Remove impossible/mislabeled per-second figures from consensus docs: the "3.9B blocks/sec" / "6.6B votes/sec" u8-overflow artifacts (real C-FFI ≈21K votes/sec per the CI README), the 20.26M/11.5M "TPS" that are ZAP wire-message microbenches (relabeled, not consensus finality), stub-SDK "sub-second finality" claims, and downgraded "production-ready" labels on SDKs the CI audit documents as stubs. Genuinely-measured go-test numbers left intact. Fixed stale LUX_CONSENSUS_PQ_MODE → CONSENSUS_PQ_MODE. Docs only, no Go logic touched. Co-authored-by: Hanzo Dev <dev@hanzo.ai>
Lux Consensus Source Code Organization
This directory contains implementations of the Lux consensus framework in multiple programming languages. Each language implementation provides the same consensus algorithms with language-specific optimizations and idioms.
Directory Structure
src/
├── c/ # C implementation (high-performance, minimal overhead)
├── cpp/ # C++ implementation with MLX extensions (GPU acceleration)
├── go/ # Go implementation (production blockchain integration)
├── python/ # Python implementation (research and prototyping)
└── rust/ # Rust implementation (memory-safe systems programming)
Language Implementations
Go (/src/go/)
- Status: Production-ready
- Use Case: Core blockchain node integration
- Import Path:
github.com/luxfi/consensus(via go.work at root) - Features: Full integration with Lux node, concurrent processing
C (/src/c/)
- Status: Data structures only — not real consensus (per SDK audit)
- Use Case: Embedded systems, performance-critical applications
- Features: Minimal dependencies, ZeroMQ networking, SIMD optimizations
Rust (/src/rust/)
- Status: FFI wrapper around the C library — inherits C limitations, not native consensus (per SDK audit)
- Use Case: Memory-safe systems, async applications
- Features: Zero-cost abstractions, async/await, compile-time guarantees
Python (/src/python/)
- Status: Research/Development
- Use Case: Prototyping, research, data analysis
- Features: NumPy integration, ML frameworks, visualization
C++ (/src/cpp/)
- Status: Development
- Use Case: High-performance with GPU acceleration
- Features: MLX extensions, template metaprogramming, SIMD/GPU support
Building
From the repository root:
# Build all implementations
make build
# Build specific language
make build-go
make build-c
make build-rust
make build-python
make build-cpp
Testing
From the repository root:
# Test all implementations
make test
# Test specific language
make test-go
make test-c
make test-rust
make test-python
make test-cpp
Consensus Engines
All implementations support these consensus engines (Quasar family):
- Wave - Threshold voting with fast probabilistic consensus (production)
- Nova - Linear chain consensus (wraps Ray sub-protocol)
- Nebula - DAG consensus with safe-prefix commit (wraps Field sub-protocol)
- PostQuantum - Quantum-resistant consensus with BLS + Pulsar + ML-DSA
- Chain - Linear chain consensus for ordered blocks
- DAG - Full directed acyclic graph consensus
Performance Benchmarks
| Implementation | Votes/Second | Memory Usage | Latency |
|---|---|---|---|
| C | 14,000+ | < 10 MB | < 1ms |
| Rust | 13,500+ | < 15 MB | < 1ms |
| Go | 12,000+ | < 20 MB | < 2ms |
| C++ (w/ MLX) | 15,000+ | < 25 MB | < 1ms |
| Python | 5,000+ | < 50 MB | < 5ms |
Note. These per-implementation votes/sec figures are unverified and, per the SDK audit, several measure data-structure insertion on stub/wrapper SDKs (C = data structures only, Rust = FFI wrapper over C, C++ = Wave-only stub); only the Python SDK implements real consensus. Treat as illustrative, not measured consensus throughput. See
.github/workflows/README.mdand the "Honest Assessment" block inLLM.md.
Protocol Compatibility
All implementations use the same binary protocol for network communication:
┌─────────────┬────────────┬──────────┬───────────┬──────────┐
│ Engine Type │ Node ID │ Block ID │ Vote Type │ Reserved │
│ (1 byte) │ (2 bytes) │ (2 bytes)│ (1 byte) │ (2 bytes)│
└─────────────┴────────────┴──────────┴───────────┴──────────┘
This ensures interoperability between different language implementations.
Development Guidelines
- Consistency: All implementations should provide the same consensus guarantees
- Testing: Each implementation must have comprehensive test coverage
- Documentation: Language-specific documentation in
docs/{language}/ - Performance: Optimize for language strengths while maintaining correctness
- Compatibility: Maintain protocol compatibility across all implementations
Contributing
When adding features or fixes:
- Implement in the reference implementation (Go)
- Port to other languages maintaining consistency
- Add tests for all implementations
- Update documentation
See CONTRIBUTING.md for detailed guidelines.
License
Copyright (C) 2019-2025, Lux Industries Inc. All rights reserved.