sparkient-edge

Local decisions from Sparkient edge bundles without a Sparkient API call after download.

Train a decision model on Sparkient, export an edge bundle, and run predictions in a compatible Python 3.10+ environment without a network call.

Status: sparkient-edge is beta software. Benchmark the complete bundle on the target hardware before relying on its latency or capacity in production.

Quick Start

pip install "sparkient-edge[all]"

from sparkient_edge import EdgePredictor

# Load a bundle exported from the Sparkient dashboard or API
predictor = EdgePredictor.from_bundle("my_decision_type.zip")

# Make a local decision — no network; measure latency on target hardware
result = predictor.predict({
    "amount": 750,
    "account_age_days": 45,
    "risk_score": 0.55,
})

print(result.decision)            # "review"
print(result.confidence)          # 0.9985
print(result.stage)               # "classifier"
print(result.class_probabilities) # {"approve": 0.0006, "review": 0.9985, "block": 0.0009}

How It Works

1. Export an edge bundle from Sparkient (dashboard or GET /api/v1/decision-types/{id}/export)
2. Load the bundle with EdgePredictor.from_bundle("path/to/bundle.zip")
3. Predict with predictor.predict(your_input) — runs expression rules then compiled classifier locally

The bundle contains the compiled model, feature configuration, expression rules, metadata, and text-processing assets when applicable. Bundle size and local latency depend on the decision type, model, and target hardware.

Optional Dependencies

The quick start installs every runtime feature so exported rules cannot be skipped. Use the minimal base install only for rule-free bundles when you do not need MCP:

pip install "sparkient-edge[all]"    # Everything; recommended
pip install "sparkient-edge[rules]"  # Expression rule evaluation
pip install "sparkient-edge[mcp]"    # MCP only; use only with rule-free bundles

The base package includes ONNX Runtime, NumPy, and the tokenizer needed by exported text models. Compatible wheels must exist for the target Python version, operating system, and architecture.

MCP Server

Run as a local Model Context Protocol server for AI assistant integration:

pip install "sparkient-edge[all]"
sparkient-edge                    # Uses the CLI entry point
python -m sparkient_edge          # Or run as a module

Add to your MCP client config (Claude Desktop, Cursor, etc.):

{
  "mcpServers": {
    "sparkient-edge": {
      "command": "sparkient-edge",
      "args": []
    }
  }
}

EdgeDecision Fields

Limits

• The package runs exported rules and classifiers only. It does not use Sparkient's optional cloud LLM escalation.
• Bundle size, local latency, memory use, and throughput depend on the model and target hardware.
• Training, retraining, and bundle export still use the Sparkient service. Edge export is available on Growth and Scale plans.
• This Python package is not a native Android, iOS, microcontroller, or embedded-device SDK.
• Local compute and the applicable Sparkient plan still have a cost; offline inference is not the same as free inference.

Links

• Documentation
• Sparkient Platform
• API Reference