fix(examples): phase-2 validation fixes for memory/knowledge API and token metrics (#1859 )

* fix: correct TypeError in 3 example files by using proper parameter names - Fix MemoryConfig(memory=...) → user_id=... in agent_centric_api.py - Fix Session(memory=...) → user_id=... in session-example.py - Fix build_context_for_task(memory=...) → user_id=... in memory-quality-example.py Co-authored-by: Mervin Praison <[email protected]> * fix(examples): align AgentTeam memory and knowledge configs with v4.6.52 API --------- Co-authored-by: praisonai-triage-agent[bot] <272766704+praisonai-triage-agent[bot]@users.noreply.github.com> Co-authored-by: Mervin Praison <[email protected]> Co-authored-by: Dhivya-Bharathy <[email protected]>
fix: correct TypeError in 3 example files by using proper parameter names
2026-11-08 08:11:07 -10:00 · 2026-06-08 07:47:59 +01:00 · 2026-06-04 11:12:18 +00:00 · 2026-06-04 16:30:02 +05:30
51 changed files with 129 additions and 99 deletions
@@ -35,7 +35,7 @@ def run_quick_comparison():
        prompt="What is 2+2?",
        iterations=2,
        paths=["openai_sdk", "praisonai_agent"],
-        output="verbose"
+        verbose=True
    )
    
    # Print comparison table
@@ -56,7 +56,7 @@ def run_full_benchmark():
    report = handler.run_full_benchmark(
        prompt="Hi",
        iterations=3,
-        output="verbose"
+        verbose=True
    )
    
    # Print full report
@@ -78,7 +78,7 @@ def run_agent_benchmark():
        prompt="Explain Python in one sentence",
        iterations=3,
        paths=["openai_sdk", "praisonai_agent"],
-        output="verbose"
+        verbose=True
    )
    
    # Show timeline diagrams
@@ -22,7 +22,7 @@ custom_modes = {
 }

 # Create handler with verbose output
-handler = CompareHandler(output="verbose")
+handler = CompareHandler(verbose=True)

 # Example: Manually create comparison results for analysis
 # (useful for testing without API calls)
@@ -22,7 +22,7 @@ modes = parse_modes("basic,tools,planning")
 print(f"\nParsed modes: {modes}")

 # Create compare handler
-handler = CompareHandler(output="verbose")
+handler = CompareHandler(verbose=True)

 # Compare modes (this will run actual agent calls)
 # Uncomment to run:
@@ -15,7 +15,7 @@ agent = Agent(
 # With array [preset, overrides]
 agent_override = Agent(
    instructions="You are a helpful assistant.",
-    memory=["redis", {"user_id": "custom_user"}],
+    memory=MemoryConfig(backend="redis", user_id="custom_user"),
 )

 if __name__ == "__main__":
@@ -15,8 +15,20 @@ workflow = AgentFlow(
    ],
    output="verbose",
    planning=True,
+    variables={"topic": "AI agents"},
 )

 if __name__ == "__main__":
-    result = workflow.run(variables={"topic": "AI agents"})
+    import os
+    import sys
+
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Set OPENAI_API_KEY to run this example.")
+        sys.exit(0)
+    if sys.platform == "win32":
+        try:
+            sys.stdout.reconfigure(encoding="utf-8")
+        except Exception:
+            pass
+    result = workflow.run("AI agents")
    print(result)
@@ -24,7 +24,7 @@ if has_api_key:
        input_text="What is 2 + 2?",
        expected_output="4",
        num_iterations=3,  # Run 3 times for statistical significance
-        output="verbose"
+        verbose=True
    )

    # Run evaluation
@@ -42,7 +42,7 @@ else:
        input_text="What is 2 + 2?",
        expected_output="4",
        num_iterations=1,
-        output="verbose"
+        verbose=True
    )
    
    # Test with mock output
@@ -27,7 +27,7 @@ if has_api_key:
        scoring_type="numeric",  # Score 1-10
        threshold=7.0,           # Pass if score >= 7
        num_iterations=2,
-        output="verbose"
+        verbose=True
    )

    # Run evaluation
@@ -45,7 +45,7 @@ if has_api_key:
        agent=agent,
        input_text="Tell me a joke",
        scoring_type="binary",  # Pass or Fail
-        output="verbose"
+        verbose=True
    )

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

    # Run evaluation
@@ -48,7 +48,7 @@ if has_api_key:
        input_text="Search for the weather in Paris and calculate 25 * 4",
        expected_tools=["search_web", "calculate"],  # Tools that should be called
        forbidden_tools=["delete_file"],  # Tools that should NOT be called
-        output="verbose"
+        verbose=True
    )

    # Run evaluation
@@ -70,7 +70,7 @@ evaluator = ReliabilityEvaluator(
    agent=mock_agent,
    expected_tools=["search_web", "calculate"],  # Tools that should be called
    forbidden_tools=["delete_file"],  # Tools that should NOT be called
-    output="verbose"
+    verbose=True
 )

 result2 = evaluator.evaluate_tool_calls(
@@ -184,7 +184,7 @@ Answer questions based ONLY on the provided knowledge context.
 When asked about codes, provide the EXACT code from the documents.
 Be concise but accurate.""",
            knowledge=[temp_dir],
-            user_id="compression_demo_user",
+            memory={"user_id": "compression_demo_user"},
            output="verbose",
        )
        
@@ -164,7 +164,7 @@ Answer questions based ONLY on the provided knowledge context.
 When asked about department codes, provide the EXACT code from the documents.
 Be specific about which department you're referring to.""",
            knowledge=[temp_dir],
-            user_id="hierarchy_demo_user",
+            memory={"user_id": "hierarchy_demo_user"},
            output="verbose",
        )
        
@@ -124,7 +124,7 @@ def main():
            # Show strategy selection for different corpus sizes
            print(f"\nStrategy selection by corpus size:")
            for size in [100, 1000, 10000, 50000, 100000]:
-                strategy = select_strategy(corpus_tokens=size)
+                strategy = select_strategy(CorpusStats(file_count=max(1, size // 1000), total_tokens=size))
                print(f"  {size:>6} tokens -> {strategy.value}")
                
        except ImportError as e:
@@ -142,7 +142,7 @@ Answer questions based ONLY on the provided knowledge context.
 When asked about verification codes, provide the EXACT code from the documents.
 If information is not in the context, say 'Information not found in documents.'""",
            knowledge=[temp_dir],
-            user_id="strategy_demo_user",
+            memory={"user_id": "strategy_demo_user"},
            output="verbose",  # Use new consolidated param
        )
        
@@ -65,7 +65,7 @@ def main():
            name="PolicyExpert",
            instructions="You are a company policy expert. Answer questions based ONLY on the provided knowledge. Quote exact values when asked about codes or numbers.",
            knowledge=[temp_dir],  # Add directory with documents
-            user_id="example_user",  # Required for mem0 backend
+            memory={"user_id": "example_user"},
        )
        
        print("\n1. Testing Agent.chat() with context-required question:")
@@ -50,7 +50,7 @@ def main():
            name="HR_Assistant_User1",
            instructions="You are an HR assistant.",
            knowledge=[temp_dir],
-            user_id="user_alice",  # Knowledge scoped to Alice
+            memory={"user_id": "user_alice"},
        )
        
        response = user1_agent.chat("What is the policy on remote work?")
@@ -80,7 +80,7 @@ def main():
            name="Personal_HR_Bot",
            instructions="You are a personal HR assistant.",
            knowledge=[temp_dir],
-            user_id="user_bob",
+            memory={"user_id": "user_bob"},
            agent_id="hr_bot_v2",
        )
        
@@ -49,7 +49,7 @@ Only authorized personnel with code WRENCH-992 may access this project.
            name="AgentA_Ingester",
            instructions="You ingest and store knowledge.",
            knowledge=[temp_dir],
-            user_id="user_alice",  # Scoped to Alice
+            memory={"user_id": "user_alice"},
            output="silent",
        )
        
@@ -103,7 +103,7 @@ Only authorized personnel with code WRENCH-992 may access this project.
            name="AgentB_Querier",
            instructions="Answer questions based on the provided knowledge. If you don't have the information, say so.",
            knowledge=[temp_dir],
-            user_id="user_alice",  # Same scope as Agent A
+            memory={"user_id": "user_alice"},
            output="silent",
        )
        
@@ -50,10 +50,12 @@ print("=== Qdrant Knowledge Store Demo (Agent-First) ===")
 agent = Agent(
    name="KnowledgeAssistant",
    instructions="You are a helpful assistant with access to technical documentation.",
-    knowledge=[doc_path],
    knowledge={
-        "vector_store": "qdrant",
-        "url": "http://localhost:6333"
+        "sources": [doc_path],
+        "vector_store": {
+            "provider": "qdrant",
+            "url": "http://localhost:6333",
+        },
    }
 )

@@ -8,7 +8,7 @@ Docs: https://docs.praison.ai/cli/git-integration
 from praisonai.cli.features import GitIntegrationHandler

 # Initialize
-handler = GitIntegrationHandler(output="verbose")
+handler = GitIntegrationHandler(verbose=True)
 git = handler.initialize(repo_path=".")

 # Check if it's a git repo
@@ -8,7 +8,7 @@ Docs: https://docs.praison.ai/cli/repo-map
 from praisonai.cli.features import RepoMapHandler

 # Initialize with current directory
-handler = RepoMapHandler(output="verbose")
+handler = RepoMapHandler(verbose=True)
 repo_map = handler.initialize(root=".")

 # Get the repository map
@@ -9,7 +9,7 @@ from praisonai.cli.features import SandboxExecutorHandler
 from praisonai.cli.features.sandbox_executor import SandboxMode

 # Initialize with basic sandbox
-handler = SandboxExecutorHandler(output="verbose")
+handler = SandboxExecutorHandler(verbose=True)
 sandbox = handler.initialize(mode="basic")

 print(f"Sandbox mode: {handler.get_mode()}")
@@ -25,8 +25,7 @@ pdf_agent = Agent(
    goal="Help users understand and extract information from PDF documents",
    backstory="You are an expert at reading, analyzing, and answering questions about PDF documents. You provide accurate, detailed answers based on the document content.",
    instructions="Read the provided PDF document and answer questions about its content. Be specific and cite relevant sections when possible.",
-    knowledge=["document.pdf"],  # Replace with your PDF file path
-    knowledge=config,
+    knowledge={**config, "sources": ["document.pdf"]},  # Replace with your PDF path
    reflection=True,
    
    
@@ -1,6 +1,9 @@
+from pathlib import Path
+
 from praisonaiagents import Agent, Task, AgentTeam
 import logging
 import os
+import sys

 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -17,14 +20,18 @@ config = {
    }
 }

+_sample_path = Path(__file__).with_name("sample_knowledge.txt")
+if not _sample_path.exists():
+    _sample_path.write_text("Mervin Praison is the creator of PraisonAI.\n", encoding="utf-8")
+
 # Create an agent with knowledge capabilities
 knowledge_agent = Agent(
    name="KnowledgeAgent",
    role="Information Specialist",
    goal="Store and retrieve knowledge efficiently",
    backstory="Expert in managing and utilizing stored knowledge",
-    knowledge=["sample.pdf"],
-    knowledge=config)
+    knowledge={**config, "sources": [str(_sample_path)]},
+)

 # Define a task for the agent
 knowledge_task = Task(
@@ -39,8 +46,16 @@ agents = AgentTeam(
    agents=[knowledge_agent],
    tasks=[knowledge_task],
    process="sequential",
-    user_id="user1"
 )

-# Start execution
-result = agents.start()
+if __name__ == "__main__":
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Set OPENAI_API_KEY to run this example.")
+        sys.exit(0)
+    if sys.platform == "win32":
+        try:
+            sys.stdout.reconfigure(encoding="utf-8")
+        except Exception:
+            pass
+    result = agents.start()
+    print(result)
@@ -138,8 +138,7 @@ def main():
    agent = Agent(
        name="AI Research Assistant",
        instructions="You are an AI research assistant. Use your knowledge base to answer questions about artificial intelligence topics.",
-        knowledge=sample_documents,  # Will use default knowledge config
-        knowledge=rerank_config,  # Use reranking config
+        knowledge={**rerank_config, "sources": sample_documents},
        llm="gpt-4o-mini"
    )
    
@@ -32,8 +32,12 @@ rag_task = Task(
 agents = AgentTeam(
    agents=[rag_agent],
    tasks=[rag_task],
-    user_id="user1"
 )

-# Start Agents
-agents.start()
+if __name__ == "__main__":
+    import os
+    import sys
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Set OPENAI_API_KEY to run this example.")
+        sys.exit(0)
+    agents.start()
@@ -5,7 +5,7 @@ import os
 def main():
    # Initialize memory config
    memory_config = {
-        "provider": "rag",
+        "backend": "sqlite",
        "use_embedding": True,
        "storage": {
            "type": "sqlite",
@@ -105,13 +105,13 @@ def main():
        agents=[researcher, retriever],
        tasks=[store_task, verify_task, query_task, query_both_task],  # Use same verbose level as memory
        memory={
-            "provider": "rag",
            "embedder": {
                "provider": "openai",
                "config": {
                    "model": "text-embedding-3-small"
                }
-            }
+            },
+            "config": {"provider": "sqlite"},
        }
    )

@@ -12,8 +12,8 @@ print("=== Advanced Graph Memory Integration Example ===\n")

 # Configure simple graph memory
 memory_config = {
-    "provider": "mem0",
    "config": {
+        "provider": "mem0",
        "vector_store": {
            "provider": "chroma",
            "config": {
@@ -60,8 +60,8 @@ query_task = Task(
 agents_system = AgentTeam(
    agents=[knowledge_agent],
    tasks=[build_task, query_task],
-    memory=True,
-    memory=memory_config, output="verbose"
+    memory=memory_config,
+    output="verbose",
 )

 print("Starting graph memory demonstration...")
@@ -12,8 +12,8 @@ print("=== Advanced Memory Search and Optimization Example ===\n")

 # Configure optimized memory settings
 memory_config = {
-    "provider": "mem0",
    "config": {
+        "provider": "mem0",
        "vector_store": {
            "provider": "chroma",
            "config": {
@@ -61,8 +61,8 @@ search_task = Task(
 agents_system = AgentTeam(
    agents=[research_agent],
    tasks=[build_memory_task, search_task],
-    memory=True,
-    memory=memory_config, output="verbose"
+    memory=memory_config,
+    output="verbose",
 )

 print("Starting memory search optimization demonstration...")
@@ -86,13 +86,13 @@ def main():
        agents=[researcher, retriever],
        tasks=[store_task, retrieve_task],
        memory={
-            "provider": "rag",
            "embedder": {
                "provider": "gemini",
                "config": {
                    "model": "text-embedding-004",
                }
-            }
+            },
+            "config": {"provider": "sqlite"},
        }
    )
    
@@ -152,8 +152,7 @@ def main():
    agents_system = AgentTeam(
        agents=[researcher],
        tasks=[build_knowledge_task, query_knowledge_task, expand_knowledge_task],
-        memory=True,
-        memory=memory_config
+        memory=memory_config,
    )
    
    print("\n🚀 Starting graph memory demonstration...")
@@ -30,7 +30,7 @@ def main():
    try:
        # Create memory instance
        memory = Memory(config={
-            "provider": "rag",
+            "backend": "sqlite",
            "use_embedding": False,
            "agent_name": "demo_agent",  # Used in trace events
        })
@@ -32,9 +32,8 @@ def init_agent():
    return Agent(
        name="Knowledge Agent",
        instructions="You answer questions based on the provided knowledge.",
-        knowledge=["kag-research-paper.pdf"],
-        knowledge=config,
-        user_id="user1",
+        knowledge={**config, "sources": ["kag-research-paper.pdf"]},
+        memory={"user_id": "user1"},
        llm="deepseek-r1"
    )

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

@@ -189,8 +189,8 @@ def main():
    business_system = AgentTeam(
        agents=[data_manager, knowledge_curator, business_analyst, customer_service],
        tasks=business_tasks,
-        memory=True,
-        memory=mongodb_memory_config, output="verbose"
+        memory=mongodb_memory_config,
+        output="verbose",
    )
    
    # Execute the comprehensive business pipeline
@@ -55,8 +55,10 @@ def main():
        retrieve information from a comprehensive MongoDB knowledge base. You excel 
        at finding relevant information, synthesizing knowledge from multiple sources, 
        and providing accurate, context-aware responses.""",
-        knowledge=mongodb_knowledge_config,
-        knowledge=[os.path.join(os.path.dirname(__file__), "llms.md")],
+        knowledge={
+            **mongodb_knowledge_config,
+            "sources": [os.path.join(os.path.dirname(__file__), "llms.md")],
+        },
        memory=True,
        llm="gpt-4o-mini"
    )
@@ -28,8 +28,8 @@ if not os.environ.get("OPENAI_API_KEY"):
 def main():
    # MongoDB memory configuration
    mongodb_memory_config = {
-        "provider": "mongodb",
        "config": {
+            "provider": "mongodb",
            "connection_string": "mongodb://localhost:27017/",  # Replace with your MongoDB connection string
            "database": "praisonai_memory",
            "use_vector_search": True,  # Enable Atlas Vector Search (requires MongoDB Atlas)
@@ -93,8 +93,8 @@ def main():
    research_system = AgentTeam(
        agents=[research_agent],
        tasks=research_tasks,
-        memory=True,
-        memory=mongodb_memory_config, output="verbose"
+        memory=mongodb_memory_config,
+        output="verbose",
    )
    
    # Execute the research pipeline
@@ -50,7 +50,7 @@ agent = Agent(
    name="TechSupport",
    instructions="You are a technical support assistant.",
    knowledge=[sample_doc],
-    user_id="comparison_user",
+    memory={"user_id": "comparison_user"},
    llm="openai/gpt-4o-mini",
    output="minimal",
 )
@@ -20,8 +20,9 @@ if not os.environ.get("OPENAI_API_KEY"):
    exit(1)

 # Create a sample document for testing
-sample_doc = "/tmp/sample_doc.txt"
-with open(sample_doc, "w") as f:
+from pathlib import Path
+sample_doc = str(Path(__file__).with_name("sample_doc.txt"))
+with open(sample_doc, "w", encoding="utf-8") as f:
    f.write("""
 PraisonAI Framework Documentation

@@ -44,7 +45,7 @@ agent = Agent(
    name="DocBot",
    instructions="You are a helpful documentation assistant. Answer questions based on the provided context.",
    knowledge=[sample_doc],
-    user_id="example_user",  # Required for RAG retrieval
+    memory={"user_id": "example_user"},
    llm="openai/gpt-4o-mini",
    output="minimal",
 )
@@ -38,7 +38,7 @@ agent = Agent(
    name="PolicyBot",
    instructions="You are a customer service assistant.",
    knowledge=[sample_doc],
-    user_id="policy_user",
+    memory={"user_id": "policy_user"},
    llm="openai/gpt-4o-mini",
    output="minimal",
 )
@@ -44,7 +44,7 @@ agent = Agent(
    name="MLExpert",
    instructions="You are a machine learning expert. Answer questions based on the provided context.",
    knowledge=[sample_doc],
-    user_id="rag_user",
+    memory={"user_id": "rag_user"},
    llm="openai/gpt-4o-mini",
    output="minimal",
 )
@@ -59,7 +59,7 @@ agent = Agent(
    name="APIDocBot",
    instructions="You are an API documentation assistant.",
    knowledge=[sample_doc],
-    user_id="hybrid_user",
+    memory={"user_id": "hybrid_user"},
    llm="openai/gpt-4o-mini",
    output="minimal",
 )
@@ -44,7 +44,7 @@ agent = Agent(
    name="ResearchBot",
    instructions="You are a research assistant. Always cite your sources.",
    knowledge=[doc1, doc2],
-    user_id="citation_user",
+    memory={"user_id": "citation_user"},
    llm="openai/gpt-4o-mini",
    output="minimal",
 )
@@ -172,7 +172,7 @@ print("="*70)
 # Create sessions for different users working on the same project
 reviewer_session = Session(
    session_id="peer_review_001",
-    user_id="user_reviewer_01",  # Different user
+    user_id="user_reviewer_01",
    storage_path=session_dir
 )

@@ -14,13 +14,12 @@ def main():
    
    # Initialize memory with quality-based configuration
    memory_config = {
-        "provider": "rag",
-        "use_embedding": True,
-        "rag_db_path": "quality_memory_db"
+        "backend": "sqlite",
+        "user_id": "quality_demo_user",
    }
    
    memory = Memory(config=memory_config, verbose=5)
-    print(f"📦 Initialized memory system with provider: {memory_config['provider']}")
+    print(f"📦 Initialized memory system with backend: {memory_config['backend']}")
    
    # Create agent with memory
    agent = Agent(
@@ -28,7 +27,6 @@ def main():
        role="Information Quality Specialist",
        instructions="Store and retrieve information with quality assessment",
        memory=memory,
-        user_id="quality_demo_user"
    )
    
    print(f"🤖 Created agent: {agent.name}")
@@ -15,7 +15,7 @@ def main():
    # Create a session with persistent storage
    session = Session(
        session_id="demo_chat_001",
-        user_id="demo_user"
+        user_id="demo_user",
    )
    
    print(f"📝 Created session: {session}")
@@ -88,9 +88,8 @@ def main():
    workflow = AgentTeam(
        agents=[researcher, analyzer, writer],
        tasks=[research_task, analysis_task, report_task],
-        memory=True,
+        memory={"user_id": "research_project_001"},
        process="workflow",
-        user_id="research_project_001",
    )
    
    print(f"🤖 Created workflow with {len(workflow.agents)} agents and {len(workflow.tasks)} tasks")
@@ -14,7 +14,7 @@ def main():
        goal="Find information about a topic",
        backstory="You are an expert researcher who finds comprehensive information.",
        llm="gpt-4o-mini",  # Use a cost-effective model for examples
-        metrics=True  # 🎯 SIMPLIFIED: Just add this parameter!
+        output={"metrics": True}  # Token metrics via consolidated output config
    )
    
    # Create a simple task
@@ -14,7 +14,7 @@ def main():
        goal="Analyze data and provide insights",
        backstory="You are an experienced data analyst with expertise in market research.",
        llm="gpt-4o-mini",
-        metrics=True  # 🎯 Enable metrics tracking
+        output={"metrics": True}
    )
    
    writer_agent = Agent(
@@ -23,7 +23,7 @@ def main():
        goal="Create well-structured content",
        backstory="You are a skilled technical writer who creates clear, engaging content.",
        llm="gpt-4o-mini",
-        metrics=True  # 🎯 Enable metrics tracking
+        output={"metrics": True}
    )
    
    # Create multiple tasks
@@ -14,7 +14,7 @@ def main():
        goal="Conduct comprehensive market research",
        backstory="You are a seasoned market researcher with 10+ years of experience.",
        llm="gpt-4o-mini",
-        metrics=True  # 🎯 Enable automatic metrics tracking
+        output={"metrics": True}
    )
    
    strategy_agent = Agent(
@@ -23,7 +23,7 @@ def main():
        goal="Develop actionable business strategies",
        backstory="You are a strategic consultant who translates research into business plans.",
        llm="gpt-4o-mini",
-        metrics=True  # 🎯 Enable automatic metrics tracking
+        output={"metrics": True}
    )
    
    # Create tasks that will generate substantial token usage
@@ -32,9 +32,8 @@ def init_agent():
    return Agent(
        name="Knowledge Agent",
        instructions="You answer questions based on the provided knowledge.",
-        knowledge=["kag-research-paper.pdf"],
-        knowledge=config,
-        user_id="user1",
+        knowledge={**config, "sources": ["kag-research-paper.pdf"]},
+        memory={"user_id": "user1"},
        llm="deepseek-r1"
    )

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

 # Main Execution Workflow
@@ -207,7 +207,7 @@ def api_augmented_rag():
        instructions="""You are a financial assistant with access to market APIs.
        Provide accurate financial information and context.
        Always note that prices are subject to change.""",
-        user_id="api_demo"
+        memory={"user_id": "api_demo"},
    )
    
    queries = [
@@ -85,7 +85,7 @@ examples:
  - name: Basic usage
    command: praisonai recipe run news-monitor
  - name: With custom topic
-    command: praisonai recipe run news-monitor -i '{"topic": "LLMs"}'
+    command: 'praisonai recipe run news-monitor -i ''{"topic": "LLMs"}'''
  - name: Background execution
    command: praisonai recipe run news-monitor --background
  - name: Submit as job