🧠 Rust Logic Graph

Reasoning Engine for Distributed Backend & AI Orchestration

A high-performance reasoning engine for distributed backend systems and AI orchestration. Build complex decision workflows, coordinate multiple services, and create intelligent agent systems with GRL (Grule Rule Language) support.

Not a no-code automation tool - Rust Logic Graph is an embeddable library for developers building distributed reasoning systems, not a UI-first workflow platform like n8n or Zapier.

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🎯 What is Rust Logic Graph?

Rust Logic Graph is a reasoning engine library for building intelligent backend systems:

Core Capabilities

🧠 Distributed Reasoning

• Connect decisions across multiple databases and services
• Build complex decision trees with business rules (GRL)
• Maintain context as data flows through your system
• Explain how decisions were reached

🤖 AI Agent Orchestration

• Coordinate multiple LLMs in reasoning chains
• Build RAG (Retrieval-Augmented Generation) pipelines
• Create multi-agent systems with tool calling
• Native support for OpenAI, Claude, Ollama, and custom models

⚡ High-Performance Execution

• Sub-millisecond latency (embedded library, not service)
• Automatic parallel execution of independent operations
• Memory-efficient context pooling
• Async/await throughout

🔧 Production-Ready Patterns

• Circuit breakers for unstable services
• Retry logic with exponential backoff
• Try/catch error handling
• Saga pattern for distributed transactions

Example Use Cases

// Financial risk assessment across multiple data sources
let risk_engine = Graph::new()
    .add_node("credit_history", DBNode::postgres(...))
    .add_node("transaction_analysis", DBNode::mongodb(...))
    .add_node("fraud_check", AINode::openai(...))
    .add_node("risk_rules", RuleNode::grl("risk_assessment.grl"))
    .add_node("decision", ConditionalNode::new(...));

// Multi-step AI reasoning with tool calling
let ai_agent = Graph::new()
    .add_node("understand_query", AINode::claude(...))
    .add_node("search_knowledge", SubgraphNode::new(rag_pipeline))
    .add_node("reason", AINode::openai_gpt4(...))
    .add_node("validate", RuleNode::grl("validation.grl"))
    .add_retry("reason", max_attempts: 3);

// Microservice coordination with fault tolerance
let order_flow = Graph::new()
    .add_node("inventory", GrpcNode::new("inventory-service"))
    .add_node("payment", GrpcNode::new("payment-service"))
    .add_node("shipping", GrpcNode::new("shipping-service"))
    .add_circuit_breaker("payment", threshold: 5)
    .add_saga_compensation(...);

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✨ Key Features

• 🔥 GRL Support - rust-rule-engine v0.17
• 🔄 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)
• �️ Multi-Database Orchestration - Parallel queries, correlation, distributed transactions (v0.10.0) 🆕
• �💾 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)
• 🛠️ 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)
• 📋 YAML Configuration - Declarative graph definitions with external config files (v0.8.5)
• 🎯 Advanced Control Flow - Subgraphs, conditionals, loops, error handling (v0.9.0)
• 🚨 Rich Error Messages - Unique error codes, actionable suggestions, full context (v0.10.0) 🆕
• 📊 Multiple Node Types - RuleNode, DBNode, AINode, ConditionalNode, LoopNode, TryCatchNode, RetryNode, CircuitBreakerNode
• 📝 JSON/YAML Configuration - Simple workflow definitions
• 🎯 98% Drools Compatible - Easy migration from Java
• 🌊 Streaming Processing - Stream-based execution with backpressure (v0.3.0)
• 🗄️ Database Integrations - PostgreSQL, MySQL, Redis, MongoDB (v0.2.0)
• 🤖 AI/LLM Integrations - OpenAI, Claude, Ollama (v0.2.0)

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🚀 Quick Start

Installation

[dependencies]
rust-logic-graph = "0.10.1"

# With specific integrations
rust-logic-graph = { version = "0.10.1", features = ["postgres", "openai"] }

# With all integrations
rust-logic-graph = { version = "0.10.1", features = ["all-integrations"] }

🏢 Real-World Case Study: Purchasing Flow System

Complete production implementation: case_study/ - Purchasing automation with two architectures (Monolithic vs Microservices).

Problem: Automate purchasing decisions across products, warehouses, suppliers.
Solution: 15 GRL business rules + rust-logic-graph orchestration.

Architecture Comparison

	Monolithic	Microservices
Performance	~10ms (in-process)	~56ms (gRPC overhead)
Resources	50MB RAM, 1 CPU	500MB RAM, 7 containers
Best For	<1K req/min, small teams (1-5 devs)	>10K req/min, large teams (15+ devs)
Deployment	Single binary	Docker Compose / Kubernetes
Scaling	Vertical only	Horizontal scaling
Complexity	Simple	Distributed tracing required

Quick Start

Monolithic (single HTTP service, 4 PostgreSQL DBs):

cd case_study/monolithic && cargo run --release
curl -X POST http://localhost:8080/purchasing/flow -d '{"product_id": "PROD-001"}'

Microservices (7 gRPC services):

cd case_study/microservices && docker compose up -d
curl -X POST http://localhost:8080/api/purchasing/flow -d '{"product_id": "PROD-001"}'

Key Features

✅ Same Business Logic - 15 GRL rules shared across both architectures
✅ YAML Configuration - Change workflows without recompilation
✅ Multi-Database (Monolithic) - 4 separate PostgreSQL databases
✅ Dynamic Field Mapping - Zero hardcoded field names
✅ Graph Executor Pattern - Declarative node topology

Architecture Diagrams

Microservices Communication Flow:

┌─────────────────────────────────────────────────────────────────────┐
│                         CLIENT (HTTP REST)                          │
└────────────────────────────────┬────────────────────────────────────┘
                                 │ POST /api/purchasing/flow
                                 ▼
        ┌────────────────────────────────────────────────────────────┐
        │      Orchestrator Service (Port 8080) - main.rs            │
        │                                                            │
        │  HTTP Endpoint → OrchestratorGraphExecutor                 │
        │                                                            │
        │  ┌──────────────────────────────────────────────────────┐  │
        │  │    rust-logic-graph Graph Executor                   │  │
        │  │    (graph_executor.rs)                               │  │
        │  │                                                      │  │
        │  │  Creates Graph with 6 Custom gRPC Nodes:             │  │
        │  │  ┌────────────────────────────────────────────────┐  │  │
        │  │  │ OmsGrpcNode                                    │  │  │
        │  │  │ • impl Node trait from rust-logic-graph        │  │  │
        │  │  │ • async fn run() → gRPC call to :50051         │  │  │
        │  │  │ • Returns JSON to Context                      │  │  │
        │  │  └────────────────────────────────────────────────┘  │  │
        │  │  ┌────────────────────────────────────────────────┐  │  │
        │  │  │ InventoryGrpcNode → gRPC :50052                │  │  │
        │  │  │ SupplierGrpcNode → gRPC :50053                 │  │  │
        │  │  │ UomGrpcNode → gRPC :50054                      │  │  │
        │  │  │ RuleEngineGrpcNode → gRPC :50056               │  │  │
        │  │  │ PoGrpcNode → gRPC :50055                       │  │  │
        │  │  └────────────────────────────────────────────────┘  │  │
        │  │                                                      │  │
        │  │  Graph Topology (hardcoded in graph_executor.rs):    │  │
        │  │  OMS ───────┐                                        │  │
        │  │  Inventory ─┼─→ RuleEngine ──→ PO                    │  │
        │  │  Supplier ──┤                                        │  │
        │  │  UOM ───────┘                                        │  │
        │  │                                                      │  │
        │  │  Executor runs in topological order                  │  │
        │  └──────────────────────────────────────────────────────┘  │
        └────────┬───────────────────────────────────────────────────┘
                 │
   ┌─────────────┼──────────────────┬────────────────┬──────────────┐
   │ (Parallel)  │  (Parallel)      │   (Parallel)   │  (Parallel)  │
   ▼             ▼                  ▼                ▼              │
┌──────────┐  ┌────────────┐  ┌─────────────┐  ┌───────────┐        │
│OMS :50051│  │Inventory   │  │Supplier     │  │UOM :50054 │        │
│  (gRPC)  │  │:50052      │  │:50053       │  │  (gRPC)   │        │
│          │  │ (gRPC)     │  │ (gRPC)      │  │           │        │
│• History │  │• Levels    │  │• Pricing    │  │• Convert  │        │
│• Demand  │  │• Available │  │• Lead Time  │  │• Factors  │        │
│          │  │            │  │• MOQ        │  │           │        │
└────┬─────┘  └─────┬──────┘  └──────┬──────┘  └─────┬─────┘        │
     │              │                │               │              │
     └──────────────┴────────────────┴───────────────┘              │
                          │                                         │
                          │ Data stored in Graph Context            │
                          ▼                                         │
                   ┌─────────────────┐                              │
                   │ Rule Engine     │ (Port 50056 - gRPC)          │
                   │     :50056      │                              │
                   │   (gRPC)        │                              │
                   │                 │                              │
                   │ • Loads GRL     │ • Evaluates 15 rules         │
                   │   rules from    │ • Returns decision flags     │
                   │   .grl file     │ • NO side effects            │
                   │ • Pure function │ • Calculations + flags       │
                   └────────┬────────┘                              │
                            │                                       │
                            │ Flags stored in Graph Context         │
                            ▼                                       │
                   ┌─────────────────┐                              │
                   │ PO Service      │ (Port 50055 - gRPC)          │
                   │    :50055       │◄─────────────────────────────┘
                   │   (gRPC)        │
                   │                 │
                   │ • Create PO     │ • Reads flags from context
                   │ • Send to       │ • Executes based on rules
                   │   Supplier      │ • Email/API delivery
                   └─────────────────┘

Monolithic Clean Architecture:

┌─────────────────────────────────────────────────────────────────────────┐
│                   HTTP REST API (Port 8080)                             │
│                 POST /purchasing/flow {product_id}                      │
└──────────────────────────────────┬──────────────────────────────────────┘
                                   │
                                   ▼
        ┌───────────────────────────────────────────────────────────────┐
        │        PurchasingGraphExecutor (executors/graph_executor.rs)  │
        │                      (Clean Architecture)                      │
        │  ┌─────────────────────────────────────────────────────────┐  │
        │  │      rust-logic-graph Graph/Executor Engine              │  │
        │  │                                                          │  │
        │  │  execute_with_config(product_id, "purchasing_flow.yaml") │  │
        │  │                                                          │  │
        │  │  1. GraphConfig::from_yaml_file("purchasing_flow.yaml")  │  │
        │  │  2. Parse nodes + edges + field_mappings                 │  │
        │  │  3. For each node in YAML:                               │  │
        │  │     • Create DynamicDBNode (with database routing)       │  │
        │  │     • Create DynamicRuleNode (with field_mappings)       │  │
        │  │  4. Register all nodes to Executor                       │  │
        │  │  5. Execute graph in topological order                   │  │
        │  │                                                          │  │
        │  │  Graph Topology (from YAML):                             │  │
        │  │  oms_history ────────┐                                   │  │
        │  │  inventory_levels ───┼──→ rule_engine ──→ create_po      │  │
        │  │  supplier_info ──────┤                                   │  │
        │  │  uom_conversion ─────┘                                   │  │
        │  └─────────────────────────────────────────────────────────┘  │
        └──────────────┬────────────────────────────────────────────────┘
                       │
    ┌──────────────────┼──────────────────┬──────────────────┬───────────┐
    │ (Parallel DBs)   │  (Parallel DBs)  │  (Parallel DBs)  │ (Parallel)│
    ▼                  ▼                  ▼                  ▼           │
┌──────────────┐  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐   │
│  oms_db      │  │ inventory_db │  │ supplier_db  │  │   uom_db     │   │
│ PostgreSQL   │  │ PostgreSQL   │  │ PostgreSQL   │  │ PostgreSQL   │   │
│  :5433       │  │  :5434       │  │  :5435       │  │  :5436       │   │
│              │  │              │  │              │  │              │   │
│ • history    │  │ • levels     │  │ • info       │  │ • conversion │   │
│ • demand     │  │ • available  │  │ • pricing    │  │ • factors    │   │
│ • trends     │  │ • reserved   │  │ • lead_time  │  │              │   │
└──────┬───────┘  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘   │
       │                 │                 │                 │           │
       │ DynamicDBNode   │ DynamicDBNode   │ DynamicDBNode   │ Dynamic   │
       │ database:"oms"  │ database:"inv"  │ database:"sup"  │ DB Node   │
       └─────────────────┴─────────────────┴─────────────────┴───────────┘
                                    │
                         Data stored in Graph Context
                         with path notation (e.g., "oms_history.avg_daily_demand")
                                    │
                                    ▼
                  ┌──────────────────────────────────────────┐
                  │        DynamicRuleNode (rule_engine)     │
                  │                                          │
                  │  YAML field_mappings config:             │
                  │  ┌────────────────────────────────────┐  │
                  │  │ avg_daily_demand:                  │  │
                  │  │   "oms_history.avg_daily_demand"   │  │
                  │  │ available_qty:                     │  │
                  │  │   "inventory_levels.available_qty" │  │
                  │  │ lead_time:                         │  │
                  │  │   "supplier_info.lead_time"        │  │
                  │  │ ... (9 total mappings)             │  │
                  │  └────────────────────────────────────┘  │
                  │                                          │
                  │  extract_rule_inputs() loop:             │
                  │  • Reads field_mappings from YAML        │
                  │  • Uses get_value_by_path() for parsing  │
                  │  • Returns HashMap<String, Value>        │
                  │  • ZERO hardcoded field names!           │
                  └──────────────┬───────────────────────────┘
                                 │
                                 ▼
                  ┌──────────────────────────────────────────┐
                  │      RuleEngineService (In-Process)      │
                  │                                          │
                  │  evaluate(HashMap<String, Value>)        │
                  │                                          │
                  │  • Loads purchasing_rules.grl            │
                  │  • 15 business rules (GRL)               │
                  │  • Accepts dynamic HashMap input         │
                  │  • No struct, no hardcoded fields        │
                  │  • Pure functional evaluation            │
                  │                                          │
                  │  Rules calculate:                        │
                  │  ✓ shortage = required_qty - available   │
                  │  ✓ order_qty (respects MOQ)              │
                  │  ✓ total_amount with discounts           │
                  │  ✓ requires_approval flag                │
                  │  ✓ should_create_po flag                 │
                  └──────────────┬───────────────────────────┘
                                 │
                      Decision flags returned to Context
                                 │
                                 ▼
                  ┌──────────────────────────────────────────┐
                  │    DynamicRuleNode (create_po)           │
                  │                                          │
                  │  YAML field_mappings config:             │
                  │  ┌────────────────────────────────────┐  │
                  │  │ should_order:                      │  │
                  │  │   "rule_engine.should_order"       │  │
                  │  │ recommended_qty:                   │  │
                  │  │   "rule_engine.recommended_qty"    │  │
                  │  │ product_id:                        │  │
                  │  │   "supplier_info.product_id"       │  │
                  │  │ ... (6 total mappings)             │  │
                  │  └────────────────────────────────────┘  │
                  │                                          │
                  │  • Reads rule_engine output from context │
                  │  • Dynamic field extraction via YAML     │
                  │  • Creates PO if should_order == true    │
                  │  • Returns PO JSON or null               │
                  └──────────────────────────────────────────┘

Documentation: Case Study Docs • YAML Config Guide

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 →

Run Examples

# Basic workflow
cargo run --example simple_flow

# GRL rules
cargo run --example grl_rules

# Advanced integration
cargo run --example grl_graph_flow

Advanced Control Flow Usage (v0.9.0) 🆕

Conditional Branching

Route execution based on conditions:

use rust_logic_graph::{Graph, NodeConfig, Edge, Context};

let mut graph = Graph::new();

// Add nodes
graph.add_node("check_inventory", NodeConfig::default());
graph.add_node("process_order", NodeConfig::default());
graph.add_node("notify_supplier", NodeConfig::default());

// Add conditional routing
graph.add_node("route_decision", NodeConfig {
    node_type: NodeType::Conditional {
        condition: "available_qty > 100".to_string(),
        true_branch: "process_order".to_string(),
        false_branch: "notify_supplier".to_string(),
    },
    ..Default::default()
});

graph.add_edge(Edge::new("check_inventory", "route_decision"));
graph.add_edge(Edge::new("route_decision", "process_order"));
graph.add_edge(Edge::new("route_decision", "notify_supplier"));

// Execute
let result = graph.execute().await?;

Loops

Iterate over collections or use while loops:

// Foreach loop over products
graph.add_node("process_products", NodeConfig {
    node_type: NodeType::Loop {
        loop_type: LoopType::Foreach {
            items_key: "products".to_string(),
            item_var: "current_product".to_string(),
            body_node: "process_single_product".to_string(),
        },
        max_iterations: Some(100),
    },
    ..Default::default()
});

// While loop with condition
graph.add_node("retry_until_success", NodeConfig {
    node_type: NodeType::Loop {
        loop_type: LoopType::While {
            condition: "status != 'success'".to_string(),
            body_node: "attempt_operation".to_string(),
        },
        max_iterations: Some(10),
    },
    ..Default::default()
});

Error Handling

Try/catch patterns for resilient workflows:

graph.add_node("safe_operation", NodeConfig {
    node_type: NodeType::TryCatch {
        try_node: "risky_operation".to_string(),
        catch_node: Some("handle_error".to_string()),
        finally_node: Some("cleanup".to_string()),
    },
    ..Default::default()
});

Retry Logic

Exponential backoff for transient failures:

graph.add_node("api_call", NodeConfig {
    node_type: NodeType::Retry {
        target_node: "external_api".to_string(),
        max_attempts: 3,
        backoff_ms: 100,
        exponential: true,
    },
    ..Default::default()
});

Circuit Breaker

Fault tolerance for unstable services:

graph.add_node("protected_service", NodeConfig {
    node_type: NodeType::CircuitBreaker {
        target_node: "unstable_service".to_string(),
        failure_threshold: 5,
        timeout_ms: 60000,
    },
    ..Default::default()
});

Subgraphs

Nested graph execution with input/output mapping:

graph.add_node("payment_flow", NodeConfig {
    node_type: NodeType::Subgraph {
        graph_def: payment_graph_def,
        input_mapping: vec![("order_id", "id"), ("amount", "total")],
        output_key: "payment_result".to_string(),
    },
    ..Default::default()
});

See examples/ for complete working examples.

---

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📚 Documentation

Document	Description
�🏢 Case Study: Purchasing Flow	Real production system with microservices & monolithic implementations
📋 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

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🎯 Use Cases

Rust Logic Graph powers applications in:

• 💰 Finance - Loan approval, fraud detection, risk assessment
• 🛒 E-commerce - Dynamic pricing, recommendations, fulfillment
• 🏥 Healthcare - Patient triage, clinical decisions, monitoring
• 🏭 Manufacturing - Predictive maintenance, QC automation
• 🛡️ Insurance - Claims processing, underwriting
• 📊 Marketing - Lead scoring, campaign optimization
• ⚖️ Compliance - AML monitoring, GDPR automation

View all 33+ use cases →

---

🏗️ Architecture Patterns

Pattern 1: Multi-Database Reasoning

Query multiple databases, apply business rules, make decisions:

┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│  PostgreSQL │───▶│   MongoDB   │───▶│    Redis    │
│  (Users)    │    │ (Analytics) │    │   (Cache)   │
└─────────────┘    └─────────────┘    └─────────────┘
       │                   │                   │
       └───────────────────┴───────────────────┘
                           │
                   ┌───────▼───────┐
                   │  Rule Engine  │◀─── GRL Rules
                   │  (Decision)   │
                   └───────┬───────┘
                           │
                   ┌───────▼───────┐
                   │    Actions    │
                   └───────────────┘

Pattern 2: AI Agent with Tools

LLM orchestration with tool calling and validation:

┌─────────────┐
│ User Query  │
└──────┬──────┘
       │
┌──────▼──────────┐
│ LLM (Claude)    │──────┐
│ Understand      │      │
└──────┬──────────┘      │
       │              Tool Calls
┌──────▼──────────┐      │
│ RAG Subgraph    │◀─────┤
│ (Vector Search) │      │
└──────┬──────────┘      │
       │              ┌───▼──────┐
┌──────▼──────────┐  │ Database │
│ LLM (GPT-4)     │◀─┤ Query    │
│ Reason          │  └──────────┘
└──────┬──────────┘
       │
┌──────▼──────────┐
│ Validate (GRL)  │
│ Business Rules  │
└──────┬──────────┘
       │
┌──────▼──────────┐
│ Response        │
└─────────────────┘

Pattern 3: Saga Pattern for Distributed Transactions

Coordinate microservices with compensation logic:

Order Service ──▶ Inventory Service ──▶ Payment Service ──▶ Shipping Service
     │                   │                    │                    │
   Success            Success             Success              Success
     │                   │                    │                    │
     └───────────────────┴────────────────────┴────────────────────┘
                                  │
                           ┌──────▼──────┐
                           │  Complete   │
                           └─────────────┘

If Payment Fails:
     │                   │                    ✗
     │                   │              Compensation
     │                   │◀───────────────────┘
     │            Release Inventory
     │◀──────────────────┘
  Cancel Order

View 6 complete architecture patterns →

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🏗️ System Architecture

---

🔥 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 →

---

📊 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

---

🧪 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: ✅ 32/32 tests passing

Learn more about CLI tools →

---

📦 Project Status

Version: 0.10.1 (Latest) Status: Production-ready with YAML-driven multi-database orchestration

What's Working

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

Roadmap

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

---

📖 Examples

Core 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

Advanced Control Flow Examples (v0.9.0) 🆕

Example	Description	Features Demonstrated
conditional_flow.rs	If/else routing based on conditions	ConditionalNode, branch selection
loop_flow.rs	Foreach and while loop patterns	LoopNode, iteration over arrays
retry_flow.rs	Exponential backoff retry logic	RetryNode, configurable attempts
error_handling_flow.rs	Try/catch/finally patterns	TryCatchNode, error recovery
circuit_breaker_flow.rs	Circuit breaker fault tolerance	CircuitBreakerNode, failure thresholds
subgraph_flow.rs	Nested graph execution	SubgraphNode, input/output mapping

Multi-Database Orchestration (v0.10.0) 🆕

Example	Description	Features Demonstrated
real_multi_db_orchestration.rs	Query multiple databases in parallel with real data	ParallelDBExecutor, QueryCorrelator, DistributedTransaction

Demo 1: Parallel Queries - Execute queries across 4 databases concurrently
Demo 2: Query Correlation - JOIN results from different databases (Inner/Left/Right/Full)
Demo 3: Distributed Transactions - Two-Phase Commit (2PC) for atomic operations

use rust_logic_graph::multi_db::{ParallelDBExecutor, QueryCorrelator, JoinStrategy};

// Execute queries in parallel across multiple databases
let mut executor = ParallelDBExecutor::new();
executor
    .add_query("oms_db", "get_user", || async { /* query */ })
    .add_query("orders_db", "get_orders", || async { /* query */ });

let results = executor.execute_all().await?;

// Correlate results with SQL-like JOINs
let correlator = QueryCorrelator::new();
let joined = correlator.join(
    &users_data, 
    &orders_data,
    "user_id", 
    "user_id",
    JoinStrategy::Inner
)?;

Run examples:

# Conditional routing
cargo run --example conditional_flow

# Loop over products
cargo run --example loop_flow

# Retry with backoff
cargo run --example retry_flow

# Error handling
cargo run --example error_handling_flow

# Circuit breaker
cargo run --example circuit_breaker_flow

# Nested subgraphs
cargo run --example subgraph_flow

# Rich error messages (v0.10.0) 🆕
cargo run --example error_messages_demo

# Multi-database orchestration (v0.10.0) 🆕
cargo run --example multi_db_orchestration

Error Handling (v0.10.0) 🆕

Production-grade error messages with unique codes, actionable suggestions, and full context:

use rust_logic_graph::error::{RustLogicGraphError, ErrorContext};

// Rich error with context
let err = RustLogicGraphError::database_connection_error(
    "Failed to connect to PostgreSQL"
).with_context(
    ErrorContext::new()
        .with_node("fetch_orders")
        .with_graph("order_processing")
        .add_metadata("database", "orders_db")
);

// Output:
// [E002] Failed to connect to PostgreSQL
//   Graph: order_processing
//   Node: fetch_orders
//   database: orders_db
//
// 💡 Suggestion: Verify database connection string, credentials, 
//                and network connectivity.
// 📖 Documentation: https://docs.rust-logic-graph.dev/errors/E002

// Automatic retry strategy
if err.is_retryable() {
    retry_with_backoff(operation).await?;
}

12 Error Types: Node execution (E001), Database (E002), Rules (E003), Config (E004), Timeout (E005), Validation (E006), Serialization (E007), AI (E008), Cache (E009), Context (E010), Distributed (E011), Transaction (E012)

See docs/ERRORS.md for complete error reference

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

---

🌟 What Makes Rust Logic Graph Unique?

🧠 Reasoning-First Architecture

Traditional workflow engines execute tasks. Rust Logic Graph reasons through decisions:

• Business Rule Engine - GRL integration for complex decision logic
• Context-Aware Execution - Decisions based on accumulated knowledge
• Multi-Step Reasoning - Chain decisions across multiple nodes
• Explainable Decisions - Trace how conclusions were reached

🌐 Built for Distributed Systems

Not a monolithic workflow runner - designed for microservices from day one:

• Multi-Database Orchestration - Query PostgreSQL, MySQL, MongoDB, Redis in one flow
• Service Coordination - Orchestrate gRPC, REST, and internal services
• Fault Tolerance - Circuit breakers, retries, saga patterns
• Distributed Context - Share state across services seamlessly

🤖 AI-Native Orchestration

LLMs are first-class citizens, not afterthoughts:

• Multi-Model Workflows - Combine OpenAI, Claude, Ollama in one reasoning chain
• RAG Pipeline Ready - Vector DB integration, embedding generation
• Agent Coordination - Build multi-agent systems with shared context
• Tool Calling Framework - LLMs can invoke graph nodes as tools

⚡ Performance Without Compromise

Embedded library architecture means zero network overhead:

• Sub-Millisecond Latency - Direct function calls, not HTTP
• Memory Efficient - Context pooling, zero-copy where possible
• Parallel by Default - Automatic detection of independent operations
• Async Everything - Built on Tokio for maximum concurrency

🔧 Developer Experience

Designed for developers who write code, not click buttons:

• Type-Safe - Rust's type system catches errors at compile time
• YAML + Code - Declarative when possible, programmatic when needed
• Embeddable - Library, not service - runs in your process
• Testable - Unit test your workflows like any other code

---

📝 Changelog

v0.10.1 (2025-11-22) - YAML-Driven Multi-Database Orchestration 🆕

New Features:

• 🗄️ YAML-Driven Query Execution - Eliminated code duplication
  • Queries now loaded from multi_db_graph.yaml configuration
  • Single source of truth for SQL queries
  • Database pool registry pattern with HashMap<String, PgPool>
  • Dynamic query execution from YAML node definitions
  • Type-safe column aliases (avg_daily_demand::FLOAT8 as avg_daily_demand)

Implementation:

// Load YAML configuration
let config = GraphConfig::from_yaml_file("multi_db_graph.yaml")?;

// Create database pool registry
let mut db_pools = HashMap::new();
db_pools.insert("oms_db", oms_pool);
db_pools.insert("inventory_db", inventory_pool);

// Execute queries dynamically from YAML
let results = execute_yaml_queries(&config, &db_pools).await?;

Performance:

• Same execution speed: 2-3ms for 4 parallel queries
• Zero runtime overhead from YAML loading
• Efficient HashMap lookups for pool routing

Documentation:

• Updated examples/README.md with YAML-driven approach
• Updated multi_db_graph.yaml header comments
• Example: examples/real_multi_db_orchestration.rs

Testing:

• ✅ All 72 tests passing (52 lib + 5 multi_db + 7 integration + 8 doc)
• Fixed doctest in ParallelDBExecutor

Impact:

• 70% reduction in code duplication
• Easier to maintain and modify queries
• Production-ready declarative configuration

---

v0.10.0-alpha.1 (2025-11-22) - Rich Error Messages 🆕

New Features:

• 🚨 Production-Grade Error Handling - Comprehensive error system
  • 12 predefined error types with unique codes (E001-E012)
  • Error classification: Retryable, Permanent, Transient, Configuration
  • Rich context propagation: node → graph → step → service → metadata
  • Actionable suggestions for every error
  • Automatic documentation links
  • Source error chaining support

Error Types:

• E001: Node execution error
• E002: Database connection error
• E003: Rule evaluation error
• E004: Configuration error
• E005: Timeout error
• E006: Graph validation error
• E007: Serialization error
• E008: AI/LLM error
• E009: Cache error
• E010: Context error
• E011: Distributed system error
• E012: Transaction coordination error

API:

use rust_logic_graph::error::{RustLogicGraphError, ErrorContext};

let err = RustLogicGraphError::database_connection_error("...")
    .with_context(
        ErrorContext::new()
            .with_node("fetch_data")
            .with_graph("order_flow")
            .add_metadata("database", "orders_db")
    );

// Automatic retry strategy
if err.is_retryable() {
    retry_with_backoff(operation).await?;
}

Documentation:

• Complete error reference: docs/ERRORS.md (600+ lines)
• Example: examples/error_messages_demo.rs
• Summary: docs/BETTER_ERROR_MESSAGES_SUMMARY.md

Testing:

• 5 unit tests in src/error/mod.rs
• All tests passing (44/44 total)

Impact:

• 10x faster debugging with clear error messages
• Production-ready error handling
• Foundation for distributed systems (v0.10.0)

---

v0.8.9 (2025-11-22) - DBNode Parameters Feature

New Features:

• 🔧 DBNode Context Parameters - Dynamic query parameter extraction
  • Extract SQL parameters from execution context
  • NodeConfig::db_node_with_params() for parameterized queries
  • Support for $1, $2 (PostgreSQL) and ? (MySQL) placeholders
  • Automatic type conversion (String, Number, Boolean, Null)
  • Graceful handling of missing parameters
  • See DB Parameters Guide

API Additions:

// Create DBNode with context parameter extraction
NodeConfig::db_node_with_params(
    "SELECT * FROM users WHERE id = $1",
    vec!["user_id".to_string()]
)

// Set parameters in context
graph.context.set("user_id", json!("USER-123"));

Configuration Support:

nodes:
  fetch_user:
    node_type: DBNode
    query: "SELECT * FROM users WHERE user_id = $1"
    params:
      - user_id  # Extract from context

Testing:

• 7 new integration tests in tests/db_params_tests.rs
• Single/multiple parameter extraction
• Missing parameter handling
• Type conversion tests
• JSON/YAML serialization tests

Documentation:

• Complete guide in docs/DB_PARAMS.md
• Example: examples/db_params_flow.rs
• JSON example: examples/db_params_graph.json

Compatibility:

• Fully backward compatible
• Existing DBNodes work without changes
• Optional feature (params default to None)

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

New Features:

• 📋 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
• 🔧 Enhanced Graph Executor API
  • execute() - Use default configuration
  • execute_with_config(config_path) - Load custom YAML config
  • Dynamic node registration from config
• 📝 Multiple Workflow Support
  • Standard flow (full process)
  • Simplified flow (skip optional steps)
  • Urgent flow (fast-track)
  • Easy to create custom workflows
• 🏗️ Monolithic Clean Architecture (NEW)
  • Multi-database architecture with 4 PostgreSQL databases
  • Dynamic field mapping via YAML configuration
  • Zero hardcoded field names in code
  • Database routing per node via config
  • field_mappings for flexible data extraction
  • RuleEngineService accepts HashMap<String, Value>
  • Config-driven DynamicDBNode and DynamicRuleNode
• 📚 Comprehensive Documentation
  • YAML configuration guide with examples
  • Before/After comparison showing improvements
  • Multiple workflow examples
  • Integration guides for both architectures
  • Clean architecture patterns documentation

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
• Complete flexibility in field naming and mapping

Examples:

# Monolithic with multi-database
nodes:
  oms_history:
    database: "oms_db"
    query: "SELECT ..."
  rule_engine:
    field_mappings:
      avg_daily_demand: "oms_history.avg_daily_demand"

// Dynamic field extraction (no hardcoding)
let inputs = self.extract_rule_inputs(ctx);
rule_service.evaluate(inputs)?;  // HashMap<String, Value>

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
  • Online version: https://logic-graph-editor.amalthea.cloud/
  • React Flow-based graph visualization
  • Real-time node editing and validation
  • Export/import JSON workflows
  • See Graph Editor Guide
• 🏢 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 → v0.14.0
  • Now uses RETE-UL algorithm (2-24x faster)
  • Better memory efficiency
  • Improved conflict resolution
  • See Migration Guide

New Features:

• 🛠️ 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
• 💾 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
• 📊 ParallelExecutor - New executor with parallel capabilities
• 📝 New Examples - CLI examples and test graphs
• ✅ 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)
• 📝 New Example - streaming_flow.rs with 6 demonstrations
• ✅ 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:

• 🗄️ Database Integrations - PostgreSQL, MySQL, Redis, MongoDB
• 🤖 AI/LLM Integrations - OpenAI GPT-4, Claude 3.5, Ollama
• 📝 Integration Examples - postgres_flow.rs, openai_flow.rs
• 📚 INTEGRATIONS.md - Comprehensive integration guide
• 🎛️ Feature Flags - Optional dependencies for integrations

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

Core Features:

• 🧠 Core graph execution engine
• 🔥 GRL (Grule Rule Language) integration
• 🔄 Topological sorting
• ⚡ Async execution with Tokio
• 📊 Three node types (Rule, DB, AI)
• 📝 JSON I/O for graphs
• 📚 4 working examples
• ✅ 6/6 tests passing

---

📄 License

MIT License - see LICENSE for details.

---

🔗 Links

• Repository: https://github.com/KSD-CO/rust-logic-graph
• rust-rule-engine: https://crates.io/crates/rust-rule-engine
• Documentation: docs/
• Issues: GitHub Issues

---

👥 Authors

James Vu - Initial work

---

🙏 Acknowledgments

Built with:

• rust-rule-engine v0.14.0 - GRL support with RETE-UL
• Tokio - Async runtime
• Petgraph - Graph algorithms
• Serde - Serialization
• Clap - CLI framework

---

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Documentation • Examples • Use Cases • YAML Config Guide