langchain-brainiall 0.1.0

LangChain integration for Brainiall LLM Gateway - 113+ AI models from 17 providers

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 -- powered by AWS Bedrock with automatic prompt caching and flex pricing

Environment Variables

Variable	Description
BRAINIALL_API_KEY	API key for authentication
BRAINIALL_API_BASE	Override the default API base URL

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

Model	Provider	Context Window	Max Output
claude-opus-4-6	Anthropic	200K	64K
claude-opus-4-6-1m	Anthropic	1M	64K
claude-opus-4-5	Anthropic	200K	32K
claude-sonnet-4-6	Anthropic	200K	64K
claude-sonnet-4-6-1m	Anthropic	1M	64K
claude-haiku-4-5	Anthropic	200K	16K
claude-3-opus	Anthropic	200K	4K
deepseek-r1	DeepSeek	128K	64K
deepseek-v3	DeepSeek	128K	16K
llama-3.3-70b	Meta	128K	4K
llama-4-scout-17b	Meta	1M	16K
llama-4-maverick-17b	Meta	1M	16K
qwen-3-235b	Qwen	128K	16K
qwen-3-32b	Qwen	128K	16K
qwen-3-8b	Qwen	128K	16K
qwen-3-80b	Qwen	128K	16K
mistral-large-3	Mistral	128K	16K
mistral-small-3	Mistral	128K	16K
nova-pro	Amazon	300K	5K
nova-lite	Amazon	300K	5K
nova-micro	Amazon	128K	5K
minimax-m2	MiniMax	1M	128K
nemotron-ultra-253b	NVIDIA	128K	16K
kimi-k2.5	Moonshot	128K	16K

Embedding Models

Model	Dimensions	Max Tokens
bge-m3	1024	8192
bge-large-en-v1.5	1024	512
cohere-embed-v3	1024	512
titan-embed-v2	1024	8192

For the complete and up-to-date model list, see brainiall.com.

Getting an API Key

1. Visit brainiall.com
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