ChatParallelWeb

Get started using Parallel chat models in LangChain.

Parallel provides real-time web research capabilities through an OpenAI-compatible chat interface, allowing your AI applications to access current information from the web.

API Reference

For detailed documentation of all features and configuration options, head to the ChatParallelWeb API reference.

Overview

Integration details

Class	Package	Local	Serializable	JS/TS Support	Downloads	Latest Version
ChatParallelWeb	langchain-parallel	❌	✅	❌

Model features

Tool calling	Structured output	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
❌	❌	❌	❌	❌	✅	✅	❌	❌

Setup

To access Parallel models you’ll need to install the langchain-parallel integration package and acquire a Parallel API key.

Installation

pip

pip install -U langchain-parallel

Credentials

Head to Parallel to sign up and generate an API key. Once you’ve done this set the PARALLEL_API_KEY environment variable in your environment:

import getpass
import os

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

Instantiation

Now we can instantiate our model object and generate responses. The default model is "speed" which provides fast responses:

from langchain_parallel import ChatParallelWeb

llm = ChatParallelWeb(
    model="speed",
    # temperature=0.7,
    # max_tokens=None,
    # timeout=None,
    # max_retries=2,
    # api_key="...",  # If you prefer to pass api key in directly
    # base_url="https://api.parallel.ai",
    # other params...
)

See the ChatParallelWeb API Reference for the full set of available model parameters.

OpenAI compatibility

Parallel supports many OpenAI-compatible parameters for easy migration (e.g., response_format, tools, top_p), though most are ignored by the Parallel API. See the OpenAI Compatibility section for more details.

---

Invocation

messages = [
    (
        "system",
        "You are a helpful assistant with access to real-time web information.",
    ),
    ("human", "What are the latest developments in AI?"),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content='Here\'s a summary of the latest AI news and breakthroughs as of ...', additional_kwargs={}, response_metadata={'model': 'speed', 'finish_reason': 'stop', 'created': 1764043410}, id='run--3866fa98-6ac9-4585-8d23-99c5542b582b-0')

print(ai_msg.content)

Here's a summary of the latest AI news and breakthroughs as of...

---

Chaining

We can chain our model with a prompt template like so:

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate(
    [
        (
            "system",
            "You are a helpful research assistant with access to real-time web information. "
            "Provide comprehensive answers about {topic} with current data.",
        ),
        ("human", "{question}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "topic": "artificial intelligence",
        "question": "What are the most significant AI breakthroughs in 2025?",
    }
)

AIMessage(content="Based on the provided search results, here's a summary of the significant AI breakthroughs and trends...", additional_kwargs={}, response_metadata={'model': 'speed', 'finish_reason': 'stop', 'created': 1764043419}, id='run--9c521362-6724-4299-9e65-0565ec13d997-0')

---

Streaming

ChatParallelWeb supports streaming responses for real-time output:

for chunk in llm.stream(messages):
    print(chunk.content, end="")

---

Async

You can also use async operations:

# Async invoke
ai_msg = await llm.ainvoke(messages)

# Async streaming
async for chunk in llm.astream(messages):
    print(chunk.content, end="")

---

Token usage

No token usage tracking

Parallel does not currently provide token usage metadata. The usage_metadata field will be None.

ai_msg = llm.invoke(messages)
print(ai_msg.usage_metadata)

None

---

Response metadata

Access response metadata from the API:

ai_msg = llm.invoke(messages)
print(ai_msg.response_metadata)

{'model': 'speed', 'finish_reason': 'stop', 'created': 1703123456}

---

Error handling

The integration provides enhanced error handling for common scenarios:

from langchain_parallel import ChatParallelWeb

try:
    llm = ChatParallelWeb(api_key="invalid-key")
    response = llm.invoke([("human", "Hello")])
except ValueError as e:
    if "Authentication failed" in str(e):
        print("Invalid API key provided")
    elif "Rate limit exceeded" in str(e):
        print("API rate limit exceeded, please try again later")

---

OpenAI compatibility

OpenAI-compatible API

ChatParallelWeb is fully compatible with many OpenAI Chat Completions API parameters, making migration seamless. However, most advanced parameters (like response_format, tools, top_p) are accepted but ignored by the Parallel API.

llm = ChatParallelWeb(
    model="speed",
    # These parameters are accepted but ignored by Parallel
    response_format={"type": "json_object"},
    tools=[{"type": "function", "function": {"name": "example"}}],
    tool_choice="auto",
    top_p=1.0,
    frequency_penalty=0.0,
    presence_penalty=0.0,
    logit_bias={},
    seed=42,
    user="user-123"
)

---

Message handling

The integration automatically handles message formatting and merges consecutive messages of the same type to satisfy API requirements:

from langchain.messages import HumanMessage, SystemMessage

# These consecutive system messages will be automatically merged
messages = [
    SystemMessage("You are a helpful assistant."),
    SystemMessage("Always be polite and concise."),
    HumanMessage("What is the weather like today?")
]

# Automatically merged to single system message before API call
response = llm.invoke(messages)

---

API reference

For detailed documentation of all features and configuration options, head to the ChatParallelWeb API reference or or the Parallel chat API quickstart.

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