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Parallel is a real-time web search and content extraction platform built for LLMs and AI applications.
ParallelSearchTool calls Parallel’s Search API, which collapses the traditional search → scrape → extract pipeline into one call and returns structured, LLM-optimized excerpts.
ParallelSearchTool is the canonical class name. The earlier ParallelWebSearchTool continues to work as an alias for the same class. Looking for a BaseRetriever to drop into a RAG chain instead? See ParallelSearchRetriever.

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Overview

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Integration details

ClassPackageSerializableJS supportPackage latest
ParallelSearchToollangchain-parallel❌❌PyPI - Latest version

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Setup

The integration lives in the langchain-parallel package. 
pip install -U langchain-parallel

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Credentials

Head to Parallel to sign up and generate an API key. Set PARALLEL_API_KEY in your environment: 
import getpass
import os

if not os.environ.get("PARALLEL_API_KEY"):
    os.environ["PARALLEL_API_KEY"] = getpass.getpass("Parallel API key:\n")

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Instantiation

from langchain_parallel import ParallelSearchTool

tool = ParallelSearchTool()

# Or pass an explicit key / override the base URL:
# tool = ParallelSearchTool(
#     api_key="your-api-key",
#     base_url="https://api.parallel.ai",  # default
# )

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Invocation

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Invoke directly with args

The tool requires search_queries (one or more keyword strings). Pair it with an objective for richer relevance ranking, and add domain filtering, fetch policies, and other settings as needed. 
result = tool.invoke(
    {
        "search_queries": [
            "AI breakthroughs 2026",
            "machine learning advances",
            "generative AI news",
        ],
        "objective": "What are the latest developments in AI?",
        "max_results": 8,
        "excerpts": {"max_chars_per_result": 2000},
        "mode": "advanced",
        "source_policy": {
            "include_domains": ["arxiv.org", "nature.com"],
            "exclude_domains": ["reddit.com", "twitter.com"],
        },
        "fetch_policy": {
            "max_age_seconds": 86400,
            "timeout_seconds": 60,
        },
        "include_metadata": True,
    }
)

print(f"Found {len(result['results'])} results")
for r in result["results"][:3]:
    print(r["title"], "—", r["url"])

Found 8 results
Latest AI Developments 2026 — https://arxiv.org/abs/...
...
mode="basic" is the lower-latency setting; mode="advanced" runs a higher-quality search.

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Invoke with a ToolCall

Invoking with a model-generated ToolCall returns a ToolMessage: 
model_generated_tool_call = {
    "args": {
        "search_queries": [
            "climate change initiatives",
            "global climate policy 2026",
        ],
        "objective": "Find recent news about climate change initiatives",
        "max_results": 3,
        "source_policy": {
            "include_domains": ["ipcc.ch", "unfccc.int", "nature.com"],
        },
        "include_metadata": True,
    },
    "id": "call_123",
    "name": tool.name,  # "parallel_web_search"
    "type": "tool_call",
}

result = tool.invoke(model_generated_tool_call)

​
Async usage

async def search_async():
    return await tool.ainvoke(
        {
            "search_queries": ["quantum computing breakthroughs"],
            "objective": "Latest quantum computing breakthroughs",
            "max_results": 5,
        }
    )

result = await search_async()

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Parameters

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Required

  • search_queries: list of keyword strings (3-6 words each works best).

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Optional

  • objective: natural-language description of the retrieval goal.
  • max_results: number of results to return (default 10).
  • excerpts: per-result excerpt settings, e.g. {"max_chars_per_result": 1500}.
  • mode: "basic" (lower latency) or "advanced" (higher quality).
  • source_policy: domain filtering. Accepts a SourcePolicy pydantic model or a raw dict with include_domains / exclude_domains / after_date.
  • fetch_policy: cache control, e.g. {"max_age_seconds": 86400, "timeout_seconds": 60}.
  • max_chars_total: cap on combined excerpt length across all results.
  • client_model / session_id / location: forwarded to Parallel for downstream attribution and personalization.
  • include_metadata: include client-side timing in the response (default True).
  • timeout: per-request timeout in seconds.

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SourcePolicy pydantic model

SourcePolicy mirrors the API’s include_domains / exclude_domains / after_date. Use it for type safety; raw dicts are also accepted. 
from langchain_parallel import SourcePolicy

result = tool.invoke({
    "search_queries": ["renewable energy policy Europe"],
    "source_policy": SourcePolicy(
        include_domains=["europa.eu", "iea.org", "irena.org"],
        exclude_domains=["wikipedia.org", "reddit.com"],
    ),
    "max_results": 15,
})

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Chaining

Bind the tool to any tool-calling chat model and drive an agent with create_agent: 
from langchain.agents import create_agent
from langchain.chat_models import init_chat_model

llm = init_chat_model(model="claude-opus-4-7")
agent = create_agent(model=llm, tools=[tool])

agent.invoke({"messages": [("human", "What are the latest breakthroughs in quantum computing?")]})
ChatParallel itself does not support tool calling. Use it as a research assistant inside a chain, or use the Parallel search/extract tools alongside another tool-calling model.

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Response format

{
    "search_id": "search_abc123...",
    "session_id": "sess_...",
    "results": [
        {
            "url": "https://example.com/page",
            "title": "Page Title",
            "publish_date": "2026-04-01",
            "excerpts": [
                "First relevant excerpt...",
                "Second relevant excerpt...",
            ],
        },
    ],
    "warnings": [...],
    "usage": {...},
    "search_metadata": {  # added by this tool when include_metadata=True (the default)
        "search_duration_seconds": 2.451,
        "search_timestamp": "2026-04-15T10:30:00",
        "actual_results_returned": 5,
    },
}

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API reference

For detailed documentation, head to the ParallelSearchTool API reference or the Parallel Search reference.
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