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управление контекстом разговора

  • Open-source agent framework bridging ZhipuAI API with OpenAI-compatible function calling, tool orchestration, and multi-step workflows.
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    What is ZhipuAI Agent to OpenAI?
    ZhipuAI Agent to OpenAI is a specialized agent framework designed to bridge ZhipuAI’s chat completion services with OpenAI-style agent interfaces. It provides a Python SDK that mirrors OpenAI’s function calling paradigm and supports third-party tool integrations, enabling developers to define custom tools, call external APIs, and maintain conversation context across turns. The framework handles request orchestration, dynamic prompt construction, and response parsing, returning structured outputs compatible with OpenAI’s ChatCompletion format. By abstracting API differences, it allows seamless leveraging of ZhipuAI’s Chinese-language models within existing OpenAI-oriented workflows. Ideal for building chatbots, virtual assistants, and automated workflows that require Chinese LLM capabilities without changing established OpenAI-based codebases.
  • LazyLLM is a Python framework enabling developers to build intelligent AI agents with custom memory, tool integration, and workflows.
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    What is LazyLLM?
    LazyLL external APIs or custom utilities. Agents execute defined tasks through sequential or branching workflows, supporting synchronous or asynchronous operation. LazyLLM also offers built-in logging, testing utilities, and extension points for customizing prompts or retrieval strategies. By handling the underlying orchestration of LLM calls, memory management, and tool execution, LazyLLM enables rapid prototyping and deployment of intelligent assistants, chatbots, and automation scripts with minimal boilerplate code.
  • Modular Python framework to build AI Agents with LLMs, RAG, memory, tool integration, and vector database support.
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    What is NeuralGPT?
    NeuralGPT is designed to simplify AI Agent development by offering modular components and standardized pipelines. At its core, it features customizable Agent classes, retrieval-augmented generation (RAG), and memory layers to maintain conversational context. Developers can integrate vector databases (e.g., Chroma, Pinecone, Qdrant) for semantic search and define tool agents to execute external commands or API calls. The framework supports multiple LLM backends such as OpenAI, Hugging Face, and Azure OpenAI. NeuralGPT includes a CLI for quick prototyping and a Python SDK for programmatic control. With built-in logging, error handling, and extensible plugin architecture, it accelerates deployment of intelligent assistants, chatbots, and automated workflows.
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