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
