Langfuse LLM监控与日志
AI Skill Hub 强烈推荐：Langfuse LLM监控与日志 是一款优质的Prompt模板。在 GitHub 上收获超过 27.1k 颗 Star，AI 综合评分 8.5 分，在同类工具中表现稳健。如果你正在寻找可靠的Prompt模板解决方案，这是一个值得深入了解的选择。
优质 Prompt 模板的核心价值在于其结构化设计：明确的角色设定、精确的任务描述、具体的输出格式要求和必要的边界条件，这些要素共同构成了一个能够持续产出高质量结果的 Prompt 框架。Langfuse LLM监控与日志 提供的模板经过反复迭代和用户验证，能够有效减少 AI 的"幻觉"（Hallucination）和输出不稳定问题。
无论你使用 Claude 3.5 Sonnet、GPT-4、Gemini 还是国内的文心一言、智谱 AI，优质的 Prompt 设计都能跨模型复用。AI Skill Hub 建议将本模板保存为个人 Prompt 库的标准组件，根据具体场景调整参数后反复使用，形成自己的 AI 提效工作流。
Langfuse LLM监控与日志 是经过精心设计和反复验证的专业 Prompt 模板集合。这些 Prompt 框架能够有效激活 Claude、ChatGPT 等大型语言模型的深层能力，让 AI 生成更准确、更有价值的输出结果。无需任何安装，直接复制模板内容到 AI 对话框即可使用。
Langfuse LLM监控与日志 是经过精心设计和反复验证的专业 Prompt 模板集合。这些 Prompt 框架能够有效激活 Claude、ChatGPT 等大型语言模型的深层能力，让 AI 生成更准确、更有价值的输出结果。无需任何安装，直接复制模板内容到 AI 对话框即可使用。
- 精心设计的 Prompt 框架，快速激活 AI 的深层能力
- 支持参数化替换，灵活适配多种业务场景
- 经过反复验证的指令结构，显著提升 AI 输出质量和一致性
- 适用于 Claude、ChatGPT 等主流大语言模型
- 可作为团队标准 Prompt 模板复用和二次开发
- 快速生成高质量的专业文案、分析报告或结构化内容
- 利用 Prompt 框架引导 AI 解决特定领域的复杂问题
- 在不同 AI 工具间复用经过验证的提示词模板
# Prompt 无需安装，直接复制使用 # 支持：Claude / ChatGPT / Gemini / 通义千问 等主流模型 # 使用步骤 # 1. 复制 Prompt 模板内容 # 2. 粘贴到 AI 对话框 # 3. 替换 [占位符] 为实际内容 # 4. 发送后获取结构化输出 # 获取原始文件 git clone https://github.com/langfuse/langfuse
- 复制本工具的 Prompt 模板内容
- 打开 Claude、ChatGPT 或其他 AI 对话工具
- 将 Prompt 粘贴到对话框开头
- 根据实际需求替换 [占位符] 中的内容
- 发送后 AI 将按照模板格式执行，获得结构化输出
# 粘贴到 Claude/ChatGPT 使用 # 示例 Prompt 结构： 你是一位 [角色]，擅长 [领域]。 请根据以下要求完成任务： 任务背景：[描述背景] 具体要求：[详细说明] 输出格式：[期望格式] # 将 [] 内内容替换为实际需求
# langfuse 配置说明 # 查看配置选项 langfuse --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量（覆盖配置文件） export LANGFUSE_CONFIG="/path/to/config.yml"
简介
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Langfuse uses GitHub Discussions for Support and Feature Requests.
We're hiring. Join us in product engineering and technical go-to-market roles.
<p align="center"> <a href="https://github.com/langfuse/langfuse/blob/main/LICENSE"> <img src="https://img.shields.io/badge/License-MIT-E11311.svg" alt="MIT License"> </a> <a href="https://www.ycombinator.com/companies/langfuse"><img src="https://img.shields.io/badge/Y%20Combinator-W23-orange" alt="Y Combinator W23"></a> <a href="https://hub.docker.com/u/langfuse" target="_blank"> <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langfuse/langfuse?labelColor=%20%23FDB062&logo=Docker&labelColor=%20%23528bff"></a> <a href="https://pypi.python.org/pypi/langfuse"><img src="https://img.shields.io/pypi/dm/langfuse?logo=python&logoColor=white&label=pypi%20langfuse&color=blue" alt="langfuse Python package on PyPi"></a> <a href="https://www.npmjs.com/package/langfuse"><img src="https://img.shields.io/npm/dm/langfuse?logo=npm&logoColor=white&label=npm%20langfuse&color=blue" alt="langfuse npm package"></a> <br/> <a href="https://discord.com/invite/7NXusRtqYU" target="_blank"> <img src="https://img.shields.io/discord/1111061815649124414?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb" alt="chat on Discord"></a> <a href="https://twitter.com/intent/follow?screen_name=langfuse" target="_blank"> <img src="https://img.shields.io/twitter/follow/langfuse?logo=X&color=%20%23f5f5f5" alt="follow on X(Twitter)"></a> <a href="https://www.linkedin.com/company/langfuse/" target="_blank"> <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff" alt="follow on LinkedIn"></a> <a href="https://github.com/langfuse/langfuse/graphs/commit-activity" target="_blank"> <img alt="Commits last month" src="https://img.shields.io/github/commit-activity/m/langfuse/langfuse?labelColor=%20%2332b583&color=%20%2312b76a"></a> <a href="https://github.com/langfuse/langfuse/" target="_blank"> <img alt="Issues closed" src="https://img.shields.io/github/issues-search?query=repo%3Alangfuse%2Flangfuse%20is%3Aclosed&label=issues%20closed&labelColor=%20%237d89b0&color=%20%235d6b98"></a> <a href="https://github.com/langfuse/langfuse/discussions/" target="_blank"> <img alt="Discussion posts" src="https://img.shields.io/github/discussions/langfuse/langfuse?labelColor=%20%239b8afb&color=%20%237a5af8"></a> <a href="https://deepwiki.com/langfuse/langfuse" target="_blank"> <img alt="Ask DeepWiki" src="https://deepwiki.com/badge.svg"></a> </p>
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Langfuse is an open source LLM engineering platform. It helps teams collaboratively develop, monitor, evaluate, and debug AI applications. Langfuse can be self-hosted in minutes and is battle-tested.
✨ Core Features
<img width="4856" height="1944" alt="Langfuse Overview" src="https://github.com/user-attachments/assets/5dac68ef-d546-49fb-b06f-cfafc19282e3" />
- LLM Application Observability: Instrument your app and start ingesting traces to Langfuse, thereby tracking LLM calls and other relevant logic in your app such as retrieval, embedding, or agent actions. Inspect and debug complex logs and user sessions. Try the interactive demo to see this in action.
- Prompt Management helps you centrally manage, version control, and collaboratively iterate on your prompts. Thanks to strong caching on server and client side, you can iterate on prompts without adding latency to your application.
- Evaluations are key to the LLM application development workflow, and Langfuse adapts to your needs. It supports LLM-as-a-judge, user feedback collection, manual labeling, and custom evaluation pipelines via APIs/SDKs.
- Datasets enable test sets and benchmarks for evaluating your LLM application. They support continuous improvement, pre-deployment testing, structured experiments, flexible evaluation, and seamless integration with frameworks like LangChain and LlamaIndex.
- LLM Playground is a tool for testing and iterating on your prompts and model configurations, shortening the feedback loop and accelerating development. When you see a bad result in tracing, you can directly jump to the playground to iterate on it.
- Comprehensive API: Langfuse is frequently used to power bespoke LLMOps workflows while using the building blocks provided by Langfuse via the API. OpenAPI spec, Postman collection, and typed SDKs for Python, JS/TS are available.
Dependencies
We deploy this code base in Docker containers based on the Linux Alpine Image (source). You may find the Dockerfiles in web/Dockerfile and worker/Dockerfile.
📦 Deploy Langfuse
<img width="4856" height="1322" alt="Langfuse Deployment Options" src="https://github.com/user-attachments/assets/98f020c7-7a20-4264-a201-65c41a52a5d5" />
🚀 Quickstart
Instrument your app and start ingesting traces to Langfuse, thereby tracking LLM calls and other relevant logic in your app such as retrieval, embedding, or agent actions. Inspect and debug complex logs and user sessions.
🔌 Integrations
<img width="4856" height="1322" alt="github-integrations" src="https://github.com/user-attachments/assets/e41ea0fb-742d-41ce-bf94-1d4fb95750cd" />
Main Integrations:
| Integration | Supports | Description |
|---|---|---|
| [SDK](https://langfuse.com/docs/sdk) | Python, JS/TS | Manual instrumentation using the SDKs for full flexibility. |
| [OpenAI](https://langfuse.com/integrations/model-providers/openai-py) | Python, JS/TS | Automated instrumentation using drop-in replacement of OpenAI SDK. |
| [Langchain](https://langfuse.com/docs/integrations/langchain) | Python, JS/TS | Automated instrumentation by passing callback handler to Langchain application. |
| [LlamaIndex](https://langfuse.com/docs/integrations/llama-index/get-started) | Python | Automated instrumentation via LlamaIndex callback system. |
| [Haystack](https://langfuse.com/docs/integrations/haystack) | Python | Automated instrumentation via Haystack content tracing system. |
| [LiteLLM](https://langfuse.com/docs/integrations/litellm) | Python, JS/TS (proxy only) | Use any LLM as a drop in replacement for GPT. Use Azure, OpenAI, Cohere, Anthropic, Ollama, VLLM, Sagemaker, HuggingFace, Replicate (100+ LLMs). |
| [Vercel AI SDK](https://langfuse.com/docs/integrations/vercel-ai-sdk) | JS/TS | TypeScript toolkit designed to help developers build AI-powered applications with React, Next.js, Vue, Svelte, Node.js. |
| [Mastra](https://langfuse.com/docs/integrations/mastra) | JS/TS | Open source framework for building AI agents and multi-agent systems. |
| [API](https://langfuse.com/docs/api) | Directly call the public API. OpenAPI spec available. |
Packages integrated with Langfuse:
| Name | Type | Description |
|---|---|---|
| [Instructor](https://langfuse.com/docs/integrations/instructor) | Library | Library to get structured LLM outputs (JSON, Pydantic) |
| [DSPy](https://langfuse.com/docs/integrations/dspy) | Library | Framework that systematically optimizes language model prompts and weights |
| [Mirascope](https://langfuse.com/docs/integrations/mirascope) | Library | Python toolkit for building LLM applications. |
| [Ollama](https://langfuse.com/docs/integrations/ollama) | Model (local) | Easily run open source LLMs on your own machine. |
| [Amazon Bedrock](https://langfuse.com/docs/integrations/amazon-bedrock) | Model | Run foundation and fine-tuned models on AWS. |
| [AutoGen](https://langfuse.com/docs/integrations/autogen) | Agent Framework | Open source LLM platform for building distributed agents. |
| [Flowise](https://langfuse.com/docs/integrations/flowise) | Chat/Agent UI | JS/TS no-code builder for customized LLM flows. |
| [Langflow](https://langfuse.com/docs/integrations/langflow) | Chat/Agent UI | Python-based UI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows. |
| [Dify](https://langfuse.com/docs/integrations/dify) | Chat/Agent UI | Open source LLM app development platform with no-code builder. |
| [OpenWebUI](https://langfuse.com/docs/integrations/openwebui) | Chat/Agent UI | Self-hosted LLM Chat web ui supporting various LLM runners including self-hosted and local models. |
| [Promptfoo](https://langfuse.com/docs/integrations/promptfoo) | Tool | Open source LLM testing platform. |
| [LobeChat](https://langfuse.com/docs/integrations/lobechat) | Chat/Agent UI | Open source chatbot platform. |
| [Vapi](https://langfuse.com/docs/integrations/vapi) | Platform | Open source voice AI platform. |
| [Inferable](https://langfuse.com/docs/integrations/other/inferable) | Agents | Open source LLM platform for building distributed agents. |
| [Gradio](https://langfuse.com/docs/integrations/other/gradio) | Chat/Agent UI | Open source Python library to build web interfaces like Chat UI. |
| [Goose](https://langfuse.com/docs/integrations/goose) | Agents | Open source LLM platform for building distributed agents. |
| [smolagents](https://langfuse.com/docs/integrations/smolagents) | Agents | Open source AI agents framework. |
| [CrewAI](https://langfuse.com/docs/integrations/crewai) | Agents | Multi agent framework for agent collaboration and tool use. |
Langfuse是业界领先的LLM可观测性平台，27k stars验证其热度。功能完整度高，社区活跃，特别适合需要系统化管理提示词和监控模型应用的团队。
- 想快速复用高质量提示词模板的 AI 用户
- 先在测试环境跑通最小用例，再接入生产数据
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
- 精心设计的 Prompt 框架，快速激活 AI 的深层能力
- 支持参数化替换，灵活适配多种业务场景
- 经过反复验证的指令结构，显著提升 AI 输出质量和一致性
- 适用于 Claude、ChatGPT 等主流大语言模型
- 可作为团队标准 Prompt 模板复用和二次开发
- 想快速复用高质量提示词模板的 AI 用户
- 先在测试环境跑通最小用例，再接入生产数据
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- 快速生成高质量的专业文案、分析报告或结构化内容
- 利用 Prompt 框架引导 AI 解决特定领域的复杂问题
- 在不同 AI 工具间复用经过验证的提示词模板
- +GitHub 27.1k Star，社区高度认可
- +无需安装，立即可用
- +适配所有主流 AI 工具
- +经社区验证的最佳实践
- −效果依赖使用者对 Prompt 工程的熟悉程度
- −不同模型和版本的响应效果可能存在差异
- −复杂场景需结合实际需求二次调整
该工具使用 NOASSERTION 协议，商用场景请仔细阅读协议条款，必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
总体来看，Langfuse LLM监控与日志 是一款质量优秀的Prompt模板，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。
| 原始名称 | langfuse |
| 原始描述 | 开源Prompt模板：🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prom。⭐27.1k · TypeScript |
| Topics | LLM监控提示词管理可观测性评估工具开源平台 |
| GitHub | https://github.com/langfuse/langfuse |
| License | NOASSERTION |
| 语言 | TypeScript |
收录时间：2026-05-13 · 更新时间：2026-05-26 · License：NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。