API Documentation
Integrate powerful domain-specific intelligence into your applications with our simple REST API and Python SDK
cURL Examples
Use your API key from your dashboard to make API calls.
Basic Search Request
bash
curl -X POST "https://llama-search.com/search/web" \
-H "Authorization: Bearer your-api-key-here" \
-H "Content-Type: application/json" \
-d '{
"query": "find information about RayBan Meta Smart Glass (weight, size)",
"search_depth": "standard"
}'Advanced Search with Options
bash
curl -X POST "https://llama-search.com/search/web" \
-H "Authorization: Bearer your-api-key-here" \
-H "Content-Type: application/json" \
-d '{
"query": "Tesla Model 3 battery specifications",
"search_depth": "standard",
"domain": "manufacturing, battery, tesla",
"with_full_content": true
}'Response Example
json
{
"success": true,
"sources": [
{
"url": "https://www.reddit.com/r/RayBanStories/comments/1crmdlk",
"content": "• Product: Ray‑Ban Meta Smart Glasses\n• Camera: 12 MP\n• Feature: Meta AI capabilities (described as \"growing Meta AI capabilities\")\n• Weight: 48.6g to 50.8g",
"full_content": ""
},
{
"url": "https://www.ray-ban.com/usa/smart-glasses/ray-ban-meta",
"content": "• Ray-Ban Meta Smart Glasses Official Specs\n• Weight: 49.6g (Wayfarer), 50.8g (Headliner)\n• Dimensions: 150mm temple length\n• Frame materials: Acetate and metal options\n• Available in prescription and non-prescription",
"full_content": ""
},
{
"url": "https://about.meta.com/realitylabs/ray-ban-meta/",
"content": "• Meta partnership with Ray-Ban\n• Smart glasses with built-in camera and speakers\n• Weight range: 48.6g - 50.8g depending on frame style\n• Size options: Multiple frame sizes available\n• Battery life: All-day use with charging case",
"full_content": ""
}
],
"raw_data": "",
"error_message": "",
"id": "search_67890",
"query": "find information about RayBan Meta Smart Glass (weight, size)",
"credits_consumed": 5,
"processing_time_ms": 2840,
"status": "completed"
}Search Depth Options
| Search Depth | Credits Cost | Description |
|---|---|---|
| basic | 2 | Efficient search across multiple sources with focused results and quick delivery |
| standard | 5 | Balanced search with deeper analysis and contextual understanding (default) |
| extensive | 12 | Comprehensive search with recursive exploration and detailed source analysis |
Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| query | string | required | - | The search query to execute |
| search_depth | string | optional | "standard" | Search depth: "basic", "standard", or "extensive" |
| domain | string | optional | "" | Optional domain filter (e.g., "reddit.com") |
| with_full_content | boolean | optional | false | Whether to fetch full content from URLs |
Python Examples
Simple Python examples using requests library.
Installation
$ pip install llama-search
Async Usage (Recommended)
python
from llama_search import AsyncLlamaSearch
import asyncio
async def main():
# API key loaded from LLAMA_SEARCH_API_KEY environment variable
async with AsyncLlamaSearch() as client:
# Perform a web search with different depths
result = await client.web_search(
query="find information about RayBan Meta Smart Glass (weight, size)",
search_depth="extensive" # "basic", "standard", or "extensive"
)
print(f"Found {len(result.sources)} sources")
print(f"Credits used: {result.credits_consumed}")
print(f"Processing time: {result.processing_time_ms}ms")
for i, source in enumerate(result.sources, 1):
print(f"\n--- Source {i} ---")
print(f"URL: {source.url}")
print(f"Content: {source.content[:200]}...")
asyncio.run(main())Sync Usage
python
from llama_search import LlamaSearch
with LlamaSearch() as client:
# Perform a web search
result = client.web_search(
query="Tesla Model 3 battery specifications",
search_depth="standard",
domain="reddit.com", # Optional domain filter
with_full_content=True # Fetch full content from URLs
)
if result.success:
print(f"Found {len(result.sources)} sources")
print(f"Credits used: {result.credits_consumed}")
for source in result.sources:
print(f"URL: {source.url}")
print(f"Content: {source.content}")
if source.full_content:
print(f"Full content: {len(source.full_content)} chars")
else:
print(f"Search failed: {result.error_message}")Example Output
text
Found 3 sources
Credits used: 5
Processing time: 3240ms
--- Source 1 ---
URL: https://www.reddit.com/r/RayBanStories/comments/1crmdlk
Content: • Product: Ray‑Ban Meta Smart Glasses
• Camera: 12 MP
• Feature: Meta AI capabilities (described as "growing Meta AI capabilities")
• Weight: 48.6g to 50.8g...
--- Source 2 ---
URL: https://www.ray-ban.com/usa/smart-glasses/ray-ban-meta
Content: • Ray-Ban Meta Smart Glasses Official Specs
• Weight: 49.6g (Wayfarer), 50.8g (Headliner)
• Dimensions: 150mm temple length
• Frame materials: Acetate and metal options...
--- Source 3 ---
URL: https://about.meta.com/realitylabs/ray-ban-meta/
Content: • Meta partnership with Ray-Ban
• Smart glasses with built-in camera and speakers
• Weight range: 48.6g - 50.8g depending on frame style
• Size options: Multiple frame sizes available...Usage Statistics & History
python
# Check account usage
stats = await client.get_usage_stats()
print(f"Credits remaining: {stats.credits_remaining}")
print(f"Total searches: {stats.total_searches}")
print(f"Searches this month: {stats.searches_this_month}")
# Get search history
history = await client.get_search_history(limit=5)
for search in history.searches:
print(f"{search.created_at}: {search.query} ({search.credits_consumed} credits)")
# Get available search types
types = await client.get_search_types()
for search_type in types.search_types:
print(f"{search_type.name}: {search_type.credits} credits")