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LlamaIndex Framework
Component Guides
Models
Llms

Using LLMs as standalone modules

You can use our LLM modules on their own.

from llama_index.llms.openai import OpenAI
# non-streaming
completion = OpenAI().complete("Paul Graham is ")
print(completion)
# using streaming endpoint
from llama_index.llms.openai import OpenAI
llm = OpenAI()
completions = llm.stream_complete("Paul Graham is ")
for completion in completions:
print(completion.delta, end="")
from llama_index.core.llms import ChatMessage
from llama_index.llms.openai import OpenAI
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="What is your name"),
]
resp = OpenAI().chat(messages)
print(resp)

Check out our modules section for usage guides for each LLM.

Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/