> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# PAL

> Personal agent that learns. Knowledge base, wiki, and structured data that compounds over time.

**PAL is a Personal Agent that Learns how you work by building a compounding knowledge base.**

Feed it raw data (articles, papers, notes, URLs) and it organizes everything into two layers: a compiled wiki for text-heavy knowledge and a SQL database for structured data. Scheduled tasks compile new sources daily and run health checks to find gaps.

## How It Works

PAL is a team of five specialists coordinated by a leader:

| Agent          | Role                                                         |
| -------------- | ------------------------------------------------------------ |
| **Navigator**  | Routes queries, reads wiki, handles email/calendar/SQL/files |
| **Researcher** | Web search, source gathering, writes to raw/                 |
| **Compiler**   | Reads raw/, compiles structured wiki articles                |
| **Linter**     | Health checks on the wiki, finds gaps and contradictions     |
| **Syncer**     | Commits and pushes context/ changes to GitHub                |

### Knowledge Pipeline

Raw data flows through a compilation pipeline:

1. **Ingest** — Feed PAL URLs, articles, or text. The Researcher saves them to `context/raw/`.
2. **Compile** — The Compiler produces structured wiki articles in `context/wiki/`.
3. **Query** — The Navigator reads the wiki index first, then pulls specific articles.
4. **Lint** — The Linter finds contradictions, stale articles, and missing concepts.

### Structured Data (SQL)

The other half is PostgreSQL for structured data. When you say "save a note: met with Sarah from Acme," PAL creates a row tagged with `['sarah', 'acme']`. Later, "what do I know about Sarah?" queries across notes, people, and projects.

## Run Locally

```bash theme={null}
git clone https://github.com/agno-agi/pal.git && cd pal

cp example.env .env
# Edit .env and add your OPENAI_API_KEY

docker compose up -d --build

# Load context metadata into the knowledge base
docker compose exec pal-api python context/load_context.py
```

Confirm PAL is running at `http://localhost:8000/docs`.

### Connect to the Control Plane

1. Open [os.agno.com](https://os.agno.com)
2. Click **Connect OS** → **Local**
3. Enter `http://localhost:8000`

## Deploy to Railway

```bash theme={null}
railway login
./scripts/railway_up.sh
```

The script provisions PostgreSQL, configures environment variables, and deploys PAL.

Then connect via the control plane:

1. Open [os.agno.com](https://os.agno.com)
2. Click **Connect OS** → **Live**
3. Enter your Railway domain

## Example Prompts

Try these to see PAL in action:

* Ingest this URL: \[paste any article]
* What do I know about \[topic from ingested content]?
* Save a note: met with \[person] about \[topic]
* What notes do I have tagged with \[tag]?
* Compile the wiki and summarize what's new

## Source

For architecture details, context structure, and tool configuration, see the [GitHub repo](https://github.com/agno-agi/pal).