Use Modellix LLM with LangChain

Use LangChain against the Modellix LLM gateway through the OpenAI-compatible Chat Completions API. Configure ChatOpenAI from langchain-openai with base_url set to https://llm.modellix.ai/v1 — the same pattern as the OpenAI SDK.

This page covers LLM chat only (https://llm.modellix.ai). Modellix model IDs are provider/name (for example openai/gpt-5.5). Pass that full string as model. See Models & Pricing.The LLM gateway does not document an embeddings API. For RAG, use Modellix for generation and a separate embeddings provider, or keep embeddings on another OpenAI-compatible host.

Set Up LangChain

Install Packages

Python (recommended imports from langchain-openai):

pip install langchain langchain-openai

TypeScript:

npm install @langchain/openai @langchain/core

Set Credentials

Create a Modellix API key in the console, then export:

export OPENAI_API_KEY="mdlx-xxxxxxxx"
export OPENAI_BASE_URL="https://llm.modellix.ai/v1"

Setting	Value
`OPENAI_API_KEY`	Modellix API Key (not an OpenAI platform key)
`OPENAI_BASE_URL`	`https://llm.modellix.ai/v1` (include `/v1`)
Model	Exact Modellix Model ID (`provider/name`)

Missing /v1 on the base URL commonly causes 404s. Do not point LangChain at the media host (https://api.modellix.ai).

Create ChatOpenAI

Prefer constructor args so the base URL is explicit in code. Environment variables work as a fallback when you omit api_key / base_url.

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="openai/gpt-5.5",
    api_key="mdlx-xxxxxxxx",  # or rely on OPENAI_API_KEY
    base_url="https://llm.modellix.ai/v1",
    max_tokens=256,
)

response = llm.invoke("Introduce yourself in one sentence")
print(response.content)

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "openai/gpt-5.5",
  apiKey: "mdlx-xxxxxxxx", // or rely on OPENAI_API_KEY
  configuration: {
    baseURL: "https://llm.modellix.ai/v1",
  },
  maxTokens: 256,
});

const response = await llm.invoke("Introduce yourself in one sentence");
console.log(response.content);

You can use the same base_url with other Modellix catalog IDs (for example anthropic/claude-sonnet-5 or google/gemini-3.6-flash); traffic still goes through Chat Completions on https://llm.modellix.ai/v1.

Optional: Streaming and Fallbacks

Streaming:

for chunk in llm.stream("Write a one-line haiku about APIs"):
    print(chunk.content, end="", flush=True)

Multi-model fallback on the same gateway:

primary = ChatOpenAI(
    model="openai/gpt-5.5",
    base_url="https://llm.modellix.ai/v1",
    api_key="mdlx-xxxxxxxx",
)
fallback = ChatOpenAI(
    model="google/gemini-3.6-flash",
    base_url="https://llm.modellix.ai/v1",
    api_key="mdlx-xxxxxxxx",
)

llm = primary.with_fallbacks([fallback])

Optional: ChatAnthropic (Messages)

For native Anthropic Messages instead of Chat Completions, use langchain-anthropic with the same host shape as the Anthropic SDK (no /v1 on the base URL):

pip install langchain-anthropic

import os
from langchain_anthropic import ChatAnthropic

os.environ["ANTHROPIC_API_KEY"] = "mdlx-xxxxxxxx"
os.environ["ANTHROPIC_BASE_URL"] = "https://llm.modellix.ai"

llm = ChatAnthropic(
    model="anthropic/claude-sonnet-5",
    max_tokens=256,
)

Most LangChain apps should stay on ChatOpenAI + /v1 unless you specifically need the Messages protocol.

Troubleshooting

Symptom	Check
404 Not Found	`base_url` / `OPENAI_BASE_URL` is `https://llm.modellix.ai/v1` (include `/v1`)
401 Unauthorized	Key is a valid Modellix API key
Model not found	Model is a full ID like `openai/gpt-5.5`, not bare `gpt-5.5`
Deprecated import warnings	Use `from langchain_openai import ChatOpenAI`, not `langchain.chat_models`
Embeddings / RAG errors	LLM gateway has no documented embeddings route — use another embeddings provider

LangChain OpenAI-compatible providers — ChatOpenAI + base_url
OpenAI SDK — same /v1 gateway without LangChain
Anthropic SDK — Messages protocol used by ChatAnthropic
Models & Pricing — Model IDs and rates
LLM API guide — protocols and curl examples
Chat Completions — OpenAPI reference

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Use Modellix LLM with LangChain

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