NeuronScope

Ask a language model "why did you say that" and get back the actual attention heads and neurons responsible, as JSON, from the command line or from an agent over MCP.

Install

Not on PyPI yet, so install from source:

git clone https://github.com/RudrenduPaul/NeuronScope
cd NeuronScope
pip install -e .

That gets you the neuronscope command. A published package (pip install neuronscope-cli) is planned but not live yet; if you try it today you'll get a 404 from PyPI.

First run of any command downloads the requested model from the HuggingFace Hub (gpt2 is about 500MB) and prints two lines to stderr that are expected, not errors: a CPU-fallback notice if you don't have a CUDA GPU, and an unauthenticated-HF-Hub rate-limit notice. Neither one means anything broke.

What it does

NeuronScope is a CLI and MCP server built on top of TransformerLens. TransformerLens does the actual model loading, hooking, and activation math; NeuronScope adds a stable CLI, a versioned JSON schema, and an MCP server around it, so a script or an agent can ask "which components drove this output" without writing TransformerLens code directly.

• trace: runs a prompt through the model and ranks attention heads by direct logit attribution to the predicted token, and MLP neurons by activation magnitude at the final prompt position.
• activations: dumps shape, mean, std, min/max, and the max-activating sequence position for every layer's residual stream, MLP neuron activations, and attention pattern.
• patch: zero-ablates one component (resid_pre, resid_mid, resid_post, attn_out, mlp_out, or mlp_post) at a given layer and reports how the predicted token and its logit changed.
• circuit: a best-effort automated circuit sketch. Ranks candidate heads/neurons by logit attribution, then measures each one's individual causal effect via single-component ablation. This is not full path-patching with clean/corrupted prompt pairs and does not capture interaction effects between components. The --json output says so explicitly in its method field.
• Every command supports --json for a schema_version-stamped document instead of a table, and the same four operations are exposed as MCP tools returning the identical shape via .model_dump(), so a CLI call and an MCP tool call produce the same document for the same input.
• Model support is whatever transformer_lens.HookedTransformer.from_pretrained supports. As of the transformer-lens version this repo currently depends on, that's 247 pretrained checkpoints and aliases (OFFICIAL_MODEL_NAMES), covering GPT-2, Pythia, Llama, Gemma, Qwen, and more. Small models like gpt2 run comfortably on CPU.

NeuronScope does not replace TransformerLens, nnsight, SAELens, Anthropic's circuit-tracer, or Neuronpedia. It wraps TransformerLens for one narrower job: fast, scriptable, agent-callable component tracing on a single prompt. It leaves deeper mechanistic work (SAE training, transcoder-based circuit graphs, hosted feature browsing) to those tools.

Quickstart

neuronscope trace gpt2 "The capital of France is Paris. The capital of Japan is" --top-k 5

Real output from this exact command (stderr trimmed to the two expected warnings mentioned above):

Prompt: The capital of France is Paris. The capital of Japan is
Predicted next token: ' Tokyo'
Top attention heads (by direct logit
            attribution)
┏━━━━━━━┳━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ Layer ┃ Head ┃ Logit attribution ┃
┡━━━━━━━╇━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│     9 │    8 │            4.0679 │
│     8 │   11 │            2.9028 │
│    10 │    7 │           -1.4782 │
│     8 │   10 │           -1.3999 │
│    10 │    0 │            1.1424 │
└───────┴──────┴───────────────────┘
Top MLP neurons (by activation
          magnitude)
┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━┓
┃ Layer ┃ Neuron ┃ Activation ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━┩
│    10 │     97 │     7.8394 │
│    11 │    611 │     4.6954 │
│    11 │   2997 │     4.6468 │
│    10 │   1793 │     4.5443 │
│     9 │   1460 │     4.4196 │
└───────┴────────┴────────────┘

gpt2 predicts Tokyo correctly, and head L9H8 is the single biggest contributor to that prediction. Add --json to get the machine-readable version of the same result:

neuronscope trace gpt2 "The capital of France is Paris. The capital of Japan is" --top-k 3 --json

{
  "schema_version": 1,
  "operation": "trace",
  "model": {
    "requested_name": "gpt2",
    "resolved_name": "gpt2",
    "backend": "transformer_lens",
    "device": "cpu",
    "n_layers": 12,
    "n_heads": 12,
    "d_model": 768,
    "d_mlp": 3072
  },
  "prompt": "The capital of France is Paris. The capital of Japan is",
  "predicted_token": " Tokyo",
  "predicted_token_id": 11790,
  "top_neurons": [
    { "layer": 10, "neuron_index": 97, "activation": 7.839381217956543 },
    { "layer": 11, "neuron_index": 611, "activation": 4.695372581481934 },
    { "layer": 11, "neuron_index": 2997, "activation": 4.646785736083984 }
  ],
  "top_heads": [
    { "layer": 9, "head_index": 8, "logit_attribution": 4.067923545837402 },
    { "layer": 8, "head_index": 11, "logit_attribution": 2.9028172492980957 },
    { "layer": 10, "head_index": 7, "logit_attribution": -1.4781968593597412 }
  ]
}

CLI reference

Every command takes MODEL (any name HookedTransformer.from_pretrained accepts, for example gpt2 or EleutherAI/pythia-70m) and PROMPT as positional arguments.

Global: neuronscope --version, neuronscope <command> --help. Exit codes: 0 success, 1 a runtime error (prompt too long for the model's context window, --layer out of range, etc.), 2 a Click usage error (bad flags), 3 an unsupported model name.

MCP server

neuronscope mcp-server

Starts an MCP server over stdio that exposes trace, activations, patch, and circuit as MCP tools, with the same arguments and the same JSON schema as the CLI's --json output.

To register it with an MCP host, add:

{
  "mcpServers": {
    "neuronscope": {
      "command": "neuronscope",
      "args": ["mcp-server"]
    }
  }
}

• Claude Code reads this from a project-level .mcp.json in your repo root, or you can add it with claude mcp add neuronscope -- neuronscope mcp-server.
• Claude Desktop reads this from its claude_desktop_config.json (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows), under the same "mcpServers" key.

How it compares

All five of these are real, actively maintained projects doing different jobs. This table compares CLI/JSON-agent-output surface and model coverage, not depth of interpretability research, where TransformerLens, nnsight, SAELens, circuit-tracer, and Neuronpedia are all more mature than NeuronScope. Star counts, release info, and last-push dates below were pulled from each project's GitHub API on 2026-07-19 and will drift over time; check the repos directly for current numbers.

The honest differentiation is narrow: NeuronScope is the only one of these with a CLI, a native MCP server, and a versioned JSON schema together in one package, and it's model-agnostic across whatever TransformerLens supports rather than pinned to a fixed transcoder allowlist like circuit-tracer. It is not more capable, more mature, or more widely used than any of these projects.

What is NeuronScope and why does it exist

TransformerLens gives you a Python API for loading a model and running hooked forward passes. That's the right interface for a research notebook. It's the wrong interface for a script that needs a subprocess call and a JSON document back, or for an agent that needs a tool it can call over MCP. NeuronScope exists to be that second interface: the same underlying computation, wrapped so a CLI invocation or an MCP tool call gets back a schema-versioned document instead of a Python object graph.

FAQ

Is this a replacement for TransformerLens, nnsight, SAELens, circuit-tracer, or Neuronpedia? No. NeuronScope is built directly on TransformerLens and does not do anything TransformerLens itself can't already do at a lower level. It doesn't train SAEs (SAELens), do full path-patching circuit discovery with transcoders (circuit-tracer), give you a Python-native tracing context manager for arbitrary PyTorch models (nnsight), or host a browsable feature database (Neuronpedia). It's a CLI and MCP wrapper around one slice of TransformerLens's functionality.

What models are supported? Anything transformer_lens.HookedTransformer.from_pretrained supports, which today is 247 checkpoints and aliases spanning GPT-2, Pythia, Llama, Gemma, Qwen, and others. Run python -c "from transformer_lens.loading_from_pretrained import OFFICIAL_MODEL_NAMES; print(len(OFFICIAL_MODEL_NAMES))" in your own environment to get the exact count for your installed version, since TransformerLens adds models over time.

Does it need a GPU? No. Small models like gpt2 run fine on CPU; that's what the test suite and the quickstart above run on. Larger models will be slow on CPU. NeuronScope does not auto-select Apple Silicon's MPS backend even when available, because PyTorch's MPS backend can silently produce incorrect values for some ops that this project's activation-patching math depends on being exact. Pass device="mps" explicitly in your own code if you want it anyway.

Is it safe to expose the MCP server to an untrusted agent? Only with resource limits in place. See Known limitations below.

Known limitations

• circuit is an approximation. It ranks components by logit attribution and measures each one's individual causal effect via single-component zero-ablation on one prompt. It does not do full path-patching with clean/corrupted prompt pairs, and it will not catch interaction effects between components. The --json output states this in its method field so a caller doesn't have to trust prose to know the caveat.
• No size cap or timeout on model loading or forward passes. NeuronScope loads whatever model weights the caller asks for and runs the forward pass to completion, with no built-in limit on model size or wall-clock time. If you run the MCP server somewhere an untrusted agent can call it, put a resource limit around the process (a cgroup, ulimit, or a container memory/CPU cap) rather than relying on NeuronScope to refuse an oversized request on its own.

Contributing

Issues and pull requests are welcome. To run the test suite locally:

pip install -e ".[dev]"
pytest -v

CI runs the same suite on Python 3.10, 3.11, and 3.12 on every push and pull request against main.

License

MIT. See LICENSE.