# Agent Benchmark Card — GPT Researcher

[![Production Reliability](https://img.shields.io/badge/Production%20Reliability-Score%3A57-orange)](https://github.com/assafelovic/gpt-researcher)
[![GitHub](https://img.shields.io/badge/GitHub-View%20Repo-181717?logo=github)](https://github.com/assafelovic/gpt-researcher)

> Evaluated by [EvalMonkey](https://github.com/Corbell-AI/evalmonkey) · 2026-05-28

## Overview

| Field | Value |
|-------|-------|
| Agent | [GPT Researcher](https://github.com/assafelovic/gpt-researcher) |
| Type | Deep Research Agent |
| EvalMonkey Rank | 🏅 #1 of 10 open-source agents |
| Eval Judge | Claude Sonnet 4.5 (AWS Bedrock) |
| Chaos Profiles | `client_prompt_injection`, `client_schema_mutation` |

## Scores

| Benchmark | Baseline | Chaos | Production Reliability |
|-----------|:--------:|:-----:|:----------------------:|
| `hotpotqa` | 🟢 **66** | 17 | 46.4 |
| `truthfulqa` | 🟢 **65** | 48 | 58.2 |
| `mmlu` | 🟡 **56** | 16 | 40.0 |

| **Overall** | **66** | **43** | **57** |

## Production Reliability

```
Production Reliability = (baseline × 0.6) + (chaos × 0.4)
                       = (66 × 0.6) + (43 × 0.4)
                       = 57
```

> Production Reliability measures how your agent performs under **real-world conditions** —
> not just clean benchmark inputs, but also adversarial mutations like prompt injection,
> schema corruption, and typo flooding.

## Analysis

> Highest baseline scorer. Dropped 23 pts under chaos — sensitive to prompt injection.

## How to Re-run This Benchmark

```bash
# Install EvalMonkey
pip install git+https://github.com/Corbell-AI/evalmonkey.git

# Start GPT Researcher on port 8000 (see its own README)

# Run the same benchmarks
evalmonkey run-benchmark --scenario hotpotqa --target-url http://localhost:8000/solve
evalmonkey run-benchmark --scenario truthfulqa --target-url http://localhost:8000/solve
evalmonkey run-benchmark --scenario mmlu --target-url http://localhost:8000/solve

# Chaos test
evalmonkey run-chaos --scenario hotpotqa --chaos-profile client_prompt_injection --target-url http://localhost:8000/solve

# Generate this card
evalmonkey report --output agent_card.md
```

---

*Generated by [EvalMonkey](https://github.com/Corbell-AI/evalmonkey) — the open-source agent benchmarking and chaos framework.*