Registry / llm-agents / langchain-nebius

langchain-nebius

library0.1.3pypypi✓ verified 22d ago

This package provides LangChain integration for Nebius AI Studio, enabling seamless use of Nebius AI Studio's chat and embedding models within LangChain. It offers classes for chat models (ChatNebius), embedding models (NebiusEmbeddings), and a retriever (NebiusRetriever), facilitating common LLM application patterns like RAG. The current version is 0.1.3.

llm-agents

pip install langchain-nebius

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · LANGCHAIN-NEBIUS

langchain-nebius

llm-agentspythonv0.1.3

Install

9.9s avg

Import

5151ms

Disk

102MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.1.3 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 5.374s · 99.2MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 9.9s · import 4.928s · 108MB

102MB installed

● package 102MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

ChatNebius

✓ from langchain_nebius import ChatNebius

NebiusEmbeddings

✓ from langchain_nebius import NebiusEmbeddings

NebiusRetriever

✓ from langchain_nebius import NebiusRetriever

NebiusRetrievalTool

✓ from langchain_nebius import NebiusRetrievalTool

This example demonstrates how to initialize `ChatNebius` and invoke it with a simple query. Ensure your `NEBIUS_API_KEY` is set as an environment variable for secure credential management. You may need to select a specific model available in Nebius AI Studio.

import os from langchain_nebius import ChatNebius from langchain_core.messages import HumanMessage # Set your Nebius API key as an environment variable # os.environ["NEBIUS_API_KEY"] = "your_nebius_api_key" chat = ChatNebius( api_key=os.environ.get('NEBIUS_API_KEY', ''), model="Qwen/Qwen3-14B", # Choose an available model from Nebius AI Studio temperature=0.6 ) response = chat.invoke([ HumanMessage(content="What is 1 + 1?") ]) print(response.content)

Debug

Known issues

gotchaAPI Key Management: Nebius requires an API key for authentication. It's recommended to set it as an environment variable named `NEBIUS_API_KEY` rather than passing it directly in code for security reasons.

fix

Set `export NEBIUS_API_KEY='your_api_key'` in your environment or use a `.env` file and `dotenv`.

affects: All versions

gotchaLangChain Version Compatibility: As the LangChain ecosystem (especially `langchain-core`) evolves rapidly, ensure your installed `langchain-nebius` version is compatible with your core LangChain packages to avoid import errors or unexpected behavior.

fix

Refer to the `langchain-nebius` GitHub repository or PyPI page for explicit `langchain-core` version requirements. Use `pip install -U langchain-nebius` to get the latest compatible version.

affects: All versions

gotchaModel String Variations: The `model` parameter for `ChatNebius` and `NebiusEmbeddings` expects a specific string identifier. These identifiers may vary or new models may become available. Refer to the Nebius AI Studio documentation for the most current list of supported models.

fix

Consult the official Nebius AI Studio documentation for up-to-date model names and availability. Avoid hardcoding model names if they are subject to frequent changes.

affects: All versions

gotchaRate Limiting and Context Length: Like most LLM providers, Nebius AI Studio may impose rate limits and have maximum context window sizes for its models. Hitting these limits can lead to errors.

fix

Implement retry mechanisms with exponential backoff for API calls. Monitor token usage and truncate input or summarize history if close to context limits. Review Nebius AI Studio's API documentation for specific rate limits and model context window sizes.

affects: All versions

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'langchain_nebius'

The 'langchain-nebius' package is not installed in your Python environment, or your current environment does not have it activated.

fix

Ensure the package is installed in your active Python environment: `pip install langchain-nebius`

AuthenticationError: Invalid credentials for Nebius AI Studio

The Nebius AI Studio API key is either missing, invalid, or incorrectly configured as an environment variable or initialization parameter.

fix

Set the `NEBIUS_API_KEY` environment variable (e.g., `export NEBIUS_API_KEY='your_api_key'`) or pass it directly during initialization: `ChatNebius(api_key='your_api_key', ...)`.

ValueError: Model 'invalid-model-name' not found or unsupported by Nebius AI Studio.

The model string provided to `ChatNebius` or `NebiusEmbeddings` does not match an available or correct model identifier in Nebius AI Studio.

fix

Consult the official Nebius AI Studio documentation for the most current list of supported model names and use a valid identifier, e.g., `ChatNebius(model='Qwen/Qwen3-14B')`.

ImportError: cannot import name 'ChatNebius' from 'langchain_nebius'

This usually indicates a version incompatibility between `langchain-nebius` and other core LangChain packages (`langchain`, `langchain-core`) due to rapid updates in the LangChain ecosystem.

fix

Upgrade `langchain-nebius` and your main `langchain` and `langchain-core` packages to their latest compatible versions: `pip install -U langchain-nebius langchain langchain-core`.

Upgrade

Version history

0.1.3latest on PyPI · released Jun 29, 2025

Audit

Dependencies

langchain-corerequiredCore abstractions for LangChain integrations.

Agent activity

14 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/nebius/langchain-nebius ↗

packagehttps://pypi.org/project/langchain-nebius/ ↗

homepagehttps://nebius.com ↗