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Dora

Agentic Dataflow-Oriented Robotic Architecture – a 100% Rust framework for building real-time robotics and AI applications.

User Guide | 用户指南 (中文)

Built and maintained with agentic engineering – AI agents do the heavy lifting on code generation, reviews, refactoring, and testing; humans set direction and gate every merge.

Features
Installation
Quick Start
CLI Commands
Dataflow Configuration
Architecture
Language Support
Examples
Development
Contributing
License

Features

Performance

10-17x faster than ROS2 Python – 100% Rust internals with zero-copy shared memory IPC for messages >4KB, flat latency from 4KB to 4MB payloads
Zenoh SHM data plane – nodes publish directly via Zenoh shared memory, bypassing the daemon for 35% lower latency and 3-10x higher throughput on large payloads; automatic network fallback for cross-machine
Apache Arrow native – columnar memory format end-to-end with zero serialization overhead; optional Arrow IPC framing for self-describing wire format; shared across all language bindings
Non-blocking event loop – Zenoh publishes offloaded to a dedicated drain task; control commands respond in <500ms even under high data throughput

Developer experience

Single CLI, full lifecycle – dora run for local dev, dora up/start for distributed prod, plus build, logs, monitoring, record/replay all from one tool
Declarative YAML dataflows – define pipelines as directed graphs, connect nodes through typed inputs/outputs, optional type annotations with static validation, override with environment variables
Multi-language nodes – write nodes in Rust, Python, C, or C++ with native APIs (not wrappers); mix languages freely in one dataflow
Reusable modules – compose sub-graphs as standalone YAML files with typed inputs/outputs, parameters, optional ports, and nested composition (compile-time expansion, zero runtime overhead)
Hot reload – live-reload Python operators without restarting the dataflow
Programmatic builder – construct dataflows in Python code as an alternative to YAML

Production readiness

Fault tolerance – per-node restart policies (never/on-failure/always), exponential backoff, health monitoring, circuit breakers with configurable input timeouts
Distributed by default – local shared memory between co-located nodes, automatic Zenoh pub-sub for cross-machine communication, SSH-based cluster management with label scheduling, rolling upgrades, and auto-recovery
Coordinator HA – persistent redb-backed state store (default), daemon auto-reconnect with exponential backoff, dataflow records survive coordinator restart (running dataflow reclaim-across-restart is partial, see the open issue tracker)
Dynamic topology – add and remove nodes from running dataflows via CLI (dora node add/remove/connect/disconnect) without restarting
Soft real-time – optional --rt flag for mlockall + SCHED_FIFO; per-node cpu_affinity pinning in YAML; comprehensive tuning guide for memory locking, kernel params, and container deployment
OpenTelemetry – built-in structured logging with rotation/routing, metrics, distributed tracing, and zero-setup trace viewing via CLI

Debugging and observability

Record/replay – capture dataflow messages to .drec files, replay offline at any speed with node substitution for regression testing
Topic inspection – topic echo to print live data, topic hz TUI for frequency analysis, topic info for schema and bandwidth
Resource monitoring – dora top TUI showing per-node CPU, memory, queue depth, network I/O, restart count, and health status across all machines; --once flag for scriptable JSON snapshots
Trace inspection – trace list and trace view for viewing coordinator spans without external infrastructure
Dataflow visualization – generate interactive HTML or Mermaid graphs from YAML descriptors

Ecosystem

Communication patterns – built-in service (request/reply), action (goal/feedback/result), and streaming (session/segment/chunk) patterns via well-known metadata keys; no daemon or YAML changes required
ROS2 bridge – bidirectional interop with ROS2 topics, services, and actions; QoS mapping; Arrow-native type conversion
Pre-packaged nodes – node hub with ready-made nodes for cameras, YOLO, LLMs, TTS, and more
In-process operators – lightweight functions that run inside a shared runtime, avoiding per-node process overhead for simple transformations

Installation

From crates.io (recommended)

cargo install dora-cli           # CLI (dora command)
pip install dora-rs              # Python node/operator API

From source

git clone https://github.com/dora-rs/dora.git
cd dora
cargo build --release -p dora-cli
PATH=$PATH:$(pwd)/target/release

# Python API (requires maturin >= 1.8: pip install maturin)
# Must run from the package directory for dependency resolution
cd apis/python/node && maturin develop --uv && cd ../../..

Platform installers

macOS / Linux:

curl --proto '=https' --tlsv1.2 -LsSf \
  https://github.com/dora-rs/dora/releases/latest/download/dora-cli-installer.sh | sh

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://github.com/dora-rs/dora/releases/latest/download/dora-cli-installer.ps1 | iex"

Build features

Feature	Description	Default
`tracing`	OpenTelemetry tracing support	Yes
`metrics`	OpenTelemetry metrics collection	Yes
`python`	Python operator support (PyO3)	No
`redb-backend`	Persistent coordinator state (redb)	Yes

cargo install dora-cli --features redb-backend

Quick Start

1. Run a Python dataflow

Important: The PyPI package is dora-rs, not dora. The import name is dora (from dora import Node), but pip install dora installs an unrelated package.

cargo install dora-cli            # or use install script below
pip install dora-rs numpy pyarrow
git clone https://github.com/dora-rs/dora.git && cd dora
dora run examples/python-dataflow/dataflow.yml

This runs a sender -> transformer -> receiver pipeline. Here’s what the Python node code looks like:

# sender.py -- sends messages and polls for STOP
from dora import Node
import pyarrow as pa
import time

node = Node()
sent = 0
while sent < 100:
    event = node.try_recv()
    if event is not None and event["type"] == "STOP":
        break
    node.send_output("message", pa.array([sent]))
    sent += 1
    time.sleep(0.1)

# receiver.py -- receives and prints messages
from dora import Node

node = Node()
for event in node:
    if event["type"] == "INPUT":
        print(f"Got {event['id']}: {event['value'].to_pylist()}")
    elif event["type"] == "STOP":
        break

See the Python Getting Started Guide for a full tutorial, or the Python API Reference for complete API docs.

2. Run a Rust dataflow

cd examples/rust-dataflow
dora run dataflow.yml

3. Distributed mode (ad-hoc)

# Terminal 1: start coordinator + daemon
dora up

# Terminal 2: start a dataflow (--debug enables topic inspection)
dora start dataflow.yml --attach --debug

# Terminal 3: monitor
dora list
dora logs <dataflow-id>
dora top

# Stop or restart
dora stop <dataflow-id>
dora restart --name <name>
dora down

4. Managed cluster

# Bring up a multi-machine cluster from a config file
dora cluster up cluster.yml

# Start a dataflow across the cluster
dora start dataflow.yml --name my-app --attach

# Check cluster health
dora cluster status

# Tear down
dora cluster down

See the Distributed Deployment Guide for cluster.yml configuration, label scheduling, systemd services, rolling upgrades, and operational runbooks.

CLI Commands

Lifecycle

Command	Description
`dora run <PATH>`	Run a dataflow locally (no coordinator/daemon needed)
`dora up`	Start coordinator and daemon in local mode
`dora down`	Tear down coordinator and daemon
`dora build <PATH>`	Run build commands from a dataflow descriptor
`dora start <PATH>`	Start a dataflow on a running coordinator
`dora stop <ID>`	Stop a running dataflow
`dora restart <ID>`	Restart a running dataflow (stop + re-start)

Monitoring

Command	Description
`dora list`	List running dataflows (alias: `ps`)
`dora clean`	Remove finished and failed dataflows from the coordinator
`dora logs <ID> [--node <NAME>]`	Show logs for a dataflow or node
`dora top`	Real-time resource monitor (TUI); also `dora inspect top`
`dora topic list`	List topics in a dataflow
`dora topic hz <TOPIC>`	Measure topic publish frequency (TUI)
`dora topic echo <TOPIC>`	Print topic messages to stdout
`dora topic info <TOPIC>`	Show topic type and metadata
`dora node list`	List nodes in a dataflow
`dora node info <NODE>`	Show detailed node status, inputs, outputs, and metrics
`dora node add --from-yaml <FILE>`	Add a node to a running dataflow
`dora node remove <NODE>`	Remove a node from a running dataflow
`dora node connect <SRC> <DST>`	Add a live mapping between nodes
`dora node disconnect <SRC> <DST>`	Remove a live mapping between nodes
`dora node restart <NODE>`	Restart a single node within a running dataflow
`dora node stop <NODE>`	Stop a single node within a running dataflow
`dora topic pub <TOPIC> <DATA>`	Publish JSON data to a topic
`dora param list <NODE>`	List runtime parameters for a node
`dora param get <NODE> <KEY>`	Get a runtime parameter value
`dora param set <NODE> <KEY> <VALUE>`	Set a runtime parameter (JSON value)
`dora param delete <NODE> <KEY>`	Delete a runtime parameter
`dora trace list`	List recent traces captured by the coordinator
`dora trace view <ID>`	View spans for a specific trace (supports prefix matching)
`dora record <PATH>`	Record dataflow messages to `.drec` file
`dora replay <FILE>`	Replay recorded messages from `.drec` file

Cluster management

Command	Description
`dora cluster up <PATH>`	Bring up a cluster from a cluster.yml file
`dora cluster status`	Show connected daemons and active dataflows
`dora cluster down`	Tear down the cluster
`dora cluster install <PATH>`	Install daemons as systemd services
`dora cluster uninstall <PATH>`	Remove systemd services
`dora cluster upgrade <PATH>`	Rolling upgrade: SCP binary + restart per-machine
`dora cluster restart <NAME>`	Restart a dataflow by name or UUID

Setup and utilities

Command	Description
`dora doctor`	Diagnose environment, connectivity, and dataflow health
`dora status`	Check system health (alias: `check`)
`dora new`	Generate a new project or node
`dora graph <PATH>`	Visualize a dataflow (Mermaid or HTML)
`dora expand <PATH>`	Expand module references and print flat YAML
`dora validate <PATH>`	Validate dataflow YAML and check type annotations
`dora system`	System management (daemon/coordinator control)
`dora completion <SHELL>`	Generate shell completions
`dora self update`	Update dora CLI

For full CLI documentation, see docs/cli.md. For distributed deployment, see docs/distributed-deployment.md.

Dataflow Configuration

Dataflows are defined in YAML. Each node declares its binary/script, inputs, and outputs:

nodes:
  - id: camera
    build: pip install opencv-video-capture
    path: opencv-video-capture
    inputs:
      tick: dora/timer/millis/20
    outputs:
      - image
    env:
      CAPTURE_PATH: 0
      IMAGE_WIDTH: 640
      IMAGE_HEIGHT: 480

  - id: object-detection
    build: pip install dora-yolo
    path: dora-yolo
    inputs:
      image: camera/image
    outputs:
      - bbox

  - id: plot
    build: pip install dora-rerun
    path: dora-rerun
    inputs:
      image: camera/image
      boxes2d: object-detection/bbox

Built-in timer nodes: dora/timer/millis/<N> and dora/timer/hz/<N>.

Input format: <node-id>/<output-name> to subscribe to another node’s output. Long form supports queue_size, queue_policy (drop_oldest or backpressure), and input_timeout. See the YAML Specification for details.

Type annotations: Optionally annotate ports with type URNs for static and runtime validation. See the Type Annotations Guide for the full type library.

nodes:
  - id: camera
    path: camera.py
    outputs:
      - image
    output_types:
      image: std/media/v1/Image

dora validate dataflow.yml                        # static check (warnings)
dora validate --strict-types dataflow.yml         # fail on warnings (CI)
dora build dataflow.yml --strict-types            # type check during build
DORA_RUNTIME_TYPE_CHECK=warn dora run dataflow.yml  # runtime check

Modules: Extract reusable sub-graphs into separate files with module: instead of path:. See the Modules Guide for details.

nodes:
  - id: nav_stack
    module: modules/navigation.module.yml
    inputs:
      goal_pose: localization/goal

Architecture

Dora architecture: dora CLI drives the Coordinator over a WebSocket control plane; the Coordinator launches a Daemon per host; Daemons and Nodes exchange data over the Zenoh data plane

Layer	Protocol	Purpose
CLI <-> Coordinator	WebSocket (port 6013)	Build, run, stop commands
Coordinator <-> Daemon	WebSocket	Node spawning, dataflow lifecycle
Daemon <-> Daemon	Zenoh	Distributed cross-machine communication
Node <-> Node	Zenoh SHM	Direct zero-copy data plane for messages >4KB
Daemon <-> Node	Shared memory / TCP	Control plane + small message delivery

Key components

Coordinator – orchestrates dataflow lifecycle across daemons. Persistent redb state store by default; daemons auto-reconnect on coordinator restart.
Daemon – spawns and manages nodes on a single machine. Routes messages and manages Zenoh SHM data plane.
Runtime – in-process operator execution engine. Operators run inside the runtime process, avoiding per-operator process overhead.
Nodes – standalone processes that communicate via inputs/outputs. Written in Rust, Python, C, or C++.
Operators – lightweight functions that run inside the runtime. Faster than nodes for simple transformations.

Workspace layout

binaries/
  cli/                  # dora CLI binary
  coordinator/          # Orchestration service
  daemon/               # Node manager + IPC
  runtime/              # In-process operator runtime
  ros2-bridge-node/     # ROS2 bridge binary
  mavlink2-bridge-node/ # MAVLink 2 bridge binary
  record-node/          # Dataflow message recorder
  replay-node/          # Recorded message replayer
libraries/
  core/                 # Descriptor parsing, build utilities
  message/              # Inter-component message types
  shared-memory-server/ # Zero-copy IPC
  arrow-convert/        # Arrow data conversion
  recording/            # .drec recording format
  log-utils/            # Log parsing, merging, formatting
  coordinator-store/    # Persistent coordinator state (redb)
  extensions/
    telemetry/          # OpenTelemetry tracing + metrics
    ros2-bridge/        # ROS2 interop (bridge, msg-gen, arrow, python)
    mavlink2-bridge/    # MAVLink 2 interop (Arrow ↔ MAVLink, TCP/UDP/serial)
    download/           # Download utilities
apis/
  rust/node/            # Rust node API (dora-node-api)
  rust/operator/        # Rust operator API (dora-operator-api)
  python/node/          # Python node API (PyO3)
  python/operator/      # Python operator API (PyO3)
  python/cli/           # Python CLI interface
  c/node/               # C node API
  c/operator/           # C operator API
  c++/node/             # C++ node API (CXX bridge)
  c++/operator/         # C++ operator API (CXX bridge)
examples/               # Example dataflows

Language Support

Language	Node API	Operator API	Docs	Status
Rust	`dora-node-api`	`dora-operator-api`	API Reference	First-class
Python >= 3.11	`pip install dora-rs`	included	Getting Started, API Reference	First-class
C	`dora-node-api-c`	`dora-operator-api-c`	API Reference	Supported
C++	`dora-node-api-cxx`	`dora-operator-api-cxx`	API Reference	Supported
ROS2 >= Foxy	`dora-ros2-bridge`	–	Bridge Guide	Experimental

Platform support

Platform	Rust / Python	C / C++ templates
Linux (x86_64, ARM64, ARM32)	First-class (PR-gated)	First-class (nightly-gated)
macOS (ARM64)	First-class (nightly-gated)	Best effort (nightly-gated)
Windows (x86_64)	Best effort (nightly-gated)	Best effort (not gated)
WSL (x86_64)	Best effort	Best effort (not gated)

Gate meanings (#1716):

PR-gated — every PR to main runs these tests; merge is blocked on failure.
Nightly-gated — the daily scheduled run (.github/workflows/nightly.yml) runs these. A failure auto-files a nightly-regression issue but does NOT block PRs.
Not gated — no automated CI coverage. Regressions surface via user reports.

dora new --lang rust/python template tests run in nightly across all three platforms; C/C++ variants run in nightly on Linux only. Developers who need cross-platform verification before merge can run make qa-test / make qa-examples / make qa-nightly locally. See docs/testing-matrix.md for the full rationale.

Examples

Core language examples

Example	Language	Description
rust-dataflow	Rust	Basic Rust node pipeline
python-dataflow	Python	Python sender/transformer/receiver
python-operator-dataflow	Python	Python operators (in-process)
python-dataflow-builder	Python	Pythonic imperative API
c-dataflow	C	C node example
c++-dataflow	C++	C++ node example
c++-arrow-dataflow	C++	C++ with Arrow data
cmake-dataflow	C/C++	CMake-based build

Composition

Example	Language	Description
module-dataflow	Python	Reusable module composition
typed-dataflow	Python	Type annotations with `dora validate`

Communication patterns

Example	Language	Description
service-example	Rust	Request/reply with `request_id` correlation
action-example	Rust	Goal/feedback/result with cancellation
streaming-example	Python	Token-by-token generation with session/seq/fin metadata

See docs/patterns.md for the full guide.

Dynamic topology

Example	Language	Description
dynamic-add-remove	Python	Add/remove nodes from running dataflows
dynamic-agent-tools	Python	AI agent with dynamically-added tools

Advanced patterns

Example	Language	Description
python-async	Python	Async Python nodes
python-concurrent-rw	Python	Concurrent read-write patterns
python-multiple-arrays	Python	Multi-array handling
python-drain	Python	Event draining patterns
multiple-daemons	Rust	Distributed multi-daemon setup
rust-dataflow-git	Rust	Git-based dataflow loading
rust-dataflow-url	Rust	URL-based dataflow loading

Logging

Example	Language	Description
python-logging	Python	Python logging integration
python-log	Python	Basic Python log output
log-sink-tcp	YAML	TCP-based log sink
log-sink-file	YAML	File-based log sink
log-sink-alert	YAML	Alert-based log sink
log-aggregator	Python	Centralized log aggregation via `dora/logs`

Performance

Example	Language	Description
benchmark	Rust/Python	Latency and throughput benchmark
ros2-comparison	Python	Dora vs ROS2 comparison
cuda-benchmark	Rust/CUDA	GPU zero-copy benchmark

ROS2 integration

Example	Description
ros2-bridge/rust	Rust ROS2 topics, services, actions
ros2-bridge/python	Python ROS2 integration
ros2-bridge/c++	C++ ROS2 integration
ros2-bridge/yaml-bridge	YAML-based ROS2 topic bridge
ros2-bridge/yaml-bridge-service	YAML ROS2 service bridge
ros2-bridge/yaml-bridge-action	YAML ROS2 action client
ros2-bridge/yaml-bridge-action-server	YAML ROS2 action server

MAVLink 2 integration

Example	Description
mavlink2-bridge (Rust)	MAVLink 2 ↔ dora bridge, Rust telemetry consumer
mavlink2-bridge (Python)	Same bridge, Python telemetry consumer (`--uv`)
mavlink2-bridge (C++)	Same bridge, C++ telemetry consumer (`cargo run --example mavlink2-bridge-cxx`)
mavlink2-bridge-sitl-mission	Closed-loop ArduCopter SITL: arm + takeoff + hover + land driven from a Python dora node (Ubuntu / macOS, local-only)

Development

Rust edition 2024; MSRV and default workspace package metadata are tracked in [workspace.package] of the root Cargo.toml. Most crates inherit the workspace version via version.workspace = true; a handful (e.g. apis/rust/operator/types, the examples/error-propagation/* samples) pin their own version independently.

Build

# Build all (excluding Python packages which require maturin)
cargo build --all \
  --exclude dora-node-api-python \
  --exclude dora-operator-api-python \
  --exclude dora-ros2-bridge-python

# Build specific package
cargo build -p dora-cli

Test

# Run all tests
cargo test --all \
  --exclude dora-node-api-python \
  --exclude dora-operator-api-python \
  --exclude dora-ros2-bridge-python

# Test single package
cargo test -p dora-core

# Smoke tests (requires coordinator/daemon)
cargo test --test example-smoke -- --test-threads=1

Lint and format

cargo clippy --all
cargo fmt --all -- --check

Run examples

cargo run --example rust-dataflow
cargo run --example python-dataflow
cargo run --example benchmark --release

Quality assurance

Dora ships with a three-tier QA system designed for AI-authored code. Everything runs locally first; CI mirrors the same scripts.

make qa-install        # one-time: install cargo-audit, cargo-deny, cargo-llvm-cov, cargo-mutants, cargo-semver-checks
make qa-fast           # ~15s    -- fmt + clippy + audit + unwrap-budget + typos (pre-commit)
make qa-full           # ~5-10m  -- qa-fast + tests + coverage (pre-push)
make qa-deep           # ~15m    -- qa-full + mutation testing + semver (target Tier 1 gate, stronger than today's CI; alias: qa-tier1)
make qa-nightly        # ~3-4h -- qa-deep + proptest@1000 + miri + example-smoke + ci-nightly-jobs (full parity with .github/workflows/nightly.yml)
make qa-release-gate   #         -- qa-deep + semver (Tier 3 automatable; audit/dogfood are human)
make qa-mutation-audit # ~10-18h -- full-repo cargo-mutants; deliberate test-quality audit
make qa-examples       # ~15-20m -- run all smoke-eligible example dataflows end-to-end (skips CUDA/ROS2/C++/interactive)

On Ubuntu, install ripgrep separately and install typos-cli with Cargo:

sudo apt update
sudo apt install ripgrep
cargo install typos-cli

Gates in place:

Supply chain – cargo-audit + cargo-deny for CVEs, license policy, dependency bans
Unwrap ratchet – counts .unwrap() / .expect( in production code; can only go down (.unwrap-budget)
Coverage – cargo-llvm-cov with diff-coverage gate (70% on PR-touched lines)
Mutation testing – cargo-mutants against critical crates (library crates at package scope, binary crates with test_workspace = true)
Property testing – proptest on wire-protocol types; catches edge cases unit tests miss
Miri – UB detection on pure-Rust unsafe hotspots (e.g., dora-core::metadata)
SemVer check – cargo-semver-checks against the last git tag
Adversarial LLM review – scripts/qa/adversarial.sh runs a different model on your diff to catch single-model blind spots (local today; CI pending API secret)

Reference docs:

Contributor QA Cheat Sheet – contributor-oriented setup, day-to-day commands, and PR validation checklist
QA Runbook – day-to-day command reference, failure modes, and fixes
Agentic QA Strategy – full three-tier design and rationale
POC Report – case studies, metrics, lessons learned, recommendations for the wider ecosystem

Contributing

We welcome contributors of all experience levels. See the contributing guide to get started.

For non-trivial work, discuss the approach in a GitHub issue, discussion, or Discord thread before implementing it. Before opening or updating a PR, run the QA level appropriate for the change and include the validation you ran in the PR description. The Contributor QA Cheat Sheet is the fastest day-to-day reference; the stricter per-change policy lives in docs/agentic-qa-policy.md.

Communication

Agentic Engineering

This repository is built with agentic engineering. AI agents collaborate on day-to-day work – code generation, reviews, refactoring, testing, drafting PR comments, triaging nightly regressions – while maintainers set direction, review judgments, and authorize what ships. The two roles compound: AI agents move fast on mechanical work; humans catch the things that matter.

License

Apache-2.0. See NOTICE.md for details.

Website | Python API | Rust API | Guide | Discord

Dora

Table of Contents

Features

Performance

Developer experience

Production readiness

Debugging and observability

Ecosystem

Installation

From crates.io (recommended)

From source

Platform installers

Build features

Quick Start

1. Run a Python dataflow

2. Run a Rust dataflow

3. Distributed mode (ad-hoc)

4. Managed cluster

CLI Commands

Lifecycle

Monitoring

Cluster management

Setup and utilities

Dataflow Configuration

Architecture

Key components

Workspace layout

Language Support

Platform support

Examples

Core language examples

Composition

Communication patterns

Dynamic topology

Advanced patterns

Logging

Performance

ROS2 integration

MAVLink 2 integration

Development

Build

Test

Lint and format

Run examples

Quality assurance

Contributing

Communication

Agentic Engineering

License