Parallel.ai 🔬

High-accuracy web search API built for AI agents. Outperforms Perplexity/Exa on research benchmarks.

Setup

pip install parallel-web

API key is configured. Uses Python SDK.

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