Agent Starter Pack is a developer toolkit that scaffolds intelligent, interactive agents on Google Cloud. It offers templates in Node.js and Python to manage conversation flows, maintain long-term memory, and perform tool and API invocations. Built on Vertex AI and Cloud Functions or Cloud Run, it supports multi-step planning, dynamic routing, observability, and logging. Developers can extend connectors to custom services, build domain-specific assistants, and deploy scalable agents in minutes.

Fast-LLM-Agent-MCP is a lightweight, open-source Python framework for building AI agents that combine memory management, chain-of-thought reasoning, and multi-step planning. Developers can integrate it with OpenAI, Azure OpenAI, local Llama, and other models to maintain conversational context, generate structured reasoning traces, and decompose complex tasks into executable subtasks. Its modular design allows custom tool integration and memory stores, making it ideal for applications like virtual assistants, decision support systems, and automated customer support bots.

Imandra offers a cloud-native automated reasoning system designed to bring rigor and transparency to the analysis and design of complex algorithms. With Imandra, users can convert input into mathematical logic and automate reasoning processes, ensuring safety, fairness, and compliance of algorithms. Imandra is especially suitable for high-stakes industries such as finance, where the reliability of algorithms is critical.

Wren is a Python-based AI agent framework designed to help developers create, manage, and deploy autonomous agents. It provides abstractions for defining tools (APIs or functions), memory stores for context retention, and orchestration logic to handle multi-step reasoning. With Wren, you can rapidly prototype chatbots, task automation scripts, and research assistants by composing LLM calls, registering custom tools, and persisting conversation history. Its modular design and callback capabilities make it easy to extend and integrate with existing applications.

Java-Action-Bool integrates seamlessly with the LightJason multi-agent framework, enabling developers to leverage ready-made boolean logic actions in their agent programs. Instead of writing custom boolean checks, you can call provided actions like ActionBoolAnd, ActionBoolOr, ActionBoolNot, and more. These actions evaluate truth values at runtime to guide agent behavior, reducing boilerplate code and simplifying plan definitions in cognitive and reactive agent systems.