Running OpenClaw with Ollama

2026-07-09 · Source: KDnuggets · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

OpenClaw, a personal AI assistant, integrates local Ollama models with messaging platforms like Telegram, WhatsApp, and Slack, enabling private, persistent AI interactions. Originally released in late 2025 as Clawdbot by Peter Steinberger, it quickly garnered over 60,000 GitHub stars and was renamed in early 2026. This article details setting up OpenClaw with Ollama 0.17 or later, which streamlines installation to a single command. It emphasizes configuring a minimum 64k token context length, crucial for multi-step agent tasks, and outlines hardware prerequisites including 16GB RAM and 25GB VRAM for local models, or using cloud models like kimi-k2.5:cloud for easier setup. The guide covers connecting Telegram via BotFather, enabling web search, and deploying the system headlessly using Docker, demonstrating its utility for automated research tasks.

Key takeaway

For AI Engineers building private, persistent AI assistants, OpenClaw with Ollama 0.17+ offers a streamlined path. You should prioritize configuring a minimum 64k token context length for local models or opt for cloud models like "kimi-k2.5:cloud" to ensure robust multi-step agent performance. Deploying headlessly via Docker or "ollama launch --yes" allows for a 24/7 research assistant accessible from your messaging apps, significantly enhancing your automated information gathering capabilities.

Key insights

OpenClaw bridges local Ollama models to messaging apps, enabling persistent, multi-step AI agent tasks on personal hardware.

Principles

• Agentic tasks demand 64k+ token context length.
• A three-layer architecture enables persistent AI agents.
• Cloud models simplify agent setup and context.

Method

Install Ollama 0.17+, then "ollama launch openclaw". Configure context length (64k+), connect a messaging channel (e.g., Telegram via BotFather), and enable web search for local models. Deploy headlessly with "--yes" or Docker.

In practice

• Set "OLLAMA_CONTEXT_LENGTH" for local models.
• Truncate tool results to 8,000 characters.
• Deploy headlessly in Docker for 24/7 operation.

Topics

• OpenClaw
• Ollama
• AI Agents
• Local LLMs
• Messaging Bots
• Docker Deployment
• Web Search

Code references

• WhiskeySockets/Baileys

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

Related on AIssential

• OpenClaw — OpenClaw, launched on February 1, 2026, is a personal AI assistant that privately bridges popular messaging platforms like WhatsApp, Telegram, and Slack with AI coding agents via a centralized, locally-run gateway.
• OpenAI Codex with Ollama — OpenAI's Codex CLI now integrates with Ollama, enabling local execution of open-weight code generation models.
• How to Run OpenClaw with Open-Source Models — OpenClaw can be powered by alternative LLMs like Kimi-K2.5 following Claude's subscription ban.
• You Asked About AI: Agents, Hacking & LLMs — AI agents are democratizing complex tasks like hacking and enabling new forms of inter-agent communication.
• Ollama: Unleash the Power of Local LLMs on Your Machine — Ollama enables private, cost-effective, and low-latency local LLM inference and customization via a Python library.
• I asked the Portal One+ Telegram AI assistant to review itself. Here’s how it went — OpenClaw offers persistent, stateful AI assistance through a local filesystem, enabling deep context accumulation and multi-step task execution.

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Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.