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Documentation Index

Fetch the complete documentation index at: https://docs.langtrace.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Introduction

Langtrace provides native support for DeepSeek’s family of models. When using DeepSeek models, Langtrace automatically captures essential metrics including:
  • Token usage
  • Cost
  • Latency
  • Model hyperparameters

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Prerequisites

Before integrating DeepSeek with Langtrace, ensure you have:
  • A Langtrace account with an API key
  • DeepSeek API credentials
  • Python environment (3.6 or later)

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Installation

  1. Install the Langtrace Python SDK:
pip install -U langtrace-python-sdk
  1. Set up your environment variables:
export LANGTRACE_API_KEY=YOUR_LANGTRACE_API_KEY

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Integration

Initialize Langtrace before importing any LLM modules: 
import os
from langtrace_python_sdk import langtrace  # Must precede any llm module imports
from openai import OpenAI

# Initialize Langtrace
langtrace.init(api_key=os.environ['LANGTRACE_API_KEY'])

# Initialize DeepSeek client
client = OpenAI(
    api_key="<DeepSeek API Key>",
    base_url="https://api.deepseek.com"
)

# Example: Create a chat completion
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Hello"},
    ],
    stream=False
)

print(response.choices[0].message.content)

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Viewing Traces

After integration, you can view your DeepSeek model traces in the Langtrace dashboard. The traces will include:
  • Request/response payloads
  • Token usage metrics
  • Cost information
  • Latency measurements
  • Model configuration details

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Additional Resources