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The prior-work section describes the 2022 backend by category, and the comparison table and production section cover the systems Base is measured against. hanzo-stack-comparison and hanzo-datastore drop one reference each. hanzo-base.pdf had no source in the repo and is superseded by hanzo-base-runtime. Committed PDFs rebuilt with make. Co-authored-by: Hanzo Dev <[email protected]>
141 lines
7.0 KiB
TeX
141 lines
7.0 KiB
TeX
\section{Conclusion}
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\label{sec:conclusion}
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%% ============================================================================
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The Hanzo + Lux stack provides a uniquely integrated platform for AI-native blockchain infrastructure. Across 16 technology categories, it matches or exceeds alternatives on performance while providing capabilities---post-quantum cryptography, FHE-native confidential computing, zero-allocation wire protocols, and dual-certificate consensus---that no combination of point solutions can replicate.
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The integration advantage compounds: shared identity eliminates 15 auth integration points, shared secrets eliminate 15 KMS integration points, and the unified K8s operator replaces 45 configuration files with one. The result is a 6$\times$ reduction in first-year engineering effort and a 45\% reduction in annual TCO at scale.
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For organizations building at the intersection of AI and blockchain, the Hanzo + Lux stack is not merely a collection of tools---it is a coherent platform where each component amplifies the capabilities of every other. The post-quantum cryptographic substrate, the FHE confidential computing layer, the zero-allocation consensus protocol, and the integrated AI inference pipeline are not features that can be bolted onto an existing stack. They are architectural decisions that must be present from the foundation.
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The stack is fully open source, self-hostable, and deployable with a single Kubernetes manifest. The code is the documentation; the benchmarks are the proof.
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%% ============================================================================
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%% References
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%% ============================================================================
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\begin{thebibliography}{30}
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\bibitem{krakend2017}
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A.~Sanchez, ``KrakenD: Ultra-performant API Gateway,'' \textit{krakend.io}, 2017.
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\bibitem{infisical2023}
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T.~Bornstein et al., ``Infisical: Open-source secret management,'' \textit{infisical.com}, 2023.
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\bibitem{umami2020}
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M.~Cao, ``Umami: Privacy-focused analytics,'' \textit{umami.is}, 2020.
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\bibitem{fips203}
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NIST, ``FIPS 203: Module-Lattice-Based Key-Encapsulation Mechanism Standard,'' August 2024.
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\bibitem{fips204}
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NIST, ``FIPS 204: Module-Lattice-Based Digital Signature Standard,'' August 2024.
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\bibitem{fips205}
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NIST, ``FIPS 205: Stateless Hash-Based Digital Signature Standard,'' August 2024.
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\bibitem{cggmp21}
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R.~Canetti, R.~Gennaro, S.~Goldfeder, N.~Makriyannis, and U.~Peled, ``UC non-interactive, proactive, threshold ECDSA with identifiable aborts,'' \textit{CCS}, 2020.
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\bibitem{frost}
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C.~Komlo and I.~Goldberg, ``FROST: Flexible Round-Optimized Schnorr Threshold Signatures,'' \textit{SAC}, 2020.
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\bibitem{tfhe}
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I.~Chillotti, N.~Gama, M.~Georgieva, and M.~Izabachene, ``TFHE: Fast Fully Homomorphic Encryption over the Torus,'' \textit{Journal of Cryptology}, vol.~33, pp.~34--91, 2020.
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\bibitem{ckks}
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J.~H.~Cheon, A.~Kim, M.~Kim, and Y.~Song, ``Homomorphic Encryption for Arithmetic of Approximate Numbers,'' \textit{ASIACRYPT}, 2017.
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\bibitem{hotstuff}
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M.~Yin, D.~Malkhi, M.~K.~Reiter, G.~G.~Gueta, and I.~Abraham, ``HotStuff: BFT Consensus in the Lens of Blockchain,'' \textit{PODC}, 2019.
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\bibitem{narwhal}
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G.~Danezis, L.~Kokoris-Kogias, A.~Sonnino, and A.~Spiegelman, ``Narwhal and Tusk: A DAG-based Mempool and Efficient BFT Consensus,'' \textit{EuroSys}, 2022.
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\bibitem{tendermint}
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E.~Buchman, J.~Kwon, and Z.~Milosevic, ``The latest gossip on BFT consensus,'' \textit{arXiv:1807.04938}, 2018.
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\bibitem{vllm}
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W.~Kwon et al., ``Efficient Memory Management for Large Language Model Serving with PagedAttention,'' \textit{SOSP}, 2023.
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\bibitem{tgi}
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Hugging Face, ``Text Generation Inference,'' \textit{github.com/huggingface/text-generation-inference}, 2023.
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\bibitem{hnsw}
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Y.~Malkov and D.~Yashunin, ``Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs,'' \textit{IEEE TPAMI}, vol.~42, no.~4, 2020.
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\bibitem{casbin}
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L.~Yang, ``Casbin: An authorization library that supports access control models,'' \textit{casbin.org}, 2017.
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\bibitem{mcp}
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Anthropic, ``Model Context Protocol Specification,'' \textit{modelcontextprotocol.io}, 2024.
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\bibitem{auth0}
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Auth0 Inc., ``Auth0 Identity Platform,'' \textit{auth0.com}, 2013.
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\bibitem{clerk}
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Clerk Inc., ``Clerk: Authentication and User Management,'' \textit{clerk.com}, 2020.
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\bibitem{fireblocks}
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Fireblocks Ltd., ``Fireblocks: Digital Asset Security,'' \textit{fireblocks.com}, 2019.
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\bibitem{pinecone}
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Pinecone Systems Inc., ``Pinecone: Vector Database,'' \textit{pinecone.io}, 2019.
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\bibitem{qdrant}
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Qdrant Solutions GmbH, ``Qdrant: Vector Similarity Search Engine,'' \textit{qdrant.tech}, 2021.
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\bibitem{zama}
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Zama, ``Concrete: TFHE Compiler,'' \textit{zama.ai}, 2022.
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\bibitem{seal}
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Microsoft Research, ``Microsoft SEAL: Homomorphic Encryption Library,'' \textit{github.com/microsoft/SEAL}, 2018.
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\bibitem{vault}
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HashiCorp, ``Vault: Manage Secrets and Protect Sensitive Data,'' \textit{vaultproject.io}, 2015.
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\bibitem{langchain}
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H.~Chase, ``LangChain: Building applications with LLMs,'' \textit{langchain.com}, 2022.
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\bibitem{crewai}
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J.~Moura, ``CrewAI: Framework for orchestrating role-playing AI agents,'' \textit{crewai.com}, 2023.
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\bibitem{autogen}
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Microsoft Research, ``AutoGen: Enabling Next-Gen LLM Applications,'' \textit{microsoft.github.io/autogen}, 2023.
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% --- Named-competitor scaffold (Apr 2026) ---
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\bibitem{langgraphgithub}
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LangChain AI. \textit{LangGraph: Build resilient language agents as graphs}. \url{https://github.com/langchain-ai/langgraph}, 2024.
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\bibitem{autogengithub}
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Microsoft Research. \textit{AutoGen: Programming Framework for Agentic AI}. \url{https://github.com/microsoft/autogen}, 2023.
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\bibitem{crewaigithub}
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J.~Moura. \textit{CrewAI: Framework for orchestrating role-playing, autonomous AI agents}. \url{https://github.com/crewAIInc/crewAI}, 2023.
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\bibitem{openaiagentssdk}
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OpenAI. \textit{OpenAI Agents SDK (formerly Swarm)}. \url{https://github.com/openai/openai-agents-python}, 2024.
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\bibitem{pecollective2026agentframeworks}
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PE Collective. \textit{AI Agent Frameworks Compared: LangGraph vs CrewAI vs AutoGen (2026)}. \url{https://pecollective.com/blog/ai-agent-frameworks-compared/}, 2026.
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\bibitem{pooya2026frameworks}
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P.~Golchian. \textit{CrewAI vs LangGraph vs AutoGen 2026: Benchmarks, Pricing, and the Right Choice}. \url{https://pooya.blog/blog/crewai-vs-langgraph-autogen-comparison-2026/}, 2026.
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\bibitem{vllmrepo}
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vLLM Project. \textit{vLLM: A high-throughput and memory-efficient inference and serving engine for LLMs}. \url{https://github.com/vllm-project/vllm}, 2023.
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\bibitem{vllmpaper}
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W.~Kwon et al. \textit{Efficient Memory Management for Large Language Model Serving with PagedAttention}. SOSP 2023. arXiv:2309.06180.
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\bibitem{tgirepo}
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Hugging Face. \textit{text-generation-inference: Large Language Model Text Generation Inference}. \url{https://github.com/huggingface/text-generation-inference}, 2023.
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\bibitem{tensorrtllm}
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NVIDIA. \textit{TensorRT-LLM: Optimised inference toolkit for LLMs}. \url{https://github.com/NVIDIA/TensorRT-LLM}, 2023.
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\end{thebibliography}
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