Interlens: Framework for Multi-Agent Interaction and Interpretability

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

A harness for multi-agent (model-to-model) conversations with first-class interpretability—activation capture, steering, activation patching, and token logprobs—all hooked into the same generation path as real turns and tagged to conversation structure. Scales from one interactive dialogue to thousands of checkpointed, multi-GPU 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.

Install

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

PyTorch / CUDA note

torch is declared as a plain, build-agnostic dependency — 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 — rollout / run_conversations: multi-GPU, checkpointed, resumable, batched co-stepping, with in-worker analyze callbacks.
  • Serialization — ConversationTemplate (recipe) and full save/load (template + transcript).

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

Develop

git clone https://github.com/Sid-MB/interlens && cd interlens
pip install -e ".[dev]"
pytest                      
# fast tests; opt-in to thorough tests requiring downloading models + a GPU with: pytest -m slow

License

GNU AGPLv3 — see LICENSE.

Project links

Data verified by PyPI on Jul 6, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.1.74)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for smb from gravatar.com smb

Credits

Author: Siddharth M. Bhatia

GitHub Statistics

Data verified by PyPI on Jul 6, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

AGPL-3.0-only

Requires

Python <3.14, >=3.11

Provides Extra

api dev docs

Tags

activation-steering interpretability llm multi-agent transformers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

interlens-0.1.15.tar.gz (192.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

interlens-0.1.15-py3-none-any.whl (127.0 kB view details)

Uploaded Python 3

File details

Details for the file interlens-0.1.15.tar.gz.

File metadata

  • Download URL: interlens-0.1.15.tar.gz
  • Upload date:
  • Size: 192.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for interlens-0.1.15.tar.gz
Algorithm Hash digest
SHA256 6ca46cebdeb9acc6beb753d265c73aafd3b01051a4394c02f60b42e42f10abff
MD5 aa52e1dc53a0af1a7ba012ea1ac42567
BLAKE2b-256 2ea11bfd548970d65ed78fba6db1733f46b25851187da55e5857a485f16fe3ef

See more details on using hashes here.

Provenance

The following attestation bundles were made for interlens-0.1.15.tar.gz:

Publisher: publish.yml on Sid-MB/interlens

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file interlens-0.1.15-py3-none-any.whl.

File metadata

  • Download URL: interlens-0.1.15-py3-none-any.whl
  • Upload date:
  • Size: 127.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for interlens-0.1.15-py3-none-any.whl
Algorithm Hash digest
SHA256 2fb556d5860bf71a06709d01ca5b796cfc16fde52cb5383435c6b4512efd6af0
MD5 d402c4be80dc24bfed03741368738dcb
BLAKE2b-256 3c2040bfd98baa361692de04bdcca27524db11aa27db371205a4e1215a2b42bd

See more details on using hashes here.

Provenance

The following attestation bundles were made for interlens-0.1.15-py3-none-any.whl:

Publisher: publish.yml on Sid-MB/interlens

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.1.74

2 files

0.1.73

2 files

0.1.72

2 files

0.1.71

2 files

0.1.70

2 files

0.1.69

2 files

0.1.67

2 files

0.1.66

2 files

0.1.65

2 files

0.1.64

2 files

0.1.63

2 files

0.1.62

2 files

0.1.61

2 files

0.1.60

2 files

0.1.59

2 files

0.1.58

2 files

0.1.57

2 files

0.1.56

2 files

0.1.55

2 files

0.1.54

2 files

0.1.53

2 files

0.1.52

2 files

0.1.51

2 files

0.1.50

2 files

0.1.49

2 files

0.1.48

2 files

0.1.47

2 files

0.1.46

2 files

0.1.45

2 files

0.1.44

2 files

0.1.43

2 files

0.1.42

2 files

0.1.41

2 files

0.1.40

2 files

0.1.39

2 files

0.1.38

2 files

0.1.37

2 files

0.1.36

2 files

0.1.35

2 files

0.1.34

2 files

0.1.33

2 files

0.1.32

2 files

0.1.31

2 files

0.1.30

2 files

0.1.29

2 files

0.1.28

2 files

0.1.27

2 files

0.1.26

2 files

0.1.25

2 files

0.1.24

2 files

0.1.23

2 files

0.1.22

2 files

0.1.21

2 files

0.1.20

2 files

0.1.19

2 files

0.1.18

2 files

0.1.17

2 files

0.1.16

2 files

This release

0.1.15 This release

2 files

0.1.14

2 files

0.1.13

2 files

0.1.12

2 files

0.1.11

2 files

0.1.10

2 files

0.1.9

2 files

0.1.8

2 files

0.1.7

2 files

0.1.6

2 files

0.1.5

2 files

0.1.4

2 files

0.1.3

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page