title: "LangGraph 0.3 Release: Prebuilt Agents" source: "LangChain Blog" url: "https://blog.langchain.com/langgraph-0-3-release-prebuilt-agents/" date: "2025-03-15" scraped_at: "2026-03-03T07:37:35.229838324+00:00" language: "en-zh" translated: true
By Nuno Campos and Vadym Barda
作者：Nuno Campos 与 Vadym Barda
Over the past year, we’ve invested heavily in making LangGraph the go-to framework for building AI agents. With companies like Replit, Klarna, LinkedIn and Uber choosing to build on top of LangGraph, we have more conviction than ever that we are on the right path.
过去一年中，我们大力投入，致力于将 LangGraph 打造成构建 AI 智能体（AI agents）的首选框架。随着 Replit、Klarna、LinkedIn 和 Uber 等公司选择基于 LangGraph 构建其系统，我们比以往任何时候都更加确信——我们正走在正确的道路上。
A core principle of LangGraph is to be as low level as possible. There are no hidden prompts or no enforced “ cognitive architectures” in LangGraph. This has served to make it production ready and also distinguishes itself from all other frameworks.
LangGraph 的核心设计原则之一是尽可能保持底层抽象。LangGraph 中既不存在隐藏的提示词（hidden prompts），也不强制采用任何特定的“认知架构（cognitive architectures）”。这一设计理念使其具备了生产就绪（production-ready）能力，也使其显著区别于所有其他框架。
At the same time, we do see the value in higher level abstractions. They make it easy to get started, easy to try out new cognitive architectures, and provide a nice entrypoint to the field.
与此同时，我们确实认可更高层级抽象的价值：它们降低了入门门槛，便于快速尝试新型认知架构，并为初入该领域的开发者提供了友好的切入点。
Up to this point, we’ve had one higher level abstraction and it’s lived in the main langgraph package. It was create_react_agent, a wrapper for creating a simple tool calling agent. Today, we are splitting that out of langgraph as part of a 0.3 release, and moving it into langgraph-prebuilt.
截至目前，我们仅提供一个高层级抽象，它一直内置于主 langgraph 包中——即 create_react_agent，一个用于创建简单工具调用智能体（tool-calling agent）的封装函数。今天，作为 0.3 版本发布的一部分，我们将该功能从 langgraph 中拆分出来，并迁移至新独立包 langgraph-prebuilt。
We are also introducing a new set of prebuilt agents built on top of LangGraph, in both Python and JavaScript.
此外，我们还推出了一套全新的 预构建智能体（prebuilt agents），全部基于 LangGraph 构建，同时支持 Python 和 JavaScript 两种语言。
Over the past three weeks, we’ve already released a few of these:
过去三周内，我们已陆续发布了其中若干组件：
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Trustcall: for doing reliable structured extraction
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Trustcall：用于实现可靠的结构化信息抽取
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LangGraph Supervisor: for getting started with a supervisor multi-agent architecture
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LangGraph Supervisor：用于快速上手监督式多智能体（supervisor multi-agent）架构
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LangMem: for long term memory
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LangMem：用于长期记忆（long-term memory）
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LangGraph Swarm: for getting started with a swarm multi-agent architecture
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LangGraph Swarm：用于快速上手群体式多智能体（swarm multi-agent）架构
We believe that these prebuilt libraries can combine the best of both worlds:
我们相信，这些预构建库能够融合两大优势：
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They make it easy to get started with common agent patterns
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它们让常见智能体模式的快速上手变得轻而易举
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They are built on top of LangGraph, so if you want to modify them it’s easy and familiar
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它们均构建于 LangGraph 之上，因此如需定制修改，操作便捷且开发体验熟悉
We hope that this will foster a large collection of prebuilt agents built by the community.
我们希望此举能促进社区构建大量预置智能体（prebuilt agents）。
To that end, we have added instructions for creating your own prebuilt package and adding it to our registry of agents.
为此，我们已添加了创建您自己的预置软件包并将其加入我们的智能体注册中心的相关说明。
We have seen this work well with LangChain integrations. We have over 700 integrations, a large number maintained by the community in third party packages.
这一模式在 LangChain 的集成生态中已被证实行之有效：目前我们已有 700 多个集成组件，其中大量由社区在第三方软件包中维护。
We hope the same will happen with LangGraph prebuilt agents.
我们也期待 LangGraph 的预置智能体生态能迎来同样的蓬勃发展。