OpenMind Core

OpenMind is a local AI memory engine for your computer.

It indexes folders you explicitly approve, stores searchable memory locally, and lets you search or ask questions across your own files with sources attached.

OpenMind is not a chatbot, desktop UI, browser extension, cloud sync service, or agent that controls your machine. The first useful version does three things well:

Index local files -> Search local memory -> Ask source-grounded questions

Why am i building this?

I am building this because I have a lot of files on my computer, and sometimes I genuinely get lost. I forget a file I downloaded months ago, or something I saved years ago. sometims i just need an order id from a receipt i got last week, a flight number from a pdf, or a detail buried somewhere in my messy downloads folder or even across my system. I do not want to upload all of that to another app just to find it again, no. or even give access to my data to big tech companies. I want a local AI memory that quietly understands the folders I already have existing on my system, works in the background, and helps me ask my own computer what it already has, that's it

Current Status

OpenMind Core is early, but usable as a developer-facing CLI.

What works today:

• Local app storage under ~/.openmind.
• User-approved folder sources.
• File extraction for common text, PDF, DOCX, CSV, Markdown, and HTML files.
• LanceDB vector storage.
• SQLite source, file, and indexing job records.
• LM Studio as the user-facing local AI provider.
• Background indexing with live progress.
• Streaming answers by default.
• Interactive ask sessions with temporary conversation memory.
• Source-grounded answers.
• Developer log inspection.

What is intentionally not here yet:

• No desktop UI.
• No browser extension.
• No cloud sync.
• No file automation.
• No plugin marketplace.
• No deleting, moving, or modifying user files.

See FEATURES.md for the complete shipped feature list and roadmap. See CHANGELOG.md for release notes.

GitHub Releases are published from main using the version in pyproject.toml and the matching notes in CHANGELOG.md. A version change merged to main triggers the release workflow automatically.

Requirements

• Python 3.11+
• uv
• LM Studio for local chat and embedding models
• macOS, Linux, or another Python-supported environment

OpenMind Core 0.0.3 uses LM Studio as its only user-facing provider. The older Sentence Transformers provider remains only as a development and test fallback.

Install

Clone the project, enter the package directory, and install it into your Python environment.

If you already have a conda environment named openmind:

cd openmind-core
conda activate openmind
uv pip install -e ".[dev]"
pytest

uv pip install detects the activated conda environment and installs the packages into it, while still using uv's fast resolver and installer.

If you want uv to manage the environment itself:

cd openmind-core
uv sync --all-extras
uv run pytest

Useful dependency commands:

uv lock
uv sync --all-extras
uv pip install -e ".[dev]"

Quick Start

Start the LM Studio local server first. In LM Studio, open the Developer tab and start the server, or run:

lms server start

Then run first-time setup:

openmind setup

Setup will:

1. Initialize ~/.openmind.
2. Check that LM Studio is reachable.
3. Let you choose LM Studio as the provider.
4. List available chat and embedding models.
5. Load the selected models.
6. Ask which folders to index.
7. Start background indexing.

Watch indexing progress:

openmind index status

The live status table includes an Already indexed count for unchanged files that were indexed before and are still accessible.

Search your local memory:

openmind search "holiday plan"

Ask a question with sources:

openmind ask "What documents do I have about the cabin trip?"

Start an interactive ask session:

openmind ask

CLI Reference

Normal users should start with:

openmind setup

Lower-level initialization:

openmind init
openmind status
openmind flush
openmind flush --dry-run
openmind flush --yes
openmind flush --yes --include-sources
openmind uninstall
openmind uninstall --dry-run
openmind uninstall --yes
openmind uninstall --yes --package

Source management:

openmind source add ~/Documents
openmind source list
openmind source remove <source_id>

If a folder was already added, OpenMind tells you it is already registered and reports indexed files that are already accessible.

Indexing:

openmind index
openmind index start
openmind index status
openmind index status --once
openmind index pause
openmind index resume
openmind index stop

If an unchanged file was indexed before, OpenMind reports it as already indexed and keeps it available for search and ask.

OpenMind uses file path, size, modified time, and content hash to avoid unnecessary work. Unchanged files are not extracted, embedded, or stored again. If a file's metadata changes, OpenMind checks the content hash and only re-indexes when the content actually changed.

Search:

openmind search "holiday plan"
openmind search "OAuth redirect issue" --limit 10

Ask:

openmind ask "What do my files say about the cabin trip?"
openmind ask "What do my files say about the cabin trip?" --no-stream
openmind ask "What do my files say about the cabin trip?" --show-thinking
openmind ask "What do my files say about the cabin trip?" --limit 8
openmind ask

Interactive ask commands:

/clear  reset the current session memory
/exit   leave the chat
/quit   leave the chat

LM Studio provider commands:

openmind provider status
openmind models list
openmind models load
openmind models load <model_key>
openmind models update
openmind models update --no-load

Developer logs:

openmind dev logs
openmind dev logs --no-follow --lines 40
openmind dev logs --log all
openmind dev logs --log index
openmind dev logs --lm-studio

LM Studio Integration

OpenMind talks to LM Studio at:

http://localhost:1234

It uses LM Studio's native REST API for model setup:

GET  /api/v1/models
POST /api/v1/models/load

It uses OpenAI-compatible endpoints for inference:

POST /v1/chat/completions
POST /v1/responses
POST /v1/embeddings

OpenMind stores separate model choices because chat and embeddings are different jobs:

[provider]
name = "lmstudio"
base_url = "http://localhost:1234"
api_token_env = "LM_API_TOKEN"

[models]
chat_model = "selected-chat-model-key"
embedding_model = "selected-embedding-model-key"

[indexing]
auto_start_after_setup = true
background = true

To change saved models later, run:

openmind models update

OpenMind will ask for the provider, fetch the latest LM Studio model list, let you choose a chat model and embedding model, save the new config, and load the selected models by default.

When loading or updating models, OpenMind checks LM Studio first and skips models that are already loaded.

If LM Studio is not running, OpenMind exits with a clear message instead of a Python traceback.

Architecture Choices

OpenMind keeps the architecture intentionally simple. Each technology has one simple job.

High-Level Overview

OpenMind is the memory engine and CLI. It does not use LM Studio's chat interface, or any other provider's chat UI. It calls a local model server endpoint to reach downloaded models.

Today that local model server is LM Studio. Later, the same provider layer can support other local or OpenAI-compatible servers.

flowchart TD
    User["User"] --> CLI["OpenMind CLI"]

    CLI --> Setup["Setup / Config"]
    CLI --> Sources["Source Manager"]
    CLI --> Indexing["Indexing Engine"]
    CLI --> Search["Search"]
    CLI --> Ask["Ask"]

    Setup --> Config["~/.openmind/config.toml"]
    Setup --> ModelServer["Local Model Server<br/>(LM Studio today, others later)"]

    Sources --> SQLite["SQLite<br/>sources, files, jobs, status"]

    Indexing --> Scanner["File Scanner<br/>user-approved folders only"]
    Scanner --> Extractors["Extractors<br/>txt, md, pdf, docx, csv, html"]
    Extractors --> Normalizer["Normalize Text"]
    Normalizer --> Chunker["Chunk Text"]
    Chunker --> Embeddings["Embedding Provider<br/>calls local embedding endpoint"]
    Embeddings --> ModelServer
    Embeddings --> LanceDB["LanceDB<br/>vectors + chunks"]

    Search --> QueryEmbed["Embed Query"]
    QueryEmbed --> ModelServer
    QueryEmbed --> LanceDB
    LanceDB --> Results["Relevant Chunks<br/>path, score, snippet"]

    Ask --> Search
    Results --> Context["Build Context<br/>with sources"]
    Context --> AnswerModel["Answer Provider<br/>calls local chat/completion endpoint"]
    AnswerModel --> ModelServer
    AnswerModel --> Answer["Answer with Sources"]

    Indexing --> Jobs["Background Index Job"]
    Jobs --> SQLite
    CLI --> Logs["Dev Logs"]
    Logs --> LogFiles["~/.openmind/logs"]

Loading

SQLite

SQLite is used for project state and metadata, not the AI memory itself.

SQLite stores:

• sources and folders the user added
• file paths and file hashes
• indexing status
• indexing progress
• config and local state
• failed files or skipped files
• background job info

Why SQLite:

• it is local and embedded
• it needs no separate server
• it is reliable for small structured records
• it makes indexing progress easy to inspect and resume

LanceDB

LanceDB is used for searchable AI memory.

LanceDB stores:

• extracted text chunks
• embeddings and vectors
• chunk metadata
• source paths for search results and answers

Why LanceDB:

• it runs locally from a directory path
• it avoids a separate vector database server
• it is designed for vector search
• it keeps OpenMind's memory layer portable

Simple way to think about it:

SQLite keeps track of what OpenMind is doing. LanceDB stores what OpenMind knows.

Model Provider

OpenMind uses a model provider abstraction for embeddings and answers.

In 0.0.3, the only implemented user-facing provider is LM Studio. OpenMind talks to LM Studio's local server endpoint; it does not use the LM Studio chat interface.

OpenMind uses the provider endpoint for:

• embedding local file chunks
• embedding search queries
• generating source-grounded answers
• streaming answer tokens in ask mode

Why LM Studio first:

• it runs local models on the user's machine
• it exposes a local API server
• it supports OpenAI-compatible chat and embedding endpoints
• it lets OpenMind stay local-first without owning model runtime complexity

Future providers can fit behind the same layer, such as Ollama, llama.cpp, or another OpenAI-compatible local endpoint.

Typer and Rich

Typer powers the CLI. Rich powers readable terminal output.

Why they are used:

• Typer keeps commands small and type-friendly
• Rich makes tables, progress views, and errors easier to read
• the CLI stays usable before any desktop or web UI exists

uv

uv is used for dependency management and development setup.

Why uv:

• fast installs and dependency resolution
• works with an existing conda environment
• supports reproducible lockfiles
• keeps contributor setup simple

Supported Files

OpenMind currently indexes:

.txt
.md
.pdf
.docx
.csv
.html

OpenMind is document-first by default. It does not index source code, JSON config files, package metadata, app asset catalogs, or other low-level project internals unless a future opt-in mode is added. High-level project documents such as README.md, Markdown notes, PDFs, DOCX files, CSVs, and HTML docs can still be indexed.

PDF extraction first uses the normal embedded text layer. If a PDF looks scanned or the extracted text is too sparse, OpenMind automatically tries local OCR with RapidOCR + ONNX Runtime and then continues the normal indexing pipeline.

It ignores noisy folders such as:

.git
node_modules
venv
.venv
.env
__pycache__
dist
build
.cache
target
coverage
Assets.xcassets
hidden folders

Image files are included in the sample data/ directory for realism, but OpenMind does not index standalone image content or run screenshot OCR yet.

OCR Fallback

OCR is automatic for weak or scanned PDFs. No CLI flag is needed.

OpenMind uses RapidOCR with ONNX Runtime as the default OCR backend. It renders PDF pages locally with pypdfium2, runs OCR locally, and continues the same normalize/chunk/embed/store pipeline.

These Python OCR dependencies are installed by the normal project install:

uv pip install -e ".[dev]"

OCRmyPDF is still supported as an optional backend for users who prefer it. That mode requires OCRmyPDF, Tesseract, and Ghostscript installed separately:

brew install ocrmypdf tesseract ghostscript

OCR config lives in ~/.openmind/config.toml:

[extraction.ocr]
enabled = true
backend = "rapidocr"
min_text_chars_per_page = 80

If OCR dependencies are missing or an optional OCR backend is unavailable, OpenMind does not crash the indexing run. It records a clear extraction error for that file and continues with the rest of the source.

Search Mode

Search does not require a chat model. It embeds the query with the selected LM Studio embedding model, searches LanceDB, and returns paths, scores, and snippets.

Example:

openmind search "cabin trip checklist"

Output is shaped like:

1. ~/Documents/Holiday/checklist.md
   Score: 0.91
   Snippet: The packing checklist includes...

If search is bad, answers will be bad. OpenMind treats search quality as the foundation.

Ask Mode

Ask is search plus an answer model:

question
  -> retrieve relevant chunks
  -> build grounded context
  -> stream answer from LM Studio
  -> show sources

Answers stream by default:

openmind ask "What do my files say about the cabin trip?"

Disable streaming when needed:

openmind ask "What do my files say about the cabin trip?" --no-stream

Show provider-returned thinking or reasoning when the selected LM Studio model exposes it:

openmind ask "What do my files say about the cabin trip?" --show-thinking

If the model does not return explicit thinking or reasoning, OpenMind says so and still returns the answer with sources.

Bare openmind ask starts a chat-like session:

openmind ask

Session history is held in memory while the process is open, so follow-up questions can refer to earlier turns. The session is discarded when you exit.

Background Indexing

Start indexing in the background:

openmind index start

Watch a live table:

openmind index status

Print status once:

openmind index status --once

Pause, resume, or stop:

openmind index pause
openmind index resume
openmind index stop

Indexing has two phases:

1. Discovery: scan enabled sources and count supported files.
2. Indexing: extract, chunk, embed, and store chunks while updating SQLite progress.

The live table shows:

• Job id
• State
• Files discovered
• Files processed
• Files indexed
• Files skipped
• Files failed
• Chunks created
• Progress percentage
• Current file

Pause and stop take effect after the current file finishes. If a file is already inside a slow extraction or embedding request, the worker checks the requested state before moving to the next file.

Logs

OpenMind writes structured logs to:

~/.openmind/logs/openmind.log

Index worker logs are written to:

~/.openmind/logs/index-<job-id>.log

Watch logs:

openmind dev logs

Show recent logs once:

openmind dev logs --no-follow --lines 40

Watch all OpenMind logs:

openmind dev logs --log all

Watch only index worker logs:

openmind dev logs --log index

Watch LM Studio logs through its CLI:

openmind dev logs --lm-studio

That command runs:

lms log stream

Local Storage

OpenMind stores app data under ~/.openmind by default:

~/.openmind/
├── config.toml
├── openmind.sqlite
├── lancedb/
└── logs/

For development and tests, use a separate home:

OPENMIND_HOME=/tmp/openmind-dev openmind status

Reset indexed memory without uninstalling:

openmind flush

This clears OpenMind's indexed memory and indexing state, including SQLite file records, index jobs, LanceDB vectors/chunks, and log files. It keeps config.toml and saved source folders by default, so you can run openmind index start again from a clean memory state. To also clear saved source folder records:

openmind flush --yes --include-sources

Flush never deletes the actual files or folders you indexed.

Remove OpenMind-owned local data:

openmind uninstall

This deletes the OpenMind app home, including config.toml, openmind.sqlite, lancedb/, and logs/. It does not delete user source folders, LM Studio, or downloaded models.

To remove the installed package from the current Python environment in the same command:

openmind uninstall --yes --package

Test Data

This repo includes a small data/ folder with notes, Markdown, JSON, CSV, HTML, JavaScript, a sample PDF, and images. Only supported document-first formats are indexed by default.

Try it:

openmind source add ./data
openmind index start
openmind index status
openmind search "holiday plan"

Project Structure

openmind/
├── cli/
├── core/
├── sources/
├── extractors/
├── ingestion/
├── embeddings/
├── storage/
├── retrieval/
├── llm/
└── providers/

The design is deliberately boring inside: each stage has a small job, and the provider layer is replaceable without rewriting ingestion, storage, or retrieval.

Development

Install development dependencies:

uv pip install -e ".[dev]"

Run tests:

pytest

Or with uv:

uv run pytest

Keep docs in sync when behavior changes:

• Update FEATURES.md when a feature lands.
• Update CHANGELOG.md for user-facing release notes.
• Update TECHNICAL_SPEC.md when architecture, schema, or interfaces change.
• Update this README when normal user workflow changes.

Roadmap

Near-term work:

• Better indexing error inspection.
• Failed-file retry commands.
• Rebuild index command.
• Source enable and disable.
• Hybrid keyword plus vector search.
• Better snippets and citations.
• OCR for screenshots and scanned PDFs.
• Persistent chat sessions.
• Local API for future UI clients.
• Additional providers after LM Studio is solid.

The full roadmap lives in FEATURES.md.

Contributing

OpenMind is early and intentionally small. Good contributions make the core more trustworthy without adding premature surface area.

Start with CONTRIBUTING.md.

License

MIT. See LICENSE.