Serverless Quickstart - Fireworks AI Docs

Serverless is the fastest way to get started with using open models. This quickstart will help you make your first API call in minutes.

Step 1: Create and export an API key

Before you begin, create an API key in the Fireworks dashboard. Click Create API key and store it in a safe location. Once you have your API key, export it as an environment variable in your terminal:

macOS / Linux
Windows

export FIREWORKS_API_KEY="your_api_key_here"

setx FIREWORKS_API_KEY "your_api_key_here"

Step 2: Make your first Serverless API call

Install the Fireworks Python SDK:

The SDK is currently in alpha. Use the --pre flag when installing to get the latest version.

pip install --pre fireworks-ai

poetry add --pre fireworks-ai

uv add --pre fireworks-ai

Then make your first Serverless API call:

from fireworks import Fireworks

client = Fireworks()

response = client.chat.completions.create(
  model="accounts/fireworks/models/deepseek-v3p1",
  messages=[{
    "role": "user",
    "content": "Say hello in Spanish",
  }],
)

print(response.choices[0].message.content)

Fireworks provides an OpenAI compatible endpoint. Install the OpenAI Python SDK:

pip install openai

Then make your first Serverless API call:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference/v1"
)

response = client.chat.completions.create(
    model="accounts/fireworks/models/deepseek-v3p1",
    messages=[{
        "role": "user",
        "content": "Say hello in Spanish",
    }],
)

print(response.choices[0].message.content)

Fireworks provides an Anthropic compatible endpoint. Install the Anthropic Python SDK:

pip install anthropic

Then make your first Serverless API call:

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference"
)

response = client.messages.create(
    model="accounts/fireworks/models/deepseek-v3p1",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": "Say hello in Spanish",
    }],
)

print(response.content[0].text)

Fireworks provides an OpenAI compatible endpoint. Install the OpenAI JavaScript / TypeScript SDK:

npm install openai

Then make your first Serverless API call:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference/v1",
});

const response = await client.chat.completions.create({
  model: "accounts/fireworks/models/deepseek-v3p1",
  messages: [
    {
      role: "user",
      content: "Say hello in Spanish",
    },
  ],
});

console.log(response.choices[0].message.content);

Fireworks provides an Anthropic compatible endpoint. Install the Anthropic JavaScript / TypeScript SDK:

npm install @anthropic-ai/sdk

Then make your first Serverless API call:

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference",
});

const response = await client.messages.create({
  model: "accounts/fireworks/models/deepseek-v3p1",
  max_tokens: 1024,
  messages: [
    {
      role: "user",
      content: "Say hello in Spanish",
    },
  ],
});

console.log(response.content[0].text);

curl https://api.fireworks.ai/inference/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $FIREWORKS_API_KEY" \
  -d '{
    "model": "accounts/fireworks/models/deepseek-v3p1",
    "messages": [
      {
        "role": "user",
        "content": "Say hello in Spanish"
      }
    ]
  }'

You should see a response like: "¡Hola!"

For Priority tier (service_tier: "priority") and Fast, see Serverless Serving Paths.

Common use cases

Streaming responses

Stream responses token-by-token for a better user experience:

from fireworks import Fireworks

client = Fireworks()

stream = client.chat.completions.create(
  model="accounts/fireworks/models/deepseek-v3p1",
  messages=[{"role": "user", "content": "Tell me a short story"}],
  stream=True
)

for chunk in stream:
  if chunk.choices[0].delta.content:
    print(chunk.choices[0].delta.content, end="")

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference/v1"
)

stream = client.chat.completions.create(
    model="accounts/fireworks/models/deepseek-v3p1",
    messages=[{"role": "user", "content": "Tell me a short story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference"
)

with client.messages.stream(
    model="accounts/fireworks/models/deepseek-v3p1",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Tell me a short story"}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference/v1",
});

const stream = await client.chat.completions.create({
  model: "accounts/fireworks/models/deepseek-v3p1",
  messages: [{ role: "user", content: "Tell me a short story" }],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || "");
}

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference",
});

const stream = client.messages.stream({
  model: "accounts/fireworks/models/deepseek-v3p1",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Tell me a short story" }],
});

stream.on("text", (text) => {
  process.stdout.write(text);
});

await stream.finalMessage();

curl https://api.fireworks.ai/inference/v1/chat/completions \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $FIREWORKS_API_KEY" \
    -d '{
    "model": "accounts/fireworks/models/deepseek-v3p1",
    "messages": [
        {
        "role": "user",
        "content": "Tell me a short story"
        }
    ],
    "stream": true
    }'

Function calling

Connect your models to external tools and APIs:

from fireworks import Fireworks

client = Fireworks()

response = client.chat.completions.create(
    model="accounts/fireworks/models/kimi-k2-instruct-0905",
    messages=[
        {"role": "user", "content": "What's the weather in Paris?"}
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get the current weather for a location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "City name, e.g. San Francisco",
                        }
                    },
                    "required": ["location"],
                },
            },
        },
    ],
)

print(response.choices[0].message.tool_calls)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference/v1",
)

response = client.chat.completions.create(
    model="accounts/fireworks/models/kimi-k2-instruct-0905",
    messages=[
        {"role": "user", "content": "What's the weather in Paris?"}
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get the current weather for a location",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "City name, e.g. San Francisco",
                        }
                    },
                    "required": ["location"],
                },
            },
        },
    ],
)

print(response.choices[0].message.tool_calls)

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference"
)

response = client.messages.create(
    model="accounts/fireworks/models/kimi-k2-instruct-0905",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "What's the weather in Paris?"}
    ],
    tools=[
        {
            "name": "get_weather",
            "description": "Get the current weather for a location",
            "input_schema": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g. San Francisco",
                    }
                },
                "required": ["location"],
            },
        },
    ],
)

for block in response.content:
    if block.type == "tool_use":
        print(f"Tool: {block.name}, Input: {block.input}")

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference/v1",
});

const tools = [
  {
    type: "function",
    function: {
      name: "get_weather",
      description: "Get the current weather for a location",
      parameters: {
        type: "object",
        properties: {
          location: {
            type: "string",
            description: "City name, e.g. San Francisco",
          },
        },
        required: ["location"],
      },
    },
  },
];

const response = await client.chat.completions.create({
  model: "accounts/fireworks/models/kimi-k2-instruct-0905",
  messages: [{ role: "user", content: "What's the weather in Paris?" }],
  tools: tools,
});

console.log(response.choices[0].message.tool_calls);

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference",
});

const response = await client.messages.create({
  model: "accounts/fireworks/models/kimi-k2-instruct-0905",
  max_tokens: 1024,
  messages: [{ role: "user", content: "What's the weather in Paris?" }],
  tools: [
    {
      name: "get_weather",
      description: "Get the current weather for a location",
      input_schema: {
        type: "object",
        properties: {
          location: {
            type: "string",
            description: "City name, e.g. San Francisco",
          },
        },
        required: ["location"],
      },
    },
  ],
});

for (const block of response.content) {
  if (block.type === "tool_use") {
    console.log(`Tool: ${block.name}, Input:`, block.input);
  }
}

curl https://api.fireworks.ai/inference/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $FIREWORKS_API_KEY" \
  -d '{
    "model": "accounts/fireworks/models/kimi-k2-instruct-0905",
    "messages": [
      {
        "role": "user",
        "content": "What'\''s the weather in Paris?"
      }
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_weather",
          "description": "Get the current weather for a location",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {
                "type": "string",
                "description": "City name, e.g. San Francisco"
              }
            },
            "required": ["location"]
          }
        }
      }
    ]
  }'

Learn more about function calling →

Structured outputs (JSON mode)

Get reliable JSON responses that match your schema:

from fireworks import Fireworks

client = Fireworks()

response = client.chat.completions.create(
  model="accounts/fireworks/models/deepseek-v3p1",
  messages=[
    {
      "role": "user",
      "content": "Extract the name and age from: John is 30 years old",
    }
  ],
  response_format={
    "type": "json_schema",
    "json_schema": {
      "name": "person",
      "schema": {
        "type": "object",
        "properties": {
          "name": { "type": "string" },
          "age": { "type": "number" }
        },
        "required": ["name", "age"],
      },
    },
  },
)

print(response.choices[0].message.content)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference/v1",
)

response = client.chat.completions.create(
    model="accounts/fireworks/models/deepseek-v3p1",
    messages=[
        {
            "role": "user",
            "content": "Extract the name and age from: John is 30 years old",
        }
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "person",
            "schema": {
                "type": "object",
                "properties": {"name": {"type": "string"}, "age": {"type": "number"}},
                "required": ["name", "age"],
            },
        },
    },
)

print(response.choices[0].message.content)

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference"
)

response = client.messages.create(
    model="accounts/fireworks/models/deepseek-v3p1",
    max_tokens=1024,
    output_config={
        "format": {
            "type": "json_schema",
            "schema": {
                "type": "object",
                "properties": {
                    "name": { "type": "string" },
                    "age": { "type": "number" }
                },
                "required": ["name", "age"],
            },
        }
    },
    messages=[
        {
            "role": "user",
            "content": "Extract the name and age from: John is 30 years old",
        }
    ],
)

print(response.content[0].text)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference/v1",
});

const response = await client.chat.completions.create({
  model: "accounts/fireworks/models/deepseek-v3p1",
  messages: [
    {
      role: "user",
      content: "Extract the name and age from: John is 30 years old",
    },
  ],
  response_format: {
    type: "json_schema",
    json_schema: {
      name: "person",
      schema: {
        type: "object",
        properties: {
          name: { type: "string" },
          age: { type: "number" },
        },
        required: ["name", "age"],
      },
    },
  },
});

console.log(response.choices[0].message.content);

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference",
});

const response = await client.messages.create({
  model: "accounts/fireworks/models/deepseek-v3p1",
  max_tokens: 1024,
  output_config: {
    format: {
      type: "json_schema",
      schema: {
        type: "object",
        properties: {
          name: { type: "string" },
          age: { type: "number" },
        },
        required: ["name", "age"],
      },
    },
  },
  messages: [
    {
      role: "user",
      content: "Extract the name and age from: John is 30 years old",
    },
  ],
});

console.log(response.content[0].text);

curl https://api.fireworks.ai/inference/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $FIREWORKS_API_KEY" \
  -d '{
    "model": "accounts/fireworks/models/deepseek-v3p1",
    "messages": [
      {
        "role": "user",
        "content": "Extract the name and age from: John is 30 years old"
      }
    ],
    "response_format": {
      "type": "json_schema",
      "json_schema": {
        "name": "person",
        "schema": {
          "type": "object",
          "properties": {
            "name": {
              "type": "string"
            },
            "age": {
              "type": "number"
            }
          },
          "required": ["name", "age"]
        }
      }
    }
  }'

Learn more about structured outputs →

Reasoning

Some models support reasoning, where the model shows its thought process before giving the final answer:

from fireworks import Fireworks

client = Fireworks()

response = client.chat.completions.create(
    model="accounts/fireworks/models/glm-5p2",
    messages=[
        {"role": "user", "content": "What is 25 * 37? Show your work."}
    ],
    reasoning_effort="medium",
)

msg = response.choices[0].message
if msg.reasoning_content:
    print("Reasoning:", msg.reasoning_content)
print("Answer:", msg.content)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference/v1",
)

response = client.chat.completions.create(
    model="accounts/fireworks/models/glm-5p2",
    messages=[
        {"role": "user", "content": "What is 25 * 37? Show your work."}
    ],
    extra_body={"reasoning_effort": "medium"},
)

msg = response.choices[0].message
# Reasoning content is returned in a separate field
reasoning = getattr(msg, "reasoning_content", None)
if reasoning is None and hasattr(msg, "model_extra"):
    reasoning = msg.model_extra.get("reasoning_content")

if reasoning:
    print("Reasoning:", reasoning)
print("Answer:", msg.content)

The Anthropic SDK uses the thinking parameter to enable reasoning:

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference"
)

response = client.messages.create(
    model="accounts/fireworks/models/glm-5p2",
    max_tokens=16000,
    thinking={"type": "enabled", "budget_tokens": 4096},
    messages=[
        {"role": "user", "content": "What is 25 * 37? Show your work."}
    ],
)

for block in response.content:
    if block.type == "thinking":
        print("Thinking:", block.thinking)
    elif block.type == "text":
        print("Answer:", block.text)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference/v1",
});

const response = await client.chat.completions.create({
  model: "accounts/fireworks/models/glm-5p2",
  messages: [
    { role: "user", content: "What is 25 * 37? Show your work." },
  ],
  reasoning_effort: "medium",
});

const msg = response.choices[0].message;
if (msg.reasoning_content) {
  console.log("Reasoning:", msg.reasoning_content);
}
console.log("Answer:", msg.content);

The Anthropic SDK uses the thinking parameter to enable reasoning:

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference",
});

const response = await client.messages.create({
  model: "accounts/fireworks/models/glm-5p2",
  max_tokens: 16000,
  thinking: { type: "enabled", budget_tokens: 4096 },
  messages: [
    { role: "user", content: "What is 25 * 37? Show your work." },
  ],
});

for (const block of response.content) {
  if (block.type === "thinking") {
    console.log("Thinking:", block.thinking);
  } else if (block.type === "text") {
    console.log("Answer:", block.text);
  }
}

curl https://api.fireworks.ai/inference/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $FIREWORKS_API_KEY" \
  -d '{
    "model": "accounts/fireworks/models/glm-5p2",
    "messages": [
      {
        "role": "user",
        "content": "What is 25 * 37? Show your work."
      }
    ],
    "reasoning_effort": "medium"
  }'

Learn more about reasoning →

Vision models

Analyze images with vision-language models:

from fireworks import Fireworks

client = Fireworks()

response = client.chat.completions.create(
  model="accounts/fireworks/models/kimi-k2p6",
  messages=[
    {
      "role": "user",
      "content": [
        {"type": "text", "text": "What's in this image?"},
        {
          "type": "image_url",
          "image_url": {
            "url": "https://storage.googleapis.com/fireworks-public/image_assets/fireworks-ai-wordmark-color-dark.png"
          },
        },
      ],
    }
  ],
)

print(response.choices[0].message.content)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference/v1"
)

response = client.chat.completions.create(
    model="accounts/fireworks/models/kimi-k2p6",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://storage.googleapis.com/fireworks-public/image_assets/fireworks-ai-wordmark-color-dark.png"
                    },
                },
            ],
        }
    ],
)

print(response.choices[0].message.content)

The Anthropic SDK uses its native image format with type: "image" and a source object:

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference"
)

response = client.messages.create(
    model="accounts/fireworks/models/kimi-k2p6",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image",
                    "source": {
                        "type": "url",
                        "url": "https://storage.googleapis.com/fireworks-public/image_assets/fireworks-ai-wordmark-color-dark.png",
                    },
                },
            ],
        }
    ],
)

for block in response.content:
    if block.type == "text":
        print(block.text)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference/v1",
});

const response = await client.chat.completions.create({
  model: "accounts/fireworks/models/kimi-k2p6",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "What's in this image?" },
        {
          type: "image_url",
          image_url: {
            url: "https://storage.googleapis.com/fireworks-public/image_assets/fireworks-ai-wordmark-color-dark.png",
          },
        },
      ],
    },
  ],
});

console.log(response.choices[0].message.content);

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference",
});

const response = await client.messages.create({
  model: "accounts/fireworks/models/kimi-k2p6",
  max_tokens: 1024,
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "What's in this image?" },
        {
          type: "image",
          source: {
            type: "url",
            url: "https://storage.googleapis.com/fireworks-public/image_assets/fireworks-ai-wordmark-color-dark.png",
          },
        },
      ],
    },
  ],
});

for (const block of response.content) {
  if (block.type === "text") {
    console.log(block.text);
  }
}

curl https://api.fireworks.ai/inference/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $FIREWORKS_API_KEY" \
  -d '{
    "model": "accounts/fireworks/models/kimi-k2p6",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What'\''s in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://storage.googleapis.com/fireworks-public/image_assets/fireworks-ai-wordmark-color-dark.png"
            }
          }
        ]
      }
    ]
  }'

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