Parallel Web Systems API

Overview

This skill provides access to Parallel Web Systems APIs for web search, deep research, and content extraction. It is the primary tool for all web-related operations in the scientific writer workflow.

Primary interface: Parallel Chat API (OpenAI-compatible) for search and research. Secondary interface: Extract API for URL verification and special cases only.

API Documentation: https://docs.parallel.ai API Key: https://platform.parallel.ai Environment Variable:

PARALLEL_API_KEY

When to Use This Skill

Use this skill for ALL of the following:

• Web Search: Any query that requires searching the internet for information
• Deep Research: Comprehensive research reports on any topic
• Market Research: Industry analysis, competitive intelligence, market data
• Current Events: News, recent developments, announcements
• Technical Information: Documentation, specifications, product details
• Statistical Data: Market sizes, growth rates, industry figures
• General Information: Company profiles, facts, comparisons

Use Extract API only for:

• Citation verification (confirming a specific URL's content)
• Special cases where you need raw content from a known URL

Do NOT use this skill for:

• Academic-specific paper searches (use

  research-lookup

  which routes to Perplexity for purely academic queries)
• Google Scholar / PubMed database searches (use

  citation-management

  skill)

Two Capabilities

1. Web Search (

search

command)

Search the web via the Parallel Chat API (

base

Best for: General web searches, current events, fact-finding, technical lookups, news, market data.

# Basic search
python scripts/parallel_web.py search "latest advances in quantum computing 2025"

# Use core model for more complex queries
python scripts/parallel_web.py search "compare EV battery chemistries NMC vs LFP" --model core

# Save results to file
python scripts/parallel_web.py search "renewable energy policy updates" -o results.txt

# JSON output for programmatic use
python scripts/parallel_web.py search "AI regulation landscape" --json -o results.json

Key Parameters:

• objective

  : Natural language description of what you want to find

• --model

  : Chat model to use (

  base

  default, or

  core

  for deeper research)

• -o

  : Output file path

• --json

  : Output as JSON

Response includes: Synthesized summary organized by themes, with inline citations and a sources list.

2. Deep Research (

research

command)

Run comprehensive multi-source research via the Parallel Chat API (

core

Best for: Market research, comprehensive analysis, competitive intelligence, technology surveys, industry reports, any research question requiring synthesis of multiple sources.

# Default deep research (core model)
python scripts/parallel_web.py research "comprehensive analysis of the global EV battery market"

# Save research report to file
python scripts/parallel_web.py research "AI adoption in healthcare 2025" -o report.md

# Use base model for faster, lighter research
python scripts/parallel_web.py research "latest funding rounds in AI startups" --model base

# JSON output
python scripts/parallel_web.py research "renewable energy storage market in Europe" --json -o data.json

Key Parameters:

• query

  : Research question or topic

• --model

  : Chat model to use (

  core

  default for deep research, or

  base

  for faster results)

• -o

  : Output file path

• --json

  : Output as JSON

3. URL Extraction (

extract

command) — Verification Only

Extract content from specific URLs. Use only for citation verification and special cases.

For general research, use

search

research

# Verify a citation's content
python scripts/parallel_web.py extract "https://example.com/article" --objective "key findings"

# Get full page content for verification
python scripts/parallel_web.py extract "https://docs.example.com/api" --full-content

# Save extraction to file
python scripts/parallel_web.py extract "https://paper-url.com" --objective "methodology" -o extracted.md

Model Selection Guide

The Chat API supports two research models. Use

base

core

base

core

Recommendations:

• search

  command defaults to

  base

  — fast, good for most queries

• research

  command defaults to

  core

  — thorough, good for comprehensive reports
• Override with

  --model

  when you need different depth/speed tradeoffs

Python API Usage

Search

from parallel_web import ParallelSearch

searcher = ParallelSearch()
result = searcher.search(
    objective="Find latest information about transformer architectures in NLP",
    model="base",
)

if result["success"]:
    print(result["response"])  # Synthesized summary
    for src in result["sources"]:
        print(f"  {src['title']}: {src['url']}")

Deep Research

from parallel_web import ParallelDeepResearch

researcher = ParallelDeepResearch()
result = researcher.research(
    query="Comprehensive analysis of AI regulation in the EU and US",
    model="core",
)

if result["success"]:
    print(result["response"])  # Full research report
    print(f"Citations: {result['citation_count']}")

Extract (Verification Only)

from parallel_web import ParallelExtract

extractor = ParallelExtract()
result = extractor.extract(
    urls=["https://docs.example.com/api-reference"],
    objective="API authentication methods and rate limits",
)

if result["success"]:
    for r in result["results"]:
        print(r["excerpts"])

MANDATORY: Save All Results to Sources Folder

Every web search and deep research result MUST be saved to the project's

sources/

This ensures all research is preserved for reproducibility, auditability, and context window recovery.

Saving Rules

-o

sources/search_<topic>.md

search_YYYYMMDD_HHMMSS_<brief_topic>.md

sources/research_<topic>.md

research_YYYYMMDD_HHMMSS_<brief_topic>.md

sources/extract_<source>.md

extract_YYYYMMDD_HHMMSS_<brief_source>.md

How to Save (Always Use

-o

Flag)

CRITICAL: Every call to

parallel_web.py

-o

sources/

# Web search — ALWAYS save to sources/
python scripts/parallel_web.py search "latest advances in quantum computing 2025" \
  -o sources/search_20250217_143000_quantum_computing.md

# Deep research — ALWAYS save to sources/
python scripts/parallel_web.py research "comprehensive analysis of the global EV battery market" \
  -o sources/research_20250217_144000_ev_battery_market.md

# URL extraction (verification only) — save to sources/
python scripts/parallel_web.py extract "https://example.com/article" --objective "key findings" \
  -o sources/extract_20250217_143500_example_article.md

Why Save Everything

1. Reproducibility: Every claim in the final document can be traced back to its raw source material
2. Context Window Recovery: If context is compacted mid-task, saved results can be re-read from

   sources/

3. Audit Trail: The

   sources/

   folder provides complete transparency into how information was gathered
4. Reuse Across Sections: Saved research can be referenced by multiple sections without duplicate API calls
5. Cost Efficiency: Avoid redundant API calls by checking

   sources/

   for existing results
6. Peer Review Support: Reviewers can verify the research backing every claim

Logging

When saving research results, always log:

[HH:MM:SS] SAVED: Search results to sources/search_20250217_143000_quantum_computing.md
[HH:MM:SS] SAVED: Deep research report to sources/research_20250217_144000_ev_battery_market.md

Before Making a New Query, Check Sources First

Before calling

parallel_web.py

sources/

ls sources/  # Check existing saved results

Integration with Scientific Writer

Routing Table

parallel_web.py search

python scripts/parallel_web.py search "query" -o sources/search_<topic>.md

parallel_web.py research

python scripts/parallel_web.py research "query" -o sources/research_<topic>.md

parallel_web.py extract

python scripts/parallel_web.py extract "url" -o sources/extract_<source>.md

research_lookup.py

parallel_web.py extract

When Writing Scientific Documents

1. Before writing any section, use

   search

   or

   research

   to gather background information — save results to

   sources/

2. For academic citations, use

   research-lookup

   (which routes academic queries to Perplexity) — save results to

   sources/

3. For citation verification (confirming a specific URL), use

   parallel_web.py extract

   — save results to

   sources/

4. For current market/industry data, use

   parallel_web.py research --model core

   — save results to

   sources/

5. Before any new query, check

   sources/

   for existing results to avoid duplicate API calls

Environment Setup

# Required: Set your Parallel API key
export PARALLEL_API_KEY="your_api_key_here"

# Required Python packages
pip install openai        # For Chat API (search/research)
pip install parallel-web  # For Extract API (verification only)

Get your API key at https://platform.parallel.ai

Error Handling

The script handles errors gracefully and returns structured error responses:

{
  "success": false,
  "error": "Error description",
  "timestamp": "2025-02-14 12:00:00"
}

Common issues:

• PARALLEL_API_KEY not set

  : Set the environment variable

• openai not installed

  : Run

  pip install openai

• parallel-web not installed

  : Run

  pip install parallel-web

  (only needed for extract)

• Rate limit exceeded

  : Wait and retry (default: 300 req/min for Chat API)

Complementary Skills

research-lookup

citation-management

literature-review

scientific-schematics