# dora-rs/dora

DORA (Dataflow-Oriented Robotic Architecture) is middleware designed to streamline and simplify the creation of AI-based robotic applications. It offers low latency, composable, and distributed dataflow capabilities. Applications are modeled as directed graphs, also referred to as pipelines.

Repository: https://github.com/dora-rs/dora
Canonical: https://ross.abutalabs.com/products/dora
Homepage: https://dora-rs.ai
Language: Rust
License: Apache-2.0
License Family: permissive
Topics: dataflow, low-latency, robotics, rust, embodied-ai
Last push: 2026-08-26T16:36:32+00:00

## Health v2 (maintenance only)
Score: 87/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 64, longevity 100
- inputs: {"age_days": 1658, "days_push": 7, "days_rel": 161, "gap_med": 56, "n_releases_24m": 10}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3900, forks 432 (observed 2026-08-28T04:08:28.548899+00:00)

## What it is
Dora is a Rust-based dataflow middleware framework for building real-time robotics and AI applications, where applications are modeled as directed graphs (pipelines) of nodes communicating via zero-copy shared memory and Apache Arrow. It provides a single CLI for the full lifecycle including local development, distributed deployment, monitoring, and record/replay, with SDKs for Rust, Python, C, and C++.

## Use cases
- build low-latency robotic applications with a dataflow pipeline
- replace ROS2 with a faster Rust middleware
- orchestrate distributed AI nodes across multiple machines
- record and replay robot sensor data for offline debugging
- mix Python and Rust nodes in one robotics pipeline
- run vision models like YOLO as reusable pipeline nodes

## When to choose
- you need low-latency, zero-copy communication for robotics or embodied AI workloads
- you want distributed deployment, fault tolerance, and dynamic topology in one framework
- you prefer Rust performance with Python node support
- you need record/replay and observability for regression testing robot pipelines

## When to avoid
- your project is already deeply invested in the ROS2 ecosystem and tooling
- you need a mature, widely-adopted standard with extensive community packages
- your application is simple enough not to need a dataflow middleware
- you require platforms beyond Linux/macOS such as Windows or embedded RTOS targets

## Facets
- artifact type: framework
- maturity: active
- function: middleware, message-queue, streaming, cli, monitoring, machine-learning
- domain: robotics, machine-learning, microservices, developer-tools
- platform: rust, python, cpp, c, cross-platform
- tags: dataflow, robotics-middleware, zero-copy, shared-memory, zenoh, apache-arrow, ros2-alternative, embodied-ai, record-replay, distributed-deployment, real-time, linux, macos

## Member repositories
- dora-rs/dora (main) score 87

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:28.548899+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:25:11.559742+00:00, confidence not recorded.
  - readme: https://github.com/dora-rs/dora (fetched 2026-08-28T04:08:28.548899+00:00, sha 263d7a71ae8e)
  - homepage: https://dora-rs.ai (fetched 2026-08-29T09:19:19.905036+00:00, sha e52f824d9107)
- Data as of 2026-08-30T08:39:29.467469+00:00.