from parallel import Parallel
client = Parallel(api_key="YOUR_KEY")
response = client.beta.search(
    mode="one-shot",  # or "fast" for lower latency/cost, "agentic" for multi-hop
    max_results=10,
    objective="your query"
)

Modes

Mode	Use Case	Tradeoff
`one-shot`	Default, balanced accuracy	Best for most queries
`fast` ⚡	Quick lookups, cost-sensitive	Lower latency/cost, may sacrifice some accuracy
`agentic`	Complex multi-hop research	Higher accuracy, more expensive

Quick Usage

# Default search (one-shot mode)
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "Who is the CEO of Anthropic?" --max-results 5

# Fast mode - lower latency/cost ⚡
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "latest AI news" --mode fast

# Agentic mode - complex research
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "compare transformer architectures" --mode agentic

# JSON output
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "latest AI news" --json

Response Format

Returns structured results with:

search_id - unique search identifier
results[] - array of results with:
- url - source URL
- title - page title
- excerpts[] - relevant text excerpts
- publish_date - when available
usage - API usage stats

When to Use

Deep research requiring cross-referenced facts
Company/person research with citations
Fact-checking with evidence-based outputs
Complex queries that need multi-hop reasoning
Higher accuracy than traditional search for research tasks

API Reference

Docs: https://docs.parallel.ai Platform: https://platform.parallel.ai

parallel

Parallel.ai 🔬

Setup