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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.

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How It Works

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

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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.

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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.

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Run Locally

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

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Connect to the Control Plane

  1. Open os.agno.com
  2. Click Connect OS → Local
  3. Enter http://localhost:8000

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Deploy to Railway

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

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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

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Source

For architecture details, context structure, and tool configuration, see the GitHub repo.