LLM Wiki Blog Series

Wiki Index

A practical technical guide generated from the LLM wiki knowledge base.

Full series Next: LLM Wiki Usage Guide

Introduction

This landing page is the starting point for the LLM Wiki Blog Series, a guided set of articles about building and maintaining an Obsidian-based knowledge environment for AI-assisted work. It introduces the vault as more than a folder of notes: Obsidian provides the Markdown foundation, the obsidian-wiki skills define repeatable agent workflows, and the generated articles show how concepts, entities, references, inventories, and synthesis pages work together as a navigable knowledge graph.

Readers should use this page as the table of contents and orientation layer before jumping into individual topics. The series covers both the LLM Wiki operating model and the AI ecosystem content currently stored inside the vault, including foundation models, agents, retrieval, vector databases, embeddings, observability, memory, security, workflow automation, and tool selection. By starting here, a reader can understand the intended reading order, the role of each page type, and how the exported HTML pages map back to a maintainable wiki workflow.

Who This Guide Is For

This guide is aimed at engineers, architects, technical leads, and AI platform builders who need a structured way to explore the knowledge base. It is especially useful when evaluating tool categories, comparing system layers, or planning how a team should move from isolated experiments into maintainable AI applications.

• Use the concept pages to understand the major architectural layers.
• Use entity pages to orient around important vendors and frameworks.
• Use inventory pages to compare tool families and identify candidates for deeper review.
• Use synthesis pages when you need decision rubrics, stack patterns, and implementation trade-offs.

Recommended Reading Order

Start with the high-level knowledge base and concept pages, then move into inventories, entity pages, and synthesis articles. That path mirrors how teams usually make technical decisions: first define the problem space, then understand the layers, then compare tools, and finally choose an implementation pattern.

1. LLM Wiki Usage Guide — Practical guide for using this vault with LLM Wiki skills, Codex chat, Obsidian, and the obsidian-wiki setup CLI. Read this as step 1 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
2. Agent Development Frameworks — Agent development frameworks provide SDKs and managed services for defining, running, and deploying agent applications. Read this as step 2 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
3. Agent Orchestration — Agent orchestration coordinates stateful model calls, tools, memory, and multi-agent workflows. Read this as step 3 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
4. AI Memory Management — AI memory systems persist user preferences, conversation history, task state, and semantic or episodic context. Read this as step 4 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
5. AI Observability Evaluation — AI observability and evaluation measure quality, traces, costs, latency, safety, and reliability of LLM systems. Read this as step 5 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
6. AI Security Guardrails Governance — AI security and governance tools manage safety, privacy, prompt injection, data exposure, and policy enforcement. Read this as step 6 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
7. Embedding Layer — The embedding layer converts content into dense vectors for semantic search, clustering, ranking, and memory. Read this as step 7 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
8. Foundation Models — Foundation models provide the base language, reasoning, coding, and multimodal capabilities used by higher AI stack layers. Read this as step 8 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
9. Model Context Protocol — MCP standardizes how AI applications connect to tools, data sources, and local or remote environments. Read this as step 9 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
10. Retrieval-Augmented Generation — RAG grounds model responses in retrieved external knowledge to improve factuality, freshness, and source traceability. Read this as step 10 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
11. Vector Databases — Vector databases store and search embeddings for semantic retrieval, memory, clustering, and hybrid search. Read this as step 11 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
12. Workflow Automation Orchestration — Workflow automation connects AI systems to business processes, APIs, durable execution, and low-code integrations. Read this as step 12 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
13. Anthropic — Anthropic is the company behind Claude, a proprietary model family used for reasoning, coding, and tool-use workloads. Read this as step 13 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
14. LangChain — LangChain is a framework ecosystem for building LLM applications, including RAG, agents, orchestration, and observability. Read this as step 14 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
15. LlamaIndex — LlamaIndex is a data-centric LLM framework for ingestion, indexing, querying, and workflow construction. Read this as step 15 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
16. OpenAI — OpenAI is a frontier AI company and platform provider for models, APIs, tools, and agent development. Read this as step 16 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
17. LLM Wiki Functions Reference — Reference for the agent skills listed by obsidian-wiki list, with examples for invoking each workflow. Read this as step 17 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
18. AI Ecosystem Coverage Plan — Expansion plan for covering the AI ecosystem as one domain inside the broader LLM wiki. Read this as step 18 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
19. AI Ecosystem KB — Seed reference map for the modern AI ecosystem, organized by stack layer and connected to concept/entity pages. Read this as step 19 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
20. Agent Development Tools Inventory — Inventory of agent SDKs, managed agent services, structured-output runtimes, and tool-use platforms. Read this as step 20 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
21. Agent Orchestration Tools Inventory — Inventory of graph, role-based, event-driven, and multi-agent orchestration tools. Read this as step 21 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
22. Embedding Tools Inventory — Inventory of embedding APIs, open embedding model families, and retrieval-oriented vectorization services. Read this as step 22 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
23. Foundation Model Tools Inventory — Inventory of model providers, open-weight model sources, local runners, and inference serving engines. Read this as step 23 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
24. MCP Tools Inventory — Inventory of MCP specifications, SDKs, server frameworks, reference servers, and common integration servers. Read this as step 24 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
25. AI Memory Tools Inventory — Inventory of agent memory services, short-term state, semantic memory, long-term storage, and graph/database-backed memory layers. Read this as step 25 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
26. AI Observability Evaluation Tools Inventory — Inventory of LLM observability, tracing, evaluation, red-team, cost, latency, and monitoring tools. Read this as step 26 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
27. RAG Tools Inventory — Inventory of RAG frameworks, parsing systems, GraphRAG projects, retrievers, rerankers, and indexing tools. Read this as step 27 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
28. AI Security Governance Tools Inventory — Inventory of guardrails, content safety, PII handling, prompt-injection defense, model security, and governance tools. Read this as step 28 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
29. Vector Database Tools Inventory — Inventory of vector databases, hybrid search engines, local vector stores, and database-native vector extensions. Read this as step 29 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
30. Workflow Automation Tools Inventory — Inventory of low-code automation, API integration, durable workflow, scheduling, and data orchestration tools used around AI systems. Read this as step 30 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
31. Agent Framework Selection — Selection guide for agent frameworks by runtime style, model-provider fit, tool interface, memory/state, deployment, and governance needs. Read this as step 31 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
32. AI Ecosystem Stack Patterns — Cross-layer patterns for composing AI ecosystem tools into practical application and agent stacks. Read this as step 32 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
33. LLM Observability Stack — Stack guide for composing LLM tracing, evaluation, monitoring, cost analytics, and safety testing. Read this as step 33 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
34. Local LLM Serving Options — Selection guide for local and self-hosted LLM serving across local runners, model hubs, high-throughput servers, and API routing layers. Read this as step 34 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
35. RAG Framework Comparison — Comparison guide for RAG frameworks by pipeline shape, parsing needs, graph requirements, agentic retrieval, and evaluation loop. Read this as step 35 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.
36. Vector Database Selection — Selection guide for choosing vector databases by data gravity, deployment model, hybrid search, filtering, and operational weight. Read this as step 36 in the series to understand how this topic fits into the broader AI ecosystem knowledge base.

How The Articles Connect

The articles form a topic cluster around AI ecosystem architecture. The concept pages define vocabulary and boundaries. The reference inventories list tools and refresh points. The synthesis pages connect those raw categories into operational patterns such as RAG framework choice, vector database selection, observability stack design, and local model serving strategy.

Reader note: follow the Previous and Next links on every page when you want a guided path, or return to this landing page when you want to jump by category.

Key Takeaways

• The root page acts as the table of contents for the generated HTML export.
• Every article links back to the full series and exposes Previous and Next navigation.
• The exported folder preserves the original vault structure while adding blog-friendly reading flow.

Practical Implementation Context

For Wiki Index, practical implementation means turning the exported series into a reliable entry point for an Obsidian-backed LLM Wiki. The page should not merely introduce articles; it should help a reader move from the root index to the usage guide, then into the AI ecosystem pages with enough context to understand what the vault is, why the Blogger pages exist, and where the source of truth lives.

• Keep the root page synchronized with the generated article order and dated Blogger aliases.
• Use the usage guide as the first operational handoff before readers evaluate AI ecosystem material.
• Make Obsidian visible as the durable Markdown foundation rather than implying the HTML export is the source of truth.
• Verify that series navigation, images, FAQ links, and setup references still work after each regeneration.

Implementation note: keep this landing page aligned with the generated article order and dated Blogger aliases so the series handoff into the Obsidian setup workflow remains accurate.

Reference Implementation Pattern

The reference implementation for the full series is a working Obsidian vault backed by the obsidian-wiki skill framework. Start with a local vault path, keep raw source material in _raw/, and let an agent maintain the compiled pages, index, manifest, and links.

# .env in the vault root or a parent directory
OBSIDIAN_VAULT_PATH=/Users/example/work/llm-shared-vault
OBSIDIAN_WIKI_REPO=${OBSIDIAN_VAULT_PATH}/obsidian-wiki
OBSIDIAN_SOURCES_DIR=${OBSIDIAN_VAULT_PATH}/_raw
OBSIDIAN_CATEGORIES=concepts,entities,skills,references,synthesis,journal
OBSIDIAN_LINK_FORMAT=wikilink

A practical first pass is to run status, ingest a small source, query the result, and then lint the vault. That loop proves the foundation before a team adds larger AI ecosystem inventories or synthesis pages.

$wiki-status
$wiki-ingest promote the notes in _raw/
$wiki-query what do I know about the AI ecosystem?
$wiki-lint

Operational Depth

Series Ownership

The landing page has to stay aligned with the actual vault structure, dated Blogger aliases, and reading order. If pages are added or removed, the index should be regenerated so the series remains navigable.

Publishing Readiness

Before publishing, verify that internal links use the dated Blogger convention, images render, and the landing FAQ still points readers to Obsidian and the setup guide.

Review Cadence

Review this page whenever source material changes, linked pages are promoted, or a reader would make a different decision because of new information. The review should check both content accuracy and whether the page still connects cleanly to the rest of the LLM Wiki graph.

Reader Outcome

A reader should leave the page knowing where to start, how Obsidian fits, and which article to open next.

Frequently Asked Questions

What is the starting page for the LLM Wiki Blog Series?

Use index.html in the root of blogger_contents, or the Blogger-style alias /2026/07/llm-wiki-blog-series.html. It is the table of contents for the generated series.

What is used as the foundation for an LLM wiki environment?

Obsidian is used as the foundation because it stores the knowledge base as local Markdown files with folders, frontmatter, and links that agents can maintain. A setup guide is available from Ar9av/obsidian-wiki.

How should readers move through the series?

Start with the usage guide, then follow concepts, entities, inventories, references, and synthesis pages depending on whether the reader needs setup, vocabulary, tool comparison, source-backed facts, or decision support.

Does the export replace the Obsidian vault?

No. The HTML export is a publishing view. The source of truth remains the Obsidian Markdown vault that agents update and query.

Conclusion

Wiki Index should leave readers with a clear route through the LLM Wiki Blog Series. Start with the setup and usage material, then move through concepts, entities, inventories, references, and synthesis pages as your questions become more specific. The value of the series is not only the exported HTML; it is the operating model behind it: Obsidian stores durable Markdown knowledge, agents maintain and query it, and the linked structure keeps the knowledge graph navigable.

The next practical step is to open the usage guide, confirm how the vault is configured, and then follow the series links into the AI ecosystem pages that match your current decision. Treat this landing page as the table of contents for both reading and future maintenance.

Full series Next: LLM Wiki Usage Guide