easypaper 0.1.1

AI-powered academic paper generation SDK

EasyPaper

EasyPaper is a multi-agent academic paper generation system. It turns a small set of metadata (title, idea, method, data, experiments, references) into a structured LaTeX paper and optionally compiles it into a PDF through a typesetting agent.

Features

• Python SDK — pip install easypaper, then import easypaper in your own project
• Streaming generation — async generator yields real-time progress events at each phase
• Multi-agent pipeline: planning, writing, review, typesetting, and optional VLM review
• Optional FastAPI server mode with health and agent discovery endpoints
• LaTeX output with citation validation, figure/table injection, and review loop

Requirements

• Python 3.11+
• LaTeX toolchain (pdflatex + bibtex) for PDF compilation
• Poppler — required by pdf2image for PDF-to-image conversion
  • macOS: brew install poppler
  • Ubuntu/Debian: apt install poppler-utils
• Model API keys configured in YAML (see Config)

SDK Usage

Install from PyPI:

pip install easypaper

One-shot generation

import asyncio
from easypaper import EasyPaper, PaperMetaData

async def main():
    ep = EasyPaper(config_path="config.yaml")

    metadata = PaperMetaData(
        title="My Paper Title",
        idea_hypothesis="...",
        method="...",
        data="...",
        experiments="...",
    )

    result = await ep.generate(metadata)
    print(result.status, result.total_word_count)
    for sec in result.sections:
        print(f"  {sec.section_type}: {sec.word_count} words")

asyncio.run(main())

Streaming generation

Use generate_stream() to receive real-time progress events via async generator:

import asyncio
from easypaper import EasyPaper, PaperMetaData, EventType

async def main():
    ep = EasyPaper(config_path="config.yaml")
    metadata = PaperMetaData(
        title="My Paper Title",
        idea_hypothesis="...",
        method="...",
        data="...",
        experiments="...",
    )

    async for event in ep.generate_stream(metadata):
        if event.event_type == EventType.PHASE_START:
            print(f"▶ [{event.phase}] {event.message}")
        elif event.event_type == EventType.SECTION_COMPLETE:
            print(f"  ✎ {event.phase} done")
        elif event.event_type == EventType.COMPLETE:
            result = event.data["result"]
            print(f"Done! {result['total_word_count']} words")

asyncio.run(main())

GenerationEvent fields:

Field	Type	Description
event_type	EventType	PHASE_START, PHASE_COMPLETE, SECTION_COMPLETE, PROGRESS, WARNING, ERROR, COMPLETE
phase	str	Logical phase name (e.g. "planning", "introduction", "body")
message	str	Human-readable description
data	dict | None	Structured payload (section content, final result, etc.)
timestamp	datetime	When the event was created

A complete working example is available in user_case/.

Server Mode

To run EasyPaper as a FastAPI service (requires the server extra):

pip install "easypaper[server]"

1. Copy the example config and fill in your API keys:

cp configs/example.yaml configs/dev.yaml

2. Start the server:

uvicorn easypaper.main:app --reload --port 8000

3. Generate via API:

curl -X POST http://localhost:8000/metadata/generate \
  -H "Content-Type: application/json" \
  -d @economist_example/metadata.json

Config

The application loads configuration from AGENT_CONFIG_PATH (defaults to ./configs/dev.yaml). You can also set this variable in a .env file at the project root.

See configs/example.yaml for a fully commented configuration template. Each agent entry defines its model and optional agent-specific settings.

Key fields per agent:

• model_name — LLM model identifier
• api_key — API key for the model provider
• base_url — API endpoint URL

Additional top-level sections:

• skills — skills system toggle and active skill list
• tools — ReAct tool configuration (citation validation, paper search, etc.)
• vlm_service — shared VLM provider for visual review (supports OpenAI-compatible and Claude)

Repository Layout

• easypaper/ — SDK core, agent implementations, event system, shared utilities
• configs/ — YAML configs for agents and models
• scripts/ — CLI utilities and demos
• user_case/ — standalone usage example (independent environment)
• economist_example/ — sample metadata input