corellm-sdk 1.1.0

Python client for CoreLLM SDK — LLM gateway running Ollama on Hugging Face Spaces

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title: CoreLLM SDK emoji: 🧠 colorFrom: indigo colorTo: purple sdk: docker pinned: false

CoreLLM SDK

A fully-featured Python client and Hugging Face Space for running LLMs via Ollama — with native LangChain & LangGraph support.

corellm-sdk acts as an all-in-one unified model interface!

📦 Install from PyPI

# Minimal installation (just the client)
pip install corellm-sdk

# With LangChain support
pip install "corellm-sdk[langchain]"

# With LangChain + LangGraph support
pip install "corellm-sdk[all]"

🤖 Available Models

The following models are available on the server. Do not use any other model names.

• "gemma4:e4b" - text, vision, tools, thinking, audio, context=128k
• "devstral:24b" - text, tools, context=128k
• "cogito:14b" - text, tools, thinking, context=128k
• "ornith:9b" - Text, thinking, tools, context=256k
• "lfm2.5-thinking:1.2b" - ultra fast, tools, thinking, context=32k
• "qwen3-embedding:8b" - embedding
• "robit/ornith-vision:9b" - vision, tools, thinking

🚀 Quickstart

The new CoreLLMChat class wraps everything into a single, cohesive, Langchain-compatible chat model that also handles normal chat generation, raw completion, and OpenAI compatibility.

from corellm_sdk import CoreLLMChat

# Initialize the engine
llm = CoreLLMChat(
    model="gemma4:e4b"
)

🧩 LangChain & LangGraph Support

Use it seamlessly with your existing LangChain workflows:

from langchain_core.messages import HumanMessage
from langchain_core.prompts import ChatPromptTemplate

# Direct usage
response = llm.invoke([HumanMessage(content="Hello!")])
print(response.content)

# With Chains
chain = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("human", "{question}"),
]) | llm

print(chain.invoke({"question": "What is Python?"}).content)

💬 OpenAI Compatibility (openai_chat)

Have existing code using OpenAI structures? Just use the OpenAI method out of the box!

messages = [
    {"role": "system", "content": "You are a witty assistant."},
    {"role": "user", "content": "Tell me a joke."}
]

# Calls the /v1/chat/completions endpoint just like OpenAI
response = llm.openai_chat(messages, temperature=0.7)
print(response)

🛠 Raw APIs (raw_chat & generate)

If you want simpler formats:

# Raw Prompt Completion
print(llm.generate("Explain quantum physics in 1 sentence."))

# Standard Dict Chat
messages = [{"role": "user", "content": "Who are you?"}]
print(llm.raw_chat(messages))

🔄 Dynamic Model Switching

Switch models on the fly! The backend dynamically handles memory constraints and load transitions.

# Switch to another allowed model on your server!
llm.switch("devstral:24b")

print(llm.generate("Hello from Devstral!"))