langchain-brainiall

LangChain integration for the Brainiall LLM Gateway -- access 113+ AI models from 17 providers through a single OpenAI-compatible API.

Installation

pip install langchain-brainiall

Quick Start

from langchain_brainiall import ChatBrainiall

llm = ChatBrainiall(
    model="claude-sonnet-4-6",
    api_key="your-api-key",  # or set BRAINIALL_API_KEY env var
)

response = llm.invoke("Explain quantum computing in one sentence.")
print(response.content)

Features

• 113+ models from 17 providers (Anthropic, DeepSeek, Meta, Qwen, Mistral, Amazon, and more)
• OpenAI-compatible -- supports all ChatOpenAI features (streaming, tools, structured output)
• Single API key -- one key for all models, no need to manage multiple provider accounts
• Cost-optimized -- automatic response caching and optimized routing keep your bill low

Environment Variables

Usage Examples

Streaming

from langchain_brainiall import ChatBrainiall

llm = ChatBrainiall(model="claude-sonnet-4-6")

for chunk in llm.stream("Tell me a joke about programming"):
    print(chunk.content, end="", flush=True)

Tool Calling

from pydantic import BaseModel, Field
from langchain_brainiall import ChatBrainiall

class GetWeather(BaseModel):
    """Get current weather for a location."""
    location: str = Field(description="City name")

llm = ChatBrainiall(model="claude-sonnet-4-6")
llm_with_tools = llm.bind_tools([GetWeather])

response = llm_with_tools.invoke("What's the weather in Tokyo?")
print(response.tool_calls)

Structured Output

from pydantic import BaseModel
from langchain_brainiall import ChatBrainiall

class MovieReview(BaseModel):
    title: str
    rating: float
    summary: str

llm = ChatBrainiall(model="claude-sonnet-4-6")
structured = llm.with_structured_output(MovieReview)

review = structured.invoke("Review the movie Inception")
print(f"{review.title}: {review.rating}/10 - {review.summary}")

Multi-Model Chains

Use different models for different steps -- cheap models for drafting, powerful models for refinement:

from langchain_brainiall import ChatBrainiall

fast = ChatBrainiall(model="nova-micro", temperature=0.7)
smart = ChatBrainiall(model="claude-opus-4-6", temperature=0)

# Draft with a fast, cheap model
draft = fast.invoke("Write a short product description for wireless earbuds")

# Refine with a powerful model
final = smart.invoke(f"Improve this product description:\n{draft.content}")
print(final.content)

With LangGraph Agents

from langchain_brainiall import ChatBrainiall
from langgraph.prebuilt import create_react_agent

llm = ChatBrainiall(model="claude-sonnet-4-6")

# Define your tools
tools = [...]

agent = create_react_agent(llm, tools)
result = agent.invoke({"messages": [("human", "Help me plan a trip to Japan")]})

Embeddings

from langchain_brainiall import BrainiallEmbeddings

embeddings = BrainiallEmbeddings(
    model="bge-m3",
    api_key="your-api-key",
)

vector = embeddings.embed_query("What is machine learning?")
print(f"Dimensions: {len(vector)}")

Async Support

import asyncio
from langchain_brainiall import ChatBrainiall

async def main():
    llm = ChatBrainiall(model="claude-haiku-4-5")
    response = await llm.ainvoke("Hello!")
    print(response.content)

asyncio.run(main())

Available Models

Chat Models

Embedding Models

For the complete and up-to-date model list, see the Brainiall API documentation.

Getting an API Key

1. Visit the Brainiall dashboard
2. Sign up for an account
3. Generate an API key from the dashboard
4. Set it as BRAINIALL_API_KEY environment variable

License

MIT