llm-lens-py 0.1.1

Automatic observability for LLM API calls

llm-lens

Automatic observability for OpenAI and Anthropic API calls.
Tracks latency, token usage, cost, and errors — with a live web dashboard.

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What it does

Add one import to your project. llm-lens silently intercepts every OpenAI and Anthropic API call and logs:

• Latency (ms)
• Input and output tokens
• Cost in USD
• Model used
• Errors and status

No SDK changes. No account setup. No config files.

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Installation

pip install llm-lens-py

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Usage

import llm_lens        # patches OpenAI and Anthropic automatically
import openai

client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "hello"}]
)
# this call was silently tracked

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CLI

# show a table of all tracked calls
llm-lens

# show aggregated stats: total calls, error rate, avg latency, total cost
llm-lens stats

# start the live dashboard at http://localhost:8000
llm-lens serve

# set a cost alert threshold
llm-lens config set cost_alert_usd 0.10

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Dashboard

Run llm-lens serve and open http://localhost:8000.

• Live stats: total calls, error rate, avg latency, total cost
• Latency per call chart
• Error per call chart
• Red alert banner when cost threshold is breached
• Auto-refreshes every 5 seconds

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Docker

docker build -t llm-lens .
docker run -p 8000:8000 llm-lens

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Supported models

Provider	Models
OpenAI	gpt-4o, gpt-4o-mini, gpt-4-turbo
Anthropic	claude-3-5-sonnet, claude-3-5-haiku, claude-3-opus

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Data storage

All data is stored locally at ~/.llm_lens/calls.db (SQLite). Nothing leaves your machine unless you deploy the server yourself.

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Stack

Python · FastAPI · SQLite · Vanilla JS · Chart.js · Docker · Render

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Status

Active development. Built in 12 days as a learning project.
Feedback and PRs welcome.