> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Scientific Writer Agent

> Generate LaTeX-formatted academic papers with the CAJAL local model

<Warning>
  **Removed in PraisonAI PR #1641** — `ScientificWriterAgent` / CAJAL integration was reverted. This page is kept for historical reference only; do not use in new projects.
</Warning>

Scientific Writer Agent created LaTeX-formatted academic papers using the CAJAL local model (no longer available in current releases).

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Removed — do not use in new projects (see Warning above)
# from praisonaiagents import ScientificWriterAgent
# agent = ScientificWriterAgent()
# paper = agent.write_paper("Machine Learning in Healthcare")
```

The user requested a LaTeX paper topic; the removed Scientific Writer agent drafted academic output via CAJAL (historical only).

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Scientific Paper Generation"
        A[Research Topic] --> B[Scientific Writer]
        B --> C[LaTeX Paper]
    end

    classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
    classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef output fill:#10B981,stroke:#7C90A0,color:#fff

    class A input
    class B agent
    class C output
```

## Quick Start

<Steps>
  <Step title="Simple Usage">
    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    # Historical API — removed in PR #1641
    from praisonaiagents import ScientificWriterAgent

    agent = ScientificWriterAgent()
    paper = agent.write_paper("Machine Learning in Healthcare")

    print(f"Title: {paper.title}")
    print(f"Sections: {len(paper.sections)}")
    ```
  </Step>

  <Step title="With Configuration">
    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonaiagents import ScientificWriterAgent

    agent = ScientificWriterAgent(
        model="Agnuxo/CAJAL-4B-P2PCLAW"
    )

    paper = agent.write_paper(
        "Quantum Computing Applications",
        style="research",
        citation_style="IEEE",
    )
    ```

    <Note>
      CAJAL was a 2GB local model. Required: `pip install transformers torch`
    </Note>
  </Step>
</Steps>

***

## How It Works

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant User
    participant Agent as Scientific Writer
    participant CAJAL as CAJAL Model
    participant LaTeX as LaTeX Output
    
    User->>Agent: Topic & Requirements
    Agent->>CAJAL: Process Scientific Request
    CAJAL-->>Agent: Generated Content
    Agent->>LaTeX: Format as LaTeX
    LaTeX-->>User: Academic Paper
```

| Method              | Purpose                          | Output                     |
| ------------------- | -------------------------------- | -------------------------- |
| `write_paper()`     | Full paper generation            | Complete `ScientificPaper` |
| `write_section()`   | Single section creation          | Individual `PaperSection`  |
| `review_and_cite()` | Literature review with citations | Formatted citation text    |

***

## Choosing the Right Method

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph TB
    Start[Need Scientific Content?] --> Full{Full Paper?}
    Full -->|Yes| Paper[write_paper]
    Full -->|No| Section{One Section?}
    Section -->|Yes| Single[write_section]
    Section -->|No| Citations{Add Citations?}
    Citations -->|Yes| Review[review_and_cite]
    Citations -->|No| Paper
    
    classDef method fill:#10B981,stroke:#7C90A0,color:#fff
    classDef decision fill:#F59E0B,stroke:#7C90A0,color:#fff
    
    class Paper,Single,Review method
    class Full,Section,Citations decision
```

***

## Common Patterns

### Single Section Generation

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import ScientificWriterAgent

agent = ScientificWriterAgent()
section = agent.write_section(
    section_title="Methodology",
    content_request="Machine learning approach for medical diagnosis",
    context="Previous work on CNN architectures"
)

print(section.title)
print(section.content)
print(section.latex_content)
```

### Adding Citations to Existing Content

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import ScientificWriterAgent

agent = ScientificWriterAgent()
draft = "Machine learning shows promise in healthcare applications."

cited_content = agent.review_and_cite(
    research_query="machine learning healthcare applications",
    existing_content=draft
)
print(cited_content)
```

### Multi-Agent Workflow

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, AgentTeam, Task, ScientificWriterAgent

# Create specialized research agents
literature_reviewer = Agent(
    name="Literature Reviewer",
    instructions="Find and analyze academic papers on the topic"
)

methodology_designer = Agent(
    name="Methodology Designer", 
    instructions="Design experimental methodology and approaches"
)

scientific_writer = ScientificWriterAgent(
    model="Agnuxo/CAJAL-4B-P2PCLAW"
)

# Create research workflow
team = AgentTeam(agents=[literature_reviewer, methodology_designer, scientific_writer])

tasks = [
    Task(
        description="Review literature on quantum computing in cryptography",
        agent=literature_reviewer
    ),
    Task(
        description="Design methodology for quantum key distribution analysis", 
        agent=methodology_designer
    ),
    Task(
        description="Write complete academic paper based on research",
        agent=scientific_writer
    )
]

result = team.run(tasks=tasks)
```

***

## Configuration Options

### ScientificWriterAgent Constructor

| Parameter      | Type            | Default                                        | Description                      |
| -------------- | --------------- | ---------------------------------------------- | -------------------------------- |
| `name`         | `Optional[str]` | `"Scientific Writer"`                          | Agent display name               |
| `model`        | `Optional[str]` | `"Agnuxo/CAJAL-4B-P2PCLAW"`                    | Model identifier (CAJAL default) |
| `instructions` | `Optional[str]` | Built-in scientific instructions               | Custom behavior instructions     |
| `role`         | `Optional[str]` | `"Scientific Paper Writer"`                    | Agent role description           |
| `goal`         | `Optional[str]` | `"Generate high-quality scientific papers..."` | Agent goal definition            |
| `backstory`    | `Optional[str]` | Built-in academic backstory                    | Agent background story           |

### Method Parameters

#### `write_paper(topic, sections=None, style="academic", citation_style="APA")`

| Parameter        | Type                  | Default                                                                                       | Description                                           |
| ---------------- | --------------------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------- |
| `topic`          | `str`                 | Required                                                                                      | Research topic for the paper                          |
| `sections`       | `Optional[List[str]]` | `["Introduction", "Literature Review", "Methodology", "Results", "Discussion", "Conclusion"]` | Paper section structure                               |
| `style`          | `str`                 | `"academic"`                                                                                  | Writing style: `"academic"`, `"review"`, `"research"` |
| `citation_style` | `str`                 | `"APA"`                                                                                       | Citation format: `"APA"`, `"IEEE"`, `"Nature"`        |

#### `write_section(section_title, content_request, context=None)`

| Parameter         | Type            | Default  | Description                    |
| ----------------- | --------------- | -------- | ------------------------------ |
| `section_title`   | `str`           | Required | Title of the section           |
| `content_request` | `str`           | Required | Content requirements           |
| `context`         | `Optional[str]` | `None`   | Additional context information |

#### `review_and_cite(research_query, existing_content=None)`

| Parameter          | Type            | Default  | Description                       |
| ------------------ | --------------- | -------- | --------------------------------- |
| `research_query`   | `str`           | Required | Research query for citations      |
| `existing_content` | `Optional[str]` | `None`   | Existing text to add citations to |

### Return Types

#### `PaperSection` (dataclass)

| Field           | Type  | Default  | Description             |
| --------------- | ----- | -------- | ----------------------- |
| `title`         | `str` | Required | Section title           |
| `content`       | `str` | Required | Section content         |
| `latex_content` | `str` | `""`     | LaTeX-formatted content |

#### `ScientificPaper` (dataclass)

| Field           | Type                 | Default  | Description                                    |
| --------------- | -------------------- | -------- | ---------------------------------------------- |
| `title`         | `str`                | Required | Paper title                                    |
| `abstract`      | `str`                | Required | Paper abstract                                 |
| `sections`      | `List[PaperSection]` | `[]`     | Paper sections                                 |
| `references`    | `List[str]`          | `[]`     | Bibliography entries                           |
| `latex_content` | `str`                | `""`     | Full LaTeX document                            |
| `metadata`      | `Dict[str, Any]`     | `{}`     | Metadata (`model`, `generated_by`, `is_cajal`) |

***

## User Interaction Flow

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph TB
    User[Researcher] --> Topic[Define Topic]
    Topic --> Agent[Create ScientificWriterAgent]
    Agent --> Generate[paper = write_paper topic]
    Generate --> Inspect[Review paper.sections]
    Inspect --> Export[Save paper.latex_content]
    Export --> Compile[LaTeX Compilation]
    
    classDef user fill:#6366F1,stroke:#7C90A0,color:#fff
    classDef process fill:#F59E0B,stroke:#7C90A0,color:#fff
    classDef output fill:#10B981,stroke:#7C90A0,color:#fff
    
    class User,Topic user
    class Agent,Generate,Inspect process
    class Export,Compile output
```

A typical researcher workflow:

1. **Define Research Topic**: Specify the subject area and requirements
2. **Configure Agent**: Choose model (CAJAL for local) and citation style
3. **Generate Paper**: Call `write_paper()` with topic and preferences
4. **Review Sections**: Inspect individual sections in `paper.sections`
5. **Export LaTeX**: Save `paper.latex_content` to a `.tex` file
6. **Compile Document**: Process with LaTeX compiler for final PDF

***

## Best Practices

<AccordionGroup>
  <Accordion title="When to Use CAJAL vs General LLMs">
    **CAJAL Model Benefits:**

    * Local processing (no API calls)
    * Offline capability
    * Scientific specialization
    * Consistent academic formatting

    **General LLM Benefits:**

    * Broader knowledge base
    * Faster processing
    * No local storage requirements
    * Latest research awareness

    **Recommendation:** Use CAJAL for sensitive research or offline environments; use general LLMs for broader knowledge requirements.
  </Accordion>

  <Accordion title="Choosing Section Lists for Different Paper Types">
    **Research Papers:**

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    sections = ["Introduction", "Related Work", "Methodology", "Experiments", "Results", "Discussion", "Conclusion"]
    ```

    **Review Papers:**

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    sections = ["Introduction", "Background", "Literature Survey", "Analysis", "Future Directions", "Conclusion"]
    ```

    **Technical Reports:**

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    sections = ["Executive Summary", "Problem Statement", "Solution Approach", "Implementation", "Evaluation", "Recommendations"]
    ```
  </Accordion>

  <Accordion title="Citation Style Selection">
    * **APA**: Psychology, education, social sciences
    * **IEEE**: Engineering, computer science, technology
    * **Nature**: Natural sciences, physics, chemistry, biology
    * **MLA**: Literature, humanities (when available)

    Match the style to your target publication venue.
  </Accordion>

  <Accordion title="Multi-Agent Scientific Workflows">
    Combine Scientific Writer with complementary agents:

    1. **Literature Review Agent** → Research existing work
    2. **Data Analysis Agent** → Process experimental data
    3. **Scientific Writer Agent** → Generate formatted paper
    4. **Review Agent** → Quality check and feedback

    This creates a comprehensive research pipeline for complex projects.
  </Accordion>
</AccordionGroup>

***

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