Built-in Agent Reference

TeXRA ships with built-in agents for common research tasks—polishing prose, fixing errors, creating figures, converting formats, and more. Pick one from the dropdown in the TeXRA UI and you're ready to go.

Quick Reference

Agent	Type	Purpose
chat	Tool-use	General assistance, file editing
ask	Tool-use	Read-only questions and exploration
search	Tool-use	Literature discovery, web search
research	Tool-use	Computational verification with Wolfram
discuss	Tool-use	Academic brainstorming with literature
lean	Tool-use	Lean 4 proof development
presenter	Tool-use	Interactive presentation builder
simplifier	Tool-use	Simplify code and LaTeX for clarity
correct	Workflow	Fix errors without style changes
polish	Workflow	Improve writing quality
paper2slide	Workflow	Convert papers to beamer slides
paper2poster	Workflow	Create academic posters
draw	Workflow	Create/enhance TikZ figures
ocr	Workflow	Extract text from images/PDFs
transcribe_audio	Workflow	Transcribe audio to text
merge	Workflow	Intelligently merge document versions

Important Note

The underlying prompts and specific behaviors of these built-in agents may change slightly between TeXRA versions as we continue to optimize them. If you require precise, unchanging behavior or wish to heavily customize the process, consider creating a Custom Agent based on these examples.

For details on the underlying structure and execution flow common to all agents, see the Agent Architecture & Execution Flow guide.

Conversational Agents

chat

Your go-to research companion. It can read your project, edit files, run shell commands, and search through your workspace—all in a back-and-forth conversation.

User story: You just got reviewer comments back. Instead of manually hunting through a 40-page paper, you open chat and paste the reviewer's feedback: "Address comment 3 about missing error bars in Table 2—add them and update the caption." The agent reads your files, makes the edits, and shows you a diff to approve.

Best for: General research assistance, code/LaTeX editing, running compilations

Example instruction:

Review my introduction in paper.tex and suggest improvements for clarity.
Then update the file with your changes.

ask

A read-only assistant for exploring your workspace. It can answer questions about your project without touching any files—safe to use when you just want to understand what's there.

Best for: Quick questions, understanding existing code, safe exploration

Example instruction:

What packages does this LaTeX project use? Summarize the document structure.

Research & Discovery Agents

search

Finds papers and web content for you. Give it a topic and it comes back with relevant preprints, published articles, and web resources. Read-only—it won't touch your files.

Best for: Literature reviews, finding citations, fact-checking

Example instruction:

Find recent papers on transformer architectures for scientific document understanding.
Focus on papers from 2023-2024 that address mathematical equation handling.

research

A hands-on research agent that can edit your files and verify mathematics computationally. When you need to check a derivation or run a symbolic calculation alongside your writing, this is the one.

Best for: Mathematical derivations, computational verification, multi-step research

Example instruction:

Derive the variational equations for the Lagrangian in equations.tex.
Verify each step computationally and update the file with results.

discuss

A brainstorming partner that can pull in relevant literature as you talk. Useful for thinking through research directions, poking holes in methodology, or connecting ideas across papers. Read-only.

Best for: Brainstorming, methodology critique, research direction guidance

Example instruction:

I'm considering attention mechanisms for my theorem prover. What are the
tradeoffs compared to tree-based approaches? What does the literature say?

Formal Methods Agents

lean

Helps you write and debug Lean 4 proofs. It reads compiler diagnostics, inspects proof state, and iterates until the proof compiles.

Best for: Formalizing proofs, Lean 4 development, Mathlib projects

Example instruction:

Formalize the proof of the theorem in Proofs/GroupTheory.lean. Start with an
informal outline, then produce Lean code and iterate until it compiles.

Presentation & Simplification Agents

presenter

Builds conference-ready Beamer presentations, posters, and visual materials from your project. Point it at your paper and it reads through your work, plans the slide structure, generates figures, and compiles the result—checking every slide visually before handing it back to you.

Best for: Conference talks, poster sessions, seminar presentations, lightning talks

Example instruction:

Create a 15-slide Beamer presentation from this project. Cover motivation, the core
algorithm, key results, and future work. Use the metropolis theme and include TikZ
diagrams for the architecture.

simplifier

Cuts through complexity in your code and writing. Whether it's duplicated logic across files, overly-abstract wrappers, or LLM-generated filler prose, simplifier cleans things up while preserving correctness.

Best for: Refactoring research code, tightening manuscript prose, cleaning up verbose AI-generated text

Example instruction:

Simplify the numerical solver in solver.py. Look for duplicated code, inline any
single-use helper functions, and remove dead code. Run existing tests after each change.

Correction & Polishing Agents

correct

The correct agent focuses on fixing errors without changing the style or content of your document (think of it as a meticulous, slightly obsessive proofreader).

Purpose: Fix typos, grammatical errors, and LaTeX syntax issues.

Best for:

• Final proofreading before submission
• Fixing errors in collaborative documents
• Ensuring consistent formatting and notation

Example instruction:

Fix grammatical errors, typos, and LaTeX syntax issues throughout the document.
Ensure consistent notation for mathematical symbols and equations.
Don't change the technical content or writing style.

polish

The polish agent improves the writing quality of your document while preserving essential technical content and meaning.

User story: Your draft is technically solid but reads like it was written at 3 AM (because it was). Select polish, tell it "Improve clarity for a CVPR audience—keep all equations and citations intact," and in a couple of minutes you'll have a version that reads like it went through a professional copyedit. Review the colour-coded diff to accept or reject each change.

This agent is ideal for refining drafts that are technically sound but need language improvements before submission.

Example Output

View example

Content Generation & Transformation Agents

paper2slide

The paper2slide agent converts research papers into LaTeX beamer presentations.

Purpose: Create presentation slides from academic content.

Best for:

• Preparing conference presentations
• Converting papers for teaching purposes
• Creating seminar materials

Example instruction:

Convert this paper into a beamer presentation with approximately 15-20 slides.
Include a title slide, outline, introduction, methodology, results, and conclusion.
Use bullet points for clarity and add slide titles. Include the key figures and tables.

paper2poster

The paper2poster agent transforms papers into academic conference posters.

Purpose: Create well-structured academic posters.

Best for:

• Conference poster preparation
• Visual research summaries
• Academic showcases

Example instruction:

Convert this paper into an academic poster using the baposter template.
Include sections for Introduction, Methodology, Results, and Conclusions.
Highlight key figures and tables. Make it visually appealing with appropriate columns.

Figure & Media Agents

draw

Creates or enhances TikZ figures from textual descriptions or existing code.

User story: You need a neural-network architecture diagram for your paper. Describe the layers and connections in the instruction box, and draw generates compilable TikZ code.

Best for:

• Creating diagrams, flowcharts, or schematics from descriptions
• Improving existing TikZ figures
• Converting descriptions into LaTeX visualizations

Example instruction:

Create a TikZ figure illustrating a neural network with an input layer (3 nodes),
two hidden layers (5 nodes each), and an output layer (2 nodes).
Use appropriate colors and add labels for each layer.

ocr

The ocr agent performs Optical Character Recognition (OCR) on image or PDF files.

Purpose: Extract text content from images or non-searchable PDFs.

Best for:

• Extracting text from scanned documents or figures
• Making image-based text searchable and editable
• Processing figures containing text for analysis

Example instruction:

Perform OCR on the provided image file [figure.png] and extract all text content. Format the output as plain text.

transcribe_audio

The transcribe_audio agent converts audio files (like lectures, podcasts, or personal notes) into text transcripts. (Note: Requires native audio support, see Working with Figures).

Purpose: Create searchable text versions of spoken audio content.

Best for:

• Transcribing recorded lectures or talks
• Converting podcast episodes to text
• Transcribing personal voice memos or notes

Example instruction:

Transcribe the provided lecture audio file [lecture.mp3]. Provide the output as plain text, identifying different speakers if possible (e.g., Lecturer, Questioner 1).

Merge Agent

merge

Takes an AI-edited document and merges the improvements back into your original, keeping the best of both. It understands context, so it won't blindly overwrite your careful phrasing with generic rewrites.

Best for: Applying AI-suggested edits from output files, incorporating reviewer suggestions, combining different drafts

Example instruction:

Merge changes from the edited file into the original document. Prioritize substantive
improvements in clarity while maintaining the original's technical precision.
Preserve mathematical notation and citations from the original.

See Intelligent Merge for details on the merge workflow.

Next Steps

• Agent Architecture - How agents work internally
• Research Tools - Literature discovery and web tools
• Custom Agents - Create your own agents
• Models - AI model selection