Parallel CLI: Agent-Native Web Search, Deep Research, and Enrichment

Parallel CLI wraps the Parallel web platform into a command-line tool designed for agent workflows. If you need structured web search, multi-step deep research, entity discovery, or enrichment of tabular data, and you are working in a terminal or an agent runtime like Hermes, this skill gives you those capabilities without leaving the command line. It is a paid service with a free tier, not a free local tool, and it overlaps with Hermes' native web_search and web_extract skills. Reach for Parallel CLI when the user asks for it by name, or when the task demands capabilities like enrichment, FindAll entity discovery, or monitoring that the native tools do not cover.

What it does

Parallel CLI turns the Parallel platform into a set of composable subcommands. You can search the web, extract clean content from a URL, run deep research jobs that synthesize multiple sources, enrich a CSV or JSON dataset with new columns inferred from web research, discover entities at web scale, and monitor pages for changes over time. Every subcommand supports --json for machine-readable output, --no-wait for async long-running jobs, and --previous-interaction-id for chaining follow-up tasks that reuse earlier context. The tool is built for non-interactive execution, so it fits naturally into Hermes agent workflows.

Before you start

Prerequisites

• A Parallel account. The service has a free tier, but heavy usage requires a paid plan.
• One of the supported platforms: Linux, macOS, or Windows.
• One of the following package managers or runtimes: Homebrew, npm, pip, or a shell that can run a curl pipe.

Installation

Try the least invasive install path available for the environment.

Homebrew

brew install parallel-web/tap/parallel-cli

npm

npm install -g parallel-web-cli

Python package

pip install "parallel-web-tools[cli]"

Standalone installer

curl -fsSL https://parallel.ai/install.sh | bash

If you want an isolated Python install, pipx can also work:

pipx install "parallel-web-tools[cli]"
pipx ensurepath

Authentication

Interactive login:

parallel-cli login

Headless / SSH / CI:

parallel-cli login --device

API key environment variable:

export PARALLEL_API_KEY="***"

Verify current auth status:

parallel-cli auth

If auth requires browser interaction, run with pty=true.

Core rule set

1. Always prefer --json when you need machine-readable output.
2. Prefer explicit arguments and non-interactive flows.
3. For long-running jobs, use --no-wait and then status / poll.
4. Cite only URLs returned by the CLI output.
5. Save large JSON outputs to a temp file when follow-up questions are likely.
6. Use background processes only for genuinely long-running workflows; otherwise run in foreground.
7. Prefer Hermes native tools unless the user wants Parallel specifically or needs Parallel-only workflows.

Quick reference

parallel-cli
├── auth
├── login
├── logout
├── search
├── extract / fetch
├── research run|status|poll|processors
├── enrich run|status|poll|plan|suggest|deploy
├── findall run|ingest|status|poll|result|enrich|extend|schema|cancel
└── monitor create|list|get|update|delete|events|event-group|simulate

Common flags and patterns

Commonly useful flags:

• --json for structured output
• --no-wait for async jobs
• --previous-interaction-id for follow-up tasks that reuse earlier context
• --max-results for search result count
• --mode one-shot|agentic for search behavior
• --include-domains domain1.com,domain2.com
• --exclude-domains domain1.com,domain2.com
• --after-date YYYY-MM-DD

Read from stdin when convenient:

echo "What is the latest funding for Anthropic?" | parallel-cli search - --json
echo "Research question" | parallel-cli research run - --json

Search

Use for current web lookups with structured results.

parallel-cli search "What is Anthropic's latest AI model?" --json
parallel-cli search "SEC filings for Apple" --include-domains sec.gov --json
parallel-cli search "bitcoin price" --after-date 2026-01-01 --max-results 10 --json
parallel-cli search "latest browser benchmarks" --mode one-shot --json
parallel-cli search "AI coding agent enterprise reviews" --mode agentic --json

Useful constraints:

• --include-domains to narrow trusted sources
• --exclude-domains to strip noisy domains
• --after-date for recency filtering
• --max-results when you need broader coverage

If you expect follow-up questions, save output:

parallel-cli search "latest React 19 changes" --json -o /tmp/react-19-search.json

When summarizing results:

• lead with the answer
• include dates, names, and concrete facts
• cite only returned sources
• avoid inventing URLs or source titles

Extraction

Use to pull clean content or markdown from a URL.

parallel-cli extract https://example.com --json
parallel-cli extract https://company.com --objective "Find pricing info" --json
parallel-cli extract https://example.com --full-content --json
parallel-cli fetch https://example.com --json

Use --objective when the page is broad and you only need one slice of information.

Deep research

Use for deeper multi-step research tasks that may take time.

Common processor tiers:

• lite / base for faster, cheaper passes
• core / pro for more thorough synthesis
• ultra for the heaviest research jobs

Synchronous

parallel-cli research run \
  "Compare the leading AI coding agents by pricing, model support, and enterprise controls" \
  --processor core \
  --json

Async launch + poll

parallel-cli research run \
  "Compare the leading AI coding agents by pricing, model support, and enterprise controls" \
  --processor ultra \
  --no-wait \
  --json

parallel-cli research status trun_xxx --json
parallel-cli research poll trun_xxx --json
parallel-cli research processors --json

Context chaining / follow-up

parallel-cli research run "What are the top AI coding agents?" --json
parallel-cli research run \
  "What enterprise controls does the top-ranked one offer?" \
  --previous-interaction-id trun_xxx \
  --json

Recommended Hermes workflow:

1. launch with --no-wait --json
2. capture the returned run/task ID
3. if the user wants to continue other work, keep moving
4. later call status or poll
5. summarize the final report with citations from the returned sources

Enrichment

Use when the user has CSV/JSON/tabular inputs and wants additional columns inferred from web research.

Suggest columns

parallel-cli enrich suggest "Find the CEO and annual revenue" --json

Plan a config

parallel-cli enrich plan -o config.yaml

Inline data

parallel-cli enrich run \
  --data '[{"company": "Anthropic"}, {"company": "Mistral"}]' \
  --intent "Find headquarters and employee count" \
  --json

Non-interactive file run

parallel-cli enrich run \
  --source-type csv \
  --source companies.csv \
  --target enriched.csv \
  --source-columns '[{"name": "company", "description": "Company name"}]' \
  --intent "Find the CEO and annual revenue"

YAML config run

parallel-cli enrich run config.yaml

Status / polling

parallel-cli enrich status <task_group_id> --json
parallel-cli enrich poll <task_group_id> --json

Use explicit JSON arrays for column definitions when operating non-interactively. Validate the output file before reporting success.

FindAll

Use for web-scale entity discovery when the user wants a discovered dataset rather than a short answer.

parallel-cli findall run "Find AI coding agent startups with enterprise offerings" --json
parallel-cli findall run "AI startups in healthcare" -n 25 --json
parallel-cli findall status <run_id> --json
parallel-cli findall poll <run_id> --json
parallel-cli findall result <run_id> --json
parallel-cli findall schema <run_id> --json

This is a better fit than ordinary search when the user wants a discovered set of entities that can be reviewed, filtered, or enriched later.

Monitor

Use for ongoing change detection over time.

parallel-cli monitor list --json
parallel-cli monitor get <monitor_id> --json
parallel-cli monitor events <monitor_id> --json
parallel-cli monitor delete <monitor_id> --json

Creation is usually the sensitive part because cadence and delivery matter:

parallel-cli monitor create --help

Use this when the user wants recurring tracking of a page or source rather than a one-time fetch.

Recommended Hermes usage patterns

Fast answer with citations

1. Run parallel-cli search ... --json
2. Parse titles, URLs, dates, excerpts
3. Summarize with inline citations from the returned URLs only

URL investigation

1. Run parallel-cli extract URL --json
2. If needed, rerun with --objective or --full-content
3. Quote or summarize the extracted markdown

Long research workflow

1. Run parallel-cli research run ... --no-wait --json
2. Store the returned ID
3. Continue other work or periodically poll
4. Summarize the final report with citations

Structured enrichment workflow

1. Inspect the input file and columns
2. Use enrich suggest or provide explicit enriched columns
3. Run enrich run
4. Poll for completion if needed
5. Validate the output file before reporting success

Error handling and exit codes

The CLI documents these exit codes:

• 0 success
• 2 bad input
• 3 auth error
• 4 API error
• 5 timeout

If you hit auth errors:

1. check parallel-cli auth
2. confirm PARALLEL_API_KEY or run parallel-cli login / parallel-cli login --device
3. verify parallel-cli is on PATH

Maintenance

Check current auth / install state:

parallel-cli auth
parallel-cli --help

Update commands:

parallel-cli update
pip install --upgrade parallel-web-tools
parallel-cli config auto-update-check off

Pitfalls

• Do not omit --json unless the user explicitly wants human-formatted output.
• Do not cite sources not present in the CLI output.
• login may require PTY/browser interaction.
• Prefer foreground execution for short tasks; do not overuse background processes.
• For large result sets, save JSON to /tmp/*.json instead of stuffing everything into context.
• Do not silently choose Parallel when Hermes native tools are already sufficient.
• Remember this is a vendor workflow that usually requires account auth and paid usage beyond the free tier.

When not to use it

Do not reach for Parallel CLI for quick one-off lookups. Hermes' native web_search and web_extract skills are faster, free, and require no account setup. Only use Parallel CLI when the user explicitly asks for it, or when the task requires enrichment, FindAll entity discovery, monitoring, or deep research with processor tiers that the native tools do not provide.

Limits and gotchas

• Parallel is a paid service. The free tier exists but is limited. Heavy usage will incur costs.
• The login command may require a browser or PTY, which is not always available in headless or SSH sessions. Use --device or the PARALLEL_API_KEY environment variable for those environments.
• The CLI output is the only source of truth for citations. Do not invent URLs or source titles.
• Large JSON outputs can bloat context. Save them to /tmp/*.json instead.
• The overlap with Hermes native tools means you should not default to Parallel. Let the user's request or the task's specific needs drive the choice.

What pairs with this

Parallel CLI is one of several optional research skills for Hermes Agent. Two related skills are documented alongside it:

• duckduckgo-search: A free, no-auth web search skill for quick lookups.
• mcporter: A skill for importing and exporting data, which can feed into or consume Parallel CLI's enrichment and FindAll outputs.

Choose the skill that matches the task's depth, cost, and authentication requirements.