easypaper 0.2.4

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.

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 dependencies:

# Minimal SDK install
pip install -e .

# Recommended SDK install for full paper-generation features
pip install -e ".[docling,images]"

The SDK runs in-process and does not require the FastAPI server. The recommended docling,images extras add PDF full-text analysis and image-generation support used by richer planning, reference analysis, exemplar analysis, and visual assets.

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="...",
        # Use real BibTeX entries or citation strings for actual generation.
        references=["@article{example2026,title={Example Paper},year={2026}}"],
    ))
    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. Use examples/meta.json as a non-runnable schema template, and examples/template/meta.json as a self-contained runnable sample.

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 ".[docling]"        # Docling PDF parsing and full-text reference analysis
pip install -e ".[images]"         # image-generation support
pip install -e ".[docling,images]" # recommended SDK extras for richer generation
pip install -e ".[vlm]"            # Claude VLM review support
pip install -e ".[server]"         # FastAPI + uvicorn
pip install -e ".[dev]"            # pytest, ipython, etc.

Use the same extras without -e . when installing the published package, for example pip install "easypaper[docling,images]".

Docling is not a separate backend service that must be started. It is an optional Python dependency that EasyPaper imports only when PDF parsing is used. If tools.docling.enabled: true, exemplar.enabled: true with PDF exemplars, or standalone SDK calls such as EasyPaper.parse_pdf() are used, install the Docling extra first. Server mode is separate and is needed only for FastAPI integrations.

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)

Core generation:

• POST /metadata/generate — generate a paper from PaperGenerationRequest
• POST /metadata/generate/stream — generate with SSE progress events
• POST /metadata/generate/section — generate or rewrite a single section

Planning and resumable generation:

• POST /metadata/prepare-plan — run planning and return a resumable metadata-agent PlanResult
• POST /metadata/generate-from-plan/stream — resume generation from that PlanResult
• streaming feedback, cancel, and resume routes are registered under /metadata/*

Metadata utilities:

• POST /metadata/generate-from-folder — synthesize PaperMetaData from a materials folder
• GET /metadata/schema — inspect the request schema

Discovery and health:

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

Docling utilities are also exposed by the server for PDF parsing and paper-analysis integrations.

Metadata Loading and Path Resolution

For JSON files, prefer loading through the request model:

import json
from pathlib import Path
from easypaper import EasyPaper, PaperGenerationRequest

def metadata_relative_path(value: str, base_dir: Path) -> str:
    candidate = Path(value).expanduser()
    if candidate.is_absolute():
        return str(candidate)
    return str((base_dir / candidate).resolve())

metadata_path = Path("metadata.json").resolve()
raw = json.loads(metadata_path.read_text(encoding="utf-8"))
if not raw.get("materials_root"):
    raw["materials_root"] = str(metadata_path.parent)
if raw.get("template_path"):
    raw["template_path"] = metadata_relative_path(raw["template_path"], metadata_path.parent)
if (
    isinstance(raw.get("code_repository"), dict)
    and raw["code_repository"].get("type") == "local_dir"
    and raw["code_repository"].get("path")
):
    raw["code_repository"]["path"] = metadata_relative_path(
        raw["code_repository"]["path"],
        metadata_path.parent,
    )

request = PaperGenerationRequest.model_validate(raw)
metadata = request.to_metadata()
options = {
    "compile_pdf": request.compile_pdf,
    "enable_review": request.enable_review,
    "enable_vlm_review": request.enable_vlm_review,
    "max_review_iterations": request.max_review_iterations,
    "output_dir": request.output_dir,
    "target_pages": request.target_pages,
}

result = await EasyPaper(config_path="configs/dev.yaml").generate(metadata, **options)

Use this explicit load-and-split pattern when turning request JSON into SDK arguments.

Metadata should use relative paths where practical so examples and generated metadata remain portable. Runtime figure and table asset lookup uses materials_root first, then the current working directory. When loading a metadata file, helper scripts should set materials_root to the metadata file's parent directory if it is missing.

template_path and local code_repository.path are operational paths; the current SDK does not resolve them through materials_root. File-loading helpers such as examples/sdk_demo.py normalize those fields relative to the metadata file parent before calling EasyPaper.generate(). output_dir is an optional runtime output setting and may be omitted.

Review flags are distinct:

• enable_review controls the text review/revision loop and defaults to true.
• enable_vlm_review controls VLM/PDF visual review such as page-overflow checks and defaults to false.

Repository Layout

.
├── easypaper/          # Thin SDK package (public API)
│   ├── __init__.py     # Re-exports: EasyPaper, PaperMetaData, EventType, ...
│   └── client.py       # EasyPaper class: generate(), generate_stream(), generate_metadata_from_folder()
├── src/                # Core implementation (agents, config, skills)
│   ├── main.py         # FastAPI app (server mode entrypoint)
│   ├── agents/         # Agent implementations (metadata, writer, reviewer, ...)
│   ├── config/         # YAML config loading and schema
│   └── skills/         # Skill loader, registry, and router
├── configs/            # YAML configs for agents and models
├── skills/             # Built-in YAML skill definitions (venues, writing, reviewing)
├── scripts/            # CLI utilities and demos
├── examples/           # SDK usage examples
├── plugins/            # Claude Code plugin assets
├── tests/              # Test suite
└── pyproject.toml      # Package metadata (name: easypaper)