markdowncore-ai 1.1.4

Markdown CORE AI - Classification, Organisation, Retrieval & Entry for your personal markdown knowledge base

mdcore

Markdown CORE AI - Classification, Organisation, Retrieval & Entry

mdcore is a local, LLM-agnostic personal knowledge base engine built for engineers and architects managing context across multiple projects, workstreams, and decisions. Index your vault of markdown notes, PDFs, Word documents, and plain text files - retrieve context on demand, ingest new knowledge with automatic classification and routing, all from the terminal, a TUI, a REST API, or directly from Claude Desktop via MCP.

PyPI: markdowncore-ai | CLI: mdcore | Version: 1.1.4

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Screenshots

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What It Does

Retrieval (mdcore search) - Ask a question or give a topic. mdcore searches your vault semantically, stitches the most relevant chunks, and synthesises a coherent cited briefing. Output lands in <vault>/mdcore-output/ - ready to copy into any LLM conversation.

Ingestion (mdcore ingest) - Feed any document into mdcore - an LLM session summary, a research note, a strategy doc, an article. It classifies the content against your existing vault, routes it to the right folder, detects conflicts with existing notes, generates a proposal, and writes only after your explicit approval.

Both flows work fully local with Ollama. No subscription LLM API calls. No always-on server.

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Tech Stack

Layer	Library / Tool	Purpose
Embeddings	nomic-embed-text (Ollama), text-embedding-3-small (OpenAI), models/embedding-001 (Gemini), all-MiniLM-L6-v2 (HuggingFace)	Vector representations of vault chunks
Vector store	ChromaDB	Persistent local similarity search
Keyword pre-filter	rank-bm25	BM25 scoring over candidate chunks before vector retrieval
Markdown parsing	markdown-it-py	Heading-aware splitting, structure signal detection
LLM backends	Ollama, OpenAI, Anthropic, Gemini, HuggingFace, llm-keypool aggregator	Classification, routing, proposal generation, synthesis
LLM abstraction	LangChain	BaseChatModel wrappers, RunnableLambda chains for search and ingest
REST API	FastAPI + LangServe	/ask, /propose endpoints + auto Swagger UI
MCP server	mcp (Model Context Protocol SDK)	Claude Desktop integration via stdio JSON-RPC
TUI	Textual + Rich	Terminal UI, status panels
PDF extraction	pypdf	Page-level text extraction ([multimodal] extra)
DOCX extraction	python-docx	Paragraph and table extraction ([multimodal] extra)
Config	Pydantic v2 + YAML	Typed config models, validation
CLI	Click	Command routing
Observability	LangSmith (optional)	Traces every LLM call - token usage, latency, full prompt/response. Set langsmith_api_key in config or via mdcore init. No eval framework - tracing only.

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Installation

# Recommended - with TUI
uv tool install "markdowncore-ai[gui]"

# pipx
pipx install markdowncore-ai

Using the aggregator backend (free-tier, no paid API)

Install llm-keypool separately - it has its own CLI for managing keys:

# Install llm-keypool as standalone tool (gives llm-keypool CLI)
uv tool install "llm-keypool[gui]"

# Also wire it into mdcore's environment so mdcore can import it
uv tool install --force "markdowncore-ai[gui]" --with llm-keypool

Upgrading

# Upgrade mdcore
uv tool upgrade markdowncore-ai

# Upgrade llm-keypool + rewire into mdcore
uv tool upgrade llm-keypool
uv tool install --force "markdowncore-ai[gui]" --with llm-keypool

Ollama models (local inference)

ollama pull nomic-embed-text   # embeddings
ollama pull qwen3.5:4b         # classification, routing, proposals
ollama pull phi4-mini          # synthesis (fast, non-thinking)

First run

mdcore init     # interactive setup -> writes ~/.mdcore/config.yaml
mdcore index    # scan and index your vault

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Quick Start

# Search your vault
mdcore search "what was the decision on mTLS between services?"
# -> synthesised briefing with cited sources -> <vault>/mdcore-output/

# Ingest a document
mdcore ingest --file arch-review-notes.md
# -> classifies, routes to right folder, proposes changes -> approve to write

# Start the REST API
mdcore serve
# -> POST http://127.0.0.1:8765/ask  {"query": "kubernetes ingress"}
# -> Swagger UI at http://127.0.0.1:8765/docs

# Connect to Claude Desktop (after adding to claude_desktop_config.json)
mdcore mcp

# Launch TUI
mdcore gui

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Commands

mdcore init                        # Interactive setup wizard
mdcore index                       # Delta index - scan, show summary, diff, confirm, index
mdcore index --force               # Wipe everything and reindex from scratch
mdcore search <topic>              # Retrieve + synthesise briefing (Flow A)
mdcore search <topic> --raw        # Retrieve raw excerpts, skip synthesis
mdcore search <topic> --verbose    # Show similarity scores
mdcore ingest                      # Paste document - classify, route, propose (Flow B)
mdcore ingest --file <path>        # Ingest from file
mdcore map                         # Generate vault folder map for routing
mdcore map --repair                # Remove stale folder entries
mdcore gui                         # Launch TUI (requires [gui] extra)
mdcore status                      # Index health, drift warnings
mdcore eval [topic]                # Retrieval quality checklist
mdcore config                      # Open config in editor
mdcore config --validate           # Validate config
mdcore serve                           # Start REST API server (requires [serve])
mdcore mcp                             # Start MCP server over stdio (requires [mcp])

Multiple vaults / config profiles

mdcore search "istio auth"     --config ~/.mdcore/config-work.yaml
mdcore search "career goals"   --config ~/.mdcore/config-personal.yaml
mdcore search "topic"          --models ~/.mdcore/models-aggregator.yaml

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Backends

mdcore supports local and API-backed models. Mix and match per use case.

Backend	LLM	Embeddings	Extra needed
Ollama (local)	any pulled model	nomic-embed-text, bge-m3	none
Gemini	gemini-2.5-flash-lite	models/gemini-embedding-001	none (bundled)
OpenAI	gpt-4o-mini	text-embedding-3-small	[openai]
Anthropic	claude-haiku-4-5	use Ollama or OpenAI	[anthropic]
Aggregator	free-tier key pool	free-tier key pool	llm-keypool (separate)

uv tool install "markdowncore-ai[openai]"
uv tool install "markdowncore-ai[anthropic]"
uv tool install "markdowncore-ai[all]"           # every backend
uv tool install "markdowncore-ai[multimodal]"    # PDF, DOCX, TXT indexing
uv tool install "markdowncore-ai[serve]"         # REST API server
uv tool install "markdowncore-ai"                # MCP server bundled in core

Aggregator backend

aggregator routes calls through llm-keypool - a local SQLite-backed key pool that round-robins free-tier API keys with automatic 429 cooldown. No api_key needed in mdcore config.

Note: aggregator is for LLM calls only. Embeddings require a dedicated backend (ollama, openai, or gemini) - embedding models cannot be swapped mid-index.

Install llm-keypool separately (required - it has its own CLI for managing keys):

# Install as standalone tool so its CLI is available system-wide
uv tool install "llm-keypool[gui]"

# Also add it to mdcore's environment so mdcore can import it
uv tool install --force "markdowncore-ai[gui]" --with llm-keypool

Upgrading llm-keypool:

uv tool upgrade llm-keypool
uv tool install --force "markdowncore-ai[gui]" --with llm-keypool

Keys DB lives at ~/.llm-keypool/keys.db. Register free-tier keys:

# Groq - https://console.groq.com/keys
llm-keypool add --provider groq --key <KEY> --model llama-3.3-70b-versatile --category general_purpose

# Cerebras - https://cloud.cerebras.ai
llm-keypool add --provider cerebras --key <KEY> --model llama-3.3-70b --category general_purpose

# Mistral - https://console.mistral.ai/api-keys
llm-keypool add --provider mistral --key <KEY> --model mistral-small-latest --category general_purpose

# OpenRouter - https://openrouter.ai/settings/keys
llm-keypool add --provider openrouter --key <KEY> --model meta-llama/llama-3.3-70b-instruct:free --category general_purpose

# Check registered keys
llm-keypool status

llm:
  backend: aggregator
  aggregator_category: general_purpose
  aggregator_rotate_every: 5

embeddings:
  backend: ollama        # aggregator not supported for embeddings
  local_model: nomic-embed-text

Hardware guidance

Hardware	LLM	Embeddings
Apple M2 16GB+	qwen3.5:4b	nomic-embed-text
i5 + RTX 4070	qwen3:8b	bge-m3
Low-end / no GPU	gemini-2.5-flash-lite or gpt-4o-mini	models/gemini-embedding-001

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Configuration

Config lives at ~/.mdcore/config.yaml. Generated by mdcore init.

Section	Key fields	Purpose
vault	path, owner_name	Vault root, owner name for multi-person vaults
embeddings	backend, api_model / local_model, api_key	Embedding model
llm	backend, model, api_key, synthesise_model	Primary LLM + synthesis model
indexer	chunk_size, heading_aware_splitting	Chunking strategy
retriever	top_k, similarity_threshold	Retrieval tuning
ingester	similarity_threshold_high/low	Classification thresholds
writer	append_position, backup	Write behaviour + backups

See config.yaml.example for the full annotated reference.

Separate models config

Keep model choices in a separate ~/.mdcore/models.yaml - useful for switching backends without touching main config. Values here override llm and embeddings sections in config.yaml.

# ~/.mdcore/models.yaml
llm:
  backend: aggregator
  aggregator_category: general_purpose

embeddings:
  backend: ollama
  local_model: nomic-embed-text

Pass explicitly with --models:

mdcore search "topic" --models ~/.mdcore/models-work.yaml
mdcore ingest --file note.md --models ~/.mdcore/models-cheap.yaml

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Where LLM Calls Happen

mdcore search (Flow A)

Phase	LLM?	Notes
Keyword pre-filter	No	BM25 scoring
Vector search	No	Embedding lookup
Chunk assembly	No	Pure text
Synthesis	Yes - synthesise_model	Skip with --raw for zero LLM calls

mdcore ingest (Flow B)

Phase	LLM?	Condition
Embedding + search	No	Always
Classification	Conditional - llm.model	Only in ambiguous similarity range (0.65-0.82)
Folder routing	Yes - llm.model	NEW files only
Proposal	Yes - llm.model	Always before write

mdcore map and mdcore index make no LLM calls.

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Multi-Modal Indexing

By default mdcore indexes .md files only. Enable additional formats in ~/.mdcore/config.yaml:

vault:
  index_pdf: true    # PDF text extraction (text-based PDFs; scanned PDFs return no text)
  index_docx: true   # Word documents (.docx only, not legacy .doc)
  index_txt: true    # Plain text files

Requires the [multimodal] extra:

pip install 'markdowncore-ai[multimodal]'
# or
uv tool install "markdowncore-ai[multimodal]"

Once enabled, run mdcore index as normal - PDF/DOCX/TXT files appear in mdcore status and are searchable via mdcore search.

Limitations:

• Scanned PDFs (image-only) yield no text - extraction requires selectable text layers
• .doc (legacy binary Word format) is not supported, only .docx

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REST API

Start the HTTP server to expose vault search and ingestion as JSON endpoints:

pip install 'markdowncore-ai[serve]'
mdcore serve                          # default: http://127.0.0.1:8765
mdcore serve --host 0.0.0.0 --port 9000
mdcore serve --reload                 # dev mode, auto-reload on code change

Endpoints

Method	Path	Description
GET	/health	Index health - chunk count, vault path
POST	/ask	Ask a question, get synthesised answer + sources
POST	/propose	Classify and propose ingestion (no write)
POST	/search/invoke	LangServe chain endpoint
GET	/docs	Swagger UI

Examples

# Health check
curl http://127.0.0.1:8765/health

# Ask a question
curl -X POST http://127.0.0.1:8765/ask \
  -H "Content-Type: application/json" \
  -d '{"query": "what is the mTLS topology for the COE stack?"}'

# Propose ingestion (returns proposal, does not write)
curl -X POST http://127.0.0.1:8765/propose \
  -H "Content-Type: application/json" \
  -d '{"content": "Meeting notes from arch review...", "title": "Arch Review 2025-05"}'

# LangServe invoke
curl -X POST http://127.0.0.1:8765/search/invoke \
  -H "Content-Type: application/json" \
  -d '{"input": {"query": "kubernetes ingress"}}'

OpenAPI spec: docs/openapi.json - import into Postman, Insomnia, or any OpenAPI-compatible client.

The chain implementation uses RunnableLambda wrapping the existing two-phase retrieval pipeline - the LangServe layer adds HTTP transport without replacing mdcore's BM25 pre-filter or vector search.

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MCP Server

mdcore exposes its vault as MCP tools that any MCP-compatible client can call autonomously during a conversation. MCP is bundled in core - no extra install needed.

Tools exposed

Tool	Description
search_vault	Search vault, return synthesised answer with cited sources
ingest_note	Classify content and propose where to save it (does not write automatically)
vault_status	Current index stats - chunk count, file types, backends
index_vault	Scan vault for new/modified files. Call with dry_run=true first to get a count summary, then dry_run=false to actually index after user confirmation.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "mdcore": {
      "command": "mdcore",
      "args": ["mcp"]
    }
  }
}

Restart Claude Desktop. Ask Claude:

• "What do my notes say about the payments architecture?"
• "Save this meeting summary to my vault"
• "Check if my vault index needs updating"

Hermes Agent (NousResearch/hermes-agent)

mdcore works as a native MCP server for Hermes agent. Add to ~/.hermes/config.yaml:

mcp_servers:
  mdcore:
    command: "/Users/you/.local/bin/mdcore"
    args: ["mcp"]

Hermes registers the tools as mcp_mdcore_search_vault, mcp_mdcore_ingest_note, mcp_mdcore_vault_status, and mcp_mdcore_index_vault. Restart Hermes after editing config.

Requires a model with function calling support. Configure a direct provider in ~/.hermes/config.yaml:

model:
  default: gemini-2.5-flash
  provider: google
  api_key: '<your-google-api-key>'

Example prompts via Hermes:

• "Search my vault for notes on Kubernetes ingress"
• "Check if my vault index needs updating" - Hermes calls index_vault(dry_run=true), shows counts, asks for confirmation before indexing

Smoke test (without a client)

echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' | mdcore mcp

Multiple vaults

Run two MCP server processes with different --config paths to expose separate work and personal vaults as distinct toolsets.

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Observability

Token usage logged after every call to ~/.mdcore/logs/:

INFO llm - tokens [gemini-2.5-flash-lite] in=312 out=89 total=401

LangSmith tracing (optional) - add to ~/.mdcore/config.yaml:

llm:
  langsmith_api_key: <your-key>
  langsmith_project: mdcore

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mdcore - Markdown CORE AI v1.1.4