Release v3.9.7

api.md

@@ -148,6 +148,10 @@ Methods:
 * <code title="cli">praisonai agents info <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
 * <code title="cli">praisonai agents list <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
 * <code title="cli">praisonai audit agent-centric <a href="./src/praisonai/praisonai/cli/commands/audit.py">--help</a></code>
+* <code title="cli">praisonai batch batch-run <a href="./src/praisonai/praisonai/cli/commands/batch.py">--help</a></code>
+* <code title="cli">praisonai batch list <a href="./src/praisonai/praisonai/cli/commands/batch.py">--help</a></code>
+* <code title="cli">praisonai batch report <a href="./src/praisonai/praisonai/cli/commands/batch.py">--help</a></code>
+* <code title="cli">praisonai batch stats <a href="./src/praisonai/praisonai/cli/commands/batch.py">--help</a></code>
 * <code title="cli">praisonai benchmark agent <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
 * <code title="cli">praisonai benchmark benchmark-callback <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
 * <code title="cli">praisonai benchmark cli <a href="./src/praisonai/praisonai/cli/commands/benchmark.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>=3.9.2" \
+    "praisonai>=3.9.7" \
     "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>=3.9.2" \
+    "praisonai>=3.9.7" \
     "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>=3.9.2" \
+    "praisonai>=3.9.7" \
     "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_workflow_full_features.py

@@ -32,7 +32,7 @@ workflow = Workflow(
         ),
     ],
     # Workflow-level consolidated params
-    output=WorkflowOutputConfig(verbose=True, stream=True),
+    output=WorkflowOutputConfig(output="verbose", stream=True),
     planning=WorkflowPlanningConfig(enabled=True, reasoning=True),
     memory=WorkflowMemoryConfig(backend="file"),
     # Agent-like params

examples/context/04_agent_integration.py

@@ -37,7 +37,7 @@ def main():
         name="CodeAssistant",
         instructions="You are a helpful code assistant.",
         context=config,
-        verbose=False
+        output="silent"
     )
     
     print(f"   Agent name: {agent.name}")

examples/interactive_defaults/interactive_tools_example.py

@@ -105,7 +105,7 @@ def example_agent_with_default_tools():
         name="InteractiveAgent",
         instructions="You are a helpful coding assistant with file and code analysis capabilities.",
         tools=tools,
-        verbose=False
+        output="silent"
     )
     
     print(f"Agent created with {len(tools)} tools")

examples/knowledge/context_required_example.py

@@ -68,7 +68,7 @@ Answer questions based ONLY on the provided knowledge context.
 If the answer is not in the context, say 'I don't have that information.'
 Be precise and quote exact values when asked about codes or numbers.""",
             knowledge=[temp_dir],
-            user_id="demo_user",
+            memory={"user_id": "demo_user"},
             output="verbose",
         )
         

examples/knowledge/scope_isolation_example.py

@@ -50,7 +50,7 @@ Only authorized personnel with code WRENCH-992 may access this project.
             instructions="You ingest and store knowledge.",
             knowledge=[temp_dir],
             user_id="user_alice",  # Scoped to Alice
-            verbose=False,
+            output="silent",
         )
         
         # Ensure knowledge is processed
@@ -104,7 +104,7 @@ Only authorized personnel with code WRENCH-992 may access this project.
             instructions="Answer questions based on the provided knowledge. If you don't have the information, say so.",
             knowledge=[temp_dir],
             user_id="user_alice",  # Same scope as Agent A
-            verbose=False,
+            output="silent",
         )
         
         response = agent_b.chat("What is the secret access code for Project PHOENIX?")

examples/performance/thread_safety_example.py

@@ -74,7 +74,7 @@ def example_agent_locks():
     agent = Agent(
         name="LockTestAgent",
         instructions="Test agent",
-        verbose=False
+        output="silent"
     )
     
     # Check for locks
@@ -112,7 +112,7 @@ def example_concurrent_real_api():
         name="ConcurrentAgent",
         instructions="Reply with one word only.",
         llm="gpt-4o-mini",
-        verbose=False
+        output="silent"
     )
     
     results = []

examples/persistence/knowledge_qdrant.py

@@ -51,7 +51,7 @@ agent = Agent(
     name="KnowledgeAssistant",
     instructions="You are a helpful assistant with access to technical documentation.",
     knowledge=[doc_path],
-    knowledge_config={
+    knowledge={
         "vector_store": "qdrant",
         "url": "http://localhost:6333"
     }

examples/persistence/simple_db_agent.py

@@ -39,7 +39,7 @@ agent = Agent(
     instructions="You are a helpful assistant. Keep responses brief.",
     db=db_instance,
     session_id="demo-session-001",  # Same session_id = resume conversation
-    verbose=False
+    output="silent"
 )
 
 # Chat with persistence

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

@@ -26,7 +26,7 @@ pdf_agent = Agent(
     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=["document.pdf"],  # Replace with your PDF file path
-    knowledge_config=config,
+    knowledge=config,
     reflection=True,
     
     

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

@@ -24,7 +24,7 @@ knowledge_agent = Agent(
     goal="Store and retrieve knowledge efficiently",
     backstory="Expert in managing and utilizing stored knowledge",
     knowledge=["sample.pdf"],
-    knowledge_config=config)
+    knowledge=config)
 
 # Define a task for the agent
 knowledge_task = Task(

examples/python/concepts/knowledge-agents.py

@@ -13,8 +13,7 @@ config = {
 agent = Agent(
     name="Knowledge Agent",
     instructions="You answer questions based on the provided knowledge.",
-    knowledge=["small.pdf"],
-    knowledge_config=config
+    knowledge=["small.pdf"]
 )
 
 agent.start("What is KAG in one line?")

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

@@ -139,7 +139,7 @@ def main():
         name="AI Research Assistant",
         instructions="You are an AI research assistant. Use your knowledge base to answer questions about artificial intelligence topics.",
         knowledge=sample_documents,  # Will use default knowledge config
-        knowledge_config=rerank_config,  # Use reranking config
+        knowledge=rerank_config,  # Use reranking config
         llm="gpt-4o-mini"
     )
     

examples/python/dict_config_example.py

@@ -16,7 +16,7 @@ def example_dict_output_config():
     """Example: Using dict for output configuration."""
     from praisonaiagents import Agent
     
-    # Dict shorthand - equivalent to OutputConfig(verbose=True, stream=False)
+    # Dict shorthand - equivalent to OutputConfig(output="verbose", stream=False)
     agent = Agent(
         name="Assistant",
         instructions="You are a helpful assistant.",

examples/python/general/memory_example.py

@@ -104,11 +104,13 @@ def main():
     agents = Agents(
         agents=[researcher, retriever],
         tasks=[store_task, verify_task, query_task, query_both_task],  # Use same verbose level as memory
-        memory=True,
-        embedder={
-            "provider": "openai",
-            "config": {
-                "model": "text-embedding-3-small"
+        memory={
+            "provider": "rag",
+            "embedder": {
+                "provider": "openai",
+                "config": {
+                    "model": "text-embedding-3-small"
+                }
             }
         }
     )

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

@@ -61,7 +61,7 @@ agents_system = Agents(
     agents=[knowledge_agent],
     tasks=[build_task, query_task],
     memory=True,
-    memory_config=memory_config, output="verbose"
+    memory=memory_config, output="verbose"
 )
 
 print("Starting graph memory demonstration...")

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

@@ -62,7 +62,7 @@ agents_system = Agents(
     agents=[research_agent],
     tasks=[build_memory_task, search_task],
     memory=True,
-    memory_config=memory_config, output="verbose"
+    memory=memory_config, output="verbose"
 )
 
 print("Starting memory search optimization demonstration...")

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

@@ -85,13 +85,13 @@ def main():
     agents = Agents(
         agents=[researcher, retriever],
         tasks=[store_task, retrieve_task],
-        memory=True,
-        embedder={
-            "provider": "gemini",
-            "config": {
-                "model": "text-embedding-004",
-                # Optional: specify task type for better results
-                # "task_type": "RETRIEVAL_DOCUMENT"  
+        memory={
+            "provider": "rag",
+            "embedder": {
+                "provider": "gemini",
+                "config": {
+                    "model": "text-embedding-004",
+                }
             }
         }
     )

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

@@ -153,7 +153,7 @@ def main():
         agents=[researcher],
         tasks=[build_knowledge_task, query_knowledge_task, expand_knowledge_task],
         memory=True,
-        memory_config=memory_config
+        memory=memory_config
     )
     
     print("\n🚀 Starting graph memory demonstration...")

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

@@ -33,7 +33,7 @@ def init_agent():
         name="Knowledge Agent",
         instructions="You answer questions based on the provided knowledge.",
         knowledge=["kag-research-paper.pdf"],
-        knowledge_config=config,
+        knowledge=config,
         user_id="user1",
         llm="deepseek-r1"
     )

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

@@ -31,7 +31,7 @@ agent = Agent(
     name="Knowledge Agent",
     instructions="You answer questions based on the provided knowledge.",
     knowledge=["kag-research-paper.pdf"], # Indexing
-    knowledge_config=config,
+    knowledge=config,
     user_id="user1",
     llm="deepseek-r1"
 )

examples/python/mongodb/mongodb_comprehensive_example.py

@@ -81,7 +81,7 @@ def main():
         backstory="""You are a knowledge management specialist who excels at 
         organizing, categorizing, and retrieving information from the knowledge base. 
         You ensure that knowledge is properly stored, indexed, and accessible.""",
-        knowledge_config=mongodb_knowledge_config,
+        knowledge=mongodb_knowledge_config,
         memory=True,
         llm="gpt-4o-mini"
     )
@@ -107,7 +107,7 @@ def main():
         backstory="""You are a customer service representative who uses the 
         knowledge base and customer data to provide excellent support. You can 
         access customer history, product information, and troubleshooting guides.""",
-        knowledge_config=mongodb_knowledge_config,
+        knowledge=mongodb_knowledge_config,
         tools=[mongodb_tools],
         memory=True,
         llm="gpt-4o-mini"
@@ -190,7 +190,7 @@ def main():
         agents=[data_manager, knowledge_curator, business_analyst, customer_service],
         tasks=business_tasks,
         memory=True,
-        memory_config=mongodb_memory_config, output="verbose"
+        memory=mongodb_memory_config, output="verbose"
     )
     
     # Execute the comprehensive business pipeline

examples/python/mongodb/mongodb_knowledge_example.py

@@ -55,7 +55,7 @@ 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_config=mongodb_knowledge_config,
+        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

@@ -94,7 +94,7 @@ def main():
         agents=[research_agent],
         tasks=research_tasks,
         memory=True,
-        memory_config=mongodb_memory_config, output="verbose"
+        memory=mongodb_memory_config, output="verbose"
     )
     
     # Execute the research pipeline

examples/python/profiling/profile_results/suite_results.json

@@ -1,155 +1,155 @@
 {
-  "timestamp": "2025-12-31T18:45:38.187539Z",
+  "timestamp": "2026-01-16T05:23:04.919943Z",
   "metadata": {
     "python_version": "3.12.11",
-    "platform": "macOS-15.7.4-arm64-arm-64bit",
-    "praisonai_version": "2.9.2",
-    "timestamp": "2025-12-31T18:45:38.226558Z"
+    "platform": "macOS-26.3-arm64-arm-64bit",
+    "praisonai_version": "3.9.2",
+    "timestamp": "2026-01-16T05:23:04.932474Z"
   },
   "startup": {
-    "cold_ms": 80.9305000002496,
-    "warm_ms": 55.266541996388696
+    "cold_ms": 27.866583000104583,
+    "warm_ms": 27.75670800019725
   },
   "import_analysis": [
     {
       "module": "praisonaiagents",
-      "self_us": 2419,
-      "cumulative_us": 1645390,
-      "self_ms": 2.419,
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+      "self_us": 403,
+      "cumulative_us": 188329,
+      "self_ms": 0.403,
+      "cumulative_ms": 188.329
     },
     {
       "module": "praisonaiagents.workflows",
-      "self_us": 670,
-      "cumulative_us": 1375919,
-      "self_ms": 0.67,
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+      "self_us": 106,
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+      "self_ms": 0.106,
+      "cumulative_ms": 112.18
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+      "self_us": 298,
+      "cumulative_us": 13168,
+      "self_ms": 0.298,
+      "cumulative_ms": 13.168
+    },
+    {
+      "module": "pydantic",
+      "self_us": 223,
+      "cumulative_us": 11119,
+      "self_ms": 0.223,
+      "cumulative_ms": 11.119
     }
   ],
   "scenarios": [
@@ -162,28 +162,22 @@
         "iterations": 2
       },
       "import_time_stats": {
-        "mean": 0.008833496394800022,
-        "min": 0.005916997906751931,
-        "max": 0.011749994882848114,
-        "stdev": 0.0041245517164382375,
-        "median": 0.008833496394800022
+        "mean": 0,
+        "min": 0,
+        "max": 0,
+        "stdev": 0,
+        "median": 0
       },
       "total_time_stats": {
-        "mean": 1727.4090415012324,
-        "min": 1367.2264170018025,
-        "max": 2087.5916660006624,
-        "stdev": 509.3751524982296,
-        "median": 1727.4090415012324
+        "mean": 0,
+        "min": 0,
+        "max": 0,
+        "stdev": 0,
+        "median": 0
       },
       "first_token_stats": null,
-      "raw_import_times_ms": [
-        0.011749994882848114,
-        0.005916997906751931
-      ],
-      "raw_total_times_ms": [
-        1367.2264170018025,
-        2087.5916660006624
-      ]
+      "raw_import_times_ms": [],
+      "raw_total_times_ms": []
     },
     {
       "name": "simple_stream",
@@ -194,34 +188,22 @@
         "iterations": 2
       },
       "import_time_stats": {
-        "mean": 0.008792001608526334,
-        "min": 0.006375004886649549,
-        "max": 0.01120899833040312,
-        "stdev": 0.0034181495442894613,
-        "median": 0.008792001608526334
+        "mean": 0,
+        "min": 0,
+        "max": 0,
+        "stdev": 0,
+        "median": 0
       },
       "total_time_stats": {
-        "mean": 2321.0982290001994,
-        "min": 1697.1089580038097,
-        "max": 2945.087499996589,
-        "stdev": 882.4540898183949,
-        "median": 2321.0982290001994
+        "mean": 0,
+        "min": 0,
+        "max": 0,
+        "stdev": 0,
+        "median": 0
       },
-      "first_token_stats": {
-        "mean": 2312.8511880022415,
-        "min": 1681.2440420035273,
-        "max": 2944.458334000956,
-        "stdev": 893.2273919631452,
-        "median": 2312.8511880022415
-      },
-      "raw_import_times_ms": [
-        0.01120899833040312,
-        0.006375004886649549
-      ],
-      "raw_total_times_ms": [
-        2945.087499996589,
-        1697.1089580038097
-      ]
+      "first_token_stats": null,
+      "raw_import_times_ms": [],
+      "raw_total_times_ms": []
     }
   ]
 }

examples/python/ui/mcp-streamlit-simple.py

@@ -50,5 +50,5 @@ with st.expander("🔧 Troubleshooting"):
     **If you still have issues:**
     - Make sure you have Node.js installed for the MCP server
     - Set your OpenAI API key: `export OPENAI_API_KEY="your-key"`
-    - Try the debug mode by setting `verbose=True`
+    - Try the debug mode by setting `output="verbose"`
     """)

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

@@ -33,7 +33,7 @@ def init_agent():
         name="Knowledge Agent",
         instructions="You answer questions based on the provided knowledge.",
         knowledge=["kag-research-paper.pdf"],
-        knowledge_config=config,
+        knowledge=config,
         user_id="user1",
         llm="deepseek-r1"
     )

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

@@ -265,7 +265,7 @@ agents = Agents(
     process="hierarchical",
     manager_llm="gpt-4o-mini",
     memory=True,  # Enable shared memory for domain context
-    memory_config={
+    memory={
         "provider": "rag",
         "config": {
             "collection_name": f"{domain.replace('.', '_')}_security_analysis"

examples/rag/auto_retrieval.py

@@ -58,7 +58,7 @@ def auto_rag_basic():
         
         KNOWLEDGE BASE:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     # Wrap with AutoRagAgent for intelligent retrieval
@@ -105,7 +105,7 @@ def auto_rag_with_policies():
         
         KNOWLEDGE BASE:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     print("\n" + "=" * 60)
@@ -153,7 +153,7 @@ def auto_rag_config_options():
         
         KNOWLEDGE BASE:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     print("\n" + "=" * 60)

examples/rag/basic_retrieval.py

@@ -79,7 +79,7 @@ def rag_with_query_context():
     agent = Agent(
         name="Research Assistant",
         instructions="You answer questions based on the context provided in each query.",
-        verbose=False
+        output="silent"
     )
     
     # Simulate retrieved context (in real RAG, this comes from vector search)

examples/rag/chunking_strategies.py

@@ -204,7 +204,7 @@ def rag_with_section_chunks():
         
         PRODUCT DOCUMENTATION:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     queries = [

examples/rag/citations.py

@@ -103,7 +103,7 @@ def basic_citations():
         
         POLICY DOCUMENTS:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     queries = [
@@ -146,7 +146,7 @@ def structured_citations():
         
         POLICY DOCUMENTS:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     query = "What are the requirements for remote work eligibility?"
@@ -183,7 +183,7 @@ def inline_citations():
         
         POLICY DOCUMENTS:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     query = "Summarize the key employee benefits."
@@ -267,7 +267,7 @@ def multi_source_synthesis():
         
         POLICY DOCUMENTS:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     query = "Give me a complete overview of employee benefits and policies."

examples/rag/external_sources.py

@@ -125,7 +125,7 @@ def web_augmented_rag():
         
         COMPANY KNOWLEDGE:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     # Simulate web-augmented queries
@@ -171,7 +171,7 @@ def database_augmented_rag():
         
         COMPANY KNOWLEDGE:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     queries = [
@@ -249,7 +249,7 @@ def hybrid_source_rag():
         
         COMPANY KNOWLEDGE:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     query = "Give me a complete business briefing for today"

examples/rag/hybrid_search.py

@@ -156,7 +156,7 @@ def hybrid_rag_example():
         
         TROUBLESHOOTING KNOWLEDGE BASE:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     # Test with different query types
@@ -204,7 +204,7 @@ def compare_retrieval_modes():
         
         KNOWLEDGE BASE:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     print(f"\n📝 Query: {query}")

examples/rag/knowledge_graph.py

@@ -124,7 +124,7 @@ def graph_aware_rag():
         
         KNOWLEDGE GRAPH:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     # Relationship-focused queries
@@ -167,7 +167,7 @@ def multi_hop_reasoning():
         
         KNOWLEDGE GRAPH:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     # Multi-hop query requiring traversal
@@ -200,7 +200,7 @@ def entity_type_filtering():
         
         SCIENTISTS:
         {people_context}""",
-        verbose=False
+        output="silent"
     )
     
     # Filter to only concepts
@@ -213,7 +213,7 @@ def entity_type_filtering():
         
         CONCEPTS:
         {concepts_context}""",
-        verbose=False
+        output="silent"
     )
     
     query = "Tell me about the major developments in early 20th century physics."

examples/rag/multi_document.py

@@ -112,7 +112,7 @@ def multi_document_rag():
         
         REPORTS:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     # Questions that require multi-document synthesis
@@ -157,7 +157,7 @@ def cross_reference_example():
         
         REPORTS:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     query = """
@@ -185,7 +185,7 @@ def document_filtering():
         
         POLICY REPORT:
         {POLICY_REPORT['content']}""",
-        verbose=False
+        output="silent"
     )
     
     tech_agent = Agent(
@@ -194,7 +194,7 @@ def document_filtering():
         
         TECHNOLOGY REPORT:
         {TECH_REPORT['content']}""",
-        verbose=False
+        output="silent"
     )
     
     query = "What progress has been made in addressing climate change?"

examples/rag/rag_evaluation.py

@@ -124,7 +124,7 @@ def main():
         
         KNOWLEDGE:
         {SAMPLE_KNOWLEDGE}""",
-        verbose=False
+        output="silent"
     )
     
     # Get test queries

examples/rag/rag_multi_agent.py

@@ -61,7 +61,7 @@ def main():
         
         KNOWLEDGE BASE:
         {SHARED_KNOWLEDGE}""",
-        verbose=False
+        output="silent"
     )
     
     summarizer = Agent(
@@ -75,7 +75,7 @@ def main():
         
         KNOWLEDGE BASE:
         {SHARED_KNOWLEDGE}""",
-        verbose=False
+        output="silent"
     )
     
     writer = Agent(
@@ -89,7 +89,7 @@ def main():
         
         KNOWLEDGE BASE:
         {SHARED_KNOWLEDGE}""",
-        verbose=False
+        output="silent"
     )
     
     # Define tasks
@@ -118,7 +118,7 @@ def main():
         agents=[researcher, summarizer, writer],
         tasks=[research_task, summary_task, report_task],
         process="sequential",
-        verbose=False
+        output="silent"
     )
     
     result = agents.start()

examples/rag/rag_pdf_qa.py

@@ -67,7 +67,7 @@ def main():
         
         DOCUMENT:
         {DOCUMENT_CONTENT}""",
-        verbose=False
+        output="silent"
     )
     
     query = "What are the main topics covered in this document?"
@@ -88,7 +88,7 @@ def main():
         
         DOCUMENT:
         {DOCUMENT_CONTENT}""",
-        verbose=False
+        output="silent"
     )
     
     queries = [
@@ -114,7 +114,7 @@ def main():
         
         DOCUMENT:
         {DOCUMENT_CONTENT}""",
-        verbose=False
+        output="silent"
     )
     
     analysis_query = "Compare the benefits vs challenges of remote work based on this study"

examples/rag/reranking.py

@@ -131,7 +131,7 @@ def rag_with_reranking():
         
         RESEARCH PAPERS:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     queries = [
@@ -170,7 +170,7 @@ def compare_with_without_reranking():
         
         RESEARCH PAPERS:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     print(f"\n📝 Query: {query}")
@@ -196,7 +196,7 @@ def reranking_configuration():
     agent = Agent(
         name="My Agent",
         knowledge=documents,
-        knowledge_config={
+        knowledge={
             # Enable reranking
             "rerank": True,
             

examples/rag/retrieval_policies.py

@@ -134,7 +134,7 @@ def policy_based_retrieval():
         
         IT SUPPORT DOCS:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     query = "How do I set up VPN?"
@@ -217,7 +217,7 @@ def priority_based_retrieval():
         
         HIGH PRIORITY DOCS:
         {high_context}""",
-        verbose=False
+        output="silent"
     )
     
     # Full knowledge agent
@@ -227,7 +227,7 @@ def priority_based_retrieval():
         
         IT SUPPORT DOCS:
         {full_context}""",
-        verbose=False
+        output="silent"
     )
     
     query = "I'm locked out of my account!"

examples/rag/structured_output.py

@@ -110,7 +110,7 @@ def json_output_rag():
         
         PRODUCT CATALOG:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     # Request structured product info
@@ -154,7 +154,7 @@ def pydantic_guided_rag():
         
         PRODUCT CATALOG:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     query = "Compare the UltraWidget Pro and SmartHome Hub. Which should I buy?"
@@ -184,7 +184,7 @@ def table_format_rag():
         
         PRODUCT CATALOG:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     query = "Show me all products in a table with name, price, and rating."
@@ -212,7 +212,7 @@ def list_format_rag():
         
         PRODUCT CATALOG:
         {context}""",
-        verbose=False
+        output="silent"
     )
     
     queries = [
@@ -321,7 +321,7 @@ def multi_format_rag():
             
             PRODUCT CATALOG:
             {context}""",
-            verbose=False
+            output="silent"
         )
         
         response = agent.chat(query)

examples/vector/chroma_wow.py

@@ -6,7 +6,7 @@ agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant with access to documents.",
     knowledge=["./docs/guide.pdf"],  # Add your documents here
-    knowledge_config={
+    knowledge={
         "vector_store": "chroma",
         "path": "/tmp/chroma_demo"
     }

examples/vector/lancedb_real_wow.py

@@ -6,7 +6,7 @@ agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant with access to documents.",
     knowledge=["./docs/guide.pdf"],  # Add your documents here
-    knowledge_config={
+    knowledge={
         "vector_store": "lancedb",
         "path": "/tmp/lancedb_test"
     }

examples/vector/lancedb_wow.py

@@ -6,7 +6,7 @@ agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant with access to documents.",
     knowledge=["./docs/guide.pdf"],  # Add your documents here
-    knowledge_config={
+    knowledge={
         "vector_store": "lancedb",
         "path": "/tmp/lancedb_demo"
     }

examples/vector/pgvector_real_wow.py

@@ -12,7 +12,7 @@ agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant with access to documents.",
     knowledge=["./docs/guide.pdf"],  # Add your documents here
-    knowledge_config={
+    knowledge={
         "vector_store": "pgvector",
         "url": url
     }

examples/vector/pgvector_wow.py

@@ -12,7 +12,7 @@ agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant with access to documents.",
     knowledge=["./docs/guide.pdf"],  # Add your documents here
-    knowledge_config={
+    knowledge={
         "vector_store": "pgvector",
         "url": url
     }

examples/vector/pinecone_wow.py

@@ -16,7 +16,7 @@ agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant with access to documents.",
     knowledge=["./docs/guide.pdf"],  # Add your documents here
-    knowledge_config={
+    knowledge={
         "vector_store": "pinecone",
         "api_key": api_key,
         "environment": "us-east-1"

examples/vector/qdrant_wow.py

@@ -12,7 +12,7 @@ agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant with access to documents.",
     knowledge=["./docs/guide.pdf"],  # Add your documents here
-    knowledge_config={
+    knowledge={
         "vector_store": "qdrant",
         "url": url
     }

examples/vector/weaviate_wow.py

@@ -17,7 +17,7 @@ agent = Agent(
     name="Assistant",
     instructions="You are a helpful assistant with access to documents.",
     knowledge=["./docs/guide.pdf"],  # Add your documents here
-    knowledge_config={
+    knowledge={
         "vector_store": "weaviate",
         "url": weaviate_url,
         "api_key": weaviate_key

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/01_basic_token_tracking.py

@@ -7,15 +7,14 @@ will be automatically displayed when tasks complete!
 from praisonaiagents import Agents, Agent, Task
 
 def main():
-    # Create a simple agent with metrics=True - that's it!
+    # Create a simple agent with output="debug" - includes metrics!
     research_agent = Agent(
         name="Research Agent",
         role="Research Specialist", 
         goal="Find information about a topic",
         backstory="You are an expert researcher who finds comprehensive information.",
-        output="verbose",
+        output="debug",  # 🎯 SIMPLIFIED: debug preset includes metrics!
         llm="gpt-4o-mini",  # Use a cost-effective model for examples
-        metrics=True  # 🎯 SIMPLIFIED: Just add this parameter!
     )
     
     # Create a simple task

src/praisonai-agents/10_comprehensive_dashboard.py

@@ -143,7 +143,7 @@ def simulate_complex_workflow():
             agents=[planner, executor, reviewer],
             tasks=[planning_task, execution_task, review_task],
             process="sequential",
-            verbose=False
+            output="silent"
         )
         
         result = workflow.start()

src/praisonai-agents/agent_guardrails_example.py

@@ -3,9 +3,14 @@
 Agent-level guardrails example.
 This example demonstrates how to use guardrails at the Agent level
 which will apply to all tasks executed by that agent.
+
+Note: Agent guardrails only support function-based validation or preset strings
+(permissive, safety, strict). For LLM-based guardrails, use Task guardrails.
 """
 from typing import Tuple, Any
 from praisonaiagents import Agent, TaskOutput
+
+
 def validate_content_length(task_output: TaskOutput) -> Tuple[bool, Any]:
     """
     Validate that task output content meets minimum length requirement.
@@ -17,6 +22,8 @@ def validate_content_length(task_output: TaskOutput) -> Tuple[bool, Any]:
     if len(task_output.raw) < 50:
         return False, "Content too short - must be at least 50 characters"
     return True, task_output
+
+
 def validate_professional_tone(task_output: TaskOutput) -> Tuple[bool, Any]:
     """
     Validate that the content has a professional tone.
@@ -31,14 +38,17 @@ def validate_professional_tone(task_output: TaskOutput) -> Tuple[bool, Any]:
         if word in content:
             return False, f"Content contains unprofessional word: '{word}'"
     return True, task_output
+
+
 def main():
-    """Demonstrate Agent-level guardrails with function-based and LLM-based validation."""
+    """Demonstrate Agent-level guardrails with function-based validation."""
     print("=== Agent Guardrail Examples ===\n")
-    # Example 1: Function-based guardrail
+    
+    # Example 1: Function-based guardrail for content length
     print("1. Function-based guardrail (content length validation):")
     agent1 = Agent(
-        name="ContentWriter"
-        instructions="You are a professional content writer who creates detailed responses"
+        name="ContentWriter",
+        instructions="You are a professional content writer who creates detailed responses",
         guardrails=validate_content_length
     )
     try:
@@ -46,30 +56,37 @@ def main():
         print(f"Result: {result1}\n")
     except Exception as e:
         print(f"Error: {e}\n")
-    # Example 2: LLM-based guardrail (string description)
-    print("2. LLM-based guardrail (professional tone validation):")
+    
+    # Example 2: Function-based guardrail for professional tone
+    print("2. Function-based guardrail (professional tone validation):")
     agent2 = Agent(
         name="BusinessWriter", 
-        instructions="You are a business communication expert"
-        guardrails="Ensure the content is professional, formal, and suitable for business communication. No casual language or slang."
+        instructions="You are a business communication expert. Write formal, professional content.",
+        guardrails=validate_professional_tone
     )
     try:
         result2 = agent2.start("Write a welcome message for new employees")
         print(f"Result: {result2}\n")
     except Exception as e:
         print(f"Error: {e}\n")
-    # Example 3: Multiple agents with different guardrails
-    print("3. Professional tone function-based guardrail:")
+    
+    # Example 3: Using preset guardrails (built-in options)
+    print("3. Using preset guardrail ('safety'):")
     agent3 = Agent(
-        name="ProfessionalWriter"
-        instructions="Write professional business content"
-        guardrails=validate_professional_tone
+        name="SafeWriter",
+        instructions="Write helpful content",
+        guardrails="safety"  # Valid presets: permissive, safety, strict
     )
     try:
-        result3 = agent3.start("Write a casual greeting message")
+        result3 = agent3.start("Write a friendly greeting message")
         print(f"Result: {result3}\n")
     except Exception as e:
         print(f"Error: {e}\n")
+    
     print("=== Agent Guardrails Demonstration Complete ===")
+    print("\nNote: For LLM-based guardrails with custom descriptions,")
+    print("use Task-level guardrails instead of Agent-level guardrails.")
+
+
 if __name__ == "__main__":
     main()

src/praisonai-agents/autoagents-tavily.py

@@ -11,7 +11,7 @@ agents = AutoAgents(
     instructions="Search for information about AI Agents",
     tools=[tavily],
     process="sequential",
-    verbose=True,
+    output="verbose",
     max_agents=3  # Maximum number of agents to create
 )
 
@@ -53,7 +53,7 @@ agents = AutoAgents(
     process="sequential",
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
     reflection=False,
-    verbose=False,
+    output="silent",
     max_agents=3  # Maximum number of agents to create
 )
 

src/praisonai-agents/deepresearch_gemini.py

@@ -26,7 +26,6 @@ agent = DeepResearchAgent(
     - Clear structure with executive summary
     """,
     model="deep-research-pro",  # Auto-detected as Gemini
-    verbose=True,
 )
 
 print(f"Agent: {agent}")
@@ -39,7 +38,7 @@ result = agent.research(
     # stream=True is the default, set stream=False to disable
 )
 
-# The report is already printed during streaming when verbose=True
+# The report is already printed during streaming when output="verbose"
 # But we can also access it programmatically:
 print("\n" + "=" * 60)
 print(f"Report length: {len(result.report)} characters")

src/praisonai-agents/deepresearch_openai.py

@@ -25,7 +25,6 @@ agent = DeepResearchAgent(
     - Clear, structured responses
     """,
     model="o4-mini-deep-research",  # or "o3-deep-research" for higher quality
-    verbose=True
 )
 
 print(f"Agent: {agent}")
@@ -38,7 +37,7 @@ result = agent.research(
     # stream=True is the default, set stream=False to disable
 )
 
-# The report is already printed during streaming when verbose=True
+# The report is already printed during streaming when output="verbose"
 # But we can also access it programmatically:
 print("\n" + "=" * 60)
 print(f"Report length: {len(result.report)} characters")

src/praisonai-agents/gemini_self_reflection.py

@@ -15,9 +15,7 @@ agent = Agent(
     instructions="You are a helpful assistant. Be concise and clear.",
     llm=llm_config,
     output="verbose",
-    reflection=True,
-    max_reflect=2,
-    min_reflect=1
+    reflection=True  # Use simplest form - enables defaults
 )
 
 # Test with a simple prompt

src/praisonai-agents/langchain_example.py

@@ -24,7 +24,7 @@ task = Task(
 agents = Agents(
     agents=[agent],
     tasks=[task],
-    verbose=True
+    output="verbose"
 )
 
 agents.start()

src/praisonai-agents/llm-gemini-advanced.py

@@ -32,11 +32,8 @@ agent = Agent(
     instructions="You are a helpful Assistant specialized in scientific explanations. "
                 "Provide clear, accurate, and engaging responses.",
     llm=llm_config,                  # Pass the detailed configuration
-    output="verbose",                    # Enable detailed output
-    markdown=True,                   # Format responses in markdown
-    reflection=True,              # Enable self-reflection
-    max_reflect=3,                  # Maximum reflection iterations
-    min_reflect=1                   # Minimum reflection iterations
+    output="verbose",                # Enable detailed output (includes markdown)
+    reflection=True                  # Enable self-reflection with defaults
 )
 
 # Test the agent

src/praisonai-agents/mongodb_comprehensive_example.py

@@ -70,7 +70,7 @@ def main():
         indexing, and ensure data quality and accessibility across the organization.""",
         tools=[mongodb_tools],
         memory=True,
-        verbose=True,
+        output="verbose",
         llm="gpt-4o-mini"
     )
     
@@ -82,9 +82,9 @@ def main():
         backstory="""You are a knowledge management specialist who excels at 
         organizing, categorizing, and retrieving information from the knowledge base. 
         You ensure that knowledge is properly stored, indexed, and accessible.""",
-        knowledge_config=mongodb_knowledge_config,
+        knowledge=mongodb_knowledge_config,
         memory=True,
-        verbose=True,
+        output="verbose",
         llm="gpt-4o-mini"
     )
     
@@ -98,7 +98,7 @@ def main():
         and provide strategic recommendations based on data analysis.""",
         tools=[mongodb_tools],
         memory=True,
-        verbose=True,
+        output="verbose",
         llm="gpt-4o-mini"
     )
     
@@ -110,10 +110,10 @@ def main():
         backstory="""You are a customer service representative who uses the 
         knowledge base and customer data to provide excellent support. You can 
         access customer history, product information, and troubleshooting guides.""",
-        knowledge_config=mongodb_knowledge_config,
+        knowledge=mongodb_knowledge_config,
         tools=[mongodb_tools],
         memory=True,
-        verbose=True,
+        output="verbose",
         llm="gpt-4o-mini"
     )
     
@@ -195,7 +195,7 @@ def main():
         tasks=business_tasks,
         memory=True,
         memory_config=mongodb_memory_config,
-        verbose=True
+        output="verbose"
     )
     
     # Execute the comprehensive business pipeline

src/praisonai-agents/mongodb_knowledge_example.py

@@ -55,10 +55,9 @@ 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_config=mongodb_knowledge_config,
         knowledge=[os.path.join(os.path.dirname(__file__), "llms.md")],
         memory=True,
-        verbose=True,
+        output="verbose",
         llm="gpt-4o-mini"
     )
     
@@ -72,7 +71,7 @@ def main():
         knowledge base. You ensure information is accurate, well-structured, 
         and properly categorized.""",
         memory=True,
-        verbose=True,
+        output="verbose",
         llm="gpt-4o-mini"
     )
     
@@ -128,7 +127,7 @@ def main():
         agents=[research_agent, knowledge_agent],
         tasks=knowledge_tasks,
         memory=True,
-        verbose=True
+        output="verbose"
     )
     
     # Execute the knowledge management pipeline

src/praisonai-agents/mongodb_memory_example.py

@@ -49,7 +49,7 @@ def main():
         You excel at maintaining context across multiple research sessions and can 
         retrieve relevant information from your persistent memory store.""",
         memory=True,
-        verbose=True,
+        output="verbose",
         llm="gpt-4o-mini"
     )
     
@@ -94,9 +94,8 @@ def main():
     research_system = Agents(
         agents=[research_agent],
         tasks=research_tasks,
-        memory=True,
-        memory_config=mongodb_memory_config,
-        verbose=True
+        memory=True,  # Use default memory instead of mongodb config (requires proper MemoryConfig)
+        output="verbose"
     )
     
     # Execute the research pipeline

src/praisonai-agents/mongodb_tools_example.py

@@ -37,7 +37,7 @@ def main():
         collections, and analyze data patterns. You excel at using MongoDB tools 
         for data management and retrieval.""",
         tools=[mongodb_tools],
-        verbose=True,
+        output="verbose",
         llm="gpt-4o-mini"
     )
     
@@ -50,7 +50,7 @@ def main():
         from MongoDB databases. You can query data, perform aggregations, and 
         identify patterns and trends in the stored information.""",
         tools=[mongodb_tools],
-        verbose=True,
+        output="verbose",
         llm="gpt-4o-mini"
     )
     
@@ -116,7 +116,7 @@ def main():
     mongodb_system = Agents(
         agents=[db_agent, analysis_agent],
         tasks=mongodb_tasks,
-        verbose=True
+        output="verbose"
     )
     
     # Execute the MongoDB operations pipeline

src/praisonai-agents/performance_monitoring_demo.py

@@ -200,7 +200,7 @@ research_agent = Agent(
     goal="Research and analyze performance topics quickly",
     backstory="You are an expert at researching performance optimization topics.",
     tools=[duckduckgo],
-    verbose=False  # Reduce output for cleaner demo
+    output="silent"  # Reduce output for cleaner demo
 )
 
 # Monitor agent operations
@@ -216,7 +216,7 @@ def execute_monitored_task(agent, task_description):
     agents_workflow = Agents(
         agents=[agent],
         tasks=[task],
-        verbose=False
+        output="silent"
     )
     
     # Simulate API calls during agent execution

src/praisonai-agents/planning_example.py

@@ -4,11 +4,11 @@ from praisonaiagents import Agent
 from praisonaiagents.planning import PlanningAgent, TodoList
 
 # Step 1: Create agents
-researcher = Agent(name="Researcher", role="Research Analyst", verbose=True)
-writer = Agent(name="Writer", role="Content Writer", verbose=True)
+researcher = Agent(name="Researcher", role="Research Analyst", output="verbose")
+writer = Agent(name="Writer", role="Content Writer", output="verbose")
 
 # Step 2: Create a plan using PlanningAgent
-planner = PlanningAgent(llm="gpt-4o-mini", verbose=1)
+planner = PlanningAgent(llm="gpt-4o-mini")
 plan = planner.create_plan_sync(
     request="Write a short article about the top 3 benefits of meditation",
     agents=[researcher, writer],

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

@@ -12,6 +12,9 @@ class Chunking:
         'semantic': ['chunk_size', 'embedding_model'],
         'sdpm': ['chunk_size', 'embedding_model'],
         'late': ['chunk_size', 'embedding_model'],
+        'code': ['chunk_size', 'tokenizer_or_token_counter'],
+        'neural': ['chunk_size', 'tokenizer_or_token_counter'],
+        'slumber': ['chunk_size', 'genie'],
     }
     
     @cached_property
@@ -24,7 +27,10 @@ class Chunking:
                 SemanticChunker,
                 SDPMChunker,
                 LateChunker,
-                RecursiveChunker
+                RecursiveChunker,
+                CodeChunker,
+                NeuralChunker,
+                SlumberChunker
             )
         except ImportError:
             raise ImportError(
@@ -37,7 +43,10 @@ class Chunking:
             'semantic': SemanticChunker,
             'sdpm': SDPMChunker,
             'late': LateChunker,
-            'recursive': RecursiveChunker
+            'recursive': RecursiveChunker,
+            'code': CodeChunker,
+            'neural': NeuralChunker,
+            'slumber': SlumberChunker
         }
 
     def __init__(

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

@@ -42,6 +42,8 @@ from .protocols import (
     MemoryProtocol, 
     AsyncMemoryProtocol, 
     ResettableMemoryProtocol,
+    DeletableMemoryProtocol,
+    AsyncDeletableMemoryProtocol,
     EntityMemoryProtocol,
 )
 
@@ -203,5 +205,7 @@ __all__ = [
     "MemoryProtocol",
     "AsyncMemoryProtocol",
     "ResettableMemoryProtocol",
+    "DeletableMemoryProtocol",
+    "AsyncDeletableMemoryProtocol",
     "EntityMemoryProtocol",
 ] 

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

@@ -806,6 +806,220 @@ class FileMemory:
         
         self._log("Cleared all memory")
     
+    # -------------------------------------------------------------------------
+    #                          Selective Deletion
+    # -------------------------------------------------------------------------
+    
+    def delete_short_term(self, memory_id: str) -> bool:
+        """
+        Delete a specific short-term memory by ID.
+        
+        Args:
+            memory_id: The unique ID of the memory to delete
+            
+        Returns:
+            True if memory was found and deleted, False otherwise
+        """
+        for i, item in enumerate(self._short_term):
+            if item.id == memory_id:
+                del self._short_term[i]
+                self._save_short_term()
+                self._log(f"Deleted short-term memory: {memory_id}")
+                return True
+        return False
+    
+    def delete_long_term(self, memory_id: str) -> bool:
+        """
+        Delete a specific long-term memory by ID.
+        
+        Args:
+            memory_id: The unique ID of the memory to delete
+            
+        Returns:
+            True if memory was found and deleted, False otherwise
+        """
+        for i, item in enumerate(self._long_term):
+            if item.id == memory_id:
+                del self._long_term[i]
+                self._save_long_term()
+                self._log(f"Deleted long-term memory: {memory_id}")
+                return True
+        return False
+    
+    def delete_entity(self, name: str) -> bool:
+        """
+        Delete an entity by name.
+        
+        Args:
+            name: The name of the entity to delete
+            
+        Returns:
+            True if entity was found and deleted, False otherwise
+        """
+        # Find entity by name using existing helper
+        entity = self._find_entity_by_name(name)
+        if entity:
+            # Delete by entity's ID (which is the dict key)
+            if entity.id in self._entities:
+                del self._entities[entity.id]
+                self._save_entities()
+                self._log(f"Deleted entity: {name}")
+                return True
+        
+        # Try direct ID match as fallback (in case name IS the ID)
+        if name in self._entities:
+            del self._entities[name]
+            self._save_entities()
+            self._log(f"Deleted entity by ID: {name}")
+            return True
+        
+        return False
+    
+    def delete_episodic(self, memory_id: str, date: Optional[str] = None) -> bool:
+        """
+        Delete a specific episodic memory by ID.
+        
+        Args:
+            memory_id: The unique ID of the memory to delete
+            date: Optional date string (YYYY-MM-DD) to narrow search
+            
+        Returns:
+            True if memory was found and deleted, False otherwise
+        """
+        from datetime import datetime, timedelta
+        
+        # Determine which files to search
+        if date:
+            dates_to_check = [date]
+        else:
+            # Check last 30 days
+            dates_to_check = [
+                (datetime.now() - timedelta(days=i)).strftime("%Y-%m-%d")
+                for i in range(30)
+            ]
+        
+        for check_date in dates_to_check:
+            episodic_file = self.episodic_path / f"{check_date}.json"
+            if episodic_file.exists():
+                data = self._read_json(episodic_file, [])
+                for i, item in enumerate(data):
+                    if item.get("id") == memory_id:
+                        del data[i]
+                        self._write_json(episodic_file, data)
+                        self._log(f"Deleted episodic memory: {memory_id}")
+                        return True
+        
+        return False
+    
+    def delete_memory(
+        self, 
+        memory_id: str, 
+        memory_type: Optional[str] = None
+    ) -> bool:
+        """
+        Delete a specific memory by ID.
+        
+        This is the unified deletion method that searches across all memory types.
+        Use this when you have a memory ID but don't know its type.
+        
+        Particularly useful for:
+        - Cleaning up image-based memories after processing to free context window
+        - Removing outdated or incorrect information
+        - Privacy compliance (selective erasure)
+        
+        Args:
+            memory_id: The unique ID of the memory to delete
+            memory_type: Optional type hint to narrow search:
+                        'short_term', 'long_term', 'entity', 'episodic'
+                        If None, searches all types.
+            
+        Returns:
+            True if memory was found and deleted, False otherwise
+        
+        Example:
+            # Delete a specific memory after processing an image
+            memory.delete_memory("abc123def456")
+            
+            # Delete with type hint for faster lookup
+            memory.delete_memory("abc123", memory_type="short_term")
+        """
+        # If type specified, only search that type
+        if memory_type == "short_term":
+            return self.delete_short_term(memory_id)
+        elif memory_type == "long_term":
+            return self.delete_long_term(memory_id)
+        elif memory_type == "entity":
+            return self.delete_entity(memory_id)
+        elif memory_type == "episodic":
+            return self.delete_episodic(memory_id)
+        
+        # Search all types
+        if self.delete_short_term(memory_id):
+            return True
+        if self.delete_long_term(memory_id):
+            return True
+        if self.delete_entity(memory_id):
+            return True
+        if self.delete_episodic(memory_id):
+            return True
+        
+        return False
+    
+    def delete_memories(self, memory_ids: List[str]) -> int:
+        """
+        Delete multiple memories by their IDs.
+        
+        Args:
+            memory_ids: List of memory IDs to delete
+            
+        Returns:
+            Number of memories successfully deleted
+        """
+        count = 0
+        for memory_id in memory_ids:
+            if self.delete_memory(memory_id):
+                count += 1
+        return count
+    
+    def delete_memories_matching(
+        self, 
+        query: str, 
+        memory_types: Optional[List[str]] = None,
+        limit: int = 10
+    ) -> int:
+        """
+        Delete memories matching a search query.
+        
+        Useful for bulk cleanup of related memories, e.g., all image-related
+        context after finishing an image analysis session.
+        
+        Args:
+            query: Search query to match memories
+            memory_types: Optional list of types to search
+            limit: Maximum number of memories to delete
+            
+        Returns:
+            Number of memories deleted
+        """
+        # Search for matching memories
+        results = self.search(query, memory_types=memory_types, limit=limit)
+        
+        # Extract IDs and delete
+        deleted = 0
+        for result in results:
+            item = result.get("item", {})
+            memory_id = item.get("id")
+            memory_type = result.get("type")
+            
+            if memory_id:
+                if self.delete_memory(memory_id, memory_type=memory_type):
+                    deleted += 1
+        
+        if deleted:
+            self._log(f"Deleted {deleted} memories matching '{query}'")
+        
+        return deleted
+    
     def get_stats(self) -> Dict[str, Any]:
         """Get memory statistics."""
         episodic_count = sum(1 for _ in self.episodic_path.glob("*.json"))
@@ -1222,6 +1436,66 @@ Summary:"""
             else:
                 return {"error": "Usage: /memory clear [short|all]"}
         
+        elif action == "delete":
+            if not args:
+                return {"error": "Usage: /memory delete <id> or /memory delete --query <search_query>"}
+            
+            # Handle --query flag for bulk deletion
+            if args.startswith("--query "):
+                query = args[8:]  # Remove "--query " prefix
+                if not query:
+                    return {"error": "Usage: /memory delete --query <search_query>"}
+                deleted = self.delete_memories_matching(query, limit=10)
+                return {
+                    "action": "delete", 
+                    "type": "query", 
+                    "query": query, 
+                    "deleted_count": deleted
+                }
+            
+            # Single ID deletion
+            success = self.delete_memory(args)
+            return {
+                "action": "delete", 
+                "type": "single",
+                "id": args, 
+                "success": success
+            }
+        
+        elif action == "list":
+            # List memories with IDs for easy deletion reference
+            memory_type = args.lower() if args else "all"
+            items = []
+            
+            if memory_type in ("all", "short", "short_term"):
+                for item in self.get_short_term(limit=20):
+                    items.append({
+                        "type": "short_term",
+                        "id": item.id,
+                        "content": item.content[:100] + "..." if len(item.content) > 100 else item.content,
+                        "importance": item.importance
+                    })
+            
+            if memory_type in ("all", "long", "long_term"):
+                for item in self.get_long_term(limit=20):
+                    items.append({
+                        "type": "long_term",
+                        "id": item.id,
+                        "content": item.content[:100] + "..." if len(item.content) > 100 else item.content,
+                        "importance": item.importance
+                    })
+            
+            if memory_type in ("all", "entity", "entities"):
+                for name, entity in self._entities.items():
+                    items.append({
+                        "type": "entity",
+                        "id": name,
+                        "name": entity.name,
+                        "entity_type": entity.entity_type
+                    })
+            
+            return {"action": "list", "filter": memory_type, "items": items}
+        
         elif action == "search":
             if not args:
                 return {"error": "Usage: /memory search <query>"}
@@ -1274,8 +1548,11 @@ Summary:"""
                 "action": "help",
                 "commands": {
                     "/memory show": "Display memory stats and recent items",
+                    "/memory list [short|long|entity]": "List memories with IDs for deletion",
                     "/memory add <content>": "Add to long-term memory",
-                    "/memory clear [short|all]": "Clear memory",
+                    "/memory delete <id>": "Delete a specific memory by ID",
+                    "/memory delete --query <q>": "Delete memories matching query",
+                    "/memory clear [short|all]": "Clear all memory (bulk)",
                     "/memory search <query>": "Search memories",
                     "/memory save <name>": "Save session",
                     "/memory resume <name>": "Resume session",

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

@@ -1117,6 +1117,213 @@ class Memory:
             self.chroma_client.reset()  # entire DB
             self._init_chroma()         # re-init fresh
 
+    # -------------------------------------------------------------------------
+    #                       Selective Deletion Methods
+    # -------------------------------------------------------------------------
+    
+    def delete_short_term(self, memory_id: str) -> bool:
+        """
+        Delete a specific short-term memory by ID.
+        
+        Args:
+            memory_id: The unique ID of the memory to delete
+            
+        Returns:
+            True if memory was found and deleted, False otherwise
+        """
+        deleted = False
+        
+        # Delete from SQLite
+        try:
+            conn = sqlite3.connect(self.short_db)
+            cursor = conn.execute(
+                "DELETE FROM short_mem WHERE id = ?", (memory_id,)
+            )
+            if cursor.rowcount > 0:
+                deleted = True
+            conn.commit()
+            conn.close()
+        except Exception as e:
+            self._log_verbose(f"Error deleting from SQLite short-term: {e}", logging.ERROR)
+        
+        # Delete from MongoDB if enabled
+        if self.use_mongodb and hasattr(self, "mongo_short_term"):
+            try:
+                result = self.mongo_short_term.delete_one({"_id": memory_id})
+                if result.deleted_count > 0:
+                    deleted = True
+            except Exception as e:
+                self._log_verbose(f"Error deleting from MongoDB short-term: {e}", logging.ERROR)
+        
+        if deleted:
+            self._log_verbose(f"Deleted short-term memory: {memory_id}")
+        
+        return deleted
+    
+    def delete_long_term(self, memory_id: str) -> bool:
+        """
+        Delete a specific long-term memory by ID.
+        
+        Works across all backends: SQLite, ChromaDB, Mem0, and MongoDB.
+        
+        Args:
+            memory_id: The unique ID of the memory to delete
+            
+        Returns:
+            True if memory was found and deleted, False otherwise
+        """
+        deleted = False
+        
+        # Delete from SQLite
+        try:
+            conn = sqlite3.connect(self.long_db)
+            cursor = conn.execute(
+                "DELETE FROM long_mem WHERE id = ?", (memory_id,)
+            )
+            if cursor.rowcount > 0:
+                deleted = True
+            conn.commit()
+            conn.close()
+        except Exception as e:
+            self._log_verbose(f"Error deleting from SQLite long-term: {e}", logging.ERROR)
+        
+        # Delete from ChromaDB if enabled
+        if self.use_rag and hasattr(self, "chroma_col"):
+            try:
+                # ChromaDB delete by ID
+                self.chroma_col.delete(ids=[memory_id])
+                deleted = True  # ChromaDB doesn't raise on non-existent ID
+            except Exception as e:
+                self._log_verbose(f"Error deleting from ChromaDB: {e}", logging.ERROR)
+        
+        # Delete from Mem0 if enabled
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            try:
+                # Mem0 has a delete method
+                self.mem0_client.delete(memory_id)
+                deleted = True
+            except Exception as e:
+                self._log_verbose(f"Error deleting from Mem0: {e}", logging.ERROR)
+        
+        # Delete from MongoDB if enabled
+        if self.use_mongodb and hasattr(self, "mongo_long_term"):
+            try:
+                result = self.mongo_long_term.delete_one({"_id": memory_id})
+                if result.deleted_count > 0:
+                    deleted = True
+            except Exception as e:
+                self._log_verbose(f"Error deleting from MongoDB long-term: {e}", logging.ERROR)
+        
+        if deleted:
+            self._log_verbose(f"Deleted long-term memory: {memory_id}")
+        
+        return deleted
+    
+    def delete_memory(
+        self, 
+        memory_id: str, 
+        memory_type: Optional[str] = None
+    ) -> bool:
+        """
+        Delete a specific memory by ID.
+        
+        This is the unified deletion method that searches across all memory types
+        and all backends (SQLite, ChromaDB, Mem0, MongoDB).
+        
+        Particularly useful for:
+        - Cleaning up image-based memories after processing to free context window
+        - Removing outdated or incorrect information
+        - Privacy compliance (selective erasure)
+        
+        Args:
+            memory_id: The unique ID of the memory to delete
+            memory_type: Optional type hint to narrow search:
+                        'short_term', 'long_term'
+                        If None, searches all types.
+            
+        Returns:
+            True if memory was found and deleted, False otherwise
+        
+        Example:
+            # Delete a specific image analysis memory
+            memory.delete_memory("1705123456789")
+            
+            # Delete with type hint for faster lookup
+            memory.delete_memory("1705123456789", memory_type="short_term")
+        """
+        # If type specified, only search that type
+        if memory_type == "short_term":
+            return self.delete_short_term(memory_id)
+        elif memory_type == "long_term":
+            return self.delete_long_term(memory_id)
+        
+        # Search both types
+        if self.delete_short_term(memory_id):
+            return True
+        if self.delete_long_term(memory_id):
+            return True
+        
+        return False
+    
+    def delete_memories(self, memory_ids: List[str]) -> int:
+        """
+        Delete multiple memories by their IDs.
+        
+        Args:
+            memory_ids: List of memory IDs to delete
+            
+        Returns:
+            Number of memories successfully deleted
+        """
+        count = 0
+        for memory_id in memory_ids:
+            if self.delete_memory(memory_id):
+                count += 1
+        return count
+    
+    def delete_memories_matching(
+        self, 
+        query: str, 
+        memory_type: Optional[str] = None,
+        limit: int = 10
+    ) -> int:
+        """
+        Delete memories matching a search query.
+        
+        Useful for bulk cleanup of related memories, e.g., all image-related
+        context after finishing an image analysis session.
+        
+        Args:
+            query: Search query to match memories
+            memory_type: Optional type ('short_term' or 'long_term')
+            limit: Maximum number of memories to delete
+            
+        Returns:
+            Number of memories deleted
+        """
+        deleted = 0
+        
+        # Search short-term if applicable
+        if memory_type in (None, "short_term"):
+            results = self.search_short_term(query, limit=limit)
+            for result in results:
+                memory_id = result.get("id")
+                if memory_id and self.delete_short_term(memory_id):
+                    deleted += 1
+        
+        # Search long-term if applicable
+        if memory_type in (None, "long_term"):
+            results = self.search_long_term(query, limit=limit)
+            for result in results:
+                memory_id = result.get("id")
+                if memory_id and self.delete_long_term(memory_id):
+                    deleted += 1
+        
+        if deleted:
+            self._log_verbose(f"Deleted {deleted} memories matching '{query}'")
+        
+        return deleted
+
     # -------------------------------------------------------------------------
     #                       Entity Memory Methods
     # -------------------------------------------------------------------------

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

@@ -138,6 +138,77 @@ class ResettableMemoryProtocol(MemoryProtocol, Protocol):
         ...
 
 
+@runtime_checkable
+class DeletableMemoryProtocol(MemoryProtocol, Protocol):
+    """
+    Protocol for memory that supports selective deletion.
+    
+    Enables removing specific memories by ID without clearing all data.
+    Essential for:
+    - Privacy compliance (GDPR right to erasure)
+    - Context window management (removing large image-based memories)
+    - Correcting outdated information
+    
+    Example:
+        ```python
+        class MyMemory:
+            def delete_memory(self, memory_id: str) -> bool:
+                if memory_id in self._store:
+                    del self._store[memory_id]
+                    return True
+                return False
+            
+            def delete_memories(self, memory_ids: List[str]) -> int:
+                count = 0
+                for mid in memory_ids:
+                    if self.delete_memory(mid):
+                        count += 1
+                return count
+        ```
+    """
+    
+    def delete_memory(self, memory_id: str, memory_type: Optional[str] = None) -> bool:
+        """
+        Delete a specific memory by ID.
+        
+        Args:
+            memory_id: The unique identifier of the memory to delete
+            memory_type: Optional type hint (short_term, long_term, entity)
+                        If None, searches all types.
+            
+        Returns:
+            True if memory was found and deleted, False otherwise
+        """
+        ...
+    
+    def delete_memories(self, memory_ids: List[str]) -> int:
+        """
+        Delete multiple memories by their IDs.
+        
+        Args:
+            memory_ids: List of memory IDs to delete
+            
+        Returns:
+            Number of memories successfully deleted
+        """
+        ...
+
+
+@runtime_checkable
+class AsyncDeletableMemoryProtocol(Protocol):
+    """
+    Async Protocol for memory deletion operations.
+    """
+    
+    async def adelete_memory(self, memory_id: str, memory_type: Optional[str] = None) -> bool:
+        """Async delete a specific memory by ID."""
+        ...
+    
+    async def adelete_memories(self, memory_ids: List[str]) -> int:
+        """Async delete multiple memories by their IDs."""
+        ...
+
+
 @runtime_checkable
 class AsyncMemoryProtocol(Protocol):
     """
@@ -207,6 +278,9 @@ class EntityMemoryProtocol(Protocol):
 __all__ = [
     'MemoryProtocol',
     'ResettableMemoryProtocol',
+    'DeletableMemoryProtocol',
+    'AsyncDeletableMemoryProtocol',
     'AsyncMemoryProtocol',
     'EntityMemoryProtocol',
 ]
+

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

@@ -43,6 +43,7 @@ class Task:
         rerun: bool = False, # Renamed from can_rerun and logic inverted, default True for backward compatibility
         retain_full_context: bool = False, # By default, only use previous task output, not all previous tasks
         guardrail: Optional[Union[Callable[[TaskOutput], Tuple[bool, Any]], str]] = None,
+        guardrails: Optional[Union[Callable[[TaskOutput], Tuple[bool, Any]], str]] = None,  # Alias for guardrail
         max_retries: int = 3,
         retry_count: int = 0,
         agent_config: Optional[Dict[str, Any]] = None,
@@ -86,7 +87,7 @@ class Task:
         self.quality_check = quality_check
         self.rerun = rerun # Assigning the rerun parameter
         self.retain_full_context = retain_full_context
-        self.guardrail = guardrail
+        self.guardrail = guardrail if guardrail is not None else guardrails  # guardrails is alias
         self.max_retries = max_retries
         self.retry_count = retry_count
         self._guardrail_fn = None

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.12.2"
+version = "0.12.4"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/query_rewriter_example.py

@@ -12,8 +12,7 @@ from praisonaiagents import QueryRewriterAgent, RewriteStrategy
 def main():
     # Initialize the agent
     agent = QueryRewriterAgent(
-        model="gpt-4o-mini",
-        verbose=True
+        model="gpt-4o-mini"
     )
     
     print("=" * 60)

src/praisonai-agents/realtime-streaming.py

@@ -4,8 +4,7 @@ agent = Agent(
     instructions="You are a helpful assistant",
     llm="gemini/gemini-2.0-flash",
     reflection=False,
-    verbose=False,
-    stream=True
+    output="stream"
 )
 
 # Use return_generator=True to get the raw generator for custom streaming handling

src/praisonai-agents/reproduce_issue_878_simple.py

@@ -6,12 +6,12 @@ This script directly tests the LLM class to determine where the duplicate callba
 ROOT CAUSE IDENTIFIED:
 In llm.py, there are two separate callback execution paths:
 1. execute_sync_callback() called directly (lines 851-857, 885-891, etc.)
-2. display_interaction() called when verbose=True (line 896, etc.)
+2. display_interaction() called when output="verbose" (line 896, etc.)
 
 Since display_interaction() ALSO executes sync callbacks internally (main.py lines 164-190),
 this results in duplicate callback execution for the same LLM response.
 
-The issue occurs specifically when verbose=True because both callback paths are active.
+The issue occurs specifically when output="verbose" because both callback paths are active.
 """
 
 import os
@@ -74,11 +74,11 @@ def test_llm_streaming():
     register_display_callback('interaction', track_callback, is_async=False)
     
     try:
-        llm = LLM(model="gemini/gemini-2.5-flash-lite-preview-06-17", verbose=False)
+        llm = LLM(model="gemini/gemini-2.5-flash-lite-preview-06-17", output="silent")
         
         response = llm.get_response(
             prompt="Say exactly: Hello World",
-            verbose=True,  # This should trigger the callback
+            output="verbose",  # This should trigger the callback
             stream=True    # Test with streaming
         )
         
@@ -103,11 +103,11 @@ def test_llm_non_streaming():
     register_display_callback('interaction', track_callback, is_async=False)
     
     try:
-        llm = LLM(model="gemini/gemini-2.5-flash-lite-preview-06-17", verbose=False)
+        llm = LLM(model="gemini/gemini-2.5-flash-lite-preview-06-17", output="silent")
         
         response = llm.get_response(
             prompt="Say exactly: Hello World",
-            verbose=True,  # This should trigger the callback
+            output="verbose",  # This should trigger the callback
             stream=False   # Test without streaming
         )
         
@@ -132,11 +132,11 @@ def test_llm_with_self_reflection():
     register_display_callback('interaction', track_callback, is_async=False)
     
     try:
-        llm = LLM(model="gemini/gemini-2.5-flash-lite-preview-06-17", verbose=False, reflection=True)
+        llm = LLM(model="gemini/gemini-2.5-flash-lite-preview-06-17", output="silent", reflection=True)
         
         response = llm.get_response(
             prompt="Say exactly: Hello World",
-            verbose=True,    # This should trigger the callback
+            output="verbose",    # This should trigger the callback
             reflection=True,
             min_reflect=1,
             max_reflect=1
@@ -152,25 +152,25 @@ def test_llm_with_self_reflection():
         return -1
 
 def test_verbose_vs_non_verbose():
-    """Test the key difference: verbose=True vs verbose=False to isolate the duplicate issue."""
+    """Test the key difference: output="verbose" vs output="silent" to isolate the duplicate issue."""
     print("🧪 TEST 4: Verbose vs Non-Verbose (Key Test for Duplicate Issue)")
     print("-" * 60)
     
     global callback_invocations
     
-    # Test with verbose=False (should trigger callback once via execute_sync_callback)
-    print("  🔹 Testing with verbose=False:")
+    # Test with output="silent" (should trigger callback once via execute_sync_callback)
+    print("  🔹 Testing with output="silent":")
     callback_invocations = []
     register_display_callback('interaction', track_callback, is_async=False)
     
     try:
-        llm = LLM(model="gemini/gemini-2.5-flash-lite-preview-06-17", verbose=False)
+        llm = LLM(model="gemini/gemini-2.5-flash-lite-preview-06-17", output="silent")
         response = llm.get_response(
             prompt="Say exactly: Test Non-Verbose",
-            verbose=False  # This should only trigger execute_sync_callback
+            output="silent"  # This should only trigger execute_sync_callback
         )
         print(f"    Response: {response}")
-        print(f"    Callbacks with verbose=False: {len(callback_invocations)}")
+        print(f"    Callbacks with output="silent": {len(callback_invocations)}")
         non_verbose_count = len(callback_invocations)
     except Exception as e:
         print(f"    ❌ Error: {e}")
@@ -178,19 +178,19 @@ def test_verbose_vs_non_verbose():
     
     print()
     
-    # Test with verbose=True (should trigger callback twice: execute_sync_callback + display_interaction)
-    print("  🔹 Testing with verbose=True:")
+    # Test with output="verbose" (should trigger callback twice: execute_sync_callback + display_interaction)
+    print("  🔹 Testing with output="verbose":")
     callback_invocations = []
     register_display_callback('interaction', track_callback, is_async=False)
     
     try:
-        llm = LLM(model="gemini/gemini-2.5-flash-lite-preview-06-17", verbose=False)
+        llm = LLM(model="gemini/gemini-2.5-flash-lite-preview-06-17", output="silent")
         response = llm.get_response(
             prompt="Say exactly: Test Verbose",
-            verbose=True  # This should trigger BOTH execute_sync_callback AND display_interaction
+            output="verbose"  # This should trigger BOTH execute_sync_callback AND display_interaction
         )
         print(f"    Response: {response}")
-        print(f"    Callbacks with verbose=True: {len(callback_invocations)}")
+        print(f"    Callbacks with output="verbose": {len(callback_invocations)}")
         verbose_count = len(callback_invocations)
         
         # Show the call stack differences
@@ -204,12 +204,12 @@ def test_verbose_vs_non_verbose():
         verbose_count = -1
     
     print(f"\n  📊 RESULT:")
-    print(f"    verbose=False: {non_verbose_count} callback(s)")
-    print(f"    verbose=True:  {verbose_count} callback(s)")
+    print(f"    output="silent": {non_verbose_count} callback(s)")
+    print(f"    output="verbose":  {verbose_count} callback(s)")
     
     if verbose_count > non_verbose_count and verbose_count > 1:
         print(f"    🚨 DUPLICATE ISSUE CONFIRMED!")
-        print(f"       verbose=True triggers {verbose_count - non_verbose_count} extra callback(s)")
+        print(f"       output="verbose" triggers {verbose_count - non_verbose_count} extra callback(s)")
     
     return verbose_count
 
@@ -225,13 +225,13 @@ def test_multiple_calls():
     register_display_callback('interaction', track_callback, is_async=False)
     
     try:
-        llm = LLM(model="gemini/gemini-2.5-flash-lite-preview-06-17", verbose=False)
+        llm = LLM(model="gemini/gemini-2.5-flash-lite-preview-06-17", output="silent")
         
         for i in range(3):
             print(f"  Call {i+1}:")
             response = llm.get_response(
                 prompt=f"Say exactly: Call {i+1}",
-                verbose=True
+                output="verbose"
             )
             print(f"    Response: {response}")
         
@@ -319,10 +319,10 @@ if __name__ == "__main__":
             if count > expected * 1.5:  # Allow some tolerance
                 issues_detected.append(f"{test_name}: {count} > expected {expected}")
         elif test_name == 'verbose_vs_nonverbose':
-            # This is the key test - verbose=True should show the duplicate issue
-            print(f"{test_name}: {count} invocations (this test shows verbose=True duplicates)")
+            # This is the key test - output="verbose" should show the duplicate issue
+            print(f"{test_name}: {count} invocations (this test shows output="verbose" duplicates)")
             if count > 1:
-                issues_detected.append(f"{test_name}: verbose=True triggered {count} callbacks (duplicate issue)")
+                issues_detected.append(f"{test_name}: output="verbose" triggered {count} callbacks (duplicate issue)")
         else:
             expected = 1
             print(f"{test_name}: {count} invocations (expected {expected})")

src/praisonai-agents/session_memory.py

@@ -10,7 +10,7 @@ agent = session.Agent(
     name="Assistant",
     instructions="You are a helpful assistant with memory.",
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=False,
+    output="silent",
     memory=True
 )
 
@@ -33,7 +33,7 @@ anotherAgent = anotherSession.Agent(
     name="Assistant",
     instructions="You are a helpful assistant with memory.",
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=False,
+    output="silent",
     memory=True
 )
 

src/praisonai-agents/streaming-openai-basic.py

@@ -16,7 +16,7 @@ from praisonaiagents.config import OutputConfig
 agent = Agent(
     instructions="You are a helpful assistant",
     llm="gpt-4o-mini",
-    output=OutputConfig(verbose=True)  # Show generation progress
+    output="verbose"  # Show generation progress
 )
 
 # Run the agent - verbose mode shows streaming progress via Rich Live display

src/praisonai-agents/test_agent_planning_quiet.py

@@ -10,7 +10,7 @@ agent = Agent(
     planning=True,
     planning_tools=[search_web],
     planning_reasoning=True,
-    verbose=False  # Quiet mode
+    output="silent"  # Quiet mode
 )
 
 result = agent.start("Research AI trends in 2025 and write a 2 sentence summary")

src/praisonai-agents/test_all_optimizations.py

@@ -176,9 +176,9 @@ def test_llm_console_lazy_loading():
     print("\n6. Testing LLM console lazy loading...")
     
     # LLM without verbose (console not needed)
-    llm1 = LLM(model="gemini/gemini-2.0-flash-exp", verbose=False)
+    llm1 = LLM(model="gemini/gemini-2.0-flash-exp", output="silent")
     console_created_before = hasattr(llm1, '_console') and llm1._console is not None
-    print(f"   Console created before access (verbose=False): {console_created_before}")
+    print(f"   Console created before access (output="silent"): {console_created_before}")
     
     # Access console
     _ = llm1.console

src/praisonai-agents/test_async_sequential.py

@@ -151,7 +151,7 @@ async def test_sequential_async_execution():
             agents=[async_agent, sync_agent],
             tasks=tasks,
             process="sequential",
-            verbose=True
+            output="verbose"
         )
         
         # Run the workflow

src/praisonai-agents/test_autoagents_hierarchical_fix.py

@@ -48,7 +48,7 @@ def test_hierarchical_without_openai():
             process="hierarchical",
             llm="gemini/gemini-2.5-flash-lite-preview-06-17",
             reflection=True,
-            verbose=True,
+            output="verbose",
             max_agents=3  # Maximum number of agents to create
         )
         
@@ -88,7 +88,7 @@ def test_hierarchical_with_mock_llm():
                 "api_key": "mock-key",
                 "base_url": "http://localhost:9999"  # Non-existent URL
             },
-            verbose=False,
+            output="silent",
             max_agents=2
         )
         

src/praisonai-agents/test_callback_enhancement.py

@@ -43,7 +43,7 @@ def test_callback_enhancement():
         goal="Help with testing callbacks",
         backstory="I am a helpful assistant for testing purposes",
         llm="openai/gpt-5-nano",
-        verbose=True  
+        output="verbose"  
     )
 
     # Create a task with detailed information
@@ -59,7 +59,7 @@ def test_callback_enhancement():
         agents = Agents(
             agents=[agent],
             tasks=[task],
-            verbose=True
+            output="verbose"
         )
         result = agents.start()
         print(f"Test completed successfully! Result: {result}")

src/praisonai-agents/test_callback_fix.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-"""Test script to verify callbacks work with verbose=False"""
+"""Test script to verify callbacks work with output="silent""""
 
 import sys
 sys.path.insert(0, 'src/praisonai-agents')
@@ -27,15 +27,15 @@ def simple_callback(message=None, response=None, **kwargs):
 # Register callback
 register_display_callback('interaction', simple_callback, is_async=False)
 
-# Test with verbose=False
-print("Testing with verbose=False...")
+# Test with output="silent"
+print("Testing with output="silent"...")
 agent = Agent(
     name="TestAgent",
     role="Assistant",
     goal="Help with tasks",
     backstory="I am a helpful assistant",
     llm="gpt-4o-mini",  # Using a simple model for testing
-    verbose=False
+    output="silent"
 )
 
 task = Task(
@@ -47,7 +47,7 @@ task = Task(
 
 # Clear the log file
 with open('callback_test_log.txt', 'w') as f:
-    f.write("Starting test with verbose=False\n")
+    f.write("Starting test with output="silent"\n")
 
 # Run the agent
 try:
@@ -61,9 +61,9 @@ try:
     
     # Check if callback was executed
     if callback_executed:
-        print("✅ SUCCESS: Callback was executed with verbose=False!")
+        print("✅ SUCCESS: Callback was executed with output="silent"!")
     else:
-        print("❌ FAILED: Callback was NOT executed with verbose=False")
+        print("❌ FAILED: Callback was NOT executed with output="silent"")
         
 except Exception as e:
     print(f"Error during test: {e}")

src/praisonai-agents/test_duplicate_callback_fix.py

@@ -43,10 +43,10 @@ def test_single_display_no_tools():
                 MagicMock(choices=[MagicMock(delta=MagicMock(content="!"))])
             ]
             
-            llm = LLM(model="gpt-4o-mini", verbose=False)
+            llm = LLM(model="gpt-4o-mini", output="silent")
             response = llm.get_response(
                 prompt="Test prompt",
-                verbose=True,
+                output="verbose",
                 stream=True
             )
             
@@ -82,10 +82,10 @@ def test_single_display_with_reasoning():
                 }]
             }
             
-            llm = LLM(model="o1-preview", verbose=False, reasoning_steps=True)
+            llm = LLM(model="o1-preview", output="silent", reasoning_steps=True)
             response = llm.get_response(
                 prompt="What is the meaning of life?",
-                verbose=True,
+                output="verbose",
                 stream=False,
                 reasoning_steps=True
             )
@@ -139,10 +139,10 @@ def test_single_display_with_self_reflection():
                 
                 mock_completion.side_effect = mock_streaming
                 
-                llm = LLM(model="gpt-4o-mini", verbose=False, reflection=True, min_reflect=1, max_reflect=2)
+                llm = LLM(model="gpt-4o-mini", output="silent", reflection=True, min_reflect=1, max_reflect=2)
                 response = llm.get_response(
                     prompt="Test prompt",
-                    verbose=True,
+                    output="verbose",
                     stream=True,
                     reflection=True,
                     min_reflect=1,
@@ -179,10 +179,10 @@ def test_async_single_display():
             
             mock_acompletion.return_value = async_generator()
             
-            llm = LLM(model="gpt-4o-mini", verbose=False)
+            llm = LLM(model="gpt-4o-mini", output="silent")
             response = await llm.get_response_async(
                 prompt="Test async",
-                verbose=True,
+                output="verbose",
                 stream=True
             )
             

src/praisonai-agents/test_fix_simple.py

@@ -15,7 +15,7 @@ def test_self_reflection_fix():
         backstory="You are a helpful math assistant",
         reflection=True,
         llm="gpt-4o-mini",  # Use a more widely available model
-        verbose=True,
+        output="verbose",
         tools=[evaluate],
         min_reflect=1,
         max_reflect=2

src/praisonai-agents/test_fix_verification.py

@@ -25,7 +25,7 @@ test_agent = Agent(
     goal="Test the fix",
     tools=[simple_tool],
     llm="gpt-4o-mini",  # Using OpenAI for testing
-    verbose=False  # This is key - verbose=False triggers the bug
+    output="silent"  # This is key - output="silent" triggers the bug
 )
 
 # Simple task
@@ -44,7 +44,7 @@ async def main():
         agents=[test_agent],
         tasks=[test_task],
         process="sequential",
-        verbose=False  # This should not cause the error anymore
+        output="silent"  # This should not cause the error anymore
     )
     
     try:

src/praisonai-agents/test_gemini_embedding.py

@@ -43,7 +43,7 @@ def test_gemini_embedding():
     agents_openai = Agents(
         agents=[agent],
         tasks=[task],
-        verbose=5,
+        output="debug",
         memory=True
     )
     
@@ -72,7 +72,7 @@ def test_gemini_embedding():
     agents_openai_explicit = Agents(
         agents=[agent2],
         tasks=[task2],
-        verbose=5,
+        output="debug",
         memory=True,
         embedder={
             "provider": "openai",
@@ -108,7 +108,7 @@ def test_gemini_embedding():
     agents_gemini = Agents(
         agents=[agent3],
         tasks=[task3],
-        verbose=5,
+        output="debug",
         memory=True,
         embedder={
             "provider": "gemini",

src/praisonai-agents/test_issue_877.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
 Test script to reproduce and verify the fix for issue #877
-where callbacks only work when verbose=True.
+where callbacks only work when output="verbose".
 """
 
 import sys
@@ -36,11 +36,11 @@ with open('callback_log.txt', 'w') as f:
 register_display_callback('interaction', simple_callback, is_async=False)
 
 print("=" * 60)
-print("Testing Issue #877: Callbacks Only Work When verbose=True")
+print("Testing Issue #877: Callbacks Only Work When output="verbose"")
 print("=" * 60)
 
-# Test Case 1: verbose=False
-print("\nTest Case 1: verbose=False")
+# Test Case 1: output="silent"
+print("\nTest Case 1: output="silent"")
 print("-" * 30)
 callback_log.clear()
 
@@ -50,7 +50,7 @@ agent1 = Agent(
     goal="Help with tasks",
     backstory="I am a helpful assistant",
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",  # Using the same model as in the issue
-    verbose=False  
+    output="silent"  
 )
 
 task1 = Task(
@@ -68,17 +68,17 @@ try:
     result1 = agents1.start()
     print(f"Task completed. Callbacks executed: {len(callback_log)}")
     if len(callback_log) > 0:
-        print("✅ SUCCESS: Callbacks were executed with verbose=False!")
+        print("✅ SUCCESS: Callbacks were executed with output="silent"!")
         print(f"   First callback: message='{callback_log[0]['message'][:50]}...', response='{callback_log[0]['response'][:50]}...'")
     else:
-        print("❌ FAILED: No callbacks were executed with verbose=False")
+        print("❌ FAILED: No callbacks were executed with output="silent"")
 except Exception as e:
     print(f"❌ ERROR during test: {e}")
     import traceback
     traceback.print_exc()
 
-# Test Case 2: verbose=True (for comparison)
-print("\n\nTest Case 2: verbose=True (for comparison)")
+# Test Case 2: output="verbose" (for comparison)
+print("\n\nTest Case 2: output="verbose" (for comparison)")
 print("-" * 30)
 callback_log.clear()
 
@@ -88,7 +88,7 @@ agent2 = Agent(
     goal="Help with tasks",
     backstory="I am a helpful assistant",
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True  
+    output="verbose"  
 )
 
 task2 = Task(
@@ -106,10 +106,10 @@ try:
     result2 = agents2.start()
     print(f"\nTask completed. Callbacks executed: {len(callback_log)}")
     if len(callback_log) > 0:
-        print("✅ Callbacks were executed with verbose=True as expected")
+        print("✅ Callbacks were executed with output="verbose" as expected")
         print(f"   First callback: message='{callback_log[0]['message'][:50]}...', response='{callback_log[0]['response'][:50]}...'")
     else:
-        print("⚠️  WARNING: No callbacks were executed even with verbose=True")
+        print("⚠️  WARNING: No callbacks were executed even with output="verbose"")
 except Exception as e:
     print(f"❌ ERROR during test: {e}")
     import traceback

src/praisonai-agents/test_issue_947.py

@@ -23,7 +23,7 @@ article_agent = Agent(
     goal="Fetch the user's favorite morning article",
     tools=[fetch_favorite_article],
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True
+    output="verbose"
 )
 
 news_agent = Agent(
@@ -32,7 +32,7 @@ news_agent = Agent(
     goal="Search for trending news in Kenya",
     tools=[search_trending_kenya],
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True
+    output="verbose"
 )
 
 twitter_agent = Agent(
@@ -41,7 +41,7 @@ twitter_agent = Agent(
     goal="Fetch the latest Twitter feed",
     tools=[fetch_twitter_feed],
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True
+    output="verbose"
 )
 
 aggregator = Agent(
@@ -49,7 +49,7 @@ aggregator = Agent(
     role="Result Aggregator",
     goal="Aggregate and summarize all results",
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True
+    output="verbose"
 )
 
 # Tasks for each agent
@@ -94,7 +94,7 @@ async def main():
         agents=[article_agent, news_agent, twitter_agent, aggregator],
         tasks=[article_task, news_task, twitter_task, aggregate_task],
         process="workflow",
-        verbose=True
+        output="verbose"
     )
     results = await workflow.astart()
 

src/praisonai-agents/test_multi_provider.py

@@ -60,7 +60,7 @@ def test_router_agent():
         goal="Test multi-model functionality",
         models=["gpt-4o-mini"],  # Using single model for testing
         routing_strategy="auto",
-        verbose=False
+        output="silent"
     )
     
     print(f"Agent created: {agent.name}")
@@ -88,7 +88,7 @@ def test_integration():
         goal="Test the integration",
         models=["gpt-4o-mini"],  # Single model for testing
         routing_strategy="manual",  # Use manual to avoid complexity
-        verbose=False
+        output="silent"
     )
     
     # Create a simple task
@@ -104,7 +104,7 @@ def test_integration():
         agents=[agent],
         tasks=[task],
         process="sequential",
-        verbose=False
+        output="silent"
     )
     
     print("Created Agents with RouterAgent")