Descriptions:
Fahd Mirza demonstrates ByteRover, a memory management skill for OpenClaw (an open-source AI agent framework), designed to fix a persistent problem with the tool’s default flat-file memory system. Out of the box, OpenClaw stores all agent memory in growing markdown files with no deduplication or semantic search — causing agents to burn tokens loading stale context into the context window on every session before any real work begins.
ByteRover, available on ClawHub (OpenClaw’s skills repository), replaces this with a structured context tree: organized markdown files each containing a single clean piece of knowledge. Before each response, the skill semantically queries the tree and injects only relevant entries; after meaningful exchanges, it automatically curates new knowledge back into the tree. The tool reports 90% accuracy on the Locomo benchmark after eight months of development and reached 26,000 installs within its first week of release.
Mirza runs the complete installation walkthrough on Ubuntu using an NVIDIA RTX 6000 GPU with 48GB of VRAM, pairing OpenClaw with a locally-hosted Qwen 2.5 35B model via Ollama — making the entire stack self-contained with no external API calls required. The video covers OpenClaw installation and token configuration, Ollama model setup, ClawHub installation, ByteRover setup and initialization, and a live demo showing the agent correctly storing and later retrieving personal user context. All commands are available in Mirza’s linked GitHub repository.
📺 Source: Fahd Mirza · Published March 11, 2026
🏷️ Format: Hands On Build
