Scientific Writer

Generate LaTeX-formatted academic papers using specialized AI models designed for scientific writing.

Quick Start

Setup

Install PraisonAI and set up HuggingFace access:

pip install "praisonaiagents[llm]"
export HUGGINGFACE_API_KEY="your-key"   # for CAJAL-4B via litellm
# Optional fallback: export OPENAI_API_KEY="your-key"

Simple Scientific Writing

Create a specialized scientific writer agent:

from praisonaiagents import Agent, tool


@tool
def format_latex_section(title: str, content: str) -> str:
    """Wrap prose in a LaTeX \\section{} block."""
    return f"\\section{{{title}}}\n{content}\n"


@tool
def format_citation(authors: str, year: int, title: str, venue: str) -> str:
    """Render an APA citation."""
    return f"{authors} ({year}). {title}. *{venue}*."


agent = Agent(
    name="Scientific Writer",
    instructions="You are a specialised scientific paper writer. Use LaTeX formatting and APA citations.",
    llm="huggingface/Agnuxo/CAJAL-4B-P2PCLAW",
    tools=[format_latex_section, format_citation],
)

agent.start(
    "Write a short paper (Abstract + Introduction + Conclusion) on "
    "'Climate change effects on coral reef biodiversity'."
)

Swap llm="huggingface/Agnuxo/CAJAL-4B-P2PCLAW" for any other model (e.g. gpt-4o-mini) to use a general-purpose LLM if HuggingFace access is unavailable.

How It Works

The scientific writing system uses specialized agents for different parts of academic paper creation:

Stage	Purpose	Output
Literature Review	Survey existing research	APA-formatted citations
Methodology Design	Create research approach	LaTeX methods section
Paper Writing	Assemble final document	Complete LaTeX paper

Multi-Agent Workflow

Use multiple specialized agents for comprehensive paper generation:

from praisonaiagents import Agent, AgentTeam, Task, tool


@tool
def format_latex_section(title: str, content: str) -> str:
    """Wrap prose in a LaTeX \\section{} block."""
    return f"\\section{{{title}}}\n{content}\n"


@tool
def format_citation(authors: str, year: int, title: str, venue: str) -> str:
    """Render an APA citation."""
    return f"{authors} ({year}). {title}. *{venue}*."


literature_reviewer = Agent(
    name="Literature Reviewer",
    instructions="Survey academic literature. Produce 5–8 APA citations.",
    tools=[format_citation],
)

methodology_designer = Agent(
    name="Methodology Designer",
    instructions="Design a reproducible research methodology.",
    tools=[format_latex_section],
)

scientific_writer = Agent(
    name="Scientific Writer",
    instructions="Assemble the final paper. Use LaTeX + APA.",
    llm="huggingface/Agnuxo/CAJAL-4B-P2PCLAW",
    tools=[format_latex_section, format_citation],
)

topic = "Transformer architectures for protein folding prediction"

t1 = Task(name="review_literature", description=f"Review literature on: {topic}",
          agent=literature_reviewer, expected_output="APA literature review.")
t2 = Task(name="design_methodology", description=f"Design methodology for: {topic}",
          agent=methodology_designer, expected_output="LaTeX methods section.")
t3 = Task(name="write_paper",
          description=f"Combine review + methodology into a full paper on: {topic}.",
          agent=scientific_writer, expected_output="Complete LaTeX paper.")

team = AgentTeam(
    agents=[literature_reviewer, methodology_designer, scientific_writer],
    tasks=[t1, t2, t3],
)
print(team.start())

YAML Configuration

agents.yaml
Run Command

framework: praisonai
topic: "Climate change effects on coral reef biodiversity"

roles:
  literature_reviewer:
    role: "Literature Reviewer"
    goal: "Survey recent academic work on {topic} and produce an APA review."
    backstory: "Expert in academic literature surveying and citation hygiene."
    llm: "gpt-4o-mini"
    tasks:
      review_literature:
        description: "Produce a concise literature review with 5-8 APA citations."
        expected_output: "APA-formatted literature review."

  methodology_designer:
    role: "Methodology Designer"
    goal: "Design a reproducible research methodology for {topic}."
    backstory: "Specialises in rigorous experimental design."
    llm: "gpt-4o-mini"
    tasks:
      design_methodology:
        description: "Write a reproducible methods section in LaTeX."
        expected_output: "LaTeX methods section."

  scientific_writer:
    role: "Scientific Writer"
    goal: "Assemble the final LaTeX paper on {topic}."
    backstory: >-
      Specialised in LaTeX-formatted academic papers, fine-tuned on
      scientific literature (CAJAL-4B checkpoint).
    llm: "huggingface/Agnuxo/CAJAL-4B-P2PCLAW"
    tasks:
      write_paper:
        description: >-
          Combine the literature review and methodology into a full
          scientific paper on {topic}. Use LaTeX formatting.
        expected_output: "Complete LaTeX paper."

praisonai agents run --file agents.yaml

Which Option to Choose?

Configuration Options

Tool	Purpose	Inputs
`format_latex_section`	Wrap prose in `\section{}`	`title: str`, `content: str`
`format_citation`	Build APA citation line	`authors: str`, `year: int`, `title: str`, `venue: str`

Model Options

Model	Best For	Setup
`huggingface/Agnuxo/CAJAL-4B-P2PCLAW`	LaTeX-fluent academic writing	Requires HuggingFace API key
`gpt-4o-mini`	General-purpose fallback	Requires OpenAI API key
Local models	Privacy/cost control	Run your own inference server

Best Practices

Choose the Right Model

Use CAJAL-4B for LaTeX-fluent output when HuggingFace access is available. Fall back to gpt-4o-mini for general-purpose writing when needed.

Extend with More Tools

Add more @tool functions like format_equation, format_table, or format_figure_caption to enhance the writer’s capabilities.

Split Long Papers

For lengthy papers, create separate tasks for each section (Introduction, Methods, Results, Discussion) to maintain focus and quality.

Handle API Access

Set up both HuggingFace and OpenAI API keys for maximum flexibility. Consider local inference servers for high-volume usage.

Research Assistant

Multi-agent research workflow for comprehensive analysis

Agents Concepts

Learn about agent fundamentals and architecture

Tasks & AgentTeam

Understand task coordination and team workflows

YAML Configuration

Complete YAML configuration reference

Agent Recipes

Examples

Quick Start

How It Works

Multi-Agent Workflow

YAML Configuration

Which Option to Choose?

Configuration Options

Model Options

Best Practices

Research Assistant

Agents Concepts

Tasks & AgentTeam

YAML Configuration

​Quick Start

​How It Works

​Multi-Agent Workflow

​YAML Configuration

​Which Option to Choose?

​Configuration Options

​Model Options

​Best Practices

​Related

Research Assistant

Agents Concepts

Tasks & AgentTeam

YAML Configuration

Quick Start

How It Works

Multi-Agent Workflow

YAML Configuration

Which Option to Choose?

Configuration Options

Model Options

Best Practices

Related