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<p align="center"> <img src="./docs/deepflow-logo.png" alt="DeepFlow" width="300" /> <p align="center">Instant Observability for Cloud & AI Applications</p> <p align="center">Zero Code, Full Stack, eBPF & Wasm</p> </p> <p align="center"> <a href="https://zenodo.org/badge/latestdoi/448599559"><img src="https://zenodo.org/badge/448599559.svg" alt="DOI"></a> <img alt="GitHub Release" src="https://img.shields.io/github/v/release/deepflowio/deepflow"> </a> <img alt="docker pulls" src="https://img.shields.io/docker/pulls/deepflowce/deepflow-agent?color=green?label=docker pulls"> </a> <img alt="License" src="https://img.shields.io/github/license/deepflowio/deepflow?color=purple"> </a> </p>

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What is DeepFlow

The DeepFlow open-source project aims to provide deep observability for complex cloud-native and AI applications. DeepFlow implemented Zero Code data collection with eBPF for metrics, distributed tracing, request logs and function profiling, and is further integrated with SmartEncoding to achieve Full Stack correlation and efficient access to all observability data. With DeepFlow, cloud-native and AI applications automatically gain deep observability, removing the heavy burden of developers continually instrumenting code and providing monitoring and diagnostic capabilities covering everything from code to infrastructure for DevOps/SRE teams.

Key Features

Documentation

For more information, please visit the documentation website.

Quick start

There are three editions of DeepFlow:

The DeepFlow Community Edition consists of the core components of the Enterprise Edition.

DeepFlow Community

Please refer to the deployment documentation.

At the same time, we have also built a complete DeepFlow Community Demo, welcome to experience it. Login account/password: deepflow/deepflow.

DeepFlow Enterprise

You can visit the DeepFlow Enterprise Demo, currently available in Chinese only.

Compile DeepFlow from Source

Software Architecture

DeepFlow Community Edition consists of two components, Agent and Server. An Agent runs in each K8s node, legacy host and cloud host, and is responsible for AutoMetrics and AutoTracing data collection of all application processes on the host. Server runs in a K8s cluster and provides Agent management, tag injection, data ingest and query services.

DeepFlow Architecture

Milestones

Here is our future feature plan. Issues and Pull Requests are welcome.

Contact Us

Acknowledgments

Honors