trim-sdk 0.2.1

Trim SDK.

🧠 Trim LangChain SDK

Trim LangChain is a lightweight, OpenAI-compatible SDK designed for smart routing and efficient interaction with LLMs. It integrates seamlessly with LangChain agents, chat models, and OpenAI-style APIs.

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📦 Installation

Install the SDK via pip:

pip install trim-sdk

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🚀 Quick Start

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🔐 Environment Variables

To use the SDK, you need to set the following environment variables in a .env file or your system environment:

TRIM_API_KEY=your_trim_api_key_here
OPENAI_API_KEY=your_openai_api_key_here

• TRIM_API_KEY: Your API key for authenticating with the Trim backend.
• OPENAI_API_KEY: Your OpenAI API key used internally by Trim to route to OpenAI models.

Make sure to load these using Python's dotenv:

from dotenv import load_dotenv
load_dotenv()

🔗 LangChain ReAct Agent

from dotenv import load_dotenv
from langgraph.prebuilt import create_react_agent

from examples.utils import print_agent_response
from trim import TrimChatModel

load_dotenv()


def calculator(expression: str) -> str:
    """Evaluates a basic math expression."""
    try:
        return str(eval(expression))
    except Exception as e:
        return f"Error: {e}"


llm = TrimChatModel()

agent = create_react_agent(model=llm, tools=[calculator])

response = agent.invoke({"messages": "What is (12 * 3) + 5?"})
print_agent_response(response['messages'])

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💬 LangChain ChatModel

from trim import TrimChatModel
from dotenv import load_dotenv

load_dotenv()

llm = TrimChatModel()

response = llm.invoke([
    {"role": "user", "content": "Tell me about Elon Musk in 20 words"}
])

print(response.content)

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🧩 OpenAI-Compatible API

from trim import TrimClient
from dotenv import load_dotenv

load_dotenv()

client = TrimClient()

# Way 1: Simple completion
completion = client.responses.create(
    model="gpt-4.1",
    input="Write a one-sentence bedtime story about a unicorn."
)
print(completion.output_text)

# Way 2: Chat completion
completion = client.chat.completions.create(
    model="gpt-4.1",
    messages=[{"role": "user", "content": "Write a one-sentence bedtime story about bears"}]
)
print(completion.choices[0].message.content)

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🧠 LangChain LLM Wrapper

from trim import TrimLLM
from dotenv import load_dotenv

load_dotenv()

llm = TrimLLM()

prompt = "Explain quantum computing in less than 20 words."

result = llm.invoke(prompt)
print(result)

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📚 Documentation

Coming soon. For now, explore the examples/ directory for use cases and integration patterns.

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🤝 Contributing

Contributions are welcome! Please open issues or submit pull requests.

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🛡️ License

MIT License