Descriptions:
Bart Slodyczka walks through a practical strategy for cutting OpenClaw AI agent token costs by up to 97%, targeting three main drivers of context bloat: file management, model management, and session management. The video opens by demystifying exactly what OpenClaw sends with every API call — beyond the user’s message, each request carries core system instructions, all agent configuration files (agents.md, soul.md, memory.md), and the full accumulated conversation history. This context window compounds with every interaction, turning a seemingly simple query into a call that can cost 10–20 cents when using Claude Opus 4.6.
The tutorial covers concrete mitigations: regularly resetting sessions to clear history, auditing and trimming bloated memory files, and matching model tier to task complexity rather than defaulting to the most capable model. Slodyczka recommends OpenRouter as a unified API gateway, providing access to over 630 models — including Anthropic, OpenAI, Google Gemini, and DeepSeek — through a single API key. A particularly useful tip is offloading scheduled or repetitive tasks such as daily reports and cron jobs to n8n workflows, which consume far fewer tokens than routing the same tasks through OpenClaw’s fully autonomous context pipeline.
The video is grounded in real-world usage patterns and includes hands-on guidance for editing OpenClaw’s config file to implement multi-model routing. It is aimed at users who have moved past the novelty phase and want to run OpenClaw cost-effectively at scale.
📺 Source: Bart Slodyczka · Published February 28, 2026
🏷️ Format: Tutorial Demo
