Knowledge Base Server

knowledge-base-server

Make every AI agent you use smarter.

A persistent memory system that captures, classifies, synthesizes, and retrieves knowledge for AI agents. One brain, multiple agents, compounding intelligence. Production-proven with 200+ documents, three agents (Claude, Codex, Gemini), and daily use.

This is not just a search engine. It is an intelligence pipeline that turns raw information into refined, retrievable knowledge -- and gets smarter every session.

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The Problem

AI agents are stateless. Every session starts from zero. You end up:

• Copy-pasting context between sessions
• Re-explaining your codebase, architecture, and preferences
• Watching agents hallucinate because they lack YOUR information
• Losing debugging insights that took hours to discover
• Running multiple agents that can't share what they know

The smarter the model, the more painful the amnesia. Context is the bottleneck, not intelligence.

The Solution

knowledge-base-server sits between your knowledge and your AI agents. It ingests everything -- your Obsidian vault, code, documents, YouTube transcripts, X bookmarks, terminal sessions -- and serves it through MCP and REST APIs. Every agent you use reads from and writes to the same brain.

Your AI gets smarter every day. Every session builds on the last.

Obsidian Vault ------+
Code Repos ----------+
YouTube Transcripts --+---> [ KB Server ] ---> SQLite + FTS5 ---> [ MCP ] ---> Claude Code
X/Twitter Bookmarks -+         |                   |                          Codex CLI
Web Clippings -------+     Port 3838          Embeddings          [ REST ] --> Gemini CLI
Terminal Sessions ----+    Web Dashboard      Semantic Search                  ChatGPT
Bug Fix Captures -----+                                                       Any MCP Client

Obsidian is the source of truth -- the human layer where you organize, link, and curate knowledge. The KB server is the optimized retrieval layer that makes it AI-ready with minimal token usage.

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How It Works: The Intelligence Pipeline

This is the core differentiator. Raw information flows through seven layers to become actionable intelligence:

1. Capture Layer

Ingest from everywhere:

• Obsidian vault -- bidirectional sync, auto-indexes on changes
• YouTube transcripts -- via yt-dlp or kb_capture_youtube
• X/Twitter bookmarks -- browser export to markdown, auto-ingest
• Web articles -- Obsidian Web Clipper or kb_capture_web
• Terminal sessions -- kb_capture_session records what worked, failed, and why
• Bug fixes -- kb_capture_fix records symptom, cause, and resolution
• Code, PDFs, text -- 20+ file types with automatic type detection

Every source enters the system as a raw capture, ready for processing.

2. Classification Layer

The kb_classify tool runs AI classification on unprocessed content:

• Assigns document type (research, idea, workflow, lesson, fix, decision, session)
• Auto-generates tags and project routing
• Writes structured summaries and key topics
• Adds confidence scoring
• Enforces frontmatter discipline on every note

Raw clippings become structured, searchable knowledge automatically.

3. Promotion Pipeline

Raw captures get refined into higher-value knowledge artifacts:

• Captures in inbox get normalized into sources
• Sources get promoted to insights, lessons, decisions, runbooks
• The kb_promote tool extracts structured knowledge from raw material
• Higher-value knowledge ranks higher in retrieval
• The system distinguishes signal from noise automatically

4. Synthesis Layer

The kb_synthesize tool connects dots across sources:

• Reads recent notes across all types and projects
• Identifies recurring themes, patterns, and bottlenecks
• Surfaces opportunities and workflow improvements
• Generates cross-cutting insights that no single source contains
• This is where raw information becomes actionable intelligence

5. Three-Tier Memory (Cold / Warm / Hot)

Not all knowledge is equal. The system uses three tiers to prevent context pollution:

| Tier | What Lives Here | When Retrieved | Example | |------|----------------|----------------|---------| | Hot | Active project context, recent sessions, current decisions | First, always | "How did we fix auth yesterday?" | | Warm | Accumulated lessons, research, validated workflows | When relevant to query | "What's our Docker networking pattern?" | | Cold | Raw captures, archived sources, old terminal logs | Only when deep-diving | "What did that YouTube video say about caching?" |

Why three tiers? Without tiering, a search for "authentication" returns every raw article you ever clipped alongside your actual auth architecture decision. The tiers ensure agents get signal, not noise. Hot knowledge is always fresh and relevant. Cold knowledge is preserved but doesn't pollute active context.

6. Token-Optimized Retrieval

Every token costs money and context window space. The retrieval system minimizes waste:

• kb_context returns summaries and metadata WITHOUT full content -- 90%+ token savings
• kb_search uses FTS5 with BM25 ranking, title boosting, and AND-first precision
• kb_search_smart combines keyword and semantic search for conceptual queries
• kb_read loads full content only when needed, after context identifies relevant docs
• Agents get the right information in the fewest possible tokens

7. The Self-Learning Loop

This is the core innovation. The system compounds intelligence over time:

Sessions 1-10:    AI makes mistakes. You correct. Fixes get captured.
Sessions 10-50:   AI recognizes patterns. Starts getting things right more often.
Sessions 50-100:  AI knows your codebase, preferences, architecture patterns.
Sessions 100+:    One-shot clean code. Accumulated context covers everything.

The loop in detail:

1. AI agent attempts a task
2. Output gets evaluated (human judgment -- the irreplaceable element)
3. Corrections and findings get captured (kb_capture_session, kb_capture_fix)
4. KB server ingests the session data
5. kb_synthesize organizes and connects to existing knowledge
6. Agent instructions auto-update with lessons learned
7. Next session starts with better context
8. Repeat -- compounding improvement

This is NOT fine-tuning. NOT RLHF. It is self-modifying instructions through operational history. The model doesn't change. The context it receives does. And context is everything.

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Features

Core

• SQLite FTS5 full-text search with BM25 ranking and highlighted snippets
• Semantic/vector search via local HuggingFace embeddings (Xenova/all-MiniLM-L6-v2) — no API keys needed
• Hybrid search mode (kb_search_smart) combining keyword + semantic for conceptual queries
• Web dashboard for browsing, searching, and managing documents
• CLI for all operations (kb start, kb search, kb ingest, kb setup)
• One-command MCP registration (kb register)
• Interactive setup wizard (kb setup) with agent-compatible auto mode

Ingestion

• 20+ file types: Markdown, text, code, PDFs, JSON, YAML, XML, CSV
• Auto-ingest on first run (~/knowledgebase + Claude memory directories)
• Duplicate detection via content hashing
• Obsidian vault bidirectional sync with incremental indexing
• YouTube transcript capture
• X/Twitter bookmark ingestion
• Web article capture with metadata

Intelligence

• AI auto-classification (type, tags, summary, key topics, confidence)
• Knowledge promotion pipeline (raw -> structured)
• Cross-source synthesis (themes, patterns, opportunities)
• Three-tier memory (cold/warm/hot) with tier-aware retrieval
• Token-optimized context briefings (90%+ savings over full reads)
• Session and fix capture for operational learning
• Safety review for destructive actions (multi-model consensus)

Integration

• MCP server (stdio for local, StreamableHTTP for remote)
• REST API with OpenAPI 3.1 specification
• ChatGPT Custom GPT Actions via OpenAPI import
• OAuth 2.1 with OIDC discovery for remote access
• API key authentication for scripts and automation
• systemd service with auto-restart for production deployment

Security

• bcrypt password hashing with HttpOnly session cookies
• Dual auth: API keys (fast path) + OAuth Bearer tokens
• Three separate API keys for Claude, OpenAI, and Gemini
• Safety review tool checks KB history before destructive actions
• No external dependencies for core functionality

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Quickstart

Prerequisites

• Node.js >= 18.0.0
• That's it. No external databases, no Docker required, no cloud dependencies.

Install

git clone https://github.com/willynikes2/knowledge-base-server.git
cd knowledge-base-server
npm install
npm link

First Run (Interactive Setup Wizard)

kb setup

The wizard detects your environment, asks which AI agents you use, configures everything, and runs your first ingest. Takes about 60 seconds.

For agent-driven installation (no prompts):

kb setup --auto --password=yourpass --vault=~/obsidian-vault --agents=claude,codex --deploy=systemd

Manual Setup

KB_PASSWORD=yourpassword kb start    # Start server on port 3838
kb register                          # Register MCP with Claude Code
kb ingest ~/obsidian-vault           # Ingest your knowledge
kb search "docker networking"        # Search from terminal
kb status                            # Check stats

Connect to Claude Code

After kb register, Claude Code automatically has access to all KB tools. Test it:

> Search the knowledge base for recent bug fixes

Claude will use kb_search and return results from your accumulated knowledge.

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Don't Want to Self-Host? Use Memstalker

Memstalker is the hosted version of knowledge-base-server. Same engine, zero infrastructure.

| | Self-Hosted (Free) | Memstalker Hosted | |---|---|---| | Cost | Free forever | From $12/mo | | Setup | Clone, install, configure | Sign up, connect your agents | | Infrastructure | You run the VPS | We handle everything | | Updates | git pull | Automatic | | Multi-device sync | You configure | Built-in | | Backups | You manage | Automatic | | All 16 MCP tools | Yes | Yes | | Obsidian integration | Yes | Yes | | REST API + ChatGPT | Yes | Yes |

Self-host if: You want full control, have a VPS, and enjoy running your own infrastructure.

Use Memstalker if: You want persistent AI memory without managing servers. Connect your agents in minutes.

First 500 early adopters get Pro at $12/mo forever (normally $25): memstalker.com

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MCP Tools

All 16 tools available via MCP (stdio and HTTP):

| Tool | Description | |------|-------------| | kb_search | Full-text search with BM25 ranking and highlighted snippets | | kb_search_smart | Hybrid keyword + semantic search for conceptual queries | | kb_context | Token-efficient briefing -- summaries only, 90%+ savings. Use BEFORE kb_read | | kb_read | Read full document content by ID | | kb_list | List documents filtered by type or tag | | kb_write | Write a new note to the Obsidian vault | | kb_ingest | Ingest raw text directly into the database | | kb_classify | Auto-classify unprocessed notes (type, tags, summary) | | kb_promote | Promote raw source into structured knowledge | | kb_synthesize | Generate cross-source synthesis connecting recent knowledge | | kb_capture_session | Record a debugging/coding session with findings | | kb_capture_fix | Record a bug fix with symptom, cause, resolution | | kb_capture_web | Capture a web article with content and metadata | | kb_capture_youtube | Capture a YouTube transcript with metadata | | kb_vault_status | Show vault indexing stats by type and project | | kb_safety_check | Review a destructive action against KB history |

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CLI Commands

kb setup              Interactive setup wizard (--auto for agent mode)
kb start              Start the dashboard + API server (default :3838)
kb stop               Stop the running server
kb mcp                Start MCP stdio server (used by AI tools)
kb register           Register MCP server with Claude Code
kb ingest       Ingest a file or directory
kb search      Search documents from terminal
kb status             Show stats and server status
kb classify           Auto-classify unprocessed vault notes
kb summarize          Generate AI summaries for unsummarized notes
kb capture-x          Ingest X/Twitter bookmarks from export
kb safety-check       Review a planned action against KB history

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Architecture

                         +------------------+
                         |   AI Agents      |
                         | Claude | Codex   |
                         | Gemini | ChatGPT |
                         +--------+---------+
                                  |
                    MCP (stdio)   |   REST API (/api/v1/)
                    MCP (HTTP)    |   OpenAPI 3.1
                                  |
                         +--------+---------+
                         |    KB Server     |
                         |   Express :3838  |
                         +--------+---------+
                                  |
              +-------------------+-------------------+
              |                   |                   |
     +--------+-------+  +-------+--------+  +-------+--------+
     | SQLite + FTS5   |  |  Embeddings    |  |  Obsidian      |
     | documents       |  |  all-MiniLM    |  |  Vault Sync    |
     | vault_files     |  |  L6-v2 local   |  |  (bidirectional)|
     | embeddings      |  |  cosine sim    |  |  source of truth|
     +----------------+  +----------------+  +----------------+

Data directory: ~/.knowledge-base/

• kb.db -- SQLite database with FTS5 index
• files/ -- Ingested file copies
• config.json -- Password hash and settings

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Supported File Types

| Category | Extensions | |----------|-----------| | Markdown | .md | | Text | .txt, .log, .json, .yaml, .yml, .xml, .csv | | Code | .js, .ts, .jsx, .tsx, .py, .go, .rs, .java, .rb, .sh, .bash, .c, .cpp, .h, .hpp, .html, .css, .sql, .toml | | PDF | .pdf (with text extraction) | | Config | .env, .ini, .cfg, .conf |

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Content Ingestion Examples

Obsidian Vault

# Set vault path in .env
OBSIDIAN_VAULT_PATH=~/obsidian-vault

# Or ingest manually
kb ingest ~/obsidian-vault

The vault indexer tracks file hashes for incremental updates -- only changed files get re-indexed.

YouTube Transcripts

# Download transcript
yt-dlp --write-auto-sub --skip-download -o transcript https://youtube.com/watch?v=VIDEO_ID

# Or use the MCP tool directly from your AI agent:
# kb_capture_youtube with URL and transcript text

X/Twitter Bookmarks

# Export bookmarks to markdown using twitter-web-exporter (browser extension)
# Then ingest:
kb ingest ~/bookmarks/x_bookmarks.md

# Or use the built-in capture command:
kb capture-x ~/path/to/x_bookmarks.md

Terminal Sessions

From your AI agent after a debugging session:

Use kb_capture_session to record:
- goal: "Fix authentication flow for email users"
- commands_worked: "docker exec postgres psql query"
- commands_failed: "curl returned 401 - missing jellyfin_user_id"
- root_causes: "Email login path didn't propagate jellyfin_user_id to frontend"
- fixes: "Added jellyfin_user_id to auth response schema"
- lessons: "Always check all auth paths when adding fields to user response"

Bug Fixes

Use kb_capture_fix to record:
- title: "Library page blank for email login users"
- symptom: "Library page shows empty, no media items loaded"
- cause: "jellyfin_user_id not included in email auth response"
- resolution: "Added field to shared auth schema, redeployed"

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Multi-Agent Setup

Clau

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Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: willynikes2
• Source: willynikes2/knowledge-base-server
• License: MIT

Install and usage instructions live in the source repository linked above.