LLM 已从根本上改变了我们与数据交互、自动化推理以及构建智能系统的方式。然而，尽管其生成式能力令人印象深刻，LLM 天生并不理解关系、结构或长期的事实一致性。这一缺陷在我们尝试将 LLM 用于企业级知识系统、多跳推理或决策关键型应用时尤为明显。

If you've been searching for a faster, simpler, and more efficient way to build and deploy customized AI solutions, Flowise might be exactly what you need. It removes much of the technical complexity ...

UQLM provides a suite of response-level scorers for quantifying the uncertainty of Large Language Model (LLM) outputs. Each scorer returns a confidence score between 0 and 1, where higher scores ...

uv run agent-memory mcp Claude Desktop / local stdio MCP Defaults to asyncio; no worker required For LangChain users, the SDK provides automatic conversion of memory client tools to ...

人工智能（AI）领域对多智能体系统的潜力充满期待——这种框架允许多个专业化 AI 智能体协作，以比单一模型更高效地解决复杂问题。这一转变预示着更精细的自动化、复杂的问题解决能力以及自主化工作流程。 近期，Google 推出了其智能体开发工具包（Agent ...