looselips 0.2.0

Scan your LLM chat exports for personal information

looselips

Scan your ChatGPT and Claude chat exports for personal information you might not want sitting in the cloud.

Install

pip install looselips

Basic usage

1. Export your data (both services email you a download link):
   • ChatGPT: Settings -> Data controls -> Export.
   • Claude: Settings -> Privacy -> Export Data.
2. Create a looselips.toml config defining what to look for (see below).
3. Run:

looselips --config looselips.toml export.zip

The format (ChatGPT vs Claude) is auto-detected. Accepts .zip exports or raw conversations.json files from either service.

Config file

Define your matchers in a looselips.toml. See examples/example_config.toml for a full example with regex and LLM matchers.

[[matcher]]
type = "regex"
category = "My Phone"
pattern = '212.?867.?5309'

[[matcher]]
type = "regex"
category = "Home Address"
pattern = '(?i)742\s+Evergreen\s+Terrace'

Patterns use the Python re module. Inline flags like (?i) for case-insensitive, (?s) for dotall, and (?x) for verbose mode (comments and ignored whitespace) work in the pattern string itself.

LLM matchers

For things regex can't catch, add LLM matchers to your config. Each one runs a separate inference pass per conversation chunk, so prefer a few focused matchers over many broad ones.

model = "ollama/qwen3:32b"

[[matcher]]
type = "llm"
name = "Employment & Financial"
prompt = "Find employment and financial information: company names, job titles, salary figures, stock grants."

[[matcher]]
type = "llm"
name = "Medical & Health"
prompt = "Find medical and health information: conditions, medications, doctor names."

You can override the model per-matcher with the model key.

Choosing a model

The model field in your config is a LiteLLM model string in the format provider/model-name. For cloud providers, set the standard API key environment variable. For Ollama, no key is needed.

Provider	Example model string	Environment variable
Ollama	ollama/qwen3:32b	OLLAMA_API_BASE (optional)
OpenAI	openai/gpt-5.2	OPENAI_API_KEY
Anthropic	anthropic/claude-sonnet-4-5-20250929	ANTHROPIC_API_KEY

--model on the command line overrides the config file. These are just a few examples; LiteLLM supports many more providers, each with its own environment variable for authentication.

For local/private scanning, Ollama keeps everything on your machine. Install and start it separately (ollama serve), then pull a model (ollama pull qwen3:32b). We've seen good results with ollama/qwen3:32b, which runs on consumer GPUs (needs ~20GB VRAM).

Benchmarking models

The looselips-bench command runs your matchers against built-in labeled test conversations and produces an HTML report comparing models. Use it to evaluate whether a model is accurate enough before running a full scan.

# Benchmark a model
looselips-bench --backend local --model ollama/qwen3:32b -c looselips.toml

# Compare two models (results accumulate in a SQLite DB between runs)
looselips-bench --backend local --model ollama/qwen3:32b -c looselips.toml
looselips-bench --backend local --model ollama/qwen3:8b -c looselips.toml

# Re-render the report from cached results without re-running inference
looselips-bench --report-only --db bench_report.db -o bench_report.html

Results are saved incrementally, so you can interrupt and resume. The report shows accuracy, recall, and per-testcase breakdowns for each model.

Scan output

The scan produces a self-contained HTML report. Default path is <input>_report.html; override with --output:

looselips --config looselips.toml --output=report.html export.zip

Each flagged conversation links directly to chatgpt.com or claude.ai so you can review or delete it in one click. Click a conversation to expand it and see each match highlighted in context.

Everything runs locally. No conversation data leaves your machine unless you use a cloud LLM model.