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]>

examples/consolidated_params/advanced_workflow_full_features.py

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

examples/consolidated_params/basic_workflow.py

@@ -30,5 +30,5 @@ if __name__ == "__main__":
             sys.stdout.reconfigure(encoding="utf-8")
         except Exception:
             pass
-    result = workflow.run()
+    result = workflow.run("AI agents")
     print(result)

examples/managed-agents/persistence/cockroachdb_managed.py

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

examples/managed-agents/persistence/neon_managed.py

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

examples/managed-agents/persistence/supabase_direct_managed.py

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

examples/managed-agents/persistence/turso_managed.py

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

examples/middleware/configurable_model.py

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

examples/middleware/injected_state.py

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

examples/policy/basic_policy.py

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

examples/python/camera/camera-continuous.py

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

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

@@ -1,8 +1,9 @@
-import tempfile
+from pathlib import Path
+
+from praisonaiagents import Agent, Task, AgentTeam
+import logging
 import os
 import sys
-import logging
-from praisonaiagents import Agent, Task, AgentTeam
 
 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -19,11 +20,9 @@ config = {
     }
 }
 
-# Create sample file in system temp directory
-_sample_path = os.path.join(tempfile.gettempdir(), "sample_knowledge.txt")
-if not os.path.exists(_sample_path):
-    with open(_sample_path, "w", encoding="utf-8") as f:
-        f.write("Mervin Praison is the creator of PraisonAI.\n")
+_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(
@@ -31,7 +30,7 @@ knowledge_agent = Agent(
     role="Information Specialist",
     goal="Store and retrieve knowledge efficiently",
     backstory="Expert in managing and utilizing stored knowledge",
-    knowledge={**config, "sources": [_sample_path]},
+    knowledge={**config, "sources": [str(_sample_path)]},
 )
 
 # Define a task for the agent

examples/python/concepts/repetitive-agents.py

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

examples/python/custom_tools/example_latency_tracking.py

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

examples/python/data/cot-agents.py

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

examples/python/failover_example.py

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

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

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

examples/python/linear_agent_example.py

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

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

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

examples/python/rag/auto_rag_agent_basic.py

@@ -12,7 +12,6 @@ Environment:
 """
 
 import os
-import tempfile
 from praisonaiagents import Agent, AutoRagAgent
 
 # Ensure API key is set
@@ -21,7 +20,8 @@ if not os.environ.get("OPENAI_API_KEY"):
     exit(1)
 
 # Create a sample document for testing
-sample_doc = os.path.join(tempfile.gettempdir(), "sample_doc.txt")
+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

examples/python/session/session_persistence_example.py

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

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

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

examples/python/workflows/workflow_mixed_steps.py

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

examples/retrieval/basic_retrieval.py

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

examples/skills/basic_skill_usage.py

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

examples/yaml/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']}")