undercurrent 0.1.0

See the undercurrent before it surfaces.

_{An open-source project by Wrynx.}

Undercurrent is production-grade activation probing for live LLM inference. It runs your probes on a model's internal activations while it generates, so a probe can stop a generation mid-flight, or observe asynchronously without adding latency to the response.

Why Undercurrent

• Declarative YAML specs. A few lines say which tensor to capture, at which layers and token positions, and which probe receives it. No model surgery, no forked serving code.
• Inline abort mid-generation. An inline probe runs on the generation path and can stop a request before the next token is produced.
• Production-ready async execution. Observe-only probes run off the hot path on bounded per-binding queues with overflow policies, and report to metrics and to file or webhook sinks (with retries, dead-lettering and redaction).
• Runs where your model runs. Inside your existing vLLM deployment, with continuous batching (the vLLM adapter is experimental, see Deploy with vLLM), and with Hugging Face Transformers on a laptop CPU.

Known issues (v0.1): the vLLM adapter is experimental: its GPU tests pass on an NVIDIA L4 with vLLM 0.28.0, run by hand (there's no GPU CI yet), and only vLLM 0.28 is supported. Fixed in 0.1.0: on vLLM, residual_stream on Llama-family (fused-residual) models is captured as hidden_states + residual, the same residual stream as the HF backend (GPU-verified). See Known issues.

How it works

In short: an engine adapter (Hugging Face or vLLM) captures the activations your YAML spec asks for, and the router hands them to your probes, which either abort the generation (inline) or report to sinks and metrics (async).

flowchart LR
    spec["YAML spec"] --> adapter
    subgraph engine["Inference engine (HF Transformers or vLLM)"]
        model["LLM generating tokens"] -- forward hooks --> adapter["Engine adapter"]
    end
    adapter -- ActivationRecords --> router["Router"]
    router -- inline --> inline["Inline probes"]
    inline -- ABORT --> model
    router -- async, bounded queues --> async["Async probes"]
    async --> sinks["Sinks: file / webhook / metrics"]

Install

pip install undercurrent

Python 3.10+. The Hugging Face Transformers backend is included; there are no extras to choose. For vLLM, install Undercurrent into your existing vLLM environment or image, or use pip install "undercurrent[vllm]" to get a vLLM from the tested range (currently vllm>=0.28,<0.29). The adapter checks the installed vLLM version at runtime; set UNDERCURRENT_ALLOW_UNSUPPORTED_VLLM=1 to try an untested vLLM anyway.

Supported versions, tested combinations and known issues are on the Compatibility page.

Quickstart

Attach a probe to GPT-2 and read its result. This runs on CPU:

from undercurrent.core import ActivationRecord, probe
from undercurrent.model import ProbedModel

SPEC = """
version: "1"
extraction_points:
  - name: last_prompt_token
    layers: 6
    tensor_type: residual_stream
    position: "prompt[-1]"
    probe_type: norm_gate
    probe_kind: single_shot
    execution_mode: inline
"""


@probe("norm_gate", threshold=500.0)
def norm_gate(record: ActivationRecord) -> float:
    """L2 norm of the residual stream at this token."""
    return float(record.tensor.norm())


with ProbedModel.from_pretrained("openai-community/gpt2", spec=SPEC, probes={"norm_gate": norm_gate}) as model:
    out = model.generate("The quick brown fox", max_new_tokens=20, temperature=0)

print(repr(out.text))
print(out.probe_results["last_prompt_token"].verdict)

A score at or above threshold would abort the generation. The Quickstart continues with a trajectory probe that stops a generation part-way through, and an async probe that logs to a file without touching the response.

How it compares

Last reviewed: 2026-10-01. Footnotes, sources and when to pick something else: full comparison.

Documentation

• Quickstart: your first probe on GPT-2, in five minutes.
• Concepts: extraction points, position selectors, probes, inline vs async.
• Guides: write, test and package your own probe.
• Production: vLLM deployment, embedding the router, backpressure, sinks and metrics.
• Examples: complete, runnable projects.
• API reference: every public class and function, and the CLI.
• Intended use and limitations and privacy and data handling: what Undercurrent is and isn't for, and what data it captures and writes.
• Colab notebook: the quickstart in your browser.

examples/content_safety/ is a demo of the wiring (probes, specs and a vLLM pipeline), not a safety product. Its probes use random-initialised or dummy weights.

Project status

Undercurrent is on 0.x: the API may change between minor releases. The API stability policy says what is public, what is experimental, and how deprecations work. Planned work is in the roadmap.

Contributing and community

• Contributing guide
• Security policy: report vulnerabilities privately to security@wrynx.com.
• Code of Conduct

Citation

If you use Undercurrent in research, please cite it using CITATION.cff (GitHub's "Cite this repository" button reads it).

License

Apache-2.0. See LICENSE and NOTICE. Copyright Wrynx.