StarSeaAPI

LlamaIndex

OpenAILike(api_base=...)

LlamaIndex documents an OpenAI-like LLM class for exactly this case. Set api_base and the rest of the pipeline is unchanged.

Code

from llama_index.llms.openai_like import OpenAILike
llm = OpenAILike(api_base="https://starseaapi.com/v1", api_key="sk-your-key",
                 model="glm-5.3-flash", is_chat_model=True)

Quickstart

1. Create an API key — Sign up, then create a token in the console. A key works across the whole catalogue; scope and quota can be restricted per key.
2. Point your client at the endpoint — Set base_url to https://starseaapi.com/v1 and use the key. Any OpenAI-compatible SDK, IDE or desktop client works.
3. Pick a model — Start with a low-cost tier for prototyping. Every id in the catalogue can be swapped without touching code.
4. Watch cost, not just quality — Output tokens are usually priced at a multiple of input, and reasoning models emit thinking tokens. Keep stable prompt prefixes first so caching applies.

Integrations