# Programmatically manage binary, file-based assets

Clarifai artifacts are binary, file-based assets associated with your apps. An artifact acts as a logical container for any binary or structured output produced by a user’s workload.

At a high level, artifacts are:

- **User-defined** — Created explicitly by users or automatically by [Pipelines](https://docs.clarifai.com/compute/pipelines/create-api). Pipelines support long-running workloads such as model training, fine-tuning, and data preprocessing, which produce artifact [files](https://docs.clarifai.com/create/artifacts/#common-artifact-examples) used in downstream tasks.
- **Versioned** — Each artifact contains one or more immutable `ArtifactVersions`, representing distinct snapshots or checkpoints over time.
- **Taggable and searchable** — Artifacts support metadata and tagging, making them easy to organize, filter, and discover.
- **Linked** — Artifacts can be associated with Clarifai resources such as `PipelineVersionRuns`, Models, or Datasets, establishing clear data lineage and traceability.
- **Timestamped** — Artifacts track lifecycle timestamps including `created_at`, `modified_at`, and `deleted_at`.
- **Resumable** — Runs can resume from stored artifact checkpoints (exact URI and checksum) instead of recomputing from scratch, significantly reducing training time and cost.
- **Reproducible and auditable** — Pipeline runs can persist the exact files used to produce results (weights, checkpoints, configs, logs), ensuring experiments are reproducible and verifiable.
- **Access-controlled and workspace-scoped** — Artifacts inherit app and project permissions, enabling secure sharing and [collaboration](https://docs.clarifai.com/create/applications/manage#collaborators). Shared artifacts can be reused across multiple pipeline version runs without duplication.
- **Modality-agnostic** — Unlike entities such as inputs, models, or datasets — which are semantically tied to specific modalities or roles — an artifact provides a generic abstraction for storing arbitrary data, regardless of format or purpose.

## Common Artifact Examples

Artifacts can store a wide range of assets, including:

- Model checkpoints and weights (`.ckpt`, `.pt`, `.bin`)
- Training logs or metrics (`.json`, `.csv`)
- Preprocessed embeddings (`.npy`, `.pkl`)
- Packaged datasets (`.tar.gz`, `.parquet`)
- Serialized configuration or tokenizer files (`.yaml`, `.json`)

## Artifact Resource Paths

Artifacts are addressed using fully qualified Clarifai resource paths:

```
users/{user-id}/apps/{app-id}/artifacts/{artifact-id}
```

Artifact versions are addressed as:

```
users/{user-id}/apps/{app-id}/artifacts/{artifact-id}/versions/{version-id}
```

These paths are used consistently across the CLI and SDK when managing artifacts and their versions.

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