Skip to main content
Glama

AWS DevOps Study KB

A self-hosted memo knowledge base for AWS DevOps Engineer Professional exam prep — you don't write the notes, your AI agent does. Ask Claude, OpenWebUI, or any other agent a study question ("what's the baseline performance difference between gp3 and io2, and when would I pick each?"), and it saves a well-structured, tagged memo here as a side effect of answering — grounded in official AWS docs if it's also connected to the AWS Knowledge MCP server. Ask again next week and the agent finds the memo instead of re-deriving the answer.

No folders, no manual organizing — every memo is flat, tagged, and full-text searchable. The web UI is read-only by design: agents own all writes.

you: "explain EBS volume types, baseline performance, and when to pick each"
agent: [searches this KB — nothing found] [answers, grounded in AWS docs] [saves a memo,
        tagged ebs, storage, exam:resilient-cloud-solutions]

 ...two weeks later...

you: "remind me about EBS gp3 vs io2"
agent: [searches this KB — finds the memo] "Yep, saved this one already — gp3 baseline is
        3,000 IOPS / 125 MiB/s regardless of size..."

How it works

One Node.js/TypeScript service exposes the same memo store three ways, so it works with whatever your AI client speaks:

  • MCP (/mcp, streamable-HTTP) — for Claude Code, Claude Desktop, OpenWebUI, or any other MCP-native client. Tools: create_memo, search_memos, get_memo, list_tags, update_memo, delete_memo.

  • REST + OpenAPI (/api/v1/*, spec at /openapi.json) — for agent frameworks and bots that only do OpenAPI/REST tool-calling (custom GPT Actions, LibreChat, a homegrown Telegram bot, etc.).

  • Web UI (/) — read-only browse/search/tag-filter view for you, gated by a simple cookie-session login. No create/edit forms exist at all — that's not an oversight, it's the point.

All three are thin adapters over one SQLite-backed service (search is FTS5 full-text + tag filtering), so nothing can drift between what an MCP client sees and what a REST client sees — see CLAUDE.md for the architecture in more detail.

Related MCP server: XMemo

Quickstart

git clone <this-repo-url>
cd aws-devops-study-kb
cp .env.example .env        # fill in API_BEARER_TOKEN and SESSION_COOKIE_SECRET (see below)
npm install
npm run dev                 # http://localhost:8000

Generate real secrets rather than leaving the .env.example placeholders:

openssl rand -hex 32   # → API_BEARER_TOKEN
openssl rand -hex 32   # → SESSION_COOKIE_SECRET

For a persistent, internet-reachable deployment (so a cloud-hosted agent or a Telegram bot can reach it too), see docs/deployment.md — Docker Compose + a Cloudflare Tunnel, with no inbound ports to open.

Connect an AI agent

Every consumer needs the bearer token from .env (API_BEARER_TOKEN) as Authorization: Bearer <token>. Pick whichever path matches your client.

MCP clients (Claude Code, Claude Desktop, OpenWebUI, …)

Claude Code, against a running deployment:

claude mcp add --transport http aws-devops-study-kb https://<your-host>/mcp \
  --header "Authorization: Bearer <API_BEARER_TOKEN>"

Claude Desktop or any client configured via a mcpServers JSON block:

{
  "mcpServers": {
    "aws-devops-study-kb": {
      "type": "http",
      "url": "https://<your-host>/mcp",
      "headers": { "Authorization": "Bearer <API_BEARER_TOKEN>" }
    }
  }
}

OpenWebUI: add it as an MCP tool server (Settings → Tools) with the same URL + header, if your OpenWebUI version has native MCP support.

Once connected, the server's MCP instructions field (read automatically by clients that honor it) already nudges the model to search before answering and save memos unprompted — see docs/agent-persona.md if you want to make that behavior explicit and reliable via a system prompt instead of relying on the client picking it up.

OpenAPI/REST tool-calling clients

For anything that imports an OpenAPI spec rather than speaking MCP (custom GPT Actions, LibreChat, a custom bot):

  • Spec URL: https://<your-host>/openapi.json (bearer-token protected — fetch it with the header)

  • A static copy is committed at openapi/generated.json if your client needs a file instead of a live fetch (regenerate with npm run generate:openapi after any schema change).

  • Every call needs the same Authorization: Bearer <API_BEARER_TOKEN> header.

Tag convention

There are no folders — tags are the only organizing structure, so agents need to use them consistently. exam:<domain> for the 6 AWS DevOps Pro exam domains, plus freeform topic tags (ebs, s3, codepipeline, …). Full convention, and where it's enforced/documented for agents, in docs/tag-conventions.md.

Development

npm test              # vitest — memoService, REST API, and MCP tool round-trips
npm run typecheck      # tsc --noEmit
npm run build           # compiles to dist/ (used by the Docker image)

See CLAUDE.md for the full architecture writeup (why SQLite+FTS5, why one shared service layer, the auth model, known gotchas) if you're extending this.

License

MIT

Maintenance

ActivitySlowing
ResponsivenessNo issues

Related MCP Connectors

  • XMemo is a user-owned Memory OS for AI agents, providing a shared, persistent memory layer across AI assistants, IDEs, CLIs, tools, projects, and sessions. It enables ChatGPT, Claude, Codex, Cursor, Gemini, and other supported AI clients to access authorized long-term context without requiring users to repeatedly explain their preferences, project decisions, or previous work. Beyond basic memory storage and retrieval, XMemo supports semantic search, contextual recall, memory updates and corrections, source attribution, version history, project-scoped context, task tracking, and governed memory lifecycle management. Identity-aware access controls, scoped authorization, and memory isolation help users manage which agents and workflows can access their information. XMemo also provides advanced capabilities for structured knowledge, reusable procedures, and memory consolidation through its broader Memory OS platform. Connect through hosted MCP with OAuth or bearer-token authentication, or integrate directly through REST APIs and supported client tools. Memory remains available across authorized clients and sessions, with user-controlled access, export, and deletion. Website: https://xmemo.dev Documentation: https://xmemo.dev/docs

  • Persistent memory layer for AI tools. Save and recall notes across Claude and other MCP clients.

  • Markdown-based note-taking with a hosted MCP server. Your notes serve you and your AI.

  • MCP-native notes and memory for ChatGPT, Claude, and other AI tools.

Related MCP Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    Self-hosted semantic memory for AI agents. Save worklogs, decisions, and notes via MCP, then recall them across sessions by meaning rather than keyword. Backed by Postgres + pgvector with local embeddings (multilingual-e5-base).
    1
    MIT
  • A
    license
    A
    quality
    A
    maintenance
    XMemo is a secure, user-owned memory substrate and context engine for AI agents, CLIs, IDEs, and LLM workspaces. Exposed over Streamable HTTP MCP, it empowers agents with cross-session memory, task continuity, and personalized context. Key Features: * Personalized Context: Stores and recalls developer preferences, project guidelines, and coding patterns via semantic vector search. * Agent Daily Me
    31
    1,128 npm
    12
    MIT
  • A
    license
    Not graded
    quality
    A
    maintenance
    A self-hosted Markdown knowledge base and Agent Harness with an MCP server that enables AI agents to read and write notes, providing persistent memory and a shared workspace for multi-agent collaboration.
    2
    MIT
  • A
    license
    A
    quality
    B
    maintenance
    Self-hosted knowledge base that gives AI agents persistent memory over MCP. 18 tools covering notes, folders, tags and trash; hybrid search combining pgvector embeddings with bilingual full-text ranking via RRF; [[wikilink]] graph traversal and backlinks; document import from PDF, DOCX and EPUB. Connects over Streamable HTTP or stdio, with Bearer-token or OAuth auth.
    18
    3
    AGPL 3.0