---
license: cc-by-4.0
language:
- en
tags:
- ai-agents
- agent-economy
- rankings
- mcp
- erc-8004
- x402
- crewai
- langchain
pretty_name: AgentCrush Agent Index
size_categories:
- 1K<n<10K
task_categories:
- text-classification
- question-answering
configs:
- config_name: agents
  data_files:
  - split: train
    path: data/agents.jsonl
- config_name: evidence_ranked
  data_files:
  - split: train
    path: data/evidence-ranked.jsonl
- config_name: snapshots_latest
  data_files:
  - split: train
    path: data/snapshots-latest.jsonl
---

# AgentCrush Agent Index

Evidence-ranked index of the AI agent economy. Updated daily from [agentcrush.xyz](https://agentcrush.xyz).

## Overview

- **1,403 agents** indexed across categories: developer tools, tokenized agents, service agents, model families
- **145 evidence-ranked** with verified multi-signal scores
- Updated: **2026-07-14**

## Configs

| Config | Description | Rows |
|---|---|---|
| `agents` | All indexed agents with metadata | ~1,403 |
| `evidence_ranked` | Evidence-ranked tier only | ~145 |
| `snapshots_latest` | Most recent snapshot per agent | ~1,403 |

## Schema

| Field | Type | Description |
|---|---|---|
| `handle` | string | Unique identifier (e.g. `crewai`, `aixbt_agent`) |
| `name` | string | Display name |
| `category` | string | `developer` | `tokenized` | `service` | `model_family` |
| `tier` | string | `evidence_ranked` | `indexed` | `archived` |
| `score` | float | 0–100 composite score |
| `rank` | int | Rank within category |
| `weekly_delta` | int | Rank change vs previous week |
| `github_stars` | int | GitHub stars (if applicable) |
| `follower_count` | int | X/Farcaster follower count |
| `erc8004_verified` | bool | On-chain ERC-8004 identity verified |
| `x402_enabled` | bool | x402 payment endpoint active |
| `profile_url` | string | Full profile URL on agentcrush.xyz |

## Usage

```python
from datasets import load_dataset

# All agents
ds = load_dataset("agentcrush/agents-index", "agents")

# Evidence-ranked only
top = load_dataset("agentcrush/agents-index", "evidence_ranked")

# Check a specific agent
df = ds["train"].to_pandas()
agent = df[df["handle"] == "crewai"].iloc[0]
```

```python
# Filter by category
developer_agents = df[df["category"] == "developer"].sort_values("rank")

# Top movers this week
movers = df[df["weekly_delta"] > 5].sort_values("weekly_delta", ascending=False)

# ERC-8004 verified agents
verified = df[df["erc8004_verified"] == True]
```

## Methodology

Rankings use per-category multi-signal scoring:
- **Developer**: GitHub stars, forks, follower counts, activity signals
- **Tokenized**: market cap, liquidity, holder count, momentum
- **Service**: adoption, protocol presence, activity, forks
- **Model family**: HF downloads, LMArena scores, derivatives, citations

Full methodology: [agentcrush.xyz/methodology](https://agentcrush.xyz/methodology)

## License

[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
Attribution: AgentCrush (agentcrush.xyz)

## Citation

```bibtex
@misc{agentcrush2026,
  title={AgentCrush Agent Economy Index},
  author={AgentCrush},
  year={2026},
  url={https://agentcrush.xyz},
  note={Daily-updated dataset. agentcrush.xyz/methodology}
}
```