AIBrain

https://myaibrain.org/docs
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AIBrain is a persistent memory layer and operating system for AI agents, offering multi-agent...
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### Run AIBrain setup

Source: https://myaibrain.org

Initial configuration command to be run after installation.

```bash
aibrain setup
```

--------------------------------

### Local installation

Source: https://myaibrain.org/docs/getting-started.html

Manual installation for full control over service execution.

```bash
$ pip install aibrain && aibrain setup
$ cp config.json.example config.json
```

--------------------------------

### Install all MCP servers and add individually

Source: https://myaibrain.org/docs/guide.html

Commands to install all MCP servers at once with 'aibrain mcp install' or add individual servers via 'claude mcp add'. The examples show adding aibrain-memory, it-support, and peer-discovery servers.

```bash
# Install all MCP servers at once
$ aibrain mcp install

# Or add individually
$ claude mcp add aibrain-memory -- python mcp_server.py --db aibrain.db
$ claude mcp add it-support -- python mcp_it_support.py
$ claude mcp add peer-discovery -- python mcp_peer_discovery.py
```

--------------------------------

### Install via pip

Source: https://myaibrain.org/docs/getting-started.html

The simplest installation method providing the CLI, MCP server, and memory system.

```bash
$ pip install aibrain
$ aibrain setup
```

--------------------------------

### Launch services

Source: https://myaibrain.org/docs/getting-started.html

Start the services using the provided scripts for your operating system.

```bash
# Windows
> start.bat

# Unix / macOS
$ ./start.sh
```

--------------------------------

### Install via Docker

Source: https://myaibrain.org/docs/getting-started.html

Runs the full stack including backend and frontend dashboard in containers.

```bash
$ pip install aibrain && aibrain setup
$ cp config.json.example config.json
# Edit config.json with your settings
$ docker compose up --build
```

--------------------------------

### Install AIBrain via pip

Source: https://myaibrain.org

Use pip to install the AIBrain Python package.

```bash
pip install aibrain
```

--------------------------------

### Install AIBrain on Mac, Linux, or WSL

Source: https://myaibrain.org

Use the curl command to install AIBrain on Unix-based systems.

```bash
curl -fsSL https://myaibrain.org/install.sh | bash
```

--------------------------------

### Install AIBrain on Windows PowerShell

Source: https://myaibrain.org

Use the PowerShell command to install AIBrain on Windows systems.

```powershell
irm https://myaibrain.org/install.ps1 | iex
```

--------------------------------

### Install MCP dependencies

Source: https://myaibrain.org/docs/getting-started.html

Install required packages for the MCP server functionality.

```bash
$ pip install mcp sentence-transformers sqlite-vec
```

--------------------------------

### Start services manually

Source: https://myaibrain.org/docs/getting-started.html

Run the backend and frontend services manually in separate terminals.

```bash
# Terminal 1 — Backend
$ cd backend
$ pip install -r ../requirements.txt
$ python -m uvicorn main:app --host 0.0.0.0 --port 8001 --reload

# Terminal 2 — Frontend (optional)
$ cd frontend
$ npm install && npm run dev -- --host
```

--------------------------------

### Initialize and Use Brain API

Source: https://myaibrain.org

A basic example showing how to initialize the Brain, add a memory, and perform a search.

```python
from aibrain import Brain
brain = Brain()
brain.add("I love tennis", user_id="alice")
brain.search("hobbies", user_id="alice")
```

--------------------------------

### Marketplace Operations

Source: https://myaibrain.org/docs/guide.html

Commands for packaging, publishing, searching, installing, and viewing revenue for brain configurations.

```bash
$ aibrain marketplace package --name "devops-brain"  # Package for sale
$ aibrain marketplace publish devops-brain.brain     # Publish
$ aibrain marketplace search "machine learning"      # Browse
$ aibrain marketplace install <id>                   # Install a brain
$ aibrain marketplace revenue                        # Revenue dashboard
```

--------------------------------

### View workflow dependency map

Source: https://myaibrain.org/docs/workflows.html

Use the deps command to view the full dependency map or dependencies for a specific workflow. The example shows viewing dependencies for contradiction_detector.

```bash
# View the full dependency map
$ aibrain workflows deps

# View dependencies for a specific workflow
$ aibrain workflows deps contradiction_detector
```

--------------------------------

### Define schedules in scheduled_jobs.json

Source: https://myaibrain.org/docs/workflows.html

Manually define schedules for workflows in the scheduled_jobs.json file. Each entry specifies whether the workflow is enabled, its cron expression, and the script path. The example shows a weather briefing at 6:30 AM daily and memory consolidation at 3:00 AM on Sundays.

```json
{
  "weather_briefing": {
    "enabled": true,
    "cron": "30 6 * * *",
    "script": "workflows/weather_briefing.py"
  },
  "memory_consolidation": {
    "enabled": true,
    "cron": "0 3 * * 0",
    "script": "workflows/memory_consolidation.py"
  }
}
```

--------------------------------

### AIBrain CLI: Search, Summary, Export, Import, Forget

Source: https://myaibrain.org/docs/guide.html

Shows the AIBrain CLI commands for searching memories, getting memory stats, exporting/importing the brain, and deleting memories. Uses '--confirm' flag for the forget command.

```bash
aibrain search "kubernetes deployment"     # Search memories
aibrain summary                            # Memory stats
aibrain brain export                       # Export as JSON
aibrain brain import backup.json           # Import from file
aibrain forget "outdated topic" --confirm  # Delete memories

```

--------------------------------

### Enable recommended starter set with aibrain workflows enable --recommended

Source: https://myaibrain.org/docs/workflows.html

Command to enable the recommended starter set of workflows. The set includes 7 agent improvement, 5 personal intelligence, and 2 utility workflows (daily planner + weekly review).

```bash
# Enable the recommended starter set
$ aibrain workflows enable --recommended

# Sync to your OS scheduler
$ aibrain workflows sync

# Preview what would be created (optional)
$ aibrain workflows sync --dry-run
```

--------------------------------

### AIBrain workflow commands

Source: https://myaibrain.org/docs/guide.html

Shows how to list workflows, enable the recommended starter set, sync OS scheduled tasks, and run a specific workflow immediately. Workflows use the OS scheduler (cron on Linux, schtasks on Windows, launchctl on macOS) and require no daemon process.

```bash
$ aibrain workflows list                  # Show all workflows + status
$ aibrain workflows enable --recommended  # Enable the starter set
$ aibrain workflows sync                  # Create OS scheduled tasks
$ aibrain workflows run weekly_wins       # Run one immediately
```

--------------------------------

### Manage aibrain licenses via CLI

Source: https://myaibrain.org/docs/guide.html

Use these commands to view current pricing details or activate a license key.

```bash
$ aibrain license tiers              # See pricing details
$ aibrain license activate <key>     # Activate your license
```

--------------------------------

### Execute Swarm Training

Source: https://myaibrain.org/docs/guide.html

Commands to initialize a multi-agent swarm and harvest the resulting training data.

```bash
$ aibrain mesh swarm create research -n 100   # Create 100-agent swarm
$ aibrain mesh swarm harvest research          # Merge swarm results
```

--------------------------------

### Configure environment variables

Source: https://myaibrain.org/docs/getting-started.html

Optional step to set environment overrides.

```bash
$ cp .env.example .env
# Edit .env with any overrides
```

--------------------------------

### Configure MCP server

Source: https://myaibrain.org/docs/getting-started.html

Add the AIBrain memory server to your MCP configuration file.

```json
{
  "mcpServers": {
    "aibrain-memory": {
      "command": "python",
      "args": ["/path/to/aibrain/mcp_server.py"]
    }
  }
}
```

--------------------------------

### Create custom workflow by subclassing UserWorkflow

Source: https://myaibrain.org/docs/workflows.html

Create a custom workflow by subclassing UserWorkflow. Define the name, schedule (cron), description, and implement the run method. The run method can read from the brain, process data, and store results back.

```python
# my_workflow.py
from aibrain.workflows import UserWorkflow

class MyCustomWorkflow(UserWorkflow):
    name = "my_custom_workflow"
    schedule = "0 9 * * *"  # 9am daily
    description = "My custom daily workflow"

    def run(self, brain):
        # Read from the brain
        memories = brain.search("project status")

        # Do your work
        summary = self.process(memories)

        # Write back to the brain
        brain.store(
            content=summary,
            memory_type="project",
            importance=7
        )
```

--------------------------------

### Manage Brain Packs

Source: https://myaibrain.org/docs/getting-started.html

Commands to browse, activate, and check active domain specialization bundles.

```bash
$ aibrain packs                       # Browse all packs
$ aibrain packs activate developer     # Activate the developer pack
$ aibrain packs active                 # See what's active
```

--------------------------------

### Enable workflow with auto-enabled dependencies

Source: https://myaibrain.org/docs/workflows.html

Enabling a Tier 3 workflow automatically enables its Tier 2 dependencies. The output shows the enabled workflow and the list of auto-enabled dependencies.

```bash
$ aibrain workflows enable contradiction_detector

Enabled: contradiction_detector
Auto-enabled dependencies:
  correction_learner
  memory_hygiene
  user_model
```

--------------------------------

### Sync workflows to OS scheduler

Source: https://myaibrain.org/docs/workflows.html

Creates native scheduled tasks from enabled workflows. Use --dry-run to preview changes without applying. On Linux, entries are added to the user crontab; on Windows, tasks are created via schtasks; on macOS, launchctl plist files are used.

```bash
# Create native scheduled tasks from enabled workflows
$ aibrain workflows sync

# Preview changes without applying
$ aibrain workflows sync --dry-run
```

--------------------------------

### AIBrain pack management commands

Source: https://myaibrain.org/docs/guide.html

Commands to browse, activate, and show active Brain Packs. Use 'aibrain packs' to list available packs, 'aibrain packs activate developer' to activate a specific pack, and 'aibrain packs active' to display currently active packs.

```bash
$ aibrain packs                       # Browse available packs
$ aibrain packs activate developer     # Activate a pack
$ aibrain packs active                 # Show active packs
```

--------------------------------

### Manage AIBrain Mesh Agents

Source: https://myaibrain.org/docs/guide.html

Commands to check status, register agents, merge consensus, and distribute brain updates across the mesh.

```bash
$ aibrain mesh status                        # Show mesh agent status
$ aibrain mesh register myagent agent.db      # Register an agent
$ aibrain mesh merge                          # Merge all agents (consensus)
$ aibrain mesh distribute                     # Push merged brain to all
```

--------------------------------

### Pull Ollama model

Source: https://myaibrain.org/docs/getting-started.html

Command to pull a local model for zero-cost inference.

```bash
$ ollama pull llama3.2
```
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