scoutml 0.1.3

Scout ML research papers with intelligent agents - CLI and Python library

ScoutML

Scout ML research papers with intelligent agents. A powerful command-line interface and Python library for discovering, analyzing, and implementing ML research.

Installation

pip install scoutml

Or install from source:

git clone https://github.com/prospectml/scoutml
cd scoutml
pip install -e .

Configuration

Set your API key using one of these methods:

Option 1: Environment Variable

export SCOUTML_API_KEY="your-api-key-here"

Option 2: Configuration File

Create a .env file in your working directory:

SCOUTML_API_KEY=your-api-key-here

Option 3: CLI Configuration Command

scoutml configure --api-key your-api-key-here

Quick Start

Command Line Interface

# Search for papers
scoutml search "transformer models computer vision" --limit 10

# Get implementation guide for a paper
scoutml agent implement 2010.11929 --framework pytorch

# Compare multiple papers
scoutml compare 1810.04805 2005.14165 1910.10683

# Generate a literature review
scoutml review "few-shot learning" --year-min 2020

# Find similar papers
scoutml similar --paper-id 1810.04805 --limit 5

Python Library

import scoutml

# Search for papers
results = scoutml.search("vision transformers", limit=5, year_min=2021)
for paper in results['papers']:
    print(f"{paper['title']} - {paper['citations']} citations")

# Get paper details
paper = scoutml.get_paper("2010.11929", include_similar=True)
print(paper['paper']['abstract'])

# Compare papers
comparison = scoutml.compare_papers("1810.04805", "2005.14165")
print(comparison['analysis']['key_differences'])

# Get implementation guide
guide = scoutml.get_implementation_guide("2010.11929", framework="pytorch")
print(guide['implementation']['overview'])

# Find reproducible papers
papers = scoutml.get_reproducible_papers(domain="computer vision", limit=10)

Commands Overview

Search Commands

search - Semantic Search

Search papers using natural language queries with advanced filtering.

scoutml search "your query" [OPTIONS]

Options:
  --limit INTEGER          Number of results (default: 20)
  --year-min INTEGER       Minimum publication year
  --year-max INTEGER       Maximum publication year
  --min-citations INTEGER  Minimum citation count
  --venue TEXT            Filter by venue (e.g., "CVPR", "NeurIPS")
  --sota-only             Only show state-of-the-art papers
  --domain TEXT           Filter by domain (e.g., "computer vision")
  --output FORMAT         Output format: table/json/csv (default: table)
  --export PATH           Export results to file

Example:
  scoutml search "vision transformers" --year-min 2021 --sota-only

method-search - Search by Method

Find papers using specific methods or techniques.

scoutml method-search METHOD [OPTIONS]

Options:
  --limit INTEGER      Number of results (default: 20)
  --sort-by TEXT      Sort by: citations/year/novelty (default: citations)
  --year-min INTEGER  Minimum year
  --year-max INTEGER  Maximum year
  --output FORMAT     Output format: table/json/csv

Example:
  scoutml method-search "BERT" --sort-by citations --limit 10

dataset-search - Search by Dataset

Find papers that use specific datasets.

scoutml dataset-search DATASET [OPTIONS]

Options:
  --limit INTEGER               Number of results (default: 20)
  --include-benchmarks          Include benchmark results
  --no-benchmarks              Exclude benchmark results
  --year-min INTEGER           Minimum year
  --year-max INTEGER           Maximum year
  --output FORMAT              Output format: table/json/csv

Example:
  scoutml dataset-search "ImageNet" --include-benchmarks --year-min 2020

Paper Analysis Commands

paper - Get Paper Details

Get detailed information about a specific paper.

scoutml paper ARXIV_ID [OPTIONS]

Options:
  --similar/--no-similar    Include similar papers (default: no)
  --similar-limit INTEGER   Number of similar papers (default: 5)

Example:
  scoutml paper 1810.04805 --similar --similar-limit 10

compare - Compare Papers

AI-powered comparison of multiple papers.

scoutml compare PAPER_ID1 PAPER_ID2 [PAPER_ID3...] [OPTIONS]

Options:
  --from-file PATH    Read paper IDs from file (one per line)
  --output FORMAT     Output format: rich/json/markdown (default: rich)

Example:
  scoutml compare 1810.04805 2005.14165 1910.10683 --output markdown

similar - Find Similar Papers

Find papers similar to a given paper or abstract.

scoutml similar [OPTIONS]

Options:
  --paper-id TEXT         ArXiv ID of source paper
  --abstract TEXT         Abstract text to match
  --abstract-file PATH    File containing abstract
  --limit INTEGER         Number of results (default: 10)
  --threshold FLOAT       Similarity threshold 0-1 (default: 0.7)
  --output FORMAT         Output format: table/json

Example:
  scoutml similar --paper-id 1810.04805 --limit 20
  scoutml similar --abstract "We propose a new method for..." --threshold 0.8

Research Synthesis Commands

review - Generate Literature Review

Generate an AI-synthesized literature review on a topic.

scoutml review TOPIC [OPTIONS]

Options:
  --year-min INTEGER       Minimum year
  --year-max INTEGER       Maximum year
  --min-citations INTEGER  Minimum citations (default: 0)
  --limit INTEGER         Max papers to analyze (default: 50)
  --output FORMAT         Output format: rich/markdown/json
  --export PATH           Export review to file

Example:
  scoutml review "few-shot learning" --year-min 2020 --limit 100 --export review.md

Intelligent Agent Commands

agent implement - Implementation Guide

Generate a step-by-step implementation guide for a paper.

scoutml agent implement ARXIV_ID [OPTIONS]

Options:
  --framework CHOICE     Target framework: pytorch/tensorflow/jax/other (default: pytorch)
  --level CHOICE        Experience level: beginner/intermediate/advanced (default: intermediate)
  --output FORMAT       Output format: rich/json (default: rich)

Example:
  scoutml agent implement 2010.11929 --framework pytorch --level intermediate

agent critique - Research Critique

Get comprehensive research critique and peer review analysis.

scoutml agent critique ARXIV_ID [OPTIONS]

Options:
  --aspects TEXT        Aspects to critique (can specify multiple):
                       methodology/experiments/claims/reproducibility
  --output FORMAT      Output format: rich/json

Example:
  scoutml agent critique 1810.04805 --aspects methodology --aspects experiments

agent solve-limitations - Limitation Solver

Get solutions for paper limitations with practical approaches.

scoutml agent solve-limitations ARXIV_ID [OPTIONS]

Options:
  --focus TEXT         Specific limitation to focus on
  --tradeoffs TEXT     Acceptable tradeoffs (can specify multiple):
                      accuracy/speed/memory/complexity/data_requirements/quality
  --output FORMAT     Output format: rich/json

Example:
  scoutml agent solve-limitations 1810.04805 --focus computational --tradeoffs speed

agent design-experiment - Experiment Designer

Design experiments to validate or extend research hypotheses.

scoutml agent design-experiment BASE_PAPER HYPOTHESIS [OPTIONS]

Options:
  --gpu-hours INTEGER    Available GPU hours
  --datasets TEXT        Available datasets (can specify multiple)
  --output FORMAT        Output format: rich/json

Example:
  scoutml agent design-experiment 2010.11929 "ViT works on small datasets with augmentation" \
    --gpu-hours 100 --datasets CIFAR-10 --datasets CIFAR-100

Research Intelligence Commands

insights reproducibility - Reproducibility Analysis

Analyze papers ranked by reproducibility score.

scoutml insights reproducibility [OPTIONS]

Options:
  --domain TEXT        Filter by domain
  --year-min INTEGER   Minimum year
  --year-max INTEGER   Maximum year
  --limit INTEGER      Number of results (default: 20)
  --output FORMAT      Output format: rich/json/csv

Example:
  scoutml insights reproducibility --domain "computer vision" --year-min 2021

insights compute - Compute Requirements Analysis

Analyze GPU/compute trends across papers.

scoutml insights compute [OPTIONS]

Options:
  --method TEXT        Filter by method/technique
  --year-min INTEGER   Minimum year
  --year-max INTEGER   Maximum year
  --output FORMAT      Output format: rich/json/csv

Example:
  scoutml insights compute --method transformer --year-min 2020

insights funding - Funding Analysis

Analyze funding sources and their impact.

scoutml insights funding [OPTIONS]

Options:
  --institution TEXT   Filter by institution
  --source TEXT       Filter by funding source
  --year-min INTEGER  Minimum year
  --year-max INTEGER  Maximum year
  --limit INTEGER     Number of top sources (default: 20)
  --output FORMAT     Output format: rich/json/csv

Example:
  scoutml insights funding --source NSF --institution MIT

Advanced Examples

Complex Search with Multiple Filters

# Find recent SOTA transformer papers in computer vision
scoutml search "vision transformer" \
  --year-min 2022 \
  --min-citations 50 \
  --sota-only \
  --domain "computer vision" \
  --venue "CVPR" \
  --export sota_transformers.json

Complete Paper Analysis Pipeline

# 1. Find a paper
scoutml search "BERT" --limit 1

# 2. Get implementation guide
scoutml agent implement 1810.04805 --framework pytorch

# 3. Get research critique
scoutml agent critique 1810.04805

# 4. Find and compare similar papers
scoutml similar --paper-id 1810.04805 --limit 3 > similar_ids.txt
scoutml compare 1810.04805 1906.08237 1907.11692

Literature Review Workflow

# Generate comprehensive review with export
scoutml review "federated learning privacy" \
  --year-min 2020 \
  --year-max 2024 \
  --min-citations 20 \
  --limit 75 \
  --output markdown \
  --export federated_learning_review.md

Batch Processing

# Create a file with ArXiv IDs
cat > papers.txt << EOF
1810.04805
2005.14165
1910.10683
2010.11929
EOF

# Compare all papers
scoutml compare --from-file papers.txt --output markdown > comparison.md

# Get implementation guides for all
while read -r paper_id; do
  echo "=== Implementation guide for $paper_id ===" >> implementations.txt
  scoutml agent implement "$paper_id" --output json >> implementations.txt
done < papers.txt

Output Formats

• table (default): Rich terminal tables with colors and formatting
• json: Structured JSON for programmatic use
• csv: Comma-separated values for spreadsheet analysis
• markdown: Formatted markdown for documentation
• rich: Enhanced terminal output with panels and formatting

Tips and Best Practices

1. API Key Security: Never commit your API key to version control. Use environment variables or .env files.

2. Efficient Searching: Start with broader queries and use filters to narrow results rather than overly specific initial queries.

3. Batch Operations: When analyzing multiple papers, use --from-file options or shell scripts for efficiency.

4. Output Formats: Use --output json when piping to other tools or for programmatic processing.

5. Export Results: Use --export to save results for later analysis or sharing.

Error Messages

The CLI provides helpful error messages:

• Missing API Key: Clear instructions on how to set your API key
• Invalid Paper ID: Suggests checking the ArXiv ID format
• No Results Found: Suggests broadening search terms or adjusting filters
• Rate Limiting: Shows when to retry the request

Support

• Documentation: https://docs.scoutml.com
• Issues: https://github.com/prospectml/scoutml/issues
• Email: [e-mail]

Python Library Usage

ScoutML can be used as a Python library for programmatic access to all features:

Basic Usage

import scoutml

# All CLI commands are available as functions
results = scoutml.search("bert", limit=10)
paper = scoutml.get_paper("1810.04805")
review = scoutml.generate_review("transformers", year_min=2020)

Advanced Usage

# Get a client instance for more control
client = scoutml.get_client()

# Batch operations
paper_ids = ["2103.00020", "2010.11929", "1810.04805"]
papers = client.batch_get_papers(paper_ids, show_progress=True)

# Custom configuration
from scoutml import Config
config = Config()
config.api_key = "your-api-key"
config.base_url = "https://api.scoutml.com"
client = scoutml.ScoutMLClient(config)

Context Manager

# Automatic session management
with scoutml.Scout() as scout:
    papers = scout.semantic_search("nlp", limit=5)
    comparison = scout.compare_papers([p['arxiv_id'] for p in papers['papers'][:2]])

Error Handling

from scoutml import ScoutMLError, NotFoundError, AuthenticationError

try:
    paper = scoutml.get_paper("invalid-id")
except NotFoundError:
    print("Paper not found")
except AuthenticationError:
    print("Invalid API key")
except ScoutMLError as e:
    print(f"Error: {e}")

Available Functions

All CLI commands are available as Python functions:

• Search: search(), method_search(), dataset_search()
• Analysis: get_paper(), compare_papers(), find_similar_papers()
• Synthesis: generate_review()
• Insights: get_reproducible_papers(), analyze_compute_trends(), analyze_funding()
• Agents: get_implementation_guide(), critique_paper(), solve_limitations(), design_experiment()

See examples/python_usage.py for comprehensive examples.

License

MIT License - see LICENSE file.