Agent Logging provides a unified logging framework for AI agent frameworks and custom workflows. It intercepts and records each stage of an agent’s execution—prompt generation, tool invocation, LLM response, and final output—along with timestamps and metadata. Logs can be exported in JSON, CSV, or sent to monitoring services. The library supports customizable log levels, hooks for integration with observability platforms, and visualization tools to trace decision paths. With Agent Logging, teams gain insights into agent behavior, spot performance bottlenecks, and maintain transparent records for auditing.

Multi-Agent-Conversation-AutoGen is engineered to automate the creation of interactive dialogue sequences among multiple AI agents for testing, research, and educational applications. Users supply a configuration file to define agent profiles, personas, and conversation flows. The framework orchestrates turn-based interactions, leveraging OpenAI GPT APIs to generate each message dynamically. Key features include customizable prompt templates, flexible API integration, conversation length control, and exportable logs in JSON or text formats. With this tool, developers can simulate complex group discussions, stress-test conversational agents in diverse scenarios, and rapidly produce large sets of dialogue data without manual scripting. The modular architecture allows extension to other LLM providers and integration into existing development pipelines.