These docs are built from the langchain-parallel repo and have not been verified for accuracy by the LangChain team.
For issues, please open an issue in the langchain-parallel repo.
Refer to the docs for a high-level guide on how to use each module. These reference pages contain auto-generated API documentation for each module, focusing on the "what" rather than the "how" or "why" (i.e. no end-to-end tutorials or conceptual overviews).
LangChain retriever that returns Parallel Search results as Documents.
Drops in to any RAG pipeline that expects a BaseRetriever. Each
Document.page_content is the joined excerpt list from one Pa
Input schema for Parallel Extract Tool.
Parallel Extract Tool.
Calls Parallel's Extract API to pull clean, structured content from web pages. Returns a compact summary string the LLM sees and the full structured response as a tool artifact
Settings for excerpt extraction.
Settings for full content extraction.
Fetch policy for cache vs live content.
Domain allow/deny lists and freshness floor for web research.
Apex-domain semantics: include nature.com, not https://www.nature.com.
Wildcards permitted (e.g. .org).
Webhook config for a FindAll run.
One boolean condition the API uses to filter candidates.
The pair (name, description) names a True/False question the API answers per candidate; only candidates with all conditions True are returned.
One entry in the FindAll exclude_list.
Input schema for :class:ParallelFindAllTool.
Run a Parallel FindAll discovery and return the matched candidates.
FindAll discovers entities from the web that satisfy a natural-language objective plus a set of boolean match conditions. Useful fo
Webhook config for a Parallel monitor.
Per the create-monitor API, the webhook object is {url, event_types}.
The signing secret is configured at the org webhook-endpoint level (in
the Parallel da
Manage scheduled web monitors via the Parallel Monitor API (alpha).
Each method is a thin wrapper around an HTTP call. Returned dicts are the API response bodies as-is.
Input schema for ParallelWeb search tool.
Parallel Search tool with web research capabilities.
This tool calls Parallel's Search API, which streamlines the traditional search -> scrape -> extract pipeline into a single API call. It supports
Parallel Web chat model integration.
This integration connects to Parallel's Chat API, which provides real-time web research capabilities through an OpenAI-compatible interface.
One BYOMCP server description for a Task Run.
Mirrors :class:parallel.types.McpServerParam so callers don't have to
import from the SDK directly.
Input schema for :class:ParallelTaskRunTool.
Run a single Parallel Task synchronously and return the structured result.
This is the agent-friendly path: an LLM calls the tool with input, the
tool blocks until the Parallel Task Run completes,
High-level Runnable for Parallel deep-research tasks.
Defaults to the pro-fast processor -- the -fast variant of
pro ("Exploratory web research") at 2-5x the speed for similar
accuracy. D
Batch task runner backed by the Task Group API.
Use when you have a list of inputs and want them all processed in parallel. Returns a list of result dicts in the same order as the input list.
High-level Runnable for the structured-batch enrichment pattern.
Wraps :class:ParallelTaskGroup with a default_task_spec so callers
can pass a list of records (pydantic instances or dicts) and
Retrieve the Parallel API key from argument or environment variables.
Returns a configured sync OpenAI client for the Chat API.
Returns a configured async OpenAI client for the Chat API.
Returns a configured sync Parallel SDK client.
Returns a configured async Parallel SDK client.
Build a TaskSpecParam dict from pydantic classes / schemas / text.
Mirrors the helper used in https://docs.parallel.ai/task-api/examples/task-enrichment.
Verify a Parallel webhook signature (Standard Webhooks scheme).
Parallel signs webhook payloads using HMAC-SHA256 over
"<webhook-id>.<webhook-timestamp>.<body>", base64-encoded
with padding. The
Extract citations, low-confidence fields, and interaction_id.
Saves Task-API consumers the boilerplate of walking the result shape to pull out per-field citations, find which fields the model wasn'
