Divine Agent is a comprehensive AI agent platform that simplifies the design, development, and deployment of autonomous digital workers. Through its intuitive visual workflow builder, users can define agent behavior as a sequence of nodes, connect to any REST or GraphQL API, and select from supported LLMs like OpenAI and Google PaLM. The built-in memory module preserves context across sessions, while real-time analytics track usage, performance, and errors. Once tested, agents can be deployed as HTTP endpoints or integrated with channels like Slack, email, and custom applications, enabling rapid automation of customer support, sales, and knowledge tasks.

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.

AI-Agent-Framework offers a comprehensive foundation for building AI-powered agents in Python. It includes modules for managing conversation memory, integrating external tools, and constructing prompt templates. Developers can connect to various LLM providers, equip agents with custom plugins, and orchestrate multiple agents in coordinated workflows. Built-in logging and monitoring tools help track agent performance and debug behaviors. The framework's extensible design allows seamless addition of new connectors or domain-specific capabilities, making it ideal for rapid prototyping, research projects, and production-grade automation.