llama-index-llms-litellm
llama-index llms litellm integration
Decision gist · record as of 2026-08-14
Yes, if you are building with LlamaIndex and need to support multiple LLM providers without provider-specific code. The low install friction, permissive license, active maintenance status, and lack of known vulnerabilities make it a straightforward choice. Install only if you actually need multi-provider flexibility; single-provider projects may not benefit.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later and valid API keys for the LLM providers you intend to use.
- Low install friction with just two runtime dependencies.
- Marked active with a recent release in 2026, though repository details are not publicly available.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions.
last release 2026-03-20 (147 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 81,251 downloads/mo, #14,240 on PyPI
Alternatives
Verify before relying
pip install llama-index-llms-litellm
from llama_index.llms.litellm import LiteLLM
from llama_index.core.llms import ChatMessage
import os
os.environ["OPENAI_API_KEY"] = "your-key"
message = ChatMessage(role="user", content="Hello")
llm = LiteLLM("gpt-3.5-turbo")
response = llm.chat([message])
print(response)- Whether LiteLLM's full provider roster is accessible through this integration or only a subset
- Performance characteristics and latency overhead of the abstraction layer
- Whether streaming and async features work uniformly across all supported providers
What it is and what it does
This package bridges LiteLLM and LlamaIndex, allowing you to use multiple LLM providers through LlamaIndex's standardized interface. Instead of writing provider-specific code for OpenAI, Cohere, or other services, you instantiate a single LiteLLM object with a model identifier and call the same methods—chat, complete, stream_chat, stream_complete, and async variants—regardless of which backend you're targeting.
The integration handles the translation between LlamaIndex's message and response formats and LiteLLM's provider abstractions. You set API keys as environment variables and switch providers by changing the model string passed to the LiteLLM constructor. It supports both synchronous and asynchronous calls, as well as streaming responses.
Use it for
- Build a RAG pipeline that can swap between OpenAI and Cohere without changing application code.
- Prototype LLM features against multiple providers to compare cost, latency, or output quality.
- Run LlamaIndex agents that fall back to alternative providers if one API is unavailable.
- Implement multi-provider chat applications where users can select their preferred LLM backend.
- Stream LLM responses in a LlamaIndex application without rewriting for each provider's API.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building with LlamaIndex and need to support multiple LLM providers without provider-specific code.
The low install friction, permissive license, active maintenance status, and lack of known vulnerabilities make it a straightforward choice. Install only if you actually need multi-provider flexibility; single-provider projects may not benefit.
Install
llama-index-llms-litellm on PyPI
Before you install
Low install friction with just two runtime dependencies. Marked active with a recent release in 2026, though repository details are not publicly available.
Requires Python 3.10 or later and valid API keys for the LLM providers you intend to use.
License in practice
MIT license permits commercial and private use with minimal restrictions.
Quickstart
pip install llama-index-llms-litellm
from llama_index.llms.litellm import LiteLLM
from llama_index.core.llms import ChatMessage
import os
os.environ["OPENAI_API_KEY"] = "your-key"
message = ChatMessage(role="user", content="Hello")
llm = LiteLLM("gpt-3.5-turbo")
response = llm.chat([message])
print(response)Verify before relying
- Whether LiteLLM's full provider roster is accessible through this integration or only a subset
- Performance characteristics and latency overhead of the abstraction layer
- Whether streaming and async features work uniformly across all supported providers
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 packageslitellmllama-index-core |
| Maintenance | Actively maintained 147 days since the last release |
| First released | |
| Downloads | 81,251 / month, #14,240 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_llms_litellm-0.7.1-py3-none-any.whl
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See also llama-index-llms-langchain · llama-index-llms-openai · llama-index-llms-openai-like · llama-index-llms-google-genai · llama-index-llms-bedrock · llama-index-llms-ollama · pandasai-litellm · llama-index-llms-azure-openai · llama-index-llms-bedrock-converse · any-llm-sdk