LatteReview Guide

Overview

LatteReview is a low-code Python package that uses AI agents to automate systematic literature reviews. It handles title/abstract screening, full-text assessment, data extraction, and PRISMA-compliant reporting — tasks that typically consume hundreds of researcher-hours. Supports multiple LLM backends (Anthropic, OpenAI, local models).

Installation

pip install lattereview

Core Workflow

Step 1: Initialize Review

from lattereview import ReviewProject

# Create a new review project
project = ReviewProject(
    name="ML in Medical Imaging Review",
    research_question="What deep learning architectures are used for "
                      "medical image segmentation?",
    inclusion_criteria=[
        "Uses deep learning for medical image segmentation",
        "Published in peer-reviewed venue",
        "Reports quantitative evaluation metrics",
    ],
    exclusion_criteria=[
        "Review/survey articles",
        "Non-English publications",
        "Conference abstracts only",
    ],
)

Step 2: Import Papers

# Import from various sources
project.import_papers("scopus_export.csv", source="scopus")
project.import_papers("pubmed_export.csv", source="pubmed")

# Or from a DataFrame
import pandas as pd
df = pd.read_csv("papers.csv")
project.import_from_dataframe(df,
    title_col="title",
    abstract_col="abstract",
    year_col="year",
)

print(f"Imported {project.total_papers} papers")

Step 3: AI Screening

from lattereview.agents import ScreeningAgent

# Configure screening agent
screener = ScreeningAgent(
    llm_provider="anthropic",
    model="claude-sonnet-4-20250514",
    criteria=project.inclusion_criteria,
    exclusion=project.exclusion_criteria,
)

# Title/abstract screening
results = screener.screen(
    project.papers,
    mode="title_abstract",
    confidence_threshold=0.7,
)

# Results include: decision, confidence, reasoning
for paper in results[:3]:
    print(f"{paper.title}")
    print(f"  Decision: {paper.decision} "
          f"(confidence: {paper.confidence:.2f})")
    print(f"  Reason: {paper.reasoning}")