Articles tagged "OpenClaw"

**DeepReinforce released Ornith-1.0 on June 26, 2026** ([official](https://deep-reinforce.com/ornith_1_0.html) / [Hugging Face collection](https://huggingface.co/collections/deepreinforce-ai/ornith-10)). It is an **MIT-licensed open-weights family specialized for agentic coding**, **with no regional restrictions**. **Three sizes**: [Ornith-1.0-9B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B) (dense, ~19GB BF16) / [Ornith-1.0-35B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B) (MoE) / [Ornith-1.0-397B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B) (MoE, built on Qwen 3.5 + Gemma 4). **All sizes ship 262K context**, with **FP8 and GGUF quantizations released alongside**. **Benchmarks (vendor-reported, claimed SOTA at each open-weights size tier)**: | Benchmark | 9B | 35B | 397B | |---|---|---|---| | **SWE-Bench Verified** | **69.4%** | **75.6%** | **82.4%** | | **SWE-Bench Pro** | **42.9%** | **50.4%** | **62.2%** | | **SWE-Bench Multilingual** | — | — | **78.9%** | | **Terminal-Bench 2.1** | 43.1% | 64.2% | **77.5-78.2%** | | **NL2Repo** | 27.2% | 34.6% | **48.2%** | | **ClawEval** | — | — | **77.1%** | **Design thesis**: Reinforcement learning optimizes **both the solution rollouts and the scaffolding (the agent structure that drives them) itself** — a 'self-improving' agentic-coding design. It sits naturally next to the [Loop Engineering Maker-Checker](../columns/loop-engineering-ai-agent-paradigm-2026-06) paradigm. Reasoning is exposed via `<think>...</think>` blocks; function calling and tool use are first-class. **Distribution and ops**: vLLM ≥ 0.19.1 / SGLang ≥ 0.5.9 / Transformers ≥ 5.8.1 / Docker + llama.cpp / Ollama. OpenAI-compatible endpoints. The 9B fits on a single 80GB GPU; 35B and 397B want an **8×80GB GPU node (TP=8)**. Agent-framework compatibility: **OpenHands, Hermes Agent, and [OpenClaw](../services/openclaw-setup)** (Oflight's own service line — and ClawEval is in DeepReinforce's published benchmark set). **DeepReinforce lineage**: an RL-focused research organization that has previously shipped [CUDA-L1 (avg 3.12× GPU speedup)](https://github.com/deepreinforce-ai/CUDA-L1), [CUDA-L2 (HGEMM kernels beating cuBLAS)](https://github.com/deepreinforce-ai/CUDA-L2), and **IterX (MLSys 2026 NVIDIA Track)**. Ornith-1.0 applies the same RL playbook to LLM self-improvement. **Positioning**: alongside [Kimi K2.7-Code](../columns/kimi-k2-7-code-moonshot-ai-2026-06) (1T MoE / 32B active) and [GLM-5.2](../columns/local-llm-landscape-2026-june-update) (Intelligence Index v4.1 = 51, open-weights leader), **Ornith-1.0 is at the front of the June-2026 agentic-coding open-weights race**. Against Chinese-origin models (Kimi / GLM), its differentiator is **MIT license + no regional restrictions + a US-flag procurement story**. **Caveat**: benchmarks are DeepReinforce's own vendor-reported numbers. Independent third-party verification on public leaderboards has not yet appeared (as of June 26, 2026). The article closes with **three inquiry funnels for Ornith-1.0–era local-LLM evaluation, build, and ongoing maintenance**.

**June 16, 2026 Update**: On the very day of enforcement (June 15, 2026), Anthropic **paused the planned split of Claude Agent SDK, `claude -p`, GitHub Actions, and third-party app (OpenClaw, Zed, Conductor, etc.) usage from subscription rate limits**. The [official Help Center article](https://support.claude.com/en/articles/15036540-use-the-claude-agent-sdk-with-your-claude-plan) was amended with: "Update June 15: We are pausing the changes to Claude Agent SDK usage described below. For now, nothing has changed: Claude Agent SDK, `claude -p`, and third-party app usage still draw from your subscription is usage limits. The previously announced monthly credit, which would have been available to eligible claimants in connection with these changes, isn it available. We are working to update the plan to better support how users build with Claude subscriptions. When we have an update, we will share it before anything takes effect." **The previously announced monthly credits (Pro $20 / Max 5x $100 / Max 20x $200 / Team $20-100 / Enterprise $200) were not distributed**. Programmatic usage now once again draws from standard subscription limits. The change is officially a **pause, not a full rollback** — Anthropic says it is reworking the plan and will share details before anything new ships. The backlash that triggered this was substantial: [community estimates](https://gist.github.com/MagnaCapax/d9177e35b355853f03c730dfcaa693ef) projected effective price hikes of 12-175x against API-rate equivalents, Anthropic engineer Lydia Hallie was quickly Community-Noted on X, and Reddit r/ClaudeAI, HN, and [The New Stack](https://thenewstack.io/anthropic-agent-sdk-credits/) all carried critical coverage. This is Anthropic is **third subscription-policy reversal of 2026** (January OAuth block reversed within days, April 4 third-party agent ban reversed within 24 hours, and now the May 14 compromise credit pool paused on its June 15 enforcement day). This column preserves the original announced design while adding a detailed reversal section: timeline, operational implications, and the current validity of the "turn Extra Usage auto-billing off" guidance.

At Microsoft Build 2026 Day 1 Keynote on June 2, 2026, the open-source AI agent 'OpenClaw' was officially announced as a Windows-native integration, MXC sandbox-ready runtime, and the foundation for enterprise product Microsoft Scout. This column covers the full paradigm shift — from Agent 365 governance to pricing, competitive comparison, and implications for Japanese enterprises. Note: The OpenClaw discussed here is the OSS by Peter Steinberger and is unrelated to Obright's 'OpenClaw Setup Service'.

OpenClaw 2026.4.23 release overview. Following the late-April 2026 wave of major LLM updates (Kimi K2.6, Qwen 3.6-27B, GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro Deep Research), this note summarizes how OpenClaw on Mac mini now integrates with these new models — plus operational and cost updates.

Comprehensive reference guide for OpenClaw. Covers glossary, system requirements, command reference, MCP integration, troubleshooting, FAQs, and more.

An in-depth exploration of NVIDIA's NemoClaw and OpenClaw combination announced in March 2026, presenting a new approach to enterprise AI agent development. We examine OpenShell's secure execution environment, Supervisor+Worker multi-agent architecture, and integration with major frameworks like LangChain and LlamaIndex.

By combining Ollama and OpenClaw, you can build AI agents on Mac mini that dynamically switch between multiple LLMs. This article provides detailed practical steps from Ollama installation to model management, OpenClaw integration configuration, and performance comparison. We introduce how to build a local AI infrastructure that can be adopted by SMBs and startups, especially in Shinagawa, Minato, Shibuya, Setagaya, Meguro, and Ota wards.

This article explains how to build an internal AI chatbot with zero API costs using Ollama and OpenClaw. We introduce implementation methods for cost reduction crucial to SMBs, integration with existing Slack and LINE, conversation memory, and FAQ automation. Centered in Shinagawa, Minato, Ota, and Meguro wards, we propose a zero-cost AI strategy that can start with existing Mac hardware.

This article explains how to build a RAG (Retrieval-Augmented Generation) customer support system by combining Ollama's embedding models with OpenClaw agents. Through vector database integration, you can generate accurate answers from FAQ documents and deploy AI support across multiple channels like LINE and Slack.

This article provides a detailed explanation of how to create business-specific custom AI models using Ollama's Modelfile feature and deploy them with OpenClaw. We cover practical techniques including prompt engineering, parameter tuning, importing GGUF format models, and A/B testing through multi-agent routing.

Build your own AI agent easily with Mac mini M4, Ollama, and OpenClaw. This beginner-friendly guide walks you through everything from Homebrew installation to model downloads and LINE/Slack integration, step by step. Supporting Tokyo-area businesses in Shinagawa, Minato, Shibuya, and beyond with AI agent implementation.

A comprehensive guide to building high-performance AI agents with Qwen3.5-9B using Mac mini M4 and OpenClaw. Covers hardware requirements, LINE/Slack/Discord integration, and performance benchmarks.

Learn how to build a fully offline AI customer support system using OpenClaw and Qwen3.5-9B. Discover the benefits of offline operation from data privacy, cost reduction, and reliability perspectives, with detailed coverage from architecture design to FAQ automation.

Learn how to build an AI sales assistant that operates across multiple channels including LINE, Slack, Discord, and WhatsApp using OpenClaw and Qwen3.5-9B. We cover practical approaches to automating the entire sales process from lead generation to appointment scheduling and CRM integration.

Learn how SMBs can deploy AI agents cost-effectively using OpenClaw and Qwen3.5-9B without relying on cloud APIs. We cover initial investment with Mac mini M4, monthly cost savings, phased rollout plans, and success metrics setting through practical approaches.

Learn how to build a RAG-enabled internal knowledge base AI using Qwen3.5-9B and OpenClaw. This guide covers document ingestion from PDFs, Word files, and internal wikis, vector database integration, and best practices for achieving accurate information retrieval with natural dialogue. We provide AI agent implementation support for companies in Shinagawa-ku, Minato-ku, Shibuya-ku, Setagaya-ku, and Ota-ku to enhance operational efficiency.

Learn how to integrate OpenClaw with external services like GitHub, Notion, Google Drive, and databases using MCP (Model Context Protocol). This practical guide covers plugin setup, custom MCP server development, and security best practices for AI agent integrations.

Learn how to build a high-accuracy internal knowledge search system using OpenClaw and RAG (Retrieval-Augmented Generation). This guide covers local vector database setup with ChromaDB, Qdrant, and Weaviate, document indexing strategies, and practical deployment for searching across PDFs, Word documents, and internal wikis.

Learn how to integrate OpenClaw with LINE Messaging API and Slack Bot API for automated customer communication and streamlined internal workflows. This guide covers webhook setup, message handling patterns, multi-channel routing, and monitoring best practices.

Learn practical prompt engineering techniques to maximize OpenClaw's AI agent capabilities. From system prompt design and task decomposition to chain-of-thought prompting and template libraries, this guide covers proven strategies for effective AI automation.

A comprehensive comparison of OpenClaw against Claude Code, ChatGPT with Computer Use, Google Project Astra, Microsoft Copilot Studio, AutoGPT, and CrewAI. Analyze 2026's top AI agents across local execution, privacy, customizability, pricing, and more.

A comprehensive guide to OpenClaw (formerly Clawdbot/Moltbot): what it is, how it differs from ChatGPT, key features, supported messaging platforms (LINE/Slack/Discord/Telegram/WhatsApp), Mac mini as recommended hardware, and setup overview.

Step-by-step guide for setting up OpenClaw on Mac mini M4. Covers system requirements (M4 chip, 16GB RAM, 256GB SSD), recommended configurations, hardware purchasing guide, Ollama installation, LLM setup, and first task execution.

OpenClaw is a powerful AI agent capable of executing shell commands, reading and writing files, and browsing the web. This guide covers essential security measures you need to implement before deployment, including access control, data protection, audit logging, and enterprise-safe configuration practices.

OpenClaw is an always-on AI agent accessible through messaging apps that can automate multi-step tasks. This article presents five practical business use cases showing how small and medium-sized businesses are leveraging OpenClaw to streamline operations, along with ROI estimates and key success factors.

A detailed breakdown of OpenClaw running costs including hardware, API fees, and electricity, along with seven practical techniques to reduce your monthly API spending.