I turned my Obsidian vault into a second brain without touching a single plugin
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Three external tools can transform an Obsidian vault into a genuine second brain without installing any community plugins. Claude Code, pointed at the vault folder, gives AI file and terminal access with vault-specific rules set via a CLAUDE.md file. Google's Antigravity (a VS Code fork) brings multi-agent planning and can spawn subagents to audit orphaned notes, broken wikilinks, and duplicate content in a single prompt. AnythingLLM provides a fully local AI workspace with RAG, chat, and MCP support, letting users create separate workspaces for different vault sections — though RAG quality depends on embedding models and GPU resources.
Table of contents
Claude Code makes your vault the home base for your AI setupAntigravity is the tool you bring in when the vault needs a real auditAnythingLLM is how you actually chat with your vaultQuestions this post answers
How do I use Claude Code with my Obsidian vault?
Point Claude Code at your vault folder and create a CLAUDE.md file at the vault root. That file teaches Claude Code your folder structures, naming conventions, and Obsidian-specific syntax like wikilinks and frontmatter. Because Claude Code runs at the OS level it doesn't inherently know Obsidian's link-updating rules on rename, but CLAUDE.md can supply those guardrails explicitly. Developers wiring AI tools into their Obsidian workflows track new approaches like this on daily.dev.
What is Antigravity and how does it differ from Claude Code for working with files?
Antigravity is Google's agent-first IDE forked from VS Code. Unlike Claude Code, it shows a multi-step plan before acting and can spawn subagents to work in parallel or hand off tasks to each other. For a vault audit, a single prompt like 'find orphaned notes, broken wikilinks, and duplicate content' produces a full report, and the agent can write its own Python scripts mid-task to verify findings. Teams choosing between AI coding and productivity agents find comparison coverage like this on daily.dev.
What are the trade-offs of using AnythingLLM with a local LLM for Obsidian RAG?
AnythingLLM provides a fully local AI workspace with chat, RAG, agents, and MCP support, running against local models via LM Studio or Ollama. You can create separate workspaces for different vault sections, each with its own model and system prompt. The main trade-off is that RAG quality depends entirely on your embedding models and note chunking strategy, and a decently sized GPU is needed for reliable long-term use. Those building local AI stacks around their notes find relevant tooling news on daily.dev.