langchain-thalam

LangChain integration for thalam. — one OpenAI-compatible endpoint for every leading AI model.

Built in the UAE. Text, image, and video models from a single endpoint, with per-key spend caps and audit logs as a built-in governance layer.

• One endpoint — DeepSeek, Qwen, Kimi, GLM, MiniMax, Claude, GPT, Llama, Grok, Mistral and more
• Drop-in LangChain compatibility — every LangChain feature (streaming, tools, structured output, async) works unchanged
• Pay per token — no subscription, no minimums
• AED or USD invoicing on Enterprise contracts

Install

pip install langchain-thalam

Sign up at thalam.ai — no card required: new accounts get $1 in starter credit to use right away, then add a card to top up.

Quickstart

import os
from langchain_thalam import ChatThalam

os.environ["THALAM_API_KEY"] = "tl-..."

llm = ChatThalam(model="deepseek/deepseek-v3.2", temperature=0)

response = llm.invoke("Explain quantum entanglement in 2 sentences.")
print(response.content)

Or pass the key explicitly:

from langchain_thalam import ChatThalam

llm = ChatThalam(
    model="qwen/qwen3-max",
    api_key="tl-...",
)

Streaming

from langchain_thalam import ChatThalam

llm = ChatThalam(model="deepseek/deepseek-v3.2")

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

Tool calling

from langchain_thalam import ChatThalam
from langchain_core.tools import tool

@tool
def get_weather(city: str) -> dict:
    """Get the weather in a city."""
    return {"temp": 32, "unit": "C", "city": city}

llm = ChatThalam(model="deepseek/deepseek-v4-pro")
llm_with_tools = llm.bind_tools([get_weather])

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

Structured outputs

from langchain_thalam import ChatThalam
from pydantic import BaseModel, Field

class BlogMetadata(BaseModel):
    title: str = Field(description="The post title")
    tags: list[str] = Field(description="3-5 relevant tags")

llm = ChatThalam(model="deepseek/deepseek-v3.2")
structured_llm = llm.with_structured_output(BlogMetadata)

result = structured_llm.invoke("Generate metadata for a post about MENA fintech.")
print(result.title, result.tags)

Async

import asyncio
from langchain_thalam import ChatThalam

async def main():
    llm = ChatThalam(model="deepseek/deepseek-v3.2")
    result = await llm.ainvoke("Hi")
    print(result.content)

asyncio.run(main())

In a LangChain chain

from langchain_thalam import ChatThalam
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a senior MENA fintech analyst. Respond concisely."),
    ("user", "{question}"),
])

llm = ChatThalam(model="deepseek/deepseek-v3.2")
chain = prompt | llm

result = chain.invoke({"question": "Top 3 fintech trends in the UAE in 2026?"})
print(result.content)

Available models

The full live catalog is at thalam.ai/models or via:

import os, requests
r = requests.get(
    "https://api.thalam.ai/v1/models",
    headers={"Authorization": f"Bearer {os.environ['THALAM_API_KEY']}"},
)
for m in r.json()["data"]:
    print(m["id"], "-", m["display_name"], "-", m["category"])

Common text-model IDs:

Configuration

from langchain_thalam import ChatThalam

llm = ChatThalam(
    model="deepseek/deepseek-v3.2",
    api_key="tl-...",                              # default: env THALAM_API_KEY
    base_url="https://api.thalam.ai/v1",            # default
    temperature=0.7,
    max_tokens=512,
    timeout=60,
    max_retries=2,
)

Every keyword argument that langchain_openai.ChatOpenAI accepts is supported.

Links

• thalam.ai
• Documentation
• Model catalog
• LangChain docs

License

Apache-2.0