Adora

智能数据流导向机器人架构 (Agentic Dataflow-Oriented Robotic Architecture) – 一个 100% Rust 的实时机器人与 AI 应用框架。

为什么选择 Adora？

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
Apache Arrow native – columnar memory format end-to-end with zero serialization overhead; shared across all language bindings

Single CLI, full lifecycle – adora run for local dev, adora 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
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, and nested composition
Hot reload – live-reload Python operators without restarting the dataflow

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
Configurable queue policies – drop_oldest (default) or backpressure per input, with metrics on dropped messages
OpenTelemetry – built-in structured logging with rotation/routing, metrics, distributed tracing

Record/replay – capture dataflow messages to .adorec files, replay offline at any speed with node substitution
Topic inspection – topic echo to print live data, topic hz TUI for frequency analysis, topic info for schema and bandwidth
Resource monitoring – adora top TUI showing per-node CPU, memory, queue depth, network I/O across all machines
Log aggregation – subscribe to adora/logs to receive structured log messages from all nodes without extra wiring
Trace inspection – trace list and trace view for viewing coordinator spans without external infrastructure

Communication patterns – built-in service (request/reply), action (goal/feedback/result), and streaming (session/segment/chunk) patterns via well-known metadata keys
ROS2 bridge – bidirectional interop with ROS2 topics, services, and actions
In-process operators – lightweight functions that run inside a shared runtime, avoiding per-node process overhead