🧠 brainfood

Structured knowledge for hungry agents.

Your AI agent is only as smart as what you feed it. Brainfood turns your messy knowledge — YouTube transcripts, PDFs, docs, notes, websites — into clean, structured data your agent actually understands.

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What does it do?

You have knowledge trapped in files your AI can't use. Brainfood fixes that.

Source	What brainfood does
PDFs	Extracts text, structures it, outputs clean JSON/Markdown/Obsidian notes
Local docs (Markdown, HTML, text, DOCX)	Parses, organizes, builds a knowledge graph
Websites	Crawls pages, extracts content, maps structure
Sitemaps	Reads sitemap XML, fetches and processes all listed pages

Every source becomes structured, linked, agent-ready output.

Quick start

# Install
npm install -g brainfood

# Process local files (PDFs, docs, transcripts)
brainfood local ./my-knowledge --format both

# Crawl a website
brainfood crawl https://example.com --depth 2

# Generate Obsidian-ready notes
brainfood local ./research --format obsidian

# Read from a sitemap
brainfood sitemap https://example.com/sitemap.xml

Or run without installing:

npx brainfood local ./docs --format json

Output formats

--format json

Structured JSON nodes — perfect for AI agent ingestion, pipelines, and APIs.

--format markdown

Clean Markdown files — readable by humans and machines.

--format obsidian

Obsidian-ready Markdown with YAML frontmatter, tags, and [[wiki-links]] — drop directly into your vault.

--format both

JSON + Markdown together.

Every format also writes a brainfood.json knowledge graph index.

Obsidian integration

Brainfood speaks Obsidian natively:

brainfood local ./research-papers --format obsidian --output ~/Documents/Obsidian\ Vault/research/

Output includes:

• YAML frontmatter — title, date, source, tags, type
• Wiki-links — entity names automatically linked as [[Entity Name]]
• Clean filenames — slugified, no special characters
• Tags — extracted topics become Obsidian tags

Your vault becomes a living knowledge base — searchable, linked, and graph-ready.

How it works

1. Ingest — point brainfood at files, a folder, a URL, or a sitemap
2. Extract — content is parsed, cleaned, and structured using Mozilla Readability + Cheerio
3. Structure — topics, entities, and relationships are identified and linked
4. Output — clean knowledge nodes in your chosen format

Each document becomes a knowledge node:

{
  "id": "a1b2c3d4e5f6",
  "title": "Document Title",
  "content": "# Clean structured content...",
  "summary": "AI-generated or extractive summary",
  "topics": ["topic1", "topic2"],
  "entities": [
    { "name": "Key Concept", "type": "topic" }
  ],
  "relationships": [],
  "metadata": {
    "sourceType": "local",
    "wordCount": 1250,
    "generatedAt": "2026-03-17T00:00:00.000Z"
  }
}

CLI reference

brainfood local <directory>

Process local Markdown, HTML, text, PDF, or DOCX files.

brainfood crawl <url>

Crawl a website with configurable depth and rate limiting.

brainfood sitemap <url>

Parse a sitemap and fetch all listed pages.

Common options

Option	Default	Description
-o, --output <dir>	./brainfood-output	Output directory
-f, --format <format>	json	Output format: json, markdown, obsidian, or both
--summarize	off	Generate AI summaries (requires OPENAI_API_KEY)
--model <model>	gpt-4.1-mini	OpenAI model for summaries
--depth <n>	2	Max crawl depth (crawl mode)
--max-pages <n>	50	Max pages to process
--concurrency <n>	3	Concurrent requests (max 10)
--rate-limit <ms>	1000	Minimum ms between requests
--exclude <patterns>	—	Comma-separated URL patterns to skip

Using brainfood with OpenClaw

Already running an OpenClaw agent? Just tell it what to process:

"Install brainfood and process my research folder into Obsidian notes"

Your agent will run:

npm install -g brainfood
brainfood local ./research --format obsidian --output ~/Documents/Obsidian\ Vault/research/

"Crawl my company website and give me structured data"

brainfood crawl https://yoursite.com --depth 2 --format json

"Convert these PDFs into something you can actually read"

brainfood local ./documents --format both

That's it. One install, one command, your agent gets structured knowledge it can actually use.

Tip for non-technical users: Copy any of the commands above and paste them to your OpenClaw agent in chat. It handles the rest.

Use cases

Feed your AI agent — Convert your knowledge base into structured data any LLM agent can ingest.

Build an Obsidian vault — Turn PDFs, transcripts, and research into linked, searchable notes.

Audit a website — Extract and map all content from any site for analysis or migration.

Power a knowledge pipeline — Automate ingestion from docs folders, sitemaps, or web sources.

Built by Capxel

Capxel builds AI-native intelligence infrastructure. Brainfood is open source under MIT.

Contributing

PRs welcome. See issues for open work.

License

MIT