Building Agentic Document Intelligence Pipelines: Creating Scientific Figures with AutoFigure

AutoFigure, a toolkit from ResearAI, generates publication-style scientific figures from text descriptions and paper-like content, as demonstrated in a tutorial that builds a pipeline diagram for an agentic long-document intelligence system. The tutorial sets up the environment, fixes dependency issues like Pillow compatibility, and uses AutoFigure to convert a detailed pipeline description into a clean 16:9 SVG figure, with support for API-backed generation and offline rendering. The toolkit aims to streamline the creation of scientific diagrams for finance and enterprise document intelligence audiences.

In this tutorial, we explore AutoFigure https://github.com/ResearAI/AutoFigure as a practical toolkit for generating scientific figures directly from text descriptions, paper-like content, and structured methodological explanations. In this tutorial, we set up the complete AutoFigure environment, fix dependency issues such as Pillow compatibility, and prepare the required rendering tools for SVG and PNG outputs. We then build a custom reference figure, configure an API-backed generation workflow, and use AutoFigure to convert a detailed agentic document intelligence pipeline into a publication-style scientific diagram. Along the way, we also test offline SVG rendering, inspect the generated files, create a sample paper and PDF, and export the final outputs to a reusable gallery and a zip archive. python import os import sys import json import time import glob import shutil import textwrap import subprocess import importlib from pathlib import Path from getpass import getpass REPO URL = "https://github.com/ResearAI/AutoFigure.git" REPO DIR = Path "/content/AutoFigure" OUTPUT ROOT = Path "/content/autofigure colab outputs" PROVIDER = os.environ.get "AUTOFIGURE PROVIDER", "openrouter" DEFAULT MODELS = { "openrouter": "google/gemini-3.1-pro-preview", "gemini": "gemini-3.1-pro-preview", "bianxie": "gemini-3.1-pro-preview", } GENERATION MODEL = os.environ.get "AUTOFIGURE MODEL", DEFAULT MODELS.get PROVIDER, "google/gemini-3.1-pro-preview" MAX ITERATIONS = int os.environ.get "AUTOFIGURE MAX ITERATIONS", "1" QUALITY THRESHOLD = float os.environ.get "AUTOFIGURE QUALITY THRESHOLD", "8.5" RUN TEXT TO FIGURE = True RUN PAPER TO FIGURE = False RUN MXGRAPH DEMO = False RUN IMAGE ENHANCEMENT = False TEXT OUTPUT FORMAT = "svg" MXGRAPH OUTPUT FORMAT = "mxgraphxml" ART STYLE = "clean publication-ready scientific illustration, precise alignment, subtle shadows, " "clear academic typography, high contrast, minimal clutter" FIGURE DESCRIPTION = """ Create a publication-ready scientific method figure for an agentic long-document intelligence system. The figure should explain the following pipeline in a left-to-right architecture: 1. Long documents enter the system. They may be PDFs, scanned reports, markdown files, tables, or mixed-layout documents. 2. A document normalization layer extracts raw text, section hierarchy, tables, figures, and metadata. 3. A routing planner decides whether each section should go to summarization, field extraction, table reconstruction, visual analysis, or citation grounding. 4. Specialized expert modules process the routed chunks: - Summarizer expert creates hierarchical summaries. - Extraction expert returns JSON fields. - Table expert reconstructs exact tables. - Visual expert describes charts and diagrams. - Citation expert links claims to evidence spans. 5. A low-cost orchestration layer selects smaller or larger LLMs depending on complexity, confidence, and budget. 6. A verification layer checks schema validity, source grounding, table consistency, and confidence. 7. The final output is an analyst-ready workspace containing a summary, extracted fields, exact tables, cited answers, and audit logs. Design requirements: - Use a wide 16:9 layout. - Use clear module boxes, arrows, and labels. - Add small callouts for cost control, confidence scoring, and auditability. - Avoid decorative clutter. - Make the flow understandable for a finance or enterprise document intelligence audience. """ MINI PAPER MARKDOWN = """ Efficient Agentic Document Intelligence for Long Financial Reports Abstract We propose an agentic document intelligence architecture for extracting summaries, facts, tables, and grounded answers from long, heterogeneous financial documents. Method Our method first normalizes each incoming document into a structured document graph. The graph contains section nodes, paragraph nodes, table nodes, figure nodes, and metadata nodes. A routing planner assigns each node to a specialized expert according to modality, complexity, and required output schema. The system uses five experts. The summarization expert produces hierarchical summaries from section-level chunks. The extraction expert fills strict JSON schemas for entities, dates, risks, financial metrics, and obligations. The table expert reconstructs exact tables and validates row-column alignment. The visual expert describes charts and diagrams. The citation expert maps every generated claim to source spans. A budget-aware orchestration layer selects model size dynamically. Simple chunks are processed by low-cost models, while complex chunks are escalated to stronger models. A verification layer then checks schema validity, citation support, numerical consistency, and table integrity. Failed checks are routed back for repair. Experiments We evaluate on financial filings and analyst reports using extraction accuracy, grounding precision, table reconstruction quality, and total inference cost. """ def run cmd, cwd=None, check=True, quiet=False : print f"\n$ {cmd}" process = subprocess.run cmd, shell=True, cwd=str cwd if cwd else None, text=True, stdout=subprocess.PIPE if quiet else None, stderr=subprocess.STDOUT if quiet else None, if quiet and process.stdout: print process.stdout -5000: if check and process.returncode = 0: raise RuntimeError f"Command failed with exit code {process.returncode}: {cmd}" return process def heading title : print "\n" + "=" 100 print title print "=" 100 def safe read path, max chars=2500 : path = Path path if not path.exists : return "" text = path.read text encoding="utf-8", errors="ignore" return text :max chars + "\n... truncated " if len text max chars else "" def clear loaded modules prefixes : for name in list sys.modules : if any name == prefix or name.startswith prefix + "." for prefix in prefixes : del sys.modules name def get colab secret names : try: from google.colab import userdata for name in names: try: value = userdata.get name if value: return value except Exception: pass except Exception: pass return None def collect api key provider : env candidates = "AUTOFIGURE API KEY", "OPENROUTER API KEY", "GOOGLE API KEY", "GEMINI API KEY", "BIANXIE API KEY", for key name in env candidates: value = os.environ.get key name if value: print f"Using API key from environment variable: {key name}" return value secret candidates = { "openrouter": "AUTOFIGURE API KEY", "OPENROUTER API KEY" , "gemini": "AUTOFIGURE API KEY", "GOOGLE API KEY", "GEMINI API KEY" , "bianxie": "AUTOFIGURE API KEY", "BIANXIE API KEY" , }.get provider, "AUTOFIGURE API KEY" value = get colab secret secret candidates if value: print "Using API key from Colab Secrets." return value value = getpass f"Paste your {provider} API key, or press Enter to skip cloud generation: " .strip return value We begin by importing and defining the main paths, provider settings, model configuration, and tutorial options. We also prepare the detailed figure description and sample paper content that we use later for AutoFigure generation. We then create helper functions to run commands, print section headings, read files safely, clear loaded modules, and securely collect API keys. python def display file if possible path, title=None : path = Path path if path else None if not path or not path.exists : print f"Missing file: {path}" return try: from IPython.display import display, Image as IPImage, SVG, Markdown if title: display Markdown f" {title}" suffix = path.suffix.lower if suffix == ".png": display IPImage filename=str path elif suffix == ".svg": display SVG filename=str path elif suffix in ".json", ".md", ".txt", ".drawio" : print safe read path, max chars=5000 else: print path except Exception as exc: print f"Could not display {path}: {exc}" def make output gallery output dir : output dir = Path output dir gallery path = output dir / "gallery.html" blocks = for p in sorted output dir.rglob " .png" : rel = p.relative to output dir blocks.append f""" <div class="card" <h3 {rel}</h3 <img src="{rel}" / </div """ for p in sorted output dir.rglob " .svg" : rel = p.relative to output dir svg text = p.read text encoding="utf-8", errors="ignore" blocks.append f""" <div class="card" <h3 {rel}</h3 <div class="svgbox" {svg text}</div </div """ for p in sorted output dir.rglob " .drawio" : rel = p.relative to output dir code = p.read text encoding="utf-8", errors="ignore" :4000 blocks.append f""" <div class="card" <h3 {rel}</h3 <p Editable draw.io mxGraph XML file.</p <pre {code}</pre </div """ for p in sorted output dir.rglob "generation report.json" : rel = p.relative to output dir try: report text = json.dumps json.loads p.read text encoding="utf-8" , indent=2 :7000 except Exception: report text = p.read text encoding="utf-8", errors="ignore" :7000 blocks.append f""" <div class="card" <h3 {rel}</h3 <pre {report text}</pre </div """ html = f""" < doctype html <html <head <meta charset="utf-8" <title AutoFigure Colab Gallery</title <style body {{ font-family: Arial, sans-serif; margin: 24px; background: f7f7f7; }} h1 {{ margin-bottom: 8px; }} .card {{ background: white; padding: 18px; margin: 18px 0; border-radius: 14px; box-shadow: 0 2px 16px rgba 0,0,0,0.08 ; }} img {{ max-width: 100%; border: 1px solid ddd; border-radius: 10px; }} .svgbox {{ border: 1px solid ddd; border-radius: 10px; padding: 8px; overflow: auto; }} pre {{ white-space: pre-wrap; word-break: break-word; max-height: 520px; overflow: auto; background: fafafa; padding: 12px; border-radius: 10px; }} </style </head <body <h1 AutoFigure Colab Gallery</h1 {''.join blocks } </body </html """ gallery path.write text html, encoding="utf-8" return gallery path def summarize generation result result, label : print "\n" + "-" 100 print label print "-" 100 print f"Success: {result.success}" print f"Final score: {result.final score}" print f"Iterations used: {result.iterations used}" print f"SVG path: {result.svg path}" print f"mxGraph path: {result.mxgraph path}" print f"Preview path: {result.preview path}" print f"Enhanced path: {result.enhanced path}" print f"Enhanced paths: {result.enhanced paths}" print f"Error: {result.error}" if result.logs: print "\nRecent logs:" for log in result.logs -20: : print f"- {log}" display file if possible result.preview path, f"{label}: PNG Preview" if result.svg path: display file if possible result.svg path, f"{label}: SVG" if result.mxgraph path: display file if possible result.mxgraph path, f"{label}: mxGraph XML" report candidates = for candidate in result.svg path, result.mxgraph path, result.preview path : if candidate: report candidates.append Path candidate .parent / "generation report.json" for report path in report candidates: if report path.exists : print "\nGeneration report preview:" print safe read report path, max chars=6000 try: import pandas as pd from IPython.display import display report = json.loads report path.read text encoding="utf-8" rows = for row in report.get "iteration history", : rows.append { "iteration": row.get "iteration" , "quality score": row.get "quality score" , "improvement": row.get "improvement" , "has critique": row.get "critique" is not None, } if rows: display pd.DataFrame rows except Exception as exc: print f"Could not tabulate report: {exc}" break We define utility functions that help us display generated files directly inside Colab, including PNG, SVG, JSON, Markdown, text, and draw.io outputs. We also build an HTML gallery generator so that all AutoFigure outputs can be reviewed on a single, organized page. We then add a result-summary function that prints generation metadata, displays previews, and shows the iteration report in a readable format. heading "1. Installing AutoFigure and Colab dependencies" OUTPUT ROOT.mkdir parents=True, exist ok=True run "apt-get update -qq", quiet=True run "apt-get install -y -qq " "libcairo2 libpango-1.0-0 libpangocairo-1.0-0 " "libgdk-pixbuf-2.0-0 libffi-dev shared-mime-info", quiet=True, clear loaded modules "PIL", "autofigure" run f"{sys.executable} -m pip install -q -U pip 'setuptools<82' wheel jedi", quiet=True run f"{sys.executable} -m pip install -q --force-reinstall --no-cache-dir " f"'Pillow==11.3.0'", quiet=True, if REPO DIR.exists : print f"Repository already exists at {REPO DIR}. Pulling latest main branch." run "git fetch origin main", cwd=REPO DIR, quiet=True run "git checkout main", cwd=REPO DIR, quiet=True run "git pull --ff-only origin main", cwd=REPO DIR, check=False, quiet=True else: run f"git clone {REPO URL} {REPO DIR}", quiet=True run f"{sys.executable} -m pip install -q -e '. pdf,web ' " f"reportlab pandas 'Pillow==11.3.0'", cwd=REPO DIR, quiet=True, run f"{sys.executable} -m pip install -q --force-reinstall --no-cache-dir " f"'Pillow==11.3.0'", quiet=True, clear loaded modules "PIL", "autofigure" try: from PIL import Image, ImageDraw, ImageFont print f"Pillow imported successfully. Version: {Image. version }" except Exception as exc: print "Pillow import still failed after reinstall." print "Run Runtime - Restart runtime, then rerun this full cell." raise exc if RUN MXGRAPH DEMO: run f"{sys.executable} -m playwright install chromium", quiet=True sys.path.insert 0, str REPO DIR heading "2. Importing AutoFigure SDK" from autofigure import AutoFigureAgent, Config from autofigure.generator import validate code syntax, code to png, get initial prompt template, from autofigure.extractor import MethodologyExtractor print "AutoFigure imported successfully." print f"Repository directory: {REPO DIR}" print f"Output root: {OUTPUT ROOT}" heading "3. Offline SVG preflight: validation and rendering" preflight dir = OUTPUT ROOT / "00 offline preflight" preflight dir.mkdir parents=True, exist ok=True sample svg = """ <svg width="1333" height="750" viewBox="0 0 1333 750" xmlns="http://www.w3.org/2000/svg" <rect x="0" y="0" width="1333" height="750" fill=" ffffff"/ <text x="666" y="70" text-anchor="middle" font-family="Arial" font-size="36" font-weight="700" fill=" 111111" AutoFigure Offline Rendering Check </text <rect x="120" y="220" width="250" height="140" rx="18" fill=" f3f3f3" stroke=" 111111" stroke-width="3"/ <text x="245" y="285" text-anchor="middle" font-family="Arial" font-size="24" fill=" 111111" Text Prompt</text <text x="245" y="325" text-anchor="middle" font-family="Arial" font-size="17" fill=" 444444" method description</text <line x1="390" y1="290" x2="565" y2="290" stroke=" 111111" stroke-width="4" marker-end="url arrow "/ <rect x="585" y="220" width="250" height="140" rx="18" fill=" f3f3f3" stroke=" 111111" stroke-width="3"/ <text x="710" y="285" text-anchor="middle" font-family="Arial" font-size="24" fill=" 111111" AutoFigure</text <text x="710" y="325" text-anchor="middle" font-family="Arial" font-size="17" fill=" 444444" generate → evaluate → refine</text <line x1="855" y1="290" x2="1030" y2="290" stroke=" 111111" stroke-width="4" marker-end="url arrow "/ <rect x="1050" y="220" width="250" height="140" rx="18" fill=" f3f3f3" stroke=" 111111" stroke-width="3"/ <text x="1175" y="285" text-anchor="middle" font-family="Arial" font-size="24" fill=" 111111" Figure</text <text x="1175" y="325" text-anchor="middle" font-family="Arial" font-size="17" fill=" 444444" SVG + PNG output</text <defs <marker id="arrow" markerWidth="12" markerHeight="12" refX="10" refY="6" orient="auto" <path d="M2,2 L10,6 L2,10 Z" fill=" 111111"/ </marker </defs </svg """.strip is valid, validation message = validate code syntax sample svg, "svg" print f"SVG syntax valid: {is valid}" print f"Validation message: {validation message}" sample svg path = preflight dir / "offline preflight.svg" sample png path = preflight dir / "offline preflight.png" sample svg path.write text sample svg, encoding="utf-8" render ok, processed svg = code to png sample svg, str sample png path , attempt repair=False, output format="svg", print f"Rendered PNG: {render ok} - {sample png path}" display file if possible sample png path, "Offline preflight PNG" We install the required system packages, resolve Pillow compatibility issues, clone the AutoFigure repository, and install the SDK along with its PDF and web dependencies. We then import AutoFigure’s main classes and generator utilities after confirming that the environment is ready. We also run offline SVG validation and PNG rendering tests to ensure the rendering pipeline works before making any API-based generation calls. heading "4. Creating a custom reference figure" reference dir = OUTPUT ROOT / "01 custom references" reference dir.mkdir parents=True, exist ok=True reference path = reference dir / "reference architecture style.png" W, H = 1333, 750 img = Image.new "RGB", W, H , "white" draw = ImageDraw.Draw img try: title font = ImageFont.truetype "DejaVuSans-Bold.ttf", 36 box font = ImageFont.truetype "DejaVuSans-Bold.ttf", 24 small font = ImageFont.truetype "DejaVuSans.ttf", 18 except Exception: title font = None box font = None small font = None draw.text W // 2, 55 , "Reference Layout: Modular Scientific Pipeline", anchor="mm", fill="black", font=title font, boxes = 90, 215, 290, 120, "Input", "documents" , 365, 215, 290, 120, "Planner", "route by task" , 640, 215, 290, 120, "Experts", "summary / table / vision" , 915, 215, 290, 120, "Verifier", "grounded output" , for i, x, y, bw, bh, title, subtitle in enumerate boxes : draw.rounded rectangle x, y, x + bw, y + bh , radius=22, fill= 245, 245, 245 , outline= 20, 20, 20 , width=3, draw.text x + bw / 2, y + 45 , title, anchor="mm", fill="black", font=box font, draw.text x + bw / 2, y + 82 , subtitle, anchor="mm", fill= 70, 70, 70 , font=small font, if i < len boxes - 1: ax = x + bw + 20 ay = y + bh / 2 bx = boxes i + 1 0 - 20 by = ay draw.line ax, ay, bx, by , fill="black", width=5 draw.polygon bx, by , bx - 18, by - 10 , bx - 18, by + 10 , fill="black", draw.rounded rectangle 180, 500, 1150, 585 , radius=24, fill= 252, 252, 252 , outline= 80, 80, 80 , width=2, draw.text 665, 542 , "Design cue: aligned modules, sparse labels, strong flow direction, clean academic styling", anchor="mm", fill= 40, 40, 40 , font=small font, img.save reference path print f"Custom reference saved: {reference path}" display file if possible reference path, "Custom reference figure" heading "5. Configuring API-backed AutoFigure" API KEY = collect api key PROVIDER if not API KEY: print "No API key provided. Cloud generation sections will be skipped." else: print f"Provider: {PROVIDER}" print f"Generation model: {GENERATION MODEL}" print "API key received. The key is not printed." config = None agent = None if API KEY: config = Config generation api key=API KEY, generation provider=PROVIDER, generation model=GENERATION MODEL, methodology api key=API KEY, methodology provider=PROVIDER, methodology model=GENERATION MODEL, enhancement api key=API KEY if RUN IMAGE ENHANCEMENT else None, enhancement provider=PROVIDER, enhancement model=os.environ.get "AUTOFIGURE ENHANCEMENT MODEL", "google/gemini-3.1-flash-image-preview" if PROVIDER == "openrouter" else "gemini-3.1-flash-image-preview", , max iterations=MAX ITERATIONS, quality threshold=QUALITY THRESHOLD, output dir=str OUTPUT ROOT / "02 text to figure" , custom references= str reference path , art style=ART STYLE, validation errors = config.validate print f"Config validation errors: {validation errors if validation errors else 'none'}" print f"References found by config: {len config.get references }" agent = AutoFigureAgent config heading "6. Prompt template preview" prompt preview = get initial prompt template topic="paper", content=FIGURE DESCRIPTION :2500 , output format="svg", print prompt preview :2500 print "\n... prompt preview truncated ..." if API KEY and RUN TEXT TO FIGURE: heading "7. Running text-to-figure generation" text output dir = OUTPUT ROOT / "02 text to figure" text output dir.mkdir parents=True, exist ok=True text result = agent.generate description=FIGURE DESCRIPTION, max iterations=MAX ITERATIONS, quality threshold=QUALITY THRESHOLD, output format=TEXT OUTPUT FORMAT, enable enhancement=RUN IMAGE ENHANCEMENT, art style=ART STYLE, enhancement input type="code2prompt", enhancement count=1, custom references= str reference path , output dir=str text output dir , topic="paper", summarize generation result text result, "Text-to-Figure Result" else: print "Skipping text-to-figure generation." We create a custom reference image that shows the kind of clean modular scientific layout we want AutoFigure to follow. We then configure AutoFigure with the selected provider, model, API key, output directory, reference image, iteration settings, and visual style. Finally, we preview the internal prompt template and run the main text-to-figure generation workflow to produce a scientific figure from our detailed system description. heading "8. Paper methodology extraction dry check" paper dir = OUTPUT ROOT / "03 paper to figure" paper dir.mkdir parents=True, exist ok=True paper md path = paper dir / "mini paper.md" paper md path.write text MINI PAPER MARKDOWN, encoding="utf-8" if API KEY: if RUN PAPER TO FIGURE: extractor = MethodologyExtractor config extracted = extractor.extract from file str paper md path print "\nExtracted methodology preview:" print extracted or "" :2500 else: print f"Created demo paper markdown at: {paper md path}" print "Set RUN PAPER TO FIGURE = True to run LLM methodology extraction and figure generation." else: print f"Created demo paper markdown at: {paper md path}" print "No API key available, so LLM methodology extraction is skipped." if API KEY and RUN PAPER TO FIGURE: heading "9. Running paper-to-figure generation" paper result = agent.generate from paper paper path=str paper md path , max iterations=MAX ITERATIONS, output format="svg", enable enhancement=RUN IMAGE ENHANCEMENT, art style=ART STYLE, enhancement input type="code2prompt", enhancement count=1, custom references= str reference path , output dir=str paper dir , summarize generation result paper result, "Paper-to-Figure Result" heading "10. Creating a tiny PDF and testing PDF text reading" pdf path = paper dir / "mini paper.pdf" try: from reportlab.lib.pagesizes import letter from reportlab.pdfgen import canvas c = canvas.Canvas str pdf path , pagesize=letter width, height = letter y = height - 50 for line in MINI PAPER MARKDOWN.splitlines : line = line.strip if not line: y -= 12 continue for wrapped in textwrap.wrap line, width=95 : c.drawString 50, y, wrapped y -= 14 if y < 60: c.showPage y = height - 50 c.save print f"Created demo PDF: {pdf path}" if API KEY: pdf text = MethodologyExtractor config . read pdf pdf path print "PDF text extraction preview:" print pdf text or "" :1500 else: print "PDF created. LLM-based paper-to-figure generation still requires an API key." except Exception as exc: print f"PDF creation or read test failed: {exc}" if API KEY and RUN MXGRAPH DEMO: heading "11. Running editable mxGraph XML generation" mxgraph dir = OUTPUT ROOT / "04 mxgraph drawio" mxgraph dir.mkdir parents=True, exist ok=True mx result = agent.generate description=FIGURE DESCRIPTION, max iterations=MAX ITERATIONS, quality threshold=QUALITY THRESHOLD, output format=MXGRAPH OUTPUT FORMAT, enable enhancement=False, custom references= str reference path , output dir=str mxgraph dir , topic="paper", summarize generation result mx result, "mxGraph / draw.io Result" else: heading "11. mxGraph XML generation skipped" print "Set RUN MXGRAPH DEMO = True to generate editable draw.io mxGraph XML." print "This path installs Chromium through Playwright and may be slower than SVG generation." heading "12. Output inventory and export" all files = for path in sorted OUTPUT ROOT.rglob " " : if path.is file : all files.append path print f"Total files under {OUTPUT ROOT}: {len all files }" for path in all files: rel = path.relative to OUTPUT ROOT size kb = path.stat .st size / 1024 print f"{rel} {size kb:.1f} KB " gallery path = make output gallery OUTPUT ROOT print f"\nGallery HTML: {gallery path}" zip base = "/content/autofigure colab outputs" zip path = shutil.make archive zip base, "zip", root dir=str OUTPUT ROOT print f"Zip archive: {zip path}" try: from IPython.display import display, HTML display HTML f""" <h3 AutoFigure tutorial complete</h3 <p <b Output root:</b {OUTPUT ROOT}</p <p <b Gallery:</b {gallery path}</p <p <b Zip:</b {zip path}</p """ except Exception: pass print "\nDone." print "If the model is unavailable or access is denied, change PROVIDER and GENERATION MODEL near the top of the cell." We create a small paper-style Markdown file and optionally use AutoFigure’s methodology extractor to generate a figure from paper content. We also create a simple PDF version of the paper and test whether the PDF text extraction pipeline works correctly. We finish by optionally running the mxGraph draw.io workflow, listing all generated files, building the HTML gallery, and exporting the complete output folder as a zip archive. In conclusion, we completed this tutorial by building a full AutoFigure workflow that moves from environment setup to figure generation, validation, previewing, and export. We saw how AutoFigure helps us transform complex research or system descriptions into structured scientific visuals while still giving us control over references, style, output format, iterations, and optional paper-based extraction. By the end, we have a Colab-ready pipeline that can generate SVG figures and prepare editable drawings. io-style outputs when needed, test PDF extraction, and package all generated assets for later use. Check out the FULL CODES here https://github.com/MARKTECHPOST-AI-MEDIA-INC/AI-Agents-Projects-Tutorials/blob/main/LLM%20Projects/autofigure scientific figure generation Marktechpost.ipynb . Also, feel free to follow us on Twitter and don’t forget to join our and Subscribe to 150k+ML SubReddit https://www.reddit.com/r/machinelearningnews/ . Wait are you on telegram? our Newsletter https://magic.beehiiv.com/v1/f5e63dd4-5653-4f09-83e2-321a8b1ba526?email={{email}} now you can join us on telegram as well. https://t.me/machinelearningresearchnews Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us https://forms.gle/wbash1wF6efRj8G58 Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.