langtrace

Scale3-LabsAGPL-3.0更新放缓

基于 OpenTelemetry 的 LLM 应用全链路可观测性工具,提供实时追踪、评估和指标,支持主流大模型、框架与向量数据库,可用 TypeScript 和 Python 接入。

Langtrace 🔍 is an open-source, Open Telemetry based end-to-end observability tool for LLM applications, providing real-time tracing, evaluations and metrics for popular LLMs, LLM frameworks, vectorDBs and more.. Integrate using Typescript, Python. 🚀💻📊

4.0.11发布于 2025-04-17 23:33 UTC1,234star128fork同步于 2026-11-07 07:29 UTC

怎么上手

由 README 提炼,执行前请确认
pip install langtrace-python-sdk
from langtrace_python_sdk import langtrace
langtrace.init(api_key='<your_api_key>')
硬件要求按 README 说明

README 未说明显存或 GPU 要求;自托管需 Docker 与 Docker Compose,运行 Next.js 应用、Postgres 和 Clickhouse 三个服务。

有 Docker 镜像
下载:源码 zip·GitHub Releases(安装包)本站不镜像安装包

项目说明

由 README 自动翻译提炼

基于 OpenTelemetry 的开源 LLM 应用可观测性工具,实时追踪调用、评估与指标

基于 OpenTelemetry 的 LLM 应用全链路可观测性工具,提供实时追踪、评估和指标,支持主流大模型、框架与向量数据库,可用 TypeScript 和 Python 接入。

核心能力

  • 基于 OTEL 标准生成并采集链路追踪数据
  • 实时监控 LLM API 调用与向量库操作
  • 分析延迟、成本与用量等性能指标
  • 提供追踪调试工具定位 LLM 工作流问题
  • 支持自托管部署,数据不出自有服务器

容易踩的坑

SDK 初始化必须放在任何 LLM 模块导入之前;云端使用需先注册并在项目内生成 API key;自托管需同时启动 Postgres 与 Clickhouse,默认访问 http://localhost:3000。

内容由模型根据 README 生成,可能不完整或不准确,以官方文档为准。看原文 README

最近发版

5 个版本4.0.11
  1. 4.0.11最新正式版2025-04-17 · 2025-04-17 23:33 UTC

    What's Changed

    • Added support for displaying Cleanlab trustworthiness score on the traces tab
    • Cleanlab span shows additional metadata such as the explanation logs on the span attributes
  2. 4.0.102025-04-17 · 2025-04-17 14:28 UTC
    查看更新说明

    What's Changed

    • Fixed the setup instructions for the typescript sdk by @kanlanc
    • fix selfhosting missing team issue + add proper timeout to clickhouse by @alizenhom

    New Contributors

    • @kanlanc made their first contribution
  3. 4.0.92025-04-15 · 2025-04-15 12:53 UTC
    查看更新说明

    What's Changed

    • Bugfixes and query performance improvement
  4. 4.0.82025-04-14 · 2025-04-14 16:22 UTC
    查看更新说明

    What's Changed

    Full Changelog: https://github.com/Scale3-Labs/langtrace/compare/4.0.7...4.0.8

  5. 4.0.72025-04-10 · 2025-04-10 16:07 UTC
    查看更新说明

    What's Changed

    Full Changelog: https://github.com/Scale3-Labs/langtrace/compare/4.0.6...4.0.7

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    README 原文

    未翻译

    License Development Status Pull Requests NPM SDK NPM Downloads PyPI SDK PyPI Downloads Total PyPI Downloads Deploy Deploy to Northflank


    📚 Table of Contents

    Langtrace is an open source observability software which lets you capture, debug and analyze traces and metrics from all your applications that leverages LLM APIs, Vector Databases and LLM based Frameworks.

    image

    ✨ Features

    • 📊 Open Telemetry Support: Built on OTEL standards for comprehensive tracing
    • 🔄 Real-time Monitoring: Track LLM API calls, vector operations, and framework usage
    • 🎯 Performance Insights: Analyze latency, costs, and usage patterns
    • 🔍 Debug Tools: Trace and debug your LLM application workflows
    • 📈 Analytics: Get detailed metrics and visualizations
    • 🏠 Self-hosting Option: Deploy on your own infrastructure

    🚀 Quick Start

    # For TypeScript/JavaScript
    npm i @langtrase/typescript-sdk
    
    # For Python
    pip install langtrace-python-sdk
    

    Initialize in your code:

    // TypeScript
    import * as Langtrace from '@langtrase/typescript-sdk'
    Langtrace.init({ api_key: '<your_api_key>' }) // Get your API key at langtrace.ai
    
    # Python
    from langtrace_python_sdk import langtrace
    langtrace.init(api_key='<your_api_key>') # Get your API key at langtrace.ai
    

    For detailed setup instructions, see Getting Started.

    📊 Open Telemetry Support

    The traces generated by Langtrace adhere to Open Telemetry Standards(OTEL). We are developing semantic conventions for the traces generated by this project. You can checkout the current definitions in this repository. Note: This is an ongoing development and we encourage you to get involved and welcome your feedback.


    📦 SDK Repositories


    🚀 Getting Started

    Langtrace Cloud ☁️

    To use the managed SaaS version of Langtrace, follow the steps below:

    1. Sign up by going to this link.
    2. Create a new Project after signing up. Projects are containers for storing traces and metrics generated by your application. If you have only one application, creating 1 project will do.
    3. Generate an API key by going inside the project.
    4. In your application, install the Langtrace SDK and initialize it with the API key you generated in the step 3.
    5. The code for installing and setting up the SDK is shown below:

    If your application is built using typescript/javascript

    npm i @langtrase/typescript-sdk
    
    import * as Langtrace from '@langtrase/typescript-sdk' // Must precede any llm module imports
    Langtrace.init({ api_key: <your_api_key> })
    

    OR

    import * as Langtrace from "@langtrase/typescript-sdk"; // Must precede any llm module imports
    LangTrace.init(); // LANGTRACE_API_KEY as an ENVIRONMENT variable
    

    If your application is built using python

    pip install langtrace-python-sdk
    
    from langtrace_python_sdk import langtrace
    langtrace.init(api_key=<your_api_key>)
    

    OR

    from langtrace_python_sdk import langtrace
    langtrace.init() # LANGTRACE_API_KEY as an ENVIRONMENT variable
    

    🏠 Langtrace self hosted

    To run the Langtrace locally, you have to run three services:

    • Next.js app
    • Postgres database
    • Clickhouse database

    [!IMPORTANT] Checkout our documentation for various deployment options and configurations.

    Requirements:

    • Docker
    • Docker Compose
    The .env file

    Feel free to modify the .env file to suit your needs.

    Starting the servers
    docker compose up
    

    The application will be available at http://localhost:3000.

    Take down the setup

    To delete containers and volumes

    docker compose down -v
    

    -v flag is used to delete volumes

    Telemetry

    Langtrace does NOT collect any Telemetry if you are self hosting the OSS client. None of your data leaves your servers.


    🔗 Supported Integrations

    Langtrace automatically captures traces from the following vendors and frameworks:

    LLM Providers

    ProviderTypeScript SDKPython SDK
    OpenAI✅✅
    Anthropic✅✅
    Azure OpenAI✅✅
    Cohere✅✅
    DeepSeek✅✅
    xAI✅✅
    Groq✅✅
    Perplexity✅✅
    Gemini✅✅
    AWS Bedrock✅✅
    Mistral❌✅

    LLM Frameworks

    FrameworkTypeScript SDKPython SDK
    Langchain❌✅
    LlamaIndex✅✅
    Langgraph❌✅
    LiteLLM❌✅
    DSPy❌✅
    CrewAI❌✅
    Ollama❌✅
    VertexAI✅✅
    Vercel AI✅❌
    GuardrailsAI❌✅
    Arch❌✅
    Graphlit❌✅
    Agno❌✅
    Phidata❌✅
    Cleanlab❌✅

    Vector Databases

    DatabaseTypeScript SDKPython SDK
    Pinecone✅✅
    ChromaDB✅✅
    QDrant✅✅
    Weaviate✅✅
    PGVector✅✅ (SQLAlchemy)
    MongoDB❌✅
    Milvus❌✅

    📐 Langtrace System Architecture

    image


    💡 Feature Requests and Issues


    🤝 Contributions

    We welcome contributions to this project. To get started, fork this repository and start developing. To get involved, join our Slack workspace.


    🌟 Langtrace Star History

    Langtrace Star History Chart


    🔒Security

    To report security vulnerabilities, email us at [email protected]. You can read more on security here.


    📜 License

    • Langtrace application(this repository) is licensed under the AGPL 3.0 License. You can read about this license here.
    • Langtrace SDKs are licensed under the Apache 2.0 License. You can read about this license here.

    👥 Contributors


    ❓Frequently Asked Questions

    1. Can I self host and run Langtrace in my own cloud? Yes, you can absolutely do that. Follow the self hosting setup instructions in our documentation.

    2. What is the pricing for Langtrace cloud? Currently, we are not charging anything for Langtrace cloud and we are primarily looking for feedback so we can continue to improve the project. We will inform our users when we decide to monetize it.

    3. What is the tech stack of Langtrace? Langtrace uses NextJS for the frontend and APIs. It uses PostgresDB as a metadata store and Clickhouse DB for storing spans, metrics, logs and traces.

    4. Can I contribute to this project? Absolutely! We love developers and welcome contributions. Get involved early by joining our Discord Community.

    5. What skillset is required to contribute to this project? Programming Languages: Typescript and Python. Framework knowledge: NextJS. Database: Postgres and Prisma ORM. Nice to haves: Opentelemetry instrumentation framework, experience with distributed tracing.