# decoderesearch/circuit-tracer

Repository: https://github.com/decoderesearch/circuit-tracer
Canonical: https://ross.abutalabs.com/products/circuit-tracer
Language: Python
License: MIT
License Family: permissive
Last push: 2026-08-21T03:05:53+00:00

## Health v2 (maintenance only)
Score: 79/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 81, longevity 33
- inputs: {"age_days": 462, "days_push": 12, "days_rel": 46, "gap_med": 40, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2897, forks 343 (observed 2026-08-28T04:07:28.770073+00:00)

## What it is
A Python library for circuit tracing and attribution graphs in language model interpretability, based on cross-layer MLP transcoders. It computes attribution graphs, visualizes and annotates them, and enables interventions on transcoder features.

## Use cases
- trace circuits in a language model with attribution graphs
- visualize and annotate attribution graphs for LLM interpretability research
- intervene on transcoder features and observe model output changes
- study how features influence logits in transformer models
- run circuit tracing on Gemma-2 with limited GPU resources
- explore attribution graphs interactively via Neuronpedia

## When to choose
- you need to compute direct effects between transcoder features, error nodes, and tokens
- you want to replicate attribution graph methods from Anthropic's circuit tracing papers
- you need feature-level interventions on a transformer model
- you want interactive visualization of interpretability graphs

## When to avoid
- you need general-purpose sparse autoencoder training rather than circuit analysis
- you have no GPU access and cannot use hosted tools
- you need interpretability tooling for non-transformer architectures

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, data-visualization, developer-tools
- domain: machine-learning, deep-learning, large-language-models, artificial-intelligence, developer-tools
- platform: python, cli
- tags: mechanistic-interpretability, attribution-graphs, circuit-tracing, transcoders, sparse-autoencoders, llm-interpretability, feature-visualization, gpu

## Member repositories
- decoderesearch/circuit-tracer (main) score 79

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:28.770073+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:35:14.197461+00:00, confidence not recorded.
  - readme: https://github.com/decoderesearch/circuit-tracer (fetched 2026-08-28T04:07:28.770073+00:00, sha ff55b37d692f)
  - registry_pypi: https://pypi.org/pypi/circuit-tracer/json (fetched 2026-08-29T09:50:01.114784+00:00, sha fe12e9ff0d91)
- Data as of 2026-08-30T08:39:29.467469+00:00.