Examples Docs · SDK Docs · nxusKit SDK · Examples Portfolio · Website
33 production-quality examples for the nxusKit SDK in Rust, Go, and Python, plus selected CLI/Bash implementations for shell-first orchestration — covering LLM patterns, CLIPS rule engines, Z3 constraint solvers, Bayesian networks, and ZEN decision tables.
# 1. Install the SDK (see https://docs.nxus.systems/nxuskit/getting-started/installation/)
# 2. Set up this project
source ~/.nxuskit/sdk/current/scripts/setup-sdk.sh # Go, env vars, library paths
./scripts/setup-sdk-symlink.sh # Rust Cargo paths override
# 3. Run an example
cargo run --manifest-path examples/patterns/basic-chat/rust/Cargo.toml # Rust
go run -tags nxuskit ./examples/patterns/basic-chat/go # Go
python examples/patterns/basic-chat/python/main.py # Python| Example | Description | Languages |
|---|---|---|
| basic-chat | Basic chat completion with a simple prompt | Rust, Go, Python |
| streaming | Streaming chat completion with real-time output | Rust, Go, Python |
| multi-provider | Using multiple providers in one application | Rust, Go, Python, CLI/Bash |
| convenience-api | LiteLLM-style convenience API usage | Rust, Go |
| blocking-api | Synchronous blocking API for simpler use cases | Rust, Go |
| capability-detection | Detecting provider capabilities at runtime | Rust, Go, CLI/Bash |
| cost-routing | Cost-aware provider routing and selection | Rust, Go, Python, CLI/Bash |
| polymorphic | Polymorphic provider patterns with trait objects | Rust, Go |
| retry-fallback | Retry and fallback strategies across providers | Rust, Go, Python, CLI/Bash |
| structured-output | JSON mode and structured output generation | Rust, Go, Python, CLI/Bash |
| timeout-config | Timeout configuration and connection management | Rust, Go, Python |
| token-budget | Token budget management and cost estimation | Rust, Go, Python, CLI/Bash |
| vision | Vision and multimodal capabilities with images | Rust, Go, Python, CLI/Bash |
| auth-helper | OAuth login flow and credential management helper | Rust, Go |
↳ status |
List provider authentication status and stored credentials | |
↳ set |
Store an API key for a specific provider | |
↳ remove |
Remove a stored API key for a provider | |
↳ dashboard |
Open provider credential dashboard in browser | |
| solver | Z3 constraint solver integration via nxusKit SDK | Rust, Go, Python, CLI/Bash |
↳ theme-park |
Budget and space planning for a theme park with rides, food courts, and entertainment zones | |
↳ space-colony |
Resource allocation for a space colony dealing with solar storm what-if scenarios | |
↳ fantasy-draft |
Fantasy sports draft optimization under salary cap with injury what-if analysis | |
| ↳ real-world: Theme Park Planning | Facility layout, capital budgeting, resource allocation | |
| ↳ real-world: Space Colony Planning | Infrastructure sizing, capacity planning, disaster recovery modeling | |
| ↳ real-world: Fantasy Sports Draft | Portfolio optimization, team composition, auction bidding strategies | |
| bayesian-inference | Bayesian network inference via nxusKit SDK | Rust, Go, Python, CLI/Bash |
↳ haunted-house |
Investigate a haunted house — is it a ghost or a raccoon? | |
↳ coffee-shop |
Diagnose bad espresso from grind size, temperature, and bean age | |
↳ plant-doctor |
Diagnose a sick plant from overwatering, nutrient, and disease evidence | |
| ↳ real-world: Haunted House | Fault diagnosis, anomaly detection, sensor fusion from multiple noisy sensors pointing to hidden causes | |
| ↳ real-world: Coffee Shop | Manufacturing quality control, process parameter tuning, root cause analysis in production | |
| ↳ real-world: Plant Doctor | Medical diagnosis, agricultural advisory systems, multi-symptom differential diagnosis | |
| solver-what-if | What-if scenario analysis with solver scoping | Rust, Go, Python, CLI/Bash |
↳ wedding |
Wedding budget planning with $25k constraint and vendor what-if scenarios | |
↳ mars |
Mars colony resource allocation with dust storm what-if disruptions | |
↳ recipe |
Recipe scaling with vegan substitution — may be UNSAT | |
| ↳ real-world: Wedding Budget Planning | Event planning, capital budgeting, portfolio allocation | |
| ↳ real-world: Mars Colony Planning | Infrastructure sizing, supply chain planning, disaster preparedness | |
| ↳ real-world: Recipe Scaling | Manufacturing scaling, formulation optimization, process engineering |
| Example | Description | Languages |
|---|---|---|
| ollama | Using Ollama for local inference | Rust, Go, Python |
| lmstudio | Using LM Studio for local inference | Rust, Go |
| alert-triage | Alert triage with LLM-powered analysis | Rust, Go, CLI/Bash |
| cli-assistant | Interactive CLI assistant with LLM backend | Rust, Go |
| clips-basics | CLIPS rule engine basics via nxusKit SDK | Rust, Go, CLI/Bash |
| clips-llm-hybrid | Hybrid CLIPS rules + LLM reasoning | Rust, Go, Python, CLI/Bash |
| common-sense-guardrails | Progressive LLM guardrails with Community CLIPS validation and optional Pro proof stages | Python, CLI/Bash |
↳ car-wash |
Catch the classic car-wash walk-vs-drive failure with object-presence rules and optional solver proof | |
↳ coupon-stack |
Reject promotion stacking that violates eligibility and margin policy, with optional ZEN validation | |
↳ pallet-door |
Block unsafe warehouse advice that ignores dimensional clearance, with optional solver what-if | |
↳ cold-chain |
Prevent cheaper logistics recommendations that violate handling requirements, with optional ZEN validation | |
| ↳ real-world: LLM answer validation | Catch plausible recommendations that fail physical, operational, or policy preconditions before they reach users | |
| ↳ real-world: Policy enforcement | Turn free-form answers into facts, apply deterministic rules, and produce auditable repair context | |
| ↳ real-world: Operational decision support | Preserve fast LLM drafting while requiring concrete feasibility evidence for workflow-critical recommendations | |
| bn-solver-clips-pipeline | Three-stage BN prediction → Solver optimization → CLIPS safety pipeline | Rust, Go, CLI/Bash |
↳ festival |
Music festival staging — crowd predictions drive band scheduling and safety | |
↳ rescue |
Search and rescue — survivor probability drives team assignment and safety checks | |
↳ bakery |
Bakery scheduling — demand forecasts drive oven allocation and allergen separation | |
| ↳ real-world: Event planning | Predict attendance, optimize resource allocation, enforce safety codes | |
| ↳ real-world: Emergency response | Estimate survival windows, deploy rescue assets, enforce operational protocols | |
| ↳ real-world: Manufacturing | Forecast demand, schedule production, enforce quality and safety standards | |
| ↳ real-world: Logistics | Predict delivery volumes, optimize fleet routing, enforce regulatory compliance | |
| ↳ real-world: Healthcare | Predict patient load, optimize staff scheduling, enforce clinical safety protocols | |
| llm-solver-hybrid | Hybrid LLM + Z3 solver problem solving | Rust, Go, Python, CLI/Bash |
↳ seating |
Wedding dinner seating — 12 guests across 3 tables with constraints | |
↳ dungeon |
Dungeon layout — 5 rooms with boss and treasure placement rules | |
↳ road-trip |
Road trip planning — 14 days across 5 national parks with preferences | |
| bn-structure-learning | Bayesian network structure learning from data | Rust, Go, Python |
↳ golf |
Golf course conditions — weather, soil, and maintenance factor learning | |
↳ bmx |
BMX performance — skill level, technique, and jump factor learning | |
↳ sourdough |
Sourdough baking — feeding schedule, flour type, and temperature factor learning | |
| ↳ real-world: Epidemiology | Discover disease risk factor relationships from patient records | |
| ↳ real-world: Manufacturing | Identify root causes of defects from production data | |
| ↳ real-world: Finance | Map causal relationships between economic indicators | |
| ↳ real-world: Genomics | Learn gene regulatory networks from expression data | |
| ↳ real-world: Quality control | Find which process parameters affect product quality | |
| zen-decisions | ZEN decision table evaluation via nxusKit SDK | Rust, Go, Python, CLI/Bash |
↳ maze-rat |
First Hit Policy — route a maze runner through personality-driven decisions | |
↳ potion |
Collect Hit Policy — match ingredient lists against brewing recipes | |
↳ food-truck |
Expression Nodes — compute dynamic pricing with conditional logic |
| Example | Description | Languages |
|---|---|---|
| puzzler | Multi-approach puzzle solver comparing CLIPS, LLM, and hybrid strategies | Rust, Go |
↳ sudoku |
Solve Sudoku puzzles using CLIPS constraint propagation | |
↳ set-game |
Find valid SET card combinations using CLIPS pattern matching | |
↳ compare |
Side-by-side comparison of CLIPS, LLM, and hybrid solvers | |
| racer | CLIPS vs LLM head-to-head benchmarking tool | Rust, Go |
↳ race |
Head-to-head CLIPS vs LLM race on a single problem | |
↳ benchmark |
Statistical benchmarking with multiple runs and timing | |
↳ list |
List all available problems with difficulty ratings | |
↳ describe |
Show detailed description of a specific problem | |
| riffer | Music sequence analysis and transformation tool (still learning to shred) | Rust, Go |
↳ analyze |
Analyze a music sequence for key, intervals, and rhythm patterns | |
↳ score |
Score a sequence on six musical dimensions | |
↳ transform |
Transform a sequence — transpose, invert, or retrograde | |
↳ convert |
Convert between MIDI and MusicXML formats | |
| ruler | LLM-powered CLIPS rule generator with automatic validation | Rust, Go, CLI/Bash |
↳ generate |
Generate CLIPS rules from natural language descriptions | |
↳ validate |
Validate CLIPS rule syntax and semantic correctness | |
↳ save |
Save generated rules to a file for later use | |
↳ load |
Load previously saved rules from a file | |
↳ examples |
Run progressive complexity examples demonstrating rule generation | |
| arbiter | CLIPS-validated LLM retry app with rule-based answer verification | Rust, Go, CLI/Bash |
↳ classification |
Categorize input text into specified categories | |
↳ extraction |
Extract structured information from unstructured text | |
↳ reasoning |
Perform logical inference and multi-step reasoning |
| Badge | Meaning |
|---|---|
| Community | Runs with the free OSS SDK |
| Pro | Requires a Pro license (activation guide) |
See conformance/example-tiers.json for the full tier map.
examples/
├── patterns/ Community-tier reusable patterns
├── integrations/ SDK feature combinations
├── apps/ Complete applications (mostly Pro tier)
└── shared/ Shared libraries and helpers (Rust, Go, Python, CLI/Bash)
conformance/ Example manifest and tier definitions
scripts/ Build and test helpers
All examples require the nxusKit SDK. Run these once after cloning:
# Set up Go workspace, env vars, and native library paths
source ~/.nxuskit/sdk/current/scripts/setup-sdk.sh
# Set up Rust Cargo paths override (generates .cargo/config.toml)
./scripts/setup-sdk-symlink.shThe first script creates Go workspace files and exports environment variables. The second generates a .cargo/config.toml that tells Cargo where to find the installed Rust SDK (the generated file is .gitignored — each developer runs this once).
cargo run --manifest-path examples/<category>/<name>/rust/Cargo.tomlgo run -tags nxuskit ./examples/<category>/<name>/go/cmdpython examples/<category>/<name>/python/main.pycd examples/<category>/<name>/bash
make runBuilt with gratitude for the open-source projects that make nxusKit possible. See ACKNOWLEDGEMENTS.md for a curated list of key projects.
nxusKit Examples is dual-licensed under MIT or Apache 2.0, at your option. See LICENSE, LICENSE-MIT, and LICENSE-APACHE.
Copyright 2025-2026 nxus.SYSTEMS LLC.