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

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
2. Click Connect OS → Local
3. Enter http://localhost:8000

Deploy to Railway

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
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.