概览
主要功能
- 可以组合的链和代理为 LLM 应用程序
- LangGraph用于管理有状态代理的多步工作流
- LangSmith用于跟踪、评估和监控
- 集成的大型 LLM、向量数据库和 API
- Python 和 JavaScript、TypeScript SDK
- 用于检索增强生成 (RAG) 的工具
价格
- 模型
- Free
- 评分
- 4.5 / 5 (4)
使用场景
构建工具使用 LLM 代理
使用 LangChain 和 LangGraph 设计能够推理、调用 API 或工具、并跨有状态工作流维持状态的代理。
检索增强生成应用
将 LangChain 的 RAG 工具结合使用向量数据库集成来为 LLM 回应提供地物并将知识库连接到您的文档。
调试和监控 AI 管道
利用 LangSmith 进行跟踪、评估和监控代理行为，帮助团队调试故障并迭代复杂的 LLM 管道。
原型到生产的 LLM 系统
使用可组合的链和 Python或 JavaScript 中的 Python 或 JavaScript 将快速原型转化为一致的促发式、记忆和模型调用。生产级应用程序。
优点 & 缺点
优点
- 大型与模型、工具和数据源的集成生态系统
- 强大的可观察性和调试功能通过 LangSmith
- 使用 LangGraph 的灵活代理orchestration
- 活跃的社区和频繁更新
缺点
- 抽象可以使简单用例看起来很重
- 频繁的 API 修改要求持续的维护
- 跨越更广泛生态系统的学习曲线
评测
4 个评分的平均值。
登录以留下评测。
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on langSmith for tracing, evaluation, and monitoring, and flexible agent orchestration with LangGraph caught me off guard. still, I'd recommend giving it a real trial.
Years in this space
I've evaluated a lot of these over the years. What stands out here is python and JavaScript/TypeScript SDKs — handled better than most — and strong observability and debugging via LangSmith. Learning curve across the broader ecosystem is my one real gripe. Worth the time if this is your use case.
Solid for our team
We rolled this out across the team last quarter and active community and frequent updates. Integrations with major LLMs, vector databases, and APIs fits neatly into how we already work, and integrations with major LLMs, vector databases, and APIs removed a step we used to do by hand. Abstractions can feel heavy for simple use cases, which is the main caveat, but it has held up under daily use.
Solid for our team
We rolled this out across the team last quarter and large ecosystem of integrations with models, tools, and data sources. LangSmith for tracing, evaluation, and monitoring fits neatly into how we already work, and tooling for retrieval-augmented generation (RAG) removed a step we used to do by hand. but it has held up under daily use.
问答
What does uptime mean for LangSmith Deployment usage?
Uptime is the duration your deployment’s database is live and persisting state. Uptime will be tracked as soon as your deployment is live and ends when you shut it down. Dev agent deployments are typically short-lived (used during iteration, then deleted) – whereas Production agent deployments stay live and are updated via revisions (rather than being deleted).
Asked by Aaliyah Johnson · Feb 4, 2026
Does LangSmith Deployment include any free deployments?
Plus plans include 1 free small serverless deployment. If you spin up additional serverless or dedicated deployments, you’ll be charged on usage (resource time).
Asked by Paulo Cardoso · Jan 28, 2026
Why would I upgrade a base trace to an extended trace?
Base traces are short-lived (14-day retention) and ideal for quick debugging or ad-hoc analysis. They’re priced for volume and short-term utility. Extended traces are retained for 400 days. This is useful when traces include valuable feedback associated with them, such as from users, evaluators, or human labelers. This feedback makes them valuable for ongoing improvement and model tuning. LangSmith lets you choose the right retention for each trace, helping you balance cost and value.
Asked by Lorenzo Bianchi · Jan 19, 2026
What is the difference between a base trace and an extended trace?
Base traces have a shorter retention period of 14 days. Extended traces have a longer retention period of 400 days. You can "upgrade" base traces to extended traces for an additional fee.
Asked by Yaw Owusu · Jan 8, 2026
What is a trace? Can it contain multiple events?
A trace represents a single execution of your application—whether it’s an agent, evaluator, or playground session. It can include many individual steps, such as LLM calls and other tracked events. Here's an example of a single trace.
Asked by Noor Siddiqui · Dec 26, 2025
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