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Fireworks AI

info

We support ALL Fireworks AI models, just set fireworks_ai/ as a prefix when sending completion requests

PropertyDetails
DescriptionThe fastest and most efficient inference engine to build production-ready, compound AI systems.
Provider Route on LiteLLMfireworks_ai/
Provider DocFireworks AI ↗
Supported OpenAI Endpoints/chat/completions, /responses, /embeddings, /completions, /audio/transcriptions, /rerank

Overview​

This guide explains how to integrate LiteLLM with Fireworks AI. You can connect to Fireworks AI in three main ways:

  1. Using Fireworks AI serverless models – Easy connection to Fireworks-managed models.
  2. Connecting to a model in your own Fireworks account – Access models that are hosted within your Fireworks account.
  3. Connecting via a direct-route deployment – A more flexible, customizable connection to a specific Fireworks instance.

API Key​

# env variable
os.environ['FIREWORKS_AI_API_KEY']

Sample Usage - Serverless Models​

from litellm import completion
import os

os.environ['FIREWORKS_AI_API_KEY'] = ""
response = completion(
    model="fireworks_ai/glm-5p2", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
)
print(response)

A bare serverless slug like glm-5p2 is expanded to accounts/fireworks/models/glm-5p2 for you, so you can pass either the short slug or the full resource id.

Sample Usage - Serverless Models - Streaming​

from litellm import completion
import os

os.environ['FIREWORKS_AI_API_KEY'] = ""
response = completion(
    model="fireworks_ai/glm-5p2", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
    stream=True
)

for chunk in response:
    print(chunk)

Sample Usage - Models in Your Own Fireworks Account​

from litellm import completion
import os

os.environ['FIREWORKS_AI_API_KEY'] = ""
response = completion(
    model="fireworks_ai/accounts/fireworks/models/YOUR_MODEL_ID", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
)
print(response)

Sample Usage - Direct-Route Deployment​

from litellm import completion
import os

os.environ['FIREWORKS_AI_API_KEY'] = "YOUR_DIRECT_API_KEY"
response = completion(
    model="fireworks_ai/accounts/fireworks/models/qwen2p5-coder-7b#accounts/gitlab/deployments/2fb7764c", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
   api_base="https://gitlab-2fb7764c.direct.fireworks.ai/v1"
)
print(response)

Note: The above is for the chat interface, if you want to use the text completion interface it's model="text-completion-openai/accounts/fireworks/models/qwen2p5-coder-7b#accounts/gitlab/deployments/2fb7764c"

Sample Usage - Routers​

Fireworks routers are served at accounts/fireworks/routers/<router-id> rather than accounts/fireworks/models/<model-id>, so a bare slug alone cannot tell LiteLLM which one you mean. Prefix the slug with routers/ to target a router; LiteLLM expands routers/<id> to accounts/fireworks/routers/<id>. See the Fireworks routers docs for the routers available on your account.

from litellm import completion
import os

os.environ['FIREWORKS_AI_API_KEY'] = ""
response = completion(
    model="fireworks_ai/routers/glm-latest",
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
)
print(response)

The full resource id (fireworks_ai/accounts/fireworks/routers/glm-latest) is still accepted if you prefer to be explicit. Slugs ending in -fast (for example fireworks_ai/glm-5p2-fast) are treated as routers even without the routers/ prefix.

Usage with LiteLLM Proxy​

1. Set Fireworks AI Models on config.yaml​

model_list:
  - model_name: fireworks-glm-5p2
    litellm_params:
      model: fireworks_ai/glm-5p2
      api_key: "os.environ/FIREWORKS_AI_API_KEY"

2. Start Proxy​

litellm --config config.yaml

3. Test it​

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "fireworks-glm-5p2",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'

Responses API​

fireworks_ai/ models on /v1/responses go straight to Fireworks' native https://api.fireworks.ai/inference/v1/responses endpoint, so server-side features such as MCP tools ("type": "mcp"), previous_response_id, and reasoning output items work the same as they do against Fireworks directly

import os
from litellm import responses

os.environ["FIREWORKS_AI_API_KEY"] = "YOUR_API_KEY"

response = responses(
    model="fireworks_ai/accounts/fireworks/models/kimi-k3",
    input="Use the deepwiki MCP server to tell me in one sentence what the BerriAI/litellm repository is.",
    tools=[
        {
            "type": "mcp",
            "server_label": "deepwiki",
            "server_url": "https://mcp.deepwiki.com/mcp",
            "require_approval": "never",
        }
    ],
)
print(response.output)

Multi-turn tool calling works the same way it does against Fireworks directly: send back the function_call_output items together with the previous_response_id Fireworks returned, and Fireworks continues the conversation server-side

developer input items are sent to Fireworks as system messages, since Fireworks' Responses API has no developer role on models such as kimi-k3 and qwen3.8. A model whose chat template needs the system message first (qwen3.8) still rejects a developer item placed after the first input item, the same way it does when called directly

Document Inlining​

LiteLLM supports document inlining for Fireworks AI models. This is useful for models that are not vision models, but still need to parse documents/images/etc.

LiteLLM will add #transform=inline to the url of the image_url, if the model is not a vision model.See Code

from litellm import completion
import os

os.environ["FIREWORKS_AI_API_KEY"] = "YOUR_API_KEY"
os.environ["FIREWORKS_AI_API_BASE"] = "https://audio-prod.api.fireworks.ai/v1"

completion = litellm.completion(
    model="fireworks_ai/accounts/fireworks/models/llama-v3p3-70b-instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://storage.googleapis.com/fireworks-public/test/sample_resume.pdf"
                    },
                },
                {
                    "type": "text",
                    "text": "What are the candidate's BA and MBA GPAs?",
                },
            ],
        }
    ],
)
print(completion)

Disable Auto-add​

If you want to disable the auto-add of #transform=inline to the url of the image_url, set disable_add_transform_inline_image_block to True

litellm.disable_add_transform_inline_image_block = True

Reasoning Effort​

The reasoning_effort parameter is supported on select Fireworks AI models. Supported models include:

from litellm import completion
import os

os.environ["FIREWORKS_AI_API_KEY"] = "YOUR_API_KEY"

response = completion(
    model="fireworks_ai/accounts/fireworks/models/qwen3-8b",
    messages=[
        {"role": "user", "content": "What is the capital of France?"}
    ],
    reasoning_effort="low",
)
print(response)

Supported Models - ALL Fireworks AI Models Supported!​

info

We support ALL Fireworks AI models, just set fireworks_ai/ as a prefix when sending completion requests

Model NameFunction Call
glm-5p2completion(model="fireworks_ai/glm-5p2", messages)
deepseek-v4-procompletion(model="fireworks_ai/deepseek-v4-pro", messages)
kimi-k3completion(model="fireworks_ai/kimi-k3", messages)
qwen3p8-maxcompletion(model="fireworks_ai/qwen3p8-max", messages)
minimax-m3completion(model="fireworks_ai/minimax-m3", messages)
gpt-oss-120bcompletion(model="fireworks_ai/gpt-oss-120b", messages)

The table above is a small selection of popular models. For the full, current list of models and routers, see the Fireworks model library.

Supported Embedding Models​

info

We support ALL Fireworks AI models, just set fireworks_ai/ as a prefix when sending embedding requests

Model NameFunction Call
fireworks_ai/nomic-ai/nomic-embed-text-v1.5response = litellm.embedding(model="fireworks_ai/nomic-ai/nomic-embed-text-v1.5", input=input_text)
fireworks_ai/nomic-ai/nomic-embed-text-v1response = litellm.embedding(model="fireworks_ai/nomic-ai/nomic-embed-text-v1", input=input_text)
fireworks_ai/WhereIsAI/UAE-Large-V1response = litellm.embedding(model="fireworks_ai/WhereIsAI/UAE-Large-V1", input=input_text)
fireworks_ai/thenlper/gte-largeresponse = litellm.embedding(model="fireworks_ai/thenlper/gte-large", input=input_text)
fireworks_ai/thenlper/gte-baseresponse = litellm.embedding(model="fireworks_ai/thenlper/gte-base", input=input_text)

Audio Transcription​

Quick Start​

from litellm import transcription
import os

os.environ["FIREWORKS_AI_API_KEY"] = "YOUR_API_KEY"
os.environ["FIREWORKS_AI_API_BASE"] = "https://audio-prod.api.fireworks.ai/v1"

response = transcription(
    model="fireworks_ai/whisper-v3",
    audio=audio_file,
)

Pass API Key/API Base in .transcription

Rerank​

Quick Start​

from litellm import rerank
import os

os.environ["FIREWORKS_AI_API_KEY"] = "YOUR_API_KEY"

query = "What is the capital of France?"
documents = [
    "Paris is the capital and largest city of France, home to the Eiffel Tower and the Louvre Museum.",
    "France is a country in Western Europe known for its wine, cuisine, and rich history.",
    "The weather in Europe varies significantly between northern and southern regions.",
    "Python is a popular programming language used for web development and data science.",
]

response = rerank(
    model="fireworks_ai/fireworks/qwen3-reranker-8b",
    query=query,
    documents=documents,
    top_n=3,
    return_documents=True,
)
print(response)

Pass API Key/API Base in .rerank

Supported Models​

Model NameFunction Call
fireworks/qwen3-reranker-8brerank(model="fireworks_ai/fireworks/qwen3-reranker-8b", query=query, documents=documents)
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