sparkient-edge

Sub-10ms local decisions from Sparkient edge bundles — zero cloud dependencies.

Train your decision model on Sparkient, export an edge bundle, and run predictions locally with no network, no database, and no API calls.

Quick Start

pip install sparkient-edge

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 — sub-10ms, no network
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 everything needed: a compiled model, feature configuration, expression rules, metadata, and a text model for text processing (when applicable). Typical bundle size is 1–10 MB.

Optional Dependencies

The base install includes only onnxruntime and numpy. Install extras for additional features:

pip install sparkient-edge[all]    # Everything
pip install sparkient-edge[rules]  # Expression rule evaluation
pip install sparkient-edge[text]   # Text field processing (tokenizers)
pip install sparkient-edge[mcp]    # MCP server support
Extra Adds When needed
rules cel-expr-python Decision types with expression-based hard rules
text tokenizers Decision types with free-text input fields
mcp mcp Running as a local MCP server
all All of the above Recommended for full functionality

MCP Server

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

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

Field Type Description
decision str The chosen outcome (e.g., "approve")
confidence float Confidence score (0.0 to 1.0)
reason_codes list[str] Why this decision was made
stage str Which stage decided: "rules", "classifier", or "fallback"
class_probabilities dict[str, float] Probability for each option
rules_triggered list[str] Names of rules that fired

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Credits

Author: Sparkient

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.10

Provides Extra

all mcp rules text

Tags

ai decisions edge inference onnx sparkient

Classifiers

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