Skills
skills/mvanhorn/clawdbot-skill-parallel/parallel

parallel

Installation
SKILL.md

Parallel.ai - High-Accuracy Web Research Platform

Deep web research platform with 7 APIs built for AI agents. Outperforms Perplexity and Exa on research benchmarks with rich excerpts, citations, and source provenance.

Setup

pip install -r {baseDir}/requirements.txt

Requires PARALLEL_API_KEY environment variable. Get a key at https://platform.parallel.ai

Optional: BROWSERUSE_API_KEY for authenticated page access via browser-use.com (see Authenticated Sources section below).

Security Notes

  • API keys are loaded from environment variables only - never hardcoded in scripts
  • User input is safely escaped before API calls (no JSON injection)
  • Dependencies are pinned in requirements.txt to prevent supply chain attacks
  • When using BROWSERUSE_API_KEY, your key is transmitted to Parallel.ai servers which proxy it to browser-use.com. Both services see your queries and credentials. Only enable this if you understand and accept that data flow.

Search API

POST /v1/search

The primary search interface. Use for most research queries.

Modes

Mode Latency Use Case Tradeoff
one-shot ~3-5s Default, balanced accuracy Best for most queries
fast ~1s Quick lookups, cost-sensitive Lowest latency, may sacrifice depth (added Feb 2026)
agentic ~10-30s Complex multi-hop research Highest accuracy, token-efficient, more expensive

Source Policy

Control which sources are searched using source_policy:

  • Domain include list - restrict to specific domains
  • Domain exclude list - block specific domains
  • after_date freshness filtering - only return results published after a given date

When to use each mode

  • one-shot: Single-topic factual queries, company lookups, person research, current events
  • fast: Simple fact checks, quick lookups where 1-second latency matters, cost-sensitive batch jobs
  • agentic: Questions requiring cross-referencing multiple sources, comparative analysis, claims that need multi-hop verification, complex "why" and "how" questions

Basic search

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

# Fast mode - ~1 second latency (Feb 2026)
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "latest AI news" --mode fast

# Agentic mode - complex multi-hop research, token-efficient
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "compare transformer architectures for long-context tasks" --mode agentic

# Source policy - domain filtering
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "AI regulation" --include-domains "reuters.com,bloomberg.com" --after-date 2026-01-01

# Exclude domains
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "AI safety" --exclude-domains "reddit.com,twitter.com"

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

Example 1: Company research

{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "Anthropic company overview funding valuation" --max-results 8

Sample output:

Search ID: search_abc123

**1. [Anthropic raises $2B Series D at $18B valuation](https://example.com/anthropic-funding)**  (2025-12-15)
   Anthropic, the AI safety company founded by former OpenAI researchers Dario and Daniela Amodei, has closed a $2 billion Series D round led by Lightspeed Venture Partners...

**2. [Anthropic - Company Profile](https://www.crunchbase.com/organization/anthropic)**
   Founded: 2021. Headquarters: San Francisco, CA. Total funding: $7.6B. Key products: Claude AI assistant, Claude API. Investors include Google, Spark Capital, Menlo Ventures...

Usage: search_units: 1, result_count: 8

Example 2: Fact-checking with agentic mode

{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "Is it true that GPT-4 was trained on over 1 trillion parameters? Verify with sources." --mode agentic --max-results 10

Extract API

POST /v1beta/extract

Extract clean, structured content from any URL. Supports JS-rendered pages and PDF extraction.

Parameters

Parameter Required Description
urls[] Yes One or more URLs to extract from
objective No Targeted extraction instruction
mode No excerpts (default) or full_content

Usage

# Extract with relevant excerpts
{baseDir}/.venv/bin/python {baseDir}/scripts/extract.py https://stripe.com/docs/api

# Full content extraction
{baseDir}/.venv/bin/python {baseDir}/scripts/extract.py https://example.com/paper.pdf --full

# Targeted extraction with an objective
{baseDir}/.venv/bin/python {baseDir}/scripts/extract.py https://sec.gov/10-K.htm --objective "Extract risk factors"

# Multiple URLs at once
{baseDir}/.venv/bin/python {baseDir}/scripts/extract.py https://example.com/page1 https://example.com/page2

# JS-rendered page (React/Vue/Angular SPAs)
{baseDir}/.venv/bin/python {baseDir}/scripts/extract.py https://app.example.com/dashboard --full

# JSON output
{baseDir}/.venv/bin/python {baseDir}/scripts/extract.py https://example.com --json

Task API (Deep Research)

POST /v1/tasks/runs

For complex questions that benefit from being broken into sub-queries and synthesized. Supports MCP tool calling, authenticated browsing, SSE streaming, and webhooks.

Processor Tiers

8 tiers from lightweight to maximum depth:

Processor Speed Depth Cost Best for
lite Fastest Minimal Lowest Simple lookups, quick facts
base Fast Shallow Low Basic research queries
core Medium Standard Medium Most research queries (default)
core2x Medium Enhanced Medium-High Detailed analysis
ultra Slow Deep High Reports, multi-hop analysis
ultra2x Slower Very deep Higher Comprehensive research
ultra4x Slow Extensive Very high Exhaustive coverage
ultra8x Slowest Maximum Highest Maximum depth research

Output Modes

Mode Description
auto Parallel chooses best format (default)
json Structured JSON output - supports json_schema for custom schemas
text Markdown with inline citations

Basic usage

# Generate a comprehensive research report
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py --report "Market analysis of the AI code assistant industry in 2025"

# Deep research with specific processor tier
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py "What are the key technical differences between Claude, GPT-4, and Gemini?" --processor ultra

# Use the maximum depth tier
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py "Comprehensive geopolitical analysis of AI chip export controls" --processor ultra8x

# JSON output with schema
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py "List top 5 AI companies" --output-mode json --json-schema '{"companies": [{"name": "string", "valuation": "string"}]}'

# Text output with citations
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py "State of quantum computing 2026" --output-mode text

MCP Tool Calling

Connect up to 10 external MCP servers per task. The task processor can invoke tools from connected servers during research.

# Task with MCP tools
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py "Analyze our Stripe revenue data" --mcp-server "stripe-mcp://localhost:3001"

Authenticated Page Access (Jan 2026)

Use a browser agent to access login-protected content. Requires BROWSERUSE_API_KEY.

export BROWSERUSE_API_KEY="your-browseruse-key"

# Access authenticated pages
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py "Extract migration docs from https://nxp.com/products/K66_180"

Data flow warning: When using authenticated sources, your query and BROWSERUSE_API_KEY flow through: Your machine -> Parallel.ai API -> browser-use.com -> target website. Only use this for non-sensitive queries.

SSE Streaming

Stream real-time progress updates from long-running tasks:

# Stream task progress
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py "Deep market analysis" --processor ultra --stream

Webhooks

Register webhooks for task completion notifications:

  • Event: task_run.status - fired when a task run changes status (running, completed, failed)

Enrichment

Enrich structured data with web research:

# Enrich a company
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py --enrich "company_name=Stripe" --output "founding_year,funding,employee_count,ceo"

# Enrich with domain filtering
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py --enrich "company_name=Anthropic,website=anthropic.com" --output "valuation,investors,products" --include-domains "crunchbase.com,pitchbook.com"

Source Filtering

Control which sources are used for research:

# Only search academic sources
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py "latest research on chain-of-thought prompting" --include-domains "arxiv.org,scholar.google.com,semanticscholar.org,acm.org"

# Exclude social media and forums
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py "AI regulation updates" --exclude-domains "reddit.com,twitter.com,x.com,quora.com"

Example 3: Deep research report

{baseDir}/.venv/bin/python {baseDir}/scripts/task.py --report "State of AI safety research in 2025"

Sample output:

Task: run_xyz789
   Status: completed | Processor: ultra

**Report:**
# State of AI Safety Research in 2025

## Executive Summary
AI safety research has expanded significantly in 2025, with major labs increasing their safety team headcounts by an average of 40%...

**Citations:**
  [safety_research] confidence: high
    - AI Safety Research Landscape 2025: https://example.com/safety-2025
    - Anthropic Constitutional AI v2 Paper: https://arxiv.org/abs/2025.xxxxx

Chat API

POST /v1/chat/completions

OpenAI-compatible chat endpoint with built-in web grounding. Added January 15, 2026.

Research Models

Model TTFT Basis Citations Best for
speed ~3s No Fast conversational responses without citations
lite ~5s Yes Quick research with source attribution
base ~10s Yes Standard research conversations
core ~20s Yes Deep research with comprehensive citations

Features

  • OpenAI-compatible - drop-in replacement using standard chat completions format
  • Web grounding - all models (except speed) include basis citations in responses
  • response_format - supports JSON schema for structured output
  • Streaming - SSE streaming with stream: true

Usage

# Chat with web grounding (uses Python SDK)
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py --chat "What happened in AI this week?" --model base

# Structured JSON response
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py --chat "List the top 3 AI companies by valuation" --model core --response-format json

# Fast response without citations
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py --chat "Explain transformers briefly" --model speed

API format

Standard OpenAI chat completions format:

{
  "model": "base",
  "messages": [{"role": "user", "content": "What is Anthropic's latest funding?"}],
  "stream": true,
  "response_format": {"type": "json_schema", "json_schema": {"name": "result", "schema": {...}}}
}

Response includes basis[] array with source URLs, titles, and confidence scores (except speed model).

FindAll API

POST /v1beta/findall/runs

Entity discovery at web scale. Turns natural language queries into structured datasets.

Generators

Generator Candidates Speed Best for
preview ~10 Fast Quick sampling, testing queries
base ~50 Medium Standard discovery
core ~100 Slow Thorough discovery
pro ~200+ Slowest Comprehensive, exhaustive discovery

4-Step Process

  1. Ingest - submit your natural language query
  2. Create run - the API generates candidate entities
  3. Poll - check status until completed
  4. Retrieve - get matched and enriched entities

Entity Exclusion (Feb 2026)

Prevent duplicates across runs by passing previously discovered entity IDs:

# Exclude entities from a previous run
{baseDir}/.venv/bin/python {baseDir}/scripts/findall.py "AI startups Series A" --exclude-entities "entity_abc,entity_def"

Usage

# Find matching entities
{baseDir}/.venv/bin/python {baseDir}/scripts/findall.py "AI startups that raised Series A in the last 6 months"

# With enrichment fields
{baseDir}/.venv/bin/python {baseDir}/scripts/findall.py "dental practices in Ohio with 4+ star reviews" --enrich "phone,address,rating" --limit 50

# Pro tier for comprehensive discovery
{baseDir}/.venv/bin/python {baseDir}/scripts/findall.py "portfolio companies of Khosla Ventures" --generator pro

# Preview tier for quick sampling (~10 candidates)
{baseDir}/.venv/bin/python {baseDir}/scripts/findall.py "cybersecurity startups" --generator preview

# Check status of a long-running job
{baseDir}/.venv/bin/python {baseDir}/scripts/findall.py --status findall_abc123

# Don't wait, get the ID and check later
{baseDir}/.venv/bin/python {baseDir}/scripts/findall.py "SaaS companies in Europe with 50+ employees" --no-wait

Use Cases

  • Lead generation - find companies matching your ICP
  • Market mapping - discover all players in a segment
  • Competitive landscape - enumerate competitors and their attributes

Example 4: Entity discovery with enrichment

{baseDir}/.venv/bin/python {baseDir}/scripts/findall.py "AI safety research labs" --enrich "funding,employee_count,founded_year" --limit 10

Sample output:

FindAll: findall_abc789
   Status: completed
   Candidates: 10 matched / 47 generated

**Matched Entities:**

**1. Anthropic**
   URL: https://www.anthropic.com
   AI safety company building reliable, interpretable AI systems.
   - funding: $7.6B
   - employee_count: ~1500
   - founded_year: 2021

**2. Redwood Research**
   URL: https://www.redwoodresearch.org
   Non-profit AI alignment research lab focused on mechanistic interpretability.
   - funding: $35M (grants)
   - employee_count: ~30
   - founded_year: 2021

Monitor API

POST /v1alpha/monitors

Scheduled web change tracking. Monitors run at a configured frequency and fire webhooks when events are detected.

Frequency

Supported intervals: 1h, 2h, 4h, 8h, 12h, 1d, 7d, 30d

Features

  • Webhook notifications - event: monitor.event.detected fires when a monitored condition triggers
  • Event simulation (Feb 2026) - test your webhook integrations without waiting for real events
  • Structured outputs (Jan 2026) - use predefined schemas to get structured event data

Usage

# Create a daily monitor
{baseDir}/.venv/bin/python {baseDir}/scripts/monitor.py create "Track AI funding news" --cadence daily

# Hourly monitor with webhook notifications
{baseDir}/.venv/bin/python {baseDir}/scripts/monitor.py create "Alert when AirPods Pro drop below $150" --cadence hourly --webhook https://hooks.example.com/notify

# Monitor with structured output schema
{baseDir}/.venv/bin/python {baseDir}/scripts/monitor.py create "Track competitor pricing changes" --cadence 4h --schema '{"competitor": "string", "old_price": "number", "new_price": "number"}'

# Simulate an event for testing (Feb 2026)
{baseDir}/.venv/bin/python {baseDir}/scripts/monitor.py simulate monitor_abc123

# List all active monitors
{baseDir}/.venv/bin/python {baseDir}/scripts/monitor.py list

# Get events from a monitor
{baseDir}/.venv/bin/python {baseDir}/scripts/monitor.py events monitor_abc123 --lookback 10d

# Delete a monitor
{baseDir}/.venv/bin/python {baseDir}/scripts/monitor.py delete monitor_abc123

Use Cases

  • Competitor tracking - monitor product launches, pricing changes, hiring
  • Price monitoring - track price drops for products or services
  • Regulatory changes - watch for new regulations, policy updates, compliance requirements

Task Group API

POST /v1beta/tasks/groups

Batch up to 1,000 task runs in a single POST. Supports dynamic expansion and SSE streaming.

Features

  • Batch execution - submit up to 1,000 runs per POST
  • Dynamic expansion - add more tasks to an active group while it runs
  • SSE event streaming - real-time completion events for each task in the group

Usage

# Create a task group with multiple queries
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py --group \
  "Research Anthropic funding history" \
  "Research OpenAI funding history" \
  "Research Google DeepMind funding history"

# Task group with specific processor
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py --group --processor core \
  "Market analysis: cloud computing" \
  "Market analysis: edge computing" \
  "Market analysis: quantum computing"

# Stream group completion events
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py --group --stream \
  "Company profile: Stripe" \
  "Company profile: Plaid" \
  "Company profile: Adyen"

# Add tasks to an existing group
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py --group-add group_abc123 \
  "Company profile: Square" \
  "Company profile: Marqeta"

# Check group status
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py --group-status group_abc123

Batch Search

Run multiple queries in parallel for comparison research or bulk fact-checking:

# Run 3 searches in parallel for comparison research
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "Claude 3 capabilities" --json > /tmp/claude.json &
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "GPT-4 capabilities" --json > /tmp/gpt4.json &
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "Gemini Ultra capabilities" --json > /tmp/gemini.json &
wait

For structured batch entity research, use the FindAll API. For batch task execution, use the Task Group API.

Shell Script (parallel.sh)

Lightweight bash wrapper for the Task API. Requires jq and curl.

# General research
{baseDir}/scripts/parallel.sh research "What are the latest developments in AI safety?"

# Company research
{baseDir}/scripts/parallel.sh company "Anthropic"

# Person research
{baseDir}/scripts/parallel.sh person "Dario Amodei"

# Check task status
{baseDir}/scripts/parallel.sh status run_abc123

Citation Formatting

Results include source URLs and titles. Format citations based on your needs:

Inline citations (default)

The output format uses markdown links: **[Title](URL)** with excerpts below each result.

Academic style

When writing reports, reformat results as numbered references:

[1] Author/Source. "Title." URL. Published: Date.
[2] Author/Source. "Title." URL. Published: Date.

Markdown links

For embedding in documents, extract URL and title:

- [Title](URL) - key excerpt
- [Title](URL) - key excerpt

Use --json output and post-process for custom citation formats.

Response Formats

Search API response

  • 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

Task API response

  • run_id - unique task identifier
  • status - completed/failed/running
  • processor - lite/base/core/core2x/ultra/ultra2x/ultra4x/ultra8x
  • output - result content (text or JSON)
  • basis[] - citations with confidence scores

Chat API response

  • Standard OpenAI chat completions format
  • basis[] - source citations (except speed model)

FindAll API response

  • findall_id - unique findall run identifier
  • status - completed/running/failed
  • candidates - matched count / generated count
  • entities[] - matched entities with enrichment fields

Monitor API response

  • monitor_id - unique monitor identifier
  • status - active/paused/deleted
  • events[] - detected events with timestamps

Task Group API response

  • group_id - unique group identifier
  • status - completed/running/partial
  • runs[] - individual task run results

SDK and CLI Reference

Python SDK

pip install parallel-web  # v0.4.2

from parallel import Parallel
client = Parallel(api_key="...")

TypeScript SDK

npm install parallel-web

import { Parallel } from 'parallel-web';
const client = new Parallel({ apiKey: '...' });

CLI

brew install parallel-web/tap/parallel-cli

Vercel AI SDK

npm install @parallel-web/ai-sdk-tools

Error Recovery

Invalid API key

Error: PARALLEL_API_KEY environment variable is required

Fix: Set export PARALLEL_API_KEY="your-key" in your shell profile. Get a key at https://platform.parallel.ai

Rate limits

The API may return 429 errors during heavy usage. Wait 30-60 seconds and retry, or reduce --max-results to lower request weight.

Empty results

If search returns no results:

  1. Broaden your query - remove specific dates or narrow terms
  2. Try a different mode - agentic mode searches more broadly than one-shot
  3. Check if the topic is too recent - very new events may not be indexed yet

Timeout errors

Task API operations (especially ultra8x processor and FindAll pro generator) can take minutes:

{baseDir}/.venv/bin/python {baseDir}/scripts/task.py "complex query" --timeout 600

Or use --no-wait to get the run ID and check status later.

SDK import errors

If from parallel import Parallel fails:

pip install -r {baseDir}/requirements.txt

Example 5: Complete research workflow

Combine multiple Parallel APIs for comprehensive research:

# Step 1: Quick search to scope the topic (fast mode - ~1s)
{baseDir}/.venv/bin/python {baseDir}/scripts/search.py "AI code assistants market 2025" --mode fast --max-results 5

# Step 2: Deep research report (ultra processor)
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py --report "Comprehensive analysis of the AI code assistant market: key players, market size, growth trends, and competitive dynamics"

# Step 3: Find specific companies in the space (FindAll)
{baseDir}/.venv/bin/python {baseDir}/scripts/findall.py "AI code assistant companies" --enrich "funding,product_name,pricing" --limit 20

# Step 4: Extract detailed info from key sources (Extract)
{baseDir}/.venv/bin/python {baseDir}/scripts/extract.py https://example.com/ai-code-tools-report --objective "Extract market size estimates and growth projections"

# Step 5: Batch compare top players (Task Groups)
{baseDir}/.venv/bin/python {baseDir}/scripts/task.py --group --processor core \
  "Detailed profile: GitHub Copilot" \
  "Detailed profile: Cursor" \
  "Detailed profile: Windsurf"

# Step 6: Set up monitoring for ongoing tracking (Monitor)
{baseDir}/.venv/bin/python {baseDir}/scripts/monitor.py create "New AI code assistant launches and funding rounds" --cadence daily

Follow-Up Questions

After receiving search results, consider asking follow-up queries to deepen understanding:

  • "Tell me more about [specific result]" - drill into a particular finding
  • "What are the counterarguments to [claim]?" - get opposing viewpoints
  • "Find primary sources for [excerpt]" - trace claims to original research
  • "How has [topic] changed in the last year?" - temporal analysis
  • "Compare [result A] with [result B]" - comparative analysis

When to Use Parallel vs. Other Tools

Need Best tool
High-accuracy research with citations Parallel (this skill)
OpenAI-compatible chat with web grounding Parallel Chat API
Entity discovery at scale Parallel FindAll API
Batch research (up to 1,000 queries) Parallel Task Groups
X/Twitter social sentiment and trends /search-x skill
Recency-focused research (last 30 days) /last30days skill
Quick web page content Browser/fetch tools
Code search GitHub search, grep

Parallel excels at research tasks requiring accuracy, citations, and cross-referencing. For social media analysis or very recent events (hours-old), consider combining with other tools.

API Reference

  • Docs: https://docs.parallel.ai
  • Platform: https://platform.parallel.ai
  • Python SDK: pip install parallel-web (v0.4.2)
  • TypeScript SDK: npm install parallel-web
  • CLI: brew install parallel-web/tap/parallel-cli
  • Vercel AI SDK: npm install @parallel-web/ai-sdk-tools
Weekly Installs
12
Repository
mvanhorn/clawdb…parallel
GitHub Stars
15
First Seen
Jan 29, 2026
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