Sherpa by CartographAI is a Python-based agent framework designed to streamline the creation of intelligent assistants and automated workflows. It enables developers to define agents that can interpret user input, select appropriate LLM endpoints or external APIs, and orchestrate complex tasks such as document summarization, data retrieval, and conversational Q&A. With its plugin architecture, Sherpa supports easy integration of custom tools, memory stores, and routing strategies to optimize response relevance and cost. Users can configure multi-step pipelines where each module performs a distinct function—like semantic search, text analysis, or code generation—while Sherpa manages context propagation and fallback logic. This modular approach accelerates prototype development, improves maintainability, and empowers teams to build scalable AI-driven solutions for diverse applications.

CArtAgO (Common ARTifact Infrastructure for AGents Open environments) is a lightweight, extensible framework for implementing environment infrastructures in multi-agent systems. It introduces the concept of artifacts: first-class entities representing environment resources with defined operations, observable properties, and event interfaces. Developers define artifact types in Java, register them in environment classes, and expose operations and events for agent consumption. Agents interact with artifacts using standard actions (e.g., createArtifact, observe), receive asynchronous notifications of state changes, and coordinate through shared resources. CArtAgO integrates easily with agent platforms such as Jason, JaCaMo, JADE, and Spring Agent, enabling hybrid system development. The framework provides built-in support for artifact documentation, dynamic loading, and runtime monitoring, facilitating rapid prototyping of complex agent-based applications.