CoDA: Agentic Systems for Collaborative Data Visualization

CoDA: Agentic Systems for Collaborative Data Visualization Zichen Chen, Jiefeng Chen, Sercan O. Arik, Misha Sra, Tomas Pfister, Jinsung Yoon International Conference on Learning Representations (ICLR 2026)

CoDA (Collaborative Data-visualization Agents) is a multi-agent framework that turns natural language queries into publication-quality visualizations. Instead of treating visualization as a monolithic code-generation problem, CoDA distributes the work across 8 specialized LLM agents in four sequential phases — Understanding, Planning, Generation, and Self-Reflection — that iteratively refine the output until explicit quality thresholds are met.

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Results

Benchmark	CoDA	Improvement over Prior SOTA
MatplotBench	79.5%	+24.5%
Qwen Code Interpreter	89.0%	new state-of-the-art
DA-Code	39.0%	+2× prior best
Human Evaluation (Elo)	1701	outperforms all baselines

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Architecture

CoDA employs 8 specialized LLM agents across 4 phases. A global TODO list is generated by QueryAnalyzer at the start and is propagated through every subsequent agent; VisualEvaluator uses image-level analysis to verify completion before the workflow terminates.

Phase	Agent	Responsibility
1 — Understanding	QueryAnalyzer	Parses intent, expands ambiguous queries, produces a structured TODO checklist
	DataProcessor	Loads heterogeneous data files, infers schema and statistics
2 — Planning	VisualizationMappingAgent	Assigns data columns to visual roles (axes, color, size) and selects chart type
	DesignExplorer	Specifies color scheme, layout, typography, and accessibility choices
3 — Generation	SearchAgent	Retrieves real matplotlib gallery examples to bias the code generator
	CodeGenerator	Produces clean, runnable matplotlib code grounded in the design spec
4 — Self-Reflection	DebugAgent	Executes the code, diagnoses runtime errors, and applies targeted fixes
	VisualEvaluator	Scores readability, aesthetics, and UX; triggers refinement if below threshold

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Gallery

CoDA consistently produces complete, accurate visualizations scoring 90–95/100, while baselines frequently produce broken outputs scoring 0–45/100.

Selected outputs

Task	Benchmark	Score	CoDA Output	Ground Truth
NBA team performance trends — multi-line chart	DA-Code	100 / 100
Steam game ratings — scatter plot with trend line	DA-Code	100 / 100
Hierarchical data — sunburst chart	MatplotBench	100 / 100

Self-reflection recovery

CoDA's self-reflection loop detected layout and labeling errors across 3 iterations, then automatically recovered at iteration 4 — demonstrating the robustness of quality-driven refinement.

Iter 3 (Failed)	Iter 4 (Recovered)

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Installation

Requirements: Python ≥ 3.8 and API credentials for at least one LLM provider.

git clone https://github.com/google-research/agentic-visualization.git
cd agentic-visualization
pip install -r requirements.txt

Configure your API keys:

cp .env.example .env
# Fill in OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, etc.

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Usage

Simple API

import coda

result = coda.plot(
    query="Create a sunburst chart showing browser market share by version.",
    data="path/to/data.csv",   # CSV, JSON, Excel, SQLite, or a directory
    output="result.png",
)
print(f"Quality: {result.quality_score:.2f}  —  saved to {result.output_file}")

Programmatic Workflow

from coda import Workflow
from coda.workflow.orchestrator import WorkflowConfiguration

cfg = WorkflowConfiguration(
    quality_threshold=0.9,
    max_iterations=3,
    enable_search_agent=True,
    output_directory="outputs/",
)

wf = Workflow(config=cfg, query_id="q001")
result = wf.execute_workflow(
    query="Plot monthly active users with a 90-day rolling average.",
    data_input="data/mau.csv",
    workflow_context={"original_data_path": "data/mau.csv"},
)

print(f"Success: {result.final_success}  |  Quality: {result.final_quality_score:.2f}")
# Output image: outputs/<model>_query_q001/query_1/final_result.png

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Benchmarks

1 — Download benchmark data

bash prepare_benchmark.sh
# Downloads MatPlotAgent benchmark into benchmark_data/

2 — Run generation

python run_benchmark.py \
    --start 1 --end 100 \
    --model-name        openai/gpt-4o \
    --search-model-name openai/gpt-4o-mini

Results are saved to benchmark_outputs/<model>_query_<id>/.

3 — Run evaluation

python run_evaluation.py \
    --results-dir   benchmark_outputs \
    --start 1 --end 100 \
    --model-name         openai/gpt-4o \
    --vision-model-name  openai/gpt-4o \
    --processes 10

Model tags follow the LiteLLM naming convention (provider/model-name). Any provider supported by LiteLLM — OpenAI, Anthropic, Google Gemini, Groq, etc. — works out of the box.

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Citation

@inproceedings{chen2026coda,
  title     = {{CoDA}: Agentic Systems for Collaborative Data Visualization},
  author    = {Chen, Zichen and Chen, Jiefeng and Arik, Sercan O. and
               Sra, Misha and Pfister, Tomas and Yoon, Jinsung},
  booktitle = {International Conference on Learning Representations},
  year      = {2026},
  url       = {https://coda-agent.github.io/CoDA/}
}

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License

Apache 2.0 License — see LICENSE for details.

Disclaimer

This is not an officially supported Google product. This project is not eligible for the Google Open Source Software Vulnerability Rewards Program.

This project is intended for demonstration purposes only. It is not intended for use in a production environment.