Local-Models

OpenClaw v2026.4.15-beta.1 ships two features that, combined, dramatically improve the experience for self-hosted and resource-constrained deployments: cloud LanceDB memory (durable remote memory indexes instead of local disk only) and lean mode (a smaller prompt footprint for weaker local models). This guide walks you through configuring both. Prerequisites OpenClaw beta installed: npm install -g openclaw@beta A running local model via Ollama, LM Studio, or equivalent (for lean mode to be relevant) An S3-compatible object store if using cloud LanceDB (AWS S3, Cloudflare R2, MinIO, etc.) Step 1: Install the Beta npm install -g [email protected] Verify the install: ...

OpenClaw’s latest beta release, v2026.4.15-beta.1, lands with a trio of features that meaningfully expand the platform’s reach — from hardened operators keeping tabs on OAuth health, to resource-constrained developers finally getting a viable local-model path, to teams who’ve been waiting for durable memory that doesn’t eat disk on every server they deploy to. Here’s what shipped. Model Auth Status Card The Control UI now has a dedicated Model Auth status card in the Overview panel. At a glance it shows OAuth token health and provider rate-limit pressure — and raises attention callouts when tokens are expiring or have already expired. ...

Running Claude Code in a Docker container isn’t just a development curiosity — it’s increasingly the recommended way to work with AI coding agents in a way that’s both powerful and secure. Docker published an official guide this week walking through the full workflow: local model execution with Docker Model Runner, real-world tool connections via MCP servers, and securing agent autonomy inside isolated sandboxes. This guide synthesizes that walkthrough into a practical tutorial for developers who want to get running quickly. ...

Alibaba’s CoPaw just went open-source and it’s one of the cleanest personal agent setups I’ve seen for developers who want full control over their stack. This guide walks you through a working deployment in under 30 minutes — locally on a Mac, or on a cheap Linux VPS. Prerequisites: Python 3.11+ or Docker A machine with at least 4GB RAM (8GB+ for local models) Optional: Anthropic/OpenAI API key, or a local model via llama.cpp or Ollama Step 1: Clone the Repository git clone https://github.com/agentscope-ai/CoPaw.git cd CoPaw The repo includes a docker-compose.yml for containerized deployment and a standard Python requirements.txt for bare-metal installs. ...

The open-source personal agent space just got a serious new contender. Alibaba’s research team quietly dropped CoPaw at the end of February — an open-source framework for deploying self-hosted AI agents that runs entirely on your own hardware, supports local models, and integrates directly with Discord, iMessage, DingTalk, and Feishu out of the box. If you’ve been following the OpenClaw community, the concept will feel familiar. But CoPaw brings a distinctly different design philosophy: it’s built from the ground up for portability and model-agnosticism, with equal-class support for local inference (via llama.cpp or Apple MLX) and remote APIs. ...