liter-llm-calling-llms

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Calling LLMs

Build a ChatCompletionRequest and send it with client.chat(request). Create the client with create_client(...). The model string is provider/model; the prefix selects the backend.

import asyncio, json, os
from liter_llm import create_client
from liter_llm._internal_bindings import ChatCompletionRequest

async def main() -> None:
    client = create_client(api_key=os.environ["OPENAI_API_KEY"])
    request = ChatCompletionRequest.from_json(json.dumps({
        "model": "openai/gpt-4o",
        "messages": [
            {"role": "system", "content": "You are concise."},
            {"role": "user", "content": "Name three Rust crates for HTTP."},
        ],
    }))
    response = await client.chat(request)
    print(response.choices[0].message.content)

asyncio.run(main())

Provider routing

The model string's prefix selects the provider; build a request per backend:

ChatCompletionRequest.from_json('{"model":"anthropic/claude-sonnet-4-20250514","messages":[...]}')
ChatCompletionRequest.from_json('{"model":"gemini/gemini-2.0-flash","messages":[...]}')
ChatCompletionRequest.from_json('{"model":"groq/llama3-70b","messages":[...]}')
ChatCompletionRequest.from_json('{"model":"mistral/mistral-large-latest","messages":[...]}')
ChatCompletionRequest.from_json('{"model":"bedrock/anthropic.claude-v2","messages":[...]}')

Set a full provider/model as model_hint at construction to select the client provider and credential source. Requests without a recognized prefix fall back to that client provider:

client = create_client(api_key=os.environ["OPENAI_API_KEY"], model_hint="openai/gpt-4o")
# the request model can now omit the provider prefix:
request = ChatCompletionRequest.from_json('{"model":"gpt-4o","messages":[...]}')
await client.chat(request)  # routes to OpenAI

Notes

• Keys come from env vars (OPENAI_API_KEY, ANTHROPIC_API_KEY, …); never hardcode them.
• Without a recognized prefix, requests fall back to the client's configured provider; a client without model_hint defaults to OpenAI.
• Environment credentials are selected when constructing the client, not independently for every request. Use a separate client with matching model_hint and credentials for each provider. A prefix changes routing but does not switch the client's API key.
• Python errors are typed exceptions exported from liter_llm: AuthenticationError, RateLimitedError, BadRequestError, ContextWindowExceededError, ContentPolicyError, NotFoundError, ServerError, ServiceUnavailableError, LiterLlmTimeoutError, BudgetExceededError — all subclasses of LiterLlmError.