> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Scientific Writer
> Generate LaTeX-formatted academic papers with the CAJAL-4B model โ single agent or multi-agent workflow
Generate LaTeX-formatted academic papers using specialized AI models designed for scientific writing.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
Topic[๐ Research Topic] --> Writer[๐งช Scientific Writer Agent]
Writer --> LaTeX[๐ LaTeX Section]
Writer --> Cite[๐ APA Citation]
LaTeX --> Paper[๐ Final Paper]
Cite --> Paper
classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff
classDef tool fill:#189AB4,stroke:#7C90A0,color:#fff
classDef result fill:#10B981,stroke:#7C90A0,color:#fff
class Topic input
class Writer agent
class LaTeX,Cite tool
class Paper result
```
## Quick Start
Install PraisonAI and set up HuggingFace access:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install "praisonaiagents[llm]"
export HUGGINGFACE_API_KEY="your-key" # for CAJAL-4B via litellm
# Optional fallback: export OPENAI_API_KEY="your-key"
```
Create a specialized scientific writer agent:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
participant User
participant Reviewer as Literature Reviewer
participant Designer as Methodology Designer
participant Writer as Scientific Writer
User->>Reviewer: Topic
Reviewer-->>Designer: Literature review (APA)
Designer-->>Writer: Methods (LaTeX)
Writer-->>User: Complete LaTeX paper
```
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:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 5โ8 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
```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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."
```
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai agents run --file agents.yaml
```
***
## Which Option to Choose?
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph TB
Q{Need full paper
with literature + methods?}
Q -->|No, just one section| Single[Single Agent
scientific_writer.py]
Q -->|Yes| Q2{Prefer code
or config file?}
Q2 -->|Python code| Multi[Multi-Agent
multi_agent_paper.py]
Q2 -->|YAML config| YAML[YAML workflow
agents.yaml]
classDef question fill:#F59E0B,stroke:#7C90A0,color:#fff
classDef option fill:#189AB4,stroke:#7C90A0,color:#fff
class Q,Q2 question
class Single,Multi,YAML option
```
***
## 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
Use CAJAL-4B for LaTeX-fluent output when HuggingFace access is available. Fall back to `gpt-4o-mini` for general-purpose writing when needed.
Add more `@tool` functions like `format_equation`, `format_table`, or `format_figure_caption` to enhance the writer's capabilities.
For lengthy papers, create separate tasks for each section (Introduction, Methods, Results, Discussion) to maintain focus and quality.
Set up both HuggingFace and OpenAI API keys for maximum flexibility. Consider local inference servers for high-volume usage.
***
## Related
Multi-agent research workflow for comprehensive analysis
Learn about agent fundamentals and architecture
Understand task coordination and team workflows
Complete YAML configuration reference