any-lang-anonymizer 2026.6.7

Self-hosted PII redaction step for LLM observability pipelines.

any-lang-anonymizer

Drop-in PII redaction for teams that already use Langfuse.

pip install any-lang-anonymizer

from langfuse import Langfuse
from pii_redactor.integrations.langfuse import make_mask

langfuse = Langfuse(mask=make_mask())

That is the main API. make_mask() recursively scans every string Langfuse sends through the mask callback, including nested input, output, metadata, messages, tool calls, and custom fields.

Supported Integrations

Framework	Status	How to use
Langfuse SDK	Ready	Langfuse(mask=make_mask())
LangChain / LangGraph	Ready	make_langfuse_callback() with config={"callbacks": [...]}
Pydantic AI	Coming soon	Planned example
OpenAI SDK	Coming soon	Planned example

How It Works

any-lang-anonymizer runs the Bards AI ONNX PII model locally. Raw observability data is redacted in your app before it is sent to Langfuse.

The model is downloaded from Hugging Face on first use and cached locally by huggingface-hub.

Optional model config:

export PII_MODEL_ID=bardsai/eu-pii-anonimization-multilang
export PII_MODEL_CACHE_DIR=/path/to/model-cache

You can narrow or exclude JSON paths if needed:

langfuse = Langfuse(
    mask=make_mask(
        include_paths=["input", "output", "metadata", "messages.*.content", "tool_calls.*.args"],
        exclude_paths=["metadata.trace_id", "metadata.model", "usage"],
    )
)

By default, no path config is needed. Everything text-like is scanned.

LangChain / LangGraph

Langfuse traces LangChain and LangGraph through a callback handler. Use the same callback, but create the Langfuse client with the anonymizer mask first.

pip install 'any-lang-anonymizer[langchain]'

from langchain.agents import create_agent
from pii_redactor.integrations.langchain import make_langfuse_callback

langfuse_handler = make_langfuse_callback()
agent = create_agent(model="groq:llama-3.1-8b-instant", tools=[])

agent.invoke(
    {"messages": [{"role": "user", "content": "Jan Kowalski, [e-mail]"}]},
    config={"callbacks": [langfuse_handler]},
)

create_agent runs on LangGraph internally, so this is the shortest LangGraph-backed agent path. A runnable example is in examples/langchain_langgraph_langfuse.py.

Model

The default model is bardsai/eu-pii-anonimization-multilang. The project is Apache-2.0 licensed, matching the model license. The model files are not bundled in this package.

Local Playground

The FastAPI playground is a demo tool, not part of the core library.

python3 -m pip install -e '.[server]'
python3 -m uvicorn playground.app:app --reload

Open:

http://127.0.0.1:8000

Examples

Runnable demos live in examples/.

python3 -m pip install -e '.[demo]'
PII_LLM_PROVIDER=demo python3 examples/langfuse_demo.py

For real LLM demo traces:

export LANGFUSE_PUBLIC_KEY=pk-lf-...
export LANGFUSE_SECRET_KEY=sk-lf-...
export LANGFUSE_BASE_URL=https://cloud.langfuse.com
export GROQ_API_KEY=gsk_...
python3 examples/langfuse_txt_demo.py --allow-leaks

The long TXT demo uses Groq llama-3.1-8b-instant by default when GROQ_API_KEY is set. It asks the LLM to append | checked to each non-empty line, then verifies locally whether known raw values remain after masking.