Interlens: Framework for Multi-Agent Interaction and Interpretability

This library provides a harness, optimized utilities, and interpretability hooks for multi-agent conversation rollouts.

from interlens import Conversation

conv = Conversation.from_models(
    ("Qwen/Qwen2.5-0.5B-Instruct", "Qwen/Qwen2.5-0.5B-Instruct"), names=("alice", "bob"),
    shared_context="Let's debate: is cereal a soup?",
)
conv.run(turns=4, first="alice")
print(conv.transcript)

See docs/examples for sample code.

*Documentation for LLMs!

Install

pip install interlens
# with hosted-API participants (APIParticipant):
pip install "interlens[api]"

PyTorch / CUDA note

torch — install the wheel matching your platform (CUDA / CPU / MPS) before or alongside interlens. E.g. for CUDA 13.0:

pip install torch --index-url https://download.pytorch.org/whl/cu130

See https://pytorch.org/get-started/locally/.

What's inside

  • Conversation — turn-taking over a shared, perspective-neutral Transcript; per-speaker view pipeline (system/private framing → context-fit → family-correct chat template).
  • AutoModelParticipant — HF-style factory (from_pretrained / from_model / from_) that returns the family-correct participant (Qwen/Gemma/…); APIParticipant for hosted models.
  • Interpretability — conv.capture(...), SteeringSpec, Patch, token_logprobs, backed by a queryable ActivationCache.
  • Scale — conv.rollout(...) / interlens.run([...]): multi-GPU, checkpointed, resumable, batched co-stepping, with in-worker analyzer callbacks; data-driven rollouts via dataset_field, matched compute via TokenBudget.
  • One object, no ceremony — a Conversation (with lazy participants) is at once the serializable recipe, the live dialogue, and the rollout driver; build it functionally (.turns(6).data(ds).analyzer(grade)), .set(...) copy-on-write, and save/load (recipe + transcript).

See docs/examples/ for a simple→advanced walkthrough of the whole API.

Develop

git clone https://github.com/Sid-MB/interlens && cd interlens
uv sync                     # installs the package + dev group (pytest, pre-commit)
uv run pre-commit install   # one-time
uv run pytest               
# fast tests; opt-in to thorough tests requiring downloading models + a GPU with: pytest -m slow

License

GNU AGPLv3 — see LICENSE.

Project links

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Credits

Author: Siddharth M. Bhatia

GitHub Statistics

Data verified by PyPI on Sep 3, 2026
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License

AGPL-3.0-only

Requires

Python <3.14, >=3.11

Provides Extra

api benchmarks inspect

Tags

activation-steering interpretability llm multi-agent transformers

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