# study8677/awesome-architecture

🧭 Architecture-first system design: 26 bilingual tutorials, 25 architecture templates, and 6 end-to-end cases covering distributed systems, AI-native systems, RAG, coding Agents, and production trade-offs.

Repository: https://github.com/study8677/awesome-architecture
Canonical: https://ross.abutalabs.com/products/study8677-awesome-architecture
Homepage: https://study8677.github.io/awesome-architecture/
Language: Vue
License: MIT
License Family: permissive
Topics: architecture-decision-records, architecture-patterns, backend, c4-model, design-patterns, distributed-systems, interview-preparation, learning-resources, microservices, scalability, software-architecture, software-engineering, system-design, system-design-interview, ai-agents, awesome-list, ai-coding, ai-native, llm, rag
Last push: 2026-08-21T09:41:29+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 98, release rhythm 35, longevity 7
- inputs: {"age_days": 102, "days_push": 12, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2211, forks 246 (observed 2026-08-28T04:06:26.356341+00:00)

## What it is
A bilingual (Chinese/English) open-source knowledge base focused on software architecture rather than code, offering 40 architecture-thinking tutorials, 31 real-system architecture templates, and 6 end-to-end case studies. It covers classic systems (e-commerce, IM, payments) as well as AI-native systems (RAG, LLM inference, AI agents, AI gateways), emphasizing design trade-offs and decision-making.

## Use cases
- learn system design for backend interviews
- study architecture patterns for distributed systems
- understand how to design a RAG knowledge base
- prepare for system design interview questions
- learn trade-offs in microservices and scalability
- find reference architectures for AI agent systems
- study real-world architecture diagrams of popular systems

## When to choose
- you want architecture-level thinking and trade-off analysis rather than code tutorials
- you are preparing for system design or staff-engineer interviews
- you need reference architectures for AI-native systems like RAG, LLM inference, or agent workflows
- you prefer bilingual Chinese/English learning material

## When to avoid
- you need runnable code, frameworks, or libraries rather than conceptual material
- you want deep hands-on implementation guides for a specific language or stack
- you need formal academic treatment of architecture theory

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: microservices, large-language-models, tutorials, awesome-lists, developer-tools
- platform: cross-platform
- tags: system-design, architecture-patterns, microservices, interview-preparation, ai-native-systems, rag, ai-agents, architecture-decision-records, bilingual, scalability, software-architecture, web-server

## Member repositories
- study8677/awesome-architecture (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:26.356341+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:46:05.731267+00:00, confidence not recorded.
  - readme: https://github.com/study8677/awesome-architecture (fetched 2026-08-28T04:06:26.356341+00:00, sha 6b07bf99b57f)
  - homepage: https://study8677.github.io/awesome-architecture/ (fetched 2026-08-29T10:26:41.712875+00:00, sha 2d0588691a1e)
- Data as of 2026-08-30T08:39:29.467469+00:00.