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  • Agent Starter Pack

    An open-source Google Cloud framework offering templates and samples to build conversational AI agents with memory, planning, and API integrations.
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    What is Agent Starter Pack?
    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.
    Agent Starter Pack Core Features
    • Conversation scaffolding with multi-turn dialogue
    • Long-term memory management
    • Multi-step reasoning and planning
    • API and tool invocation connectors
    • Integration with Vertex AI LLMs
    • Deployment on Cloud Functions or Cloud Run
    • Observability via Cloud Logging and Monitoring
    Agent Starter Pack Pro & Cons

    The Cons

    No explicit pricing information available on the page.
    Potential complexity in customizing templates for users without advanced knowledge.
    Documentation may require prior familiarity with Google Cloud and AI agent concepts.

    The Pros

    Pre-built templates enable rapid development of AI agents.
    Integration with Vertex AI allows for effective experimentation and evaluation.
    Production-ready infrastructure supports reliable deployment with monitoring and CI/CD.
    Highly customizable and extendable to suit various use cases.
    Open-source under Apache 2.0 License facilitating community contribution and transparency.

  • Fast-LLM-Agent-MCP

    An open-source Python framework providing fast LLM agents with memory, chain-of-thought reasoning, and multi-step planning.
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    What is Fast-LLM-Agent-MCP?
    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.
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