrail, max_retries=3),
-)
+    guardrails=content_guardrail,
+    max_retries=3)
 
 # Create secure task with validation
 secure_task = Task(

examples/python/linear_agent_example.py

@@ -40,7 +40,7 @@ agent = Agent(
         "execute_command"
     ],
     memory=True,
-    web=True,
+    web_search=True,
     auto_approve_tools=True,
 )
 

examples/python/mcp/mcp-tools-server-sse.py

@@ -36,18 +36,10 @@ def calculate(expression: str) -> dict:
         expression: Math expression (e.g., "2 + 2 * 3")
     """
     try:
-        import ast, operator
-        _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                ast.Mod: operator.mod, ast.Pow: operator.pow,
-                ast.USub: operator.neg, ast.UAdd: operator.pos}
-        def _ev(n):
-            if isinstance(n, ast.Expression): return _ev(n.body)
-            if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-            if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-            if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-            raise ValueError(f"Unsupported: {ast.dump(n)}")
-        return {"expression": expression, "result": _ev(ast.parse(expression, mode="eval"))}
+        allowed = set("0123456789+-*/.() ")
+        if not all(c in allowed for c in expression):
+            return {"error": "Invalid characters"}
+        return {"expression": expression, "result": eval(expression)}
     except Exception as e:
         return {"error": str(e)}
 

examples/python/mcp/mcp-tools-server.py

@@ -47,18 +47,12 @@ def calculate(expression: str) -> Dict[str, Any]:
         The result of the calculation
     """
     try:
-        import ast, operator
-        _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                ast.Mod: operator.mod, ast.Pow: operator.pow,
-                ast.USub: operator.neg, ast.UAdd: operator.pos}
-        def _ev(n):
-            if isinstance(n, ast.Expression): return _ev(n.body)
-            if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-            if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-            if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-            raise ValueError(f"Unsupported: {ast.dump(n)}")
-        result = _ev(ast.parse(expression, mode="eval"))
+        # Safe evaluation of mathematical expressions
+        allowed_chars = set("0123456789+-*/.() ")
+        if not all(c in allowed_chars for c in expression):
+            return {"error": "Invalid characters in expression"}
+        
+        result = eval(expression)  # Use safer alternative in production
         return {"expression": expression, "result": result}
     except Exception as e:
         return {"error": str(e)}

examples/python/mcp_server_example.py

@@ -49,18 +49,13 @@ def calculate(expression: str) -> Dict[str, Any]:
         The result of the calculation
     """
     try:
-        import ast, operator
-        _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                ast.Mod: operator.mod, ast.Pow: operator.pow,
-                ast.USub: operator.neg, ast.UAdd: operator.pos}
-        def _ev(n):
-            if isinstance(n, ast.Expression): return _ev(n.body)
-            if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-            if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-            if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-            raise ValueError(f"Unsupported: {ast.dump(n)}")
-        result = _ev(ast.parse(expression, mode="eval"))
+        # Safe evaluation of mathematical expressions
+        # In production, use a proper math parser
+        allowed_chars = set("0123456789+-*/.() ")
+        if not all(c in allowed_chars for c in expression):
+            return {"error": "Invalid characters in expression"}
+        
+        result = eval(expression)  # Note: Use safer alternative in production
         return {"expression": expression, "result": result}
     except Exception as e:
         return {"error": str(e)}

examples/python/memory/advanced-graph-memory-integration.py

@@ -12,8 +12,8 @@ print("=== Advanced Graph Memory Integration Example ===\n")
 
 # Configure simple graph memory
 memory_config = {
+    "provider": "mem0",
     "config": {
-        "provider": "mem0",
         "vector_store": {
             "provider": "chroma",
             "config": {
@@ -60,8 +60,8 @@ query_task = Task(
 agents_system = AgentTeam(
     agents=[knowledge_agent],
     tasks=[build_task, query_task],
-    memory=memory_config,
-    output="verbose",
+    memory=True,
+    memory=memory_config, output="verbose"
 )
 
 print("Starting graph memory demonstration...")

examples/python/memory/advanced-memory-search-optimization.py

@@ -12,8 +12,8 @@ print("=== Advanced Memory Search and Optimization Example ===\n")
 
 # Configure optimized memory settings
 memory_config = {
+    "provider": "mem0",
     "config": {
-        "provider": "mem0",
         "vector_store": {
             "provider": "chroma",
             "config": {
@@ -61,8 +61,8 @@ search_task = Task(
 agents_system = AgentTeam(
     agents=[research_agent],
     tasks=[build_memory_task, search_task],
-    memory=memory_config,
-    output="verbose",
+    memory=True,
+    memory=memory_config, output="verbose"
 )
 
 print("Starting memory search optimization demonstration...")

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

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

examples/python/memory/graph-memory-agent.py

@@ -152,7 +152,8 @@ def main():
     agents_system = AgentTeam(
         agents=[researcher],
         tasks=[build_knowledge_task, query_knowledge_task, expand_knowledge_task],
-        memory=memory_config,
+        memory=True,
+        memory=memory_config
     )
     
     print("\n🚀 Starting graph memory demonstration...")

examples/python/memory_with_tracing_example.py

@@ -30,7 +30,7 @@ def main():
     try:
         # Create memory instance
         memory = Memory(config={
-            "backend": "sqlite",
+            "provider": "rag",
             "use_embedding": False,
             "agent_name": "demo_agent",  # Used in trace events
         })

examples/python/models/deepseek/deepseek-rag-agents-streamlit.py

@@ -32,8 +32,9 @@ def init_agent():
     return Agent(
         name="Knowledge Agent",
         instructions="You answer questions based on the provided knowledge.",
-        knowledge={**config, "sources": ["kag-research-paper.pdf"]},
-        memory={"user_id": "user1"},
+        knowledge=["kag-research-paper.pdf"],
+        knowledge=config,
+        user_id="user1",
         llm="deepseek-r1"
     )
 

examples/python/models/deepseek/deepseek-rag-agents.py

@@ -30,12 +30,9 @@ config = {
 agent = Agent(
     name="Knowledge Agent",
     instructions="You answer questions based on the provided knowledge.",
-    knowledge={
-        "sources": ["kag-research-paper.pdf"],
-        "vector_store": config["vector_store"],
-        "embedder": config["embedder"],
-    },
-    memory={"user_id": "user1"},
+    knowledge=["kag-research-paper.pdf"], # Indexing
+    knowledge=config,
+    user_id="user1",
     llm="deepseek-r1"
 )
 

examples/python/models/googlegemini/any-llm-agents-advanced.py

@@ -31,9 +31,10 @@ llm_config = {
 agent = Agent(
     instructions="You are a helpful Assistant specialized in scientific explanations. "
                 "Provide clear, accurate, and engaging responses.",
-    llm=llm_config,
-    output={"markdown": True},
-    reflection=True,
+    llm=llm_config,                  # Pass the detailed configuration
+    # Enable detailed output
+    markdown=True,                   # Format responses in markdown
+    reflection=True               # Enable self-reflection
 )
 
 # Test the agent

examples/python/mongodb/mongodb_comprehensive_example.py

@@ -189,8 +189,8 @@ def main():
     business_system = AgentTeam(
         agents=[data_manager, knowledge_curator, business_analyst, customer_service],
         tasks=business_tasks,
-        memory=mongodb_memory_config,
-        output="verbose",
+        memory=True,
+        memory=mongodb_memory_config, output="verbose"
     )
     
     # Execute the comprehensive business pipeline

examples/python/mongodb/mongodb_knowledge_example.py

@@ -55,10 +55,8 @@ def main():
         retrieve information from a comprehensive MongoDB knowledge base. You excel 
         at finding relevant information, synthesizing knowledge from multiple sources, 
         and providing accurate, context-aware responses.""",
-        knowledge={
-            **mongodb_knowledge_config,
-            "sources": [os.path.join(os.path.dirname(__file__), "llms.md")],
-        },
+        knowledge=mongodb_knowledge_config,
+        knowledge=[os.path.join(os.path.dirname(__file__), "llms.md")],
         memory=True,
         llm="gpt-4o-mini"
     )

examples/python/mongodb/mongodb_memory_example.py

@@ -28,8 +28,8 @@ if not os.environ.get("OPENAI_API_KEY"):
 def main():
     # MongoDB memory configuration
     mongodb_memory_config = {
+        "provider": "mongodb",
         "config": {
-            "provider": "mongodb",
             "connection_string": "mongodb://localhost:27017/",  # Replace with your MongoDB connection string
             "database": "praisonai_memory",
             "use_vector_search": True,  # Enable Atlas Vector Search (requires MongoDB Atlas)
@@ -93,8 +93,8 @@ def main():
     research_system = AgentTeam(
         agents=[research_agent],
         tasks=research_tasks,
-        memory=mongodb_memory_config,
-        output="verbose",
+        memory=True,
+        memory=mongodb_memory_config, output="verbose"
     )
     
     # Execute the research pipeline

examples/python/rag/agent_vs_auto_rag.py

@@ -50,7 +50,7 @@ agent = Agent(
     name="TechSupport",
     instructions="You are a technical support assistant.",
     knowledge=[sample_doc],
-    memory={"user_id": "comparison_user"},
+    user_id="comparison_user",
     llm="openai/gpt-4o-mini",
     output="minimal",
 )

examples/python/rag/auto_rag_agent_basic.py

@@ -12,7 +12,6 @@ Environment:
 """
 
 import os
-import tempfile
 from praisonaiagents import Agent, AutoRagAgent
 
 # Ensure API key is set
@@ -21,8 +20,8 @@ if not os.environ.get("OPENAI_API_KEY"):
     exit(1)
 
 # Create a sample document for testing
-sample_doc = os.path.join(tempfile.gettempdir(), "sample_doc.txt")
-with open(sample_doc, "w", encoding="utf-8") as f:
+sample_doc = "/tmp/sample_doc.txt"
+with open(sample_doc, "w") as f:
     f.write("""
 PraisonAI Framework Documentation
 
@@ -45,7 +44,7 @@ agent = Agent(
     name="DocBot",
     instructions="You are a helpful documentation assistant. Answer questions based on the provided context.",
     knowledge=[sample_doc],
-    memory={"user_id": "example_user"},
+    user_id="example_user",  # Required for RAG retrieval
     llm="openai/gpt-4o-mini",
     output="minimal",
 )

examples/python/rag/auto_rag_agent_policy.py

@@ -38,7 +38,7 @@ agent = Agent(
     name="PolicyBot",
     instructions="You are a customer service assistant.",
     knowledge=[sample_doc],
-    memory={"user_id": "policy_user"},
+    user_id="policy_user",
     llm="openai/gpt-4o-mini",
     output="minimal",
 )

examples/python/rag/rag_basic.py

@@ -44,7 +44,7 @@ agent = Agent(
     name="MLExpert",
     instructions="You are a machine learning expert. Answer questions based on the provided context.",
     knowledge=[sample_doc],
-    memory={"user_id": "rag_user"},
+    user_id="rag_user",
     llm="openai/gpt-4o-mini",
     output="minimal",
 )

examples/python/rag/rag_hybrid.py

@@ -59,7 +59,7 @@ agent = Agent(
     name="APIDocBot",
     instructions="You are an API documentation assistant.",
     knowledge=[sample_doc],
-    memory={"user_id": "hybrid_user"},
+    user_id="hybrid_user",
     llm="openai/gpt-4o-mini",
     output="minimal",
 )

examples/python/rag/rag_with_citations.py

@@ -44,7 +44,7 @@ agent = Agent(
     name="ResearchBot",
     instructions="You are a research assistant. Always cite your sources.",
     knowledge=[doc1, doc2],
-    memory={"user_id": "citation_user"},
+    user_id="citation_user",
     llm="openai/gpt-4o-mini",
     output="minimal",
 )

examples/python/session/session_persistence_example.py

@@ -28,7 +28,7 @@ def example_basic_persistence():
     agent = Agent(
         name="Assistant",
         instructions="You are a helpful assistant. Be very brief.",
-        memory={"session_id": session_id},
+        session_id=session_id,
         output="minimal",
     )
     
@@ -59,7 +59,7 @@ def example_session_restoration():
     agent1 = Agent(
         name="MemoryBot",
         instructions="You are a helpful assistant. Be very brief.",
-        memory={"session_id": session_id},
+        session_id=session_id,
         output="minimal",
     )
     
@@ -72,7 +72,7 @@ def example_session_restoration():
     agent2 = Agent(
         name="MemoryBot",
         instructions="You are a helpful assistant. Be very brief.",
-        memory={"session_id": session_id},
+        session_id=session_id,
         output="minimal",
     )
     

examples/python/sessions/comprehensive-session-management.py

@@ -172,7 +172,7 @@ print("="*70)
 # Create sessions for different users working on the same project
 reviewer_session = Session(
     session_id="peer_review_001",
-    user_id="user_reviewer_01",
+    user_id="user_reviewer_01",  # Different user
     storage_path=session_dir
 )
 

examples/python/stateful/memory-quality-example.py

@@ -14,12 +14,13 @@ def main():
     
     # Initialize memory with quality-based configuration
     memory_config = {
-        "backend": "sqlite",
-        "user_id": "quality_demo_user",
+        "provider": "rag",
+        "use_embedding": True,
+        "rag_db_path": "quality_memory_db"
     }
     
     memory = Memory(config=memory_config, verbose=5)
-    print(f"📦 Initialized memory system with backend: {memory_config['backend']}")
+    print(f"📦 Initialized memory system with provider: {memory_config['provider']}")
     
     # Create agent with memory
     agent = Agent(
@@ -27,6 +28,7 @@ def main():
         role="Information Quality Specialist",
         instructions="Store and retrieve information with quality assessment",
         memory=memory,
+        user_id="quality_demo_user"
     )
     
     print(f"🤖 Created agent: {agent.name}")

examples/python/stateful/session-example.py

@@ -15,7 +15,7 @@ def main():
     # Create a session with persistent storage
     session = Session(
         session_id="demo_chat_001",
-        user_id="demo_user",
+        user_id="demo_user"
     )
     
     print(f"📝 Created session: {session}")

examples/python/stateful/workflow-state-example.py

@@ -88,8 +88,9 @@ def main():
     workflow = AgentTeam(
         agents=[researcher, analyzer, writer],
         tasks=[research_task, analysis_task, report_task],
-        memory={"user_id": "research_project_001"},
+        memory=True,
         process="workflow",
+        user_id="research_project_001",
     )
     
     print(f"🤖 Created workflow with {len(workflow.agents)} agents and {len(workflow.tasks)} tasks")

examples/python/token-metrics/01_basic_token_tracking.py

@@ -14,7 +14,7 @@ def main():
         goal="Find information about a topic",
         backstory="You are an expert researcher who finds comprehensive information.",
         llm="gpt-4o-mini",  # Use a cost-effective model for examples
-        output={"metrics": True}  # Token metrics via consolidated output config
+        metrics=True  # 🎯 SIMPLIFIED: Just add this parameter!
     )
     
     # Create a simple task

examples/python/token-metrics/02_session_metrics.py

@@ -14,7 +14,7 @@ def main():
         goal="Analyze data and provide insights",
         backstory="You are an experienced data analyst with expertise in market research.",
         llm="gpt-4o-mini",
-        output={"metrics": True}
+        metrics=True  # 🎯 Enable metrics tracking
     )
     
     writer_agent = Agent(
@@ -23,7 +23,7 @@ def main():
         goal="Create well-structured content",
         backstory="You are a skilled technical writer who creates clear, engaging content.",
         llm="gpt-4o-mini",
-        output={"metrics": True}
+        metrics=True  # 🎯 Enable metrics tracking
     )
     
     # Create multiple tasks

examples/python/token-metrics/03_cost_estimation.py

@@ -14,7 +14,7 @@ def main():
         goal="Conduct comprehensive market research",
         backstory="You are a seasoned market researcher with 10+ years of experience.",
         llm="gpt-4o-mini",
-        output={"metrics": True}
+        metrics=True  # 🎯 Enable automatic metrics tracking
     )
     
     strategy_agent = Agent(
@@ -23,7 +23,7 @@ def main():
         goal="Develop actionable business strategies",
         backstory="You are a strategic consultant who translates research into business plans.",
         llm="gpt-4o-mini",
-        output={"metrics": True}
+        metrics=True  # 🎯 Enable automatic metrics tracking
     )
     
     # Create tasks that will generate substantial token usage

examples/python/tools/exa-tool/rag_examples/agentic_rag/rag_agent.py

@@ -22,8 +22,7 @@ rag_agent = Agent(
     
     You can use the internet_search function to search the web when needed.""",
     llm="gpt-4o",
-    output={"markdown": True},
-)
+    markdown=True)
 
 if __name__ == "__main__":
     print("🤖 Thai Recipe RAG Agent is ready!")

examples/python/ui/ollama-rag-agents-streamlit.py

@@ -32,8 +32,9 @@ def init_agent():
     return Agent(
         name="Knowledge Agent",
         instructions="You answer questions based on the provided knowledge.",
-        knowledge={**config, "sources": ["kag-research-paper.pdf"]},
-        memory={"user_id": "user1"},
+        knowledge=["kag-research-paper.pdf"],
+        knowledge=config,
+        user_id="user1",
         llm="deepseek-r1"
     )
 

examples/python/usecases/domain-context-solution.py

@@ -264,12 +264,13 @@ agents = AgentTeam(
     tasks=[tool_query_task, pentester_analysis_task, ciso_review_task],
     process="hierarchical",
     manager_llm="gpt-4o-mini",
+    memory=True,  # Enable shared memory for domain context
     memory={
-        "backend": "sqlite",
+        "provider": "rag",
         "config": {
             "collection_name": f"{domain.replace('.', '_')}_security_analysis"
-        },
-    },
+        }
+    }
 )
 
 # Main Execution Workflow

examples/python/workflows/workflow_conditional.py

@@ -5,7 +5,7 @@ Demonstrates using should_run to conditionally execute agent steps
 based on input or previous results.
 """
 
-from praisonaiagents import Agent, AgentFlow, Task
+from praisonaiagents import Agent, Workflow, Task
 
 # Condition functions
 def is_technical(ctx) -> bool:

examples/python/workflows/workflow_mixed_steps.py

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

examples/rag/external_sources.py

@@ -207,7 +207,7 @@ def api_augmented_rag():
         instructions="""You are a financial assistant with access to market APIs.
         Provide accurate financial information and context.
         Always note that prices are subject to change.""",
-        memory={"user_id": "api_demo"},
+        user_id="api_demo"
     )
     
     queries = [

examples/retrieval/basic_retrieval.py

@@ -51,15 +51,13 @@ print("=" * 60)
 agent = Agent(
     name="Knowledge Agent",
     instructions="You are a helpful assistant that answers questions based on the provided knowledge. Be concise.",
-    knowledge={
-        "sources": [".praison/test_docs/overview.txt"],
-        "config": {
-            "policy": "always",
-            "top_k": 3,
-            "citations": True,
-            "max_context_tokens": 2000,
-        },
-    },
+    knowledge=[".praison/test_docs/overview.txt"],
+    retrieval_config={
+        "policy": "always",      # Always retrieve for this demo
+        "top_k": 3,              # Get top 3 chunks
+        "citations": True,       # Include citations
+        "max_context_tokens": 2000,
+    }
 )
 
 # Test 1: Basic chat with retrieval

examples/skills/basic_skill_usage.py

@@ -60,7 +60,7 @@ print("=" * 50)
 agent_with_discovery = Agent(
     name="Multi-Skill Agent",
     instructions="You are a versatile assistant with multiple skills.",
-    skills=["./"],  # Scan current directory for skill subdirectories
+    skills_dirs=["./"],  # Scan current directory for skill subdirectories
 )
 
 if agent_with_discovery.skill_manager:

examples/yaml/agent-with-mcp.yaml

@@ -1,40 +0,0 @@
-# Example: Agent with MCP Tool Integration
-#
-# This example shows how to configure an agent with MCP (Model Context Protocol)
-# servers for additional tool capabilities.
-#
-# Usage:
-#   praisonai workflow run --file examples/yaml/agent-with-mcp.yaml
-
-agent:
-  name: mcp_assistant
-  instructions: |
-    You are a helpful assistant with access to filesystem and time tools.
-    Use the MCP tools when needed to help users with file operations or time queries.
-  llm: gpt-4o-mini
-
-# MCP server configuration
-mcp:
-  servers:
-    filesystem:
-      # Filesystem access for the agent
-      command: npx
-      args:
-        - "-y"
-        - "@modelcontextprotocol/server-filesystem"
-        - "."
-      env: {}
-      enabled: true
-    
-    time:
-      # Time and date utilities
-      command: npx
-      args:
-        - "-y" 
-        - "@modelcontextprotocol/server-time"
-      env: {}
-      enabled: true
-
-# NOTE: Per-server tool filtering (tools.include/exclude) is not yet supported
-# by the MCP server config schema. Tool filtering should be applied after
-# calling mcp.get_tools() in your application code.

examples/yaml/workflows/example_usage.py

@@ -93,7 +93,7 @@ def example_3_execute_yaml_directly():
     result = manager.execute_yaml(
         yaml_file,
         input_data="The future of AI",
-        verbose=True,
+        output="verbose"
     )
     
     print(f"Status: {result['status']}")

src/praisonai-agents/.gitignore

@@ -95,5 +95,4 @@ chroma_db/
 .windsurf
 .ruff_cache
 *.sfdx
-.sfdx/
-.cursor/
+.sfdx/

src/praisonai-agents/benchmarks/real_benchmark.py

@@ -79,20 +79,10 @@ def get_time(timezone: Literal["est", "pst"]) -> str:
 
 
 def calculate(expression: str) -> str:
-    """Calculate a math expression safely."""
+    """Calculate a math expression."""
     try:
-        import ast, operator
-        _OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,
-                ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv,
-                ast.Mod: operator.mod, ast.Pow: operator.pow,
-                ast.USub: operator.neg, ast.UAdd: operator.pos}
-        def _ev(n):
-            if isinstance(n, ast.Expression): return _ev(n.body)
-            if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return n.value
-            if isinstance(n, ast.UnaryOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.operand))
-            if isinstance(n, ast.BinOp) and type(n.op) in _OPS: return _OPS[type(n.op)](_ev(n.left), _ev(n.right))
-            raise ValueError(f"Unsupported: {ast.dump(n)}")
-        return f"Result: {_ev(ast.parse(expression, mode='eval'))}"
+        result = eval(expression)
+        return f"Result: {result}"
     except Exception:
         return "Error: Invalid expression"
 

src/praisonai-agents/praisonaiagents/__init__.py

@@ -384,7 +384,6 @@ _LAZY_IMPORTS = {
     'MultiAgentExecutionConfig': ('praisonaiagents.config.feature_configs', 'MultiAgentExecutionConfig'),
     'MultiAgentPlanningConfig': ('praisonaiagents.config.feature_configs', 'MultiAgentPlanningConfig'),
     'MultiAgentMemoryConfig': ('praisonaiagents.config.feature_configs', 'MultiAgentMemoryConfig'),
-    'ToolSearchConfig': ('praisonaiagents.config.feature_configs', 'ToolSearchConfig'),
     
     # Parameter resolver
     'resolve': ('praisonaiagents.config.param_resolver', 'resolve'),

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

@@ -556,7 +556,6 @@ class Agent(SandboxMixin, UnifiedExecutionMixin, ToolExecutionMixin, ChatHandler
         backend: Optional[Any] = None,  # External managed agent backend (e.g., ManagedAgentIntegration)
         cli_backend: Optional[Union[str, Any]] = None,  # CLI backend for delegating turns (e.g., "claude-code")
         interrupt_controller: Optional['InterruptController'] = None,  # G2: Cooperative cancellation
-        tool_search: Optional[Union[bool, str, Dict[str, Any], 'ToolSearchConfig']] = False,  # Progressive tool disclosure
         sandbox: Optional[Union[bool, 'SandboxConfig']] = None,  # Sandbox for safe code execution
     ):
         """Initialize an Agent instance.
@@ -661,14 +660,6 @@ class Agent(SandboxMixin, UnifiedExecutionMixin, ToolExecutionMixin, ChatHandler
                 When provided, agent delegates entire conversation turns to the CLI tool
                 instead of using the built-in LLM. Enables session continuity and 
                 tool integration through external AI coding assistants.
-            tool_search: Progressive tool disclosure configuration. Accepts:
-                - bool: False=disabled (default), True=auto mode
-                - str: Mode ("auto", "on", "off")  
-                - Dict[str, Any]: Config overrides (e.g. {"threshold_pct": 15})
-                - ToolSearchConfig: Custom configuration
-                When enabled, replaces large tool schemas with bridge tools (tool_search,
-                tool_describe, tool_call) to save context. Core SDK tools never defer.
-                Auto mode activates based on token threshold. Opt-in feature.
 
         Raises:
             ValueError: If all of name, role, goal, backstory, and instructions are None.
@@ -1439,34 +1430,6 @@ class Agent(SandboxMixin, UnifiedExecutionMixin, ToolExecutionMixin, ChatHandler
             web_search = False
             web_fetch = False
         
-        # ─────────────────────────────────────────────────────────────────────
-        # Resolve TOOL_SEARCH param
-        # ─────────────────────────────────────────────────────────────────────
-        # Fast path: None/False -> disabled (zero overhead)
-        if tool_search is None or tool_search is False:
-            self._tool_search_config = None
-        elif tool_search is True:
-            # True -> auto mode with defaults
-            from ..tools.tool_search import ToolSearchConfig as _ToolSearchConfig
-            self._tool_search_config = _ToolSearchConfig(enabled="auto")
-        elif isinstance(tool_search, str):
-            # String mode ("auto", "on", "off")
-            from ..tools.tool_search import ToolSearchConfig as _ToolSearchConfig
-            self._tool_search_config = _ToolSearchConfig.from_raw(tool_search)
-        elif isinstance(tool_search, dict):
-            # Dict -> config overrides
-            from ..tools.tool_search import ToolSearchConfig as _ToolSearchConfig
-            self._tool_search_config = _ToolSearchConfig(**tool_search)
-        else:
-            from ..tools.tool_search import ToolSearchConfig as _ToolSearchConfig
-            if isinstance(tool_search, _ToolSearchConfig):
-                self._tool_search_config = tool_search
-            else:
-                raise TypeError(
-                    "tool_search must be False/None, True, a mode string, "
-                    "a dict of ToolSearchConfig fields, or ToolSearchConfig"
-                )
-        
         # ============================================================
         # END CONSOLIDATED PARAMS EXTRACTION
         # ============================================================
@@ -2063,7 +2026,6 @@ Your Goal: {self.goal}
             'skills': getattr(self, '_skills_config', None),
             'approval': getattr(self, '_approval_config', None),
             'learn': getattr(self, '_learn_config', None),
-            'tool_search': getattr(self, '_tool_search_config', None),
             
             # Tool configuration
             'tool_timeout': getattr(self, '_tool_timeout', None),

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

@@ -331,7 +331,6 @@ Your Goal: {self.goal}"""
         - Callable functions
         - String function names
         - Objects with to_openai_tool() method
-        - Tool Search progressive disclosure (if enabled)
         
         Args:
             tools: List of tools in various formats or None to use self.tools
@@ -345,14 +344,10 @@ Your Goal: {self.goal}"""
         if not tools:
             return []
         
-        # Check cache first - include tool_search config in cache key for safety
+        # Check cache first
         tools_key = self._get_tools_cache_key(tools)
-        tool_search_enabled = getattr(self, '_tool_search_config', None) is not None
-        cache_key = f"{tools_key}:tool_search={tool_search_enabled}"
-        cached_entry = self._cache_get(self._formatted_tools_cache, cache_key)
-        if cached_entry is not None:
-            cached_tools, cached_metadata = cached_entry
-            self._tool_search_metadata = cached_metadata
+        cached_tools = self._cache_get(self._formatted_tools_cache, tools_key)
+        if cached_tools is not None:
             return cached_tools
             
         formatted_tools = []
@@ -404,46 +399,9 @@ Your Goal: {self.goal}"""
                 logging.error(f"Tools are not JSON serializable: {e}")
                 return []
         
-        # Apply tool search assembly if enabled (after formatting, before caching)
-        if hasattr(self, '_tool_search_config') and self._tool_search_config is not None:
-            try:
-                from ..tools.tool_search import assemble_tool_defs
-                # Get context length from LLM config if available
-                context_length = getattr(self, '_context_window_size', None)
-                
-                # Assemble tools with bridge mode check
-                assembled_tools, metadata = assemble_tool_defs(
-                    tool_defs=formatted_tools,
-                    config=self._tool_search_config,
-                    context_length=context_length
-                )
-                
-                # Store metadata for bridge tool dispatch
-                self._tool_search_metadata = metadata
-                formatted_tools = assembled_tools
-                
-            except ImportError:
-                # Tool search module not available, continue with original tools
-                logging.warning("Tool search requested but tool_search module not available")
-        
-        # Strip __praisonai_deferrable__ from provider-facing tool payloads
-        # Keep the marker only for internal tool classification
-        cleaned_tools = []
-        for tool in formatted_tools:
-            if isinstance(tool, dict) and "__praisonai_deferrable__" in tool:
-                tool_copy = tool.copy()
-                tool_copy.pop("__praisonai_deferrable__", None)
-                cleaned_tools.append(tool_copy)
-            else:
-                cleaned_tools.append(tool)
-        
-        # Cache the formatted tools with LRU eviction, including tool search metadata
-        self._cache_put(
-            self._formatted_tools_cache,
-            cache_key,
-            (cleaned_tools, getattr(self, "_tool_search_metadata", None)),
-        )
-        return cleaned_tools
+        # Cache the formatted tools with LRU eviction
+        self._cache_put(self._formatted_tools_cache, tools_key, formatted_tools)
+        return formatted_tools
 
     def _build_multimodal_prompt(
         self, 
@@ -549,7 +507,7 @@ Your Goal: {self.goal}"""
             emit_events=True
         )
 
-    def _chat_completion(self, messages, temperature=1.0, tools=None, stream=None, reasoning_steps=False, task_name=None, task_description=None, task_id=None, response_format=None, _retry_depth=0):
+    def _chat_completion(self, messages, temperature=1.0, tools=None, stream=True, reasoning_steps=False, task_name=None, task_description=None, task_id=None, response_format=None, _retry_depth=0):
         start_time = time.time()
 
         # --- Context compaction (opt-in via ExecutionConfig.context_compaction) ---
@@ -619,39 +577,6 @@ Your Goal: {self.goal}"""
         # Use the new _format_tools_for_completion helper method
         formatted_tools = self._format_tools_for_completion(tools)
 
-        # Smart fallback for streaming: try streaming first, fall back to non-streaming if unsupported
-        if stream is None:
-            # Auto-detect: prefer streaming for better UX, fallback if adapter doesn't support it
-            try:
-                # First attempt: try with streaming enabled for better user experience
-                stream_callback = self.stream_emitter.emit if hasattr(self, 'stream_emitter') else None
-                final_response = self._execute_unified_chat_completion(
-                    messages=messages,
-                    temperature=temperature,
-                    tools=formatted_tools,
-                    stream=True,  # Try streaming first
-                    reasoning_steps=reasoning_steps,
-                    task_name=task_name,
-                    task_description=task_description,
-                    task_id=task_id,
-                    response_format=response_format,
-                    stream_callback=stream_callback,
-                    emit_events=True
-                )
-                return final_response
-            except ValueError as e:
-                if "Streaming is not supported" in str(e):
-                    # Fallback: retry with non-streaming for sync adapters
-                    logging.debug(f"{self.name}: Streaming not supported by adapter, falling back to non-streaming")
-                    stream = False  # Set for the main execution below
-                else:
-                    raise  # Re-raise if it's a different ValueError
-            except Exception:
-                # For any other exception, fall back to non-streaming
-                logging.debug(f"{self.name}: Streaming attempt failed, falling back to non-streaming")
-                stream = False  # Set for the main execution below
-        
-        # If stream was explicitly set or fallback occurred, use the specified/fallback value
         try:
             # NEW: Unified protocol dispatch path (Issue #1304, #1362)
             # UNIFIED: Single protocol-driven dispatch path (fixes DRY violation)
@@ -849,7 +774,7 @@ Your Goal: {self.goal}"""
         messages, 
         temperature=1.0, 
         tools=None, 
-        stream=None,
+        stream=True,
         reasoning_steps=False,
         task_name=None,
         task_description=None,
@@ -881,47 +806,6 @@ Your Goal: {self.goal}"""
             # Cache the dispatcher
             self._unified_dispatcher = dispatcher
         
-        # Smart fallback for streaming: try streaming first, fall back to non-streaming if unsupported
-        if stream is None:
-            # Auto-detect: prefer streaming for better UX, fallback if adapter doesn't support it
-            try:
-                # First attempt: try with streaming enabled for better user experience
-                if stream_callback is None and hasattr(self, 'stream_emitter'):
-                    stream_callback = getattr(self.stream_emitter, 'emit', None)
-                final_response = self._unified_dispatcher.chat_completion(
-                    messages=messages,
-                    tools=tools,
-                    tool_choice=getattr(self, 'tool_choice', None),
-                    temperature=temperature,
-                    max_tokens=getattr(self, 'max_tokens', None),
-                    stream=True,  # Try streaming first
-                    response_format=response_format,
-                    execute_tool_fn=getattr(self, 'execute_tool', None),
-                    console=self.console if (self.verbose or True) else None,  # Enable console for streaming
-                    display_fn=self._display_generating if self.verbose else None,
-                    stream_callback=stream_callback,
-                    emit_events=emit_events,
-                    verbose=self.verbose,
-                    max_iterations=10,
-                    reasoning_steps=reasoning_steps,
-                    task_name=task_name,
-                    task_description=task_description,
-                    task_id=task_id,
-                    agent_name=getattr(self, 'name', 'assistant')
-                )
-                return final_response
-            except ValueError as e:
-                if "Streaming is not supported" in str(e):
-                    # Fallback: retry with non-streaming for sync adapters
-                    logging.debug(f"Agent: Streaming not supported by adapter, falling back to non-streaming")
-                    stream = False  # Set for the main execution below
-                else:
-                    raise  # Re-raise if it's a different ValueError
-            except Exception:
-                # For any other exception, fall back to non-streaming
-                logging.debug(f"Agent: Streaming attempt failed, falling back to non-streaming")
-                stream = False  # Set for the main execution below
-
         # Execute unified dispatch with all necessary parameters
         # Includes all parameters from both legacy paths to ensure full compatibility
         try:
@@ -973,7 +857,7 @@ Your Goal: {self.goal}"""
         messages, 
         temperature=1.0, 
         tools=None, 
-        stream=True,  # Async methods keep stream=True default (async adapters support streaming vs sync smart fallback)
+        stream=True,
         reasoning_steps=False,
         task_name=None,
         task_description=None,

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

@@ -135,12 +135,6 @@ class ToolExecutionMixin:
         """
         logging.debug(f"{self.name} executing tool {function_name} with arguments: {arguments}")
         
-        # Handle bridge tool unwrapping BEFORE trace/stream/hooks (design invariant #6)
-        # Only intercept when tool_search is active; otherwise fall through to real tool execution
-        if (getattr(self, '_tool_search_config', None) is not None and
-                function_name in ("tool_search", "tool_describe", "tool_call")):
-            return self._handle_bridge_tool_call(function_name, arguments, tool_call_id)
-        
         # NOTE: tool_call callback is triggered by display_tool_call in openai_client.py
         # Do NOT call it here to avoid duplicate output
         
@@ -960,97 +954,3 @@ class ToolExecutionMixin:
     def pending_approval_count(self) -> int:
         """Number of approval requests still waiting."""
         return len(self._pending_approvals)
-    
-    def _handle_bridge_tool_call(self, function_name: str, arguments: Dict[str, Any], tool_call_id: Optional[str] = None) -> Any:
-        """
-        Handle bridge tool calls (tool_search, tool_describe, tool_call).
-        
-        This implements the tool search unwrapping logic before trace/stream/hooks
-        as required by design invariant #6.
-        
-        Args:
-            function_name: Bridge tool name
-            arguments: Arguments passed to bridge tool
-            tool_call_id: Optional tool call ID
-            
-        Returns:
-            Result of bridge tool execution or unwrapped real tool call
-        """
-        # Ensure tool search metadata is available
-        if not hasattr(self, '_tool_search_metadata') or self._tool_search_metadata is None:
-            return "Tool search not available or not in bridge mode"
-        
-        metadata = self._tool_search_metadata
-        
-        # Check if we're in bridge mode
-        if not metadata.get("bridge_mode", False):
-            return "Tool search not in bridge mode"
-        
-        # Get deferrable tools from metadata
-        deferrable_tools = metadata.get("deferrable_tools", [])
-        
-        if function_name == "tool_search":
-            # Handle tool_search bridge call
-            try:
-                from ..tools.tool_search import dispatch_tool_search
-                query = arguments.get("query", "")
-                limit = arguments.get("limit", None)
-                
-                result = dispatch_tool_search(
-                    query=query,
-                    limit=limit, 
-                    deferrable_tools=deferrable_tools,
-                    config=self._tool_search_config
-                )
-                return json.dumps(result, indent=2)
-            except ImportError:
-                return "Tool search module not available"
-            except Exception as e:
-                logging.error(f"Error in tool_search: {e}")
-                return f"Error searching tools: {e}"
-        
-        elif function_name == "tool_describe":
-            # Handle tool_describe bridge call
-            try:
-                from ..tools.tool_search import dispatch_tool_describe
-                tool_name = arguments.get("tool_name", "")
-                
-                result = dispatch_tool_describe(
-                    tool_name=tool_name,
-                    deferrable_tools=deferrable_tools
-                )
-                return json.dumps(result, indent=2)
-            except ImportError:
-                return "Tool search module not available"
-            except Exception as e:
-                logging.error(f"Error in tool_describe: {e}")
-                return f"Error describing tool: {e}"
-        
-        elif function_name == "tool_call":
-            # Handle tool_call bridge - unwrap and recurse with real tool
-            try:
-                from ..tools.tool_search import resolve_underlying_call
-                
-                # Unwrap the real tool call
-                real_function_name, real_arguments = resolve_underlying_call(function_name, arguments)
-                
-                # Validate that the real tool is in our deferrable set (security check)
-                deferrable_names = {
-                    tool_def.get("function", {}).get("name", "") 
-                    for tool_def in deferrable_tools
-                }
-                
-                if real_function_name not in deferrable_names:
-                    return f"Tool '{real_function_name}' is not available for execution"
-                
-                # Recursively execute the real tool (this will go through normal execution path)
-                return self.execute_tool(real_function_name, real_arguments, tool_call_id)
-                
-            except ImportError:
-                return "Tool search module not available"
-            except Exception as e:
-                logging.error(f"Error in tool_call unwrap: {e}")
-                return f"Error executing tool: {e}"
-        
-        else:
-            return f"Unknown bridge tool: {function_name}"

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

@@ -39,7 +39,6 @@ __all__ = [
     "CachingConfig",
     "HooksConfig",
     "SkillsConfig",
-    "ToolSearchConfig",
     # Type aliases
     "MemoryParam",
     "KnowledgeParam",
@@ -123,7 +122,6 @@ _MODULE_MAP = {
     "CachingConfig": "feature_configs",
     "HooksConfig": "feature_configs",
     "SkillsConfig": "feature_configs",
-    "ToolSearchConfig": "feature_configs",
     "MemoryParam": "feature_configs",
     "KnowledgeParam": "feature_configs",
     "PlanningParam": "feature_configs",

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

@@ -8,7 +8,6 @@ Provides dataclasses for consolidated feature configuration:
 - ReflectionConfig: Self-reflection settings
 - GuardrailConfig: Safety and validation
 - WebConfig: Web search and fetch
-- ToolSearchConfig: Progressive tool disclosure
 
 All configs follow the agent-centric pattern:
 - False: Feature disabled (zero overhead)
@@ -17,22 +16,21 @@ All configs follow the agent-centric pattern:
 - Instance: Pre-configured manager/engine
 
 Usage:
-    from praisonaiagents import Agent, MemoryConfig, KnowledgeConfig, ToolSearchConfig
+    from praisonaiagents import Agent, MemoryConfig, KnowledgeConfig
     
     # Simple enable
-    agent = Agent(instructions="...", memory=True, tool_search=True)
+    agent = Agent(instructions="...", memory=True)
     
     # With config
     agent = Agent(
         instructions="...",
         memory=MemoryConfig(backend="redis", user_id="user123"),
         knowledge=KnowledgeConfig(sources=["docs/"], rerank=True),
-        tool_search=ToolSearchConfig(enabled="auto", threshold_pct=15),
     )
 """
 
 from dataclasses import dataclass, field
-from typing import Dict, List, Any, Optional, Callable, Tuple, Union, FrozenSet
+from typing import Dict, List, Any, Optional, Callable, Tuple, Union
 from enum import Enum
 
 # Import AutonomyConfig from canonical location (no circular dep)
@@ -1074,25 +1072,6 @@ class MultiAgentMemoryConfig:
         }
 
 
-# Import ToolSearchConfig from tools module to avoid duplication
-def __get_tool_search_config():
-    try:
-        from ..tools.tool_search import ToolSearchConfig as _ToolSearchConfig
-        return _ToolSearchConfig
-    except ImportError:
-        # Fallback minimal config if tools module not available
-        @dataclass
-        class FallbackToolSearchConfig:
-            enabled: Union[bool, str] = "auto"
-            threshold_pct: float = 10.0
-            search_default_limit: int = 5
-            max_search_limit: int = 20
-            core_tools: Optional[FrozenSet[str]] = None
-        return FallbackToolSearchConfig
-
-ToolSearchConfig = __get_tool_search_config()
-
-
 class AutonomyLevel(str, Enum):
     """Autonomy levels for agent behavior."""
     SUGGEST = "suggest"
@@ -1113,7 +1092,6 @@ CachingParam = Union[bool, CachingConfig]
 HooksParam = Union[List[Any], HooksConfig]
 SkillsParam = Union[List[str], SkillsConfig]
 AutonomyParam = Union[bool, Dict[str, Any], "AutonomyConfig"]
-ToolSearchParam = Union[bool, str, Dict[str, Any], ToolSearchConfig]
 
 
 # =============================================================================
@@ -1365,21 +1343,6 @@ def resolve_autonomy(value: AutonomyParam) -> Optional[AutonomyConfig]:
     return value
 
 
-def resolve_tool_search(value: ToolSearchParam) -> Optional[ToolSearchConfig]:
-    """Resolve tool_search= parameter following precedence ladder."""
-    if value is None or value is False:
-        return None
-    if value is True:
-        return ToolSearchConfig()
-    if isinstance(value, str):
-        return ToolSearchConfig(enabled=value)
-    if isinstance(value, dict):
-        return ToolSearchConfig(**value)
-    if isinstance(value, ToolSearchConfig):
-        return value
-    return value
-
-
 __all__ = [
     # Enums
     "MemoryBackend",
@@ -1405,7 +1368,6 @@ __all__ = [
     "HooksConfig",
     "SkillsConfig",
     "AutonomyConfig",
-    "ToolSearchConfig",
     # Config classes (Multi-Agent)
     "MultiAgentHooksConfig",
     "MultiAgentOutputConfig",
@@ -1426,7 +1388,6 @@ __all__ = [
     "HooksParam",
     "SkillsParam",
     "AutonomyParam",
-    "ToolSearchParam",
     # Precedence ladder resolvers
     "resolve_memory",
     "resolve_knowledge",
@@ -1438,5 +1399,4 @@ __all__ = [
     "resolve_execution",
     "resolve_caching",
     "resolve_autonomy",
-    "resolve_tool_search",
 ]

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

@@ -83,23 +83,11 @@ def __getattr__(name):
     elif name == "OAUTH_CALLBACK_PATH":
         from .mcp_oauth_callback import OAUTH_CALLBACK_PATH
         return OAUTH_CALLBACK_PATH
-    # Protocols (lazy loaded)
-    elif name == "MCPClientProtocol":
-        from .protocols import MCPClientProtocol
-        return MCPClientProtocol
-    # Loader (lazy loaded)
-    elif name == "load_mcp_tools":
-        from .loader import load_mcp_tools
-        return load_mcp_tools
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 __all__ = [
     # Client
     "MCP",
-    # Protocols (lazy loaded)
-    "MCPClientProtocol",
-    # Loader (lazy loaded)
-    "load_mcp_tools",
     # Server (lazy loaded)
     "ToolsMCPServer",
     "launch_tools_mcp_server",

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

@@ -1,92 +0,0 @@
-"""
-MCP Tools Loader for PraisonAI Agents.
-
-Provides a thin helper function to load MCP tools for agents
-following the agent-centric design principles in AGENTS.md.
-"""
-
-from typing import List, Optional
-
-from ..memory.mcp_config import MCPConfig, MCPConfigManager
-from .mcp import MCP
-
-
-def load_mcp_tools(
-    names: Optional[List[str]] = None,
-    *,
-    configs: Optional[List[MCPConfig]] = None,
-    prefix_tools: bool = True,
-) -> List[MCP]:
-    """
-    Load MCP tools for use with agents.
-    
-    This is a thin helper that bridges the gap between configuration
-    and agent tool setup, following the "few lines to run an agent"
-    principle from AGENTS.md.
-    
-    Args:
-        names: Specific config names to load (None = all enabled)
-        configs: Optional injected configs from wrapper TOML loader
-        prefix_tools: Whether to prefix tool names when multiple servers
-            are loaded to avoid collisions (default: True).
-            NOTE: Currently not implemented - collisions possible with multiple servers.
-    
-    Returns:
-        List of MCP client instances ready for agent use
-    
-    Examples:
-        # Load all enabled MCP servers from JSON config
-        >>> from praisonaiagents import Agent
-        >>> from praisonaiagents.mcp import load_mcp_tools
-        >>> agent = Agent(name="assistant", tools=load_mcp_tools())
-        
-        # Load specific servers only
-        >>> tools = load_mcp_tools(["filesystem", "github"])
-        >>> agent = Agent(name="coder", tools=tools)
-        
-        # Inject configs from wrapper TOML
-        >>> from praisonai.cli.configuration import get_config_loader
-        >>> loader = get_config_loader()
-        >>> config = loader.load()
-        >>> toml_configs = [MCPConfig(...) for server in config.mcp.servers]
-        >>> tools = load_mcp_tools(configs=toml_configs)
-    """
-    mcp_clients: List[MCP] = []
-    
-    if configs is not None:
-        # Use injected configs from wrapper (TOML loading)
-        target_configs = configs
-        if names:
-            # Filter by names if specified
-            target_configs = [c for c in configs if c.name in names]
-    else:
-        # Load from MCPConfigManager (JSON files)
-        manager = MCPConfigManager()
-        if names:
-            target_configs = [manager.get_config(name) for name in names]
-            target_configs = [c for c in target_configs if c and c.enabled]
-        else:
-            target_configs = manager.get_enabled_configs()
-    
-    for config in target_configs:
-        if not config.enabled:
-            continue
-            
-        # Convert config to MCP instance
-        mcp = config.to_mcp_instance()
-        if mcp:
-            # Apply tool filtering if configured (B3)
-            # TODO: Wire include/exclude filters when B3 is implemented
-            
-            # Apply tool prefix if multiple servers (B4)
-            if prefix_tools and len(target_configs) > 1:
-                # Sanitize server name for use as prefix
-                server_name = config.name.replace('-', '_').replace(' ', '_')
-                # TODO: Tool name prefixing not yet implemented
-                # Need to modify tool names in MCP instance to avoid collisions
-                # e.g., "read_file" -> "filesystem_read_file"
-                pass
-            
-            mcp_clients.append(mcp)
-    
-    return mcp_clients

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

@@ -29,8 +29,8 @@ class MCPToolRunner(threading.Thread):
         super().__init__(daemon=True)
         self.server_params = server_params
         self.queue = queue.Queue()
+        self.result_queue = queue.Queue()
         self.initialized = threading.Event()
-        self._init_error = None
         self.tools = []
         self.timeout = timeout
         self._tool_timings = {}
@@ -66,12 +66,12 @@ class MCPToolRunner(threading.Thread):
                                 if item is None:  # Shutdown signal
                                     break
                                 
-                                response_queue, tool_name, arguments = item
+                                tool_name, arguments = item
                                 try:
                                     result = await session.call_tool(tool_name, arguments)
-                                    response_queue.put((True, result))
+                                    self.result_queue.put((True, result))
                                 except Exception as e:
-                                    response_queue.put((False, str(e)))
+                                    self.result_queue.put((False, str(e)))
                             except queue.Empty:
                                 pass
                             
@@ -80,8 +80,8 @@ class MCPToolRunner(threading.Thread):
                         except asyncio.CancelledError:
                             break
         except Exception as e:
-            self._init_error = f"MCP initialization error: {str(e)}"
             self.initialized.set()  # Ensure we don't hang
+            self.result_queue.put((False, f"MCP initialization error: {str(e)}"))
     
     def call_tool(self, tool_name, arguments):
         """Call an MCP tool and wait for the result."""
@@ -100,25 +100,16 @@ class MCPToolRunner(threading.Thread):
                 if telemetry:
                     telemetry.track_tool_usage(tool_name, success=False, execution_time=0)
                 return f"Error: MCP initialization timed out after {self.timeout} seconds"
-
-        if self._init_error:
-            if telemetry:
-                telemetry.track_tool_usage(tool_name, success=False, execution_time=0)
-            return f"Error: {self._init_error}"
         
         # Start timing after initialization check
         start_time = time.time()
         is_success = False
-        response_queue = queue.Queue(maxsize=1)
         try:
-            # Put request in queue with caller-specific response channel
-            self.queue.put((response_queue, tool_name, arguments))
+            # Put request in queue
+            self.queue.put((tool_name, arguments))
             
-            # Wait for result with timeout
-            try:
-                success, result = response_queue.get(timeout=self.timeout)
-            except queue.Empty:
-                return f"Error: MCP tool call timed out after {self.timeout} seconds"
+            # Wait for result
+            success, result = self.result_queue.get()
             if not success:
                 return f"Error: {result}"
             
@@ -196,8 +187,7 @@ class MCP:
         ```
     """
     
-    def __init__(self, command_or_string=None, args=None, *, command=None, timeout=60, debug=False, 
-                 allowed_tools: Optional[List[str]] = None, disabled_tools: Optional[List[str]] = None, **kwargs):
+    def __init__(self, command_or_string=None, args=None, *, command=None, timeout=60, debug=False, **kwargs):
         """
         Initialize the MCP connection and get tools.
         
@@ -211,13 +201,7 @@ class MCP:
             command: Alternative parameter name for backward compatibility
             timeout: Timeout in seconds for MCP server initialization and tool calls (default: 60)
             debug: Enable debug logging for MCP operations (default: False)
-            allowed_tools: Include whitelist - only these tools will be available (default: None = all tools)
-            disabled_tools: Exclude blacklist - these tools will be filtered out (default: None = no exclusions)
             **kwargs: Additional parameters for StdioServerParameters
-            
-        Note:
-            If both allowed_tools and disabled_tools are specified, allowed_tools takes precedence
-            (include wins over exclude).
         """
         # Check if MCP is available
         if not MCP_AVAILABLE:
@@ -256,8 +240,6 @@ class MCP:
         # Store additional parameters
         self.timeout = timeout
         self.debug = debug
-        self.allowed_tools = allowed_tools
-        self.disabled_tools = disabled_tools
         
         # Check if this is a WebSocket URL (ws:// or wss://)
         if isinstance(command_or_string, str) and re.match(r'^wss?://', command_or_string):
@@ -272,7 +254,7 @@ class MCP:
                 auth_token=auth_token,
                 options=kwargs
             )
-            self._tools = self._apply_tool_filters(list(self.websocket_client.tools))
+            self._tools = list(self.websocket_client.tools)
             self.is_sse = False
             self.is_http_stream = False
             self.is_websocket = True
@@ -286,7 +268,7 @@ class MCP:
                 # Legacy SSE URL - use SSE transport for backward compatibility
                 from .mcp_sse import SSEMCPClient
                 self.sse_client = SSEMCPClient(command_or_string, debug=debug, timeout=timeout)
-                self._tools = self._apply_tool_filters(list(self.sse_client.tools))
+                self._tools = list(self.sse_client.tools)
                 self.is_sse = True
                 self.is_http_stream = False
                 self.is_npx = False
@@ -313,7 +295,7 @@ class MCP:
                     timeout=timeout,
                     options=transport_options
                 )
-                self._tools = self._apply_tool_filters(list(self.http_stream_client.tools))
+                self._tools = list(self.http_stream_client.tools)
                 self.is_sse = False
                 self.is_http_stream = True
                 self.is_npx = False
@@ -343,10 +325,20 @@ class MCP:
         self.is_sse = False
         self.is_http_stream = False
         
-        # Build safe environment for stdio MCP servers
-        # Use safe baseline + explicit env from config (B5 security policy)
-        custom_env = kwargs.get('env', {})
-        env = self._build_safe_env(custom_env)
+        # Ensure UTF-8 encoding in environment for Docker compatibility
+        env = kwargs.get('env', {})
+        if not env:
+            env = os.environ.copy()
+        
+        # Always set Python encoding
+        env['PYTHONIOENCODING'] = 'utf-8'
+        
+        # Only set locale variables on Unix systems
+        if platform.system() != 'Windows':
+            env.update({
+                'LC_ALL': 'C.UTF-8',
+                'LANG': 'C.UTF-8'
+            })
         
         kwargs['env'] = env
         
@@ -377,7 +369,7 @@ class MCP:
             self._initialize_npx_mcp_tools(cmd, arguments)
         else:
             # Generate tool functions immediately and store them
-            self._tools = self._apply_tool_filters(self._generate_tool_functions())
+            self._tools = self._generate_tool_functions()
     
     def _generate_tool_functions(self) -> List[Callable]:
         """
@@ -497,7 +489,7 @@ class MCP:
                 logging.debug(f"Initializing NPX MCP tools with command: {cmd} {' '.join(arguments)}")
             
             # Generate tool functions using the regular MCP approach
-            self._tools = self._apply_tool_filters(self._generate_tool_functions())
+            self._tools = self._generate_tool_functions()
             
             if self.debug:
                 logging.debug(f"Generated {len(self._tools)} NPX MCP tools")
@@ -507,100 +499,6 @@ class MCP:
                 logging.error(f"Failed to initialize NPX MCP tools: {e}")
             raise RuntimeError(f"Failed to initialize NPX MCP tools: {e}")
     
-    def _apply_tool_filters(self, raw_tools: List[Any]) -> List[Any]:
-        """
-        Apply tool filtering based on allowed_tools and disabled_tools.
-        
-        Args:
-            raw_tools: Unfiltered tool list
-            
-        Returns:
-            Filtered tool list
-        """
-        if not self.allowed_tools and not self.disabled_tools:
-            return raw_tools
-            
-        # Import filter functions
-        from .mcp_utils import filter_tools_by_allowlist, filter_disabled_tools
-        
-        # Convert tools to the format expected by filter functions (list of dicts with 'name')
-        # Raw tools might be function objects, so we need to extract names
-        tool_defs = []
-        for tool in raw_tools:
-            if hasattr(tool, '__name__'):
-                tool_defs.append({"name": tool.__name__, "_tool_obj": tool})
-            elif isinstance(tool, dict) and 'name' in tool:
-                tool_defs.append(tool)
-            else:
-                # Skip tools that don't have a clear name
-                continue
-        
-        # Include wins over exclude - apply allowlist exclusively if provided
-        if self.allowed_tools:
-            tool_defs = filter_tools_by_allowlist(tool_defs, self.allowed_tools)
-        elif self.disabled_tools:
-            tool_defs = filter_disabled_tools(tool_defs, self.disabled_tools)
-        
-        # Convert back to original format
-        filtered_tools = []
-        for tool_def in tool_defs:
-            if "_tool_obj" in tool_def:
-                filtered_tools.append(tool_def["_tool_obj"])
-            else:
-                filtered_tools.append(tool_def)
-        
-        return filtered_tools
-    
-    def _build_safe_env(self, custom_env: Optional[dict] = None) -> dict:
-        """
-        Build a safe environment for stdio MCP servers.
-        
-        Merges only safe baseline environment variables with explicit custom ones,
-        following the security policy outlined in AGENTS.md.
-        
-        Args:
-            custom_env: Optional custom environment variables from config
-            
-        Returns:
-            Safe environment dictionary
-        """
-        # Safe baseline environment variables
-        safe_baseline = {
-            'PATH': os.environ.get('PATH', ''),
-            'HOME': os.environ.get('HOME', os.path.expanduser('~')),
-            'USER': os.environ.get('USER', ''),
-            'LANG': os.environ.get('LANG', 'C.UTF-8'),
-            'LC_ALL': os.environ.get('LC_ALL', 'C.UTF-8'),
-            'PYTHONIOENCODING': 'utf-8',
-        }
-        
-        # Platform-specific safe variables
-        if platform.system() == 'Windows':
-            safe_baseline.update({
-                'SYSTEMROOT': os.environ.get('SYSTEMROOT', ''),
-                'COMSPEC': os.environ.get('COMSPEC', ''),
-                'USERNAME': os.environ.get('USERNAME', ''),
-                'USERPROFILE': os.environ.get('USERPROFILE', ''),
-                'APPDATA': os.environ.get('APPDATA', ''),
-                'LOCALAPPDATA': os.environ.get('LOCALAPPDATA', ''),
-                'TEMP': os.environ.get('TEMP', ''),
-                'TMP': os.environ.get('TMP', ''),
-            })
-        else:
-            safe_baseline.update({
-                'TMPDIR': os.environ.get('TMPDIR', '/tmp'),
-                'SHELL': os.environ.get('SHELL', '/bin/sh'),
-            })
-        
-        # Start with safe baseline
-        env = {k: v for k, v in safe_baseline.items() if v}  # Only include non-empty values
-        
-        # Add explicit custom environment variables from config
-        if custom_env:
-            env.update(custom_env)
-        
-        return env
-
     def __iter__(self) -> Iterable[Callable]:
         """
         Allow the MCP instance to be used directly as an iterable of tools.
@@ -711,8 +609,7 @@ class MCP:
                     "name": tool.name,
                     "description": tool.description if hasattr(tool, 'description') else f"Call the {tool.name} tool",
                     "parameters": parameters
-                },
-                "__praisonai_deferrable__": True  # Mark MCP tools as deferrable for tool search
+                }
             })
         
         return openai_tools

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

@@ -156,8 +156,7 @@ class HTTPStreamMCPTool:
                 "name": self.name,
                 "description": self.description,
                 "parameters": fixed_schema
-            },
-            "__praisonai_deferrable__": True  # Mark MCP tools as deferrable for tool search
+            }
         }
 
 class HTTPStreamTransport:

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

@@ -121,8 +121,7 @@ class SSEMCPTool:
                 "name": self.name,
                 "description": self.description,
                 "parameters": fixed_schema
-            },
-            "__praisonai_deferrable__": True  # Mark MCP tools as deferrable for tool search
+            }
         }
 
 class SSEMCPClient:

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

@@ -363,8 +363,7 @@ class WebSocketMCPTool:
                 "name": self.name,
                 "description": self.description,
                 "parameters": fixed_schema
-            },
-            "__praisonai_deferrable__": True  # Mark MCP tools as deferrable for tool search
+            }
         }
 
 class WebSocketMCPClient:

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

@@ -1,110 +0,0 @@
-"""
-Model Context Protocol (MCP) client protocols for PraisonAI Agents.
-
-Defines protocol interfaces for MCP client extensibility following
-the protocol-driven design pattern outlined in AGENTS.md.
-"""
-
-from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
-
-
-@runtime_checkable
-class MCPClientProtocol(Protocol):
-    """
-    Protocol interface for MCP client implementations.
-    
-    Defines the minimal contract that all MCP clients must follow,
-    enabling extensibility through different transport mechanisms
-    (stdio, SSE, HTTP stream, WebSocket) while maintaining a
-    consistent interface for agents.
-    """
-
-    def list_tools(self) -> List[Dict[str, Any]]:
-        """
-        List available tools from the MCP server.
-        
-        Returns:
-            List of tool definitions from the server
-        """
-        ...
-
-    def get_tools(self) -> List[Dict[str, Any]]:
-        """
-        Get available tools from the MCP server.
-        
-        Alias for list_tools() to maintain backward compatibility.
-        
-        Returns:
-            List of tool definitions from the server
-        """
-        ...
-
-    def call_tool(self, name: str, args: Dict[str, Any]) -> Any:
-        """
-        Call a tool on the MCP server.
-        
-        Args:
-            name: Tool name to call
-            args: Tool arguments
-            
-        Returns:
-            Tool execution result
-            
-        Raises:
-            Exception: If tool call fails
-        """
-        ...
-
-    def shutdown(self) -> None:
-        """
-        Shutdown the MCP client and clean up resources.
-        
-        This should gracefully close connections, terminate
-        subprocesses, and release any held resources.
-        """
-        ...
-
-    async def async_list_tools(self) -> List[Dict[str, Any]]:
-        """
-        Asynchronously list available tools from the MCP server.
-        
-        Returns:
-            List of tool definitions from the server
-        """
-        ...
-
-    async def async_get_tools(self) -> List[Dict[str, Any]]:
-        """
-        Asynchronously get available tools from the MCP server.
-        
-        Alias for async_list_tools() to maintain backward compatibility.
-        
-        Returns:
-            List of tool definitions from the server
-        """
-        ...
-
-    async def async_call_tool(self, name: str, args: Dict[str, Any]) -> Any:
-        """
-        Asynchronously call a tool on the MCP server.
-        
-        Args:
-            name: Tool name to call
-            args: Tool arguments
-            
-        Returns:
-            Tool execution result
-            
-        Raises:
-            Exception: If tool call fails
-        """
-        ...
-
-    async def async_shutdown(self) -> None:
-        """
-        Asynchronously shutdown the MCP client and clean up resources.
-        
-        This should gracefully close connections, terminate
-        subprocesses, and release any held resources.
-        """
-        ...