Building a Self-Improving Knowledge System with Claude Code and Obsidian

Architecture Overview
A developer created a self-improving knowledge system that runs 25 automated tools hourly to solve Claude Code's session amnesia problem. The system connects Claude Code to an Obsidian vault (~350 notes) with local semantic search, knowledge graphs, and automated processing.
Technical Stack
- Obsidian vault as the knowledge store
- Claude Code (Opus) as the AI that reads/writes the vault
- Ollama + bge-m3 (1024-dim embeddings, RTX 3080) for local semantic search
- SQLite (better-sqlite3) for search index, graph DB, codebase index
- Express server for a React dashboard
- 2 MCP servers giving Claude native vault + graph access
- Windows Task Scheduler running everything hourly
Tool Layers and Functions
Layer 1: Data Collection
vault-live-sync.mjs: Watches Claude Code JSONL sessions in real-time, converts to Obsidian notesvault-sync.mjs: Hourly sync of Supabase stats, AutoPost status, git activity, project contextvault-voice.mjs: Voice-to-vault with Whisper transcription + Sonnet summary of audio filesvault-clip.mjs: Web clipping from RSS feeds + Brave Search topic monitoring + AI summaryvault-git-stats.mjs: Git metrics including commit streaks, file hotspots, hourly distribution
Layer 2: Processing & Intelligence
vault-digest.mjs: Daily digest aggregating all sessions into one readable pagevault-reflect.mjs: Uses Sonnet to extract key decisions from sessions, auto-promotes to MEMORY.mdvault-autotag.mjs: AI auto-tagging with Sonnet suggesting tags + wikilink connectionsvault-schema.mjs: Frontmatter validator with 10 note types, compliance reporting, auto-fix modevault-handoff.mjs: Generates machine-readablehandoff.json(survives compaction better than markdown)vault-session-start.mjs: Assembles optimal context package for new Claude sessions
Layer 3: Search & Retrieval
vault-search.mjs: FTS5 + chunked semantic search (512-char chunks, bge-m3 1024-dim). Flags include--semantic,--hybrid,--scope,--since,--between,--recent. Includes retrieval logging + heat map.vault-codebase.mjs: Indexes 2,011 source files: exports, routes, imports, JSDocvault-graph.mjs: Knowledge graph with 375 nodes, 275 edges, betweenness centrality, community detection, link suggestionsvault-graph-mcp.mjs: Graph as MCP server with 6 tools (search, neighbors, paths, common, bridges, communities) Claude can use natively
Layer 4: Self-Improvement
vault-patterns.mjs: Weekly patterns including momentum score (1-10), project attention %, velocity trends, token burn ($), stuck detection, frustration/energy tracking, burnout riskvault-spaced.mjs: Spaced repetition (FSRS) with 348 notes tracked, priority-based review schedulingvault-prune.mjs: Hot/warm/cold decay scoring, auto-archives stale notes, flags never-retrieved notesvault-contradict.mjs: Contradiction detection with rule-based (stale references, metric drift, date conflicts) + AI-powered (Sonnet compares related docs)vault-research.mjs: Autonomous research with Brave Search + Sonnet, scheduled topic monitoring
Layer 5: Visualization & Monitoring
vault-canvas.mjs: Auto-generates Obsidian Canvas files from knowledge graph (5 modes: full map, per-project, hub-centered, communities, daily)vault-heartbeat.mjs: Proactive agent that gathers state from all services, uses Sonnet to reason about what needs attention
The system was built by a solo dev agency owner who runs 4 interconnected projects, manages 64K business leads, and conducts hundreds of Claude Code sessions per week. The tools are all Node.js ES modules with zero external dependencies beyond what's already in the repo.
📖 Read the full source: r/ClaudeAI
👀 See Also

Tendr Skill Adds CLI-Based Long-Term Memory with Hierarchy to Reduce Token Usage
A new OpenClaw skill separates reasoning from execution for long-term memory operations, using a CLI tool to handle structural changes deterministically. It supports wikilinks and explicit semantic hierarchy across files to reduce token consumption and prevent error accumulation.

AVP Protocol Enables LLM Agents to Share KV-Cache Instead of Text for Token Efficiency
AVP (Agent Vector Protocol) allows LLM agents to pass KV-cache directly between them instead of text, reducing token processing by 73-78% and achieving 2-4x speedups across Qwen, Llama, and DeepSeek models. The protocol works with HuggingFace and vLLM connectors and is available as a Python package.

Eqho: Local Voice-to-Text App for Claude Code Sessions
Eqho is a free, open-source voice-to-text app that uses OpenAI's Whisper model locally to type spoken input into any focused application. Currently Windows-only with command-line setup required.

Claude Code Skill Refactors React Components Using 'Don't Make Me Think' Principles
A new Claude Code skill automatically refactors React components for usability based on Steve Krug's principles — cuts happy talk, surfaces primary CTAs, fixes empty/error states, and tightens labels.