rust-logic-graph 0.8.5

A modular reasoning graph framework for distributed logic orchestration
Documentation

🧠 Rust Logic Graph

Rust License: MIT GitHub CI

A high-performance reasoning graph framework for Rust with GRL (Grule Rule Language) support. Build complex workflows with conditional execution, topological ordering, and async processing.


✨ Key Features

  • πŸ”₯ GRL Support - rust-rule-engine v0.14.0 with RETE-UL algorithm (2-24x faster)
  • πŸ”„ Topological Execution - Automatic DAG-based node ordering
  • ⚑ Async Runtime - Built on Tokio for high concurrency
  • ⚑ Parallel Execution - Automatic parallel execution of independent nodes (v0.5.0)
  • πŸ’Ύ Caching Layer - High-performance result caching with TTL, eviction policies, and memory limits (v0.5.0)
  • 🧠 Memory Optimization - Context pooling and allocation tracking (v0.7.0)
  • πŸ› οΈ CLI Developer Tools - Graph validation, dry-run, profiling, and visualization (v0.5.0)
  • 🎨 Web Graph Editor - Next.js visual editor with drag-and-drop interface (v0.8.0)
  • οΏ½ YAML Configuration - Declarative graph definitions with external config files (v0.8.5)
  • οΏ½πŸ“Š Multiple Node Types - RuleNode, DBNode, AINode
  • πŸ“ JSON/YAML Configuration - Simple workflow definitions
  • 🎯 98% Drools Compatible - Easy migration from Java
  • 🌊 Streaming Processing - Stream-based execution with backpressure (v0.3.0)
  • πŸ—„οΈ Database Integrations - PostgreSQL, MySQL, Redis, MongoDB (v0.2.0)
  • πŸ€– AI/LLM Integrations - OpenAI, Claude, Ollama (v0.2.0)

πŸš€ Quick Start

Installation

[dependencies]
rust-logic-graph = "0.8.5"

# With specific integrations
rust-logic-graph = { version = "0.8.5", features = ["postgres", "openai"] }

# With all integrations
rust-logic-graph = { version = "0.8.5", features = ["all-integrations"] }

Simple Example

use rust_logic_graph::{RuleEngine, GrlRule};

let grl = r#"
rule "Discount" {
    when
        cart_total > 100 && is_member == true
    then
        discount = 0.15;
}
"#;

let mut engine = RuleEngine::new();
engine.add_grl_rule(grl)?;

🏒 Real-World Case Study: Purchasing Flow System

See a complete production implementation in case_study/ - A full-featured purchasing automation system built with Rust Logic Graph.

πŸ“Š System Overview

Problem: Automate purchasing decisions for inventory replenishment across multiple products, warehouses, and suppliers.

Solution: Business rules in GRL decide when/how much to order. Orchestrator executes the workflows.

🎯 Two Architecture Implementations

1. Microservices (v4.0) - 7 services with gRPC

  • Orchestrator (port 8080) - Workflow coordination
  • OMS Service (port 50051) - Order management data
  • Inventory Service (port 50052) - Stock levels
  • Supplier Service (port 50053) - Supplier information
  • UOM Service (port 50054) - Unit conversions
  • Rule Engine (port 50055) - GRL business rules
  • PO Service (port 50056) - Purchase order management

2. Monolithic - Single HTTP service

  • Same business logic as microservices
  • Single process on port 8080
  • Shared GRL rules file
  • Direct function calls instead of gRPC

πŸ”₯ GRL Business Rules (15 Rules)

rule "CalculateShortage" salience 120 no-loop {
  when
    required_qty > 0
  then
    Log("Calculating shortage...");
    shortage = required_qty - available_qty;
    Log("Shortage calculated");
}

rule "OrderMOQWhenShortageIsLess" salience 110 no-loop {
  when
    shortage > 0 && shortage < moq && is_active == true
  then
    Log("Shortage less than MOQ, ordering MOQ");
    order_qty = moq;
}

See full rules: purchasing_rules.grl

YAML Configuration (NEW in v0.8.5)

Both Monolithic and Microservices implementations now support YAML-based graph configuration:

# purchasing_flow_graph.yaml
nodes:
  oms_grpc:
    type: DBNode
    description: "Fetch order management data"
  
  inventory_grpc:
    type: DBNode
    description: "Fetch inventory levels"
  
  rule_engine_grpc:
    type: RuleNode
    description: "Evaluate business rules"
    dependencies:
      - oms_grpc
      - inventory_grpc

edges:
  - from: oms_grpc
    to: rule_engine_grpc
  - from: inventory_grpc
    to: rule_engine_grpc

Benefits:

  • βœ… 70% less code - Graph definition moves from code to YAML
  • βœ… No recompile - Change workflows without rebuilding
  • βœ… Multiple workflows - Easy to create variants (urgent, standard, approval)
  • βœ… Better readability - Clear, declarative graph structure
  • βœ… Easy testing - Test with different configurations

Usage:

// Default config
executor.execute("PROD-001").await?;

// Custom workflow
executor.execute_with_config("PROD-001", "urgent_flow.yaml").await?;

Documentation: See YAML_CONFIGURATION_SUMMARY.md

Microservices Communication Flow

After v0.8.0 refactor, the Orchestrator now uses rust-logic-graph's Graph/Executor pattern to coordinate microservices:

  • The Orchestrator receives a purchasing request (HTTP) and creates a Graph with 6 custom gRPC Nodes.
  • Each Node wraps a gRPC call to a service: OmsGrpcNode, InventoryGrpcNode, SupplierGrpcNode, UomGrpcNode, RuleEngineGrpcNode, PoGrpcNode.
  • The Executor runs the graph in topological order:
    1. Data Collection Phase (parallel): OMS, Inventory, Supplier, UOM nodes execute simultaneously via gRPC
    2. Rule Evaluation Phase: RuleEngineGrpcNode waits for all data, then evaluates GRL rules
    3. Execution Phase: PoGrpcNode creates/sends PO based on rule decisions
  • All business logic (decision flags, calculations) comes from GRL rules. The Orchestrator is a pure executor.

Graph Topology:

OMS Node ────┐
             β”‚
Inventory ───┼──→ RuleEngine Node ──→ PO Node
             β”‚
Supplier ─────
             β”‚
UOM Node β”€β”€β”€β”€β”˜

Benefits of Graph/Executor Pattern:

  • βœ… Declarative: Define workflow as nodes + edges instead of imperative code
  • βœ… Parallel Execution: Data nodes run concurrently automatically
  • βœ… Type Safety: Custom Node implementations with Rust's type system
  • βœ… Testable: Each node can be tested in isolation
  • βœ… Consistent: Same pattern used in monolithic and microservices
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚                         CLIENT (HTTP REST)                          β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                                 β”‚ POST /purchasing/flow
                                 β–Ό
        β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
        β”‚            Orchestrator Service (Port 8080)                β”‚
        β”‚  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”   β”‚
        β”‚  β”‚          rust-logic-graph Graph Executor            β”‚   β”‚
        β”‚  β”‚                                                     β”‚   β”‚
        β”‚  β”‚  Creates Graph with 6 gRPC Nodes:                   β”‚   β”‚
        β”‚  β”‚  β€’ OmsGrpcNode      β†’ gRPC to OMS :50051            β”‚   β”‚
        β”‚  β”‚  β€’ InventoryGrpcNode β†’ gRPC to Inventory :50052     β”‚   β”‚
        β”‚  β”‚  β€’ SupplierGrpcNode β†’ gRPC to Supplier :50053       β”‚   β”‚
        β”‚  β”‚  β€’ UomGrpcNode      β†’ gRPC to UOM :50054            β”‚   β”‚
        β”‚  β”‚  β€’ RuleEngineGrpcNode β†’ gRPC to Rules :50055        β”‚   β”‚
        β”‚  β”‚  β€’ PoGrpcNode       β†’ gRPC to PO :50056             β”‚   β”‚
        β”‚  β”‚                                                     β”‚   β”‚
        β”‚  β”‚  Graph Topology:                                    β”‚   β”‚
        β”‚  β”‚  OMS ───────┐                                       β”‚   β”‚
        β”‚  β”‚  Inventory ─┼─→ RuleEngine ──→ PO                   β”‚   β”‚
        β”‚  β”‚  Supplier ───                                       β”‚   β”‚
        β”‚  β”‚  UOM β”€β”€β”€β”€β”€β”€β”€β”˜                                       β”‚   β”‚
        β”‚  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜   β”‚
        β””β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                 β”‚
   β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
   β”‚ (Parallel)  β”‚  (Parallel)      β”‚   (Parallel)   β”‚  (Parallel)  β”‚
   β–Ό             β–Ό                  β–Ό                β–Ό              β”‚
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”        β”‚
β”‚OMS :50051β”‚  β”‚Inventory   β”‚  β”‚Supplier     β”‚  β”‚UOM :50054 β”‚        β”‚
β”‚          β”‚  β”‚:50052      β”‚  β”‚:50053       β”‚  β”‚           β”‚        β”‚
β”‚β€’ History β”‚  β”‚β€’ Levels    β”‚  β”‚β€’ Pricing    β”‚  β”‚β€’ Convert  β”‚        β”‚
β”‚β€’ Demand  β”‚  β”‚β€’ Available β”‚  β”‚β€’ Lead Time  β”‚  β”‚β€’ Factors  β”‚        β”‚
β””β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”˜  β””β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜  β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜  β””β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”˜        β”‚
     β”‚              β”‚                β”‚               β”‚              β”‚
     β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜              β”‚
                          β”‚                                         β”‚
                          β”‚ Data stored in Graph Context            β”‚
                          β–Ό                                         β”‚
                   β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”                              β”‚
                   β”‚ Rule Engine     β”‚ (Port 50055 - gRPC)          β”‚
                   β”‚     :50055      β”‚                              β”‚
                   β”‚                 β”‚                              β”‚
                   β”‚ β€’ GRL Rules     β”‚ β€’ Evaluates 15 rules         β”‚
                   β”‚ β€’ Calculations  β”‚ β€’ Returns decision flags     β”‚
                   β”‚ β€’ Decision Flagsβ”‚ β€’ NO side effects            β”‚
                   β””β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”˜                              β”‚
                            β”‚                                       β”‚
                            β”‚ Flags stored in Graph Context         β”‚
                            β–Ό                                       β”‚
                   β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”                              β”‚
                   β”‚ PO Service      β”‚ (Port 50056 - gRPC)          β”‚
                   β”‚    :50056       β”‚β—„β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                   β”‚                 β”‚
                   β”‚ β€’ Create PO     β”‚ β€’ Reads flags from context
                   β”‚ β€’ Send to       β”‚ β€’ Executes based on rules
                   β”‚   Supplier      β”‚ β€’ Email/API delivery
                   β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Note: The Rule Engine service returns decision flags and calculations to the Graph Context. The PoGrpcNode then reads these flags from the context to determine whether to create/send the PO.

Where rust-logic-graph is Used

Monolithic App (case_study/monolithic/):

  • Uses Graph, Executor, and custom Node implementations
  • 6 DB nodes query local MySQL databases directly
  • RuleEngineNode calls in-process RuleEngine
  • Single process, no network calls

Orchestrator Microservice (case_study/microservices/services/orchestrator-service/):

  • Uses Graph, Executor, and custom gRPC Node implementations
  • 6 gRPC nodes make network calls to remote services
  • Same graph topology as monolithic
  • Distributed across multiple processes

Rule Engine Service (case_study/microservices/services/rule-engine-service/):

  • Uses RuleEngine for GRL evaluation
  • Exposed via gRPC endpoint
  • Stateless service (no graph execution)

Other Microservices (OMS, Inventory, Supplier, UOM, PO):

  • Standard gRPC services with database access
  • Do NOT use rust-logic-graph directly
  • Called by Orchestrator's Graph Executor

Web Graph Editor (NEW in v0.8.0)

🌐 Online Editor: https://logic-graph-editor.amalthea.cloud/

Try the visual graph editor online - no installation required! Create workflows, define rules, and visualize your logic graphs with drag-and-drop.

CLI Tools (v0.5.0)

# Build the CLI tool
cargo build --release --bin rlg

# Validate a graph
./target/release/rlg validate --file examples/sample_graph.json

# Visualize graph structure
./target/release/rlg visualize --file examples/sample_graph.json --details

# Profile performance
./target/release/rlg profile --file examples/sample_graph.json --iterations 100

# Dry-run without execution
./target/release/rlg dry-run --file examples/sample_graph.json --verbose

Full CLI Documentation β†’

Run Examples

# Basic workflow
cargo run --example simple_flow

# GRL rules
cargo run --example grl_rules

# Advanced integration
cargo run --example grl_graph_flow


πŸ“š Documentation

Document Description
🏒 Case Study: Purchasing Flow Real production system with microservices & monolithic implementations
πŸ“‹ YAML Configuration Guide Declarative graph configuration with YAML (NEW in v0.8.5)
Graph Editor Guide Visual web-based graph editor with Next.js (NEW in v0.8.0)
Memory Optimization Guide Context pooling and allocation tracking (v0.7.0)
CLI Tool Guide Developer tools for validation, profiling, and visualization (v0.5.0)
Cache Guide Caching layer with TTL and eviction policies (v0.5.0)
Migration Guide Upgrade guide to v0.14.0 with RETE-UL (v0.5.0)
Integrations Guide Database & AI integrations (v0.2.0)
GRL Guide Complete GRL syntax and examples
Use Cases 33+ real-world applications
Extending Create custom nodes and integrations
Implementation Technical details

🎯 Use Cases

Rust Logic Graph powers applications in:

  • πŸ’° Finance - Loan approval, fraud detection, risk assessment
  • πŸ›’ E-commerce - Dynamic pricing, recommendations, fulfillment
  • πŸ₯ Healthcare - Patient triage, clinical decisions, monitoring
  • 🏭 Manufacturing - Predictive maintenance, QC automation
  • πŸ›‘οΈ Insurance - Claims processing, underwriting
  • πŸ“Š Marketing - Lead scoring, campaign optimization
  • βš–οΈ Compliance - AML monitoring, GDPR automation

View all 33+ use cases β†’


πŸ—οΈ Architecture

Rust Logic Graph architecture diagram


πŸ”₯ GRL Example

rule "HighValueLoan" salience 100 {
    when
        loan_amount > 100000 &&
        credit_score < 750
    then
        requires_manual_review = true;
        approval_tier = "senior";
}

rule "AutoApproval" salience 50 {
    when
        credit_score >= 700 &&
        income >= loan_amount * 3 &&
        debt_ratio < 0.4
    then
        auto_approve = true;
        interest_rate = 3.5;
}

Learn more about GRL β†’


πŸ“Š Performance

  • RETE-UL Algorithm: Advanced pattern matching with unlinking (v0.14.0)
  • 2-24x Faster: Than v0.10 at 50+ rules
  • 98% Drools Compatible: Easy migration path
  • Async by Default: High concurrency support
  • Parallel Execution: Automatic layer-based parallelism
  • Smart Caching: Result caching with TTL and eviction policies

πŸ§ͺ Testing & CLI Tools

# Run all tests
cargo test

# Build CLI tool
cargo build --release --bin rlg

# Validate graph
./target/release/rlg validate --file examples/sample_graph.json

# Visualize graph structure
./target/release/rlg visualize --file examples/sample_graph.json

# Profile performance
./target/release/rlg profile --file examples/sample_graph.json --iterations 100

# Dry-run execution
./target/release/rlg dry-run --file examples/sample_graph.json --verbose

Test Results: βœ… 32/32 tests passing

Learn more about CLI tools β†’


πŸ“¦ Project Status

Version: 0.8.5 (Latest) Status: Production-ready with YAML configuration, web graph editor, and real-world case study

What's Working

  • βœ… Core graph execution engine
  • βœ… RETE-UL algorithm (v0.14.0) - 2-24x faster
  • βœ… Three node types (Rule, DB, AI)
  • βœ… Topological sorting
  • βœ… Async execution
  • βœ… JSON I/O
  • βœ… Database integrations (PostgreSQL, MySQL, Redis, MongoDB)
  • βœ… AI integrations (OpenAI, Claude, Ollama)
  • βœ… Streaming processing with backpressure and chunking
  • βœ… Parallel execution with automatic layer detection
  • βœ… Caching layer with TTL, eviction policies, memory limits (v0.5.0)
  • βœ… Memory optimization with context pooling (v0.7.0)
  • βœ… CLI Developer Tools - validate, profile, visualize, dry-run (v0.5.0)
  • βœ… Web Graph Editor - Next.js visual editor with drag-and-drop (v0.8.0)
  • βœ… Production Case Study - Purchasing flow with microservices & monolithic (v0.8.0)
  • βœ… YAML Configuration - Declarative graph definitions (v0.8.5)
  • βœ… Stream operators (map, filter, fold)
  • βœ… Comprehensive documentation

Roadmap

  • Streaming processing (v0.3.0) - COMPLETED βœ…
  • Parallel node execution (v0.4.0) - COMPLETED βœ…
  • Caching layer (v0.5.0) - COMPLETED βœ…
  • CLI Developer Tools (v0.5.0) - COMPLETED βœ…
  • RETE-UL upgrade (v0.5.0) - COMPLETED βœ…
  • Memory Optimization (v0.7.0) - COMPLETED βœ…
  • Web Graph Editor (v0.8.0) - COMPLETED βœ…
  • Production Case Study (v0.8.0) - COMPLETED βœ…
  • YAML Configuration (v0.8.5) - COMPLETED βœ…
  • GraphQL API (v0.9.0)
  • Production release (v1.0.0)

See ROADMAP.md for details


🀝 Contributing

Contributions welcome! Please:

  1. Fork the repository
  2. Create your feature branch
  3. Write tests for new features
  4. Submit a pull request

πŸ“– Examples

Example Description Lines
simple_flow.rs Basic 3-node pipeline 36
advanced_flow.rs Complex 6-node workflow 120
grl_rules.rs GRL rule examples 110
grl_graph_flow.rs GRL + Graph integration 140
postgres_flow.rs PostgreSQL integration 100
openai_flow.rs OpenAI GPT integration 150
streaming_flow.rs Streaming with backpressure 200
parallel_execution.rs Parallel node execution 250

CLI Tool Examples (v0.5.0)

File Description
examples/sample_graph.json Linear workflow with 5 nodes
examples/cyclic_graph.json Graph with cycle for testing
examples/sample_context.json Sample input data

See CLI_TOOL.md for usage examples


🌟 Why Rust Logic Graph?

vs. Traditional Rule Engines

  • βœ… Async by default - No blocking I/O
  • βœ… Type safety - Rust's type system
  • βœ… Modern syntax - GRL support
  • βœ… Graph-based - Complex workflows

vs. Workflow Engines

  • βœ… Embedded - No external services
  • βœ… Fast - Compiled Rust code
  • βœ… Flexible - Custom nodes
  • βœ… Rule-based - Business logic in rules

πŸ“ Changelog

v0.8.5 (2025-11-20) - YAML Configuration Release

New Features:

  • πŸ“‹ YAML Configuration Support - Declarative graph definitions
    • Load graph structure from YAML files instead of hardcoded
    • GraphConfig module for parsing YAML configurations
    • Support for both JSON and YAML formats
    • 70% code reduction in graph executors
    • See YAML Configuration Guide
  • πŸ”§ Enhanced Graph Executor API
    • execute() - Use default configuration
    • execute_with_config(config_path) - Load custom YAML config
    • Dynamic node registration from config
  • πŸ“ Multiple Workflow Support
    • Standard flow (full process)
    • Simplified flow (skip optional steps)
    • Urgent flow (fast-track)
    • Easy to create custom workflows
  • πŸ“š Comprehensive Documentation
    • YAML configuration guide with examples
    • Before/After comparison showing improvements
    • Multiple workflow examples
    • Integration guides for both architectures

Improvements:

  • Monolithic and Microservices both support YAML configs
  • Reduced boilerplate code by 70% in executors
  • Better separation of concerns (config vs. code)
  • Easier testing with multiple configurations
  • No recompilation needed for workflow changes

Examples:

# purchasing_flow_graph.yaml
nodes:
  oms_grpc:
    type: DBNode
    description: "Fetch OMS data"
  rule_engine_grpc:
    type: RuleNode
    dependencies: [oms_grpc]

edges:
  - from: oms_grpc
    to: rule_engine_grpc
// Use default config
executor.execute("PROD-001").await?;

// Use custom config
executor.execute_with_config("PROD-001", "urgent_flow.yaml").await?;

Compatibility:

  • All tests passing
  • API backward compatible
  • Existing hardcoded graphs still work

v0.8.0 (2025-11-20) - Web Editor & Production Case Study Release

New Features:

  • 🎨 Web Graph Editor - Next.js visual editor with drag-and-drop
  • 🏒 Production Case Study - Complete purchasing flow system
    • Microservices architecture (7 services with gRPC)
    • Monolithic architecture (single HTTP service)
    • 15 GRL business rules for purchasing decisions
    • Kubernetes deployment manifests
    • Docker Compose for local development
    • Shared GRL rules proving portability
    • See Case Study Documentation

Improvements:

  • Updated README with case study section
  • Added online graph editor link
  • Comprehensive production examples

Compatibility:

  • All tests passing
  • API backward compatible

v0.5.0 (2025-11-06) - Performance & Developer Tools Release

Breaking Changes:

  • ⚑ Upgraded rust-rule-engine from v0.10 β†’ v0.14.0
    • Now uses RETE-UL algorithm (2-24x faster)
    • Better memory efficiency
    • Improved conflict resolution
    • See Migration Guide

New Features:

  • πŸ› οΈ CLI Developer Tools (rlg binary)
    • Graph validation with comprehensive checks
    • Dry-run execution mode
    • Performance profiling with statistics
    • ASCII graph visualization
    • See CLI Tool Guide
  • πŸ’Ύ Caching Layer - High-performance result caching
    • TTL-based expiration
    • Multiple eviction policies (LRU, LFU, FIFO)
    • Memory limits and statistics
    • See Cache Guide
  • ⚑ Parallel Node Execution - Automatic detection and parallel execution
    • Layer detection algorithm using topological sort
    • Concurrent execution within layers
    • Parallelism analysis and statistics
  • πŸ“Š ParallelExecutor - New executor with parallel capabilities
  • πŸ“ New Examples - CLI examples and test graphs
  • βœ… 32 Tests - Comprehensive test coverage

Improvements:

  • Updated documentation with CLI tools, caching, and migration guides
  • Performance benchmarking utilities
  • Example graph files for testing

Compatibility:

  • All 32 tests passing
  • API is backward compatible (100%)
  • Performance: 2-24x faster rule matching

v0.3.0 (2025-11-03) - Streaming & Performance Release

New Features:

  • 🌊 Streaming Processing - Stream-based node execution
    • Backpressure handling with bounded channels
    • Large dataset support with chunking
    • Stream operators (map, filter, fold, async map)
  • πŸ“ New Example - streaming_flow.rs with 6 demonstrations
  • βœ… 8 New Tests - Streaming module testing

Performance:

  • Processed 10,000 items in chunks
  • ~432 items/sec throughput with backpressure

v0.2.0 (2025-11-02) - Integrations Release

New Features:

  • πŸ—„οΈ Database Integrations - PostgreSQL, MySQL, Redis, MongoDB
  • πŸ€– AI/LLM Integrations - OpenAI GPT-4, Claude 3.5, Ollama
  • πŸ“ Integration Examples - postgres_flow.rs, openai_flow.rs
  • πŸ“š INTEGRATIONS.md - Comprehensive integration guide
  • πŸŽ›οΈ Feature Flags - Optional dependencies for integrations

v0.1.0 (2025-11-01) - Initial Release

Core Features:

  • 🧠 Core graph execution engine
  • πŸ”₯ GRL (Grule Rule Language) integration
  • πŸ”„ Topological sorting
  • ⚑ Async execution with Tokio
  • πŸ“Š Three node types (Rule, DB, AI)
  • πŸ“ JSON I/O for graphs
  • πŸ“š 4 working examples
  • βœ… 6/6 tests passing

πŸ“„ License

MIT License - see LICENSE for details.


πŸ”— Links


πŸ‘₯ Authors

James Vu - Initial work


πŸ™ Acknowledgments

Built with:


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Documentation β€’ Examples β€’ Use Cases β€’ YAML Config Guide