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Send a URL and a question. Slaash returns only the relevant nodes — 99% fewer tokens than raw HTML.
cURL
curl -X POST https://api.slaash.ai/v1/extract \
-H "Authorization: Bearer sk-your-key-here" \
-H "Content-Type: application/json" \
-d '{
"url": "https://en.wikipedia.org/wiki/Rust_(programming_language)",
"goal": "when was Rust created and by whom"
}'
Python
from slaash import Slaash
s = Slaash(api_key="sk-your-key-here")
result = s.extract(
url="https://en.wikipedia.org/wiki/Rust_(programming_language)",
goal="when was Rust created and by whom"
)
for node in result.nodes:
print(f"[{node.role}] {node.label}")
JavaScript
import Slaash from 'slaash'
const s = new Slaash({ apiKey: 'sk-your-key-here' })
const result = await s.extract(
'https://en.wikipedia.org/wiki/Rust_(programming_language)',
{ goal: 'when was Rust created and by whom' }
)
result.nodes.forEach(n => console.log(`[${n.role}] ${n.label}`))
Tell Slaash which nodes had the right answer. Next time, they'll rank higher automatically.
curl -X POST https://api.slaash.ai/v1/learn \
-H "Authorization: Bearer sk-your-key-here" \
-H "Content-Type: application/json" \
-d '{
"url": "https://en.wikipedia.org/wiki/Rust_(programming_language)",
"goal": "when was Rust created and by whom",
"node_ids": [142, 289]
}'
Every extract call supports 3 output formats:
JSON — structured nodes with role, label, relevance, causal_boost. Best for programmatic access.
Markdown — readable text with headings and links. Best for LLM consumption.
TOON — Token-Oriented Object Notation. 86% smaller than JSON. Best for minimal token budgets.
REST API Reference — all 12 primitives with request/response specs.
Slaash Guide — how goal expansion, causal learning, and feedback work.
MCP Integration — connect to Claude Desktop, Cursor, VS Code.
Playground — try all primitives live in your browser.