easypaper

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.

EasyPaper can be used in two modes:

• SDK mode — pip install -e . and call from Python directly (no server needed)
• Server mode — pip install -e ".[server]" and run as a FastAPI service

Features

• Multi-agent pipeline: planning, writing, review, typesetting, and optional VLM review
• Python SDK for in-process paper generation (from easypaper import EasyPaper)
• Optional FastAPI service with health and agent discovery endpoints
• Streaming progress via generate_stream() (SDK) or SSE (server)
• CLI scripts for metadata-driven generation and paper assembly demos
• 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)

Quickstart (SDK mode)

1. Install core dependencies:

pip install -e .

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

cp examples/config.example.yaml configs/dev.yaml
# Edit configs/dev.yaml — replace YOUR_API_KEY with real keys

3. Set the config path (or create a .env file):

export AGENT_CONFIG_PATH=./configs/dev.yaml

4. Use from Python:

import asyncio
from easypaper import EasyPaper, PaperMetaData

async def main():
    ep = EasyPaper(config_path="configs/dev.yaml")
    result = await ep.generate(PaperMetaData(
        title="My Paper",
        idea_hypothesis="...",
        method="...",
        data="...",
        experiments="...",
    ))
    print(f"Status: {result.status}, Words: {result.total_word_count}")

asyncio.run(main())

5. Or use streaming for progress updates:

async for event in ep.generate_stream(metadata):
    print(f"{event.get('phase', '')}: {event.get('message', '')}")

See examples/sdk_demo.py for a complete working example.

Server Mode

To run as a FastAPI service (for external integrations):

1. Install with server extras:

pip install -e ".[server]"

2. Start the server:

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

3. Verify health:

curl http://localhost:8000/healthz

Generate a Paper via API

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

Generate via CLI

python scripts/generate_paper.py --input economist_example/metadata.json

Optional Dependencies

pip install -e ".[dev]"    # pytest, ipython, etc.
pip install -e ".[vlm]"    # Claude VLM review support
pip install -e ".[server]" # FastAPI + uvicorn

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)

Service Endpoints (Server Mode)

• GET /healthz — health check
• GET /config — current app config
• GET /list_agents — list registered agents and endpoints
• Agent-specific routes are registered under /agent/* and /metadata/*

Repository Layout