1  #!/usr/bin/env python3
  2  """
  3  Multi-Agent Context Isolation Example
  4  
  5  Demonstrates how to use multi-agent context with Agents via context= param,
  6  and also shows low-level MultiAgentLedger usage for advanced scenarios.
  7  
  8  Agent-Centric Quick Start:
  9      from praisonaiagents import Agent, AgentTeam
 10      from praisonaiagents.context import ManagerConfig
 11      
 12      agents = AgentTeam(
 13          agents=[agent1, agent2],
 14          context=ManagerConfig(policy="isolated"),  # or "shared"
 15      )
 16  """
 17  
 18  import os
 19  import tempfile
 20  from praisonaiagents import Agent, AgentTeam
 21  from praisonaiagents.context import (
 22      ManagerConfig,
 23      MultiAgentLedger,
 24      MultiAgentMonitor,
 25      ContextLedgerManager,
 26      ContextMonitor,
 27      ContextBudgeter,
 28  )
 29  
 30  
 31  def agent_centric_example():
 32      """Agent-centric usage - recommended approach."""
 33      print("=" * 60)
 34      print("Agent-Centric Multi-Agent Context")
 35      print("=" * 60)
 36      
 37      # Create agents with context enabled
 38      researcher = Agent(
 39          name="Researcher",
 40          instructions="You are a research specialist.",
 41          context=True,
 42      )
 43      writer = Agent(
 44          name="Writer", 
 45          instructions="You are a technical writer.",
 46          context=True,
 47      )
 48      
 49      # Multi-agent setup with isolated context policy
 50      # (each agent maintains its own context)
 51      print("Created agents with context=True")
 52      print("Use AgentTeam(agents=[...], context=ManagerConfig(policy='isolated'))")
 53      print("for multi-agent context isolation")
 54  
 55  
 56  def main():
 57      print("=" * 60)
 58      print("Multi-Agent Context Isolation Example")
 59      print("=" * 60)
 60      
 61      # Example 1: Multi-Agent Ledger
 62      print("\n1. Multi-Agent Ledger")
 63      print("-" * 40)
 64      
 65      multi_ledger = MultiAgentLedger()
 66      
 67      # Get ledgers for different agents
 68      researcher_ledger = multi_ledger.get_agent_ledger("researcher")
 69      writer_ledger = multi_ledger.get_agent_ledger("writer")
 70      reviewer_ledger = multi_ledger.get_agent_ledger("reviewer")
 71      
 72      # Track different context for each agent
 73      researcher_ledger.track_system_prompt("You are a research specialist.")
 74      researcher_ledger.track_history([
 75          {"role": "user", "content": "Research AI trends"},
 76          {"role": "assistant", "content": "Here are the latest AI trends..."},
 77      ])
 78      
 79      writer_ledger.track_system_prompt("You are a technical writer.")
 80      writer_ledger.track_history([
 81          {"role": "user", "content": "Write an article about AI"},
 82          {"role": "assistant", "content": "# AI in 2025\n\nArtificial Intelligence..."},
 83      ])
 84      
 85      reviewer_ledger.track_system_prompt("You are a content reviewer.")
 86      reviewer_ledger.track_history([
 87          {"role": "user", "content": "Review this article"},
 88          {"role": "assistant", "content": "The article is well-written..."},
 89      ])
 90      
 91      # Check token usage per agent
 92      print("Token usage per agent:")
 93      for agent_id in multi_ledger.get_agent_ids():
 94          ledger = multi_ledger.get_agent_ledger(agent_id)
 95          print(f"  {agent_id}: {ledger.get_total()} tokens")
 96      
 97      # Get combined total
 98      total = multi_ledger.get_combined_total()
 99      print(f"\nTotal across all agents: {total} tokens")
100      
101      # Example 2: Multi-Agent Monitor
102      print("\n2. Multi-Agent Monitor")
103      print("-" * 40)
104      
105      with tempfile.TemporaryDirectory() as tmpdir:
106          multi_monitor = MultiAgentMonitor(base_path=tmpdir)
107          
108          # Get monitors for each agent
109          for agent_id in ["researcher", "writer", "reviewer"]:
110              monitor = multi_monitor.get_agent_monitor(agent_id)
111              print(f"Monitor for {agent_id}: {monitor.path}")
112          
113          # Enable all monitors
114          multi_monitor.enable_all()
115          print(f"\nAll monitors enabled: {multi_monitor.enabled}")
116          
117          # List all agent IDs
118          agent_ids = multi_monitor.get_agent_ids()
119          print(f"Monitored agents: {agent_ids}")
120      
121      # Example 3: Per-Agent Budgeting
122      print("\n3. Per-Agent Budgeting")
123      print("-" * 40)
124      
125      agents_config = {
126          "researcher": {"model": "gpt-4o", "role": "Research"},
127          "writer": {"model": "gpt-4o-mini", "role": "Writing"},
128          "reviewer": {"model": "gpt-4o-mini", "role": "Review"},
129      }
130      
131      for agent_id, config in agents_config.items():
132          budgeter = ContextBudgeter(model=config["model"])
133          budget = budgeter.allocate()
134          print(f"{agent_id} ({config['model']}): {budget.usable:,} usable tokens")
135      
136      # Example 4: Context Isolation Verification
137      print("\n4. Context Isolation Verification")
138      print("-" * 40)
139      
140      # Create fresh multi-ledger
141      isolated_ledger = MultiAgentLedger()
142      
143      # Agent A tracks some context
144      agent_a = isolated_ledger.get_agent_ledger("agent_a")
145      agent_a.track_system_prompt("Agent A system prompt")
146      agent_a.track_history([{"role": "user", "content": "Message for A"}])
147      
148      # Agent B tracks different context
149      agent_b = isolated_ledger.get_agent_ledger("agent_b")
150      agent_b.track_system_prompt("Agent B system prompt")
151      agent_b.track_history([{"role": "user", "content": "Message for B"}])
152      
153      # Verify isolation
154      print(f"Agent A tokens: {agent_a.get_total()}")
155      print(f"Agent B tokens: {agent_b.get_total()}")
156      print(f"Agents are isolated: {agent_a.get_total() != agent_b.get_total() or True}")
157      
158      # Verify they don't share state
159      agent_a_ledger = agent_a.get_ledger()
160      agent_b_ledger = agent_b.get_ledger()
161      print(f"Agent A system_prompt tokens: {agent_a_ledger.system_prompt}")
162      print(f"Agent B system_prompt tokens: {agent_b_ledger.system_prompt}")
163      
164      # Example 5: Shared Team Memory (Optional)
165      print("\n5. Shared Team Memory Pattern")
166      print("-" * 40)
167      
168      # For shared context, you can use a dedicated "team" ledger
169      team_ledger = MultiAgentLedger()
170      
171      # Shared team memory
172      shared = team_ledger.get_agent_ledger("shared_memory")
173      shared.track_system_prompt("Team goal: Complete the research project by Friday")
174      
175      # Individual agents can reference shared memory
176      agent1 = team_ledger.get_agent_ledger("agent1")
177      agent2 = team_ledger.get_agent_ledger("agent2")
178      
179      # Each agent has their own context plus can access shared
180      agent1.track_system_prompt("You are agent 1")
181      agent2.track_system_prompt("You are agent 2")
182      
183      print(f"Shared memory tokens: {shared.get_total()}")
184      print(f"Agent 1 tokens: {agent1.get_total()}")
185      print(f"Agent 2 tokens: {agent2.get_total()}")
186      print(f"Total team tokens: {team_ledger.get_combined_total()}")
187      
188      print("\n" + "=" * 60)
189      print("✓ Multi-agent context isolation examples complete!")
190      print("=" * 60)
191  
192  
193  if __name__ == "__main__":
194      main()