Agent Transparency Tool offers a comprehensive framework for instrumenting AI agents with transparency features. It provides logging interfaces to record state transitions and decisions, modules to compute key transparency metrics (e.g., confidence scores, decision lineage), and visualization dashboards to explore agent behavior over time. By integrating seamlessly with popular agent frameworks, it generates structured transparency logs, supports export to JSON or CSV formats, and includes utilities to plot transparency curves for audit and performance analysis. This toolkit empowers teams to identify biases, debug workflows, and demonstrate responsible AI practices.

LLMFlow provides a declarative way to design, test, and deploy complex language model workflows. Developers create Nodes which represent prompts or actions, then chain them into Flows that can branch based on conditions or external tool outputs. Built-in memory management tracks context between steps, while adapters enable seamless integration with OpenAI, Hugging Face, and others. Extend functionality via plugins for custom tools or data sources. Execute Flows locally, in containers, or as serverless functions. Use cases include creating conversational agents, automated report generation, and data extraction pipelines—all with transparent execution and logging.

NaturalAgents is an open-source Python library designed to streamline the creation and deployment of LLM-powered agents. It provides modules for memory management, context tracking, and tool integration, allowing agents to store and recall information over long sessions. A hierarchical planner orchestrates multi-step reasoning and actions, while an extension system supports custom plugins and external API calls. Built-in logging and analytics enable developers to monitor agent performance and debug workflow issues. NaturalAgents also supports synchronous and asynchronous execution, making it flexible for both interactive use cases and automated pipelines.