Build
Physical AI.
The development platform for Physical AI.
From first run to production. Full lineage. No gaps.
Physical AI Tooling
Is Broken.
No lineage between your data, models, failures, and rollouts.
Evaluation is an afterthought bolted onto training pipelines.
Local dev and remote training are completely different worlds.
Debugging means stitching together disconnected tools.
Anveld Closes the Loop.
Local Development
RunSpec Parity
One frozen spec runs locally or on Kubernetes. No field changes, no drift.
Simulator-First
Bootstrap to your first replayable run in under 30 minutes on supported Linux.
Hot Reload
Iterate on models and policies without restarting your environment.
Offline-Capable
Work without connectivity. Artifacts spool locally and sync when reconnected.
Data & Lineage
Artifact Graph
Every model, config, dataset, and recording is versioned with full provenance.
Recordings
Multimodal capture with replay-oriented indexing. Seek within 2 seconds across 50 GB runs.
Dataset Slicing
Extract reusable data subsets from any recording with lineage preserved end to end.
Retention Policies
Ephemeral, standard, or durable retention classes for every artifact you produce.
Training & Evaluation
DAG Pipelines
Define multi-stage training pipelines in Python. Run locally or on Kubernetes with the same spec.
Scenario Engine
Versioned test cases with inputs, seed policies, and validators. Deterministic and reproducible.
EvalPacks
Bundle scenarios, metrics, thresholds, and baselines into repeatable evaluation suites.
Promotion Gates
Auditable promotion decisions with exact evidence linking model, config, eval, and actor.
Debugging & Observability
Unified Timeline
Align replay, logs, metrics, traces, and events on a single scrubable timeline.
Run Diffing
Compare any two runs or a candidate against a baseline side by side.
Failure Clustering
Group failures by tags and export them directly as training data or new scenarios.
Annotations
Comment on runs, tag artifacts, and share investigation context with your team.
Built for Engineers.
01from anveld import train02from anveld.config import load_config0304config = load_config("anveld.yaml")0506result = train(07 config=config,08 env="mujoco://franka-pick",09 trainer="ppo",10)1112result.evaluate(pack="pick-place-v2")13result.promote(baseline="production")