How Can OpenClaw Users Drastically Reduce Token Consumption Using Local Semantic Search?

Implementing qmd for Cost-Effective, Precise Context Recall

High token consumption in agents like OpenClaw often stems from stuffing large, often irrelevant document chunks into the context window. To solve this, you can integrate qmd, a local, free, and permanent semantic search engine built by Shopify founder Tobi. By running locally using GGUF models, qmd eliminates API costs entirely and significantly boosts precision.

Key Advantages of Using qmd with Your Agent

• Zero API Cost: Fully offline operation means you save substantial money, especially critical for frequent Claude interactions.
• High Accuracy: qmd supports hybrid search (BM25 full-text + vector semantic search + LLM re-ranking), achieving impressive precision (tested at 93% for hybrid search).
• Proactive Agent Recall: Integration via MCP (Memory Context Porter) allows the agent to proactively recall relevant snippets rather than relying on you to manually remind it or stuffing entire files into context.

Quick Setup Guide for Integration

Setting up qmd is straightforward, requiring about 10 minutes:

1. Installation: Install qmd globally using Bun: bun install -g https://github.com/tobi/qmd. It will automatically download necessary models (Embedding: jina-embeddings-v3, Reranker: jina-reranker-v2-base-multilingual).
2. Indexing Your Knowledge Base: Add your data directories (e.g., markdown notes in memory/*.md) as collections. For instance: qmd collection add memory/*.md --name daily-logs. Then, generate embeddings: qmd embed daily-logs memory/*.md.
3. Agent Integration (MCP): Configure config/mcporter.json to point to your local qmd binary, enabling tools like query (hybrid search) for the agent to use automatically.

By using qmd query instead of sending a 2000-token document, you can retrieve just the relevant 200 tokens, resulting in up to a 90% token saving per query while maintaining high accuracy. Regularly update your index using the qmd embed command, perhaps scheduled via a cron job.