Runtime Model

BlazeRules has one rule engine and several ways to feed it.

Runtime	What it is	Use it when
`blazerules` Python module	Direct Python binding to the engine.	Your app already controls batching or uses PyArrow/JSON.
C++ library	Native API for embedding in a service.	You want the engine inside a C++ process.
`blazerules_io`	Kafka, binary decoders, file readers, CDC helpers.	You need broker/file/format adapters but still own the loop.
`blazerules_agent`	Local ingest binary for HTTP, stdin, and file tails.	You want a small local collector without writing code.
`blazerules_dashboard`	Read-only UI over decision/DLQ/metrics/result files.	You want to inspect decisions and failures locally.
Helm chart	Agent DaemonSet plus dashboard Deployment.	You want node-local Kubernetes log collection.

Embedded Python

import blazerules

engine = blazerules.RuleEngine()
engine.load_rules("rules.yaml")
result = engine.evaluate_ndjson(payload)

Use this when your application already has events in memory and can call the engine once per batch.

IO Module

import blazerules_io

payload = blazerules_io.read_ndjson_bytes("s3://bucket/events/day.ndjson")

Use this when you need file, Kafka, CDC, or binary decoding helpers but still want your application to own control flow.

Agent

blazerules_agent --rules rules.yaml --input http --port 9480 --output ndjson --output-path decisions.ndjson

Use the agent for local log-style ingestion: HTTP /v1/logs, stdin, or file tails. The agent still evaluates in batches; it is not a per-record rule server.

Dashboard

blazerules_dashboard \
  --decision-log decisions.ndjson \
  --dead-letter-log dead_letters.ndjson \
  --rules rules.yaml

The dashboard is read-only. It does not mutate rules or engine state.

Kubernetes

The Helm chart runs the agent as a DaemonSet and the dashboard as a Deployment. The default mode tails container logs from each node and writes decision/DLQ files under /var/log/blazerules.

See Kubernetes Log Ingestion and Deployment.