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Benchmarking multimodal agents on realistic, ultra-challenging visual scenarios requiring long-horizon hybrid tool use.

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<div align="center">
  <img src="docs/logo.png" alt="Logo" width="300">
  <h1 align="center">AgentVista: Evaluating Multimodal Agents in<br>Ultra-Challenging Realistic Visual Scenarios</h1>

---

> *Real-world multimodal agents solve multi-step workflows grounded in visual evidence.*

**AgentVista** is the first benchmark that evaluates multimodal agents on realistic, ultra-challenging visual scenarios requiring long-horizon hybrid tool use. It spans **25 sub-domains** across **7 categories** with **209** expert-curated tasks. Even the best model, **Gemini-3-Pro**, achieves only **27.3%** overall accuracy. This repository provides a **lightweight yet general agent framework** for reproducible evaluation.

<div align="center">
  <img src="docs/data_examples.png" alt="AgentVista Examples" width="800">
  <br>
  <em>Sampled AgentVista examples from each domain. Each query is grounded in complex, real-world visual scenes and is designed to elicit agentic tool use with multi-step reasoning toward a unique, verifiable answer.</em>
</div>

---

## News

- **[2026/02]** AgentVista benchmark, evaluation codebase, and dataset are publicly released.

---

## What is AgentVista?

AgentVista is a benchmark designed to evaluate generalist multimodal agents on **diverse, realistic, and ultra-challenging tasks**. It contains **209 tasks** spanning 25 sub-domains across 7 categories:

| Category | Examples |
|---|---|
| **Commerce** | Product matching, price comparison, nutritional constraint checking |
| **Geography** | Route planning, landmark identification, map-based reasoning |
| **Entertainment** | Media analysis, event identification |
| **Technology** | Hardware troubleshooting, technical diagram interpretation |
| **Society** | Policy lookup, news verification |
| **Academics** | Scientific figure analysis, formula derivation |
| **Culture** | Art identification, cultural artifact matching |

Each task is grounded in detail-rich visual states (daily photos, screenshots, technical diagrams) and requires **multi-step reasoning with interleaved tool use** -- the agent must repeatedly ground visual cues, retrieve external information, and verify intermediate decisions to arrive at a unique, verifiable answer.

### Key Features

- **Vision-centric tasks with realistic images** -- key evidence must come from the visual input, not textual shortcuts.
- **Natural interleaved hybrid tool use** -- solutions mix visual tools and text-based tools across at least two tool categories.
- **Easy to verify and stable over time** -- concise target answers in deterministic formats (number, entity name, short description).
- **Expert-curated with rigorous quality control** -- each instance undergoes agent-centric filtering, expert finalization, execution filtering, and two-round verification.

---

## Benchmark Statistics

| Statistic | Value |
|---|---|
| Total queries | 209 |
| Total images | 308 |
| Primary categories | 7 |
| Secondary categories | 25 |
| Average query length | 401.4 tokens |
| Average answer length | 40.8 tokens |
| Single-image queries | 151 (72.2%) |
| Multi-image queries | 58 (27.8%) |

---

## Tool Environment

AgentVista provides four tools covering common multimodal agent workflows:

| Tool | Description |
|---|---|
| **`web_search`** | Retrieve web pages via Serper.dev Google Search API |
| **`image_search`** | Text-to-image search and reverse image search via Serper.dev |
| **`visit`** | Open a URL and extract its main textual content (Trafilatura + Jina API fallback) |
| **`code_interpreter`** | Execute Python code in a stateful Jupyter kernel for image processing, arithmetic, structured extraction, and general programming |

All tools are exposed with detailed descriptions and structured inputs/outputs so the model can decide autonomously when and how to call each tool.

---

## Main Results

Performance on AgentVista (accuracy %). Domain abbreviations: **Comm.** (Commerce), **Geog.** (Geography), **Ent.** (Entertainment), **Tech.** (Technology), **Soc.** (Society), **Acad.** (Academics), **Cult.** (Culture).

| Model | Comm. | Geog. | Ent. | Tech. | Soc. | Acad. | Cult. | Single | Multi | **Overall** | Avg Turns |
|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| Qwen3-VL-235B | 7.14 | 7.69 | 7.69 | 26.47 | 16.00 | 20.00 | 13.33 | 11.84 | 15.79 | 12.92 | 2.34 |
| GPT-4.1 | 16.67 | 15.38 | 10.26 | 29.41 | 20.00 | 20.00 | 13.33 | 15.13 | 24.56 | 17.70 | 1.74 |
| o3 | 21.43 | 15.38 | 7.69 | 23.53 | **40.00** | 26.67 | 13.33 | 17.76 | 26.32 | 20.10 | 13.18 |
| o4-mini | 2.38 | 10.26 | 2.56 | 8.82 | 8.00 | 13.33 | 0.00 | 6.58 | 5.26 | 6.22 | 1.89 |
| GPT-5 | **23.81** | 23.08 | 12.82 | 35.29 | 28.00 | 26.67 | 26.67 | **24.34** | 24.56 | 24.40 | 12.67 |
| GPT-5.1 | 23.81 | 12.82 | 15.38 | 26.47 | 24.00 | **40.00** | **40.00** | 19.74 | 31.58 | 22.97 | 17.14 |
| GPT-5.2 | 21.43 | 17.95 | **20.51** | **38.24** | 24.00 | 33.33 | 20.00 | 23.03 | 28.07 | 24.40 | 13.85 |
| Grok-4 | 11.90 | 23.08 | 7.69 | 20.59 | 28.00 | 0.00 | 0.00 | 13.82 | 17.54 | 14.83 | 16.44 |
| Claude-Sonnet-4 | 9.52 | 15.38 | 2.56 | 29.41 | 16.00 | 20.00 | 6.67 | 11.18 | 21.05 | 13.88 | 5.37 |
| Claude-Opus-4 | 19.05 | 12.82 | 5.13 | 26.47 | 20.00 | 20.00 | 6.67 | 11.84 | 26.32 | 15.79 | 6.89 |
| Claude-Opus-4.1 | 11.90 | 23.08 | 10.26 | 29.41 | 16.00 | 26.67 | 13.33 | 16.45 | 22.81 | 18.18 | 7.28 |
| Claude-Sonnet-4.5 | 11.90 | 23.08 | 7.69 | 26.47 | 24.00 | 20.00 | 13.33 | 17.11 | 19.30 | 17.70 | 9.99 |
| Gemini-3-Flash | 16.67 | 17.95 | 10.26 | 29.41 | 28.00 | **40.00** | 20.00 | 18.42 | 28.07 | 21.05 | 7.78 |
| **Gemini-3-Pro** | 16.67 | **28.21** | **20.51** | 32.35 | 32.00 | **40.00** | **40.00** | 23.68 | **36.84** | **27.27** | 6.67 |

**Key Takeaways:**
- AgentVista is **ultra-challenging**: the best model (Gemini-3-Pro) achieves only 27.27% overall accuracy.
- Performance varies across domains, revealing complementary strengths among model families.
- Multi-image inputs are not uniformly harder -- additional views often provide complementary evidence.
- A sizable gap exists between open-source (Qwen3-VL-235B at 12.92%) and closed-source models, highlighting room for improvement in open-source multimodal agents.

---

## Dataset

The AgentVista benchmark dataset is hosted on Hugging Face. Download it before running evaluation:

```bash
pip install huggingface_hub
huggingface-cli download Warrieryes/AgentVista --repo-type dataset --local-dir ./datasets

```

The dataset includes task queries (JSON) and associated images. After downloading, your directory structure should look like:

```
datasets/
  val.json                   # Evaluation queries with ground truth
  <image files>              # Referenced images
```

For more details, visit the dataset page: [https://huggingface.co/datasets/Warrieryes/AgentVista](https://huggingface.co/datasets/Warrieryes/AgentVista)

---

## Installation

### Prerequisites

- Python 3.10+
- A reasoning model API key (OpenAI, OpenRouter, or compatible endpoint)
- A [Serper.dev](https://serper.dev) API key for web/image search
- A [Jina](https://jina.ai) API key for webpage content extraction (optional but recommended)

### Setup

```bash
# Clone the repository
git clone https://github.com/hkust-nlp/AgentVista-Bench.git
cd AgentVista-Bench

# (Recommended) Create a clean Conda environment
conda create -n agentvista python=3.10
conda activate agentvista

# Install dependencies
pip install -r requirements.txt
```

### Environment Variables

All API keys are provided via environment variables. **Never hardcode credentials.**

```bash
# Required: Reasoning model
export REASONING_MODEL_NAME="openai/gpt-4o"          # or any supported model identifier
export REASONING_API_KEY="your-api-key"
export REASONING_END_POINT="https://openrouter.ai/api/v1/chat/completions"

# Required: Search tools
export SERPAPI_KEY="your-serper-api-key"
export JINA_API_KEY="your-jina-api-key"

# Optional: Verifier for automatic accuracy scoring (requires ground truth)
export VERIFIER_MODEL_NAME="openai/gpt-4.1"
export VERIFIER_API_KEY="your-verifier-api-key"
export VERIFIER_END_POINT="https://openrouter.ai/api/v1/chat/completions"

# Optional: Control which tools are enabled (default: all)
export ENABLED_TOOLS="web_search,image_search,visit,code_interpreter"
```

---

## How to Run

### Quick Start

The simplest way to run the agent:

```bash
# 1. Set environment variables (see above)
# 2. Run the evaluation script
bash run.sh
```

### Running Directly with Python

For more control, call `infer.py` directly:

```bash
python infer.py \
  --input-file /path/to/val.json \
  --image-folder /path/to/images \
  --output-dir /path/to/output \
  --max-turns 30 \
  --max-images 100 \
  --max-total-tokens 65536 \
  --skip-completed
```

### Data Format

- **Input**: A JSON or JSONL file where each item contains:
  - `question_id` or `doc_id` -- unique identifier
  - `problem` or `question` -- the task query
  - `images` (optional) -- list of image paths relative to the image folder
  - `solution` (optional) -- ground truth answer for automatic scoring

- **Images**: A single folder containing all referenced images.

### Output Structure

Results are written to `--output-dir` with the following structure:

```
output/
  results.jsonl              # Aggregated results
  summary_metrics.json       # Overall accuracy and statistics
  <question_id>/
    traj.jsonl               # Full agent trajectory (turns, tool calls, responses)
    metrics.json             # Per-sample accuracy and analysis
```

---

## Project Structure

```
agenvista/
  infer.py                   # Main inference entry point
  run.sh                     # Convenience script to launch evaluation
  requirements.txt           # Python dependencies
  configs/
    tool_configs.yaml        # Tool-specific configuration
    prompt_loader.py         # Inference prompt loader
  prompts/
    inference_prompts.yaml   # System and turn prompts
  engine/
    api/
      api_caller.py          # LLM API caller with retry and fallback
      api_model_caller.py    # Model-level orchestration
      api_processors.py      # Single-sample processing pipeline
      api_tool_handler.py    # Tool dispatch and execution
  tools/
    base.py                  # Abstract base class for tools
    tool_registry.py         # Tool registration and retrieval
    web_search.py            # Web search via Serper.dev
    image_search.py          # Image search (text-to-image + reverse)
    visit.py                 # Webpage content extraction
    code_interpreter.py      # Python code execution via Jupyter kernel
  search/
    tree.py                  # Search tree for reasoning state management
  utils/
    context_utils.py         # Token estimation and image processing
    function_call_parser.py  # Parse API responses for tool calls
    result_utils.py          # Result writing and summary statistics
    tool_schema_builder.py   # Build OpenAI-compatible tool schemas
    general_qa_tool.py       # QA verifier for accuracy scoring
```

---

## Citation

If you find AgentVista useful, please cite our paper:

```bibtex
@article{su2026agentvista,
  title={AgentVista: Evaluating Multimodal Agents in Ultra-Challenging Realistic Visual Scenarios},
  author={Su, Zhaochen and Gao, Jincheng and Guo, Hangyu and Liu, Zhenhua and Zhang, Lueyang and Geng, Xinyu and Huang, Shijue and Xia, Peng and Jiang, Guanyu and Wang, Cheng and others},
  journal={arXiv preprint arXiv:2602.23166},
  year={2026}
}
```

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