ruledsl

Python binding and desktop workbench for RuleDSL — a deterministic, embedded business-rule engine with a stable C ABI. Same input, same bytecode, same decision — on every run and every platform.

This package is pure Python (ctypes, zero dependencies). The engine library itself (ruledsl_capi.dll / libruledsl_capi.so) is not inside — download an SDK bundle from GitHub Releases and point the binding at it.

The workbench — authoring & replay companion

pip install ruledsl
ruledsl-workbench --dll path/to/ruledsl_capi.dll

A zero-dependency Tkinter desktop tool for the two human ends of the rule pipeline (it is not a server component — servers embed the C ABI directly):

• Authoring: edit a ruleset (or File > Open Rules…), run it against sample inputs, read the decision, its output fields, the engine's own evaluation trace (why this decision), and a canonical decision hash. Run 100× asserts every run produced the identical hash — the determinism contract, live.
• Replay: File > Open Bytecode (Replay)… loads a compiled .axbc exactly as your server ran it (no recompilation; the editor is disabled), and File > Load Inputs from JSON… restores an incident's inputs. Same bytecode, same input → the same decision and trace, on your desk.
• Deliberately no .axbc export: production artifacts come from ruledslc, which stamps the authenticity manifest.

The binding

from ruledsl import RuleDSL

with RuleDSL("path/to/ruledsl_capi.dll") as engine:   # .so on Linux
    bytecode = engine.compile('rule r1 { when amount > 100; then decline; }')
    decision = engine.evaluate(bytecode, {"amount": 250.0})
    print(decision.action, decision.rule_name)         # DECLINE r1

    trace = []
    engine.evaluate(bytecode, {"amount": 250.0}, on_trace=trace.append)
    print("\n".join(trace))                            # why this decision

Deterministic by contract: the engine never reads the system clock (time-based rules require an explicit now_utc_ms), never touches the network, and returns stable, append-only error codes.

The MCP server — optional [mcp] extra (EXPERIMENTAL)

pip install "ruledsl[mcp]"
ruledsl-mcp --rules path/to/rules --decision-log decisions.jsonl --engine-lib path/to/libruledsl_capi.so

An MCP (Model Context Protocol) stdio server that lets an AI agent invoke deterministic rule evaluation — never produce the decision itself. Exactly three tools (list_rules, evaluate_case, engine_info), a hash-verified rule-library manifest, a mandatory explicit now_utc_ms (the server never reads a clock), and a canonical JSONL decision log with a replayable decision_hash per decision. Requires Python 3.10+; setup and Claude Desktop configuration: MCP quickstart.

Changelog

• 1.1.1 — self-contained pip path for MCP: the example rule library ships inside the wheel (ruledsl-mcp --print-example-rules), the smoke client is a module (python -m ruledsl_mcp.smoke, no checkout needed), a clear Python 3.10+ runtime message, and serverInfo.version reports the package.
• 1.1.0 — MCP server migrated into the package as the optional [mcp] extra + ruledsl-mcp console command (EXPERIMENTAL). Base package unchanged and still zero-dependency.
• 1.0.3 — workbench: Cases batch runner (JSON/JSONL) + humane explicit clock (ISO-8601 ⇄ epoch-ms echo, Now (UTC) button).
• 1.0.2 — first public release: binding (on_trace, cached buffers) + workbench (authoring, replay, JSON inputs).

Version & license

Package 1.x targets engine 1.0.x (ABI 1); the minor version moves when the package itself gains capability (e.g. 1.1.0 added the [mcp] extra). Licensed under the PolyForm Free Trial License 1.0.0 — evaluation use; commercial production use requires a separate agreement (see the repository).