ChatFireworks integration

Integrate with the ChatFireworks chat model using LangChain Python.

This doc helps you get started with Fireworks AI chat models. For a list of all models served by Fireworks see the Fireworks docs.

API Reference

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

Overview

Integration details

Class	Package	Serializable	JS/TS Support	Downloads	Version
ChatFireworks	langchain-fireworks	beta	✅ (npm)

Model features

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

Setup

To access Fireworks models you’ll need to create a Fireworks account, get an API key, and install the langchain-fireworks integration package.

Credentials

Head to fireworks.ai to sign up to Fireworks and generate an API key. Once you’ve done this set the FIREWORKS_API_KEY environment variable:

import getpass
import os

if "FIREWORKS_API_KEY" not in os.environ:
    os.environ["FIREWORKS_API_KEY"] = getpass.getpass("Enter your Fireworks API key: ")

To enable automated tracing of your model calls, set your LangSmith API key:

os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

Installation

The LangChain Fireworks integration lives in the langchain-fireworks package:

pip install -qU langchain-fireworks

Instantiation

Now we can instantiate our model object and generate chat completions:

from langchain_fireworks import ChatFireworks

llm = ChatFireworks(
    model="accounts/fireworks/models/kimi-k2-instruct-0905",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # other params...
)

Invocation

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content="J'adore la programmation.", additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 31, 'total_tokens': 41, 'completion_tokens': 10}, 'system_fingerprint': '', 'finish_reason': 'stop', 'logprobs': None, 'model_provider': 'fireworks', 'model_name': 'accounts/fireworks/models/kimi-k2-instruct-0905'}, id='lc_run--a2bdeca3-6394-4c80-97ad-2fc8db9f54bb-0', usage_metadata={'input_tokens': 31, 'output_tokens': 10, 'total_tokens': 41})

print(ai_msg.content)

J'adore la programmation.

API reference

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

---

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Edit this page on GitHub or file an issue.