> ## Documentation Index > Fetch the complete documentation index at: https://docs.fireworks.ai/llms.txt > Use this file to discover all available pages before exploring further. # OpenAI compatibility You can use the [OpenAI Python client library](https://github.com/openai/openai-python) to interact with Fireworks. This makes migration of existing applications already using OpenAI particularly easy. For Anthropic SDK support, see [Anthropic compatibility](/tools-sdks/anthropic-compatibility). ## Specify endpoint and API key ### Using the OpenAI client You can use the OpenAI client by initializing it with your Fireworks configuration: ```python theme={null} from openai import OpenAI # Initialize with Fireworks parameters client = OpenAI( base_url="https://api.fireworks.ai/inference/v1", api_key="", ) ``` You can also use environment variables with the client: ```python theme={null} import os from openai import OpenAI # Initialize using environment variables client = OpenAI( base_url=os.environ.get("OPENAI_API_BASE", "https://api.fireworks.ai/inference/v1"), api_key=os.environ.get("OPENAI_API_KEY"), # Set to your Fireworks API key ) ``` ### Using environment variables ```shell theme={null} export OPENAI_API_BASE="https://api.fireworks.ai/inference/v1" export OPENAI_API_KEY="" ``` ### Alternative approach ```python theme={null} import openai # warning: it has a process-wide effect openai.api_base = "https://api.fireworks.ai/inference/v1" openai.api_key = "" ``` ## Usage Use OpenAI's SDK how you'd normally would. Just ensure that the `model` parameter refers to one of [Fireworks models](https://fireworks.ai/models). ### Completion Simple completion API that doesn't modify provided prompt in any way: ```python theme={null} from openai import OpenAI client = OpenAI( base_url="https://api.fireworks.ai/inference/v1", api_key="", ) completion = client.completions.create( model="accounts/fireworks/models/llama-v3p1-8b-instruct", prompt="The quick brown fox", ) print(completion.choices[0].text) ``` ### Chat Completion Works best for models fine-tuned for conversation (e.g. llama\*-chat variants): ```python theme={null} from openai import OpenAI client = OpenAI( base_url="https://api.fireworks.ai/inference/v1", api_key="", ) chat_completion = client.chat.completions.create( model="accounts/fireworks/models/llama-v3p1-8b-instruct", messages=[ { "role": "system", "content": "You are a helpful assistant.", }, { "role": "user", "content": "Say this is a test", }, ], ) print(chat_completion.choices[0].message.content) ``` ## Fine-tuning compatibility Fireworks fine-tuning uses the same **OpenAI-compatible chat completion format** for training data. If you have datasets formatted for OpenAI SFT, they work on Fireworks with no conversion required — the same `messages` array with `role`, `content`, `tool_calls`, and `weight` fields. Fireworks also supports additional features in the training schema: * **Thinking traces** via `reasoning_content` on assistant messages (for models like DeepSeek R1 and Qwen3) * **Per-message weights** to control which turns the model learns from * **Per-sample weights** for weighted training * **Vision inputs** using the same OpenAI-compatible multimodal content format To get started with fine-tuning, see the [Supervised Fine-Tuning guide](/fine-tuning/fine-tuning-models) or the [Fine-Tuning overview](/fine-tuning/finetuning-intro). ## API compatibility ### Differences The following options have minor differences: * `max_tokens`: behaves differently if the model context length is exceeded. If the length of `prompt` or `messages` plus `max_tokens` is higher than the model's context window, `max_tokens` will be adjusted lower accordingly. OpenAI returns an invalid request error in this situation. Control this behavior with the `context_length_exceeded_behavior` parameter: * `truncate` (default): Automatically adjusts `max_tokens` to fit within the context window * `error`: Returns an error like OpenAI does ### Token usage for streaming responses OpenAI API returns usage stats (number of tokens in prompt and completion) for non-streaming responses but doesn't for the streaming ones (see [forum post](https://community.openai.com/t/chat-completion-stream-api-token-usage/352964)). Fireworks API returns usage stats in both cases. For streaming responses, the `usage` field is returned in the very last chunk on the response (i.e. the one having `finish_reason` set). For example: ```bash cURL theme={null} curl --request POST \ --url https://api.fireworks.ai/inference/v1/completions \ --header "accept: application/json" \ --header "authorization: Bearer $API_KEY" \ --header "content-type: application/json" \ --data '{"model": "accounts/fireworks/models/starcoder-16b-w8a16", "prompt": "def say_hello_world():", "max_tokens": 100, "stream": true}' ``` ``` data: {..., "choices":[{"text":"\n print('Hello,","index":0,"finish_reason":null,"logprobs":null}],"usage":null} data: {..., "choices":[{"text":" World!')\n\n\n","index":0,"finish_reason":null,"logprobs":null}],"usage":null} data: {..., "choices":[{"text":"say_hello_","index":0,"finish_reason":null,"logprobs":null}],"usage":null} data: {..., "choices":[{"text":"world()\n","index":0,"finish_reason":"stop","logprobs":null}],"usage":{"prompt_tokens":7,"total_tokens":24,"completion_tokens":17}} data: [DONE] ``` Note, that if you're using OpenAI SDK, they `usage` field won't be listed in the SDK's structure definition. But it can be accessed directly. For example: ```python Python theme={null} for chunk in client.chat.completions.create(stream=True, ...): if chunk.usage: # Available in final chunk print(f"Tokens: {chunk.usage.total_tokens}") ``` ```typescript TypeScript theme={null} for await (const chunk of await openai.chat.completions.create(...)) { console.log((chunk as any).usage); } ```