# HKUDS/ClawWork

"ClawWork: OpenClaw as Your AI Coworker - 💰 $15K earned in 11 Hours"

Repository: https://github.com/HKUDS/ClawWork
Canonical: https://ross.abutalabs.com/products/clawwork
Language: Python
License: MIT
License Family: permissive
Last push: 2026-03-03T15:32:37+00:00

## Health v2 (maintenance only)
Score: 47/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 70, release rhythm 35, longevity 14
- inputs: {"age_days": 199, "days_push": 183, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8522, forks 1101 (observed 2026-08-28T04:10:23.335797+00:00)

## What it is
ClawWork is an open-source benchmark and framework that turns AI assistants into 'AI coworkers' which must earn income by completing real professional tasks from the GDPVal dataset while paying for their own token usage. It includes an economic survival evaluation system, leaderboards across LLM agents, and a local dashboard for tracking agent income, cost, and work quality.

## Use cases
- benchmark ai agents on real-world economic tasks
- compare llm agents by income earned and cost efficiency
- evaluate ai work quality across 44 professions
- run an economic survival simulation for autonomous agents
- track agent performance on the gdpval task dataset
- visualize agent leaderboard results in a local dashboard

## When to choose
- you want to measure LLM agents on real professional work tasks rather than static benchmarks
- you need cost-aware agent evaluation that factors in token expenses
- you want to compare models like Qwen, Gemini, GLM, or Kimi on economic survival metrics

## When to avoid
- you need a production AI assistant for actual business work rather than an evaluation harness
- you only need standard agent benchmarks like SWE-bench or MMLU
- you require guaranteed reproducible results, since live LLM APIs introduce variance and cost

## Facets
- artifact type: framework
- maturity: active
- function: agent-framework, benchmarking, llm-inference, data-visualization, analytics
- domain: artificial-intelligence, large-language-models, data-science, developer-tools
- platform: python, cli, cross-platform
- tags: economic-benchmark, ai-coworker, gdpval, agent-evaluation, llm-agents, dashboard, ai-agents, docker

## Member repositories
- HKUDS/ClawWork (main) score 47

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:23.335797+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:26:40.488798+00:00, confidence not recorded.
  - readme: https://github.com/HKUDS/ClawWork (fetched 2026-08-28T04:10:23.335797+00:00, sha baae12e6c69d)
- Data as of 2026-08-30T08:39:29.467469+00:00.