llama-index-llms-openai
llama-index llms openai integration
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has low installation friction, carries no known vulnerabilities, and uses a permissive MIT license. Install it if you are building with llama-index-core and want to use OpenAI models; it is the standard integration point for that use case. The only gotcha is the requirement for a valid OpenAI API key and Python 3.10 or later.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a valid OpenAI API key set via OPENAI_API_KEY environment variable or passed at instance creation; Python 3.10 or later (requires_python: >=3.10,<4.0).
- Low installation friction with only 2 runtime dependencies.
- Actively maintained with a release 24 days ago.
License · maintenance · safety
MIT (permissive) — MIT license permits free use, modification, and distribution with minimal restrictions, suitable for both open-source and commercial projects.
last release 2026-07-21 (24 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 39,166,216 downloads/mo, #701 on PyPI
Alternatives
Verify before relying
pip install llama-index-llms-openai
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.llms.openai import OpenAI
llm = OpenAI()
resp = llm.complete("Paul Graham is ")
print(resp)- Whether the package supports all current OpenAI models or has model-specific limitations.
- Rate limiting or quota handling behavior when used with high-volume requests.
- Cost implications and billing integration with OpenAI's API pricing.
What it is and what it does
This package provides a bridge between llama-index-core and OpenAI's language models, allowing developers to use OpenAI's models within applications built on llama-index-core. It wraps OpenAI's API behind a standard LLM interface, supporting both synchronous and asynchronous calls, streaming responses, and chat-based interactions.
The integration handles API key management at both the environment and per-instance level, supports model selection, and provides methods for text completion, chat messaging, and streaming variants of both. It depends on llama-index-core for the base LLM abstraction and openai for direct API communication, keeping the dependency footprint minimal.
Use it for
- Build retrieval-augmented generation pipelines using llama-index-core with OpenAI models as the language backend.
- Stream chat responses in real-time applications where immediate feedback is needed.
- Use different OpenAI models or API keys for different LLM instances within the same application.
- Generate text completions for prompts using OpenAI models integrated into a workflow.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has low installation friction, carries no known vulnerabilities, and uses a permissive MIT license. Install it if you are building with llama-index-core and want to use OpenAI models; it is the standard integration point for that use case. The only gotcha is the requirement for a valid OpenAI API key and Python 3.10 or later.
Install
llama-index-llms-openai on PyPI
Before you install
Low installation friction with only 2 runtime dependencies. Actively maintained with a release 24 days ago.
Requires a valid OpenAI API key set via OPENAI_API_KEY environment variable or passed at instance creation; Python 3.10 or later (requires_python: >=3.10,<4.0).
License in practice
MIT license permits free use, modification, and distribution with minimal restrictions, suitable for both open-source and commercial projects.
Quickstart
pip install llama-index-llms-openai
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.llms.openai import OpenAI
llm = OpenAI()
resp = llm.complete("Paul Graham is ")
print(resp)
Verify before relying
- Whether the package supports all current OpenAI models or has model-specific limitations.
- Rate limiting or quota handling behavior when used with high-volume requests.
- Cost implications and billing integration with OpenAI's API pricing.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesllama-index-coreopenai |
| Maintenance | Actively maintained 24 days since the last release |
| First released | |
| Downloads | 39,166,216 / month, #701 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_llms_openai-0.7.10-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “openai llm integration”
- llama-index-llms-openaiIntegrates OpenAI's language models into applications using…
- llm-openai-pluginA plugin that extends LLM to access OpenAI models via the Responses…
- llama-index-llms-openai-likeProvides a thin wrapper to use OpenAI-compatible APIs (including…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also ai21 · llama-index-llms-azure-openai · llama-index-llms-google-genai · llama-index-llms-litellm · llama-index-llms-ollama · open-interpreter · llama-index-llms-bedrock · llama-index-llms-anthropic · llama-index-llms-openai-like · llama-index-llms-bedrock-converse