A personal agent that learns your preferences, context, and history. Built with Agno.

Pal creates SQL tables on demand, remembers everything you tell it, and gets better over time using LearningMachine. It connects notes, people, bookmarks, and anything else you want to track.

# Clone the repo
cp example.env .env
# Edit .env and add your OPENAI_API_KEY and EXA_API_KEY

# Start the application
docker compose up -d --build

Confirm Pal is running at http://localhost:8000/docs.

1. Open os.agno.com and login
2. Add OS → Local → http://localhost:8000
3. Click "Connect"

Try it:

Save a note: Met with Sarah Chen from Acme Corp
What do I know about Sarah?
Create a projects table and track my current projects

Requires:

• Railway CLI
• OPENAI_API_KEY set in your environment

railway login

./scripts/railway_up.sh

The script provisions PostgreSQL, configures environment variables, and deploys your application.

1. Open os.agno.com
2. Click "Add OS" → "Live"
3. Enter your Railway domain

railway logs --service pal      # View logs
railway open                    # Open dashboard
railway up --service pal -d     # Update after changes

To stop services:

railway down --service pal
railway down --service pgvector

SQL Database — Pal's structured storage for your data. Notes, bookmarks, people, projects, decisions — tables are created on demand with a pal_ prefix. Uses SQLTools against the shared PostgreSQL database.

LearningMachine — Meta-knowledge about you and the database. Preferences, patterns, schemas created, query patterns that work. Stored in a separate vector knowledge base (pal_learnings).

Exa MCP — Web research via the Exa search API. Pal can look things up and optionally save findings to the database.

├── pal/
│   └── agent.py              # Pal agent implementation
├── app/
│   ├── main.py               # AgentOS entry point
│   └── config.yaml           # Quick prompts config
├── db/
│   ├── session.py            # PostgreSQL database helpers
│   └── url.py                # Connection URL builder
├── scripts/                  # Helper scripts
├── compose.yaml              # Docker Compose config
├── Dockerfile
└── pyproject.toml            # Dependencies

Agno includes 100+ tool integrations. See the full list.

from agno.tools.slack import SlackTools

pal = Agent(
    ...
    tools=[
        SQLTools(db_url=db_url),
        MCPTools(url=EXA_MCP_URL),
        SlackTools(),
    ],
)

1. Edit pyproject.toml
2. Regenerate requirements: ./scripts/generate_requirements.sh
3. Rebuild: docker compose up -d --build

1. Add your API key to .env (e.g., ANTHROPIC_API_KEY)
2. Update pal/agent.py to use the new provider:

from agno.models.anthropic import Claude

model=Claude(id="claude-sonnet-4-5")

3. Add dependency: anthropic in pyproject.toml

For development without Docker:

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Setup environment
./scripts/venv_setup.sh
source .venv/bin/activate

# Start PostgreSQL (required)
docker compose up -d pal-db

# Run the app
python -m app.main

• Agno Documentation
• AgentOS Documentation
• Agno Discord