> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Parallel extract integration
> Integrate with the ParallelExtractTool tool using LangChain Python.
> [Parallel](https://platform.parallel.ai/) is a real-time web search and content extraction platform built for LLMs and AI applications.
`ParallelExtractTool` calls Parallel's [Extract API](https://docs.parallel.ai/extract/extract-quickstart), which returns clean, markdown-formatted content from web pages, with optional focused excerpts driven by a `search_objective`. Pair it with [ParallelSearchTool](/oss/python/integrations/tools/parallel_search) to build a search → extract pipeline.
## Overview
### Integration details
| Class | Package | Serializable | JS support | Package latest |
| :------------------------------------------------------------------------------------------------------------------ | :--------------------------------------------------------------------------------- | :----------: | :--------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [`ParallelExtractTool`](https://reference.langchain.com/python/langchain-parallel/extract_tool/ParallelExtractTool) | [`langchain-parallel`](https://reference.langchain.com/python/langchain-parallel/) | ❌ | ❌ | 
|
## Setup
The integration lives in the `langchain-parallel` package.
```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -U langchain-parallel
```
```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
uv add langchain-parallel
```
### Credentials
Head to [Parallel](https://platform.parallel.ai) to sign up and generate an API key. Set `PARALLEL_API_KEY` in your environment:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import getpass
import os
if not os.environ.get("PARALLEL_API_KEY"):
os.environ["PARALLEL_API_KEY"] = getpass.getpass("Parallel API key:\n")
```
## Instantiation
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_parallel import ParallelExtractTool
tool = ParallelExtractTool()
# Or pass an explicit key, override the base URL, or cap per-URL `full_content` size:
# tool = ParallelExtractTool(
# api_key="your-api-key",
# base_url="https://api.parallel.ai",
# max_chars_per_extract=5000,
# )
```
## Invocation
### Invoke directly with args
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
result = tool.invoke(
{"urls": ["https://en.wikipedia.org/wiki/Artificial_intelligence"]}
)
print(result[0]["title"])
print(result[0]["url"])
print(result[0]["content"][:200], "...")
```
Multiple URLs in a single call:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
result = tool.invoke(
{
"urls": [
"https://en.wikipedia.org/wiki/Machine_learning",
"https://en.wikipedia.org/wiki/Deep_learning",
"https://en.wikipedia.org/wiki/Natural_language_processing",
]
}
)
for item in result:
print(item["title"], "—", item["url"])
```
### Invoke with a ToolCall
Invoking with a model-generated `ToolCall` returns a `ToolMessage`:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
model_generated_tool_call = {
"args": {
"urls": [
"https://en.wikipedia.org/wiki/Climate_change",
"https://en.wikipedia.org/wiki/Renewable_energy",
]
},
"id": "call_123",
"name": tool.name, # "parallel_extract"
"type": "tool_call",
}
result = tool.invoke(model_generated_tool_call)
```
### Async usage
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
async def extract_async():
return await tool.ainvoke(
{
"urls": [
"https://en.wikipedia.org/wiki/Python_(programming_language)",
"https://en.wikipedia.org/wiki/JavaScript",
]
}
)
result = await extract_async()
```
### Focused excerpts
Drive excerpt selection with a `search_objective` (or `search_queries`). Setting `full_content=False` skips the full markdown body and returns only matched excerpts:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
result = tool.invoke(
{
"urls": ["https://en.wikipedia.org/wiki/Artificial_intelligence"],
"search_objective": "What are the main applications and ethical concerns of AI?",
"excerpts": {"max_chars_per_result": 2000},
"full_content": False,
}
)
```
### Fetch policy and full-content sizing
Control caching, timeouts, and the per-URL `full_content` cap independently:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
result = tool.invoke(
{
"urls": ["https://en.wikipedia.org/wiki/Quantum_computing"],
"fetch_policy": {
"max_age_seconds": 86400,
"timeout_seconds": 60,
"disable_cache_fallback": False,
},
"full_content": {"max_chars_per_result": 5000},
}
)
```
**`full_content` precedence.** An explicit `FullContentSettings` (or dict) on the call always wins over the tool-level `max_chars_per_extract`. The latter only applies when you pass `full_content=True` as a plain bool.
### Per-URL error handling
Failed URLs are returned as items with `error_type` set, so partial-success is the default:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
result = tool.invoke(
{
"urls": [
"https://en.wikipedia.org/wiki/Artificial_intelligence",
"https://this-domain-does-not-exist-12345.com/",
]
}
)
for item in result:
if "error_type" in item:
print("failed:", item["url"], "—", item["content"])
else:
print("ok:", item["url"], f"({len(item['content'])} chars)")
```
### Parameters
#### Required
* `urls`: list of URLs to extract.
#### Optional
* `search_objective`: natural-language description that drives excerpt selection.
* `search_queries`: list of keyword strings used together with (or in place of) `search_objective`.
* `excerpts`: per-result excerpt settings. Pass `ExcerptSettings(max_chars_per_result=…)` (or a dict) to control per-result excerpt size; omit for the API default.
* `full_content`: `True` to return full markdown content (sized by the tool-level `max_chars_per_extract`), `False` to skip it, or `FullContentSettings(max_chars_per_result=…)` for fine-grained control.
* `fetch_policy`: cache control, e.g. `{"max_age_seconds": 86400, "timeout_seconds": 60}`.
* `max_chars_total`: cap on combined output length across all URLs.
* `client_model` / `session_id`: forwarded to Parallel for downstream attribution.
## Chaining
Bind the tool to any tool-calling chat model and drive an agent with [`create_agent`](/oss/python/langchain/agents):
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain.agents import create_agent
from langchain.chat_models import init_chat_model
llm = init_chat_model(model="claude-opus-4-8")
agent = create_agent(model=llm, tools=[tool])
agent.invoke({"messages": [("human", "Summarize https://en.wikipedia.org/wiki/Quantum_computing")]})
```
### Search → extract
Hand `ParallelSearchTool` and `ParallelExtractTool` to the same agent. The model uses search to find URLs and extract to drill into the ones it picks.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_parallel import ParallelSearchTool
search = ParallelSearchTool()
extract = ParallelExtractTool()
agent = create_agent(model=llm, tools=[search, extract])
agent.invoke({
"messages": [
("human", "Find a recent peer-reviewed paper on net-energy-gain fusion and summarize it."),
]
})
```
## Response format
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[
{
"url": "https://example.com/article",
"title": "Article Title",
"content": "# Article Title\n\nMain content formatted as markdown...",
"publish_date": "2026-01-15",
"excerpts": ["...", "..."], # if excerpts/search_objective requested
},
# Failed extractions:
{
"url": "https://failed-site.com",
"title": None,
"content": "Error: 404 Not Found",
"error_type": "http_error",
},
]
```
## API reference
For detailed documentation, head to the [`ParallelExtractTool`](https://reference.langchain.com/python/langchain-parallel/extract_tool/ParallelExtractTool) API reference or the [Parallel Extract reference](https://docs.parallel.ai/api-reference/extract/extract).
***
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