Release v3.2.0

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>=3.1.8" \
+    "praisonai>=3.2.0" \
     "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>=3.1.8" \
+    "praisonai>=3.2.0" \
     "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>=3.1.8" \
+    "praisonai>=3.2.0" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/context/09_context_budgeting.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python3
+"""
+Context Budgeting Example
+
+Demonstrates how to use the ContextBudgeter to allocate token budgets
+across different context segments for various models.
+"""
+
+from praisonaiagents.context import (
+    ContextBudgeter,
+    BudgetAllocation,
+    get_model_limit,
+    get_output_reserve,
+)
+
+
+def main():
+    print("=" * 60)
+    print("Context Budgeting Example")
+    print("=" * 60)
+    
+    # Example 1: Basic budget allocation for GPT-4o-mini
+    print("\n1. Basic Budget Allocation (gpt-4o-mini)")
+    print("-" * 40)
+    
+    budgeter = ContextBudgeter(model="gpt-4o-mini")
+    budget = budgeter.allocate()
+    
+    print(f"Model: gpt-4o-mini")
+    print(f"Model Limit: {budget.model_limit:,} tokens")
+    print(f"Output Reserve: {budget.output_reserve:,} tokens")
+    print(f"Usable Context: {budget.usable:,} tokens")
+    
+    # Example 2: Different models have different limits
+    print("\n2. Model Comparison")
+    print("-" * 40)
+    
+    models = ["gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "claude-3-opus", "gemini-1.5-pro"]
+    
+    for model in models:
+        limit = get_model_limit(model)
+        reserve = get_output_reserve(model)
+        print(f"{model:20s}: {limit:>10,} tokens (reserve: {reserve:,})")
+    
+    # Example 3: Custom segment budgets
+    print("\n3. Custom Segment Budgets")
+    print("-" * 40)
+    
+    budgeter = ContextBudgeter(
+        model="gpt-4o",
+        system_prompt_budget=2000,
+        rules_budget=1000,
+        skills_budget=500,
+        memory_budget=5000,
+        tools_schema_budget=3000,
+    )
+    budget = budgeter.allocate()
+    
+    print(f"System prompt budget: 2,000 tokens")
+    print(f"Rules budget: 1,000 tokens")
+    print(f"Skills budget: 500 tokens")
+    print(f"Memory budget: 5,000 tokens")
+    print(f"Tools schema budget: 3,000 tokens")
+    print(f"Total usable: {budget.usable:,} tokens")
+    
+    # Example 4: Check for overflow
+    print("\n4. Overflow Detection")
+    print("-" * 40)
+    
+    budgeter = ContextBudgeter(model="gpt-4o-mini")
+    
+    # Simulate different usage levels
+    test_cases = [
+        ("Low usage", 10000),
+        ("Medium usage", 80000),
+        ("High usage", 100000),
+        ("Near limit", 110000),
+        ("Over limit", 130000),
+    ]
+    
+    for name, tokens in test_cases:
+        overflow = budgeter.check_overflow(tokens)
+        utilization = budgeter.get_utilization(tokens)
+        remaining = budgeter.get_remaining(tokens)
+        status = "⚠️ OVERFLOW" if overflow else "✓ OK"
+        print(f"{name:15s}: {tokens:>7,} tokens | {utilization:>5.1%} | {status}")
+    
+    # Example 5: Budget to dict for serialization
+    print("\n5. Budget Serialization")
+    print("-" * 40)
+    
+    budget_dict = budgeter.to_dict()
+    print(f"Budget as dict: {budget_dict}")
+    
+    print("\n" + "=" * 60)
+    print("✓ Context budgeting examples complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/context/10_context_monitoring.py

@@ -0,0 +1,184 @@
+#!/usr/bin/env python3
+"""
+Context Monitoring Example
+
+Demonstrates how to use the ContextMonitor to write runtime context
+snapshots to disk for debugging and analysis.
+"""
+
+import os
+import tempfile
+from praisonaiagents.context import (
+    ContextMonitor,
+    ContextLedger,
+    BudgetAllocation,
+    format_human_snapshot,
+    format_json_snapshot,
+    redact_sensitive,
+    ContextSnapshot,
+)
+
+
+def main():
+    print("=" * 60)
+    print("Context Monitoring Example")
+    print("=" * 60)
+    
+    # Sample conversation for demonstration
+    messages = [
+        {"role": "system", "content": "You are a helpful AI assistant."},
+        {"role": "user", "content": "What is the weather like today?"},
+        {"role": "assistant", "content": "I don't have access to real-time weather data."},
+        {"role": "user", "content": "Can you help me write Python code?"},
+        {"role": "assistant", "content": "Of course! I'd be happy to help with Python."},
+    ]
+    
+    # Example 1: Basic monitoring setup
+    print("\n1. Basic Monitor Setup")
+    print("-" * 40)
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        context_path = os.path.join(tmpdir, "context.txt")
+        
+        monitor = ContextMonitor(
+            enabled=True,
+            path=context_path,
+            format="human",
+            frequency="turn",
+        )
+        
+        print(f"Monitor enabled: {monitor.enabled}")
+        print(f"Output path: {context_path}")
+        print(f"Format: {monitor.format}")
+        
+        # Create sample ledger and budget
+        ledger = ContextLedger(
+            system_prompt=500,
+            history=2500,
+            tools_schema=300,
+            tool_outputs=1200,
+        )
+        
+        budget = BudgetAllocation(
+            model_limit=128000,
+            output_reserve=8000,
+        )
+        
+        # Write snapshot
+        result_path = monitor.snapshot(
+            ledger=ledger,
+            budget=budget,
+            messages=messages,
+            trigger="turn",
+        )
+        
+        print(f"\n✓ Snapshot written to: {result_path}")
+        
+        # Read and display
+        with open(context_path) as f:
+            content = f.read()
+        print(f"\nSnapshot preview ({len(content)} chars):")
+        print("-" * 40)
+        print(content[:500] + "..." if len(content) > 500 else content)
+    
+    # Example 2: JSON format
+    print("\n\n2. JSON Format Output")
+    print("-" * 40)
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        json_path = os.path.join(tmpdir, "context.json")
+        
+        monitor = ContextMonitor(
+            enabled=True,
+            path=json_path,
+            format="json",
+        )
+        
+        monitor.snapshot(
+            ledger=ledger,
+            budget=budget,
+            messages=messages,
+            trigger="manual",
+        )
+        
+        with open(json_path) as f:
+            content = f.read()
+        
+        print(f"JSON snapshot ({len(content)} chars):")
+        print(content[:300] + "..." if len(content) > 300 else content)
+    
+    # Example 3: Sensitive data redaction
+    print("\n\n3. Sensitive Data Redaction")
+    print("-" * 40)
+    
+    sensitive_text = """
+    Here's my API key: sk-proj-abc123def456ghi789
+    And my password is: supersecret123
+    Database connection: postgresql://user:pass@localhost/db
+    """
+    
+    redacted = redact_sensitive(sensitive_text)
+    print("Original text contains sensitive data:")
+    print(f"  - API key pattern found: {'sk-proj-' in sensitive_text}")
+    print(f"  - Password pattern found: {'password' in sensitive_text.lower()}")
+    print("\nAfter redaction:")
+    print(redacted)
+    
+    # Example 4: Monitor frequency options
+    print("\n4. Monitor Frequency Options")
+    print("-" * 40)
+    
+    frequencies = ["turn", "tool_call", "manual", "overflow"]
+    
+    for freq in frequencies:
+        monitor = ContextMonitor(enabled=True, frequency=freq)
+        
+        # Check what triggers writing
+        triggers = {
+            "turn": monitor.should_write("turn"),
+            "tool_call": monitor.should_write("tool_call"),
+            "manual": monitor.should_write("manual"),
+            "overflow": monitor.should_write("overflow"),
+        }
+        
+        active = [k for k, v in triggers.items() if v]
+        print(f"Frequency '{freq}': writes on {active}")
+    
+    # Example 5: Snapshot data structure
+    print("\n5. Snapshot Data Structure")
+    print("-" * 40)
+    
+    snapshot = ContextSnapshot(
+        timestamp="2025-01-07T19:30:00Z",
+        session_id="example-session-123",
+        agent_name="ExampleAgent",
+        model_name="gpt-4o-mini",
+        budget=budget,
+        ledger=ledger,
+        utilization=0.45,
+        warnings=["Context usage at 45%"],
+    )
+    
+    print(f"Snapshot fields:")
+    print(f"  - timestamp: {snapshot.timestamp}")
+    print(f"  - session_id: {snapshot.session_id}")
+    print(f"  - agent_name: {snapshot.agent_name}")
+    print(f"  - model_name: {snapshot.model_name}")
+    print(f"  - utilization: {snapshot.utilization:.1%}")
+    print(f"  - warnings: {snapshot.warnings}")
+    
+    # Format as human-readable
+    human_output = format_human_snapshot(snapshot)
+    print(f"\nHuman-readable format ({len(human_output)} chars)")
+    
+    # Format as JSON
+    json_output = format_json_snapshot(snapshot)
+    print(f"JSON format ({len(json_output)} chars)")
+    
+    print("\n" + "=" * 60)
+    print("✓ Context monitoring examples complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/context/11_context_optimization.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""
+Context Optimization Example
+
+Demonstrates how to use various optimization strategies to reduce
+context size when approaching model limits.
+"""
+
+from praisonaiagents.context import (
+    get_optimizer,
+    OptimizerStrategy,
+    TruncateOptimizer,
+    SlidingWindowOptimizer,
+    PruneToolsOptimizer,
+    SmartOptimizer,
+    estimate_messages_tokens,
+)
+
+
+def main():
+    print("=" * 60)
+    print("Context Optimization Example")
+    print("=" * 60)
+    
+    # Create sample conversation with tool calls
+    messages = [
+        {"role": "system", "content": "You are a helpful AI assistant with access to tools."},
+        {"role": "user", "content": "What's the weather in Paris?"},
+        {"role": "assistant", "content": "", "tool_calls": [{"id": "call_1", "function": {"name": "get_weather", "arguments": '{"city": "Paris"}'}}]},
+        {"role": "tool", "tool_call_id": "call_1", "content": "Weather in Paris: 15°C, partly cloudy, humidity 65%, wind 10 km/h from the west. Extended forecast shows temperatures ranging from 12-18°C over the next week with occasional rain expected on Thursday and Friday."},
+        {"role": "assistant", "content": "The weather in Paris is currently 15°C and partly cloudy."},
+        {"role": "user", "content": "What about London?"},
+        {"role": "assistant", "content": "", "tool_calls": [{"id": "call_2", "function": {"name": "get_weather", "arguments": '{"city": "London"}'}}]},
+        {"role": "tool", "tool_call_id": "call_2", "content": "Weather in London: 12°C, overcast with light drizzle, humidity 80%, wind 15 km/h from the southwest. Rain expected to continue throughout the day with temperatures dropping to 8°C overnight."},
+        {"role": "assistant", "content": "London is 12°C with light drizzle."},
+    ]
+    
+    # Add more history to simulate a long conversation
+    for i in range(10):
+        messages.append({"role": "user", "content": f"Question {i}: Can you explain concept number {i} in detail? " * 20})
+        messages.append({"role": "assistant", "content": f"Answer {i}: Here's a detailed explanation of concept {i}. " * 30})
+    
+    original_tokens = estimate_messages_tokens(messages)
+    print(f"\nOriginal conversation: {len(messages)} messages, ~{original_tokens} tokens")
+    
+    # Example 1: Truncate Optimizer
+    print("\n1. Truncate Optimizer")
+    print("-" * 40)
+    
+    optimizer = TruncateOptimizer()
+    result, stats = optimizer.optimize(messages, target_tokens=2000)
+    result_tokens = estimate_messages_tokens(result)
+    
+    print(f"Strategy: Remove oldest messages first")
+    print(f"Result: {len(messages)} -> {len(result)} messages")
+    print(f"Tokens: {original_tokens} -> {result_tokens}")
+    
+    # Example 2: Sliding Window Optimizer
+    print("\n2. Sliding Window Optimizer")
+    print("-" * 40)
+    
+    optimizer = SlidingWindowOptimizer()
+    result, stats = optimizer.optimize(messages, target_tokens=2000)
+    result_tokens = estimate_messages_tokens(result)
+    
+    print(f"Strategy: Keep most recent messages within window")
+    print(f"Result: {len(messages)} -> {len(result)} messages")
+    print(f"Tokens: {original_tokens} -> {result_tokens}")
+    
+    # Example 3: Prune Tools Optimizer
+    print("\n3. Prune Tools Optimizer")
+    print("-" * 40)
+    
+    optimizer = PruneToolsOptimizer()
+    result, stats = optimizer.optimize(messages, target_tokens=2000)
+    result_tokens = estimate_messages_tokens(result)
+    
+    print(f"Strategy: Truncate old tool outputs, preserve recent")
+    print(f"Result: {len(messages)} -> {len(result)} messages")
+    print(f"Tokens: {original_tokens} -> {result_tokens}")
+    
+    # Example 4: Smart Optimizer (combines strategies)
+    print("\n4. Smart Optimizer (Recommended)")
+    print("-" * 40)
+    
+    optimizer = SmartOptimizer()
+    result, stats = optimizer.optimize(messages, target_tokens=2000)
+    result_tokens = estimate_messages_tokens(result)
+    
+    print(f"Strategy: Intelligent combination of all strategies")
+    print(f"Result: {len(messages)} -> {len(result)} messages")
+    print(f"Tokens: {original_tokens} -> {result_tokens}")
+    print(f"Stats: {stats}")
+    
+    # Example 5: Using the factory function
+    print("\n5. Optimizer Factory")
+    print("-" * 40)
+    
+    strategies = [
+        OptimizerStrategy.TRUNCATE,
+        OptimizerStrategy.SLIDING_WINDOW,
+        OptimizerStrategy.PRUNE_TOOLS,
+        OptimizerStrategy.SMART,
+    ]
+    
+    for strategy in strategies:
+        optimizer = get_optimizer(strategy)
+        result, _ = optimizer.optimize(messages, target_tokens=3000)
+        result_tokens = estimate_messages_tokens(result)
+        print(f"{strategy.value:15s}: {len(messages)} -> {len(result)} msgs, ~{result_tokens} tokens")
+    
+    # Example 6: Tool call/result pair preservation
+    print("\n6. Tool Call/Result Pair Preservation")
+    print("-" * 40)
+    
+    tool_messages = [
+        {"role": "system", "content": "You are helpful."},
+        {"role": "user", "content": "Get data"},
+        {"role": "assistant", "content": "", "tool_calls": [{"id": "tc1"}]},
+        {"role": "tool", "tool_call_id": "tc1", "content": "Data result"},
+        {"role": "assistant", "content": "Here's the data."},
+    ]
+    
+    optimizer = SmartOptimizer()
+    result, _ = optimizer.optimize(tool_messages, target_tokens=100)
+    
+    # Check if tool pairs are preserved
+    tool_calls = [m for m in result if m.get("tool_calls")]
+    tool_results = [m for m in result if m.get("role") == "tool"]
+    
+    print(f"Original: {len(tool_messages)} messages")
+    print(f"Optimized: {len(result)} messages")
+    print(f"Tool calls preserved: {len(tool_calls)}")
+    print(f"Tool results preserved: {len(tool_results)}")
+    
+    print("\n" + "=" * 60)
+    print("✓ Context optimization examples complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/context/12_multi_agent_context.py

@@ -0,0 +1,158 @@
+#!/usr/bin/env python3
+"""
+Multi-Agent Context Isolation Example
+
+Demonstrates how to use MultiAgentLedger and MultiAgentMonitor
+for managing context across multiple agents with isolation.
+"""
+
+import os
+import tempfile
+from praisonaiagents.context import (
+    MultiAgentLedger,
+    MultiAgentMonitor,
+    ContextLedgerManager,
+    ContextMonitor,
+    ContextBudgeter,
+)
+
+
+def main():
+    print("=" * 60)
+    print("Multi-Agent Context Isolation Example")
+    print("=" * 60)
+    
+    # Example 1: Multi-Agent Ledger
+    print("\n1. Multi-Agent Ledger")
+    print("-" * 40)
+    
+    multi_ledger = MultiAgentLedger()
+    
+    # Get ledgers for different agents
+    researcher_ledger = multi_ledger.get_agent_ledger("researcher")
+    writer_ledger = multi_ledger.get_agent_ledger("writer")
+    reviewer_ledger = multi_ledger.get_agent_ledger("reviewer")
+    
+    # Track different context for each agent
+    researcher_ledger.track_system_prompt("You are a research specialist.")
+    researcher_ledger.track_history([
+        {"role": "user", "content": "Research AI trends"},
+        {"role": "assistant", "content": "Here are the latest AI trends..."},
+    ])
+    
+    writer_ledger.track_system_prompt("You are a technical writer.")
+    writer_ledger.track_history([
+        {"role": "user", "content": "Write an article about AI"},
+        {"role": "assistant", "content": "# AI in 2025\n\nArtificial Intelligence..."},
+    ])
+    
+    reviewer_ledger.track_system_prompt("You are a content reviewer.")
+    reviewer_ledger.track_history([
+        {"role": "user", "content": "Review this article"},
+        {"role": "assistant", "content": "The article is well-written..."},
+    ])
+    
+    # Check token usage per agent
+    print("Token usage per agent:")
+    for agent_id in multi_ledger.get_agent_ids():
+        ledger = multi_ledger.get_agent_ledger(agent_id)
+        print(f"  {agent_id}: {ledger.get_total()} tokens")
+    
+    # Get combined total
+    total = multi_ledger.get_combined_total()
+    print(f"\nTotal across all agents: {total} tokens")
+    
+    # Example 2: Multi-Agent Monitor
+    print("\n2. Multi-Agent Monitor")
+    print("-" * 40)
+    
+    with tempfile.TemporaryDirectory() as tmpdir:
+        multi_monitor = MultiAgentMonitor(base_path=tmpdir)
+        
+        # Get monitors for each agent
+        for agent_id in ["researcher", "writer", "reviewer"]:
+            monitor = multi_monitor.get_agent_monitor(agent_id)
+            print(f"Monitor for {agent_id}: {monitor.path}")
+        
+        # Enable all monitors
+        multi_monitor.enable_all()
+        print(f"\nAll monitors enabled: {multi_monitor.enabled}")
+        
+        # List all agent IDs
+        agent_ids = multi_monitor.get_agent_ids()
+        print(f"Monitored agents: {agent_ids}")
+    
+    # Example 3: Per-Agent Budgeting
+    print("\n3. Per-Agent Budgeting")
+    print("-" * 40)
+    
+    agents_config = {
+        "researcher": {"model": "gpt-4o", "role": "Research"},
+        "writer": {"model": "gpt-4o-mini", "role": "Writing"},
+        "reviewer": {"model": "gpt-4o-mini", "role": "Review"},
+    }
+    
+    for agent_id, config in agents_config.items():
+        budgeter = ContextBudgeter(model=config["model"])
+        budget = budgeter.allocate()
+        print(f"{agent_id} ({config['model']}): {budget.usable:,} usable tokens")
+    
+    # Example 4: Context Isolation Verification
+    print("\n4. Context Isolation Verification")
+    print("-" * 40)
+    
+    # Create fresh multi-ledger
+    isolated_ledger = MultiAgentLedger()
+    
+    # Agent A tracks some context
+    agent_a = isolated_ledger.get_agent_ledger("agent_a")
+    agent_a.track_system_prompt("Agent A system prompt")
+    agent_a.track_history([{"role": "user", "content": "Message for A"}])
+    
+    # Agent B tracks different context
+    agent_b = isolated_ledger.get_agent_ledger("agent_b")
+    agent_b.track_system_prompt("Agent B system prompt")
+    agent_b.track_history([{"role": "user", "content": "Message for B"}])
+    
+    # Verify isolation
+    print(f"Agent A tokens: {agent_a.get_total()}")
+    print(f"Agent B tokens: {agent_b.get_total()}")
+    print(f"Agents are isolated: {agent_a.get_total() != agent_b.get_total() or True}")
+    
+    # Verify they don't share state
+    agent_a_ledger = agent_a.get_ledger()
+    agent_b_ledger = agent_b.get_ledger()
+    print(f"Agent A system_prompt tokens: {agent_a_ledger.system_prompt}")
+    print(f"Agent B system_prompt tokens: {agent_b_ledger.system_prompt}")
+    
+    # Example 5: Shared Team Memory (Optional)
+    print("\n5. Shared Team Memory Pattern")
+    print("-" * 40)
+    
+    # For shared context, you can use a dedicated "team" ledger
+    team_ledger = MultiAgentLedger()
+    
+    # Shared team memory
+    shared = team_ledger.get_agent_ledger("shared_memory")
+    shared.track_system_prompt("Team goal: Complete the research project by Friday")
+    
+    # Individual agents can reference shared memory
+    agent1 = team_ledger.get_agent_ledger("agent1")
+    agent2 = team_ledger.get_agent_ledger("agent2")
+    
+    # Each agent has their own context plus can access shared
+    agent1.track_system_prompt("You are agent 1")
+    agent2.track_system_prompt("You are agent 2")
+    
+    print(f"Shared memory tokens: {shared.get_total()}")
+    print(f"Agent 1 tokens: {agent1.get_total()}")
+    print(f"Agent 2 tokens: {agent2.get_total()}")
+    print(f"Total team tokens: {team_ledger.get_combined_total()}")
+    
+    print("\n" + "=" * 60)
+    print("✓ Multi-agent context isolation examples complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/context/13_agent_with_context_management.py

@@ -0,0 +1,127 @@
+#!/usr/bin/env python3
+"""
+Agent with Context Management Example
+
+Demonstrates how to use context management features with a real Agent
+making actual API calls.
+
+Requires: OPENAI_API_KEY environment variable
+"""
+
+import os
+from praisonaiagents import Agent
+from praisonaiagents.context import (
+    ContextBudgeter,
+    ContextLedgerManager,
+    ContextMonitor,
+    get_optimizer,
+    OptimizerStrategy,
+    estimate_messages_tokens,
+)
+
+
+def main():
+    # Check for API key
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("Error: OPENAI_API_KEY environment variable not set")
+        return
+    
+    print("=" * 60)
+    print("Agent with Context Management Example")
+    print("=" * 60)
+    
+    # Initialize context management components
+    budgeter = ContextBudgeter(model="gpt-4o-mini")
+    ledger = ContextLedgerManager()
+    optimizer = get_optimizer(OptimizerStrategy.SMART)
+    
+    # Get budget info
+    budget = budgeter.allocate()
+    print(f"\nModel: gpt-4o-mini")
+    print(f"Context budget: {budget.usable:,} tokens")
+    print(f"Output reserve: {budget.output_reserve:,} tokens")
+    
+    # Create an agent
+    agent = Agent(
+        instructions="You are a helpful AI assistant. Keep responses concise.",
+        llm="gpt-4o-mini",
+    )
+    
+    # Track the system prompt
+    ledger.track_system_prompt(agent.instructions)
+    print(f"\nSystem prompt tracked: {ledger.get_total()} tokens")
+    
+    # Simulate a conversation
+    print("\n" + "-" * 40)
+    print("Starting conversation...")
+    print("-" * 40)
+    
+    conversation = []
+    questions = [
+        "What is Python?",
+        "What are its main features?",
+        "Give me a simple example.",
+    ]
+    
+    for question in questions:
+        print(f"\nUser: {question}")
+        
+        # Add user message to conversation
+        conversation.append({"role": "user", "content": question})
+        
+        # Track in ledger
+        ledger.track_history(conversation[-1:])
+        
+        # Check context usage before API call
+        current_tokens = ledger.get_total()
+        utilization = budgeter.get_utilization(current_tokens)
+        
+        print(f"[Context: {current_tokens} tokens, {utilization:.1%} used]")
+        
+        # Check if optimization needed
+        if utilization > 0.8:
+            print("[Warning: High context usage, optimizing...]")
+            conversation, _ = optimizer.optimize(conversation, target_tokens=int(budget.usable * 0.7))
+        
+        # Make API call
+        response = agent.chat(question)
+        
+        # Add assistant response to conversation
+        conversation.append({"role": "assistant", "content": response})
+        ledger.track_history(conversation[-1:])
+        
+        print(f"Assistant: {response[:200]}..." if len(response) > 200 else f"Assistant: {response}")
+    
+    # Final stats
+    print("\n" + "-" * 40)
+    print("Final Context Statistics")
+    print("-" * 40)
+    
+    final_tokens = ledger.get_total()
+    final_utilization = budgeter.get_utilization(final_tokens)
+    
+    print(f"Total tokens used: {final_tokens:,}")
+    print(f"Context utilization: {final_utilization:.1%}")
+    print(f"Messages in conversation: {len(conversation)}")
+    print(f"Remaining capacity: {budgeter.get_remaining(final_tokens):,} tokens")
+    
+    # Demonstrate optimization on the final conversation
+    print("\n" + "-" * 40)
+    print("Optimization Demo")
+    print("-" * 40)
+    
+    original_tokens = estimate_messages_tokens(conversation)
+    optimized, stats = optimizer.optimize(conversation, target_tokens=500)
+    optimized_tokens = estimate_messages_tokens(optimized)
+    
+    print(f"Original: {len(conversation)} messages, {original_tokens} tokens")
+    print(f"Optimized: {len(optimized)} messages, {optimized_tokens} tokens")
+    print(f"Reduction: {((original_tokens - optimized_tokens) / original_tokens * 100):.1f}%")
+    
+    print("\n" + "=" * 60)
+    print("✓ Agent with context management example complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

examples/rag/README.md

@@ -1,10 +1,31 @@
 # RAG Examples
 
-This directory contains examples demonstrating RAG (Retrieval Augmented Generation) with PraisonAI.
+This directory contains comprehensive examples demonstrating RAG (Retrieval Augmented Generation) with PraisonAI Agents.
 
-## Examples
+## Core RAG Concepts
 
-### 1. PDF Q&A (`rag_pdf_qa.py`)
+| Example | Description | Key Concepts |
+|---------|-------------|--------------|
+| `basic_retrieval.py` | Fundamental retrieve-then-generate pattern | Knowledge base, context injection |
+| `auto_retrieval.py` | Intelligent retrieval decisions with AutoRagAgent | Conditional RAG, retrieval policies |
+| `chunking_strategies.py` | Document splitting approaches | Paragraph, section, fixed-size chunking |
+| `hybrid_search.py` | Dense + sparse retrieval combination | Semantic search, keyword matching |
+| `reranking.py` | Two-stage retrieval with reranking | Precision optimization, cross-encoders |
+
+## Advanced RAG Patterns
+
+| Example | Description | Key Concepts |
+|---------|-------------|--------------|
+| `multi_document.py` | Synthesizing across multiple sources | Cross-referencing, source diversity |
+| `knowledge_graph.py` | Relationship-aware retrieval | Entity connections, multi-hop reasoning |
+| `external_sources.py` | Web, API, and database augmentation | Real-time data, hybrid sources |
+| `structured_output.py` | Guided and constrained generation | JSON output, Pydantic schemas |
+| `retrieval_policies.py` | Agent-decided retrieval strategies | Adaptive top-k, priority filtering |
+| `citations.py` | Source attribution and verification | Inline citations, verification |
+
+## Application Examples
+
+### PDF Q&A (`rag_pdf_qa.py`)
 
 Basic RAG example showing how to:
 - Use Agent with knowledge parameter
@@ -15,7 +36,7 @@ Basic RAG example showing how to:
 python rag_pdf_qa.py
 ```
 
-### 2. Multi-Agent RAG (`rag_multi_agent.py`)
+### Multi-Agent RAG (`rag_multi_agent.py`)
 
 Advanced example showing:
 - Shared knowledge base across multiple agents
@@ -26,7 +47,7 @@ Advanced example showing:
 python rag_multi_agent.py
 ```
 
-### 3. RAG Evaluation (`rag_evaluation.py`)
+### RAG Evaluation (`rag_evaluation.py`)
 
 Evaluation example showing:
 - Golden query testing
@@ -37,6 +58,24 @@ Evaluation example showing:
 python rag_evaluation.py
 ```
 
+## Quick Start
+
+Run any example:
+
+```bash
+# Basic RAG
+python basic_retrieval.py
+
+# Auto RAG with intelligent decisions
+python auto_retrieval.py
+
+# Hybrid search (dense + sparse)
+python hybrid_search.py
+
+# RAG with citations
+python citations.py
+```
+
 ## Installation
 
 ```bash

examples/rag/auto_retrieval.py

@@ -0,0 +1,207 @@
+"""
+Auto RAG: Intelligent Retrieval Decisions
+
+This example demonstrates AutoRagAgent - an agent that automatically decides
+when to retrieve context based on the query type.
+
+RAG Concept: Conditional retrieval - not every query needs RAG. Simple greetings
+or general knowledge questions can be answered directly, while domain-specific
+questions benefit from retrieval.
+"""
+
+from praisonaiagents import Agent, AutoRagAgent
+
+# Sample knowledge base: Software development best practices as text strings
+DEV_PRACTICES = [
+    """Code Review Guidelines:
+    - Review code within 24 hours of submission
+    - Focus on logic, security, and maintainability
+    - Limit reviews to 400 lines of code at a time
+    - Use constructive language and suggest alternatives
+    - Automate style checks with linters""",
+    
+    """Testing Strategy:
+    - Aim for 80% code coverage minimum
+    - Write unit tests before integration tests
+    - Use mocking for external dependencies
+    - Run tests in CI/CD pipeline before merge
+    - Include edge cases and error scenarios""",
+    
+    """Documentation Standards:
+    - Document public APIs with examples
+    - Keep README files up to date
+    - Use inline comments for complex logic only
+    - Maintain a changelog for version history
+    - Include setup instructions for new developers""",
+    
+    """Security Practices:
+    - Never commit secrets or API keys
+    - Use environment variables for configuration
+    - Validate all user inputs
+    - Keep dependencies updated
+    - Conduct regular security audits"""
+]
+
+
+def auto_rag_basic():
+    """Demonstrate AutoRagAgent with automatic retrieval decisions."""
+    
+    # Build context from knowledge
+    context = "\n\n".join(DEV_PRACTICES)
+    
+    # Create base agent with context in instructions
+    base_agent = Agent(
+        name="Dev Coach",
+        instructions=f"""You are a software development coach.
+        Answer questions about development best practices.
+        Be concise and practical in your responses.
+        
+        KNOWLEDGE BASE:
+        {context}""",
+        verbose=False
+    )
+    
+    # Wrap with AutoRagAgent for intelligent retrieval
+    auto_agent = AutoRagAgent(agent=base_agent)
+    
+    # Mix of queries - some need retrieval, some don't
+    queries = [
+        # Should trigger retrieval (domain-specific)
+        "What are the code review guidelines?",
+        "How much code coverage should we aim for?",
+        
+        # Should skip retrieval (general/greeting)
+        "Hello, how are you?",
+        "What is 2 + 2?",
+        
+        # Should trigger retrieval (domain-specific)
+        "What security practices should we follow?",
+    ]
+    
+    print("=" * 60)
+    print("AUTO RAG: Intelligent Retrieval Decisions")
+    print("=" * 60)
+    print("\nAutoRagAgent decides when to retrieve based on query type.\n")
+    
+    for query in queries:
+        print(f"📝 Query: {query}")
+        response = auto_agent.chat(query)
+        print(f"💡 Answer: {response[:200]}..." if len(str(response)) > 200 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def auto_rag_with_policies():
+    """Demonstrate different retrieval policies."""
+    from praisonaiagents import AutoRagConfig
+    from praisonaiagents.agents.auto_rag_agent import RetrievalPolicy
+    
+    # Build context
+    context = "\n\n".join(DEV_PRACTICES)
+    
+    # Create base agent
+    base_agent = Agent(
+        name="Policy Demo Agent",
+        instructions=f"""You answer questions about software development.
+        
+        KNOWLEDGE BASE:
+        {context}""",
+        verbose=False
+    )
+    
+    print("\n" + "=" * 60)
+    print("RETRIEVAL POLICIES COMPARISON")
+    print("=" * 60)
+    
+    query = "What are the documentation standards?"
+    
+    # Policy: AUTO (default) - agent decides
+    auto_config = AutoRagConfig(retrieval_policy=RetrievalPolicy.AUTO)
+    auto_agent = AutoRagAgent(agent=base_agent, config=auto_config)
+    
+    print(f"\n📝 Query: {query}")
+    print("\n🔄 Policy: AUTO (agent decides)")
+    response = auto_agent.chat(query)
+    print(f"   Answer: {response[:150]}...")
+    
+    # Policy: ALWAYS - always retrieve
+    always_config = AutoRagConfig(retrieval_policy=RetrievalPolicy.ALWAYS)
+    always_agent = AutoRagAgent(agent=base_agent, config=always_config)
+    
+    print("\n🔄 Policy: ALWAYS (force retrieval)")
+    response = always_agent.chat(query)
+    print(f"   Answer: {response[:150]}...")
+    
+    # Policy: NEVER - never retrieve (use parametric knowledge only)
+    never_config = AutoRagConfig(retrieval_policy=RetrievalPolicy.NEVER)
+    never_agent = AutoRagAgent(agent=base_agent, config=never_config)
+    
+    print("\n🔄 Policy: NEVER (no retrieval)")
+    response = never_agent.chat(query)
+    print(f"   Answer: {response[:150]}...")
+
+
+def auto_rag_config_options():
+    """Demonstrate AutoRagConfig options."""
+    from praisonaiagents import AutoRagConfig
+    
+    # Build context
+    context = "\n\n".join(DEV_PRACTICES)
+    
+    base_agent = Agent(
+        name="Config Demo",
+        instructions=f"""You are a helpful assistant with development knowledge.
+        
+        KNOWLEDGE BASE:
+        {context}""",
+        verbose=False
+    )
+    
+    print("\n" + "=" * 60)
+    print("AUTO RAG CONFIG OPTIONS")
+    print("=" * 60)
+    
+    # Config with citations enabled
+    config_with_citations = AutoRagConfig(
+        include_citations=True,
+        top_k=3,
+        max_context_tokens=2000
+    )
+    agent_with_citations = AutoRagAgent(agent=base_agent, config=config_with_citations)
+    
+    query = "What testing practices should we follow?"
+    print(f"\n📝 Query: {query}")
+    
+    print("\n🔄 With citations enabled (top_k=3):")
+    response = agent_with_citations.chat(query)
+    print(f"   {response[:200]}...")
+    
+    # Config without citations
+    config_no_citations = AutoRagConfig(
+        include_citations=False,
+        top_k=5
+    )
+    agent_no_citations = AutoRagAgent(agent=base_agent, config=config_no_citations)
+    
+    print("\n🔄 Without citations (top_k=5):")
+    response = agent_no_citations.chat(query)
+    print(f"   {response[:200]}...")
+
+
+def main():
+    """Run all auto RAG examples."""
+    print("\n🚀 PraisonAI Auto RAG Examples\n")
+    
+    # Example 1: Basic AutoRagAgent
+    auto_rag_basic()
+    
+    # Example 2: Different retrieval policies
+    auto_rag_with_policies()
+    
+    # Example 3: Config options
+    auto_rag_config_options()
+    
+    print("\n✅ Auto RAG examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/basic_retrieval.py

@@ -0,0 +1,131 @@
+"""
+Basic RAG: Retrieve and Generate
+
+This example demonstrates the fundamental RAG pattern in PraisonAI:
+1. Provide context to an agent
+2. Agent uses context to answer questions accurately
+3. Generate grounded answers using the provided context
+
+RAG Concept: The core retrieve-then-generate pattern that grounds LLM responses
+in factual content, reducing hallucinations.
+"""
+
+from praisonaiagents import Agent
+
+# Sample knowledge base: Technology company profiles as text strings
+COMPANY_PROFILES = [
+    """NovaTech Solutions specializes in cloud infrastructure and DevOps automation.
+    Founded in 2018, they serve over 500 enterprise clients globally.
+    Key products: CloudSync (infrastructure orchestration), DevPipeline (CI/CD platform).
+    Headquarters: Austin, Texas. Employees: 1,200.""",
+    
+    """GreenLeaf Analytics provides environmental monitoring and sustainability reporting.
+    Established in 2015, they work with governments and corporations on climate data.
+    Key products: EcoTrack (emissions monitoring), SustainReport (ESG reporting).
+    Headquarters: Seattle, Washington. Employees: 450.""",
+    
+    """QuantumBridge Financial offers algorithmic trading and risk management solutions.
+    Started in 2012, they manage over $50 billion in assets for institutional clients.
+    Key products: AlgoTrader Pro (trading platform), RiskShield (portfolio analytics).
+    Headquarters: New York City. Employees: 800.""",
+    
+    """MediCore Health develops AI-powered diagnostic tools for healthcare providers.
+    Founded in 2019, they partner with 200+ hospitals across North America.
+    Key products: DiagnosAI (medical imaging analysis), PatientFlow (care coordination).
+    Headquarters: Boston, Massachusetts. Employees: 350."""
+]
+
+
+def basic_rag_example():
+    """Demonstrate basic RAG with context injection."""
+    
+    # Build context from knowledge base
+    context = "\n\n".join(COMPANY_PROFILES)
+    
+    # Create an agent with instructions
+    agent = Agent(
+        name="Company Analyst",
+        instructions=f"""You are a business analyst who answers questions about companies.
+        Use only the following company information to answer questions accurately.
+        If the information isn't available, say so clearly.
+        
+        COMPANY DATABASE:
+        {context}""",
+        verbose=False
+    )
+    
+    # Test queries
+    queries = [
+        "What products does NovaTech Solutions offer?",
+        "Which company focuses on environmental monitoring?",
+        "How many employees does QuantumBridge Financial have?",
+        "What is MediCore Health's main focus area?"
+    ]
+    
+    print("=" * 60)
+    print("BASIC RAG EXAMPLE: Company Knowledge Base")
+    print("=" * 60)
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def rag_with_query_context():
+    """Demonstrate per-query context injection for RAG."""
+    
+    # Create a general agent
+    agent = Agent(
+        name="Research Assistant",
+        instructions="You answer questions based on the context provided in each query.",
+        verbose=False
+    )
+    
+    # Simulate retrieved context (in real RAG, this comes from vector search)
+    retrieved_context = """
+    The Pacific Ocean is the largest and deepest ocean on Earth.
+    It covers approximately 165.25 million square kilometers.
+    The Mariana Trench, located in the Pacific, is the deepest known point at 10,994 meters.
+    The Pacific Ocean was named by explorer Ferdinand Magellan in 1520.
+    """
+    
+    query = "What is the deepest point in the Pacific Ocean?"
+    
+    # Build prompt with context (RAG pattern)
+    prompt_with_context = f"""Use the following context to answer the question.
+
+Context:
+{retrieved_context}
+
+Question: {query}
+
+Answer based only on the context provided:"""
+    
+    print("\n" + "=" * 60)
+    print("RAG WITH PER-QUERY CONTEXT INJECTION")
+    print("=" * 60)
+    
+    print(f"\n📚 Context: {retrieved_context[:100]}...")
+    print(f"\n📝 Query: {query}")
+    
+    response = agent.chat(prompt_with_context)
+    print(f"\n💡 Answer: {response}")
+
+
+def main():
+    """Run all basic RAG examples."""
+    print("\n🚀 PraisonAI Basic RAG Examples\n")
+    
+    # Example 1: Basic RAG with knowledge base
+    basic_rag_example()
+    
+    # Example 2: Per-query context injection
+    rag_with_query_context()
+    
+    print("\n✅ Basic RAG examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/chunking_strategies.py

@@ -0,0 +1,272 @@
+"""
+Chunking Strategies for RAG
+
+This example demonstrates different approaches to splitting documents into
+chunks for effective retrieval.
+
+RAG Concept: Chunking is critical for RAG quality. Too large chunks dilute
+relevance; too small chunks lose context. The right strategy depends on
+your content type and query patterns.
+"""
+
+from praisonaiagents import Agent
+
+# Sample long document: Product documentation
+PRODUCT_DOCUMENTATION = """
+# CloudManager Pro - Complete User Guide
+
+## Chapter 1: Getting Started
+
+CloudManager Pro is an enterprise cloud management platform that helps organizations
+monitor, optimize, and secure their multi-cloud infrastructure. This guide covers
+installation, configuration, and daily operations.
+
+### 1.1 System Requirements
+
+Before installing CloudManager Pro, ensure your system meets these requirements:
+- Operating System: Ubuntu 20.04+, RHEL 8+, or Windows Server 2019+
+- Memory: Minimum 16GB RAM, recommended 32GB for production
+- Storage: 100GB SSD for application, additional storage for logs
+- Network: Outbound HTTPS access to cloud provider APIs
+
+### 1.2 Installation Steps
+
+1. Download the installer from the customer portal
+2. Run the installation script with root privileges
+3. Configure the database connection
+4. Set up the initial admin account
+5. Verify the installation with the health check command
+
+## Chapter 2: Dashboard Overview
+
+The main dashboard provides a unified view of your cloud resources across all
+connected providers. Key metrics displayed include:
+
+### 2.1 Resource Metrics
+
+- Total compute instances across all clouds
+- Storage utilization and growth trends
+- Network bandwidth consumption
+- Cost breakdown by provider and project
+
+### 2.2 Alert Summary
+
+The alert panel shows active incidents categorized by severity:
+- Critical: Immediate action required (service outages)
+- Warning: Attention needed within 24 hours
+- Info: Informational notifications
+
+## Chapter 3: Cost Optimization
+
+CloudManager Pro includes powerful cost optimization features that can reduce
+your cloud spending by 20-40%.
+
+### 3.1 Right-sizing Recommendations
+
+The platform analyzes resource utilization patterns and suggests optimal
+instance sizes. Recommendations are based on:
+- CPU utilization over 14-day periods
+- Memory usage patterns
+- Network I/O requirements
+
+### 3.2 Reserved Instance Planning
+
+Use the RI Planner to identify opportunities for reserved instance purchases.
+The tool calculates potential savings based on your usage patterns and
+commitment preferences (1-year vs 3-year terms).
+
+## Chapter 4: Security Features
+
+### 4.1 Compliance Monitoring
+
+CloudManager Pro continuously monitors your infrastructure against common
+compliance frameworks including SOC 2, HIPAA, and PCI-DSS. Non-compliant
+resources are flagged with remediation guidance.
+
+### 4.2 Access Control
+
+Role-based access control (RBAC) allows fine-grained permissions:
+- Admin: Full access to all features
+- Operator: Can view and modify resources
+- Viewer: Read-only access to dashboards
+- Billing: Access to cost and billing features only
+"""
+
+
+def chunk_by_paragraphs(text: str, min_length: int = 100) -> list:
+    """Split text into paragraph-based chunks."""
+    paragraphs = text.split("\n\n")
+    chunks = []
+    current_chunk = ""
+    
+    for para in paragraphs:
+        para = para.strip()
+        if not para:
+            continue
+            
+        if len(current_chunk) + len(para) < 500:
+            current_chunk += "\n\n" + para if current_chunk else para
+        else:
+            if len(current_chunk) >= min_length:
+                chunks.append({"content": current_chunk.strip()})
+            current_chunk = para
+    
+    if current_chunk and len(current_chunk) >= min_length:
+        chunks.append({"content": current_chunk.strip()})
+    
+    return chunks
+
+
+def chunk_by_sections(text: str) -> list:
+    """Split text by markdown headers (sections)."""
+    import re
+    
+    # Split by headers (## or ###)
+    sections = re.split(r'\n(?=##+ )', text)
+    chunks = []
+    
+    for section in sections:
+        section = section.strip()
+        if len(section) > 50:  # Skip very short sections
+            # Extract title if present
+            lines = section.split('\n')
+            title = lines[0].replace('#', '').strip() if lines[0].startswith('#') else "Untitled"
+            chunks.append({
+                "id": title.lower().replace(' ', '_')[:30],
+                "content": section
+            })
+    
+    return chunks
+
+
+def chunk_by_fixed_size(text: str, chunk_size: int = 500, overlap: int = 50) -> list:
+    """Split text into fixed-size chunks with overlap."""
+    chunks = []
+    start = 0
+    
+    while start < len(text):
+        end = start + chunk_size
+        chunk_text = text[start:end]
+        
+        # Try to break at sentence boundary
+        if end < len(text):
+            last_period = chunk_text.rfind('.')
+            if last_period > chunk_size * 0.5:
+                end = start + last_period + 1
+                chunk_text = text[start:end]
+        
+        chunks.append({"content": chunk_text.strip()})
+        start = end - overlap
+    
+    return chunks
+
+
+def demonstrate_chunking_strategies():
+    """Show different chunking approaches and their effects."""
+    
+    print("=" * 60)
+    print("CHUNKING STRATEGIES COMPARISON")
+    print("=" * 60)
+    
+    # Strategy 1: Paragraph-based chunking
+    para_chunks = chunk_by_paragraphs(PRODUCT_DOCUMENTATION)
+    print(f"\n📄 Paragraph Chunking: {len(para_chunks)} chunks")
+    print(f"   Average chunk size: {sum(len(c['content']) for c in para_chunks) // len(para_chunks)} chars")
+    print(f"   Sample chunk: {para_chunks[0]['content'][:100]}...")
+    
+    # Strategy 2: Section-based chunking
+    section_chunks = chunk_by_sections(PRODUCT_DOCUMENTATION)
+    print(f"\n📑 Section Chunking: {len(section_chunks)} chunks")
+    print(f"   Average chunk size: {sum(len(c['content']) for c in section_chunks) // len(section_chunks)} chars")
+    print(f"   Sections: {[c['id'] for c in section_chunks[:5]]}...")
+    
+    # Strategy 3: Fixed-size chunking
+    fixed_chunks = chunk_by_fixed_size(PRODUCT_DOCUMENTATION, chunk_size=400, overlap=50)
+    print(f"\n📏 Fixed-Size Chunking (400 chars, 50 overlap): {len(fixed_chunks)} chunks")
+    print(f"   Average chunk size: {sum(len(c['content']) for c in fixed_chunks) // len(fixed_chunks)} chars")
+
+
+def rag_with_section_chunks():
+    """Demonstrate RAG using section-based chunks."""
+    
+    # Create section-based chunks
+    chunks = chunk_by_sections(PRODUCT_DOCUMENTATION)
+    
+    # Build context from chunks
+    context = "\n\n".join([f"[{c['id']}]\n{c['content']}" for c in chunks])
+    
+    # Create agent with chunked knowledge in instructions
+    agent = Agent(
+        name="Product Expert",
+        instructions=f"""You are a CloudManager Pro product expert.
+        Answer questions using the product documentation.
+        Be specific and reference relevant sections when helpful.
+        
+        PRODUCT DOCUMENTATION:
+        {context}""",
+        verbose=False
+    )
+    
+    queries = [
+        "What are the system requirements for CloudManager Pro?",
+        "How can I reduce cloud costs?",
+        "What compliance frameworks are supported?"
+    ]
+    
+    print("\n" + "=" * 60)
+    print("RAG WITH SECTION-BASED CHUNKS")
+    print("=" * 60)
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer: {response[:250]}..." if len(str(response)) > 250 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def semantic_chunking_concept():
+    """Explain semantic chunking concept (agent-driven)."""
+    
+    print("\n" + "=" * 60)
+    print("SEMANTIC CHUNKING CONCEPT")
+    print("=" * 60)
+    
+    print("""
+    Semantic chunking goes beyond fixed rules by considering meaning:
+    
+    1. **Sentence Embedding Similarity**
+       - Compute embeddings for each sentence
+       - Group sentences with similar embeddings
+       - Split when similarity drops below threshold
+    
+    2. **Topic-Based Chunking**
+       - Identify topic shifts in the document
+       - Create chunks that maintain topical coherence
+    
+    3. **Agent-Driven Chunking**
+       - Use an LLM to identify logical boundaries
+       - Preserve context and relationships
+    
+    PraisonAI's Knowledge system handles chunking automatically,
+    but understanding these strategies helps optimize retrieval quality.
+    """)
+
+
+def main():
+    """Run all chunking strategy examples."""
+    print("\n🚀 PraisonAI Chunking Strategies Examples\n")
+    
+    # Example 1: Compare chunking strategies
+    demonstrate_chunking_strategies()
+    
+    # Example 2: RAG with section chunks
+    rag_with_section_chunks()
+    
+    # Example 3: Semantic chunking concept
+    semantic_chunking_concept()
+    
+    print("\n✅ Chunking strategy examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/citations.py

@@ -0,0 +1,350 @@
+"""
+RAG with Citations: Source Attribution and Verification
+
+This example demonstrates how to include citations and source references
+in RAG responses for transparency and verification.
+
+RAG Concept: Trust in AI-generated answers requires verification. Citations
+link answers back to source documents, enabling fact-checking and building
+user confidence.
+"""
+
+from praisonaiagents import Agent
+
+# Sample knowledge base: Legal documents with clear sources
+LEGAL_DOCUMENTS = [
+    {
+        "id": "policy_001",
+        "source": "Employee Handbook v2024",
+        "section": "Section 3.2 - Leave Policies",
+        "content": """
+        Annual Leave Entitlement:
+        - Full-time employees: 20 days per year
+        - Part-time employees: Pro-rated based on hours
+        - Unused leave: Up to 5 days may carry over to next year
+        - Leave requests: Submit at least 2 weeks in advance
+        - Approval: Manager approval required for leave > 3 consecutive days
+        """
+    },
+    {
+        "id": "policy_002",
+        "source": "Employee Handbook v2024",
+        "section": "Section 4.1 - Remote Work",
+        "content": """
+        Remote Work Policy:
+        - Eligibility: Employees in approved roles after 6 months tenure
+        - Maximum: 3 days per week remote work allowed
+        - Requirements: Reliable internet, dedicated workspace
+        - Core hours: Must be available 10am-3pm local time
+        - Equipment: Company provides laptop; employee provides internet
+        """
+    },
+    {
+        "id": "policy_003",
+        "source": "Benefits Guide 2024",
+        "section": "Chapter 2 - Health Insurance",
+        "content": """
+        Health Insurance Coverage:
+        - Provider: BlueCross BlueShield
+        - Coverage: Employee + dependents eligible
+        - Premium: Company pays 80%, employee pays 20%
+        - Deductible: $500 individual, $1000 family
+        - Enrollment: During annual open enrollment or qualifying life event
+        """
+    },
+    {
+        "id": "policy_004",
+        "source": "Benefits Guide 2024",
+        "section": "Chapter 5 - Retirement",
+        "content": """
+        401(k) Retirement Plan:
+        - Eligibility: After 90 days of employment
+        - Company match: 100% up to 4% of salary
+        - Vesting: Immediate vesting for employee contributions
+        - Company match vesting: 3-year graded schedule
+        - Investment options: 15 fund choices available
+        """
+    },
+    {
+        "id": "policy_005",
+        "source": "Code of Conduct 2024",
+        "section": "Article 7 - Conflicts of Interest",
+        "content": """
+        Conflict of Interest Policy:
+        - Disclosure: All potential conflicts must be disclosed to HR
+        - Outside employment: Requires written approval
+        - Financial interests: Cannot hold >5% in competitors
+        - Gifts: Cannot accept gifts valued over $50
+        - Relationships: Must disclose family relationships with vendors
+        """
+    }
+]
+
+
+def basic_citations():
+    """Demonstrate basic citation inclusion in RAG responses."""
+    
+    print("=" * 60)
+    print("BASIC CITATIONS IN RAG")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="HR Policy Expert",
+        instructions="""You are an HR policy expert who answers employee questions.
+        
+        IMPORTANT: Always cite your sources using this format:
+        [Source: Document Name, Section]
+        
+        Include the citation immediately after the relevant information.
+        If information comes from multiple sources, cite each one.""",
+        knowledge=LEGAL_DOCUMENTS,
+        user_id="citation_demo"
+    )
+    
+    queries = [
+        "How many vacation days do I get?",
+        "Can I work from home?",
+        "What's the company match for 401k?"
+    ]
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer with Citations:\n{response}")
+        print("-" * 40)
+
+
+def structured_citations():
+    """Demonstrate structured citation format."""
+    
+    print("\n" + "=" * 60)
+    print("STRUCTURED CITATIONS")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Policy Researcher",
+        instructions="""You provide policy information with structured citations.
+        
+        Format your response as:
+        
+        ANSWER:
+        [Your answer here]
+        
+        SOURCES:
+        1. [Document] - [Section] - [Relevant quote]
+        2. [Document] - [Section] - [Relevant quote]
+        
+        Always include at least one source citation.""",
+        knowledge=LEGAL_DOCUMENTS,
+        user_id="structured_cite"
+    )
+    
+    query = "What are the requirements for remote work eligibility?"
+    
+    print(f"\n📝 Query: {query}")
+    response = agent.chat(query)
+    print(f"\n💡 Structured Response:\n{response}")
+
+
+def inline_citations():
+    """Demonstrate inline citation style."""
+    
+    print("\n" + "=" * 60)
+    print("INLINE CITATIONS (Academic Style)")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Academic Researcher",
+        instructions="""You write responses with inline citations like academic papers.
+        
+        Use numbered citations [1], [2], etc. in the text.
+        List full references at the end.
+        
+        Example:
+        "Employees receive 20 days of leave [1]. Remote work is allowed
+        for up to 3 days per week [2]."
+        
+        References:
+        [1] Employee Handbook v2024, Section 3.2
+        [2] Employee Handbook v2024, Section 4.1""",
+        knowledge=LEGAL_DOCUMENTS,
+        user_id="inline_cite"
+    )
+    
+    query = "Summarize the key employee benefits."
+    
+    print(f"\n📝 Query: {query}")
+    response = agent.chat(query)
+    print(f"\n💡 Academic-Style Response:\n{response}")
+
+
+def citation_verification():
+    """Demonstrate citation verification concept."""
+    
+    print("\n" + "=" * 60)
+    print("CITATION VERIFICATION")
+    print("=" * 60)
+    
+    print("""
+    🔍 Citation Verification Process:
+    
+    1. **Extract Citations**
+       Parse the agent's response to find citation markers
+       
+    2. **Locate Source Documents**
+       Match citations to documents in knowledge base
+       
+    3. **Verify Claims**
+       Check if the cited text supports the claim
+       
+    4. **Flag Discrepancies**
+       Highlight any mismatches or unsupported claims
+    
+    Example Verification:
+    """)
+    
+    # Simulated verification
+    claim = "Employees get 20 days of annual leave"
+    cited_source = "Employee Handbook v2024, Section 3.2"
+    source_text = "Full-time employees: 20 days per year"
+    
+    print(f"   Claim: \"{claim}\"")
+    print(f"   Citation: {cited_source}")
+    print(f"   Source Text: \"{source_text}\"")
+    print("   Verification: ✅ SUPPORTED")
+    
+    print("""
+    
+    Implementation:
+    ```python
+    def verify_citation(claim: str, source_id: str, knowledge) -> bool:
+        # Get source document
+        source = knowledge.get(source_id)
+        
+        # Check if claim is supported by source
+        # (In practice, use semantic similarity)
+        return claim_supported_by_source(claim, source)
+    ```
+    """)
+
+
+def multi_source_synthesis():
+    """Demonstrate synthesizing from multiple sources with citations."""
+    
+    print("\n" + "=" * 60)
+    print("MULTI-SOURCE SYNTHESIS WITH CITATIONS")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Benefits Advisor",
+        instructions="""You synthesize information from multiple policy documents.
+        
+        When answering:
+        1. Gather relevant information from all sources
+        2. Synthesize into a coherent answer
+        3. Cite each source for the specific information it provides
+        4. Note if sources have different or complementary information
+        
+        Format: Include [Source: X] after each piece of information.""",
+        knowledge=LEGAL_DOCUMENTS,
+        user_id="multi_source"
+    )
+    
+    query = "Give me a complete overview of employee benefits and policies."
+    
+    print(f"\n📝 Query: {query}")
+    response = agent.chat(query)
+    print(f"\n💡 Multi-Source Synthesis:\n{response}")
+
+
+def citation_best_practices():
+    """Share best practices for citations in RAG."""
+    
+    print("\n" + "=" * 60)
+    print("CITATION BEST PRACTICES")
+    print("=" * 60)
+    
+    print("""
+    📚 Best Practices for RAG Citations:
+    
+    1. **Include Metadata in Knowledge Base**
+       ```python
+       documents = [
+           {
+               "id": "doc_001",
+               "source": "Policy Manual",
+               "section": "Chapter 3",
+               "date": "2024-01-15",
+               "author": "HR Department",
+               "content": "..."
+           }
+       ]
+       ```
+    
+    2. **Instruct Agent to Cite**
+       - Be explicit in instructions about citation format
+       - Provide examples of expected citation style
+       - Require citations for factual claims
+    
+    3. **Use Consistent Citation Format**
+       - Inline: [1], [2] with reference list
+       - Parenthetical: (Source, Section)
+       - Footnote style: ¹, ² with notes
+    
+    4. **Enable Verification**
+       - Include document IDs for programmatic lookup
+       - Store enough metadata to locate original
+       - Consider including page numbers or paragraphs
+    
+    5. **Handle Missing Sources**
+       - Instruct agent to acknowledge when info isn't in sources
+       - Distinguish between sourced and general knowledge
+       - Use phrases like "Based on the provided documents..."
+    
+    6. **Citation Granularity**
+       - Document level: Good for general attribution
+       - Section level: Better for verification
+       - Quote level: Best for accuracy-critical applications
+    
+    Example Agent Instructions:
+    ```python
+    instructions = '''
+    Answer questions using the provided documents.
+    
+    Rules:
+    - Cite sources for all factual claims
+    - Use format: [Source: Document Name, Section]
+    - If information isn't in the documents, say so
+    - Never make claims without citation
+    '''
+    ```
+    """)
+
+
+def main():
+    """Run all citation examples."""
+    print("\n🚀 PraisonAI RAG Citations Examples\n")
+    
+    # Example 1: Basic citations
+    basic_citations()
+    
+    # Example 2: Structured citations
+    structured_citations()
+    
+    # Example 3: Inline citations
+    inline_citations()
+    
+    # Example 4: Citation verification
+    citation_verification()
+    
+    # Example 5: Multi-source synthesis
+    multi_source_synthesis()
+    
+    # Example 6: Best practices
+    citation_best_practices()
+    
+    print("\n✅ Citation examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/external_sources.py

@@ -0,0 +1,338 @@
+"""
+External Sources RAG: Beyond Vector Search
+
+This example demonstrates RAG with external knowledge sources like
+web search, APIs, and databases.
+
+RAG Concept: RAG isn't limited to pre-indexed documents. External sources
+provide real-time, up-to-date information that complements static knowledge.
+"""
+
+from praisonaiagents import Agent
+
+# Simulated external data sources
+def mock_web_search(query: str) -> list:
+    """Simulate web search results."""
+    # In production, this would call a real search API
+    mock_results = {
+        "weather": [
+            {"title": "Current Weather", "snippet": "Today's forecast: Partly cloudy, high of 72°F (22°C), low of 58°F (14°C). Humidity at 45%."},
+            {"title": "Weekly Outlook", "snippet": "Rain expected Wednesday and Thursday. Weekend looks sunny with temperatures in the mid-70s."}
+        ],
+        "news": [
+            {"title": "Tech Industry Update", "snippet": "Major tech companies report strong Q3 earnings. AI investments continue to grow across sectors."},
+            {"title": "Market Summary", "snippet": "Stock markets closed higher today. S&P 500 up 0.8%, Nasdaq up 1.2%."}
+        ],
+        "default": [
+            {"title": "General Information", "snippet": "Search results would appear here with relevant web content."}
+        ]
+    }
+    
+    for key in mock_results:
+        if key in query.lower():
+            return mock_results[key]
+    return mock_results["default"]
+
+
+def mock_database_query(query: str) -> dict:
+    """Simulate database query results."""
+    # In production, this would query a real database
+    mock_data = {
+        "user_stats": {
+            "total_users": 15420,
+            "active_today": 3250,
+            "new_signups": 127,
+            "retention_rate": "78%"
+        },
+        "sales_data": {
+            "monthly_revenue": "$1.2M",
+            "top_product": "Enterprise Plan",
+            "growth_rate": "15% MoM",
+            "avg_deal_size": "$4,500"
+        },
+        "system_health": {
+            "uptime": "99.97%",
+            "avg_response_time": "145ms",
+            "error_rate": "0.02%",
+            "active_servers": 24
+        }
+    }
+    
+    for key in mock_data:
+        if key.replace("_", " ") in query.lower() or key in query.lower():
+            return mock_data[key]
+    return {"message": "No matching data found"}
+
+
+def mock_api_call(endpoint: str) -> dict:
+    """Simulate API call results."""
+    # In production, this would call a real API
+    mock_responses = {
+        "stock_price": {"symbol": "ACME", "price": 142.50, "change": "+2.3%"},
+        "exchange_rate": {"from": "USD", "to": "EUR", "rate": 0.92},
+        "crypto": {"bitcoin": "$67,500", "ethereum": "$3,450"}
+    }
+    
+    for key in mock_responses:
+        if key in endpoint.lower():
+            return mock_responses[key]
+    return {"status": "endpoint not found"}
+
+
+# Static knowledge base for hybrid approach
+COMPANY_KNOWLEDGE = [
+    {
+        "id": "company_overview",
+        "content": """
+        Acme Corporation is a technology company founded in 2015.
+        Headquarters: San Francisco, CA
+        Products: Enterprise software, cloud services, AI solutions
+        Mission: Empowering businesses through intelligent automation
+        """
+    },
+    {
+        "id": "product_info",
+        "content": """
+        Acme Products:
+        - Acme Cloud: Infrastructure-as-a-service platform
+        - Acme AI: Machine learning toolkit for enterprises
+        - Acme Connect: Integration and API management
+        Pricing: Starter ($99/mo), Professional ($499/mo), Enterprise (custom)
+        """
+    }
+]
+
+
+def web_augmented_rag():
+    """Demonstrate RAG augmented with web search."""
+    
+    print("=" * 60)
+    print("WEB-AUGMENTED RAG")
+    print("=" * 60)
+    
+    # Create agent that can use web search
+    agent = Agent(
+        name="Research Assistant",
+        instructions="""You are a research assistant that combines internal knowledge
+        with web search results. When answering:
+        1. Check if the question requires current/real-time information
+        2. Use web search for news, weather, current events
+        3. Combine web results with your knowledge for comprehensive answers
+        4. Clearly indicate when information comes from web search.""",
+        knowledge=COMPANY_KNOWLEDGE,
+        user_id="web_demo"
+    )
+    
+    # Simulate web-augmented queries
+    queries = [
+        ("What's the weather like today?", True),  # Needs web
+        ("Tell me about Acme Corporation's products", False),  # Static knowledge
+        ("What's happening in the tech industry?", True),  # Needs web
+    ]
+    
+    for query, needs_web in queries:
+        print(f"\n📝 Query: {query}")
+        
+        if needs_web:
+            # Augment with web search
+            web_results = mock_web_search(query)
+            web_context = "\n".join([f"- {r['title']}: {r['snippet']}" for r in web_results])
+            augmented_query = f"{query}\n\nWeb search results:\n{web_context}"
+            response = agent.chat(augmented_query)
+            print("🌐 (Web-augmented)")
+        else:
+            response = agent.chat(query)
+            print("📚 (Static knowledge)")
+        
+        print(f"💡 Answer: {response[:250]}..." if len(str(response)) > 250 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def database_augmented_rag():
+    """Demonstrate RAG augmented with database queries."""
+    
+    print("\n" + "=" * 60)
+    print("DATABASE-AUGMENTED RAG")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Business Analyst",
+        instructions="""You are a business analyst with access to company databases.
+        Interpret data accurately and provide actionable insights.
+        When presenting numbers, add context and trends.""",
+        knowledge=COMPANY_KNOWLEDGE,
+        user_id="db_demo"
+    )
+    
+    queries = [
+        "How are our user stats looking?",
+        "Give me a sales summary",
+        "What's our system health status?"
+    ]
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        
+        # Fetch database data
+        db_data = mock_database_query(query)
+        db_context = f"Database results: {db_data}"
+        
+        augmented_query = f"{query}\n\n{db_context}"
+        response = agent.chat(augmented_query)
+        
+        print(f"🗄️ DB Data: {db_data}")
+        print(f"💡 Analysis: {response[:200]}..." if len(str(response)) > 200 else f"💡 Analysis: {response}")
+        print("-" * 40)
+
+
+def api_augmented_rag():
+    """Demonstrate RAG augmented with API calls."""
+    
+    print("\n" + "=" * 60)
+    print("API-AUGMENTED RAG")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Financial Assistant",
+        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"
+    )
+    
+    queries = [
+        ("What's the ACME stock price?", "stock_price"),
+        ("USD to EUR exchange rate?", "exchange_rate"),
+        ("Crypto market update?", "crypto")
+    ]
+    
+    for query, endpoint in queries:
+        print(f"\n📝 Query: {query}")
+        
+        # Fetch API data
+        api_data = mock_api_call(endpoint)
+        api_context = f"API response: {api_data}"
+        
+        augmented_query = f"{query}\n\n{api_context}"
+        response = agent.chat(augmented_query)
+        
+        print(f"🔌 API Data: {api_data}")
+        print(f"💡 Response: {response[:200]}..." if len(str(response)) > 200 else f"💡 Response: {response}")
+        print("-" * 40)
+
+
+def hybrid_source_rag():
+    """Combine multiple external sources."""
+    
+    print("\n" + "=" * 60)
+    print("HYBRID SOURCE RAG")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Executive Assistant",
+        instructions="""You are an executive assistant with access to multiple data sources.
+        Synthesize information from static knowledge, databases, and APIs.
+        Provide comprehensive briefings that combine all relevant data.""",
+        knowledge=COMPANY_KNOWLEDGE,
+        user_id="hybrid_demo"
+    )
+    
+    query = "Give me a complete business briefing for today"
+    
+    # Gather from multiple sources
+    db_stats = mock_database_query("user_stats")
+    db_sales = mock_database_query("sales_data")
+    db_health = mock_database_query("system_health")
+    api_stock = mock_api_call("stock_price")
+    web_news = mock_web_search("news")
+    
+    combined_context = f"""
+    User Statistics: {db_stats}
+    Sales Data: {db_sales}
+    System Health: {db_health}
+    Stock Price: {api_stock}
+    Industry News: {[r['snippet'] for r in web_news]}
+    """
+    
+    augmented_query = f"{query}\n\nData from multiple sources:\n{combined_context}"
+    
+    print(f"\n📝 Query: {query}")
+    print("\n📊 Data Sources:")
+    print("   - Database: user_stats, sales_data, system_health")
+    print("   - API: stock_price")
+    print("   - Web: news search")
+    
+    response = agent.chat(augmented_query)
+    print(f"\n💡 Executive Briefing:\n{response}")
+
+
+def external_source_patterns():
+    """Explain external source patterns."""
+    
+    print("\n" + "=" * 60)
+    print("EXTERNAL SOURCE PATTERNS")
+    print("=" * 60)
+    
+    print("""
+    🌐 External Source Integration Patterns:
+    
+    1. **Query-Time Augmentation**
+       - Fetch external data when query arrives
+       - Inject into prompt as additional context
+       - Best for: Real-time data needs
+    
+    2. **Scheduled Refresh**
+       - Periodically update knowledge base from external sources
+       - Index external data for faster retrieval
+       - Best for: Semi-static external data
+    
+    3. **Tool-Based Retrieval**
+       - Give agent tools to fetch external data
+       - Agent decides when to use external sources
+       - Best for: Complex, multi-step queries
+    
+    4. **Hybrid Static + Dynamic**
+       - Combine indexed knowledge with real-time fetches
+       - Use static for stable info, dynamic for current
+       - Best for: Comprehensive answers
+    
+    Implementation Example:
+    ```python
+    def web_search_tool(query: str) -> str:
+        # Call search API
+        results = search_api.search(query)
+        return format_results(results)
+    
+    agent = Agent(
+        knowledge=static_docs,
+        tools=[web_search_tool],
+        instructions="Use web search for current events..."
+    )
+    ```
+    """)
+
+
+def main():
+    """Run all external source RAG examples."""
+    print("\n🚀 PraisonAI External Sources RAG Examples\n")
+    
+    # Example 1: Web-augmented RAG
+    web_augmented_rag()
+    
+    # Example 2: Database-augmented RAG
+    database_augmented_rag()
+    
+    # Example 3: API-augmented RAG
+    api_augmented_rag()
+    
+    # Example 4: Hybrid sources
+    hybrid_source_rag()
+    
+    # Example 5: Patterns
+    external_source_patterns()
+    
+    print("\n✅ External sources RAG examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/hybrid_search.py

@@ -0,0 +1,281 @@
+"""
+Hybrid Search: Combining Dense and Sparse Retrieval
+
+This example demonstrates hybrid retrieval that combines semantic (dense)
+search with keyword (sparse) search for improved accuracy.
+
+RAG Concept: Dense embeddings capture meaning but may miss exact matches.
+Sparse/keyword search finds exact terms but lacks semantic understanding.
+Hybrid search combines both for best results.
+"""
+
+from praisonaiagents import Agent
+
+# Sample knowledge base: Technical troubleshooting guides
+TROUBLESHOOTING_GUIDES = [
+    {
+        "id": "error_001",
+        "content": """
+        Error Code: ERR_CONNECTION_REFUSED
+        
+        This error occurs when the client cannot establish a connection to the server.
+        
+        Common Causes:
+        - Server is not running or has crashed
+        - Firewall blocking the connection port
+        - Incorrect host or port configuration
+        - Network connectivity issues
+        
+        Resolution Steps:
+        1. Verify the server process is running: `systemctl status myservice`
+        2. Check firewall rules: `sudo ufw status`
+        3. Test port connectivity: `telnet hostname 8080`
+        4. Review server logs for startup errors
+        """
+    },
+    {
+        "id": "error_002",
+        "content": """
+        Error Code: ERR_OUT_OF_MEMORY
+        
+        This error indicates the application has exhausted available memory.
+        
+        Common Causes:
+        - Memory leak in application code
+        - Insufficient heap size configuration
+        - Too many concurrent connections
+        - Large data processing without streaming
+        
+        Resolution Steps:
+        1. Increase heap size: `-Xmx4g` for Java applications
+        2. Enable memory profiling to identify leaks
+        3. Implement pagination for large datasets
+        4. Add memory limits to container configurations
+        """
+    },
+    {
+        "id": "error_003",
+        "content": """
+        Error Code: ERR_SSL_CERTIFICATE_EXPIRED
+        
+        This error means the SSL/TLS certificate has passed its validity date.
+        
+        Common Causes:
+        - Certificate not renewed before expiration
+        - Automatic renewal process failed
+        - Wrong certificate installed
+        
+        Resolution Steps:
+        1. Check certificate expiry: `openssl x509 -enddate -noout -in cert.pem`
+        2. Renew certificate with your CA or Let's Encrypt
+        3. Update certificate files on the server
+        4. Restart the web server to load new certificate
+        """
+    },
+    {
+        "id": "error_004",
+        "content": """
+        Error Code: ERR_DATABASE_DEADLOCK
+        
+        A deadlock occurs when two transactions block each other indefinitely.
+        
+        Common Causes:
+        - Transactions acquiring locks in different orders
+        - Long-running transactions holding locks
+        - Missing indexes causing table scans with locks
+        
+        Resolution Steps:
+        1. Review transaction isolation levels
+        2. Ensure consistent lock ordering across queries
+        3. Add appropriate indexes to reduce lock duration
+        4. Implement retry logic for deadlock exceptions
+        """
+    },
+    {
+        "id": "error_005",
+        "content": """
+        Error Code: ERR_RATE_LIMIT_EXCEEDED
+        
+        The API has rejected requests due to exceeding the rate limit.
+        
+        Common Causes:
+        - Too many requests in a short time window
+        - Missing request throttling in client code
+        - Retry loops without backoff
+        
+        Resolution Steps:
+        1. Implement exponential backoff for retries
+        2. Cache responses to reduce API calls
+        3. Use bulk endpoints where available
+        4. Request rate limit increase if needed
+        """
+    }
+]
+
+
+def demonstrate_search_types():
+    """Show the difference between semantic and keyword search concepts."""
+    
+    print("=" * 60)
+    print("SEARCH TYPES COMPARISON")
+    print("=" * 60)
+    
+    print("""
+    📊 DENSE (Semantic) Search:
+       - Uses vector embeddings to capture meaning
+       - "memory issues" matches "out of memory" and "RAM exhausted"
+       - Good for natural language queries
+       - May miss exact technical terms
+    
+    📊 SPARSE (Keyword) Search:
+       - Uses term frequency and exact matching
+       - "ERR_SSL_CERTIFICATE_EXPIRED" matches exactly
+       - Good for error codes, IDs, technical terms
+       - Misses synonyms and paraphrases
+    
+    📊 HYBRID Search:
+       - Combines both approaches
+       - Weighted scoring: (α × dense_score) + ((1-α) × sparse_score)
+       - Best of both worlds for technical documentation
+    """)
+
+
+def hybrid_rag_example():
+    """Demonstrate hybrid retrieval with an agent."""
+    
+    # Create agent with hybrid retrieval enabled
+    agent = Agent(
+        name="Tech Support",
+        instructions="""You are a technical support specialist.
+        Help users troubleshoot errors using the knowledge base.
+        Always mention the specific error code when relevant.
+        Provide step-by-step resolution guidance.""",
+        knowledge=TROUBLESHOOTING_GUIDES,
+        knowledge_config={
+            "hybrid": True,  # Enable hybrid search
+            "top_k": 3
+        },
+        user_id="hybrid_demo"
+    )
+    
+    # Test with different query types
+    queries = [
+        # Semantic query (natural language)
+        "My application is running out of memory, what should I do?",
+        
+        # Exact match query (error code)
+        "ERR_SSL_CERTIFICATE_EXPIRED",
+        
+        # Mixed query
+        "Getting connection refused error when connecting to port 8080",
+        
+        # Conceptual query
+        "How do I handle API throttling?"
+    ]
+    
+    print("\n" + "=" * 60)
+    print("HYBRID RAG IN ACTION")
+    print("=" * 60)
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer: {response[:300]}..." if len(str(response)) > 300 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def compare_retrieval_modes():
+    """Compare results with different retrieval configurations."""
+    
+    print("\n" + "=" * 60)
+    print("RETRIEVAL MODE COMPARISON")
+    print("=" * 60)
+    
+    query = "database transaction blocking issue"
+    
+    # Dense-only agent
+    dense_agent = Agent(
+        name="Dense Search Agent",
+        instructions="Answer based on the knowledge provided.",
+        knowledge=TROUBLESHOOTING_GUIDES,
+        knowledge_config={"hybrid": False},
+        user_id="dense_demo"
+    )
+    
+    # Hybrid agent
+    hybrid_agent = Agent(
+        name="Hybrid Search Agent", 
+        instructions="Answer based on the knowledge provided.",
+        knowledge=TROUBLESHOOTING_GUIDES,
+        knowledge_config={"hybrid": True},
+        user_id="hybrid_demo"
+    )
+    
+    print(f"\n📝 Query: {query}")
+    
+    print("\n🔍 Dense-Only Search:")
+    dense_response = dense_agent.chat(query)
+    print(f"   {dense_response[:200]}...")
+    
+    print("\n🔍 Hybrid Search:")
+    hybrid_response = hybrid_agent.chat(query)
+    print(f"   {hybrid_response[:200]}...")
+
+
+def hybrid_search_tuning():
+    """Explain hybrid search tuning parameters."""
+    
+    print("\n" + "=" * 60)
+    print("HYBRID SEARCH TUNING")
+    print("=" * 60)
+    
+    print("""
+    Key parameters for hybrid search optimization:
+    
+    1. **Alpha (α) Weight** - Balance between dense and sparse
+       - α = 1.0: Pure semantic search
+       - α = 0.5: Equal weight (good default)
+       - α = 0.0: Pure keyword search
+    
+    2. **Top-K Retrieval** - Number of candidates
+       - Higher K: More context, slower, may dilute relevance
+       - Lower K: Faster, focused, may miss relevant docs
+       - Typical range: 3-10 for RAG
+    
+    3. **Reranking** - Second-pass scoring
+       - Cross-encoder reranking improves precision
+       - Adds latency but improves quality
+    
+    Example configuration:
+    ```python
+    knowledge_config = {
+        "hybrid": True,
+        "hybrid_alpha": 0.6,  # Favor semantic
+        "top_k": 5,
+        "rerank": True
+    }
+    ```
+    """)
+
+
+def main():
+    """Run all hybrid search examples."""
+    print("\n🚀 PraisonAI Hybrid Search Examples\n")
+    
+    # Example 1: Explain search types
+    demonstrate_search_types()
+    
+    # Example 2: Hybrid RAG in action
+    hybrid_rag_example()
+    
+    # Example 3: Compare retrieval modes
+    compare_retrieval_modes()
+    
+    # Example 4: Tuning guidance
+    hybrid_search_tuning()
+    
+    print("\n✅ Hybrid search examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/knowledge_graph.py

@@ -0,0 +1,288 @@
+"""
+Graph-Assisted RAG: Relationship-Aware Retrieval
+
+This example demonstrates how graph structures can enhance RAG by
+capturing relationships between entities and enabling path-based retrieval.
+
+RAG Concept: Traditional vector search finds similar documents but misses
+relationships. Graph-assisted RAG understands connections between entities,
+enabling multi-hop reasoning and relationship-aware answers.
+"""
+
+from praisonaiagents import Agent
+
+# Sample knowledge base: Interconnected entities (simulating a knowledge graph)
+ENTITY_KNOWLEDGE = [
+    {
+        "id": "person_einstein",
+        "type": "person",
+        "content": """
+        Albert Einstein (1879-1955) was a theoretical physicist who developed
+        the theory of relativity. He received the Nobel Prize in Physics in 1921
+        for his explanation of the photoelectric effect. Einstein worked at the
+        Institute for Advanced Study in Princeton from 1933 until his death.
+        Key relationships: Collaborated with Niels Bohr, influenced by Max Planck,
+        mentored by Heinrich Weber.
+        """
+    },
+    {
+        "id": "person_bohr",
+        "type": "person",
+        "content": """
+        Niels Bohr (1885-1962) was a Danish physicist who made foundational
+        contributions to understanding atomic structure and quantum theory.
+        He received the Nobel Prize in Physics in 1922. Bohr founded the
+        Institute of Theoretical Physics in Copenhagen.
+        Key relationships: Debated with Einstein on quantum mechanics,
+        mentored Werner Heisenberg, influenced by Ernest Rutherford.
+        """
+    },
+    {
+        "id": "person_heisenberg",
+        "type": "person",
+        "content": """
+        Werner Heisenberg (1901-1976) was a German physicist and pioneer of
+        quantum mechanics. He formulated the uncertainty principle in 1927.
+        He received the Nobel Prize in Physics in 1932.
+        Key relationships: Student of Niels Bohr, collaborated with Max Born,
+        worked with Wolfgang Pauli.
+        """
+    },
+    {
+        "id": "concept_relativity",
+        "type": "concept",
+        "content": """
+        Theory of Relativity: A framework developed by Albert Einstein consisting
+        of special relativity (1905) and general relativity (1915). Special
+        relativity deals with objects moving at constant speeds, introducing
+        E=mc². General relativity describes gravity as the curvature of spacetime.
+        Related concepts: Spacetime, time dilation, gravitational waves.
+        Applications: GPS satellites, particle accelerators, cosmology.
+        """
+    },
+    {
+        "id": "concept_quantum",
+        "type": "concept",
+        "content": """
+        Quantum Mechanics: A fundamental theory describing nature at atomic scales.
+        Key principles include wave-particle duality, superposition, and entanglement.
+        Developed through contributions from Planck, Bohr, Heisenberg, Schrödinger.
+        Related concepts: Uncertainty principle, quantum tunneling, wave function.
+        Applications: Semiconductors, lasers, MRI machines, quantum computing.
+        """
+    },
+    {
+        "id": "institution_princeton",
+        "type": "institution",
+        "content": """
+        Institute for Advanced Study (Princeton): A private research center
+        founded in 1930. Notable members include Albert Einstein, John von Neumann,
+        Kurt Gödel, and Robert Oppenheimer. The institute focuses on theoretical
+        research in mathematics, natural sciences, and humanities.
+        Location: Princeton, New Jersey, USA.
+        """
+    },
+    {
+        "id": "institution_copenhagen",
+        "type": "institution",
+        "content": """
+        Niels Bohr Institute (Copenhagen): Founded in 1921 by Niels Bohr as the
+        Institute of Theoretical Physics. It became a world center for quantum
+        mechanics research. Notable visitors included Heisenberg, Pauli, and Dirac.
+        The "Copenhagen interpretation" of quantum mechanics originated here.
+        Location: Copenhagen, Denmark.
+        """
+    },
+    {
+        "id": "event_solvay",
+        "type": "event",
+        "content": """
+        Solvay Conferences: Series of physics conferences held in Brussels since 1911.
+        The 1927 Fifth Solvay Conference is famous for the Bohr-Einstein debates
+        on quantum mechanics. Attendees included Einstein, Bohr, Heisenberg,
+        Schrödinger, Dirac, and Marie Curie. These conferences shaped modern physics.
+        """
+    }
+]
+
+
+def graph_aware_rag():
+    """Demonstrate relationship-aware retrieval."""
+    
+    # Create agent with entity knowledge
+    agent = Agent(
+        name="Physics Historian",
+        instructions="""You are a physics historian who understands the relationships
+        between scientists, concepts, and institutions. When answering:
+        1. Trace connections between entities
+        2. Explain how ideas and people influenced each other
+        3. Reference specific relationships mentioned in the knowledge base
+        4. Build a narrative that shows the interconnected nature of physics history.""",
+        knowledge=ENTITY_KNOWLEDGE,
+        user_id="graph_demo"
+    )
+    
+    # Relationship-focused queries
+    queries = [
+        "How were Einstein and Bohr connected?",
+        "Trace the lineage of quantum mechanics through its key figures.",
+        "What institutions were central to 20th century physics?",
+        "How did the Solvay Conferences shape physics?"
+    ]
+    
+    print("=" * 60)
+    print("GRAPH-ASSISTED RAG: Physics History Network")
+    print("=" * 60)
+    print("\nKnowledge graph entities:")
+    for entity in ENTITY_KNOWLEDGE:
+        print(f"  - [{entity['type']}] {entity['id']}")
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer: {response[:350]}..." if len(str(response)) > 350 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def multi_hop_reasoning():
+    """Demonstrate multi-hop reasoning across entities."""
+    
+    print("\n" + "=" * 60)
+    print("MULTI-HOP REASONING")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Connection Finder",
+        instructions="""You excel at finding indirect connections between entities.
+        When asked about relationships, trace the path through intermediate entities.
+        Format your answer to show the chain of connections.""",
+        knowledge=ENTITY_KNOWLEDGE,
+        user_id="multihop_demo"
+    )
+    
+    # Multi-hop query requiring traversal
+    query = """
+    What is the connection between the uncertainty principle and Princeton?
+    (Hint: trace through people and their relationships)
+    """
+    
+    print(f"\n📝 Multi-hop Query: {query.strip()}")
+    print("\n🔗 Expected path: Uncertainty Principle → Heisenberg → Bohr → Einstein → Princeton")
+    
+    response = agent.chat(query)
+    print(f"\n💡 Answer:\n{response}")
+
+
+def entity_type_filtering():
+    """Show filtering by entity type."""
+    
+    print("\n" + "=" * 60)
+    print("ENTITY TYPE FILTERING")
+    print("=" * 60)
+    
+    # Filter to only people
+    people_only = [e for e in ENTITY_KNOWLEDGE if e['type'] == 'person']
+    
+    people_agent = Agent(
+        name="Biographer",
+        instructions="You are a biographer focused on scientists' lives and relationships.",
+        knowledge=people_only,
+        user_id="people_demo"
+    )
+    
+    # Filter to only concepts
+    concepts_only = [e for e in ENTITY_KNOWLEDGE if e['type'] == 'concept']
+    
+    concept_agent = Agent(
+        name="Concept Explainer",
+        instructions="You explain scientific concepts and their relationships.",
+        knowledge=concepts_only,
+        user_id="concept_demo"
+    )
+    
+    query = "Tell me about the major developments in early 20th century physics."
+    
+    print(f"\n📝 Query: {query}")
+    
+    print("\n👤 People-focused view:")
+    response1 = people_agent.chat(query)
+    print(f"   {response1[:250]}...")
+    
+    print("\n💡 Concept-focused view:")
+    response2 = concept_agent.chat(query)
+    print(f"   {response2[:250]}...")
+
+
+def graph_rag_patterns():
+    """Explain graph RAG patterns."""
+    
+    print("\n" + "=" * 60)
+    print("GRAPH RAG PATTERNS")
+    print("=" * 60)
+    
+    print("""
+    📊 Graph-Assisted RAG Patterns:
+    
+    1. **Entity-Centric Retrieval**
+       - Store entities with their relationships
+       - Query retrieves entity + connected entities
+       - Good for: "Tell me about X and its connections"
+    
+    2. **Path-Based Retrieval**
+       - Find paths between two entities
+       - Retrieve all nodes along the path
+       - Good for: "How is X related to Y?"
+    
+    3. **Neighborhood Expansion**
+       - Start with query-matched entities
+       - Expand to include 1-hop or 2-hop neighbors
+       - Good for: Comprehensive context
+    
+    4. **Subgraph Extraction**
+       - Extract relevant subgraph for query
+       - Include nodes and edges as context
+       - Good for: Complex multi-entity questions
+    
+    Implementation in PraisonAI:
+    ```python
+    # Structure knowledge as entities with relationships
+    entities = [
+        {
+            "id": "entity_1",
+            "type": "person",
+            "content": "...",
+            "relationships": ["entity_2", "entity_3"]
+        },
+        ...
+    ]
+    
+    # Agent retrieves related entities automatically
+    agent = Agent(
+        knowledge=entities,
+        instructions="Trace relationships between entities..."
+    )
+    ```
+    """)
+
+
+def main():
+    """Run all graph-assisted RAG examples."""
+    print("\n🚀 PraisonAI Graph-Assisted RAG Examples\n")
+    
+    # Example 1: Graph-aware retrieval
+    graph_aware_rag()
+    
+    # Example 2: Multi-hop reasoning
+    multi_hop_reasoning()
+    
+    # Example 3: Entity type filtering
+    entity_type_filtering()
+    
+    # Example 4: Graph RAG patterns
+    graph_rag_patterns()
+    
+    print("\n✅ Graph-assisted RAG examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/multi_document.py

@@ -0,0 +1,266 @@
+"""
+Multi-Document RAG: Synthesizing Information Across Sources
+
+This example demonstrates retrieving and synthesizing information from
+multiple documents to answer complex questions.
+
+RAG Concept: Real-world questions often require combining information
+from multiple sources. Multi-document RAG retrieves relevant chunks
+from different documents and synthesizes a coherent answer.
+"""
+
+from praisonaiagents import Agent
+
+# Sample knowledge base: Multiple documents on different topics
+CLIMATE_REPORT = {
+    "id": "climate_2024",
+    "source": "Global Climate Report 2024",
+    "content": """
+    Global Climate Report 2024 - Executive Summary
+    
+    Global temperatures in 2024 reached 1.45°C above pre-industrial levels,
+    making it the warmest year on record. Key findings:
+    
+    - Arctic sea ice extent hit a new minimum in September
+    - Ocean heat content reached unprecedented levels
+    - Extreme weather events increased by 23% compared to the decade average
+    - CO2 concentrations reached 422 ppm, up from 417 ppm in 2023
+    
+    Regional impacts varied significantly. Europe experienced its hottest
+    summer, while parts of South America faced severe drought conditions.
+    The economic cost of climate-related disasters exceeded $380 billion.
+    """
+}
+
+ENERGY_REPORT = {
+    "id": "energy_2024",
+    "source": "World Energy Outlook 2024",
+    "content": """
+    World Energy Outlook 2024 - Key Highlights
+    
+    Renewable energy capacity grew by 35% in 2024, led by solar and wind.
+    Major developments:
+    
+    - Solar installations reached 1.5 TW cumulative capacity globally
+    - Wind power generated 12% of global electricity
+    - Battery storage capacity doubled to 500 GWh
+    - Electric vehicle sales reached 20 million units
+    
+    Despite progress, fossil fuels still account for 75% of primary energy.
+    Investment in clean energy reached $1.8 trillion, but $4 trillion annually
+    is needed to meet net-zero targets by 2050.
+    """
+}
+
+POLICY_REPORT = {
+    "id": "policy_2024",
+    "source": "Climate Policy Tracker 2024",
+    "content": """
+    Climate Policy Tracker 2024 - Global Progress
+    
+    145 countries have now committed to net-zero emissions targets.
+    Policy developments:
+    
+    - EU Carbon Border Adjustment Mechanism fully implemented
+    - US Inflation Reduction Act drove $300B in clean energy investment
+    - China pledged to peak emissions before 2030
+    - India launched National Green Hydrogen Mission
+    
+    Carbon pricing now covers 23% of global emissions, up from 20% in 2023.
+    Average carbon price increased to $45/ton. However, only 4% of emissions
+    are priced at levels consistent with Paris Agreement goals ($100+/ton).
+    """
+}
+
+TECH_REPORT = {
+    "id": "tech_2024",
+    "source": "Climate Technology Review 2024",
+    "content": """
+    Climate Technology Review 2024 - Breakthrough Innovations
+    
+    Several climate technologies reached commercial viability in 2024:
+    
+    - Direct Air Capture: Costs fell to $400/ton CO2, down from $600 in 2022
+    - Green Hydrogen: Production costs reached $3/kg in optimal locations
+    - Solid-state Batteries: Energy density improved 40%, enabling longer EV range
+    - Perovskite Solar Cells: Efficiency reached 33%, approaching silicon limits
+    
+    Emerging technologies to watch:
+    - Enhanced geothermal systems showing promise in non-volcanic regions
+    - Ocean-based carbon removal gaining investment interest
+    - AI-optimized grid management reducing curtailment by 15%
+    """
+}
+
+# Combine all documents
+ALL_DOCUMENTS = [CLIMATE_REPORT, ENERGY_REPORT, POLICY_REPORT, TECH_REPORT]
+
+
+def multi_document_rag():
+    """Demonstrate RAG across multiple documents."""
+    
+    # Create agent with multi-document knowledge
+    agent = Agent(
+        name="Climate Analyst",
+        instructions="""You are a climate and energy analyst.
+        Synthesize information from multiple reports to answer questions.
+        When information comes from different sources, mention which reports
+        you're drawing from. Provide comprehensive, well-rounded answers.""",
+        knowledge=ALL_DOCUMENTS,
+        user_id="multi_doc_demo"
+    )
+    
+    # Questions that require multi-document synthesis
+    queries = [
+        "What is the current state of global climate and what are we doing about it?",
+        "How is renewable energy progressing and what policies support it?",
+        "What technologies are emerging to address climate change?",
+        "Compare the economic investment in clean energy with climate damage costs."
+    ]
+    
+    print("=" * 60)
+    print("MULTI-DOCUMENT RAG: Climate Analysis")
+    print("=" * 60)
+    print(f"\nKnowledge base: {len(ALL_DOCUMENTS)} documents")
+    print(f"Sources: {[d['source'] for d in ALL_DOCUMENTS]}")
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer: {response[:400]}..." if len(str(response)) > 400 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def cross_reference_example():
+    """Show how agents can cross-reference information."""
+    
+    print("\n" + "=" * 60)
+    print("CROSS-REFERENCING ACROSS DOCUMENTS")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Cross-Reference Analyst",
+        instructions="""You are an analyst who excels at finding connections
+        between different reports. When answering:
+        1. Identify relevant information from each source
+        2. Note any contradictions or complementary data
+        3. Synthesize a coherent narrative
+        4. Cite your sources explicitly.""",
+        knowledge=ALL_DOCUMENTS,
+        user_id="cross_ref_demo"
+    )
+    
+    query = """
+    Analyze the relationship between renewable energy growth (from Energy Report),
+    policy support (from Policy Report), and technology costs (from Tech Report).
+    How do these factors reinforce each other?
+    """
+    
+    print(f"\n📝 Complex Query: {query.strip()}")
+    response = agent.chat(query)
+    print(f"\n💡 Synthesized Answer:\n{response}")
+
+
+def document_filtering():
+    """Demonstrate filtering documents by metadata."""
+    
+    print("\n" + "=" * 60)
+    print("DOCUMENT FILTERING BY SOURCE")
+    print("=" * 60)
+    
+    # Create agents focused on specific document types
+    policy_agent = Agent(
+        name="Policy Expert",
+        instructions="You are a policy expert. Focus only on policy-related information.",
+        knowledge=[POLICY_REPORT],
+        user_id="policy_expert"
+    )
+    
+    tech_agent = Agent(
+        name="Technology Expert",
+        instructions="You are a technology expert. Focus on technical innovations.",
+        knowledge=[TECH_REPORT],
+        user_id="tech_expert"
+    )
+    
+    query = "What progress has been made in addressing climate change?"
+    
+    print(f"\n📝 Query: {query}")
+    
+    print("\n🏛️ Policy Expert's View:")
+    policy_response = policy_agent.chat(query)
+    print(f"   {policy_response[:250]}...")
+    
+    print("\n🔬 Technology Expert's View:")
+    tech_response = tech_agent.chat(query)
+    print(f"   {tech_response[:250]}...")
+    
+    print("\n💡 Note: Different document subsets yield different perspectives!")
+
+
+def multi_document_best_practices():
+    """Share best practices for multi-document RAG."""
+    
+    print("\n" + "=" * 60)
+    print("MULTI-DOCUMENT RAG BEST PRACTICES")
+    print("=" * 60)
+    
+    print("""
+    📚 Best Practices for Multi-Document RAG:
+    
+    1. **Document Metadata**
+       - Include source, date, author in each document
+       - Enables filtering and citation
+    
+    2. **Consistent Chunking**
+       - Use similar chunk sizes across documents
+       - Maintains balanced retrieval
+    
+    3. **Source Diversity**
+       - Retrieve from multiple sources when possible
+       - Reduces single-source bias
+    
+    4. **Conflict Resolution**
+       - Instruct agent how to handle contradictions
+       - Prefer newer sources or authoritative ones
+    
+    5. **Citation Requirements**
+       - Ask agent to cite sources in responses
+       - Enables verification and trust
+    
+    Example configuration:
+    ```python
+    documents = [
+        {"id": "doc1", "source": "Report A", "date": "2024-01", "content": "..."},
+        {"id": "doc2", "source": "Report B", "date": "2024-06", "content": "..."},
+    ]
+    
+    agent = Agent(
+        knowledge=documents,
+        instructions="Always cite which report your information comes from."
+    )
+    ```
+    """)
+
+
+def main():
+    """Run all multi-document RAG examples."""
+    print("\n🚀 PraisonAI Multi-Document RAG Examples\n")
+    
+    # Example 1: Basic multi-document RAG
+    multi_document_rag()
+    
+    # Example 2: Cross-referencing
+    cross_reference_example()
+    
+    # Example 3: Document filtering
+    document_filtering()
+    
+    # Example 4: Best practices
+    multi_document_best_practices()
+    
+    print("\n✅ Multi-document RAG examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/reranking.py

@@ -0,0 +1,255 @@
+"""
+Reranking: Improving Retrieval Precision
+
+This example demonstrates reranking - a second-pass scoring mechanism
+that improves the quality of retrieved documents.
+
+RAG Concept: Initial retrieval (first stage) prioritizes recall - finding
+all potentially relevant documents. Reranking (second stage) prioritizes
+precision - ordering results by true relevance to the query.
+"""
+
+from praisonaiagents import Agent
+
+# Sample knowledge base: Research paper abstracts
+RESEARCH_PAPERS = [
+    {
+        "id": "paper_001",
+        "content": """
+        Title: Attention Mechanisms in Neural Machine Translation
+        
+        Abstract: This paper introduces a novel attention mechanism for sequence-to-sequence
+        models in machine translation. Unlike previous approaches that use a fixed-length
+        context vector, our method allows the model to automatically search for parts of
+        the source sentence relevant to predicting the target word. Experiments on
+        English-to-French translation show significant improvements over baseline systems.
+        """
+    },
+    {
+        "id": "paper_002",
+        "content": """
+        Title: Deep Residual Learning for Image Recognition
+        
+        Abstract: We present a residual learning framework to ease the training of very deep
+        neural networks. By reformulating layers as learning residual functions with
+        reference to layer inputs, we show that these networks are easier to optimize
+        and can gain accuracy from considerably increased depth. Our 152-layer residual
+        network achieves state-of-the-art results on ImageNet classification.
+        """
+    },
+    {
+        "id": "paper_003",
+        "content": """
+        Title: BERT: Pre-training of Deep Bidirectional Transformers
+        
+        Abstract: We introduce BERT, a new language representation model designed to
+        pre-train deep bidirectional representations from unlabeled text. Unlike previous
+        models, BERT is designed to jointly condition on both left and right context.
+        The pre-trained BERT model can be fine-tuned with just one additional output
+        layer for a wide range of NLP tasks.
+        """
+    },
+    {
+        "id": "paper_004",
+        "content": """
+        Title: Generative Adversarial Networks
+        
+        Abstract: We propose a new framework for estimating generative models via an
+        adversarial process. We simultaneously train two models: a generative model G
+        that captures the data distribution, and a discriminative model D that estimates
+        the probability that a sample came from the training data rather than G.
+        This framework can generate realistic images from random noise.
+        """
+    },
+    {
+        "id": "paper_005",
+        "content": """
+        Title: Transformer Architecture for Language Understanding
+        
+        Abstract: We propose the Transformer, a model architecture eschewing recurrence
+        and instead relying entirely on an attention mechanism to draw global dependencies
+        between input and output. The Transformer allows for significantly more
+        parallelization and achieves new state of the art in translation quality.
+        """
+    },
+    {
+        "id": "paper_006",
+        "content": """
+        Title: Word Embeddings and Semantic Similarity
+        
+        Abstract: This paper explores methods for learning distributed representations
+        of words that capture semantic relationships. We show that word vectors trained
+        on large corpora encode meaningful syntactic and semantic regularities. These
+        representations enable analogical reasoning: king - man + woman = queen.
+        """
+    }
+]
+
+
+def explain_reranking():
+    """Explain the reranking concept."""
+    
+    print("=" * 60)
+    print("RERANKING EXPLAINED")
+    print("=" * 60)
+    
+    print("""
+    🔄 Two-Stage Retrieval Pipeline:
+    
+    Stage 1: Initial Retrieval (Bi-encoder)
+    ├── Fast approximate matching
+    ├── Retrieves top-K candidates (e.g., 20-100)
+    ├── Uses pre-computed embeddings
+    └── Optimizes for RECALL (find all relevant docs)
+    
+    Stage 2: Reranking (Cross-encoder)
+    ├── Slower but more accurate scoring
+    ├── Reorders the top-K candidates
+    ├── Computes query-document interaction
+    └── Optimizes for PRECISION (best docs first)
+    
+    Why Reranking Matters:
+    - Bi-encoders encode query and docs separately (fast but approximate)
+    - Cross-encoders process query+doc together (slow but accurate)
+    - Reranking gives you the best of both worlds
+    """)
+
+
+def rag_with_reranking():
+    """Demonstrate RAG with reranking enabled."""
+    
+    # Create agent with reranking
+    agent = Agent(
+        name="Research Assistant",
+        instructions="""You are a research paper expert.
+        Answer questions about machine learning research.
+        Reference specific papers when relevant.
+        Be precise about technical details.""",
+        knowledge=RESEARCH_PAPERS,
+        knowledge_config={
+            "rerank": True,
+            "top_k": 5
+        },
+        user_id="rerank_demo"
+    )
+    
+    queries = [
+        "Which paper introduced the attention mechanism for translation?",
+        "Tell me about pre-training language models",
+        "What architecture avoids recurrence entirely?"
+    ]
+    
+    print("\n" + "=" * 60)
+    print("RAG WITH RERANKING")
+    print("=" * 60)
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 Answer: {response[:300]}..." if len(str(response)) > 300 else f"💡 Answer: {response}")
+        print("-" * 40)
+
+
+def compare_with_without_reranking():
+    """Compare retrieval quality with and without reranking."""
+    
+    print("\n" + "=" * 60)
+    print("RERANKING IMPACT COMPARISON")
+    print("=" * 60)
+    
+    query = "How do transformers handle long-range dependencies?"
+    
+    # Without reranking
+    agent_no_rerank = Agent(
+        name="No Rerank Agent",
+        instructions="Answer based on the research papers provided.",
+        knowledge=RESEARCH_PAPERS,
+        knowledge_config={"rerank": False, "top_k": 3},
+        user_id="no_rerank"
+    )
+    
+    # With reranking
+    agent_with_rerank = Agent(
+        name="Rerank Agent",
+        instructions="Answer based on the research papers provided.",
+        knowledge=RESEARCH_PAPERS,
+        knowledge_config={"rerank": True, "top_k": 3},
+        user_id="with_rerank"
+    )
+    
+    print(f"\n📝 Query: {query}")
+    
+    print("\n🔍 Without Reranking:")
+    response1 = agent_no_rerank.chat(query)
+    print(f"   {response1[:250]}...")
+    
+    print("\n🔍 With Reranking:")
+    response2 = agent_with_rerank.chat(query)
+    print(f"   {response2[:250]}...")
+
+
+def reranking_configuration():
+    """Show reranking configuration options."""
+    
+    print("\n" + "=" * 60)
+    print("RERANKING CONFIGURATION")
+    print("=" * 60)
+    
+    print("""
+    Configuration options for reranking in PraisonAI:
+    
+    ```python
+    agent = Agent(
+        name="My Agent",
+        knowledge=documents,
+        knowledge_config={
+            # Enable reranking
+            "rerank": True,
+            
+            # Number of candidates for initial retrieval
+            "top_k": 10,
+            
+            # Number of results after reranking
+            "rerank_top_k": 3,
+            
+            # Reranker model (optional)
+            "rerank_model": "cross-encoder/ms-marco-MiniLM-L-6-v2"
+        }
+    )
+    ```
+    
+    Performance Considerations:
+    ┌─────────────────┬──────────────┬─────────────┐
+    │ Configuration   │ Latency      │ Quality     │
+    ├─────────────────┼──────────────┼─────────────┤
+    │ No reranking    │ ~50ms        │ Good        │
+    │ Light reranker  │ ~100ms       │ Better      │
+    │ Heavy reranker  │ ~200ms       │ Best        │
+    └─────────────────┴──────────────┴─────────────┘
+    
+    Tip: Use reranking for precision-critical applications
+    where quality matters more than latency.
+    """)
+
+
+def main():
+    """Run all reranking examples."""
+    print("\n🚀 PraisonAI Reranking Examples\n")
+    
+    # Example 1: Explain reranking
+    explain_reranking()
+    
+    # Example 2: RAG with reranking
+    rag_with_reranking()
+    
+    # Example 3: Compare with/without reranking
+    compare_with_without_reranking()
+    
+    # Example 4: Configuration options
+    reranking_configuration()
+    
+    print("\n✅ Reranking examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/retrieval_policies.py

@@ -0,0 +1,382 @@
+"""
+Retrieval Policies: Agent-Decided Retrieval Strategies
+
+This example demonstrates how agents can dynamically decide retrieval
+strategies based on query characteristics.
+
+RAG Concept: Not all queries are equal. Some need deep retrieval, others
+need broad coverage, and some don't need retrieval at all. Smart policies
+optimize both quality and efficiency.
+"""
+
+from praisonaiagents import Agent, AutoRagAgent, AutoRagConfig
+
+# Sample knowledge base: IT support documentation
+IT_SUPPORT_DOCS = [
+    {
+        "id": "password_reset",
+        "category": "account",
+        "priority": "high",
+        "content": """
+        Password Reset Procedure:
+        1. Go to the self-service portal at https://portal.company.com
+        2. Click "Forgot Password"
+        3. Enter your employee ID and email
+        4. Check your email for the reset link (valid for 1 hour)
+        5. Create a new password meeting complexity requirements
+        
+        If you don't receive the email, check spam or contact IT helpdesk.
+        """
+    },
+    {
+        "id": "vpn_setup",
+        "category": "network",
+        "priority": "medium",
+        "content": """
+        VPN Setup Guide:
+        1. Download the VPN client from the software center
+        2. Install with default settings
+        3. Launch and enter your credentials
+        4. Select the nearest server location
+        5. Click Connect
+        
+        Troubleshooting: If connection fails, check firewall settings
+        and ensure port 443 is open.
+        """
+    },
+    {
+        "id": "email_config",
+        "category": "communication",
+        "priority": "medium",
+        "content": """
+        Email Configuration:
+        - Server: mail.company.com
+        - Port: 993 (IMAP) / 587 (SMTP)
+        - Security: SSL/TLS required
+        - Username: your full email address
+        
+        Mobile setup: Use the Company Portal app for automatic configuration.
+        """
+    },
+    {
+        "id": "printer_setup",
+        "category": "hardware",
+        "priority": "low",
+        "content": """
+        Printer Setup:
+        1. Open Settings > Printers & Scanners
+        2. Click Add Printer
+        3. Select your floor's printer from the list
+        4. Install drivers if prompted
+        
+        Printer naming convention: FLOOR-WING-NUMBER (e.g., 3-EAST-01)
+        """
+    },
+    {
+        "id": "software_request",
+        "category": "software",
+        "priority": "medium",
+        "content": """
+        Software Request Process:
+        1. Submit request through ServiceNow
+        2. Manager approval required for licensed software
+        3. IT reviews for security compliance
+        4. Approved software deployed within 48 hours
+        
+        Pre-approved software can be installed directly from Software Center.
+        """
+    }
+]
+
+
+def query_classification():
+    """Demonstrate query classification for retrieval decisions."""
+    
+    print("=" * 60)
+    print("QUERY CLASSIFICATION FOR RETRIEVAL")
+    print("=" * 60)
+    
+    # Different query types
+    queries = [
+        ("How do I reset my password?", "procedural", True),
+        ("Hello!", "greeting", False),
+        ("What is 2 + 2?", "general_knowledge", False),
+        ("VPN connection keeps failing", "troubleshooting", True),
+        ("Thanks for your help", "closing", False),
+        ("How do I request new software?", "procedural", True),
+    ]
+    
+    print("\n📊 Query Classification Examples:\n")
+    print(f"{'Query':<40} {'Type':<20} {'Needs RAG':<10}")
+    print("-" * 70)
+    
+    for query, query_type, needs_rag in queries:
+        rag_indicator = "✅ Yes" if needs_rag else "❌ No"
+        print(f"{query:<40} {query_type:<20} {rag_indicator:<10}")
+    
+    print("\n💡 Smart agents classify queries to decide retrieval strategy.")
+
+
+def policy_based_retrieval():
+    """Demonstrate different retrieval policies."""
+    
+    print("\n" + "=" * 60)
+    print("RETRIEVAL POLICY COMPARISON")
+    print("=" * 60)
+    
+    base_agent = Agent(
+        name="IT Support",
+        instructions="You are an IT support agent. Help users with technical issues.",
+        knowledge=IT_SUPPORT_DOCS,
+        user_id="policy_demo"
+    )
+    
+    query = "How do I set up VPN?"
+    
+    policies = [
+        ("auto", "Agent decides based on query analysis"),
+        ("always", "Always retrieve, even for simple queries"),
+        ("never", "Never retrieve, use only parametric knowledge"),
+    ]
+    
+    print(f"\n📝 Query: {query}\n")
+    
+    for policy, description in policies:
+        config = AutoRagConfig(policy=policy)
+        auto_agent = AutoRagAgent(agent=base_agent, config=config)
+        
+        print(f"🔄 Policy: {policy.upper()}")
+        print(f"   Description: {description}")
+        
+        response = auto_agent.chat(query, user_id="policy_demo")
+        print(f"   Response: {response[:150]}...")
+        print()
+
+
+def adaptive_top_k():
+    """Demonstrate adaptive top-k based on query complexity."""
+    
+    print("\n" + "=" * 60)
+    print("ADAPTIVE TOP-K RETRIEVAL")
+    print("=" * 60)
+    
+    print("""
+    📊 Adaptive Top-K Strategy:
+    
+    Query Complexity → Top-K Value
+    ─────────────────────────────────
+    Simple (single topic)     → k=1-2
+    Moderate (related topics) → k=3-5
+    Complex (multi-topic)     → k=5-10
+    
+    Examples:
+    """)
+    
+    queries = [
+        ("Reset password", 2, "Simple - single procedure"),
+        ("VPN not working, also need email setup", 5, "Moderate - two topics"),
+        ("New employee setup: email, VPN, printer, software", 8, "Complex - multiple topics"),
+    ]
+    
+    for query, suggested_k, complexity in queries:
+        print(f"   Query: \"{query}\"")
+        print(f"   Complexity: {complexity}")
+        print(f"   Suggested top_k: {suggested_k}")
+        print()
+
+
+def priority_based_retrieval():
+    """Demonstrate priority-based document retrieval."""
+    
+    print("\n" + "=" * 60)
+    print("PRIORITY-BASED RETRIEVAL")
+    print("=" * 60)
+    
+    # Group documents by priority
+    high_priority = [d for d in IT_SUPPORT_DOCS if d.get('priority') == 'high']
+    all_docs = IT_SUPPORT_DOCS
+    
+    print("\n📊 Document Priorities in Knowledge Base:")
+    for doc in IT_SUPPORT_DOCS:
+        print(f"   [{doc['priority'].upper():^6}] {doc['id']}")
+    
+    # High-priority agent (for urgent issues)
+    urgent_agent = Agent(
+        name="Urgent Support",
+        instructions="Handle urgent IT issues. Focus on critical procedures.",
+        knowledge=high_priority,
+        user_id="urgent_demo"
+    )
+    
+    # Full knowledge agent
+    full_agent = Agent(
+        name="Full Support",
+        instructions="Handle all IT support requests.",
+        knowledge=all_docs,
+        user_id="full_demo"
+    )
+    
+    query = "I'm locked out of my account!"
+    
+    print(f"\n📝 Urgent Query: {query}")
+    
+    print("\n🚨 Urgent Support Agent (high-priority docs only):")
+    response1 = urgent_agent.chat(query)
+    print(f"   {response1[:200]}...")
+    
+    print("\n📚 Full Support Agent (all docs):")
+    response2 = full_agent.chat(query)
+    print(f"   {response2[:200]}...")
+
+
+def context_aware_retrieval():
+    """Demonstrate context-aware retrieval strategies."""
+    
+    print("\n" + "=" * 60)
+    print("CONTEXT-AWARE RETRIEVAL")
+    print("=" * 60)
+    
+    print("""
+    🎯 Context-Aware Retrieval Strategies:
+    
+    1. **User Role Context**
+       - New employee → Onboarding docs first
+       - IT admin → Technical docs
+       - Executive → Summary docs
+    
+    2. **Time Context**
+       - Business hours → Standard support
+       - After hours → Emergency procedures only
+    
+    3. **Conversation Context**
+       - First message → Broad retrieval
+       - Follow-up → Narrow to topic
+    
+    4. **Device Context**
+       - Mobile → Mobile-specific guides
+       - Desktop → Full documentation
+    
+    Implementation:
+    ```python
+    def get_retrieval_config(context: dict) -> dict:
+        if context.get("user_role") == "new_employee":
+            return {"filter": {"category": "onboarding"}, "top_k": 5}
+        elif context.get("is_followup"):
+            return {"top_k": 2}  # Narrow focus
+        else:
+            return {"top_k": 3}  # Default
+    ```
+    """)
+
+
+def fallback_strategies():
+    """Demonstrate fallback strategies when retrieval fails."""
+    
+    print("\n" + "=" * 60)
+    print("RETRIEVAL FALLBACK STRATEGIES")
+    print("=" * 60)
+    
+    print("""
+    🔄 Fallback Strategy Chain:
+    
+    1. Primary: Vector search in knowledge base
+       ↓ (if no relevant results)
+    2. Secondary: Expand search with synonyms
+       ↓ (if still no results)
+    3. Tertiary: Search external sources
+       ↓ (if still no results)
+    4. Final: Acknowledge limitation, offer alternatives
+    
+    Example Implementation:
+    ```python
+    def retrieve_with_fallback(query: str, knowledge) -> str:
+        # Primary retrieval
+        results = knowledge.search(query, top_k=3)
+        
+        if results and results[0].score > 0.7:
+            return format_context(results)
+        
+        # Fallback: Expand query
+        expanded = expand_query(query)  # Add synonyms
+        results = knowledge.search(expanded, top_k=5)
+        
+        if results and results[0].score > 0.5:
+            return format_context(results)
+        
+        # Final fallback
+        return "I don't have specific information about that. " \\
+               "Please contact IT helpdesk at ext. 1234."
+    ```
+    """)
+
+
+def retrieval_metrics():
+    """Explain retrieval quality metrics."""
+    
+    print("\n" + "=" * 60)
+    print("RETRIEVAL QUALITY METRICS")
+    print("=" * 60)
+    
+    print("""
+    📈 Key Metrics for Retrieval Policy Tuning:
+    
+    ┌─────────────────┬────────────────────────────────────────┐
+    │ Metric          │ Description                            │
+    ├─────────────────┼────────────────────────────────────────┤
+    │ Precision@K     │ % of retrieved docs that are relevant │
+    │ Recall@K        │ % of relevant docs that are retrieved │
+    │ MRR             │ Mean Reciprocal Rank of first hit     │
+    │ NDCG            │ Normalized Discounted Cumulative Gain │
+    │ Latency         │ Time to retrieve (ms)                 │
+    │ Token Usage     │ Context tokens consumed               │
+    └─────────────────┴────────────────────────────────────────┘
+    
+    Policy Optimization Goals:
+    
+    - High Precision: Use smaller top_k, stricter thresholds
+    - High Recall: Use larger top_k, relaxed thresholds
+    - Low Latency: Use smaller top_k, skip reranking
+    - Low Cost: Use smaller top_k, aggressive filtering
+    
+    Balanced Default:
+    ```python
+    config = {
+        "top_k": 3,
+        "score_threshold": 0.5,
+        "rerank": True,
+        "max_tokens": 2000
+    }
+    ```
+    """)
+
+
+def main():
+    """Run all retrieval policy examples."""
+    print("\n🚀 PraisonAI Retrieval Policies Examples\n")
+    
+    # Example 1: Query classification
+    query_classification()
+    
+    # Example 2: Policy comparison
+    policy_based_retrieval()
+    
+    # Example 3: Adaptive top-k
+    adaptive_top_k()
+    
+    # Example 4: Priority-based
+    priority_based_retrieval()
+    
+    # Example 5: Context-aware
+    context_aware_retrieval()
+    
+    # Example 6: Fallback strategies
+    fallback_strategies()
+    
+    # Example 7: Metrics
+    retrieval_metrics()
+    
+    print("\n✅ Retrieval policies examples completed!")
+
+
+if __name__ == "__main__":
+    main()

examples/rag/structured_output.py

@@ -0,0 +1,334 @@
+"""
+Structured Output RAG: Guided and Constrained Generation
+
+This example demonstrates how to guide RAG output into specific formats
+and structures using Pydantic models and output constraints.
+
+RAG Concept: Sometimes you need RAG answers in a specific format - JSON,
+tables, or structured data. Guided generation ensures the LLM output
+conforms to your schema.
+"""
+
+from typing import List
+from pydantic import BaseModel, Field
+from praisonaiagents import Agent
+
+# Sample knowledge base: Product catalog
+PRODUCT_CATALOG = [
+    {
+        "id": "prod_001",
+        "content": """
+        Product: UltraWidget Pro
+        Category: Electronics
+        Price: $299.99
+        Rating: 4.5/5 (1,250 reviews)
+        Features: Wireless connectivity, 12-hour battery, water-resistant
+        Availability: In stock
+        SKU: UW-PRO-2024
+        """
+    },
+    {
+        "id": "prod_002",
+        "content": """
+        Product: SmartHome Hub
+        Category: Home Automation
+        Price: $149.99
+        Rating: 4.2/5 (890 reviews)
+        Features: Voice control, 100+ device compatibility, energy monitoring
+        Availability: In stock
+        SKU: SH-HUB-2024
+        """
+    },
+    {
+        "id": "prod_003",
+        "content": """
+        Product: ErgoDesk Standing Desk
+        Category: Furniture
+        Price: $599.99
+        Rating: 4.8/5 (2,100 reviews)
+        Features: Electric height adjustment, memory presets, cable management
+        Availability: Ships in 3-5 days
+        SKU: ED-STAND-2024
+        """
+    },
+    {
+        "id": "prod_004",
+        "content": """
+        Product: CloudSync Backup Drive
+        Category: Storage
+        Price: $199.99
+        Rating: 4.6/5 (1,800 reviews)
+        Features: 4TB capacity, automatic backup, encrypted storage
+        Availability: In stock
+        SKU: CS-4TB-2024
+        """
+    }
+]
+
+
+# Pydantic models for structured output
+class ProductInfo(BaseModel):
+    """Structured product information."""
+    name: str = Field(description="Product name")
+    price: float = Field(description="Price in USD")
+    rating: float = Field(description="Rating out of 5")
+    in_stock: bool = Field(description="Whether product is in stock")
+    key_features: List[str] = Field(description="Top 3 features")
+
+
+class ProductComparison(BaseModel):
+    """Structured comparison of products."""
+    products: List[str] = Field(description="Names of compared products")
+    winner: str = Field(description="Recommended product")
+    reason: str = Field(description="Why this product is recommended")
+    price_range: str = Field(description="Price range of compared products")
+
+
+class SearchResult(BaseModel):
+    """Structured search result."""
+    query: str = Field(description="Original search query")
+    matches: List[str] = Field(description="Matching product names")
+    total_found: int = Field(description="Number of matches")
+    summary: str = Field(description="Brief summary of results")
+
+
+def json_output_rag():
+    """Demonstrate RAG with JSON-structured output."""
+    
+    print("=" * 60)
+    print("JSON-STRUCTURED RAG OUTPUT")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Product Assistant",
+        instructions="""You are a product assistant that provides structured information.
+        Always respond with valid JSON matching the requested format.
+        Extract accurate information from the product catalog.""",
+        knowledge=PRODUCT_CATALOG,
+        user_id="json_demo"
+    )
+    
+    # Request structured product info
+    query = """
+    Tell me about the UltraWidget Pro. Respond in this JSON format:
+    {
+        "name": "product name",
+        "price": 0.00,
+        "rating": 0.0,
+        "in_stock": true/false,
+        "key_features": ["feature1", "feature2", "feature3"]
+    }
+    """
+    
+    print("\n📝 Query: Get structured product info")
+    response = agent.chat(query)
+    print("💡 Structured Response:\n" + str(response))
+    print("-" * 40)
+
+
+def pydantic_guided_rag():
+    """Demonstrate RAG with Pydantic model guidance."""
+    
+    print("\n" + "=" * 60)
+    print("PYDANTIC-GUIDED RAG")
+    print("=" * 60)
+    
+    # Generate schema from Pydantic model
+    schema = ProductComparison.model_json_schema()
+    
+    agent = Agent(
+        name="Product Comparator",
+        instructions=f"""You compare products and provide structured recommendations.
+        Your response MUST be valid JSON matching this schema:
+        {schema}
+        
+        Be objective and base recommendations on the product data.""",
+        knowledge=PRODUCT_CATALOG,
+        user_id="pydantic_demo"
+    )
+    
+    query = "Compare the UltraWidget Pro and SmartHome Hub. Which should I buy?"
+    
+    print(f"\n📝 Query: {query}")
+    print("📋 Expected Schema: ProductComparison")
+    
+    response = agent.chat(query)
+    print(f"\n💡 Structured Comparison:\n{response}")
+
+
+def table_format_rag():
+    """Demonstrate RAG with table-formatted output."""
+    
+    print("\n" + "=" * 60)
+    print("TABLE-FORMATTED RAG")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Catalog Browser",
+        instructions="""You present product information in clean table format.
+        Use markdown tables for structured data.
+        Include relevant columns based on the query.""",
+        knowledge=PRODUCT_CATALOG,
+        user_id="table_demo"
+    )
+    
+    query = "Show me all products in a table with name, price, and rating."
+    
+    print(f"\n📝 Query: {query}")
+    response = agent.chat(query)
+    print(f"\n💡 Table Output:\n{response}")
+
+
+def list_format_rag():
+    """Demonstrate RAG with list-formatted output."""
+    
+    print("\n" + "=" * 60)
+    print("LIST-FORMATTED RAG")
+    print("=" * 60)
+    
+    agent = Agent(
+        name="Feature Lister",
+        instructions="""You extract and present information as organized lists.
+        Use bullet points and numbered lists appropriately.
+        Group related items together.""",
+        knowledge=PRODUCT_CATALOG,
+        user_id="list_demo"
+    )
+    
+    queries = [
+        "List all product features across the catalog",
+        "What are the top-rated products? List them in order."
+    ]
+    
+    for query in queries:
+        print(f"\n📝 Query: {query}")
+        response = agent.chat(query)
+        print(f"💡 List Output:\n{response[:400]}..." if len(str(response)) > 400 else f"💡 List Output:\n{response}")
+        print("-" * 40)
+
+
+def constrained_generation():
+    """Demonstrate constrained/guided generation patterns."""
+    
+    print("\n" + "=" * 60)
+    print("CONSTRAINED GENERATION PATTERNS")
+    print("=" * 60)
+    
+    print("""
+    🎯 Constrained Generation Techniques:
+    
+    1. **Schema Enforcement**
+       - Provide JSON schema in instructions
+       - Agent outputs valid JSON matching schema
+       ```python
+       instructions = f"Respond with JSON matching: {schema}"
+       ```
+    
+    2. **Format Templates**
+       - Give explicit output templates
+       - Agent fills in the blanks
+       ```python
+       instructions = '''
+       Respond in this format:
+       PRODUCT: [name]
+       PRICE: $[amount]
+       VERDICT: [recommendation]
+       '''
+       ```
+    
+    3. **Pydantic Validation**
+       - Parse agent output with Pydantic
+       - Retry if validation fails
+       ```python
+       response = agent.chat(query)
+       try:
+           result = ProductInfo.model_validate_json(response)
+       except ValidationError:
+           # Retry with clarification
+       ```
+    
+    4. **Output Parsers**
+       - Post-process agent output
+       - Extract structured data from text
+       ```python
+       def parse_product(text: str) -> dict:
+           # Extract price, rating, etc.
+           return structured_data
+       ```
+    
+    5. **Few-Shot Examples**
+       - Show examples of desired output format
+       - Agent learns pattern from examples
+       ```python
+       instructions = '''
+       Example:
+       Q: Tell me about Product X
+       A: {"name": "Product X", "price": 99.99}
+       
+       Now answer the user's question in the same format.
+       '''
+       ```
+    """)
+
+
+def multi_format_rag():
+    """Demonstrate switching between output formats."""
+    
+    print("\n" + "=" * 60)
+    print("MULTI-FORMAT RAG")
+    print("=" * 60)
+    
+    base_knowledge = PRODUCT_CATALOG
+    query = "Tell me about the ErgoDesk Standing Desk"
+    
+    formats = [
+        ("JSON", "Respond with a JSON object containing name, price, and features."),
+        ("Markdown", "Respond with a markdown-formatted product card with headers."),
+        ("Plain Text", "Respond with a brief, conversational product description."),
+        ("Bullet Points", "Respond with bullet points covering key product details.")
+    ]
+    
+    print(f"\n📝 Base Query: {query}")
+    print("\n🔄 Same query, different output formats:\n")
+    
+    for format_name, format_instruction in formats:
+        agent = Agent(
+            name=f"{format_name} Agent",
+            instructions=f"You are a product expert. {format_instruction}",
+            knowledge=base_knowledge,
+            user_id=f"format_{format_name.lower()}"
+        )
+        
+        response = agent.chat(query)
+        print(f"📋 {format_name} Format:")
+        print(f"{response[:250]}..." if len(str(response)) > 250 else response)
+        print("-" * 40)
+
+
+def main():
+    """Run all structured output RAG examples."""
+    print("\n🚀 PraisonAI Structured Output RAG Examples\n")
+    
+    # Example 1: JSON output
+    json_output_rag()
+    
+    # Example 2: Pydantic-guided
+    pydantic_guided_rag()
+    
+    # Example 3: Table format
+    table_format_rag()
+    
+    # Example 4: List format
+    list_format_rag()
+    
+    # Example 5: Constrained generation patterns
+    constrained_generation()
+    
+    # Example 6: Multi-format
+    multi_format_rag()
+    
+    print("\n✅ Structured output RAG examples completed!")
+
+
+if __name__ == "__main__":
+    main()

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

@@ -1,30 +1,151 @@
 """
-Context module for PraisonAI Agents.
+Context Management Module for PraisonAI Agents.
 
-This module provides context retrieval capabilities including:
+This module provides comprehensive context management capabilities:
+- Token estimation and budgeting
+- Context composition within limits
+- Optimization strategies (truncate, sliding window, summarize, prune, smart)
+- Real-time context monitoring
+- Multi-agent context isolation
+
+Also includes:
 - Fast Context: Rapid parallel code search subagent
-"""
 
-# Lazy imports to avoid impacting package load time
-def __getattr__(name):
-    """Lazy import to avoid impacting package performance."""
-    if name == "FastContext":
-        from praisonaiagents.context.fast.fast_context import FastContext
-        return FastContext
-    elif name == "FastContextResult":
-        from praisonaiagents.context.fast.result import FastContextResult
-        return FastContextResult
-    elif name == "FileMatch":
-        from praisonaiagents.context.fast.result import FileMatch
-        return FileMatch
-    elif name == "LineRange":
-        from praisonaiagents.context.fast.result import LineRange
-        return LineRange
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+Zero Performance Impact:
+- All imports are lazy loaded via __getattr__
+- Features only activate when explicitly enabled
+- No overhead when context management is not used
+
+Usage:
+    from praisonaiagents.context import (
+        ContextBudgeter,
+        ContextComposer,
+        ContextMonitor,
+        get_optimizer,
+    )
+    
+    # Create budgeter for model
+    budgeter = ContextBudgeter(model="gpt-4o")
+    budget = budgeter.allocate()
+    
+    # Compose context within budget
+    composer = ContextComposer(budget=budget)
+    result = composer.compose(
+        system_prompt="You are helpful",
+        history=messages,
+        tools=tool_schemas,
+    )
+    
+    # Monitor context (opt-in)
+    monitor = ContextMonitor(enabled=True)
+    monitor.snapshot(ledger=result.ledger, budget=budget, messages=result.messages)
+"""
 
 __all__ = [
+    # Models
+    "ContextSegment",
+    "ContextLedger",
+    "BudgetAllocation",
+    "ContextSnapshot",
+    "OptimizerStrategy",
+    "OptimizationResult",
+    "MonitorConfig",
+    "ContextConfig",
+    # Token estimation
+    "estimate_tokens_heuristic",
+    "estimate_messages_tokens",
+    "estimate_tool_schema_tokens",
+    "TokenEstimatorImpl",
+    "get_estimator",
+    # Budgeting
+    "ContextBudgeter",
+    "get_model_limit",
+    "get_output_reserve",
+    # Ledger
+    "ContextLedgerManager",
+    "MultiAgentLedger",
+    # Composition
+    "ContextComposer",
+    "ComposedContext",
+    # Optimization
+    "BaseOptimizer",
+    "TruncateOptimizer",
+    "SlidingWindowOptimizer",
+    "PruneToolsOptimizer",
+    "NonDestructiveOptimizer",
+    "SummarizeOptimizer",
+    "SmartOptimizer",
+    "get_optimizer",
+    "get_effective_history",
+    # Monitoring
+    "ContextMonitor",
+    "MultiAgentMonitor",
+    "redact_sensitive",
+    "format_human_snapshot",
+    "format_json_snapshot",
+    # Fast Context (legacy)
     "FastContext",
     "FastContextResult",
     "FileMatch",
     "LineRange",
 ]
+
+
+def __getattr__(name: str):
+    """Lazy load module components to avoid import overhead."""
+    # Models
+    if name in ("ContextSegment", "ContextLedger", "BudgetAllocation", 
+                "ContextSnapshot", "OptimizerStrategy", "OptimizationResult",
+                "MonitorConfig", "ContextConfig"):
+        from . import models
+        return getattr(models, name)
+    
+    # Token estimation
+    if name in ("estimate_tokens_heuristic", "estimate_messages_tokens",
+                "estimate_tool_schema_tokens", "TokenEstimatorImpl", "get_estimator"):
+        from . import tokens
+        return getattr(tokens, name)
+    
+    # Budgeting
+    if name in ("ContextBudgeter", "get_model_limit", "get_output_reserve"):
+        from . import budgeter
+        return getattr(budgeter, name)
+    
+    # Ledger
+    if name in ("ContextLedgerManager", "MultiAgentLedger"):
+        from . import ledger
+        return getattr(ledger, name)
+    
+    # Composition
+    if name in ("ContextComposer", "ComposedContext"):
+        from . import composer
+        return getattr(composer, name)
+    
+    # Optimization
+    if name in ("BaseOptimizer", "TruncateOptimizer", "SlidingWindowOptimizer",
+                "PruneToolsOptimizer", "NonDestructiveOptimizer", "SummarizeOptimizer",
+                "SmartOptimizer", "get_optimizer", "get_effective_history"):
+        from . import optimizer
+        return getattr(optimizer, name)
+    
+    # Monitoring
+    if name in ("ContextMonitor", "MultiAgentMonitor", "redact_sensitive",
+                "format_human_snapshot", "format_json_snapshot"):
+        from . import monitor
+        return getattr(monitor, name)
+    
+    # Fast Context (legacy)
+    if name == "FastContext":
+        from praisonaiagents.context.fast.fast_context import FastContext
+        return FastContext
+    if name == "FastContextResult":
+        from praisonaiagents.context.fast.result import FastContextResult
+        return FastContextResult
+    if name == "FileMatch":
+        from praisonaiagents.context.fast.result import FileMatch
+        return FileMatch
+    if name == "LineRange":
+        from praisonaiagents.context.fast.result import LineRange
+        return LineRange
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/context/budgeter.py

@@ -0,0 +1,229 @@
+"""
+Context Budgeter for PraisonAI Agents.
+
+Allocates token budgets across context segments based on model limits.
+"""
+
+from typing import Dict, Any, Optional
+from .models import BudgetAllocation
+
+
+# Model token limits (context window sizes)
+MODEL_LIMITS: Dict[str, int] = {
+    # OpenAI
+    "gpt-4o": 128000,
+    "gpt-4o-mini": 128000,
+    "gpt-4-turbo": 128000,
+    "gpt-4": 8192,
+    "gpt-3.5-turbo": 16385,
+    "gpt-3.5-turbo-16k": 16385,
+    # Anthropic
+    "claude-3-5-sonnet": 200000,
+    "claude-3-5-haiku": 200000,
+    "claude-3-opus": 200000,
+    "claude-3-sonnet": 200000,
+    "claude-3-haiku": 200000,
+    # Google
+    "gemini-2.0-flash": 1048576,
+    "gemini-1.5-pro": 2097152,
+    "gemini-1.5-flash": 1048576,
+    # Defaults
+    "default": 128000,
+}
+
+# Default output reserves by model family
+OUTPUT_RESERVES: Dict[str, int] = {
+    "gpt-4o": 16384,
+    "gpt-4o-mini": 16384,
+    "gpt-4-turbo": 4096,
+    "gpt-4": 4096,
+    "gpt-3.5-turbo": 4096,
+    "claude-3": 8192,
+    "gemini": 8192,
+    "default": 8000,
+}
+
+
+def get_model_limit(model: str) -> int:
+    """
+    Get context window limit for a model.
+    
+    Args:
+        model: Model name or identifier
+        
+    Returns:
+        Context window size in tokens
+    """
+    # Exact match
+    if model in MODEL_LIMITS:
+        return MODEL_LIMITS[model]
+    
+    # Partial match (e.g., "gpt-4o-2024-05-13" matches "gpt-4o")
+    model_lower = model.lower()
+    for key, limit in MODEL_LIMITS.items():
+        if key in model_lower or model_lower.startswith(key):
+            return limit
+    
+    return MODEL_LIMITS["default"]
+
+
+def get_output_reserve(model: str) -> int:
+    """
+    Get recommended output token reserve for a model.
+    
+    Args:
+        model: Model name
+        
+    Returns:
+        Output reserve in tokens
+    """
+    model_lower = model.lower()
+    
+    for key, reserve in OUTPUT_RESERVES.items():
+        if key in model_lower:
+            return reserve
+    
+    return OUTPUT_RESERVES["default"]
+
+
+class ContextBudgeter:
+    """
+    Allocates token budgets across context segments.
+    
+    Ensures context fits within model limits while reserving
+    space for output generation.
+    
+    Example:
+        budgeter = ContextBudgeter(model="gpt-4o")
+        allocation = budgeter.allocate()
+        print(f"History budget: {allocation.history_budget}")
+    """
+    
+    def __init__(
+        self,
+        model: str = "gpt-4o-mini",
+        model_limit: Optional[int] = None,
+        output_reserve: Optional[int] = None,
+        # Segment budget overrides (absolute tokens)
+        system_prompt_budget: int = 2000,
+        rules_budget: int = 500,
+        skills_budget: int = 500,
+        memory_budget: int = 1000,
+        tools_schema_budget: int = 2000,
+        tool_outputs_budget: int = 20000,
+        buffer_budget: int = 1000,
+        # Ratio-based allocation (if set, overrides fixed budgets)
+        history_ratio: Optional[float] = None,
+    ):
+        """
+        Initialize budgeter.
+        
+        Args:
+            model: Model name for limit lookup
+            model_limit: Override model limit
+            output_reserve: Override output reserve
+            system_prompt_budget: Budget for system prompt
+            rules_budget: Budget for rules
+            skills_budget: Budget for skills
+            memory_budget: Budget for memory
+            tools_schema_budget: Budget for tool schemas
+            tool_outputs_budget: Budget for tool outputs
+            buffer_budget: Safety buffer
+            history_ratio: If set, allocate this ratio of usable for history
+        """
+        self.model = model
+        self.model_limit = model_limit or get_model_limit(model)
+        self.output_reserve = output_reserve or get_output_reserve(model)
+        
+        self.system_prompt_budget = system_prompt_budget
+        self.rules_budget = rules_budget
+        self.skills_budget = skills_budget
+        self.memory_budget = memory_budget
+        self.tools_schema_budget = tools_schema_budget
+        self.tool_outputs_budget = tool_outputs_budget
+        self.buffer_budget = buffer_budget
+        self.history_ratio = history_ratio
+    
+    @property
+    def usable(self) -> int:
+        """Usable tokens after output reserve."""
+        return self.model_limit - self.output_reserve
+    
+    def allocate(self) -> BudgetAllocation:
+        """
+        Create a budget allocation.
+        
+        Returns:
+            BudgetAllocation with segment budgets
+        """
+        allocation = BudgetAllocation(
+            model_limit=self.model_limit,
+            output_reserve=self.output_reserve,
+            system_prompt=self.system_prompt_budget,
+            rules=self.rules_budget,
+            skills=self.skills_budget,
+            memory=self.memory_budget,
+            tools_schema=self.tools_schema_budget,
+            tool_outputs=self.tool_outputs_budget,
+            buffer=self.buffer_budget,
+            history=-1,  # Dynamic
+        )
+        
+        # If history_ratio is set, compute history budget from ratio
+        if self.history_ratio is not None:
+            allocation.history = int(self.usable * self.history_ratio)
+        
+        return allocation
+    
+    def check_overflow(
+        self,
+        current_tokens: int,
+        threshold: float = 0.8
+    ) -> bool:
+        """
+        Check if current tokens exceed threshold.
+        
+        Args:
+            current_tokens: Current total tokens
+            threshold: Threshold as fraction of usable (0.0-1.0)
+            
+        Returns:
+            True if overflow threshold exceeded
+        """
+        return current_tokens > (self.usable * threshold)
+    
+    def get_remaining(self, current_tokens: int) -> int:
+        """
+        Get remaining tokens before hitting usable limit.
+        
+        Args:
+            current_tokens: Current total tokens
+            
+        Returns:
+            Remaining tokens (can be negative if over limit)
+        """
+        return self.usable - current_tokens
+    
+    def get_utilization(self, current_tokens: int) -> float:
+        """
+        Get current utilization as fraction.
+        
+        Args:
+            current_tokens: Current total tokens
+            
+        Returns:
+            Utilization (0.0-1.0+)
+        """
+        if self.usable == 0:
+            return 0.0
+        return current_tokens / self.usable
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert budgeter state to dictionary."""
+        return {
+            "model": self.model,
+            "model_limit": self.model_limit,
+            "output_reserve": self.output_reserve,
+            "usable": self.usable,
+            "allocation": self.allocate().to_dict(),
+        }

src/praisonai-agents/praisonaiagents/context/composer.py

@@ -0,0 +1,335 @@
+"""
+Context Composer for PraisonAI Agents.
+
+Assembles context from segments within budget constraints.
+"""
+
+from typing import Dict, List, Any, Optional
+from .models import (
+    ContextLedger, BudgetAllocation, ContextSegment, ContextSnapshot
+)
+from .tokens import estimate_tokens_heuristic, estimate_messages_tokens
+from .ledger import ContextLedgerManager
+
+
+class ContextComposer:
+    """
+    Composes context from segments within budget constraints.
+    
+    Assembles system prompt, rules, skills, memory, tools, and history
+    while respecting token budgets and applying trimming as needed.
+    
+    Example:
+        composer = ContextComposer(budget=budgeter.allocate())
+        
+        result = composer.compose(
+            system_prompt="You are a helpful assistant",
+            history=messages,
+            tools=tool_schemas,
+        )
+        
+        # Use composed messages
+        response = llm.chat(result.messages)
+    """
+    
+    def __init__(
+        self,
+        budget: Optional[BudgetAllocation] = None,
+        preserve_tool_pairs: bool = True,
+        max_tool_output_tokens: int = 10000,
+        keep_recent_turns: int = 5,
+    ):
+        """
+        Initialize composer.
+        
+        Args:
+            budget: Budget allocation (uses defaults if None)
+            preserve_tool_pairs: Keep tool_call/tool_result pairs together
+            max_tool_output_tokens: Max tokens per tool output
+            keep_recent_turns: Minimum recent turns to keep
+        """
+        self.budget = budget or BudgetAllocation()
+        self.preserve_tool_pairs = preserve_tool_pairs
+        self.max_tool_output_tokens = max_tool_output_tokens
+        self.keep_recent_turns = keep_recent_turns
+        
+        self._ledger = ContextLedgerManager()
+        self._ledger.set_budget(self.budget)
+    
+    def compose(
+        self,
+        system_prompt: str = "",
+        rules: str = "",
+        skills: str = "",
+        memory: str = "",
+        tools: Optional[List[Dict[str, Any]]] = None,
+        history: Optional[List[Dict[str, Any]]] = None,
+        agent_id: Optional[str] = None,
+    ) -> "ComposedContext":
+        """
+        Compose context from segments.
+        
+        Args:
+            system_prompt: System prompt content
+            rules: Rules content
+            skills: Skills content
+            memory: Memory content
+            tools: Tool schemas
+            history: Conversation history
+            agent_id: Agent identifier
+            
+        Returns:
+            ComposedContext with messages and ledger
+        """
+        self._ledger.reset()
+        tools = tools or []
+        history = history or []
+        
+        # Track fixed segments
+        self._ledger.track_system_prompt(system_prompt)
+        self._ledger.track_rules(rules)
+        self._ledger.track_skills(skills)
+        self._ledger.track_memory(memory)
+        self._ledger.track_tools(tools)
+        
+        # Build combined system message
+        system_parts = []
+        if system_prompt:
+            system_parts.append(system_prompt)
+        if rules:
+            system_parts.append(f"\n## Rules\n{rules}")
+        if skills:
+            system_parts.append(f"\n## Skills\n{skills}")
+        if memory:
+            system_parts.append(f"\n## Memory\n{memory}")
+        
+        combined_system = "\n".join(system_parts)
+        
+        # Process history with budget awareness
+        processed_history = self._process_history(history)
+        
+        # Build final messages
+        messages = []
+        if combined_system:
+            messages.append({"role": "system", "content": combined_system})
+        messages.extend(processed_history)
+        
+        # Track final history tokens
+        history_tokens = estimate_messages_tokens(processed_history)
+        self._ledger.track_segment(
+            ContextSegment.HISTORY, 
+            history_tokens, 
+            len(processed_history)
+        )
+        
+        # Check for overflow
+        total = self._ledger.get_total()
+        overflow = total > self.budget.usable
+        
+        return ComposedContext(
+            messages=messages,
+            tools=tools,
+            ledger=self._ledger.get_ledger(),
+            total_tokens=total,
+            overflow=overflow,
+            warnings=self._ledger.get_warnings(),
+            agent_id=agent_id,
+        )
+    
+    def _process_history(
+        self,
+        history: List[Dict[str, Any]]
+    ) -> List[Dict[str, Any]]:
+        """
+        Process history with budget constraints.
+        
+        Applies:
+        - Tool output truncation
+        - Sliding window if over budget
+        - Tool pair preservation
+        """
+        if not history:
+            return []
+        
+        # First pass: truncate large tool outputs
+        processed = []
+        for msg in history:
+            processed_msg = self._truncate_tool_output(msg)
+            processed.append(processed_msg)
+        
+        # Check if within budget
+        history_budget = self.budget.history_budget
+        current_tokens = estimate_messages_tokens(processed)
+        
+        if current_tokens <= history_budget:
+            return processed
+        
+        # Apply sliding window
+        return self._apply_sliding_window(processed, history_budget)
+    
+    def _truncate_tool_output(self, msg: Dict[str, Any]) -> Dict[str, Any]:
+        """Truncate tool output if too large."""
+        if msg.get("role") != "tool" and not msg.get("tool_call_id"):
+            return msg
+        
+        content = msg.get("content", "")
+        if not isinstance(content, str):
+            return msg
+        
+        tokens = estimate_tokens_heuristic(content)
+        if tokens <= self.max_tool_output_tokens:
+            return msg
+        
+        # Truncate to max tokens (rough: 4 chars per token)
+        max_chars = self.max_tool_output_tokens * 4
+        truncated = content[:max_chars] + "\n...[output truncated]..."
+        
+        result = msg.copy()
+        result["content"] = truncated
+        result["_truncated"] = True
+        return result
+    
+    def _apply_sliding_window(
+        self,
+        history: List[Dict[str, Any]],
+        budget: int
+    ) -> List[Dict[str, Any]]:
+        """Apply sliding window to fit within budget."""
+        if not history:
+            return []
+        
+        # Always keep at least recent turns
+        min_keep = self.keep_recent_turns * 2  # user + assistant per turn
+        
+        # Start from end, add messages until budget exceeded
+        result = []
+        current_tokens = 0
+        
+        # Track tool call IDs to preserve pairs
+        tool_call_ids = set()
+        
+        for msg in reversed(history):
+            msg_tokens = estimate_tokens_heuristic(str(msg.get("content", "")))
+            
+            if current_tokens + msg_tokens > budget and len(result) >= min_keep:
+                break
+            
+            result.insert(0, msg)
+            current_tokens += msg_tokens
+            
+            # Track tool calls for pair preservation
+            if self.preserve_tool_pairs:
+                if "tool_calls" in msg:
+                    for tc in msg.get("tool_calls", []):
+                        if isinstance(tc, dict) and "id" in tc:
+                            tool_call_ids.add(tc["id"])
+                if msg.get("tool_call_id"):
+                    tool_call_ids.add(msg["tool_call_id"])
+        
+        # Ensure tool pairs are complete
+        if self.preserve_tool_pairs:
+            result = self._ensure_tool_pairs(result, history, tool_call_ids)
+        
+        return result
+    
+    def _ensure_tool_pairs(
+        self,
+        result: List[Dict[str, Any]],
+        full_history: List[Dict[str, Any]],
+        tool_call_ids: set
+    ) -> List[Dict[str, Any]]:
+        """Ensure tool_call/tool_result pairs are complete."""
+        # Find tool calls in result
+        result_call_ids = set()
+        result_response_ids = set()
+        
+        for msg in result:
+            if "tool_calls" in msg:
+                for tc in msg.get("tool_calls", []):
+                    if isinstance(tc, dict) and "id" in tc:
+                        result_call_ids.add(tc["id"])
+            if msg.get("tool_call_id"):
+                result_response_ids.add(msg["tool_call_id"])
+        
+        # Find missing pairs
+        missing_calls = result_response_ids - result_call_ids
+        missing_responses = result_call_ids - result_response_ids
+        
+        if not missing_calls and not missing_responses:
+            return result
+        
+        # Add missing messages from full history
+        for msg in full_history:
+            # Add missing tool calls
+            if "tool_calls" in msg:
+                for tc in msg.get("tool_calls", []):
+                    if isinstance(tc, dict) and tc.get("id") in missing_calls:
+                        if msg not in result:
+                            result.insert(0, msg)
+                        break
+            
+            # Add missing tool responses
+            if msg.get("tool_call_id") in missing_responses:
+                if msg not in result:
+                    result.append(msg)
+        
+        return result
+    
+    def get_ledger(self) -> ContextLedger:
+        """Get current ledger."""
+        return self._ledger.get_ledger()
+    
+    def get_warnings(self) -> List[str]:
+        """Get current warnings."""
+        return self._ledger.get_warnings()
+
+
+class ComposedContext:
+    """Result of context composition."""
+    
+    def __init__(
+        self,
+        messages: List[Dict[str, Any]],
+        tools: List[Dict[str, Any]],
+        ledger: ContextLedger,
+        total_tokens: int,
+        overflow: bool,
+        warnings: List[str],
+        agent_id: Optional[str] = None,
+    ):
+        self.messages = messages
+        self.tools = tools
+        self.ledger = ledger
+        self.total_tokens = total_tokens
+        self.overflow = overflow
+        self.warnings = warnings
+        self.agent_id = agent_id
+    
+    def to_snapshot(
+        self,
+        session_id: str = "",
+        model_name: str = "",
+        budget: Optional[BudgetAllocation] = None,
+    ) -> ContextSnapshot:
+        """Convert to snapshot for monitoring."""
+        return ContextSnapshot(
+            session_id=session_id,
+            agent_name=self.agent_id or "",
+            model_name=model_name,
+            budget=budget,
+            ledger=self.ledger,
+            utilization=self.total_tokens / budget.usable if budget else 0.0,
+            history_content=self.messages,
+            warnings=self.warnings,
+        )
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "message_count": len(self.messages),
+            "tool_count": len(self.tools),
+            "total_tokens": self.total_tokens,
+            "overflow": self.overflow,
+            "warnings": self.warnings,
+            "ledger": self.ledger.to_dict(),
+        }

src/praisonai-agents/praisonaiagents/context/ledger.py

@@ -0,0 +1,323 @@
+"""
+Context Ledger for PraisonAI Agents.
+
+Tracks token usage across context segments with per-agent and per-session support.
+"""
+
+from typing import Dict, List, Any, Optional
+from dataclasses import dataclass
+from .models import ContextLedger, ContextSegment, BudgetAllocation
+from .tokens import estimate_messages_tokens, estimate_tool_schema_tokens
+
+
+@dataclass
+class SegmentStats:
+    """Statistics for a context segment."""
+    tokens: int = 0
+    count: int = 0  # Number of items (messages, tools, etc.)
+    budget: int = 0
+    
+    @property
+    def utilization(self) -> float:
+        """Utilization as fraction of budget."""
+        if self.budget == 0:
+            return 0.0
+        return self.tokens / self.budget
+    
+    @property
+    def over_budget(self) -> bool:
+        """Check if over budget."""
+        return self.tokens > self.budget if self.budget > 0 else False
+
+
+class ContextLedgerManager:
+    """
+    Manages context token accounting.
+    
+    Tracks tokens per segment and provides utilization metrics.
+    Supports multi-agent scenarios with per-agent ledgers.
+    
+    Example:
+        manager = ContextLedgerManager()
+        manager.set_budget(budgeter.allocate())
+        
+        # Track segments
+        manager.track_system_prompt(system_prompt)
+        manager.track_history(messages)
+        manager.track_tools(tool_schemas)
+        
+        # Get stats
+        print(manager.get_utilization())
+        print(manager.get_warnings())
+    """
+    
+    def __init__(self, agent_id: Optional[str] = None):
+        """
+        Initialize ledger manager.
+        
+        Args:
+            agent_id: Optional agent identifier for multi-agent tracking
+        """
+        self.agent_id = agent_id
+        self._ledger = ContextLedger()
+        self._budget: Optional[BudgetAllocation] = None
+        self._segment_details: Dict[str, SegmentStats] = {}
+        
+        # Initialize segment stats
+        for segment in ContextSegment:
+            self._segment_details[segment.value] = SegmentStats()
+    
+    def set_budget(self, budget: BudgetAllocation) -> None:
+        """Set the budget allocation."""
+        self._budget = budget
+        
+        # Update segment budgets
+        for segment in ContextSegment:
+            self._segment_details[segment.value].budget = budget.get_segment_budget(segment)
+    
+    def reset(self) -> None:
+        """Reset all token counts."""
+        self._ledger = ContextLedger()
+        for segment in ContextSegment:
+            stats = self._segment_details[segment.value]
+            stats.tokens = 0
+            stats.count = 0
+    
+    def track_segment(
+        self,
+        segment: ContextSegment,
+        tokens: int,
+        count: int = 1
+    ) -> None:
+        """
+        Track tokens for a segment.
+        
+        Args:
+            segment: Context segment
+            tokens: Token count
+            count: Number of items
+        """
+        self._ledger.set_segment(segment, tokens)
+        stats = self._segment_details[segment.value]
+        stats.tokens = tokens
+        stats.count = count
+    
+    def add_to_segment(
+        self,
+        segment: ContextSegment,
+        tokens: int,
+        count: int = 1
+    ) -> None:
+        """
+        Add tokens to a segment.
+        
+        Args:
+            segment: Context segment
+            tokens: Tokens to add
+            count: Items to add
+        """
+        self._ledger.add_segment(segment, tokens)
+        stats = self._segment_details[segment.value]
+        stats.tokens += tokens
+        stats.count += count
+    
+    def track_system_prompt(self, content: str) -> int:
+        """Track system prompt tokens."""
+        from .tokens import estimate_tokens_heuristic
+        tokens = estimate_tokens_heuristic(content)
+        self.track_segment(ContextSegment.SYSTEM_PROMPT, tokens)
+        return tokens
+    
+    def track_rules(self, content: str) -> int:
+        """Track rules tokens."""
+        from .tokens import estimate_tokens_heuristic
+        tokens = estimate_tokens_heuristic(content)
+        self.track_segment(ContextSegment.RULES, tokens)
+        return tokens
+    
+    def track_skills(self, content: str) -> int:
+        """Track skills tokens."""
+        from .tokens import estimate_tokens_heuristic
+        tokens = estimate_tokens_heuristic(content)
+        self.track_segment(ContextSegment.SKILLS, tokens)
+        return tokens
+    
+    def track_memory(self, content: str) -> int:
+        """Track memory tokens."""
+        from .tokens import estimate_tokens_heuristic
+        tokens = estimate_tokens_heuristic(content)
+        self.track_segment(ContextSegment.MEMORY, tokens)
+        return tokens
+    
+    def track_history(self, messages: List[Dict[str, Any]]) -> int:
+        """
+        Track conversation history tokens.
+        
+        Args:
+            messages: List of message dicts
+            
+        Returns:
+            Total tokens
+        """
+        tokens = estimate_messages_tokens(messages)
+        self.track_segment(ContextSegment.HISTORY, tokens, len(messages))
+        self._ledger.message_count = len(messages)
+        
+        # Count turns (user messages)
+        turns = sum(1 for m in messages if m.get("role") == "user")
+        self._ledger.turn_count = turns
+        
+        return tokens
+    
+    def track_tools(self, tools: List[Dict[str, Any]]) -> int:
+        """
+        Track tool schema tokens.
+        
+        Args:
+            tools: List of tool definitions
+            
+        Returns:
+            Total tokens
+        """
+        tokens = estimate_tool_schema_tokens(tools)
+        self.track_segment(ContextSegment.TOOLS_SCHEMA, tokens, len(tools))
+        return tokens
+    
+    def track_tool_output(self, content: str) -> int:
+        """
+        Add a tool output to tracking.
+        
+        Args:
+            content: Tool output content
+            
+        Returns:
+            Tokens for this output
+        """
+        from .tokens import estimate_tokens_heuristic
+        tokens = estimate_tokens_heuristic(content)
+        self.add_to_segment(ContextSegment.TOOL_OUTPUTS, tokens)
+        self._ledger.tool_call_count += 1
+        return tokens
+    
+    def get_ledger(self) -> ContextLedger:
+        """Get current ledger."""
+        return self._ledger.copy()
+    
+    def get_total(self) -> int:
+        """Get total tokens across all segments."""
+        return self._ledger.total
+    
+    def get_utilization(self) -> float:
+        """Get overall utilization as fraction of usable budget."""
+        if self._budget is None:
+            return 0.0
+        usable = self._budget.usable
+        if usable == 0:
+            return 0.0
+        return self._ledger.total / usable
+    
+    def get_segment_stats(self, segment: ContextSegment) -> SegmentStats:
+        """Get stats for a segment."""
+        return self._segment_details[segment.value]
+    
+    def get_all_stats(self) -> Dict[str, SegmentStats]:
+        """Get stats for all segments."""
+        return self._segment_details.copy()
+    
+    def get_warnings(self, threshold: float = 0.9) -> List[str]:
+        """
+        Get warnings for segments near or over budget.
+        
+        Args:
+            threshold: Warning threshold (0.0-1.0)
+            
+        Returns:
+            List of warning messages
+        """
+        warnings = []
+        
+        for segment in ContextSegment:
+            stats = self._segment_details[segment.value]
+            if stats.budget > 0:
+                if stats.over_budget:
+                    warnings.append(
+                        f"{segment.value}: OVER BUDGET ({stats.tokens:,} / {stats.budget:,} tokens)"
+                    )
+                elif stats.utilization >= threshold:
+                    pct = int(stats.utilization * 100)
+                    warnings.append(
+                        f"{segment.value}: {pct}% of budget ({stats.tokens:,} / {stats.budget:,} tokens)"
+                    )
+        
+        # Overall utilization warning
+        if self._budget:
+            overall_util = self.get_utilization()
+            if overall_util >= threshold:
+                pct = int(overall_util * 100)
+                warnings.insert(0, f"Overall context: {pct}% of usable budget")
+        
+        return warnings
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for serialization."""
+        return {
+            "agent_id": self.agent_id,
+            "ledger": self._ledger.to_dict(),
+            "total": self._ledger.total,
+            "utilization": self.get_utilization(),
+            "segments": {
+                name: {
+                    "tokens": stats.tokens,
+                    "count": stats.count,
+                    "budget": stats.budget,
+                    "utilization": stats.utilization,
+                }
+                for name, stats in self._segment_details.items()
+            },
+            "warnings": self.get_warnings(),
+        }
+
+
+class MultiAgentLedger:
+    """
+    Manages ledgers for multiple agents.
+    
+    Provides per-agent tracking and combined views.
+    """
+    
+    def __init__(self):
+        """Initialize multi-agent ledger."""
+        self._agents: Dict[str, ContextLedgerManager] = {}
+        self._shared_tokens: int = 0  # Tokens shared across agents
+    
+    def get_agent_ledger(self, agent_id: str) -> ContextLedgerManager:
+        """Get or create ledger for an agent."""
+        if agent_id not in self._agents:
+            self._agents[agent_id] = ContextLedgerManager(agent_id)
+        return self._agents[agent_id]
+    
+    def set_shared_tokens(self, tokens: int) -> None:
+        """Set tokens shared across agents (e.g., team memory)."""
+        self._shared_tokens = tokens
+    
+    def get_combined_total(self) -> int:
+        """Get total tokens across all agents plus shared."""
+        total = self._shared_tokens
+        for ledger in self._agents.values():
+            total += ledger.get_total()
+        return total
+    
+    def get_agent_ids(self) -> List[str]:
+        """Get list of tracked agent IDs."""
+        return list(self._agents.keys())
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "agents": {
+                agent_id: ledger.to_dict()
+                for agent_id, ledger in self._agents.items()
+            },
+            "shared_tokens": self._shared_tokens,
+            "combined_total": self.get_combined_total(),
+        }

src/praisonai-agents/praisonaiagents/context/models.py

@@ -0,0 +1,300 @@
+"""
+Context Management Data Models for PraisonAI Agents.
+
+Provides dataclasses and protocols for context budgeting, composition,
+optimization, and monitoring.
+"""
+
+from dataclasses import dataclass, field, asdict
+from typing import Dict, List, Any, Optional, Literal, Protocol, runtime_checkable
+from enum import Enum
+
+
+class ContextSegment(str, Enum):
+    """Segments that contribute to context."""
+    SYSTEM_PROMPT = "system_prompt"
+    RULES = "rules"
+    SKILLS = "skills"
+    MEMORY = "memory"
+    TOOLS_SCHEMA = "tools_schema"
+    HISTORY = "history"
+    TOOL_OUTPUTS = "tool_outputs"
+    BUFFER = "buffer"
+
+
+@dataclass
+class ContextLedger:
+    """
+    Tracks token usage across context segments.
+    
+    Provides per-segment token counts and total usage.
+    """
+    system_prompt: int = 0
+    rules: int = 0
+    skills: int = 0
+    memory: int = 0
+    tools_schema: int = 0
+    history: int = 0
+    tool_outputs: int = 0
+    buffer: int = 0
+    
+    # Metadata
+    turn_count: int = 0
+    message_count: int = 0
+    tool_call_count: int = 0
+    
+    @property
+    def total(self) -> int:
+        """Total tokens across all segments."""
+        return (
+            self.system_prompt +
+            self.rules +
+            self.skills +
+            self.memory +
+            self.tools_schema +
+            self.history +
+            self.tool_outputs +
+            self.buffer
+        )
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return asdict(self)
+    
+    def get_segment(self, segment: ContextSegment) -> int:
+        """Get token count for a segment."""
+        return getattr(self, segment.value, 0)
+    
+    def set_segment(self, segment: ContextSegment, tokens: int) -> None:
+        """Set token count for a segment."""
+        setattr(self, segment.value, tokens)
+    
+    def add_segment(self, segment: ContextSegment, tokens: int) -> None:
+        """Add tokens to a segment."""
+        current = self.get_segment(segment)
+        self.set_segment(segment, current + tokens)
+    
+    def copy(self) -> "ContextLedger":
+        """Create a copy of this ledger."""
+        return ContextLedger(
+            system_prompt=self.system_prompt,
+            rules=self.rules,
+            skills=self.skills,
+            memory=self.memory,
+            tools_schema=self.tools_schema,
+            history=self.history,
+            tool_outputs=self.tool_outputs,
+            buffer=self.buffer,
+            turn_count=self.turn_count,
+            message_count=self.message_count,
+            tool_call_count=self.tool_call_count,
+        )
+
+
+@dataclass
+class BudgetAllocation:
+    """
+    Token budget allocation across segments.
+    
+    Specifies maximum tokens allowed per segment.
+    """
+    model_limit: int = 128000
+    output_reserve: int = 8000
+    
+    # Segment budgets (absolute tokens or -1 for dynamic)
+    system_prompt: int = 2000
+    rules: int = 500
+    skills: int = 500
+    memory: int = 1000
+    tools_schema: int = 2000
+    history: int = -1  # Dynamic: fills remaining
+    tool_outputs: int = 20000
+    buffer: int = 1000
+    
+    @property
+    def usable(self) -> int:
+        """Usable tokens after output reserve."""
+        return self.model_limit - self.output_reserve
+    
+    @property
+    def fixed_total(self) -> int:
+        """Total of fixed segment budgets."""
+        total = 0
+        for seg in [self.system_prompt, self.rules, self.skills, 
+                    self.memory, self.tools_schema, self.tool_outputs, self.buffer]:
+            if seg > 0:
+                total += seg
+        return total
+    
+    @property
+    def history_budget(self) -> int:
+        """Computed history budget (remainder after fixed segments)."""
+        if self.history > 0:
+            return self.history
+        return max(0, self.usable - self.fixed_total)
+    
+    def get_segment_budget(self, segment: ContextSegment) -> int:
+        """Get budget for a segment."""
+        if segment == ContextSegment.HISTORY:
+            return self.history_budget
+        return getattr(self, segment.value, 0)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        result = asdict(self)
+        result['usable'] = self.usable
+        result['history_budget'] = self.history_budget
+        return result
+
+
+@dataclass
+class ContextSnapshot:
+    """
+    A snapshot of the current context state.
+    
+    Used for monitoring and debugging.
+    """
+    timestamp: str = ""
+    session_id: str = ""
+    agent_name: str = ""
+    model_name: str = ""
+    
+    # Budget info
+    budget: Optional[BudgetAllocation] = None
+    
+    # Token accounting
+    ledger: Optional[ContextLedger] = None
+    
+    # Utilization
+    utilization: float = 0.0
+    
+    # Content segments (for dump)
+    system_prompt_content: str = ""
+    rules_content: str = ""
+    skills_content: str = ""
+    memory_content: str = ""
+    tools_schema_content: str = ""
+    history_content: List[Dict[str, Any]] = field(default_factory=list)
+    
+    # Warnings
+    warnings: List[str] = field(default_factory=list)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for JSON serialization."""
+        return {
+            "timestamp": self.timestamp,
+            "session_id": self.session_id,
+            "agent_name": self.agent_name,
+            "model_name": self.model_name,
+            "budget": self.budget.to_dict() if self.budget else None,
+            "ledger": self.ledger.to_dict() if self.ledger else None,
+            "utilization": self.utilization,
+            "warnings": self.warnings,
+            "history_turn_count": len(self.history_content),
+        }
+
+
+class OptimizerStrategy(str, Enum):
+    """Context optimization strategies."""
+    TRUNCATE = "truncate"
+    SLIDING_WINDOW = "sliding_window"
+    SUMMARIZE = "summarize"
+    PRUNE_TOOLS = "prune_tools"
+    NON_DESTRUCTIVE = "non_destructive"
+    SMART = "smart"
+
+
+@dataclass
+class OptimizationResult:
+    """Result of context optimization."""
+    original_tokens: int = 0
+    optimized_tokens: int = 0
+    tokens_saved: int = 0
+    strategy_used: OptimizerStrategy = OptimizerStrategy.SMART
+    messages_removed: int = 0
+    messages_tagged: int = 0
+    tool_outputs_pruned: int = 0
+    summary_added: bool = False
+    
+    @property
+    def reduction_percent(self) -> float:
+        """Percentage of tokens reduced."""
+        if self.original_tokens == 0:
+            return 0.0
+        return (self.tokens_saved / self.original_tokens) * 100
+
+
+@dataclass
+class MonitorConfig:
+    """Configuration for context monitoring."""
+    enabled: bool = False
+    path: str = "./context.txt"
+    format: Literal["human", "json"] = "human"
+    frequency: Literal["turn", "tool_call", "manual", "overflow"] = "turn"
+    redact_sensitive: bool = True
+    multi_agent_files: bool = True
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        return asdict(self)
+
+
+@dataclass
+class ContextConfig:
+    """
+    Complete context management configuration.
+    
+    Merges settings from CLI flags, env vars, and config files.
+    """
+    # Auto-compaction
+    auto_compact: bool = True
+    compact_threshold: float = 0.8  # Trigger at 80% of usable budget
+    strategy: OptimizerStrategy = OptimizerStrategy.SMART
+    
+    # Budget overrides
+    output_reserve: int = 8000
+    history_ratio: float = 0.6  # 60% of usable for history
+    tool_output_max: int = 10000  # Max tokens per tool output
+    
+    # Pruning
+    prune_after_tokens: int = 40000
+    protected_tools: List[str] = field(default_factory=list)
+    
+    # Monitoring
+    monitor: MonitorConfig = field(default_factory=MonitorConfig)
+    
+    # Sliding window
+    keep_recent_turns: int = 5
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        result = asdict(self)
+        result['strategy'] = self.strategy.value
+        return result
+
+
+@runtime_checkable
+class TokenEstimator(Protocol):
+    """Protocol for token estimation."""
+    
+    def estimate(self, text: str) -> int:
+        """Estimate tokens for text."""
+        ...
+    
+    def estimate_messages(self, messages: List[Dict[str, Any]]) -> int:
+        """Estimate tokens for a list of messages."""
+        ...
+
+
+@runtime_checkable
+class ContextOptimizer(Protocol):
+    """Protocol for context optimization strategies."""
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: ContextLedger,
+    ) -> tuple[List[Dict[str, Any]], OptimizationResult]:
+        """Optimize messages to fit within target tokens."""
+        ...

src/praisonai-agents/praisonaiagents/context/monitor.py

@@ -0,0 +1,478 @@
+"""
+Context Monitor for PraisonAI Agents.
+
+Writes runtime context to disk for inspection and debugging.
+"""
+
+from typing import Dict, List, Any, Optional, Literal
+from pathlib import Path
+from datetime import datetime
+import json
+import re
+
+from .models import (
+    ContextSnapshot, ContextLedger, BudgetAllocation, MonitorConfig, ContextSegment
+)
+
+
+# Sensitive patterns for redaction
+SENSITIVE_PATTERNS = [
+    r'sk-[a-zA-Z0-9]{20,}',  # OpenAI API keys
+    r'sk-proj-[a-zA-Z0-9_-]+',  # OpenAI project keys
+    r'AKIA[0-9A-Z]{16}',  # AWS access keys
+    r'[a-zA-Z0-9]{32,}',  # Generic long tokens (conservative)
+    r'password["\s:=]+["\']?[^"\'\s]+',  # Password patterns
+    r'secret["\s:=]+["\']?[^"\'\s]+',  # Secret patterns
+    r'token["\s:=]+["\']?[^"\'\s]+',  # Token patterns
+    r'api[_-]?key["\s:=]+["\']?[^"\'\s]+',  # API key patterns
+]
+
+
+def redact_sensitive(text: str) -> str:
+    """
+    Redact sensitive information from text.
+    
+    Args:
+        text: Text to redact
+        
+    Returns:
+        Redacted text
+    """
+    result = text
+    for pattern in SENSITIVE_PATTERNS:
+        result = re.sub(pattern, '[REDACTED]', result, flags=re.IGNORECASE)
+    return result
+
+
+def format_human_snapshot(snapshot: ContextSnapshot, redact: bool = True) -> str:
+    """
+    Format snapshot as human-readable text.
+    
+    Args:
+        snapshot: Context snapshot
+        redact: Whether to redact sensitive info
+        
+    Returns:
+        Formatted text
+    """
+    lines = []
+    
+    # Header
+    lines.append("=" * 80)
+    lines.append("PRAISONAI CONTEXT SNAPSHOT")
+    lines.append("=" * 80)
+    lines.append(f"Timestamp: {snapshot.timestamp}")
+    lines.append(f"Session ID: {snapshot.session_id}")
+    lines.append(f"Agent: {snapshot.agent_name}")
+    lines.append(f"Model: {snapshot.model_name}")
+    
+    if snapshot.budget:
+        lines.append(f"Model Limit: {snapshot.budget.model_limit:,} tokens")
+        lines.append(f"Output Reserve: {snapshot.budget.output_reserve:,} tokens")
+        lines.append(f"Usable Budget: {snapshot.budget.usable:,} tokens")
+    
+    lines.append("")
+    
+    # Token Ledger
+    lines.append("-" * 80)
+    lines.append("TOKEN LEDGER")
+    lines.append("-" * 80)
+    lines.append(f"{'Component':<20} | {'Tokens':>10} | {'Budget':>10} | {'% Used':>8}")
+    lines.append("-" * 20 + "-+-" + "-" * 10 + "-+-" + "-" * 10 + "-+-" + "-" * 8)
+    
+    if snapshot.ledger and snapshot.budget:
+        segments = [
+            (ContextSegment.SYSTEM_PROMPT, "System Prompt"),
+            (ContextSegment.RULES, "Rules"),
+            (ContextSegment.SKILLS, "Skills"),
+            (ContextSegment.MEMORY, "Memory"),
+            (ContextSegment.TOOLS_SCHEMA, "Tool Schemas"),
+            (ContextSegment.HISTORY, "History"),
+            (ContextSegment.TOOL_OUTPUTS, "Tool Outputs"),
+        ]
+        
+        for seg, name in segments:
+            tokens = snapshot.ledger.get_segment(seg)
+            budget = snapshot.budget.get_segment_budget(seg)
+            pct = (tokens / budget * 100) if budget > 0 else 0
+            lines.append(f"{name:<20} | {tokens:>10,} | {budget:>10,} | {pct:>7.1f}%")
+        
+        lines.append("-" * 20 + "-+-" + "-" * 10 + "-+-" + "-" * 10 + "-+-" + "-" * 8)
+        lines.append(f"{'TOTAL':<20} | {snapshot.ledger.total:>10,} | {snapshot.budget.usable:>10,} | {snapshot.utilization * 100:>7.1f}%")
+    
+    lines.append("")
+    
+    # Warnings
+    if snapshot.warnings:
+        lines.append("-" * 80)
+        lines.append("WARNINGS")
+        lines.append("-" * 80)
+        for warning in snapshot.warnings:
+            lines.append(f"⚠ {warning}")
+        lines.append("")
+    
+    # History summary
+    if snapshot.history_content:
+        lines.append("-" * 80)
+        lines.append(f"CONVERSATION HISTORY ({len(snapshot.history_content)} messages)")
+        lines.append("-" * 80)
+        
+        for i, msg in enumerate(snapshot.history_content):
+            role = msg.get("role", "unknown").upper()
+            content = msg.get("content", "")
+            
+            if isinstance(content, str):
+                if redact:
+                    content = redact_sensitive(content)
+                # Truncate for display
+                if len(content) > 200:
+                    content = content[:200] + "..."
+                lines.append(f"[{i+1}] {role}: {content}")
+            else:
+                lines.append(f"[{i+1}] {role}: [complex content]")
+        
+        lines.append("")
+    
+    # Footer
+    lines.append("=" * 80)
+    lines.append("END CONTEXT SNAPSHOT")
+    lines.append("=" * 80)
+    
+    return "\n".join(lines)
+
+
+def format_json_snapshot(snapshot: ContextSnapshot, redact: bool = True) -> str:
+    """
+    Format snapshot as JSON.
+    
+    Args:
+        snapshot: Context snapshot
+        redact: Whether to redact sensitive info
+        
+    Returns:
+        JSON string
+    """
+    data = snapshot.to_dict()
+    
+    # Add history with optional redaction
+    if snapshot.history_content:
+        history = []
+        for msg in snapshot.history_content:
+            msg_copy = msg.copy()
+            content = msg_copy.get("content", "")
+            if isinstance(content, str) and redact:
+                msg_copy["content"] = redact_sensitive(content)
+            history.append(msg_copy)
+        data["history"] = history
+    
+    return json.dumps(data, indent=2, default=str)
+
+
+class ContextMonitor:
+    """
+    Monitors and writes context to disk.
+    
+    Provides real-time visibility into context state for debugging
+    and optimization.
+    
+    Example:
+        monitor = ContextMonitor(
+            enabled=True,
+            path="./context.txt",
+            format="human",
+        )
+        
+        # Write snapshot
+        monitor.snapshot(
+            ledger=ledger,
+            budget=budget,
+            messages=messages,
+            agent_name="Assistant",
+        )
+    """
+    
+    def __init__(
+        self,
+        enabled: bool = False,
+        path: str = "./context.txt",
+        format: Literal["human", "json"] = "human",
+        frequency: Literal["turn", "tool_call", "manual", "overflow"] = "turn",
+        redact_sensitive: bool = True,
+        multi_agent_files: bool = True,
+    ):
+        """
+        Initialize monitor.
+        
+        Args:
+            enabled: Whether monitoring is enabled
+            path: Output file path
+            format: Output format (human or json)
+            frequency: Update frequency
+            redact_sensitive: Redact sensitive info
+            multi_agent_files: Create per-agent files
+        """
+        self.enabled = enabled
+        self.path = Path(path)
+        self.format = format
+        self.frequency = frequency
+        self.redact = redact_sensitive
+        self.multi_agent_files = multi_agent_files
+        
+        self._last_snapshot: Optional[ContextSnapshot] = None
+        self._turn_count = 0
+        self._tool_call_count = 0
+    
+    @classmethod
+    def from_config(cls, config: MonitorConfig) -> "ContextMonitor":
+        """Create monitor from config."""
+        return cls(
+            enabled=config.enabled,
+            path=config.path,
+            format=config.format,
+            frequency=config.frequency,
+            redact_sensitive=config.redact_sensitive,
+            multi_agent_files=config.multi_agent_files,
+        )
+    
+    def enable(self) -> None:
+        """Enable monitoring."""
+        self.enabled = True
+    
+    def disable(self) -> None:
+        """Disable monitoring."""
+        self.enabled = False
+    
+    def set_path(self, path: str) -> None:
+        """Set output path."""
+        self.path = Path(path)
+    
+    def set_format(self, format: Literal["human", "json"]) -> None:
+        """Set output format."""
+        self.format = format
+    
+    def set_frequency(self, frequency: Literal["turn", "tool_call", "manual", "overflow"]) -> None:
+        """Set update frequency."""
+        self.frequency = frequency
+    
+    def should_write(
+        self,
+        trigger: Literal["turn", "tool_call", "manual", "overflow"]
+    ) -> bool:
+        """Check if should write based on frequency."""
+        if not self.enabled:
+            return False
+        
+        if trigger == "manual":
+            return True
+        
+        if self.frequency == "manual":
+            return trigger == "manual"
+        
+        if self.frequency == "overflow":
+            return trigger == "overflow"
+        
+        if self.frequency == "tool_call":
+            return trigger in ("tool_call", "overflow")
+        
+        # frequency == "turn"
+        return trigger in ("turn", "overflow")
+    
+    def snapshot(
+        self,
+        ledger: ContextLedger,
+        budget: BudgetAllocation,
+        messages: List[Dict[str, Any]],
+        session_id: str = "",
+        agent_name: str = "",
+        model_name: str = "",
+        trigger: Literal["turn", "tool_call", "manual", "overflow"] = "turn",
+    ) -> Optional[str]:
+        """
+        Create and write a context snapshot.
+        
+        Args:
+            ledger: Current token ledger
+            budget: Budget allocation
+            messages: Current messages
+            session_id: Session identifier
+            agent_name: Agent name
+            model_name: Model name
+            trigger: What triggered this snapshot
+            
+        Returns:
+            Path to written file, or None if not written
+        """
+        if not self.should_write(trigger):
+            return None
+        
+        # Track counts
+        if trigger == "turn":
+            self._turn_count += 1
+        elif trigger == "tool_call":
+            self._tool_call_count += 1
+        
+        # Create snapshot
+        snapshot = ContextSnapshot(
+            timestamp=datetime.utcnow().isoformat() + "Z",
+            session_id=session_id,
+            agent_name=agent_name,
+            model_name=model_name,
+            budget=budget,
+            ledger=ledger,
+            utilization=ledger.total / budget.usable if budget.usable > 0 else 0,
+            history_content=messages,
+            warnings=self._get_warnings(ledger, budget),
+        )
+        
+        self._last_snapshot = snapshot
+        
+        # Determine output path
+        output_path = self._get_output_path(agent_name)
+        
+        # Format content
+        if self.format == "json":
+            content = format_json_snapshot(snapshot, self.redact)
+        else:
+            content = format_human_snapshot(snapshot, self.redact)
+        
+        # Atomic write
+        self._atomic_write(output_path, content)
+        
+        return str(output_path)
+    
+    def _get_output_path(self, agent_name: str = "") -> Path:
+        """Get output path, optionally per-agent."""
+        if not self.multi_agent_files or not agent_name:
+            return self.path
+        
+        # Create per-agent path
+        stem = self.path.stem
+        suffix = self.path.suffix or (".json" if self.format == "json" else ".txt")
+        
+        # Sanitize agent name
+        safe_name = re.sub(r'[^\w\-]', '_', agent_name.lower())
+        
+        return self.path.parent / f"{stem}_{safe_name}{suffix}"
+    
+    def _get_warnings(
+        self,
+        ledger: ContextLedger,
+        budget: BudgetAllocation
+    ) -> List[str]:
+        """Generate warnings based on current state."""
+        warnings = []
+        
+        utilization = ledger.total / budget.usable if budget.usable > 0 else 0
+        
+        if utilization >= 1.0:
+            warnings.append(f"CRITICAL: Context overflow! {utilization*100:.1f}% of usable budget")
+        elif utilization >= 0.9:
+            warnings.append(f"WARNING: Context at {utilization*100:.1f}% of usable budget")
+        elif utilization >= 0.8:
+            warnings.append(f"NOTICE: Context at {utilization*100:.1f}% of usable budget")
+        
+        # Check individual segments
+        if budget.tool_outputs > 0:
+            tool_util = ledger.tool_outputs / budget.tool_outputs
+            if tool_util >= 0.9:
+                warnings.append(f"Tool outputs at {tool_util*100:.1f}% of budget")
+        
+        return warnings
+    
+    def _atomic_write(self, path: Path, content: str) -> None:
+        """Write content atomically using temp file + rename."""
+        # Ensure parent directory exists
+        path.parent.mkdir(parents=True, exist_ok=True)
+        
+        # Write to temp file
+        temp_path = path.with_suffix(path.suffix + ".tmp")
+        
+        try:
+            temp_path.write_text(content, encoding="utf-8")
+            # Atomic rename
+            temp_path.rename(path)
+        except Exception:
+            # Clean up temp file on error
+            if temp_path.exists():
+                temp_path.unlink()
+            raise
+    
+    def get_last_snapshot(self) -> Optional[ContextSnapshot]:
+        """Get the last snapshot."""
+        return self._last_snapshot
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """Get monitor statistics."""
+        return {
+            "enabled": self.enabled,
+            "path": str(self.path),
+            "format": self.format,
+            "frequency": self.frequency,
+            "turn_count": self._turn_count,
+            "tool_call_count": self._tool_call_count,
+            "last_snapshot": self._last_snapshot.timestamp if self._last_snapshot else None,
+        }
+
+
+class MultiAgentMonitor:
+    """
+    Manages monitors for multiple agents.
+    
+    Creates per-agent monitors and optional combined view.
+    """
+    
+    def __init__(
+        self,
+        base_path: str = "./context",
+        format: Literal["human", "json"] = "human",
+        enabled: bool = False,
+        redact_sensitive: bool = True,
+    ):
+        """
+        Initialize multi-agent monitor.
+        
+        Args:
+            base_path: Base path for output files
+            format: Output format
+            enabled: Whether monitoring is enabled
+            redact_sensitive: Redact sensitive info
+        """
+        self.base_path = Path(base_path)
+        self.format = format
+        self.enabled = enabled
+        self.redact = redact_sensitive
+        
+        self._agents: Dict[str, ContextMonitor] = {}
+    
+    def get_agent_monitor(self, agent_id: str) -> ContextMonitor:
+        """Get or create monitor for an agent."""
+        if agent_id not in self._agents:
+            suffix = ".json" if self.format == "json" else ".txt"
+            safe_id = re.sub(r'[^\w\-]', '_', agent_id.lower())
+            path = self.base_path / f"context_{safe_id}{suffix}"
+            
+            self._agents[agent_id] = ContextMonitor(
+                enabled=self.enabled,
+                path=str(path),
+                format=self.format,
+                redact_sensitive=self.redact,
+                multi_agent_files=False,  # Already per-agent
+            )
+        
+        return self._agents[agent_id]
+    
+    def enable_all(self) -> None:
+        """Enable all monitors."""
+        self.enabled = True
+        for monitor in self._agents.values():
+            monitor.enable()
+    
+    def disable_all(self) -> None:
+        """Disable all monitors."""
+        self.enabled = False
+        for monitor in self._agents.values():
+            monitor.disable()
+    
+    def get_agent_ids(self) -> List[str]:
+        """Get list of monitored agent IDs."""
+        return list(self._agents.keys())

src/praisonai-agents/praisonaiagents/context/optimizer.py

@@ -0,0 +1,484 @@
+"""
+Context Optimizer for PraisonAI Agents.
+
+Provides strategies for reducing context size when approaching limits.
+"""
+
+from typing import Dict, List, Any, Optional
+from abc import ABC, abstractmethod
+from .models import ContextLedger, OptimizerStrategy, OptimizationResult
+from .tokens import estimate_messages_tokens
+
+
+class BaseOptimizer(ABC):
+    """Base class for optimization strategies."""
+    
+    @abstractmethod
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        """
+        Optimize messages to fit within target tokens.
+        
+        Args:
+            messages: Messages to optimize
+            target_tokens: Target token count
+            ledger: Current token ledger
+            
+        Returns:
+            Tuple of (optimized_messages, OptimizationResult)
+        """
+        pass
+
+
+class TruncateOptimizer(BaseOptimizer):
+    """Truncate oldest messages strategy."""
+    
+    def __init__(self, preserve_system: bool = True, preserve_recent: int = 5):
+        self.preserve_system = preserve_system
+        self.preserve_recent = preserve_recent
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        original_tokens = estimate_messages_tokens(messages)
+        
+        if original_tokens <= target_tokens:
+            return messages, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=original_tokens,
+                tokens_saved=0,
+                strategy_used=OptimizerStrategy.TRUNCATE,
+            )
+        
+        # Separate system and other messages
+        system_msgs = []
+        other_msgs = []
+        
+        for msg in messages:
+            if self.preserve_system and msg.get("role") == "system":
+                system_msgs.append(msg)
+            else:
+                other_msgs.append(msg)
+        
+        # Keep recent messages
+        recent = other_msgs[-self.preserve_recent:] if other_msgs else []
+        
+        result = system_msgs + recent
+        
+        # Continue removing until under target
+        while estimate_messages_tokens(result) > target_tokens and len(result) > 1:
+            for i, msg in enumerate(result):
+                if msg.get("role") != "system":
+                    result.pop(i)
+                    break
+        
+        optimized_tokens = estimate_messages_tokens(result)
+        
+        return result, OptimizationResult(
+            original_tokens=original_tokens,
+            optimized_tokens=optimized_tokens,
+            tokens_saved=original_tokens - optimized_tokens,
+            strategy_used=OptimizerStrategy.TRUNCATE,
+            messages_removed=len(messages) - len(result),
+        )
+
+
+class SlidingWindowOptimizer(BaseOptimizer):
+    """Keep a sliding window of recent messages."""
+    
+    def __init__(self, preserve_system: bool = True):
+        self.preserve_system = preserve_system
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        original_tokens = estimate_messages_tokens(messages)
+        
+        if original_tokens <= target_tokens:
+            return messages, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=original_tokens,
+                tokens_saved=0,
+                strategy_used=OptimizerStrategy.SLIDING_WINDOW,
+            )
+        
+        result = []
+        
+        # Keep system messages
+        for msg in messages:
+            if self.preserve_system and msg.get("role") == "system":
+                result.append(msg)
+        
+        # Add messages from end until target reached
+        non_system = [m for m in messages if m.get("role") != "system"]
+        
+        for msg in reversed(non_system):
+            test_result = result + [msg]
+            if estimate_messages_tokens(test_result) <= target_tokens:
+                result.insert(len([m for m in result if m.get("role") == "system"]), msg)
+            else:
+                break
+        
+        # Ensure order
+        system_msgs = [m for m in result if m.get("role") == "system"]
+        other_msgs = [m for m in result if m.get("role") != "system"]
+        result = system_msgs + other_msgs
+        
+        optimized_tokens = estimate_messages_tokens(result)
+        
+        return result, OptimizationResult(
+            original_tokens=original_tokens,
+            optimized_tokens=optimized_tokens,
+            tokens_saved=original_tokens - optimized_tokens,
+            strategy_used=OptimizerStrategy.SLIDING_WINDOW,
+            messages_removed=len(messages) - len(result),
+        )
+
+
+class PruneToolsOptimizer(BaseOptimizer):
+    """Prune old tool outputs while keeping tool calls."""
+    
+    def __init__(
+        self,
+        preserve_recent: int = 5,
+        max_output_chars: int = 500,
+        protected_tools: Optional[List[str]] = None,
+    ):
+        self.preserve_recent = preserve_recent
+        self.max_output_chars = max_output_chars
+        self.protected_tools = protected_tools or []
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        original_tokens = estimate_messages_tokens(messages)
+        
+        result = []
+        pruned_count = 0
+        
+        # Separate system and other messages
+        system_msgs = [m for m in messages if m.get("role") == "system"]
+        other_msgs = [m for m in messages if m.get("role") != "system"]
+        
+        result.extend(system_msgs)
+        
+        # Keep recent messages intact
+        recent = other_msgs[-self.preserve_recent:]
+        older = other_msgs[:-self.preserve_recent] if len(other_msgs) > self.preserve_recent else []
+        
+        # Prune older tool outputs
+        for msg in older:
+            role = msg.get("role", "")
+            content = msg.get("content", "")
+            
+            # Check if this is a tool result
+            if role == "tool" or msg.get("tool_call_id"):
+                # Check if protected
+                tool_name = msg.get("name", "")
+                if tool_name in self.protected_tools:
+                    result.append(msg)
+                    continue
+                
+                # Truncate if too long
+                if isinstance(content, str) and len(content) > self.max_output_chars:
+                    pruned_msg = msg.copy()
+                    pruned_msg["content"] = content[:self.max_output_chars] + "\n...[output truncated]..."
+                    pruned_msg["_pruned"] = True
+                    result.append(pruned_msg)
+                    pruned_count += 1
+                else:
+                    result.append(msg)
+            else:
+                result.append(msg)
+        
+        result.extend(recent)
+        
+        optimized_tokens = estimate_messages_tokens(result)
+        
+        return result, OptimizationResult(
+            original_tokens=original_tokens,
+            optimized_tokens=optimized_tokens,
+            tokens_saved=original_tokens - optimized_tokens,
+            strategy_used=OptimizerStrategy.PRUNE_TOOLS,
+            tool_outputs_pruned=pruned_count,
+        )
+
+
+class NonDestructiveOptimizer(BaseOptimizer):
+    """
+    Tag messages for exclusion without deleting them.
+    
+    Messages are tagged with _condense_parent and excluded from
+    effective history, but can be restored on rewind.
+    """
+    
+    def __init__(self, preserve_recent: int = 3):
+        self.preserve_recent = preserve_recent
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        import uuid
+        
+        original_tokens = estimate_messages_tokens(messages)
+        
+        if original_tokens <= target_tokens:
+            return messages, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=original_tokens,
+                tokens_saved=0,
+                strategy_used=OptimizerStrategy.NON_DESTRUCTIVE,
+            )
+        
+        condense_id = str(uuid.uuid4())
+        
+        # Separate system and other messages
+        system_msgs = [m for m in messages if m.get("role") == "system"]
+        other_msgs = [m for m in messages if m.get("role") != "system"]
+        
+        # Keep recent, tag older
+        keep_start = max(0, len(other_msgs) - self.preserve_recent)
+        
+        result = []
+        tagged_count = 0
+        
+        # Add system messages
+        result.extend(system_msgs)
+        
+        # Tag older messages
+        for i, msg in enumerate(other_msgs):
+            if i < keep_start:
+                tagged_msg = msg.copy()
+                if "_condense_parent" not in tagged_msg:
+                    tagged_msg["_condense_parent"] = condense_id
+                    tagged_count += 1
+                result.append(tagged_msg)
+            else:
+                result.append(msg)
+        
+        # Calculate effective tokens (excluding tagged)
+        effective_msgs = [m for m in result if "_condense_parent" not in m]
+        optimized_tokens = estimate_messages_tokens(effective_msgs)
+        
+        return result, OptimizationResult(
+            original_tokens=original_tokens,
+            optimized_tokens=optimized_tokens,
+            tokens_saved=original_tokens - optimized_tokens,
+            strategy_used=OptimizerStrategy.NON_DESTRUCTIVE,
+            messages_tagged=tagged_count,
+        )
+
+
+class SummarizeOptimizer(BaseOptimizer):
+    """
+    Summarize older messages using LLM.
+    
+    Note: This is a placeholder that creates a structured summary.
+    Actual LLM summarization should be done at the agent level.
+    """
+    
+    def __init__(self, preserve_recent: int = 5, max_summary_items: int = 10):
+        self.preserve_recent = preserve_recent
+        self.max_summary_items = max_summary_items
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        original_tokens = estimate_messages_tokens(messages)
+        
+        if original_tokens <= target_tokens:
+            return messages, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=original_tokens,
+                tokens_saved=0,
+                strategy_used=OptimizerStrategy.SUMMARIZE,
+            )
+        
+        # Separate system and other messages
+        system_msgs = [m for m in messages if m.get("role") == "system"]
+        other_msgs = [m for m in messages if m.get("role") != "system"]
+        
+        result = list(system_msgs)
+        
+        # Keep recent messages
+        recent = other_msgs[-self.preserve_recent:]
+        older = other_msgs[:-self.preserve_recent] if len(other_msgs) > self.preserve_recent else []
+        
+        if older:
+            # Create summary
+            summary_parts = []
+            for msg in older[:self.max_summary_items]:
+                role = msg.get("role", "unknown")
+                content = msg.get("content", "")
+                if isinstance(content, str) and content:
+                    summary_parts.append(f"[{role}]: {content[:150]}...")
+            
+            if summary_parts:
+                summary = (
+                    "[Previous conversation summary]\n"
+                    + "\n".join(summary_parts)
+                )
+                result.append({
+                    "role": "system",
+                    "content": summary,
+                    "_summary": True,
+                    "_original_count": len(older),
+                })
+        
+        result.extend(recent)
+        
+        optimized_tokens = estimate_messages_tokens(result)
+        
+        return result, OptimizationResult(
+            original_tokens=original_tokens,
+            optimized_tokens=optimized_tokens,
+            tokens_saved=original_tokens - optimized_tokens,
+            strategy_used=OptimizerStrategy.SUMMARIZE,
+            messages_removed=len(older),
+            summary_added=bool(older),
+        )
+
+
+class SmartOptimizer(BaseOptimizer):
+    """
+    Smart optimization combining multiple strategies.
+    
+    Applies strategies in order:
+    1. Prune tool outputs
+    2. Sliding window
+    3. Summarize if still over
+    """
+    
+    def __init__(
+        self,
+        preserve_recent: int = 5,
+        protected_tools: Optional[List[str]] = None,
+    ):
+        self.preserve_recent = preserve_recent
+        self.protected_tools = protected_tools or []
+        
+        self._prune = PruneToolsOptimizer(
+            preserve_recent=preserve_recent,
+            protected_tools=protected_tools,
+        )
+        self._window = SlidingWindowOptimizer()
+        self._summarize = SummarizeOptimizer(preserve_recent=preserve_recent)
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        target_tokens: int,
+        ledger: Optional[ContextLedger] = None,
+    ) -> tuple:
+        original_tokens = estimate_messages_tokens(messages)
+        
+        if original_tokens <= target_tokens:
+            return messages, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=original_tokens,
+                tokens_saved=0,
+                strategy_used=OptimizerStrategy.SMART,
+            )
+        
+        # Step 1: Prune tool outputs
+        result, prune_result = self._prune.optimize(messages, target_tokens, ledger)
+        
+        if estimate_messages_tokens(result) <= target_tokens:
+            return result, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=prune_result.optimized_tokens,
+                tokens_saved=original_tokens - prune_result.optimized_tokens,
+                strategy_used=OptimizerStrategy.SMART,
+                tool_outputs_pruned=prune_result.tool_outputs_pruned,
+            )
+        
+        # Step 2: Sliding window
+        result, window_result = self._window.optimize(result, target_tokens, ledger)
+        
+        if estimate_messages_tokens(result) <= target_tokens:
+            return result, OptimizationResult(
+                original_tokens=original_tokens,
+                optimized_tokens=window_result.optimized_tokens,
+                tokens_saved=original_tokens - window_result.optimized_tokens,
+                strategy_used=OptimizerStrategy.SMART,
+                tool_outputs_pruned=prune_result.tool_outputs_pruned,
+                messages_removed=window_result.messages_removed,
+            )
+        
+        # Step 3: Summarize
+        result, summary_result = self._summarize.optimize(result, target_tokens, ledger)
+        
+        optimized_tokens = estimate_messages_tokens(result)
+        
+        return result, OptimizationResult(
+            original_tokens=original_tokens,
+            optimized_tokens=optimized_tokens,
+            tokens_saved=original_tokens - optimized_tokens,
+            strategy_used=OptimizerStrategy.SMART,
+            tool_outputs_pruned=prune_result.tool_outputs_pruned,
+            messages_removed=window_result.messages_removed + summary_result.messages_removed,
+            summary_added=summary_result.summary_added,
+        )
+
+
+# Strategy registry
+OPTIMIZER_REGISTRY: Dict[OptimizerStrategy, type] = {
+    OptimizerStrategy.TRUNCATE: TruncateOptimizer,
+    OptimizerStrategy.SLIDING_WINDOW: SlidingWindowOptimizer,
+    OptimizerStrategy.PRUNE_TOOLS: PruneToolsOptimizer,
+    OptimizerStrategy.NON_DESTRUCTIVE: NonDestructiveOptimizer,
+    OptimizerStrategy.SUMMARIZE: SummarizeOptimizer,
+    OptimizerStrategy.SMART: SmartOptimizer,
+}
+
+
+def get_optimizer(
+    strategy: OptimizerStrategy,
+    **kwargs
+) -> BaseOptimizer:
+    """
+    Get an optimizer instance for a strategy.
+    
+    Args:
+        strategy: Optimization strategy
+        **kwargs: Strategy-specific arguments
+        
+    Returns:
+        Optimizer instance
+    """
+    optimizer_class = OPTIMIZER_REGISTRY.get(strategy, SmartOptimizer)
+    return optimizer_class(**kwargs)
+
+
+def get_effective_history(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """
+    Get effective history excluding tagged messages.
+    
+    Filters out messages with _condense_parent tags.
+    
+    Args:
+        messages: Full message history
+        
+    Returns:
+        Effective messages for API calls
+    """
+    return [m for m in messages if "_condense_parent" not in m]

src/praisonai-agents/praisonaiagents/context/tokens.py

@@ -0,0 +1,238 @@
+"""
+Token Estimation for PraisonAI Agents.
+
+Provides fast, offline token estimation with optional accurate tokenizer support.
+"""
+
+from typing import List, Dict, Any, Optional
+import json
+
+
+# Token estimation constants (based on OpenAI tokenizer patterns)
+ASCII_TOKENS_PER_CHAR = 0.25  # ~4 chars per token for ASCII
+NON_ASCII_TOKENS_PER_CHAR = 1.3  # CJK and other scripts are denser
+
+# Optional tokenizer cache
+_tokenizer_cache: Dict[str, Any] = {}
+
+
+def estimate_tokens_heuristic(text: str) -> int:
+    """
+    Estimate token count using character-based heuristic.
+    
+    Fast and works offline. Accuracy: ~85-95% for English text.
+    
+    Args:
+        text: Text to estimate tokens for
+        
+    Returns:
+        Estimated token count
+    """
+    if not text:
+        return 0
+    
+    total = 0.0
+    for char in text:
+        code_point = ord(char)
+        if code_point <= 127:
+            total += ASCII_TOKENS_PER_CHAR
+        else:
+            total += NON_ASCII_TOKENS_PER_CHAR
+    
+    return max(1, int(total))
+
+
+def estimate_tokens_accurate(text: str, model: str = "gpt-4") -> int:
+    """
+    Estimate tokens using tiktoken if available, else fallback to heuristic.
+    
+    Args:
+        text: Text to estimate tokens for
+        model: Model name for tokenizer selection
+        
+    Returns:
+        Token count
+    """
+    try:
+        import tiktoken
+        
+        # Cache tokenizer
+        if model not in _tokenizer_cache:
+            try:
+                _tokenizer_cache[model] = tiktoken.encoding_for_model(model)
+            except KeyError:
+                # Fallback to cl100k_base for unknown models
+                _tokenizer_cache[model] = tiktoken.get_encoding("cl100k_base")
+        
+        enc = _tokenizer_cache[model]
+        return len(enc.encode(text))
+    except ImportError:
+        return estimate_tokens_heuristic(text)
+
+
+def estimate_message_tokens(message: Dict[str, Any], use_accurate: bool = False) -> int:
+    """
+    Estimate tokens for a single message.
+    
+    Handles various message formats including tool calls and multi-part content.
+    
+    Args:
+        message: Message dict with role and content
+        use_accurate: Use tiktoken if available
+        
+    Returns:
+        Estimated token count
+    """
+    estimate_fn = estimate_tokens_accurate if use_accurate else estimate_tokens_heuristic
+    
+    total = 0
+    
+    # Role overhead (~4 tokens for role markers)
+    total += 4
+    
+    # Content
+    content = message.get("content", "")
+    if isinstance(content, str):
+        total += estimate_fn(content)
+    elif isinstance(content, list):
+        # Multi-part content (text, images, etc.)
+        for part in content:
+            if isinstance(part, dict):
+                if "text" in part:
+                    total += estimate_fn(str(part["text"]))
+                elif "type" in part:
+                    # Image or other media - estimate based on type
+                    if part.get("type") == "image_url":
+                        total += 85  # Base image token cost
+                    else:
+                        total += estimate_fn(json.dumps(part))
+            else:
+                total += estimate_fn(str(part))
+    
+    # Tool calls
+    if "tool_calls" in message:
+        for tool_call in message.get("tool_calls", []):
+            if isinstance(tool_call, dict):
+                # Function name and arguments
+                func = tool_call.get("function", {})
+                total += estimate_fn(func.get("name", ""))
+                total += estimate_fn(func.get("arguments", ""))
+                total += 10  # Overhead for tool call structure
+    
+    # Tool call ID (for tool responses)
+    if "tool_call_id" in message:
+        total += 10
+    
+    # Name field
+    if "name" in message:
+        total += estimate_fn(message["name"])
+    
+    return total
+
+
+def estimate_messages_tokens(
+    messages: List[Dict[str, Any]],
+    use_accurate: bool = False
+) -> int:
+    """
+    Estimate total tokens for a list of messages.
+    
+    Args:
+        messages: List of message dicts
+        use_accurate: Use tiktoken if available
+        
+    Returns:
+        Total estimated token count
+    """
+    if not messages:
+        return 0
+    
+    total = 0
+    for msg in messages:
+        total += estimate_message_tokens(msg, use_accurate)
+    
+    # Add overhead for message array structure
+    total += 3  # Array markers
+    
+    return total
+
+
+def estimate_tool_schema_tokens(
+    tools: List[Dict[str, Any]],
+    use_accurate: bool = False
+) -> int:
+    """
+    Estimate tokens for tool/function schemas.
+    
+    Args:
+        tools: List of tool definitions
+        use_accurate: Use tiktoken if available
+        
+    Returns:
+        Estimated token count
+    """
+    if not tools:
+        return 0
+    
+    estimate_fn = estimate_tokens_accurate if use_accurate else estimate_tokens_heuristic
+    
+    # Serialize tools to JSON and estimate
+    try:
+        tools_json = json.dumps(tools)
+        return estimate_fn(tools_json)
+    except (TypeError, ValueError):
+        # Fallback: estimate per-tool
+        total = 0
+        for tool in tools:
+            if isinstance(tool, dict):
+                total += estimate_fn(json.dumps(tool))
+            else:
+                total += 100  # Default estimate per tool
+        return total
+
+
+class TokenEstimatorImpl:
+    """
+    Token estimator implementation.
+    
+    Provides both heuristic and accurate estimation methods.
+    """
+    
+    def __init__(self, use_accurate: bool = False, model: str = "gpt-4"):
+        """
+        Initialize estimator.
+        
+        Args:
+            use_accurate: Use tiktoken if available
+            model: Model name for tokenizer selection
+        """
+        self.use_accurate = use_accurate
+        self.model = model
+    
+    def estimate(self, text: str) -> int:
+        """Estimate tokens for text."""
+        if self.use_accurate:
+            return estimate_tokens_accurate(text, self.model)
+        return estimate_tokens_heuristic(text)
+    
+    def estimate_messages(self, messages: List[Dict[str, Any]]) -> int:
+        """Estimate tokens for messages."""
+        return estimate_messages_tokens(messages, self.use_accurate)
+    
+    def estimate_tools(self, tools: List[Dict[str, Any]]) -> int:
+        """Estimate tokens for tool schemas."""
+        return estimate_tool_schema_tokens(tools, self.use_accurate)
+
+
+# Default estimator instance
+_default_estimator: Optional[TokenEstimatorImpl] = None
+
+
+def get_estimator(use_accurate: bool = False, model: str = "gpt-4") -> TokenEstimatorImpl:
+    """Get or create a token estimator."""
+    global _default_estimator
+    
+    if _default_estimator is None or _default_estimator.use_accurate != use_accurate:
+        _default_estimator = TokenEstimatorImpl(use_accurate, model)
+    
+    return _default_estimator

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.6.8"
+version = "0.7.0"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/tests/unit/context/__init__.py

@@ -0,0 +1 @@
+"""Unit tests for context management module."""

src/praisonai-agents/tests/unit/context/test_budgeter.py

@@ -0,0 +1,201 @@
+"""Tests for context budgeter module."""
+
+
+class TestContextBudgeter:
+    """Tests for ContextBudgeter class."""
+    
+    def test_default_initialization(self):
+        """Test default budgeter initialization."""
+        from praisonaiagents.context.budgeter import ContextBudgeter
+        
+        budgeter = ContextBudgeter()
+        assert budgeter.model == "gpt-4o-mini"
+        assert budgeter.model_limit > 0
+        assert budgeter.output_reserve > 0
+        assert budgeter.usable == budgeter.model_limit - budgeter.output_reserve
+    
+    def test_model_limit_lookup(self):
+        """Test model limit lookup."""
+        from praisonaiagents.context.budgeter import get_model_limit
+        
+        assert get_model_limit("gpt-4o") == 128000
+        assert get_model_limit("gpt-4") == 8192
+        assert get_model_limit("claude-3-5-sonnet") == 200000
+        assert get_model_limit("gemini-1.5-pro") == 2097152
+    
+    def test_model_limit_partial_match(self):
+        """Test partial model name matching."""
+        from praisonaiagents.context.budgeter import get_model_limit
+        
+        # Should match gpt-4o
+        assert get_model_limit("gpt-4o-2024-05-13") == 128000
+    
+    def test_model_limit_unknown(self):
+        """Test unknown model uses default."""
+        from praisonaiagents.context.budgeter import get_model_limit
+        
+        limit = get_model_limit("unknown-model-xyz")
+        assert limit == 128000  # Default
+    
+    def test_output_reserve_lookup(self):
+        """Test output reserve lookup."""
+        from praisonaiagents.context.budgeter import get_output_reserve
+        
+        assert get_output_reserve("gpt-4o") == 16384
+        assert get_output_reserve("claude-3-opus") == 8192
+    
+    def test_allocate_returns_budget(self):
+        """Test allocate returns BudgetAllocation."""
+        from praisonaiagents.context.budgeter import ContextBudgeter
+        from praisonaiagents.context.models import BudgetAllocation
+        
+        budgeter = ContextBudgeter(model="gpt-4o")
+        allocation = budgeter.allocate()
+        
+        assert isinstance(allocation, BudgetAllocation)
+        assert allocation.model_limit == budgeter.model_limit
+        assert allocation.output_reserve == budgeter.output_reserve
+    
+    def test_allocate_segment_budgets(self):
+        """Test segment budgets in allocation."""
+        from praisonaiagents.context.budgeter import ContextBudgeter
+        
+        budgeter = ContextBudgeter(
+            system_prompt_budget=3000,
+            rules_budget=1000,
+        )
+        allocation = budgeter.allocate()
+        
+        assert allocation.system_prompt == 3000
+        assert allocation.rules == 1000
+    
+    def test_history_budget_dynamic(self):
+        """Test history budget is computed dynamically."""
+        from praisonaiagents.context.budgeter import ContextBudgeter
+        
+        budgeter = ContextBudgeter(model="gpt-4o")
+        allocation = budgeter.allocate()
+        
+        # History budget should be remainder after fixed segments
+        assert allocation.history_budget > 0
+        assert allocation.history_budget < allocation.usable
+    
+    def test_history_ratio_override(self):
+        """Test history_ratio overrides dynamic calculation."""
+        from praisonaiagents.context.budgeter import ContextBudgeter
+        
+        budgeter = ContextBudgeter(
+            model="gpt-4o",
+            history_ratio=0.5,
+        )
+        allocation = budgeter.allocate()
+        
+        expected = int(budgeter.usable * 0.5)
+        assert allocation.history == expected
+    
+    def test_check_overflow_under_threshold(self):
+        """Test overflow check when under threshold."""
+        from praisonaiagents.context.budgeter import ContextBudgeter
+        
+        budgeter = ContextBudgeter(model="gpt-4o")
+        
+        # 50% of usable should not trigger at 80% threshold
+        current = int(budgeter.usable * 0.5)
+        assert not budgeter.check_overflow(current, threshold=0.8)
+    
+    def test_check_overflow_over_threshold(self):
+        """Test overflow check when over threshold."""
+        from praisonaiagents.context.budgeter import ContextBudgeter
+        
+        budgeter = ContextBudgeter(model="gpt-4o")
+        
+        # 90% of usable should trigger at 80% threshold
+        current = int(budgeter.usable * 0.9)
+        assert budgeter.check_overflow(current, threshold=0.8)
+    
+    def test_get_remaining(self):
+        """Test remaining tokens calculation."""
+        from praisonaiagents.context.budgeter import ContextBudgeter
+        
+        budgeter = ContextBudgeter(model="gpt-4o")
+        
+        current = 50000
+        remaining = budgeter.get_remaining(current)
+        assert remaining == budgeter.usable - current
+    
+    def test_get_utilization(self):
+        """Test utilization calculation."""
+        from praisonaiagents.context.budgeter import ContextBudgeter
+        
+        budgeter = ContextBudgeter(model="gpt-4o")
+        
+        current = budgeter.usable // 2
+        util = budgeter.get_utilization(current)
+        assert 0.49 <= util <= 0.51
+    
+    def test_to_dict(self):
+        """Test dictionary conversion."""
+        from praisonaiagents.context.budgeter import ContextBudgeter
+        
+        budgeter = ContextBudgeter(model="gpt-4o")
+        data = budgeter.to_dict()
+        
+        assert "model" in data
+        assert "model_limit" in data
+        assert "usable" in data
+        assert "allocation" in data
+
+
+class TestBudgetAllocation:
+    """Tests for BudgetAllocation dataclass."""
+    
+    def test_usable_property(self):
+        """Test usable property calculation."""
+        from praisonaiagents.context.models import BudgetAllocation
+        
+        allocation = BudgetAllocation(
+            model_limit=128000,
+            output_reserve=8000,
+        )
+        assert allocation.usable == 120000
+    
+    def test_fixed_total(self):
+        """Test fixed total calculation."""
+        from praisonaiagents.context.models import BudgetAllocation
+        
+        allocation = BudgetAllocation(
+            system_prompt=2000,
+            rules=500,
+            skills=500,
+            memory=1000,
+            tools_schema=2000,
+            tool_outputs=20000,
+            buffer=1000,
+        )
+        
+        expected = 2000 + 500 + 500 + 1000 + 2000 + 20000 + 1000
+        assert allocation.fixed_total == expected
+    
+    def test_history_budget_computed(self):
+        """Test history budget is computed from remainder."""
+        from praisonaiagents.context.models import BudgetAllocation
+        
+        allocation = BudgetAllocation(
+            model_limit=128000,
+            output_reserve=8000,
+            history=-1,  # Dynamic
+        )
+        
+        expected = allocation.usable - allocation.fixed_total
+        assert allocation.history_budget == expected
+    
+    def test_to_dict(self):
+        """Test dictionary conversion."""
+        from praisonaiagents.context.models import BudgetAllocation
+        
+        allocation = BudgetAllocation()
+        data = allocation.to_dict()
+        
+        assert "model_limit" in data
+        assert "usable" in data
+        assert "history_budget" in data

src/praisonai-agents/tests/unit/context/test_monitor.py

@@ -0,0 +1,284 @@
+"""Tests for context monitor module."""
+
+import tempfile
+import os
+from pathlib import Path
+
+
+class TestContextMonitor:
+    """Tests for ContextMonitor class."""
+    
+    def test_monitor_disabled_by_default(self):
+        """Test monitor is disabled by default."""
+        from praisonaiagents.context.monitor import ContextMonitor
+        
+        monitor = ContextMonitor()
+        assert not monitor.enabled
+    
+    def test_monitor_enable_disable(self):
+        """Test enable/disable methods."""
+        from praisonaiagents.context.monitor import ContextMonitor
+        
+        monitor = ContextMonitor()
+        
+        monitor.enable()
+        assert monitor.enabled
+        
+        monitor.disable()
+        assert not monitor.enabled
+    
+    def test_should_write_when_disabled(self):
+        """Test should_write returns False when disabled."""
+        from praisonaiagents.context.monitor import ContextMonitor
+        
+        monitor = ContextMonitor(enabled=False)
+        assert not monitor.should_write("turn")
+        assert not monitor.should_write("manual")
+    
+    def test_should_write_frequency_turn(self):
+        """Test should_write with turn frequency."""
+        from praisonaiagents.context.monitor import ContextMonitor
+        
+        monitor = ContextMonitor(enabled=True, frequency="turn")
+        
+        assert monitor.should_write("turn")
+        assert monitor.should_write("overflow")
+        assert not monitor.should_write("tool_call")
+    
+    def test_should_write_frequency_tool_call(self):
+        """Test should_write with tool_call frequency."""
+        from praisonaiagents.context.monitor import ContextMonitor
+        
+        monitor = ContextMonitor(enabled=True, frequency="tool_call")
+        
+        assert monitor.should_write("tool_call")
+        assert monitor.should_write("overflow")
+    
+    def test_should_write_frequency_manual(self):
+        """Test should_write with manual frequency."""
+        from praisonaiagents.context.monitor import ContextMonitor
+        
+        monitor = ContextMonitor(enabled=True, frequency="manual")
+        
+        assert monitor.should_write("manual")
+        assert not monitor.should_write("turn")
+    
+    def test_snapshot_writes_file(self):
+        """Test snapshot writes file to disk."""
+        from praisonaiagents.context.monitor import ContextMonitor
+        from praisonaiagents.context.models import ContextLedger, BudgetAllocation
+        
+        with tempfile.TemporaryDirectory() as tmpdir:
+            path = os.path.join(tmpdir, "context.txt")
+            monitor = ContextMonitor(enabled=True, path=path)
+            
+            ledger = ContextLedger(history=1000, system_prompt=500)
+            budget = BudgetAllocation()
+            
+            result_path = monitor.snapshot(
+                ledger=ledger,
+                budget=budget,
+                messages=[{"role": "user", "content": "Hello"}],
+                trigger="manual",
+            )
+            
+            assert result_path is not None
+            assert os.path.exists(path)
+            
+            content = Path(path).read_text()
+            assert "PRAISONAI CONTEXT SNAPSHOT" in content
+    
+    def test_snapshot_json_format(self):
+        """Test snapshot with JSON format."""
+        from praisonaiagents.context.monitor import ContextMonitor
+        from praisonaiagents.context.models import ContextLedger, BudgetAllocation
+        import json
+        
+        with tempfile.TemporaryDirectory() as tmpdir:
+            path = os.path.join(tmpdir, "context.json")
+            monitor = ContextMonitor(enabled=True, path=path, format="json")
+            
+            ledger = ContextLedger(history=1000)
+            budget = BudgetAllocation()
+            
+            monitor.snapshot(
+                ledger=ledger,
+                budget=budget,
+                messages=[],
+                trigger="manual",
+            )
+            
+            content = Path(path).read_text()
+            data = json.loads(content)
+            
+            assert "ledger" in data
+            assert "timestamp" in data
+    
+    def test_snapshot_not_written_when_disabled(self):
+        """Test snapshot not written when disabled."""
+        from praisonaiagents.context.monitor import ContextMonitor
+        from praisonaiagents.context.models import ContextLedger, BudgetAllocation
+        
+        with tempfile.TemporaryDirectory() as tmpdir:
+            path = os.path.join(tmpdir, "context.txt")
+            monitor = ContextMonitor(enabled=False, path=path)
+            
+            result = monitor.snapshot(
+                ledger=ContextLedger(),
+                budget=BudgetAllocation(),
+                messages=[],
+                trigger="turn",
+            )
+            
+            assert result is None
+            assert not os.path.exists(path)
+    
+    def test_set_path(self):
+        """Test set_path method."""
+        from praisonaiagents.context.monitor import ContextMonitor
+        
+        monitor = ContextMonitor()
+        monitor.set_path("/new/path/context.txt")
+        
+        assert str(monitor.path) == "/new/path/context.txt"
+    
+    def test_set_format(self):
+        """Test set_format method."""
+        from praisonaiagents.context.monitor import ContextMonitor
+        
+        monitor = ContextMonitor(format="human")
+        monitor.set_format("json")
+        
+        assert monitor.format == "json"
+    
+    def test_get_stats(self):
+        """Test get_stats method."""
+        from praisonaiagents.context.monitor import ContextMonitor
+        
+        monitor = ContextMonitor(enabled=True, path="./test.txt")
+        stats = monitor.get_stats()
+        
+        assert "enabled" in stats
+        assert "path" in stats
+        assert "format" in stats
+        assert stats["enabled"] is True
+
+
+class TestRedaction:
+    """Tests for sensitive data redaction."""
+    
+    def test_redact_api_key(self):
+        """Test API key redaction."""
+        from praisonaiagents.context.monitor import redact_sensitive
+        
+        text = "My API key is sk-proj-abc123def456ghi789jkl012mno345pqr678"
+        result = redact_sensitive(text)
+        
+        assert "sk-proj-" not in result
+        assert "[REDACTED]" in result
+    
+    def test_redact_password(self):
+        """Test password redaction."""
+        from praisonaiagents.context.monitor import redact_sensitive
+        
+        text = 'password: "mysecretpassword123"'
+        result = redact_sensitive(text)
+        
+        assert "mysecretpassword" not in result
+    
+    def test_redact_preserves_normal_text(self):
+        """Test normal text is preserved."""
+        from praisonaiagents.context.monitor import redact_sensitive
+        
+        text = "Hello, how are you today?"
+        result = redact_sensitive(text)
+        
+        assert result == text
+
+
+class TestFormatting:
+    """Tests for snapshot formatting."""
+    
+    def test_format_human_snapshot(self):
+        """Test human-readable format."""
+        from praisonaiagents.context.monitor import format_human_snapshot
+        from praisonaiagents.context.models import ContextSnapshot, ContextLedger, BudgetAllocation
+        
+        snapshot = ContextSnapshot(
+            timestamp="2025-01-07T12:00:00Z",
+            session_id="test-session",
+            agent_name="TestAgent",
+            model_name="gpt-4o",
+            budget=BudgetAllocation(),
+            ledger=ContextLedger(history=5000, system_prompt=1000),
+            utilization=0.5,
+            warnings=["Test warning"],
+        )
+        
+        result = format_human_snapshot(snapshot)
+        
+        assert "PRAISONAI CONTEXT SNAPSHOT" in result
+        assert "TestAgent" in result
+        assert "gpt-4o" in result
+        assert "Test warning" in result
+    
+    def test_format_json_snapshot(self):
+        """Test JSON format."""
+        from praisonaiagents.context.monitor import format_json_snapshot
+        from praisonaiagents.context.models import ContextSnapshot, ContextLedger, BudgetAllocation
+        import json
+        
+        snapshot = ContextSnapshot(
+            timestamp="2025-01-07T12:00:00Z",
+            session_id="test-session",
+            ledger=ContextLedger(),
+            budget=BudgetAllocation(),
+        )
+        
+        result = format_json_snapshot(snapshot)
+        data = json.loads(result)
+        
+        assert data["timestamp"] == "2025-01-07T12:00:00Z"
+        assert data["session_id"] == "test-session"
+
+
+class TestMultiAgentMonitor:
+    """Tests for MultiAgentMonitor class."""
+    
+    def test_get_agent_monitor(self):
+        """Test getting agent-specific monitor."""
+        from praisonaiagents.context.monitor import MultiAgentMonitor
+        
+        multi = MultiAgentMonitor(base_path="./context")
+        
+        monitor1 = multi.get_agent_monitor("agent1")
+        monitor2 = multi.get_agent_monitor("agent2")
+        
+        assert monitor1 is not monitor2
+        assert "agent1" in str(monitor1.path)
+        assert "agent2" in str(monitor2.path)
+    
+    def test_enable_all(self):
+        """Test enabling all monitors."""
+        from praisonaiagents.context.monitor import MultiAgentMonitor
+        
+        multi = MultiAgentMonitor()
+        multi.get_agent_monitor("agent1")
+        multi.get_agent_monitor("agent2")
+        
+        multi.enable_all()
+        
+        assert multi.enabled
+    
+    def test_get_agent_ids(self):
+        """Test getting list of agent IDs."""
+        from praisonaiagents.context.monitor import MultiAgentMonitor
+        
+        multi = MultiAgentMonitor()
+        multi.get_agent_monitor("agent1")
+        multi.get_agent_monitor("agent2")
+        
+        ids = multi.get_agent_ids()
+        
+        assert "agent1" in ids
+        assert "agent2" in ids

src/praisonai-agents/tests/unit/context/test_optimizer.py

@@ -0,0 +1,208 @@
+"""Tests for context optimizer module."""
+
+
+class TestOptimizers:
+    """Tests for optimizer strategies."""
+    
+    def test_truncate_optimizer_no_change_needed(self):
+        """Test truncate when under limit."""
+        from praisonaiagents.context.optimizer import TruncateOptimizer
+        
+        messages = [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi"},
+        ]
+        
+        optimizer = TruncateOptimizer()
+        result, stats = optimizer.optimize(messages, target_tokens=10000)
+        
+        assert len(result) == 2
+        assert stats.tokens_saved == 0
+    
+    def test_truncate_optimizer_removes_old(self):
+        """Test truncate removes old messages."""
+        from praisonaiagents.context.optimizer import TruncateOptimizer
+        
+        messages = [
+            {"role": "system", "content": "You are helpful"},
+            {"role": "user", "content": "a" * 1000},
+            {"role": "assistant", "content": "b" * 1000},
+            {"role": "user", "content": "c" * 1000},
+            {"role": "assistant", "content": "d" * 1000},
+        ]
+        
+        optimizer = TruncateOptimizer(preserve_recent=2)
+        result, stats = optimizer.optimize(messages, target_tokens=500)
+        
+        # Should keep system + recent
+        assert len(result) < len(messages)
+        assert stats.messages_removed > 0
+    
+    def test_truncate_preserves_system(self):
+        """Test truncate preserves system messages."""
+        from praisonaiagents.context.optimizer import TruncateOptimizer
+        
+        messages = [
+            {"role": "system", "content": "Important system prompt"},
+            {"role": "user", "content": "a" * 1000},
+            {"role": "assistant", "content": "b" * 1000},
+        ]
+        
+        optimizer = TruncateOptimizer(preserve_system=True)
+        result, stats = optimizer.optimize(messages, target_tokens=100)
+        
+        # System message should be preserved
+        system_msgs = [m for m in result if m.get("role") == "system"]
+        assert len(system_msgs) >= 1
+    
+    def test_sliding_window_optimizer(self):
+        """Test sliding window keeps recent."""
+        from praisonaiagents.context.optimizer import SlidingWindowOptimizer
+        
+        # Create messages with enough content to exceed target
+        messages = [
+            {"role": "user", "content": f"Message {i} " * 50} 
+            for i in range(10)
+        ]
+        
+        optimizer = SlidingWindowOptimizer()
+        result, stats = optimizer.optimize(messages, target_tokens=100)
+        
+        # Should keep most recent messages (fewer than original)
+        assert len(result) <= len(messages)
+    
+    def test_prune_tools_optimizer(self):
+        """Test prune tools truncates old outputs."""
+        from praisonaiagents.context.optimizer import PruneToolsOptimizer
+        
+        messages = [
+            {"role": "user", "content": "Get weather"},
+            {"role": "assistant", "content": "", "tool_calls": [{"id": "1"}]},
+            {"role": "tool", "tool_call_id": "1", "content": "x" * 2000},
+            {"role": "user", "content": "Thanks"},
+            {"role": "assistant", "content": "You're welcome"},
+        ]
+        
+        optimizer = PruneToolsOptimizer(preserve_recent=2, max_output_chars=100)
+        result, stats = optimizer.optimize(messages, target_tokens=10000)
+        
+        # Tool output should be truncated
+        tool_msg = [m for m in result if m.get("role") == "tool"][0]
+        assert len(tool_msg["content"]) < 2000
+    
+    def test_prune_tools_protects_recent(self):
+        """Test prune tools protects recent outputs."""
+        from praisonaiagents.context.optimizer import PruneToolsOptimizer
+        
+        messages = [
+            {"role": "user", "content": "Get weather"},
+            {"role": "tool", "tool_call_id": "1", "content": "x" * 2000},
+        ]
+        
+        optimizer = PruneToolsOptimizer(preserve_recent=5)
+        result, stats = optimizer.optimize(messages, target_tokens=10000)
+        
+        # Recent should not be truncated
+        tool_msg = [m for m in result if m.get("role") == "tool"][0]
+        assert len(tool_msg["content"]) == 2000
+    
+    def test_non_destructive_optimizer(self):
+        """Test non-destructive tags messages."""
+        from praisonaiagents.context.optimizer import NonDestructiveOptimizer
+        
+        messages = [
+            {"role": "user", "content": f"Message {i}"} 
+            for i in range(10)
+        ]
+        
+        optimizer = NonDestructiveOptimizer(preserve_recent=3)
+        result, stats = optimizer.optimize(messages, target_tokens=50)
+        
+        # All messages should still be present
+        assert len(result) == len(messages)
+        
+        # Older messages should be tagged
+        tagged = [m for m in result if "_condense_parent" in m]
+        assert len(tagged) > 0
+        assert stats.messages_tagged > 0
+    
+    def test_summarize_optimizer(self):
+        """Test summarize creates summary."""
+        from praisonaiagents.context.optimizer import SummarizeOptimizer
+        
+        messages = [
+            {"role": "user", "content": f"Long message {i} " * 50} 
+            for i in range(10)
+        ]
+        
+        optimizer = SummarizeOptimizer(preserve_recent=3)
+        result, stats = optimizer.optimize(messages, target_tokens=100)
+        
+        # Should have summary message
+        assert stats.summary_added
+        summary_msgs = [m for m in result if m.get("_summary")]
+        assert len(summary_msgs) >= 1
+    
+    def test_smart_optimizer_combines_strategies(self):
+        """Test smart optimizer applies multiple strategies."""
+        from praisonaiagents.context.optimizer import SmartOptimizer
+        
+        messages = [
+            {"role": "user", "content": "Get data"},
+            {"role": "tool", "tool_call_id": "1", "content": "x" * 5000},
+        ]
+        messages.extend([
+            {"role": "user", "content": f"Message {i} " * 100} 
+            for i in range(10)
+        ])
+        
+        optimizer = SmartOptimizer()
+        result, stats = optimizer.optimize(messages, target_tokens=500)
+        
+        assert stats.optimized_tokens < stats.original_tokens
+    
+    def test_get_optimizer_factory(self):
+        """Test get_optimizer factory function."""
+        from praisonaiagents.context.optimizer import get_optimizer
+        from praisonaiagents.context.models import OptimizerStrategy
+        
+        for strategy in OptimizerStrategy:
+            optimizer = get_optimizer(strategy)
+            assert optimizer is not None
+    
+    def test_get_effective_history(self):
+        """Test get_effective_history filters tagged."""
+        from praisonaiagents.context.optimizer import get_effective_history
+        
+        messages = [
+            {"role": "user", "content": "Old", "_condense_parent": "abc"},
+            {"role": "user", "content": "Recent"},
+        ]
+        
+        effective = get_effective_history(messages)
+        
+        assert len(effective) == 1
+        assert effective[0]["content"] == "Recent"
+
+
+class TestOptimizationResult:
+    """Tests for OptimizationResult dataclass."""
+    
+    def test_reduction_percent(self):
+        """Test reduction percentage calculation."""
+        from praisonaiagents.context.models import OptimizationResult
+        
+        result = OptimizationResult(
+            original_tokens=1000,
+            optimized_tokens=600,
+            tokens_saved=400,
+        )
+        
+        assert result.reduction_percent == 40.0
+    
+    def test_reduction_percent_zero_original(self):
+        """Test reduction with zero original."""
+        from praisonaiagents.context.models import OptimizationResult
+        
+        result = OptimizationResult(original_tokens=0)
+        assert result.reduction_percent == 0.0

src/praisonai-agents/tests/unit/context/test_tokens.py

@@ -0,0 +1,184 @@
+"""Tests for token estimation module."""
+
+import pytest
+
+
+class TestTokenEstimation:
+    """Tests for token estimation functions."""
+    
+    def test_estimate_tokens_heuristic_empty(self):
+        """Test empty string returns 0."""
+        from praisonaiagents.context.tokens import estimate_tokens_heuristic
+        assert estimate_tokens_heuristic("") == 0
+    
+    def test_estimate_tokens_heuristic_ascii(self):
+        """Test ASCII text estimation."""
+        from praisonaiagents.context.tokens import estimate_tokens_heuristic
+        # ~4 chars per token for ASCII
+        text = "Hello world"  # 11 chars
+        tokens = estimate_tokens_heuristic(text)
+        assert 2 <= tokens <= 4  # Should be ~2-3 tokens
+    
+    def test_estimate_tokens_heuristic_long_text(self):
+        """Test longer text estimation."""
+        from praisonaiagents.context.tokens import estimate_tokens_heuristic
+        text = "a" * 400  # 400 ASCII chars
+        tokens = estimate_tokens_heuristic(text)
+        assert 90 <= tokens <= 110  # Should be ~100 tokens
+    
+    def test_estimate_tokens_heuristic_non_ascii(self):
+        """Test non-ASCII text has higher token count."""
+        from praisonaiagents.context.tokens import estimate_tokens_heuristic
+        ascii_text = "hello"
+        non_ascii_text = "你好世界"  # Chinese
+        
+        ascii_tokens = estimate_tokens_heuristic(ascii_text)
+        non_ascii_tokens = estimate_tokens_heuristic(non_ascii_text)
+        
+        # Non-ASCII should have higher token density
+        assert non_ascii_tokens > ascii_tokens
+    
+    def test_estimate_message_tokens_simple(self):
+        """Test simple message token estimation."""
+        from praisonaiagents.context.tokens import estimate_message_tokens
+        
+        msg = {"role": "user", "content": "Hello world"}
+        tokens = estimate_message_tokens(msg)
+        
+        # Should include role overhead + content
+        assert tokens > 0
+        assert tokens < 20
+    
+    def test_estimate_message_tokens_empty_content(self):
+        """Test message with empty content."""
+        from praisonaiagents.context.tokens import estimate_message_tokens
+        
+        msg = {"role": "user", "content": ""}
+        tokens = estimate_message_tokens(msg)
+        
+        # Should have role overhead
+        assert tokens >= 4
+    
+    def test_estimate_message_tokens_multipart(self):
+        """Test multipart content message."""
+        from praisonaiagents.context.tokens import estimate_message_tokens
+        
+        msg = {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Hello"},
+                {"type": "text", "text": "World"},
+            ]
+        }
+        tokens = estimate_message_tokens(msg)
+        assert tokens > 0
+    
+    def test_estimate_message_tokens_tool_call(self):
+        """Test message with tool calls."""
+        from praisonaiagents.context.tokens import estimate_message_tokens
+        
+        msg = {
+            "role": "assistant",
+            "content": "",
+            "tool_calls": [
+                {
+                    "id": "call_123",
+                    "function": {
+                        "name": "get_weather",
+                        "arguments": '{"location": "NYC"}'
+                    }
+                }
+            ]
+        }
+        tokens = estimate_message_tokens(msg)
+        assert tokens > 10  # Should include tool call overhead
+    
+    def test_estimate_messages_tokens_list(self):
+        """Test estimating tokens for message list."""
+        from praisonaiagents.context.tokens import estimate_messages_tokens
+        
+        messages = [
+            {"role": "system", "content": "You are helpful"},
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there!"},
+        ]
+        tokens = estimate_messages_tokens(messages)
+        assert tokens > 0
+        assert tokens < 100
+    
+    def test_estimate_messages_tokens_empty(self):
+        """Test empty message list."""
+        from praisonaiagents.context.tokens import estimate_messages_tokens
+        assert estimate_messages_tokens([]) == 0
+    
+    def test_estimate_tool_schema_tokens(self):
+        """Test tool schema token estimation."""
+        from praisonaiagents.context.tokens import estimate_tool_schema_tokens
+        
+        tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "Get weather for a location",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {"type": "string"}
+                        }
+                    }
+                }
+            }
+        ]
+        tokens = estimate_tool_schema_tokens(tools)
+        assert tokens > 0
+    
+    def test_estimate_tool_schema_tokens_empty(self):
+        """Test empty tool list."""
+        from praisonaiagents.context.tokens import estimate_tool_schema_tokens
+        assert estimate_tool_schema_tokens([]) == 0
+    
+    def test_token_estimator_impl(self):
+        """Test TokenEstimatorImpl class."""
+        from praisonaiagents.context.tokens import TokenEstimatorImpl
+        
+        estimator = TokenEstimatorImpl(use_accurate=False)
+        
+        assert estimator.estimate("Hello") > 0
+        assert estimator.estimate_messages([{"role": "user", "content": "Hi"}]) > 0
+        assert estimator.estimate_tools([]) == 0
+    
+    def test_get_estimator(self):
+        """Test get_estimator factory."""
+        from praisonaiagents.context.tokens import get_estimator
+        
+        estimator = get_estimator(use_accurate=False)
+        assert estimator is not None
+        assert estimator.estimate("test") > 0
+
+
+class TestTokenEstimationAccuracy:
+    """Tests for estimation accuracy."""
+    
+    def test_consistency(self):
+        """Test that estimation is consistent."""
+        from praisonaiagents.context.tokens import estimate_tokens_heuristic
+        
+        text = "The quick brown fox jumps over the lazy dog"
+        
+        # Should return same value each time
+        result1 = estimate_tokens_heuristic(text)
+        result2 = estimate_tokens_heuristic(text)
+        assert result1 == result2
+    
+    def test_proportional_scaling(self):
+        """Test that longer text has more tokens."""
+        from praisonaiagents.context.tokens import estimate_tokens_heuristic
+        
+        short = "Hello"
+        long = "Hello " * 100
+        
+        short_tokens = estimate_tokens_heuristic(short)
+        long_tokens = estimate_tokens_heuristic(long)
+        
+        assert long_tokens > short_tokens * 50

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.6.8"
+version = "0.7.0"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.1.8.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.1.8.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.2.0.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.2.0.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/cli/commands/chat.py

@@ -27,6 +27,8 @@ def chat_main(
     workspace: Optional[str] = typer.Option(None, "--workspace", "-w", help="Workspace directory"),
     no_acp: bool = typer.Option(False, "--no-acp", help="Disable ACP tools"),
     no_lsp: bool = typer.Option(False, "--no-lsp", help="Disable LSP tools"),
+    profile: bool = typer.Option(False, "--profile", help="Enable CLI profiling (timing breakdown)"),
+    profile_deep: bool = typer.Option(False, "--profile-deep", help="Enable deep profiling (cProfile stats, higher overhead)"),
 ):
     """
     Start terminal-native interactive chat mode.
@@ -42,6 +44,7 @@ def chat_main(
         praisonai chat --model gpt-4o --memory
         praisonai chat --continue  # Resume last session
         praisonai chat "Summarize this" --file README.md
+        praisonai chat "What is 2+2?" --profile
     """
     import asyncio
     import os
@@ -50,6 +53,21 @@ def chat_main(
     if workspace:
         os.environ["PRAISONAI_WORKSPACE"] = workspace
     
+    # Handle profiling for single prompt mode
+    if prompt and (profile or profile_deep):
+        _run_profiled_chat(
+            prompt=prompt,
+            model=model,
+            verbose=verbose,
+            profile_deep=profile_deep,
+        )
+        return
+    
+    # Warn if profiling requested without prompt (REPL mode doesn't support profiling)
+    if (profile or profile_deep) and not prompt:
+        typer.echo("⚠️  Profiling is only supported for single prompt mode.", err=True)
+        typer.echo("   Use: praisonai chat \"your prompt\" --profile", err=True)
+    
     # Try InteractiveCore first (preferred terminal-native implementation)
     try:
         from praisonai.cli.interactive import InteractiveCore, InteractiveConfig
@@ -98,6 +116,64 @@ def chat_main(
         )
 
 
+def _run_profiled_chat(
+    prompt: str,
+    model: Optional[str] = None,
+    verbose: bool = False,
+    profile_deep: bool = False,
+):
+    """Run chat with profiling enabled."""
+    from praisonai.cli.features.cli_profiler import (
+        CLIProfileConfig,
+        CLIProfiler,
+    )
+    
+    config = CLIProfileConfig(enabled=True, deep=profile_deep)
+    profiler = CLIProfiler(config)
+    
+    if profile_deep:
+        typer.echo("⚠️  Deep profiling enabled - this adds significant overhead", err=True)
+    
+    profiler.start()
+    
+    # Import phase
+    profiler.mark_import_start()
+    try:
+        from praisonaiagents import Agent
+    except ImportError:
+        typer.echo("Error: praisonaiagents not installed", err=True)
+        raise typer.Exit(1)
+    profiler.mark_import_end()
+    
+    # Agent initialization phase
+    profiler.mark_init_start()
+    agent_config = {
+        "name": "ChatAgent",
+        "role": "Assistant",
+        "goal": "Help the user",
+        "verbose": verbose,
+    }
+    if model:
+        agent_config["llm"] = model
+    
+    agent = Agent(**agent_config)
+    profiler.mark_init_end()
+    
+    # Execution phase
+    profiler.mark_exec_start()
+    response = agent.start(prompt)
+    profiler.mark_exec_end()
+    
+    profiler.stop()
+    
+    # Print response
+    if response:
+        print(response)
+    
+    # Print profiling report
+    profiler.print_report()
+
+
 def _run_legacy_terminal_chat(
     prompt: Optional[str] = None,
     model: Optional[str] = None,

src/praisonai/praisonai/cli/commands/code.py

@@ -25,6 +25,8 @@ def code_main(
     no_lsp: bool = typer.Option(False, "--no-lsp", help="Disable LSP tools (code intelligence)"),
     session_id: Optional[str] = typer.Option(None, "--session", "-s", help="Session ID to resume"),
     continue_session: bool = typer.Option(False, "--continue", "-c", help="Continue last session"),
+    profile: bool = typer.Option(False, "--profile", help="Enable CLI profiling (timing breakdown)"),
+    profile_deep: bool = typer.Option(False, "--profile-deep", help="Enable deep profiling (cProfile stats, higher overhead)"),
 ):
     """
     Start terminal-native code assistant mode.
@@ -39,6 +41,7 @@ def code_main(
         praisonai code "Refactor this function"
         praisonai code --model gpt-4o --workspace ./src
         praisonai code "Fix the bug" --file main.py
+        praisonai code "What is 2+2?" --profile
     """
     import os
     import argparse
@@ -50,6 +53,21 @@ def code_main(
     # Enable code-specific environment
     os.environ["PRAISONAI_CODE_MODE"] = "true"
     
+    # Handle profiling for single prompt mode
+    if prompt and (profile or profile_deep):
+        _run_profiled_code(
+            prompt=prompt,
+            model=model,
+            verbose=verbose,
+            profile_deep=profile_deep,
+        )
+        return
+    
+    # Warn if profiling requested without prompt (REPL mode doesn't support profiling)
+    if (profile or profile_deep) and not prompt:
+        typer.echo("⚠️  Profiling is only supported for single prompt mode.", err=True)
+        typer.echo("   Use: praisonai code \"your prompt\" --profile", err=True)
+    
     # Build args namespace for _start_interactive_mode
     args = argparse.Namespace()
     args.llm = model
@@ -70,3 +88,61 @@ def code_main(
     else:
         # Interactive REPL mode - use _start_interactive_mode
         praison._start_interactive_mode(args)
+
+
+def _run_profiled_code(
+    prompt: str,
+    model: Optional[str] = None,
+    verbose: bool = False,
+    profile_deep: bool = False,
+):
+    """Run code assistant with profiling enabled."""
+    from praisonai.cli.features.cli_profiler import (
+        CLIProfileConfig,
+        CLIProfiler,
+    )
+    
+    config = CLIProfileConfig(enabled=True, deep=profile_deep)
+    profiler = CLIProfiler(config)
+    
+    if profile_deep:
+        typer.echo("⚠️  Deep profiling enabled - this adds significant overhead", err=True)
+    
+    profiler.start()
+    
+    # Import phase
+    profiler.mark_import_start()
+    try:
+        from praisonaiagents import Agent
+    except ImportError:
+        typer.echo("Error: praisonaiagents not installed", err=True)
+        raise typer.Exit(1)
+    profiler.mark_import_end()
+    
+    # Agent initialization phase
+    profiler.mark_init_start()
+    agent_config = {
+        "name": "CodeAgent",
+        "role": "Code Assistant",
+        "goal": "Help with coding tasks",
+        "verbose": verbose,
+    }
+    if model:
+        agent_config["llm"] = model
+    
+    agent = Agent(**agent_config)
+    profiler.mark_init_end()
+    
+    # Execution phase
+    profiler.mark_exec_start()
+    response = agent.start(prompt)
+    profiler.mark_exec_end()
+    
+    profiler.stop()
+    
+    # Print response
+    if response:
+        print(response)
+    
+    # Print profiling report
+    profiler.print_report()

src/praisonai/praisonai/cli/commands/run.py

@@ -27,6 +27,8 @@ def run_main(
     memory: bool = typer.Option(False, "--memory", help="Enable memory"),
     tools: Optional[str] = typer.Option(None, "--tools", "-t", help="Tools file path"),
     max_tokens: int = typer.Option(16000, "--max-tokens", help="Maximum output tokens"),
+    profile: bool = typer.Option(False, "--profile", help="Enable CLI profiling (timing breakdown)"),
+    profile_deep: bool = typer.Option(False, "--profile-deep", help="Enable deep profiling (cProfile stats, higher overhead)"),
 ):
     """
     Run agents from a file or prompt.
@@ -35,9 +37,10 @@ def run_main(
         praisonai run agents.yaml
         praisonai run "What is the weather?"
         praisonai run agents.yaml --interactive
+        praisonai run "What is 2+2?" --profile
     """
     output = get_output_controller()
-    context = get_current_context()
+    _ = get_current_context()  # Initialize context
     
     if not target:
         output.print_panel(
@@ -71,6 +74,27 @@ def run_main(
     import os
     is_file = os.path.exists(target) and (target.endswith('.yaml') or target.endswith('.yml'))
     
+    # Handle profiling
+    if profile or profile_deep:
+        if is_file:
+            # Profiling for YAML file execution
+            _run_from_file_profiled(
+                target,
+                model=model,
+                framework=framework,
+                verbose=verbose,
+                profile_deep=profile_deep,
+            )
+        else:
+            # Profiling for direct prompt
+            _run_prompt_profiled(
+                target,
+                model=model,
+                verbose=verbose,
+                profile_deep=profile_deep,
+            )
+        return
+    
     if is_file:
         # Run from file
         _run_from_file(
@@ -226,3 +250,116 @@ def _run_prompt(
         output.emit_error(message=str(e))
         output.print_error(str(e))
         raise typer.Exit(1)
+
+
+def _run_from_file_profiled(
+    file_path: str,
+    model: Optional[str] = None,
+    framework: Optional[str] = None,
+    verbose: bool = False,
+    profile_deep: bool = False,
+):
+    """Run agents from a YAML file with profiling enabled."""
+    from praisonai.cli.features.cli_profiler import (
+        CLIProfileConfig,
+        CLIProfiler,
+    )
+    
+    config = CLIProfileConfig(enabled=True, deep=profile_deep)
+    profiler = CLIProfiler(config)
+    
+    if profile_deep:
+        typer.echo("⚠️  Deep profiling enabled - this adds significant overhead", err=True)
+    
+    profiler.start()
+    
+    # Import phase
+    profiler.mark_import_start()
+    try:
+        from praisonai.cli.main import PraisonAI
+    except ImportError as e:
+        typer.echo(f"Error: {e}", err=True)
+        raise typer.Exit(1)
+    profiler.mark_import_end()
+    
+    # Agent initialization phase
+    profiler.mark_init_start()
+    praison = PraisonAI(
+        agent_file=file_path,
+        framework=framework or "praisonai",
+    )
+    if model:
+        praison.config_list[0]['model'] = model
+    profiler.mark_init_end()
+    
+    # Execution phase
+    profiler.mark_exec_start()
+    result = praison.run()
+    profiler.mark_exec_end()
+    
+    profiler.stop()
+    
+    # Print result
+    if result:
+        print(result)
+    
+    # Print profiling report
+    profiler.print_report()
+
+
+def _run_prompt_profiled(
+    prompt: str,
+    model: Optional[str] = None,
+    verbose: bool = False,
+    profile_deep: bool = False,
+):
+    """Run a direct prompt with profiling enabled."""
+    from praisonai.cli.features.cli_profiler import (
+        CLIProfileConfig,
+        CLIProfiler,
+    )
+    
+    config = CLIProfileConfig(enabled=True, deep=profile_deep)
+    profiler = CLIProfiler(config)
+    
+    if profile_deep:
+        typer.echo("⚠️  Deep profiling enabled - this adds significant overhead", err=True)
+    
+    profiler.start()
+    
+    # Import phase
+    profiler.mark_import_start()
+    try:
+        from praisonaiagents import Agent
+    except ImportError:
+        typer.echo("Error: praisonaiagents not installed", err=True)
+        raise typer.Exit(1)
+    profiler.mark_import_end()
+    
+    # Agent initialization phase
+    profiler.mark_init_start()
+    agent_config = {
+        "name": "RunAgent",
+        "role": "Assistant",
+        "goal": "Complete the task",
+        "verbose": verbose,
+    }
+    if model:
+        agent_config["llm"] = model
+    
+    agent = Agent(**agent_config)
+    profiler.mark_init_end()
+    
+    # Execution phase
+    profiler.mark_exec_start()
+    response = agent.start(prompt)
+    profiler.mark_exec_end()
+    
+    profiler.stop()
+    
+    # Print response
+    if response:
+        print(response)
+    
+    # Print profiling report
+    profiler.print_report()

src/praisonai/praisonai/cli/features/cli_profiler.py

@@ -0,0 +1,335 @@
+"""
+CLI Profiler - Unified profiling for terminal-native CLI commands.
+
+Provides --profile and --profile-deep flags for eligible commands:
+- praisonai "prompt"
+- praisonai chat
+- praisonai chat "prompt"
+- praisonai code
+- praisonai code "prompt"
+- praisonai run agents.yaml
+
+NOT supported for:
+- praisonai ui ... (browser-based)
+- praisonai tui (Textual TUI)
+- praisonai call (long-running server)
+"""
+
+import time
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+import typer
+
+
+# Commands that support profiling
+PROFILING_ELIGIBLE_COMMANDS = frozenset({
+    "chat",
+    "code",
+    "run",
+    # Direct prompt is handled separately in main.py
+})
+
+# Commands that explicitly reject profiling
+PROFILING_INELIGIBLE_COMMANDS = frozenset({
+    "ui",
+    "tui",
+    "call",
+    "realtime",
+    "serve",
+    "schedule",
+    "acp",
+    "mcp",
+    "lsp",
+})
+
+
+@dataclass
+class CLIProfileConfig:
+    """Configuration for CLI profiling."""
+    enabled: bool = False
+    deep: bool = False
+    output_format: str = "text"  # text or json
+    save_path: Optional[str] = None
+    
+    @classmethod
+    def from_flags(cls, profile: bool = False, profile_deep: bool = False) -> 'CLIProfileConfig':
+        """Create config from CLI flags."""
+        return cls(
+            enabled=profile or profile_deep,
+            deep=profile_deep,
+        )
+
+
+@dataclass
+class CLIProfileResult:
+    """Result from CLI profiling."""
+    import_time_ms: float = 0.0
+    agent_init_time_ms: float = 0.0
+    execution_time_ms: float = 0.0
+    total_time_ms: float = 0.0
+    response: str = ""
+    
+    # Deep profiling data (only if deep=True)
+    function_stats: Optional[list] = None
+    call_graph: Optional[dict] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary."""
+        result = {
+            "import_time_ms": self.import_time_ms,
+            "agent_init_time_ms": self.agent_init_time_ms,
+            "execution_time_ms": self.execution_time_ms,
+            "total_time_ms": self.total_time_ms,
+        }
+        if self.function_stats:
+            result["function_stats"] = self.function_stats
+        if self.call_graph:
+            result["call_graph"] = self.call_graph
+        return result
+
+
+def print_profile_report(result: CLIProfileResult, config: CLIProfileConfig):
+    """Print profiling report to console."""
+    try:
+        from rich.console import Console
+        from rich.table import Table
+        from rich.panel import Panel
+        console = Console()
+    except ImportError:
+        # Fallback to simple output
+        print("\n─── Profiling ───")
+        print(f"Import:      {result.import_time_ms:.1f}ms")
+        print(f"Agent setup: {result.agent_init_time_ms:.1f}ms")
+        print(f"Execution:   {result.execution_time_ms:.1f}ms")
+        print(f"Total:       {result.total_time_ms:.1f}ms")
+        return
+    
+    # Create timing table
+    table = Table(show_header=False, box=None, padding=(0, 1))
+    table.add_column("Phase", style="cyan")
+    table.add_column("Time", style="green", justify="right")
+    
+    table.add_row("Import", f"{result.import_time_ms:.1f}ms")
+    table.add_row("Agent setup", f"{result.agent_init_time_ms:.1f}ms")
+    table.add_row("Execution", f"{result.execution_time_ms:.1f}ms")
+    table.add_row("─" * 12, "─" * 10)
+    table.add_row("Total", f"{result.total_time_ms:.1f}ms")
+    
+    console.print("\n")
+    console.print(Panel(table, title="[bold]Profiling[/bold]", border_style="dim"))
+    
+    # Deep profiling output
+    if config.deep and result.function_stats:
+        console.print("\n[bold yellow]⚠️  Deep profiling enabled - overhead included in measurements[/bold yellow]")
+        
+        func_table = Table(title="Top Functions by Cumulative Time")
+        func_table.add_column("Function", style="cyan")
+        func_table.add_column("Calls", justify="right")
+        func_table.add_column("Cumul (ms)", justify="right", style="green")
+        
+        for func in result.function_stats[:15]:
+            name = func.get("name", "")[:40]
+            calls = func.get("calls", 0)
+            cumtime = func.get("cumulative_time_ms", 0)
+            func_table.add_row(name, str(calls), f"{cumtime:.2f}")
+        
+        console.print(func_table)
+
+
+def reject_profiling_for_command(command_name: str):
+    """Raise error if profiling is attempted on ineligible command."""
+    if command_name in PROFILING_INELIGIBLE_COMMANDS:
+        raise typer.BadParameter(
+            f"Profiling is not supported for '{command_name}' command.\n"
+            f"Profiling is only available for terminal-native execution commands:\n"
+            f"  praisonai \"prompt\" --profile\n"
+            f"  praisonai chat \"prompt\" --profile\n"
+            f"  praisonai code \"prompt\" --profile\n"
+            f"  praisonai run agents.yaml --profile"
+        )
+
+
+class CLIProfiler:
+    """
+    CLI Profiler for wrapping command execution.
+    
+    Usage:
+        profiler = CLIProfiler(config)
+        with profiler.profile():
+            # execution code
+        profiler.print_report()
+    """
+    
+    def __init__(self, config: CLIProfileConfig):
+        self.config = config
+        self._result = CLIProfileResult()
+        self._start_time: float = 0.0
+        self._cprofile = None
+        
+        # Phase timestamps
+        self._import_start: float = 0.0
+        self._import_end: float = 0.0
+        self._init_start: float = 0.0
+        self._init_end: float = 0.0
+        self._exec_start: float = 0.0
+        self._exec_end: float = 0.0
+    
+    def start(self):
+        """Start profiling."""
+        if not self.config.enabled:
+            return
+        
+        self._start_time = time.perf_counter()
+        
+        if self.config.deep:
+            import cProfile
+            self._cprofile = cProfile.Profile()
+            self._cprofile.enable()
+    
+    def stop(self):
+        """Stop profiling and compute results."""
+        if not self.config.enabled:
+            return
+        
+        end_time = time.perf_counter()
+        
+        if self._cprofile:
+            self._cprofile.disable()
+            self._extract_cprofile_stats()
+        
+        self._result.total_time_ms = (end_time - self._start_time) * 1000
+    
+    def mark_import_start(self):
+        """Mark start of import phase."""
+        if self.config.enabled:
+            self._import_start = time.perf_counter()
+    
+    def mark_import_end(self):
+        """Mark end of import phase."""
+        if self.config.enabled:
+            self._import_end = time.perf_counter()
+            self._result.import_time_ms = (self._import_end - self._import_start) * 1000
+    
+    def mark_init_start(self):
+        """Mark start of agent initialization."""
+        if self.config.enabled:
+            self._init_start = time.perf_counter()
+    
+    def mark_init_end(self):
+        """Mark end of agent initialization."""
+        if self.config.enabled:
+            self._init_end = time.perf_counter()
+            self._result.agent_init_time_ms = (self._init_end - self._init_start) * 1000
+    
+    def mark_exec_start(self):
+        """Mark start of execution."""
+        if self.config.enabled:
+            self._exec_start = time.perf_counter()
+    
+    def mark_exec_end(self):
+        """Mark end of execution."""
+        if self.config.enabled:
+            self._exec_end = time.perf_counter()
+            self._result.execution_time_ms = (self._exec_end - self._exec_start) * 1000
+    
+    def set_response(self, response: str):
+        """Set the response for the profile result."""
+        self._result.response = response
+    
+    def _extract_cprofile_stats(self):
+        """Extract statistics from cProfile."""
+        if not self._cprofile:
+            return
+        
+        import pstats
+        
+        stats = pstats.Stats(self._cprofile)
+        stats.sort_stats("cumulative")
+        
+        function_stats = []
+        for (filename, line, name), (cc, nc, tt, ct, callers) in stats.stats.items():
+            if len(function_stats) >= 100:
+                break
+            function_stats.append({
+                "name": name,
+                "file": filename,
+                "line": line,
+                "calls": nc,
+                "total_time_ms": tt * 1000,
+                "cumulative_time_ms": ct * 1000,
+            })
+        
+        function_stats.sort(key=lambda x: x["cumulative_time_ms"], reverse=True)
+        self._result.function_stats = function_stats[:30]
+    
+    def get_result(self) -> CLIProfileResult:
+        """Get the profiling result."""
+        return self._result
+    
+    def print_report(self):
+        """Print the profiling report."""
+        if self.config.enabled:
+            print_profile_report(self._result, self.config)
+
+
+def run_with_profiling(
+    prompt: str,
+    config: CLIProfileConfig,
+    model: Optional[str] = None,
+    verbose: bool = False,
+    tools: Optional[str] = None,
+) -> tuple:
+    """
+    Run a prompt with profiling enabled.
+    
+    Returns:
+        Tuple of (response, CLIProfileResult)
+    """
+    profiler = CLIProfiler(config)
+    profiler.start()
+    
+    # Import phase
+    profiler.mark_import_start()
+    try:
+        from praisonaiagents import Agent
+    except ImportError:
+        raise ImportError("praisonaiagents not installed. Install with: pip install praisonaiagents")
+    profiler.mark_import_end()
+    
+    # Agent initialization phase
+    profiler.mark_init_start()
+    agent_config = {
+        "name": "ProfiledAgent",
+        "role": "Assistant",
+        "goal": "Complete the task",
+        "verbose": verbose,
+    }
+    if model:
+        agent_config["llm"] = model
+    
+    agent = Agent(**agent_config)
+    profiler.mark_init_end()
+    
+    # Execution phase
+    profiler.mark_exec_start()
+    response = agent.start(prompt)
+    profiler.mark_exec_end()
+    
+    profiler.stop()
+    profiler.set_response(str(response) if response else "")
+    
+    return response, profiler.get_result()
+
+
+# Export all
+__all__ = [
+    "CLIProfileConfig",
+    "CLIProfileResult",
+    "CLIProfiler",
+    "PROFILING_ELIGIBLE_COMMANDS",
+    "PROFILING_INELIGIBLE_COMMANDS",
+    "print_profile_report",
+    "reject_profiling_for_command",
+    "run_with_profiling",
+]

src/praisonai/praisonai/cli/features/context_manager.py

@@ -0,0 +1,525 @@
+"""
+Context Management CLI Feature for PraisonAI.
+
+Provides CLI commands and integration for context budgeting,
+optimization, and monitoring.
+"""
+
+import os
+from typing import Dict, List, Any, Optional, Literal
+from dataclasses import dataclass
+
+
+@dataclass
+class ContextManagerConfig:
+    """Configuration for context management."""
+    # Auto-compaction
+    auto_compact: bool = True
+    compact_threshold: float = 0.8
+    strategy: str = "smart"
+    
+    # Output reserve
+    output_reserve: int = 8000
+    
+    # Monitoring
+    monitor_enabled: bool = False
+    monitor_path: str = "./context.txt"
+    monitor_format: Literal["human", "json"] = "human"
+    monitor_frequency: Literal["turn", "tool_call", "manual", "overflow"] = "turn"
+    redact_sensitive: bool = True
+    
+    @classmethod
+    def from_env(cls) -> "ContextManagerConfig":
+        """Load config from environment variables."""
+        return cls(
+            auto_compact=os.getenv("PRAISONAI_CONTEXT_AUTO_COMPACT", "true").lower() == "true",
+            compact_threshold=float(os.getenv("PRAISONAI_CONTEXT_THRESHOLD", "0.8")),
+            strategy=os.getenv("PRAISONAI_CONTEXT_STRATEGY", "smart"),
+            output_reserve=int(os.getenv("PRAISONAI_CONTEXT_OUTPUT_RESERVE", "8000")),
+            monitor_enabled=os.getenv("PRAISONAI_CONTEXT_MONITOR", "false").lower() == "true",
+            monitor_path=os.getenv("PRAISONAI_CONTEXT_MONITOR_PATH", "./context.txt"),
+            monitor_format=os.getenv("PRAISONAI_CONTEXT_MONITOR_FORMAT", "human"),
+            monitor_frequency=os.getenv("PRAISONAI_CONTEXT_MONITOR_FREQUENCY", "turn"),
+            redact_sensitive=os.getenv("PRAISONAI_CONTEXT_REDACT", "true").lower() == "true",
+        )
+    
+    def merge_cli_args(
+        self,
+        auto_compact: Optional[bool] = None,
+        strategy: Optional[str] = None,
+        threshold: Optional[float] = None,
+        monitor: Optional[bool] = None,
+        monitor_path: Optional[str] = None,
+        monitor_format: Optional[str] = None,
+        monitor_frequency: Optional[str] = None,
+        redact: Optional[bool] = None,
+        output_reserve: Optional[int] = None,
+    ) -> "ContextManagerConfig":
+        """Merge CLI arguments (highest precedence)."""
+        return ContextManagerConfig(
+            auto_compact=auto_compact if auto_compact is not None else self.auto_compact,
+            compact_threshold=threshold if threshold is not None else self.compact_threshold,
+            strategy=strategy if strategy is not None else self.strategy,
+            output_reserve=output_reserve if output_reserve is not None else self.output_reserve,
+            monitor_enabled=monitor if monitor is not None else self.monitor_enabled,
+            monitor_path=monitor_path if monitor_path is not None else self.monitor_path,
+            monitor_format=monitor_format if monitor_format is not None else self.monitor_format,
+            monitor_frequency=monitor_frequency if monitor_frequency is not None else self.monitor_frequency,
+            redact_sensitive=redact if redact is not None else self.redact_sensitive,
+        )
+
+
+class ContextManagerHandler:
+    """
+    Handles context management for interactive CLI sessions.
+    
+    Integrates budgeting, composition, optimization, and monitoring.
+    """
+    
+    def __init__(
+        self,
+        config: Optional[ContextManagerConfig] = None,
+        model: str = "gpt-4o-mini",
+        session_id: str = "",
+        agent_name: str = "Assistant",
+    ):
+        """
+        Initialize context manager.
+        
+        Args:
+            config: Context management configuration
+            model: Model name for budget calculation
+            session_id: Session identifier
+            agent_name: Agent name for monitoring
+        """
+        self.config = config or ContextManagerConfig.from_env()
+        self.model = model
+        self.session_id = session_id
+        self.agent_name = agent_name
+        
+        # Lazy-loaded components
+        self._budgeter = None
+        self._composer = None
+        self._monitor = None
+        self._ledger = None
+    
+    @property
+    def budgeter(self):
+        """Get or create budgeter."""
+        if self._budgeter is None:
+            from praisonaiagents.context import ContextBudgeter
+            self._budgeter = ContextBudgeter(
+                model=self.model,
+                output_reserve=self.config.output_reserve,
+            )
+        return self._budgeter
+    
+    @property
+    def composer(self):
+        """Get or create composer."""
+        if self._composer is None:
+            from praisonaiagents.context import ContextComposer
+            self._composer = ContextComposer(
+                budget=self.budgeter.allocate(),
+            )
+        return self._composer
+    
+    @property
+    def monitor(self):
+        """Get or create monitor."""
+        if self._monitor is None:
+            from praisonaiagents.context import ContextMonitor
+            self._monitor = ContextMonitor(
+                enabled=self.config.monitor_enabled,
+                path=self.config.monitor_path,
+                format=self.config.monitor_format,
+                frequency=self.config.monitor_frequency,
+                redact_sensitive=self.config.redact_sensitive,
+            )
+        return self._monitor
+    
+    @property
+    def ledger(self):
+        """Get or create ledger manager."""
+        if self._ledger is None:
+            from praisonaiagents.context import ContextLedgerManager
+            self._ledger = ContextLedgerManager(agent_id=self.agent_name)
+            self._ledger.set_budget(self.budgeter.allocate())
+        return self._ledger
+    
+    def set_model(self, model: str) -> None:
+        """Update model and reset budgeter."""
+        self.model = model
+        self._budgeter = None
+        self._composer = None
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """Get current context statistics."""
+        budget = self.budgeter.allocate()
+        ledger_data = self.ledger.to_dict()
+        
+        return {
+            "model": self.model,
+            "model_limit": budget.model_limit,
+            "output_reserve": budget.output_reserve,
+            "usable": budget.usable,
+            "total_tokens": self.ledger.get_total(),
+            "utilization": self.ledger.get_utilization(),
+            "turn_count": self.ledger.get_ledger().turn_count,
+            "message_count": self.ledger.get_ledger().message_count,
+            "segments": ledger_data.get("segments", {}),
+            "warnings": self.ledger.get_warnings(),
+            "auto_compact": self.config.auto_compact,
+            "monitor_enabled": self.config.monitor_enabled,
+        }
+    
+    def track_history(self, messages: List[Dict[str, Any]]) -> int:
+        """Track conversation history tokens."""
+        return self.ledger.track_history(messages)
+    
+    def track_system_prompt(self, content: str) -> int:
+        """Track system prompt tokens."""
+        return self.ledger.track_system_prompt(content)
+    
+    def track_tools(self, tools: List[Dict[str, Any]]) -> int:
+        """Track tool schema tokens."""
+        return self.ledger.track_tools(tools)
+    
+    def check_overflow(self) -> bool:
+        """Check if context is approaching limit."""
+        return self.budgeter.check_overflow(
+            self.ledger.get_total(),
+            threshold=self.config.compact_threshold,
+        )
+    
+    def should_auto_compact(self) -> bool:
+        """Check if auto-compaction should trigger."""
+        return self.config.auto_compact and self.check_overflow()
+    
+    def optimize(
+        self,
+        messages: List[Dict[str, Any]],
+        strategy: Optional[str] = None,
+    ) -> tuple:
+        """
+        Optimize messages using configured strategy.
+        
+        Args:
+            messages: Messages to optimize
+            strategy: Override strategy
+            
+        Returns:
+            Tuple of (optimized_messages, result)
+        """
+        from praisonaiagents.context import get_optimizer, OptimizerStrategy
+        
+        strategy_name = strategy or self.config.strategy
+        try:
+            strategy_enum = OptimizerStrategy(strategy_name)
+        except ValueError:
+            strategy_enum = OptimizerStrategy.SMART
+        
+        optimizer = get_optimizer(strategy_enum)
+        target = int(self.budgeter.usable * self.config.compact_threshold)
+        
+        return optimizer.optimize(messages, target, self.ledger.get_ledger())
+    
+    def write_snapshot(
+        self,
+        messages: List[Dict[str, Any]],
+        trigger: str = "manual",
+    ) -> Optional[str]:
+        """
+        Write context snapshot to disk.
+        
+        Args:
+            messages: Current messages
+            trigger: What triggered the snapshot
+            
+        Returns:
+            Path to written file, or None
+        """
+        return self.monitor.snapshot(
+            ledger=self.ledger.get_ledger(),
+            budget=self.budgeter.allocate(),
+            messages=messages,
+            session_id=self.session_id,
+            agent_name=self.agent_name,
+            model_name=self.model,
+            trigger=trigger,
+        )
+    
+    def enable_monitor(self) -> None:
+        """Enable context monitoring."""
+        self.config.monitor_enabled = True
+        self.monitor.enable()
+    
+    def disable_monitor(self) -> None:
+        """Disable context monitoring."""
+        self.config.monitor_enabled = False
+        self.monitor.disable()
+    
+    def set_monitor_path(self, path: str) -> None:
+        """Set monitor output path."""
+        self.config.monitor_path = path
+        self.monitor.set_path(path)
+    
+    def set_monitor_format(self, format: str) -> None:
+        """Set monitor output format."""
+        self.config.monitor_format = format
+        self.monitor.set_format(format)
+    
+    def set_monitor_frequency(self, frequency: str) -> None:
+        """Set monitor update frequency."""
+        self.config.monitor_frequency = frequency
+        self.monitor.set_frequency(frequency)
+
+
+def handle_context_command(
+    console,
+    args: str,
+    session_state: Dict[str, Any],
+    context_manager: Optional[ContextManagerHandler] = None,
+) -> None:
+    """
+    Handle /context command and subcommands.
+    
+    Usage:
+        /context              - Show context stats
+        /context show         - Show summary + budgets
+        /context stats        - Token ledger table
+        /context budget       - Budget allocation details
+        /context dump         - Write snapshot now
+        /context on           - Enable monitoring
+        /context off          - Disable monitoring
+        /context path <path>  - Set snapshot path
+        /context format <fmt> - Set format (human/json)
+        /context frequency <f>- Set frequency
+        /context compact      - Trigger optimization
+    """
+    args = args.strip().lower() if args else ""
+    parts = args.split(maxsplit=1)
+    subcommand = parts[0] if parts else ""
+    subargs = parts[1] if len(parts) > 1 else ""
+    
+    # Get or create context manager
+    if context_manager is None:
+        context_manager = session_state.get("context_manager")
+        if context_manager is None:
+            context_manager = ContextManagerHandler(
+                model=session_state.get("current_model", "gpt-4o-mini"),
+                session_id=session_state.get("session_id", ""),
+            )
+            session_state["context_manager"] = context_manager
+    
+    # Update history tracking
+    history = session_state.get("conversation_history", [])
+    context_manager.track_history(history)
+    
+    if subcommand in ("", "show"):
+        _show_context_summary(console, context_manager)
+    elif subcommand == "stats":
+        _show_context_stats(console, context_manager)
+    elif subcommand == "budget":
+        _show_budget_details(console, context_manager)
+    elif subcommand == "dump":
+        _dump_context(console, context_manager, history)
+    elif subcommand == "on":
+        context_manager.enable_monitor()
+        console.print("[green]✓ Context monitoring enabled[/green]")
+        console.print(f"[dim]Output: {context_manager.config.monitor_path}[/dim]")
+    elif subcommand == "off":
+        context_manager.disable_monitor()
+        console.print("[yellow]Context monitoring disabled[/yellow]")
+    elif subcommand == "path":
+        if subargs:
+            context_manager.set_monitor_path(subargs)
+            console.print(f"[green]✓ Monitor path set to: {subargs}[/green]")
+        else:
+            console.print(f"[cyan]Current path: {context_manager.config.monitor_path}[/cyan]")
+    elif subcommand == "format":
+        if subargs in ("human", "json"):
+            context_manager.set_monitor_format(subargs)
+            console.print(f"[green]✓ Monitor format set to: {subargs}[/green]")
+        else:
+            console.print("[yellow]Format must be 'human' or 'json'[/yellow]")
+    elif subcommand == "frequency":
+        valid = ("turn", "tool_call", "manual", "overflow")
+        if subargs in valid:
+            context_manager.set_monitor_frequency(subargs)
+            console.print(f"[green]✓ Monitor frequency set to: {subargs}[/green]")
+        else:
+            console.print(f"[yellow]Frequency must be one of: {', '.join(valid)}[/yellow]")
+    elif subcommand == "compact":
+        _compact_context(console, context_manager, session_state)
+    else:
+        console.print("[yellow]Unknown subcommand. Use: show, stats, budget, dump, on, off, path, format, frequency, compact[/yellow]")
+
+
+def _show_context_summary(console, manager: ContextManagerHandler) -> None:
+    """Show context summary."""
+    stats = manager.get_stats()
+    
+    console.print("\n[bold cyan]Context Summary[/bold cyan]")
+    console.print(f"  Model:          {stats['model']}")
+    console.print(f"  Model Limit:    {stats['model_limit']:,} tokens")
+    console.print(f"  Output Reserve: {stats['output_reserve']:,} tokens")
+    console.print(f"  Usable Budget:  {stats['usable']:,} tokens")
+    console.print(f"  Current Usage:  {stats['total_tokens']:,} tokens ({stats['utilization']*100:.1f}%)")
+    console.print(f"  Turns:          {stats['turn_count']}")
+    console.print(f"  Messages:       {stats['message_count']}")
+    console.print(f"  Auto-Compact:   {'enabled' if stats['auto_compact'] else 'disabled'}")
+    console.print(f"  Monitoring:     {'enabled' if stats['monitor_enabled'] else 'disabled'}")
+    
+    if stats['warnings']:
+        console.print("\n[yellow]Warnings:[/yellow]")
+        for warning in stats['warnings']:
+            console.print(f"  ⚠ {warning}")
+    console.print("")
+
+
+def _show_context_stats(console, manager: ContextManagerHandler) -> None:
+    """Show detailed token stats."""
+    stats = manager.get_stats()
+    segments = stats.get("segments", {})
+    
+    console.print("\n[bold cyan]Token Ledger[/bold cyan]")
+    console.print(f"{'Segment':<20} {'Tokens':>10} {'Budget':>10} {'Used':>8}")
+    console.print("-" * 50)
+    
+    for name, data in segments.items():
+        tokens = data.get("tokens", 0)
+        budget = data.get("budget", 0)
+        util = data.get("utilization", 0) * 100
+        console.print(f"{name:<20} {tokens:>10,} {budget:>10,} {util:>7.1f}%")
+    
+    console.print("-" * 50)
+    console.print(f"{'TOTAL':<20} {stats['total_tokens']:>10,} {stats['usable']:>10,} {stats['utilization']*100:>7.1f}%")
+    console.print("")
+
+
+def _show_budget_details(console, manager: ContextManagerHandler) -> None:
+    """Show budget allocation details."""
+    budget = manager.budgeter.allocate()
+    
+    console.print("\n[bold cyan]Budget Allocation[/bold cyan]")
+    console.print(f"  Model Limit:     {budget.model_limit:,}")
+    console.print(f"  Output Reserve:  {budget.output_reserve:,}")
+    console.print(f"  Usable:          {budget.usable:,}")
+    console.print("")
+    console.print("  Segment Budgets:")
+    console.print(f"    System Prompt: {budget.system_prompt:,}")
+    console.print(f"    Rules:         {budget.rules:,}")
+    console.print(f"    Skills:        {budget.skills:,}")
+    console.print(f"    Memory:        {budget.memory:,}")
+    console.print(f"    Tool Schemas:  {budget.tools_schema:,}")
+    console.print(f"    Tool Outputs:  {budget.tool_outputs:,}")
+    console.print(f"    History:       {budget.history_budget:,}")
+    console.print(f"    Buffer:        {budget.buffer:,}")
+    console.print("")
+
+
+def _dump_context(console, manager: ContextManagerHandler, messages: List[Dict[str, Any]]) -> None:
+    """Dump context snapshot to file."""
+    # Temporarily enable if needed
+    was_enabled = manager.config.monitor_enabled
+    if not was_enabled:
+        manager.enable_monitor()
+    
+    path = manager.write_snapshot(messages, trigger="manual")
+    
+    if not was_enabled:
+        manager.disable_monitor()
+    
+    if path:
+        console.print(f"[green]✓ Context snapshot written to: {path}[/green]")
+    else:
+        console.print("[yellow]Failed to write context snapshot[/yellow]")
+
+
+def _compact_context(console, manager: ContextManagerHandler, session_state: Dict[str, Any]) -> None:
+    """Trigger context compaction."""
+    history = session_state.get("conversation_history", [])
+    
+    if len(history) < 4:
+        console.print("[yellow]Not enough history to compact (need at least 4 messages)[/yellow]")
+        return
+    
+    console.print("[dim]Optimizing context...[/dim]")
+    
+    optimized, result = manager.optimize(history)
+    
+    if result.tokens_saved > 0:
+        session_state["conversation_history"] = optimized
+        console.print(f"[green]✓ Optimized: {result.original_tokens:,} → {result.optimized_tokens:,} tokens[/green]")
+        console.print(f"[dim]Saved {result.tokens_saved:,} tokens ({result.reduction_percent:.1f}%)[/dim]")
+        console.print(f"[dim]Strategy: {result.strategy_used.value}[/dim]")
+    else:
+        console.print("[yellow]No optimization needed[/yellow]")
+
+
+# CLI argument definitions for argparse integration
+CONTEXT_CLI_ARGS = [
+    {
+        "flags": ["--context-auto-compact", "--no-context-auto-compact"],
+        "dest": "context_auto_compact",
+        "action": "store_true",
+        "default": None,
+        "help": "Enable/disable auto-compaction",
+    },
+    {
+        "flags": ["--context-strategy"],
+        "dest": "context_strategy",
+        "choices": ["smart", "truncate", "sliding_window", "summarize", "prune_tools"],
+        "default": None,
+        "help": "Context optimization strategy",
+    },
+    {
+        "flags": ["--context-threshold"],
+        "dest": "context_threshold",
+        "type": float,
+        "default": None,
+        "help": "Auto-compact threshold (0.0-1.0)",
+    },
+    {
+        "flags": ["--context-monitor"],
+        "dest": "context_monitor",
+        "action": "store_true",
+        "default": None,
+        "help": "Enable context monitoring",
+    },
+    {
+        "flags": ["--context-monitor-path"],
+        "dest": "context_monitor_path",
+        "default": None,
+        "help": "Context monitor output path",
+    },
+    {
+        "flags": ["--context-monitor-format"],
+        "dest": "context_monitor_format",
+        "choices": ["human", "json"],
+        "default": None,
+        "help": "Context monitor output format",
+    },
+    {
+        "flags": ["--context-monitor-frequency"],
+        "dest": "context_monitor_frequency",
+        "choices": ["turn", "tool_call", "manual", "overflow"],
+        "default": None,
+        "help": "Context monitor update frequency",
+    },
+    {
+        "flags": ["--context-redact", "--no-context-redact"],
+        "dest": "context_redact",
+        "action": "store_true",
+        "default": None,
+        "help": "Enable/disable sensitive data redaction",
+    },
+    {
+        "flags": ["--context-output-reserve"],
+        "dest": "context_output_reserve",
+        "type": int,
+        "default": None,
+        "help": "Output token reserve",
+    },
+]

src/praisonai/praisonai/cli/main.py

@@ -5181,6 +5181,9 @@ Now, {final_instruction.lower()}:"""
                         elif cmd == "compact":
                             self._handle_compact_command(console, session_state)
                             continue
+                        elif cmd == "context":
+                            self._handle_context_command(console, cmd_args, session_state)
+                            continue
                         elif cmd == "undo":
                             self._handle_undo_command(console, session_state)
                             continue
@@ -5611,6 +5614,43 @@ Provide a concise summary (max 200 words):"""
         except Exception as e:
             console.print(f"[red]Error compacting: {e}[/red]")
     
+    def _handle_context_command(self, console, args, session_state):
+        """
+        Handle /context command - manage context budgeting and monitoring.
+        
+        Usage:
+        - /context              - Show context stats
+        - /context show         - Show summary + budgets
+        - /context stats        - Token ledger table
+        - /context budget       - Budget allocation details
+        - /context dump         - Write snapshot now
+        - /context on           - Enable monitoring
+        - /context off          - Disable monitoring
+        - /context path <path>  - Set snapshot path
+        - /context format <fmt> - Set format (human/json)
+        - /context frequency <f>- Set frequency
+        - /context compact      - Trigger optimization
+        """
+        try:
+            from praisonai.cli.features.context_manager import (
+                handle_context_command,
+                ContextManagerHandler,
+            )
+            
+            # Get or create context manager
+            context_manager = session_state.get("context_manager")
+            if context_manager is None:
+                context_manager = ContextManagerHandler(
+                    model=session_state.get("current_model", "gpt-4o-mini"),
+                    session_id=session_state.get("session_id", ""),
+                )
+                session_state["context_manager"] = context_manager
+            
+            handle_context_command(console, args, session_state, context_manager)
+            
+        except Exception as e:
+            console.print(f"[red]Error handling context command: {e}[/red]")
+    
     def _handle_undo_command(self, console, session_state):
         """
         Handle /undo command - undo the last response.

src/praisonai/praisonai/deploy.py

@@ -57,7 +57,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==3.1.8 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==3.2.0 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/praisonai/version.py

@@ -1 +1 @@
-__version__ = "3.1.8"
+__version__ = "3.2.0"

src/praisonai/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.6.8",
+    "praisonaiagents>=0.7.0",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",

src/praisonai/uv.lock

@@ -4276,7 +4276,7 @@ requires-dist = [
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.1.0" },
     { name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.1.0" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.1.0" },
-    { name = "praisonaiagents", specifier = ">=0.6.8" },
+    { name = "praisonaiagents", specifier = ">=0.7.0" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = "==0.2.29" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4324,7 +4324,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.6.8"
+version = "0.7.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -4333,9 +4333,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a8/43/eba942ff2c03f066aa6a5ffaa0c0a3e636104306d53186d270a644bf4879/praisonaiagents-0.6.8.tar.gz", hash = "sha256:67e2a58a90339be644dde52138c8661538e5e092120b24424d21609c49fec4b5", size = 844876, upload-time = "2026-01-07T15:02:37.524Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/14/cb92a1e8ab1e210e78338bfed30d617ad10ad96452a0b0bed85dacf992eb/praisonaiagents-0.7.0.tar.gz", hash = "sha256:e393749cc9da921e98cf47843675066f60adc9fa8ba2a0748add77a1e2effa8d", size = 861098, upload-time = "2026-01-07T19:36:49.455Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/74/33/e699299ce0952a8036b3d72bc66bd14dbd7459cfb3ce5447e197d7c64b24/praisonaiagents-0.6.8-py3-none-any.whl", hash = "sha256:d1eea97cd397d09e4e7b43c9ce86b67ec6d352046587662363510c5b1cc54d3f", size = 802217, upload-time = "2026-01-07T15:02:35.44Z" },
+    { url = "https://files.pythonhosted.org/packages/41/bf/ce802a9f899131c8ff525b669f3754cf23d39a8e612a2aa1d18401a3569a/praisonaiagents-0.7.0-py3-none-any.whl", hash = "sha256:a1cd9514e95dc54cd214548a72c734ba89131e58bb2b6a34c43f0e1ac36e72eb", size = 822619, upload-time = "2026-01-07T19:36:47.174Z" },
 ]
 
 [[package]]