Monitoring agent conflicts is essential for understanding patterns, optimizing workflows, and improving system behavior. OVADARE offers built-in tools and APIs to track, analyze, and visualize conflicts across multi-agent platforms like AutoGen and CrewAI.
Integrate OVADARE with visualization tools to create dashboards or charts for better insights into agent conflicts:
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import matplotlib.pyplot as pltconflict_count = len(conflict_log)labels = [conflict.related_agent_id for conflict in conflict_log]values = [1 for _ in conflict_log]plt.bar(labels, values)plt.title("Conflict Frequency by Agent")plt.xlabel("Agents")plt.ylabel("Conflicts")plt.show()
Extract patterns from logged conflicts to identify recurring issues:
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from collections import Counteragent_conflict_count = Counter(conflict.related_agent_id for conflict in conflict_log)print("Conflict frequency by agent:", agent_conflict_count)
Integrate OVADARE’s APIs for programmatic access to conflict data:
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conflict = conflict_detector.get_conflict_by_id("Conflict001")if conflict: print(f"Details of Conflict: {conflict.to_dict()}")else: print("No conflict found with the given ID.")