Using LLMs as standalone modules
You can use our LLM modules on their own.
Text Completion Example
Section titled “Text Completion Example”from llama_index.llms.openai import OpenAI
# non-streamingcompletion = OpenAI().complete("Paul Graham is ")print(completion)
# using streaming endpointfrom llama_index.llms.openai import OpenAI
llm = OpenAI()completions = llm.stream_complete("Paul Graham is ")for completion in completions: print(completion.delta, end="")Chat Example
Section titled “Chat Example”from llama_index.core.llms import ChatMessagefrom 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/