Ai Scientist

AI Scientist

Build AI agents that automate scientific research using AI-Scientist-v2 — an agentic tree search framework for hypothesis generation, experiment design, data analysis, and paper writing.

Overview

AI Scientist explores research problems as a tree search: generate candidate hypotheses, evaluate them based on evidence and feasibility, design experiments for promising branches, and prune dead ends. It covers the full research lifecycle from literature review through paper drafting.

Instructions

Installation

bashCopy code
pip install ai-scientist

Set up API key:

bashCopy code
export ANTHROPIC_API_KEY="sk-ant-..."  # or OPENAI_API_KEY

Define a Research Problem

pythonCopy code
from ai_scientist import Researcher

researcher = Researcher(
    model="claude-sonnet-4-20250514",
    domain="machine-learning",
)

result = researcher.investigate(
    question="How does data augmentation affect few-shot learning performance?",
    max_depth=3,
    max_hypotheses=5,
    budget_hours=2,
)

print(result.best_hypothesis)
print(result.evidence_summary)
print(result.suggested_experiments)

Hypothesis Generation

pythonCopy code
from ai_scientist import HypothesisGenerator

generator = HypothesisGenerator(model="claude-sonnet-4-20250514")

hypotheses = generator.generate(
    context="Recent work shows transformers struggle with compositional generalization",
    num_hypotheses=5,
    constraints=[
        "Must be testable with existing benchmarks",
        "Should suggest a concrete architectural modification",
    ],
)

for h in hypotheses:
    print(f"Hypothesis: {h.statement}")
    print(f"Novelty: {h.novelty:.2f}, Feasibility: {h.feasibility:.2f}")
    print(f"Test approach: {h.test_plan}")

Experiment Design

pythonCopy code
from ai_scientist import ExperimentDesigner

designer = ExperimentDesigner(model="claude-sonnet-4-20250514")

experiment = designer.design(
    hypothesis="Adding a symbolic reasoning layer improves compositional generalization",
    resources={
        "compute": "4x A100 GPUs",
        "time": "48 hours",
        "datasets": ["COGS", "SCAN", "CFQ"],
    },
)

print(experiment.methodology)
print(experiment.variables)
print(experiment.metrics)
print(experiment.code_outline)

Result Analysis

pythonCopy code
from ai_scientist import ResultAnalyzer

analyzer = ResultAnalyzer(model="claude-sonnet-4-20250514")

analysis = analyzer.analyze(
    hypothesis="Symbolic reasoning layer improves compositional generalization",
    results_path="./experiment_results/",
    metrics=["accuracy", "generalization_gap", "training_time"],
)

print(analysis.supports_hypothesis)
print(analysis.key_findings)
print(analysis.next_steps)

Literature Review

pythonCopy code
from ai_scientist import LiteratureReviewer

reviewer = LiteratureReviewer(model="claude-sonnet-4-20250514")

review = reviewer.review(
    topic="Compositional generalization in neural networks",
    sources=["arxiv", "semantic-scholar"],
    max_papers=50,
)

print(review.summary)
print(review.research_gaps)
print(review.taxonomy)

Paper Writing

pythonCopy code
from ai_scientist import PaperWriter

writer = PaperWriter(model="claude-sonnet-4-20250514")

paper = writer.draft(
    title="Symbolic Reasoning Layers for Compositional Generalization",
    sections=["abstract", "introduction", "related-work", "method",
              "experiments", "results", "discussion", "conclusion"],
    results=analysis,
    literature=review,
    style="neurips",
)

paper.save("draft.tex")

Examples

Example 1: End-to-End Research on RAG for Code Generation

pythonCopy code
from ai_scientist import ResearchPipeline

pipeline = ResearchPipeline(
    model="claude-sonnet-4-20250514",
    output_dir="./research_output/",
)

result = pipeline.run(
    question="Can retrieval-augmented generation reduce hallucination in code generation?",
    stages=["literature-review", "hypothesis-generation", "experiment-design",
            "result-analysis", "paper-draft"],
    config={"tree_search_depth": 3, "hypotheses_per_level": 4, "auto_prune_threshold": 0.3},
)

print(f"Hypotheses explored: {result.total_hypotheses}")
print(f"Experiments designed: {result.total_experiments}")
print(f"Best finding: {result.top_finding}")
print(f"Paper draft: {result.paper_path}")

Example 2: Quick Hypothesis Screening for Few-Shot Learning

pythonCopy code
from ai_scientist import Researcher

researcher = Researcher(model="claude-sonnet-4-20250514", domain="machine-learning")

result = researcher.investigate(
    question="Does contrastive pre-training improve few-shot classification on medical images?",
    max_depth=2,
    max_hypotheses=3,
    budget_hours=1,
)

for h in result.all_hypotheses:
    print(f"{h.statement} — score: {h.score:.2f}, pruned: {h.pruned}")
print(f"Best: {result.best_hypothesis.statement}")

Guidelines

• Start with max_depth=2 and max_hypotheses=3 to get quick results before scaling up
• Use domain-specific constraints in hypothesis generation — unconstrained search wastes compute
• The pruning threshold (auto_prune_threshold) controls exploration vs exploitation — lower values explore more
• Literature review works best with semantic-scholar for ML papers and pubmed for bio/medical
• Always review generated hypotheses and papers — the agent is a research accelerator, not a replacement
• For reproducibility, set seed in the pipeline config
• Tree search depth beyond 4 rarely improves results but significantly increases cost