Apple Silicon Openbrain

sufficiently-advanced-ai12stars

Fully local AI memory for Apple Silicon. MCP server with semantic vector search, MLX embeddings, and LLM enrichment — no cloud APIs required.

Knowledge Memory

Install Run on PlugMCP

GitHub

Run on PlugMCP

1# Apple Silicon Openbrain

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-28

Stars: 12
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Apple Silicon Openbrain?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Apple Silicon Openbrain need an API key?

No required environment variable is listed on this record.

What transport does Apple Silicon Openbrain use?

Apple Silicon Openbrain runs locally over stdio.

When was Apple Silicon Openbrain last checked?

Last checked 2026-10-28. Fully local AI memory for Apple Silicon. MCP server with semantic vector search, MLX embeddings, and LLM enrichment — no cloud APIs required.

From the publisher

Documentation

OpenBrain

Fully local AI memory for Apple Silicon. Gives any MCP client persistent semantic memory with vector search — no cloud APIs required.

Everything runs on your Mac: embeddings via MLX on Metal GPU, enrichment via a local LLM, storage in PostgreSQL with pgvector.

Built on the OB1 architecture by @NateBJones-Projects.

Architecture

OpenBrain Architecture

A Bun MCP server brokers requests from any MCP client to PostgreSQL+pgvector for storage, a local MLX embedding service for vectorization, and a local mlx-lm server for enrichment. Redis caches embeddings and search results. A separate Bun web UI reads directly from PostgreSQL. Everything runs on-device — no cloud APIs in the data path.

Requirements

Apple Silicon Mac (M1/M2/M3/M4) — required for MLX GPU inference
16 GB RAM minimum for the default model (Qwen3-8B-4bit, ~4.5 GB); 32 GB+ lets you run larger enrichment models
macOS 14+
Node.js 22+ (LTS v24 recommended) and pnpm 11+
Bun 1.1+ as the runtime (we use pnpm to install deps; bun to run them)
Python 3.13+ with uv
PostgreSQL 17 with pgvector extension
Redis (optional but recommended)
Firecrawl API key — used for all web page scraping
yt-dlp (optional — YouTube transcript extraction)

Firecrawl

Firecrawl handles all web-page scraping (markdown extraction, JS rendering, etc.). It's a cloud service; set your key in .env:

FIRECRAWL_API_KEY=fc-...

Without a key, POST /api/ingest for web URLs will return an error.

Quick install (recommended)

bash installer/bootstrap.sh

This runs the full chain: prerequisites (Homebrew packages, Bun, Python venv with mlx-lm), database setup, schema, and launchd services. Idempotent — safe to re-run.

Manual install

If you'd rather install the prerequisites yourself:

# Homebrew packages
brew install postgresql@17 pgvector redis uv yt-dlp pnpm
brew services start postgresql@17
brew services start redis

# Node 22+ via nvm (skip if you already have one)
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash
. ~/.nvm/nvm.sh && nvm install --lts && nvm alias default 'lts/*'

# Optional, for Gmail integration via the agents/ subsystem
brew install gogcli

# Bun via its official installer (no Homebrew formula). We use Bun as the
# runtime (Bun.serve, Bun.spawn) but never `bun install` — that hits a
# macOS SIP/provenance issue. pnpm handles deps.
curl -fsSL https://bun.sh/install | bash

After installing Bun, restart your shell (or source ~/.zshrc) so the bun binary is on your PATH.

Quick Start

# 1. Clone and enter the repo
git clone https://github.com/sajennings79/apple-silicon-openbrain.git
cd apple-silicon-openbrain

# 2. Configure environment
cp .env.example .env
# Edit .env if needed (defaults work for local setup)

# 3. Run setup (creates database, installs Bun + Python deps)
bash scripts/setup.sh

# 4. Create a Python venv for the LLM enrichment server
python3 -m venv ~/.mlx-venv
~/.mlx-venv/bin/pip install mlx-lm

# 5. Start all services (installs as launchd daemons)
bash scripts/install-services.sh

# 6. Verify everything is running
bun run health

Or start services manually:

bun run dev                                    # MCP server (port 6277)
cd embed-service && uv run server.py           # Embedding service (port 6278)
~/.mlx-venv/bin/python -m mlx_lm.server \
  --model mlx-community/Qwen3-8B-4bit \
  --port 8000 --max-tokens 4096               # LLM enrichment (port 8000)
bun run ui                                     # Web UI (port 6279)

First Run: Model Downloads

Models are pulled from Hugging Face Hub the first time each service starts — there's no separate download step. Both services block on startup until the download completes, then come online.

Model	Size	Used by	Triggered by
`mlx-community/Qwen3-Embedding-0.6B-4bit-DWQ`	~400 MB	Embedding service (`:6278`)	FastAPI startup
`mlx-community/Qwen3-8B-4bit` (default)	~4.5 GB	LLM enrichment (`:8000`)	`mlx_lm.server` startup

The default LLM is sized for 16 GB RAM systems. To trade RAM for quality, override LLM_MODEL in .env (and the matching --model arg in ~/Library/LaunchAgents/com.openbrain.llm.plist):

RAM	Suggested model	Disk
16 GB (default)	`mlx-community/Qwen3-8B-4bit`	~4.5 GB
32 GB	`mlx-community/Qwen3.6-27B-4bit` (dense) or `mlx-community/Qwen3.6-35B-A3B-4bit` (MoE)	~17 GB
64 GB+	`mlx-community/Qwen3.5-122B-A10B-4bit`	~60 GB

All models are public repos — no Hugging Face token required. Files cache to ~/.cache/huggingface/hub/ and subsequent starts run fully offline.

If you installed the launchd services, the first-run download happens silently in the background. Watch progress with:

tail -f logs/embed.log   # embedding model (~30s on fast connection)
tail -f logs/llm.log     # ~2 min for the default 8B model on a fast connection

bun run health will report the LLM service as down until its download finishes. Interrupted downloads resume on the next start.

MCP Client Configuration

OpenBrain exposes both HTTP and stdio MCP transports. Configure your client to point at whichever it supports.

HTTP transport (recommended — one shared server for all clients):

{
  "mcpServers": {
    "openbrain": {
      "type": "http",
      "url": "http://localhost:6277/mcp"
    }
  }
}

stdio transport (for clients that spawn the server as a subprocess):

{
  "mcpServers": {
    "openbrain": {
      "type": "stdio",
      "command": "bun",
      "args": ["run", "/path/to/openbrain/src/stdio.ts"]
    }
  }
}

The exact config-file location depends on your client (e.g. .mcp.json in a project root, or a global config under your home directory).

MCP Tools

Tool	Description
StoreMemory	Store text with automatic embedding, LLM enrichment (summary, tags, entities), and cross-memory linking
SearchMemory	Semantic vector search with optional filters (type, source, tags, date range)
RecallMemory	Retrieve a specific memory by UUID
ListMemories	Paginated list with filters
UpdateMemory	Update content or metadata; re-embeds and re-enriches on content changes
DeleteMemory	Soft-delete a memory by UUID

Services

Service	Port	Purpose
MCP Server	6277	MCP protocol + URL ingestion API
Embedding Service	6278	Local MLX embeddings (Qwen3-Embedding-0.6B, 1024-dim)
LLM Server	8000	Local MLX enrichment (Qwen3-8B by default)
Web UI	6279	Dashboard for browsing and searching memories
PostgreSQL	5432	Memory storage with pgvector
Redis	6379	Embedding + search result caching

Web UI

The web UI at http://localhost:6279 provides:

Memory search and browsing with tag/source/type filters
Dashboard with statistics (total memories, per source, per type)
Monthly histogram and 30-day sparkline
New memory creation form
Tag management

URL Ingestion

Save web pages and YouTube transcripts directly to memory using the bookmarklet.

Open scripts/bookmarklet.html in your browser
Drag the bookmarklet link to your bookmarks bar
Click it on any page to save the content to OpenBrain

Web pages are scraped via Firecrawl (markdown extraction, JS rendering). Set FIRECRAWL_API_KEY in .env — without it, web ingestion will error.

YouTube URLs are handled locally via yt-dlp (install: brew install yt-dlp). Transcripts are extracted and stored with video metadata.

Gradual Setup

You don't need all services running to get started:

Minimum: PostgreSQL + Embedding Service + MCP Server — gives you store and search
Add enrichment: Start the mlx-lm server — memories get auto-enriched with summaries and tags
Add caching: Start Redis — faster repeated searches and embedding lookups
Add UI: Start the web UI — browse and manage memories visually
Add ingestion: Set FIRECRAWL_API_KEY in .env and install the bookmarklet — save web content from your browser.

To skip enrichment entirely (e.g., while setting up), set DISABLE_ENRICHMENT=true in .env.

Sources (recurring ingestion)

OpenBrain pulls content into memory on a schedule. Each source is a row in the sources table with a kind (rss, mail, or webpage), config, and an interval.

# Create an RSS source via the REST API:
echo '{"kind":"rss","name":"HN","config":{"feedUrl":"https://news.ycombinator.com/rss"},"intervalSeconds":3600}' \
  | curl -X POST http://localhost:6277/api/sources -H 'content-type: application/json' --data @-

# Sync now:
curl -X POST http://localhost:6277/api/sources/<id>/sync

# Sync everything that's due:
curl -X POST http://localhost:6277/api/sources/poll-due

The Mac app (Phase 3+) drives poll-due from an hourly timer and exposes a UI for source CRUD. Until then, talk to the REST API directly. (The timer is just a heartbeat — each source's own intervalSeconds decides when it's actually due.)

Mail sources via `gog`

To pull Gmail into memory, install gogcli and authenticate:

brew install gogcli
gog auth add [email protected]

Then create a mail source:

{
  "kind": "mail",
  "name": "Personal Gmail (last day)",
  "config": {
    "account": "[email protected]",
    "query": "newer_than:1d -in:promotions -in:spam -in:trash",
    "maxMessages": 25
  },
  "intervalSeconds": 900
}

Important: launchd-managed services have no TTY for gog's keyring password prompt. Set GOG_KEYRING_PASSWORD in .env (gitignored) to your gog keyring password, then restart the MCP service. Without this, mail syncs will fail with "no TTY available for keyring file backend password prompt".

Recurring agents

OpenBrain ships with a small starter pack of prompt-driven agents that run on a schedule, query the memory store, call the local LLM, and write the result back as a new memory. All of this happens locally — no API keys.

# Run a shipped agent once, see the output:
bun run agents/run-agent.ts agents/prompts/newsletter-digest.md

# Or via the wrapper:
agents/run-agent.sh newsletter-digest.md

Shipped prompts live in agents/prompts/. Bring your own by dropping additional .md files into a directory of your choice (the Mac app defaults to ~/Developer/claude-cron/prompts/). See agents/prompts/README.md for the format.

Remote Access via Tailscale

To access OpenBrain from other devices on your network:

# Expose MCP server over Tailscale HTTPS
tailscale serve --bg --https=6443 http://localhost:6277

Then update your bookmarklet URL to use https://your-machine.tail-xxxxx.ts.net:6443 and set AUTH_TOKEN in .env to protect the endpoint.

Development

bun run dev          # MCP server with auto-reload
bun run ui           # Web UI
bun run health       # Check all service health
bun run db:generate  # Generate Drizzle migration from schema changes
bun run db:migrate   # Apply pending migrations

# Re-enrich memories missing summaries
bun run scripts/enrich-backlog.ts

Managing Services

# Install as launchd daemons (auto-start on boot)
bash scripts/install-services.sh

# Uninstall all services
bash scripts/uninstall-services.sh

# Restart a single service
kill $(lsof -ti :6277)  # KeepAlive restarts it automatically

# View logs
tail -f logs/mcp.log
tail -f logs/embed.log
tail -f logs/llm.log

License

MIT

Apple Silicon Openbrain

sufficiently-advanced-ai12stars

Fully local AI memory for Apple Silicon. MCP server with semantic vector search, MLX embeddings, and LLM enrichment — no cloud APIs required.

Knowledge Memory

Install Run on PlugMCP

GitHub

Run on PlugMCP

1# Apple Silicon Openbrain

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-28

Stars: 12
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Apple Silicon Openbrain?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Apple Silicon Openbrain need an API key?

No required environment variable is listed on this record.

What transport does Apple Silicon Openbrain use?

Apple Silicon Openbrain runs locally over stdio.

When was Apple Silicon Openbrain last checked?

Last checked 2026-10-28. Fully local AI memory for Apple Silicon. MCP server with semantic vector search, MLX embeddings, and LLM enrichment — no cloud APIs required.

From the publisher

Documentation

OpenBrain

Fully local AI memory for Apple Silicon. Gives any MCP client persistent semantic memory with vector search — no cloud APIs required.

Everything runs on your Mac: embeddings via MLX on Metal GPU, enrichment via a local LLM, storage in PostgreSQL with pgvector.

Built on the OB1 architecture by @NateBJones-Projects.

Architecture

OpenBrain Architecture

Requirements

Apple Silicon Mac (M1/M2/M3/M4) — required for MLX GPU inference
16 GB RAM minimum for the default model (Qwen3-8B-4bit, ~4.5 GB); 32 GB+ lets you run larger enrichment models
macOS 14+
Node.js 22+ (LTS v24 recommended) and pnpm 11+
Bun 1.1+ as the runtime (we use pnpm to install deps; bun to run them)
Python 3.13+ with uv
PostgreSQL 17 with pgvector extension
Redis (optional but recommended)
Firecrawl API key — used for all web page scraping
yt-dlp (optional — YouTube transcript extraction)

Firecrawl

Firecrawl handles all web-page scraping (markdown extraction, JS rendering, etc.). It's a cloud service; set your key in .env:

FIRECRAWL_API_KEY=fc-...

Without a key, POST /api/ingest for web URLs will return an error.

Quick install (recommended)

bash installer/bootstrap.sh

This runs the full chain: prerequisites (Homebrew packages, Bun, Python venv with mlx-lm), database setup, schema, and launchd services. Idempotent — safe to re-run.

Manual install

If you'd rather install the prerequisites yourself:

# Homebrew packages
brew install postgresql@17 pgvector redis uv yt-dlp pnpm
brew services start postgresql@17
brew services start redis

# Node 22+ via nvm (skip if you already have one)
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash
. ~/.nvm/nvm.sh && nvm install --lts && nvm alias default 'lts/*'

# Optional, for Gmail integration via the agents/ subsystem
brew install gogcli

# Bun via its official installer (no Homebrew formula). We use Bun as the
# runtime (Bun.serve, Bun.spawn) but never `bun install` — that hits a
# macOS SIP/provenance issue. pnpm handles deps.
curl -fsSL https://bun.sh/install | bash

After installing Bun, restart your shell (or source ~/.zshrc) so the bun binary is on your PATH.

Quick Start

# 1. Clone and enter the repo
git clone https://github.com/sajennings79/apple-silicon-openbrain.git
cd apple-silicon-openbrain

# 2. Configure environment
cp .env.example .env
# Edit .env if needed (defaults work for local setup)

# 3. Run setup (creates database, installs Bun + Python deps)
bash scripts/setup.sh

# 4. Create a Python venv for the LLM enrichment server
python3 -m venv ~/.mlx-venv
~/.mlx-venv/bin/pip install mlx-lm

# 5. Start all services (installs as launchd daemons)
bash scripts/install-services.sh

# 6. Verify everything is running
bun run health

Or start services manually:

bun run dev                                    # MCP server (port 6277)
cd embed-service && uv run server.py           # Embedding service (port 6278)
~/.mlx-venv/bin/python -m mlx_lm.server \
  --model mlx-community/Qwen3-8B-4bit \
  --port 8000 --max-tokens 4096               # LLM enrichment (port 8000)
bun run ui                                     # Web UI (port 6279)

First Run: Model Downloads

Models are pulled from Hugging Face Hub the first time each service starts — there's no separate download step. Both services block on startup until the download completes, then come online.

Model	Size	Used by	Triggered by
`mlx-community/Qwen3-Embedding-0.6B-4bit-DWQ`	~400 MB	Embedding service (`:6278`)	FastAPI startup
`mlx-community/Qwen3-8B-4bit` (default)	~4.5 GB	LLM enrichment (`:8000`)	`mlx_lm.server` startup

The default LLM is sized for 16 GB RAM systems. To trade RAM for quality, override LLM_MODEL in .env (and the matching --model arg in ~/Library/LaunchAgents/com.openbrain.llm.plist):

RAM	Suggested model	Disk
16 GB (default)	`mlx-community/Qwen3-8B-4bit`	~4.5 GB
32 GB	`mlx-community/Qwen3.6-27B-4bit` (dense) or `mlx-community/Qwen3.6-35B-A3B-4bit` (MoE)	~17 GB
64 GB+	`mlx-community/Qwen3.5-122B-A10B-4bit`	~60 GB

All models are public repos — no Hugging Face token required. Files cache to ~/.cache/huggingface/hub/ and subsequent starts run fully offline.

If you installed the launchd services, the first-run download happens silently in the background. Watch progress with:

tail -f logs/embed.log   # embedding model (~30s on fast connection)
tail -f logs/llm.log     # ~2 min for the default 8B model on a fast connection

bun run health will report the LLM service as down until its download finishes. Interrupted downloads resume on the next start.

MCP Client Configuration

OpenBrain exposes both HTTP and stdio MCP transports. Configure your client to point at whichever it supports.

HTTP transport (recommended — one shared server for all clients):

{
  "mcpServers": {
    "openbrain": {
      "type": "http",
      "url": "http://localhost:6277/mcp"
    }
  }
}

stdio transport (for clients that spawn the server as a subprocess):

{
  "mcpServers": {
    "openbrain": {
      "type": "stdio",
      "command": "bun",
      "args": ["run", "/path/to/openbrain/src/stdio.ts"]
    }
  }
}

The exact config-file location depends on your client (e.g. .mcp.json in a project root, or a global config under your home directory).

MCP Tools

Tool	Description
StoreMemory	Store text with automatic embedding, LLM enrichment (summary, tags, entities), and cross-memory linking
SearchMemory	Semantic vector search with optional filters (type, source, tags, date range)
RecallMemory	Retrieve a specific memory by UUID
ListMemories	Paginated list with filters
UpdateMemory	Update content or metadata; re-embeds and re-enriches on content changes
DeleteMemory	Soft-delete a memory by UUID

Services

Service	Port	Purpose
MCP Server	6277	MCP protocol + URL ingestion API
Embedding Service	6278	Local MLX embeddings (Qwen3-Embedding-0.6B, 1024-dim)
LLM Server	8000	Local MLX enrichment (Qwen3-8B by default)
Web UI	6279	Dashboard for browsing and searching memories
PostgreSQL	5432	Memory storage with pgvector
Redis	6379	Embedding + search result caching

Web UI

The web UI at http://localhost:6279 provides:

Memory search and browsing with tag/source/type filters
Dashboard with statistics (total memories, per source, per type)
Monthly histogram and 30-day sparkline
New memory creation form
Tag management

URL Ingestion

Save web pages and YouTube transcripts directly to memory using the bookmarklet.

Open scripts/bookmarklet.html in your browser
Drag the bookmarklet link to your bookmarks bar
Click it on any page to save the content to OpenBrain

Web pages are scraped via Firecrawl (markdown extraction, JS rendering). Set FIRECRAWL_API_KEY in .env — without it, web ingestion will error.

YouTube URLs are handled locally via yt-dlp (install: brew install yt-dlp). Transcripts are extracted and stored with video metadata.

Gradual Setup

You don't need all services running to get started:

Minimum: PostgreSQL + Embedding Service + MCP Server — gives you store and search
Add enrichment: Start the mlx-lm server — memories get auto-enriched with summaries and tags
Add caching: Start Redis — faster repeated searches and embedding lookups
Add UI: Start the web UI — browse and manage memories visually
Add ingestion: Set FIRECRAWL_API_KEY in .env and install the bookmarklet — save web content from your browser.

To skip enrichment entirely (e.g., while setting up), set DISABLE_ENRICHMENT=true in .env.

Sources (recurring ingestion)

OpenBrain pulls content into memory on a schedule. Each source is a row in the sources table with a kind (rss, mail, or webpage), config, and an interval.

# Create an RSS source via the REST API:
echo '{"kind":"rss","name":"HN","config":{"feedUrl":"https://news.ycombinator.com/rss"},"intervalSeconds":3600}' \
  | curl -X POST http://localhost:6277/api/sources -H 'content-type: application/json' --data @-

# Sync now:
curl -X POST http://localhost:6277/api/sources/<id>/sync

# Sync everything that's due:
curl -X POST http://localhost:6277/api/sources/poll-due

Mail sources via `gog`

To pull Gmail into memory, install gogcli and authenticate:

brew install gogcli
gog auth add [email protected]

Then create a mail source:

{
  "kind": "mail",
  "name": "Personal Gmail (last day)",
  "config": {
    "account": "[email protected]",
    "query": "newer_than:1d -in:promotions -in:spam -in:trash",
    "maxMessages": 25
  },
  "intervalSeconds": 900
}

Recurring agents

# Run a shipped agent once, see the output:
bun run agents/run-agent.ts agents/prompts/newsletter-digest.md

# Or via the wrapper:
agents/run-agent.sh newsletter-digest.md

Remote Access via Tailscale

To access OpenBrain from other devices on your network:

# Expose MCP server over Tailscale HTTPS
tailscale serve --bg --https=6443 http://localhost:6277

Then update your bookmarklet URL to use https://your-machine.tail-xxxxx.ts.net:6443 and set AUTH_TOKEN in .env to protect the endpoint.

Development

bun run dev          # MCP server with auto-reload
bun run ui           # Web UI
bun run health       # Check all service health
bun run db:generate  # Generate Drizzle migration from schema changes
bun run db:migrate   # Apply pending migrations

# Re-enrich memories missing summaries
bun run scripts/enrich-backlog.ts

Managing Services

# Install as launchd daemons (auto-start on boot)
bash scripts/install-services.sh

# Uninstall all services
bash scripts/uninstall-services.sh

# Restart a single service
kill $(lsof -ti :6277)  # KeepAlive restarts it automatically

# View logs
tail -f logs/mcp.log
tail -f logs/embed.log
tail -f logs/llm.log

License

MIT

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Apple Silicon Openbrain?

Does Apple Silicon Openbrain need an API key?

What transport does Apple Silicon Openbrain use?

When was Apple Silicon Openbrain last checked?

Documentation

OpenBrain

Architecture

Requirements

Firecrawl

Quick install (recommended)

Manual install

Quick Start

First Run: Model Downloads

MCP Client Configuration

MCP Tools

Services

Web UI

URL Ingestion

Gradual Setup

Sources (recurring ingestion)

Mail sources via gog

Recurring agents

Remote Access via Tailscale

Development

Managing Services

License

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Apple Silicon Openbrain?

Does Apple Silicon Openbrain need an API key?

What transport does Apple Silicon Openbrain use?

When was Apple Silicon Openbrain last checked?

Documentation

OpenBrain

Architecture

Requirements

Firecrawl

Quick install (recommended)

Manual install

Quick Start

First Run: Model Downloads

MCP Client Configuration

MCP Tools

Services

Web UI

URL Ingestion

Gradual Setup

Sources (recurring ingestion)

Mail sources via gog

Recurring agents

Remote Access via Tailscale

Development

Managing Services

License

Mail sources via `gog`

Mail sources via `gog`