## 009_artifact_store (completed)

**Status**: Completed  
**Completed**: 2025-12-13

---

## Final Report

### What Was Implemented

| Component | Description |
|-----------|-------------|
| `ArtifactStore` | Abstract base class for artifact storage |
| `InMemoryArtifactStore` | In-memory implementation for testing |
| `FileArtifactStore` | File-based implementation for persistence |
| `Artifact` | Data class with content and metadata |
| `ArtifactMetadata` | Metadata: ID, content_type, size, timestamps, tags |
| `artifact_ref()` | Create artifact reference for vars |
| `is_artifact_ref()` | Check if value is artifact reference |
| `resolve_artifact()` | Resolve reference to content |
| `compute_artifact_id()` | Compute content-addressed ID |
| `list_all()` | List all artifacts (for garbage collection) |
| `delete_by_run()` | Delete all artifacts for a run |

### Key Features

1. **Content-Addressed IDs**: Artifact IDs are SHA-256 hashes of content (first 32 chars), enabling deduplication.

2. **Metadata-Rich**: Each artifact stores:
   - `artifact_id`: Content-addressed or explicit ID
   - `content_type`: MIME type or semantic type
   - `size_bytes`: Content size
   - `created_at`: ISO timestamp
   - `run_id`: Optional association with a workflow run
   - `tags`: Key-value metadata

3. **Convenience Methods**:
   - `store_text()` / `load_text()`: Text content
   - `store_json()` / `load_json()`: JSON-serializable data

4. **Reference Pattern**: Store artifact, get ID, put reference in vars:
   ```python
   metadata = artifact_store.store_json(large_data, run_id=run.run_id)
   run.vars["result"] = artifact_ref(metadata.artifact_id)
   ```

5. **Runtime Integration**: Runtime accepts optional `artifact_store` parameter.

### Files Added/Modified

**New:**
- `src/abstractruntime/storage/artifacts.py` - Full implementation
- `tests/test_artifacts.py` - 31 comprehensive tests

**Modified:**
- `src/abstractruntime/__init__.py` - Exports
- `src/abstractruntime/core/runtime.py` - `artifact_store` property
- `src/abstractruntime/scheduler/convenience.py` - `artifact_store` parameter

### Security

- **Path traversal protection**: Artifact IDs are validated to contain only alphanumeric characters, hyphens, and underscores
- **Content-addressed by default**: IDs are SHA-256 hashes, preventing collisions

### Test Coverage (44 tests)

- InMemoryArtifactStore: store, load, delete, exists, list_by_run, list_all, tags
- FileArtifactStore: persistence, file structure, cross-instance, list_all
- Convenience methods: store_text, store_json, load_text, load_json
- Artifact references: artifact_ref, is_artifact_ref, resolve_artifact
- Runtime integration: artifact_store property, ScheduledRuntime
- Workflow integration: store results, pass between nodes
- Edge cases: empty content, large content, binary, unicode
- Security: path traversal prevention, artifact ID validation
- Cleanup: delete_by_run, compute_artifact_id for deduplication

### Usage Example

```python
from abstractruntime import (
    create_scheduled_runtime,
    InMemoryArtifactStore,
    artifact_ref,
    resolve_artifact,
)

# Create runtime with artifact store
artifact_store = InMemoryArtifactStore()
sr = create_scheduled_runtime(artifact_store=artifact_store)

# In a workflow node:
def process_node(run, ctx):
    large_result = {"items": [...]}  # Large data
    
    # Store as artifact instead of embedding in vars
    metadata = artifact_store.store_json(large_result, run_id=run.run_id)
    
    return StepPlan(
        node_id="process",
        complete_output={"result_ref": artifact_ref(metadata.artifact_id)},
    )

# Later, resolve the reference:
ref = state.output["result_ref"]
artifact = resolve_artifact(ref, artifact_store)
data = artifact.as_json()
```

### Recommendations for Next Steps

1. **S3/Object Storage Backend**: For production deployments, implement an S3-compatible backend.

2. **Automatic Artifact Cleanup**: Add garbage collection for orphaned artifacts.

3. **Effect Handler Integration**: Consider auto-storing large LLM responses as artifacts.

---

## Original Proposal

### Goal
Store large payloads (documents, images, tool outputs) by reference instead of embedding into `RunState.vars`.

### Rationale
Durable state must stay JSON-serializable and reasonably sized.

### MVP design
- `ArtifactStore` interface
- file-based implementation
- store `artifact_id` references inside `RunState.vars`

### Future
- S3 / object storage backend
- content-addressed storage

