langchain-nvidia-ai-endpoints
An integration package connecting NVIDIA AI Endpoints and LangChain
Decision gist · record as of 2026-08-14
Yes. The package has low install friction, active maintenance, no known vulnerabilities, permissive MIT licensing, and integrates seamlessly into LangChain workflows. It is worth installing if you need access to NVIDIA Foundation Models (especially Nemotron) or want the option to run models on-premises via NIM.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA_API_KEY environment variable set to a valid key from https://build.nvidia.com/ (format: nvapi-*), or a running NIM container endpoint if using self-hosted models.
- Low friction installation with three runtime dependencies (aiohttp, langchain-core, requests).
- Package is actively maintained with recent commits and no known vulnerabilities.
License · maintenance · safety
MIT (permissive) — MIT license permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2026-07-02 (43 days) · last repo commit 2026-08-11 · 210 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 818,671 downloads/mo, #4,979 on PyPI
Alternatives
Verify before relying
pip install langchain-nvidia-ai-endpoints
from langchain_nvidia_ai_endpoints import ChatNVIDIA
llm = ChatNVIDIA(model="nvidia/nemotron-3-super-120b-a12b")
result = llm.invoke("Write a ballad about LangChain.")
print(result.content)- Whether the package supports all NVIDIA Foundation Models listed in the API Catalog or only a subset
- Performance characteristics and latency expectations when using the NVIDIA API Catalog versus self-hosted NIM
- Cost implications of using the NVIDIA API Catalog endpoints
What it is and what it does
This package bridges LangChain and NVIDIA's AI infrastructure, letting you use NVIDIA Foundation Models—particularly Nemotron and other open models—as chat and embedding providers. It works by connecting to either live endpoints on the NVIDIA API Catalog (cloud-hosted on DGX infrastructure) or to self-hosted NIM microservices running in containers on your own infrastructure.
You instantiate a ChatNVIDIA object with a model name, then use standard LangChain interfaces: invoke, stream, batch, and their async variants all work natively. The package handles authentication via an NVIDIA_API_KEY environment variable and exposes available_models to discover which models your credentials can access. It integrates with LangChain's prompt templates and output parsers, so you can build chains and agents using NVIDIA models as the LLM backbone.
Use it for
- Build agentic AI workflows using Nemotron's reasoning and tool-calling capabilities within LangChain
- Generate code using specialized models like meta/codellama-70b or google/codegemma-7b through LangChain chains
- Process multimodal inputs (text and images) with models like nvidia/neva-22b for reasoning tasks
- Stream real-time responses from NVIDIA models in LangChain applications without blocking
- Run inference on-premises using self-hosted NIM containers for full IP and customization control
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package has low install friction, active maintenance, no known vulnerabilities, permissive MIT licensing, and integrates seamlessly into LangChain workflows. It is worth installing if you need access to NVIDIA Foundation Models (especially Nemotron) or want the option to run models on-premises via NIM.
Install
langchain-nvidia-ai-endpoints on PyPI
Before you install
Low friction installation with three runtime dependencies (aiohttp, langchain-core, requests). Package is actively maintained with recent commits and no known vulnerabilities.
Requires NVIDIA_API_KEY environment variable set to a valid key from https://build.nvidia.com/ (format: nvapi-*), or a running NIM container endpoint if using self-hosted models.
License in practice
MIT license permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
pip install langchain-nvidia-ai-endpoints
from langchain_nvidia_ai_endpoints import ChatNVIDIA
llm = ChatNVIDIA(model="nvidia/nemotron-3-super-120b-a12b")
result = llm.invoke("Write a ballad about LangChain.")
print(result.content)Verify before relying
- Whether the package supports all NVIDIA Foundation Models listed in the API Catalog or only a subset
- Performance characteristics and latency expectations when using the NVIDIA API Catalog versus self-hosted NIM
- Cost implications of using the NVIDIA API Catalog endpoints
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0.0,>=3.10.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesaiohttplangchain-corerequests |
| Maintenance | Actively maintained 43 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 818,671 / month, #4,979 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: langchain_nvidia_ai_endpoints-1.4.3-py3-none-any.whl
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