Make your OpenClaw AI agent faster, smarter, and cheaper. Speed optimization, memory architecture, context management, model selection, and one-shot development guide.

OpenClaw Optimization Guide

Tested on OpenClaw 2026.4.5 — April 7, 2026 · 22 parts · Battle-tested on a 14+ agent production deployment

Make Your OpenClaw AI Agent Faster, Smarter, and Actually Useful

Speed optimization, memory architecture, context management, model selection, graph RAG, codebase intelligence, and agent observability

By Terp - Terp AI Labs

Table of Contents

1. Speed - Trim context files, add fallbacks, manage reasoning mode
2. Context Bloat - Quadratic scaling, built-in defenses
3. Cron Session Bloat - Session file accumulation, cleanup
4. Memory - 3-tier memory system, Ollama vector search
5. Orchestration - Sub-agent delegation, CEO/COO/Worker model
6. Models - Provider comparison, pricing, local models
7. Web Search - Tavily, Brave, Serper, Gemini grounding
8. One-Shotting Big Tasks - Research-first methodology
9. Vault Memory System - Structured knowledge graph, MOCs, cross-session continuity
10. State-of-the-Art Embeddings - Upgrade from nomic to qwen3-embedding, SOTA quality, Windows gotchas
11. Auto-Capture Hook - Automatic knowledge extraction after every session, no manual memory writes
12. Self-Improving System - Micro-learning loop that compounds forever, $0/day
13. Memory Bridge - Give coding agents (Codex/Claude Code) access to your vault knowledge
14. [Quick Checklist](#part-