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Liberate your OpenClaw

How to Orchestrate a Fleet of OpenClaw Bots

Learn how to run OpenClaw bots for increased productivity The post How to Orchestrate a Fleet of OpenClaw Bots appeared first on Towards Data Science .

Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks

arXiv:2606. 12344v1 Announce Type: new Abstract: General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and prediction contract required for scoring.

Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks

General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and prediction contract required for scoring. We introduce Claw-SWE-Bench, a multilingual SWE-bench-style benchmark and adapter protocol that makes heterogeneous agent harnesses, or claws, comparable under fair settings including a fixed prompt, runtime budget, workspace contract, patch extraction procedure, and evaluator.

OpenForgeRL: Train Harness-native Agents in Any Environment

arXiv:2607. 21557v1 Announce Type: new Abstract: Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems.

Is it agentic enough? Benchmarking open models on your own tooling

Welcome to the Falcon 3 Family of Open Models!

Reachy Mini - The Open-Source Robot for Today's and Tomorrow's AI Builders

Introducing improvements to the fine-tuning API and expanding our custom models program

We’re adding new features to help developers have more control over fine-tuning and announcing new ways to build custom models with OpenAI.

OpenAI Pioneers Program

State of Open Models: Summer 2026 Observations

After the Party: Growth, Governance, and Security Scanning in the OpenClaw Agent Skill Ecosystem

The paper examines the rapid growth of the OpenClaw AI agent’s public skill registry, noting a near doubling of the observable stock in 91 days and a concentration of activity in a short period. It finds that only a small fraction of skills receive significant attention—most have no stars or comments—while a large portion contains privilege‑evident code. Automated security scanners show low agreement and sensitivity, indicating that current tools are insufficient for reliable governance of fast‑expanding agent‑skill ecosystems.