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LLM observability SDK for Revefi - Traceloop-based monitoring with custom ingestor service

Project description

Revefi LLM SDK

A minimal Python SDK for LLM observability with Revefi's monitoring platform.

Installation

pip install revefi-llm-sdk

Quick Start

from revefi_llm_sdk import init_llm_observability

# Initialize the SDK
init_llm_observability(
    api_key="your-revefi-api-key",
    agent_name="my-llm-agent",
    ingestor_url="https://your-revefi-instance.com"
)

# Your LLM calls will now be automatically tracked
import openai
client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello!"}]
)

Configuration

  • api_key: Your Revefi API key for authentication
  • agent_name: Name identifier for your agent/application
  • ingestor_url: Revefi ingestor service URL (defaults to localhost:6556)

Supported LLM Providers

  • OpenAI
  • Anthropic

Development & Distribution

Building Distribution Files

The dist/ directory contains the distribution files that are built from the source code and uploaded to PyPI. These files are generated using Python's build tools:

# Build distribution files (generates .whl and .tar.gz files in dist/)
python -m build

# This creates:
# - dist/revefi_llm_sdk-{version}-py3-none-any.whl (wheel package)
# - dist/revefi_llm_sdk-{version}.tar.gz (source distribution)

Publishing to PyPI

The distribution files are uploaded to the Python Package Index (PyPI) registry using twine:

# Upload to PyPI (requires valid PyPI credentials)
python -m twine upload dist/*

# For testing, upload to Test PyPI first:
python -m twine upload --repository testpypi dist/*

Note: The files in dist/ are automatically generated and should not be manually edited. They are created from the source code defined in revefi_llm_sdk/ and the project configuration in pyproject.toml.

License

MIT

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