This MCP server is an intelligent knowledge management system for engineering agents. It provides:
Search: Perform hybrid BM25 + vector semantic search with optional LLM reranking over the knowledge base. Filter by tags, project, role, or current task for context-aware retrieval (knowledge_search, knowledge_relevant).
Learn and organize: Import knowledge (text, code, docs); the LLM automatically extracts structured metadata (title, summary, tags, type, relationships). Knowledge can be staged for review before committing (knowledge_learn, knowledge_learn_staged).
Lifecycle management: Confirm staged entries, manage knowledge decay and refresh via FSRS-6 spaced repetition, enable role-based knowledge diffusion and activation, and run decay sweeps.
Monitoring and configuration: Check knowledge base status (entry counts, type distribution, LLM connection) and view LLM configuration without secrets (knowledge_status, knowledge_config).
Operational tools: Export/import for backup, audit operations, and identify knowledge gaps.
Self-contained architecture: Uses in-process SQLite with FTS5 and ONNX for embeddings, requiring no external services.
Planned integration for automatic discovery of research papers from arXiv to enrich the knowledge base.
Planned integration for automatic discovery of repositories and projects from GitHub to enrich the knowledge base.
Allows using a local Ollama instance as the LLM backend for knowledge processing, with OpenAI-compatible API.
Integrates with OpenAI's API to enable LLM-powered semantic understanding, knowledge extraction, and relevance reasoning.
Planned storage backend to replace JSON file storage for more robust and scalable knowledge persistence.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@auto-knowledge-base 学习关于React hooks的最佳实践"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
# 1. Install
git clone https://github.com/guyeyouhun/auto-knowledge-base.git
cd auto-knowledge-base
npm install # installs deps + auto-downloads embedding model (~55MB)
npm run build
node dist/install.js # creates .env template
# 2. Configure LLM (needed for rerank/synthesis)
# Edit .env:
LLM_BASE_URL=https://api.openai.com/v1
LLM_API_KEY=sk-...
LLM_MODEL=gpt-4o
# 3. Start
node dist/index.js # runs as MCP server over stdioNo additional services. No embedding server, no vector database, no Python runtime. BM25 and vector search both run in-process with SQLite + ONNX.
Related MCP server: Open WebUI Knowledge Base MCP Server
# Store knowledge → staging
knowledge_learn(content: "Vite uses Rollup for production bundling", title: "Vite Build")
# Confirm → committed
knowledge_confirm(id: "550e8400-e29b-41d4-a716-446655440000")
# Search (BM25 + vector hybrid + LLM rerank)
knowledge_search(query: "vite rollup")
# Role-aware knowledge push
knowledge_relevant(role: "frontend", task: "configure build tooling")
# Export backup
knowledge_exportLayer | Technology |
Retrieval | FTS5 BM25 → cosine similarity → LLM rerank |
Embedding | Process-internal ONNX via |
Storage | SQLite + WAL + FTS5 + relation graph + vector columns |
Spaced repetition | FSRS-6 for retention optimization |
Knowledge diffusion | Role-based BFS activation |
query → BM25 FTS5 → vector cosine rerank
→ if BM25 < limit: vector similarity scan → results
→ (optional) LLM rerank + synthesisEvery stage degrades gracefully. No single failure blocks the response.
learn (staging) → confirm (confirmed) → FSRS decay → frozen
↓
refresh queue → content-digester re-digestTool | Description |
| BM25 + vector hybrid + LLM rerank |
| Store knowledge (staging), auto-dedup |
| staging → confirmed |
| Role-based diffusion + BFS activation |
Tool | Description |
| Role entry nodes, diffusion depth |
| View LLM configuration |
Tool | Description |
| FSRS-6 decay sweep |
| JSON backup / restore |
| Operation log |
| Statistics (truth, temperature, relations, embeddings) |
Tool | Description |
| Request re-digestion (content-digester integration) |
| Report knowledge gaps, triggers auto-digest |
| Query gap records by status/role |
Only the LLM needs to be configured (in .env):
LLM_BASE_URL=http://localhost:11434/v1
LLM_API_KEY=your-api-key
LLM_MODEL=gpt-4oThe embedding model (fastembed + BGESmallZH) is automatically downloaded during npm install to knowledge/models/. No embedding configuration needed.
npm test # 157 tests, 21 files
npm run test:watch # watch mode
npm run build # tsc + copy schemaUnclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Local-first RAG engine with MCP server for AI agent integration.
Cross-vendor AI memory over MCP. One semantic store, readable and writeable from every MCP client.
Your memory, everywhere AI goes. Build knowledge once, access it via MCP anywhere.
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/guyeyouhun/auto-knowledge-base'
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