llama-index-multi-modal-llms-openai
llama-index multi-modal-llms openai integration
Decision gist · record as of 2026-08-14
Yes, if you are already using LlamaIndex and need OpenAI's multi-modal models integrated into your pipeline. The low install friction and permissive MIT license make it straightforward to add. However, the aging maintenance status means you should verify that supported OpenAI models and LlamaIndex versions match your requirements before production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an OpenAI API key configured in your environment and existing LlamaIndex setup with llama-index-core and llama-index-llms-openai installed.
- Low install friction with a pure-Python wheel.
- Maintenance status is aging—last release was 323 days ago—so expect slower updates to track OpenAI API changes.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects.
last release 2025-09-25 (323 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,769,563 downloads/mo, #2,896 on PyPI
Alternatives
Verify before relying
pip install llama-index-multi-modal-llms-openai
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
multi_modal_llm = OpenAIMultiModal()- Which OpenAI models are actually supported by version 0.6.2
- Whether this package automatically tracks breaking changes in llama-index-core or llama-index-llms-openai
- Performance characteristics when processing large images or batches
- Current compatibility with the latest llama-index-core and llama-index-llms-openai releases
What it is and what it does
This package bridges OpenAI's multi-modal capabilities into the LlamaIndex ecosystem. It wraps OpenAI's vision-enabled language models so they can be used as a component within LlamaIndex workflows—allowing you to build applications that reason over both images and text without managing the OpenAI API directly.
The package depends on llama-index-core and llama-index-llms-openai, so it assumes you already have LlamaIndex set up and an OpenAI API key configured. It's designed as a plug-in integration rather than a standalone tool; you use it when you want multi-modal reasoning as part of a larger LlamaIndex pipeline.
Use it for
- Build a document retrieval system that indexes and reasons over PDFs containing both text and diagrams
- Create a visual question-answering application that processes uploaded images alongside text queries
- Analyze screenshots or charts in a larger data pipeline using LlamaIndex orchestration
- Combine multi-modal reasoning with LlamaIndex agents for tasks requiring image understanding
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using LlamaIndex and need OpenAI's multi-modal models integrated into your pipeline.
The low install friction and permissive MIT license make it straightforward to add. However, the aging maintenance status means you should verify that supported OpenAI models and LlamaIndex versions match your requirements before production use.
Install
llama-index-multi-modal-llms-openai on PyPI
Before you install
Low install friction with a pure-Python wheel. Maintenance status is aging—last release was 323 days ago—so expect slower updates to track OpenAI API changes.
Requires an OpenAI API key configured in your environment and existing LlamaIndex setup with llama-index-core and llama-index-llms-openai installed.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects.
Quickstart
pip install llama-index-multi-modal-llms-openai
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
multi_modal_llm = OpenAIMultiModal()Verify before relying
- Which OpenAI models are actually supported by version 0.6.2
- Whether this package automatically tracks breaking changes in llama-index-core or llama-index-llms-openai
- Performance characteristics when processing large images or batches
- Current compatibility with the latest llama-index-core and llama-index-llms-openai releases
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesllama-index-corellama-index-llms-openai |
| Maintenance | Aging 323 days since the last release |
| First released | |
| Downloads | 2,769,563 / month, #2,896 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_multi_modal_llms_openai-0.6.2-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 multi-modal llm integration”
- llama-index-multi-modal-llms-openaiIntegrates OpenAI's multi-modal language models with LlamaIndex,…
- livekit-plugins-openaiIntegrates OpenAI's Realtime, Responses, LLM, TTS, and STT APIs into…
- unitxtUnitxt provides a unified framework for evaluating AI model…
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 llama-index-agent-openai · llama-index-workflows · llama-index-llms-openai-like · llama-index-question-gen-openai · llama-index-readers-confluence · llama-index-embeddings-openai · llama-index-program-openai · llama-index-llms-litellm · llama-index-embeddings-azure-openai · llama-index-llms-vertex