latticing 0.1.3.1

Using Behavior Latticing to infer user motivation from unstructured interactions

Behavior Latticing

Latticing is a Python library for transforming raw interaction traces into behavioral insights about the user's motivations.

Given a stream of user interactions — chat histories, screen activity logs, support tickets, behavior latticing induces user motivation through hierarchically grouping and interpreting user behavior.

Installation

pip install latticing

Set your LLM provider API key(s) in a .env file or your environment:

ANTHROPIC_API_KEY=sk-ant-...   # Anthropic (Claude)
OPENAI_API_KEY=sk-...          # OpenAI (GPT)
GOOGLE_API_KEY=...             # Google (Gemini)
TOGETHER_API_KEY=...           # Together AI

Requirements: Python 3.10+

Quick start

import os
import asyncio
from lattice import Lattice, AsyncLLM, SyncLLM
from dotenv import load_dotenv

load_dotenv()

# interaction_traces: list of sessions, each a dict with
# "interactions" (list of {interaction, metadata}) and "time"
l = Lattice(
    name="Alice",
    interactions=interaction_traces,
    description="the user's ChatGPT conversations",
    insight_model=AsyncLLM(name="claude-opus-4-6", api_key=os.getenv("ANTHROPIC_API_KEY")),
    observer_model=AsyncLLM(name="claude-sonnet-4-6", api_key=os.getenv("ANTHROPIC_API_KEY")),
    evidence_model=AsyncLLM(name="claude-sonnet-4-6", api_key=os.getenv("ANTHROPIC_API_KEY")),
    format_model=SyncLLM(name="claude-sonnet-4-6", api_key=os.getenv("ANTHROPIC_API_KEY")),
    params={"max_concurrent": 100, "min_insights": 3, "window_size": 100},
)

# Each config entry defines a layer: how to group inputs into insight clusters
config = {
    0: {"type": "session", "value": "5"},   # L1: synthesize observations across 5 chunks (i.e., chat conversations)
    1: {"type": "session", "value": "10"},  # L2: synthesize insights across 10 chunks from the layer below
}

asyncio.run(l.build(config))
l.save("lattice.json")

# Interactive Plotly figure
l.visualize().show()

How it works

1. Observe — the Observer reads sliding windows of raw interactions and prompts an LLM to infer the user's behavior.
2. Synthesize — observations are grouped by session and synthesized into titled, evidence-backed insights with context on when each pattern applies.
3. Layer — insights from multiple sessions are merged into higher-order patterns, revealing cross-session behaviors invisible at the individual session level.
4. Explore — navigate the resulting lattice interactively — hover any node to read its full text, trace it back to its supporting observations.

Lattice structure

Layer	Contents
0	Raw observations — inferred emotional/cognitive states from interaction windows
1+	Insights — higher-order inferred motivations that are induced from the layer below

Edges link each insight back to the observations or lower-level insights that support it.

Supported providers

Pass different providers per role for cost/quality tradeoffs:

from lattice import AsyncLLM, SyncLLM

model = AsyncLLM(name="claude-opus-4-6", api_key=os.getenv("ANTHROPIC_API_KEY"))
format_model = SyncLLM(name="claude-haiku-4-5", api_key=os.getenv("ANTHROPIC_API_KEY"))

Examples

Explore examples of how behavior latticing can be applied to different types of interaction data:

• LLM Chat Histories: Colab Notebook

Documentation

Full documentation is available at https://stanfordhci.github.io/lattice.

Citation

If you use Behavior Latticing in academic work, please cite:

@article{zhao2026behavior,
  title={Behavior Latticing: Inferring User Motivations from Unstructured Interactions},
  author={Zhao, Dora and Lam, Michelle S and Yang, Diyi and Bernstein, Michael S},
  journal={arXiv preprint arXiv:2604.07629},
  year={2026}
}

License

MIT — see LICENSE for details.