llm-logger 0.1.3

Lightweight LLM call logger for OpenAI/Anthropic

Log and View Python Based LLM Conversations

LLM Logger is a lightweight, local-first tool for inspecting and understanding how your application interacts with large language models like OpenAI GPT-4 or Anthropic Claude.

It helps you:

• Log and inspect each model call with request/response metadata
• View differences between turns in a conversation
• Visualize tool calls, tool responses, and system prompts
• Compare prompt strategies and debug session behavior

Ideal for developers building agent workflows, chat interfaces, or prompt-based systems.

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✨ Features

• ⚡ One-line setup – Start logging with a simple wrapper around your OpenAI client
• 🧠 Automatic session tracking – No manual session IDs or state management required
• 📀 Local-first logging – Stores structured logs as JSON on your machine
• 🔍 Rich session insights – Context diffs, tool call/response blocks, and system prompt visibility
• ⏱️ Latency + metadata capture – Track timing, models, and more with every call
• 🧹 Framework-agnostic – Works with any Python codebase
• 🛡️ Privacy-first – Fully offline, no account or server required
• 🌐 Simple UI – Static frontend served locally; no build step needed for end users
• 👐 Open source (MIT) – Lightweight, auditable, and easy to extend

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📦 Installation

🔹 Installation Options

Option 1: From PyPI (Recommended for most users)

Install the prebuilt package if you just want to use the tool:

pip install llm-logger

Option 2: Local Copy (For direct integration or customization)

Clone the repository and install:

# Clone the repo
git clone https://github.com/akhalsa/llm_debugger.git

# rebuild ui (optional)
cd llm_debugger/llm_logger/front_end
npm install
npx tsc

# Install from the local copy
pip install ./llm_debugger

Note: All installation methods include pre-compiled frontend files. No Node.js or frontend build steps are required for basic usage. The static files (HTML, CSS, JS) are packaged with the library, so the debugger UI works out of the box.

Rebuilding using npm install and npx tsc are required to update the .js files in the static/ folder

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🔸 Development Setup (Only for contributors)

If you want to modify the logger or UI code:

1. Prerequisites:

   • Python ≥ 3.8
   • Node.js & npm (only needed for UI development)

2. Setup:

   git clone https://github.com/akhalsa/llm_debugger.git
   cd llm_debugger
   
   # Optional: Create a virtual environment
   python3 -m venv venv
   source venv/bin/activate
   
   # Install in development mode
   pip install -e .
   

3. Frontend Development (only if modifying the UI):

   cd llm_logger/front_end
   npm install
   npx tsc  # Compiles TypeScript to ../static
   

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🚀 Usage

1. Wrap Your OpenAI Client

from dotenv import load_dotenv
import openai
import os
from llm_logger import wrap_openai

load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")

openai_client = wrap_openai(
    openai.OpenAI(api_key=api_key),
    logging_account_id="my_project"
)

Then use openai_client as normal:

response = openai_client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What's the capital of France?"}
    ]
)

This writes logs to .llm_logger/logs/.

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2. Launch the Log Viewer

Option A: Stand Alone Service Launched From Terminal

This option is ideal for viewing logs from an application running on your local device

# Default port (8000)
llm_logger

# Or specify a custom port
llm_logger -p 8000

Then open in your browser:

http://localhost:8000

Option B: As An Endpoint In Your Python Web Application

You can run the debugger UI alongside your application if you're using a python webapp

Same Process (using FastAPI):

from fastapi import FastAPI
import uvicorn
from llm_logger.server import app as debugger_app
# Or with a custom base URL:
# from llm_logger.server import create_app
# debugger_app = create_app(base_url="/debugger")

# Your main application
app = FastAPI()

# Mount the debugger UI at /debugger
app.mount("/debugger", debugger_app)

# Run your application
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=5000)

With Custom Base URL:

If you need to specify a custom base URL (for example, when the debugger is mounted at a specific path or accessed through a reverse proxy), you can use the create_app function:

from fastapi import FastAPI
from llm_logger.server import create_app

# Your main application
app = FastAPI()

# Create debugger app with custom base URL
debugger_app = create_app(base_url="/debugger")  # or any other path where it's mounted

# Mount the debugger UI
app.mount("/debugger", debugger_app)

This ensures that all internal links and API calls in the debugger UI will use the correct base URL.

Parallel Process (e.g., in Docker): In production or distributed development environments, it is recommended to run the llm_logger UI as a stand alone process.

# Start your main application
uvicorn your_app:app --host 0.0.0.0 --port 5000 &

# Start the debugger UI on a different port
llm_logger -p 8000 &

# Wait for both processes
wait

Option C: Docker Environment

If you're using Docker, you can include the debugger UI in your Dockerfile without requiring Node.js:

FROM python:3.9-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy your application code
COPY . .

# Expose ports for both your app and the debugger
EXPOSE 5000 8000

# Start both services
CMD ["bash", "-c", "uvicorn your_app:app --host 0.0.0.0 --port 5000 & llm_logger -p 8000 & wait"]

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🛠️ Roadmap Ideas

• Replay conversation with inline visualization
• Claude and other model support
• UI analytics and filters
• Exportable reports and session sharing
• Plugin hooks and configuration options

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📬 Feedback

Found a bug or have a feature request? Open an issue.

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📜 License

MIT