OpenClaw Memory Plugin Testing Results and Recommended Stack

Memory Plugin Performance Breakdown
A recent test of OpenClaw memory plugins reveals significant issues with default configurations and provides specific recommendations for effective setups.
Problem with Default Setup
The default markdown setup will quietly destroy your agent over time. Token bloat is real—your instructions get compressed away and your API bill climbs for nothing.
Plugin Tier Rankings
- C tier — Markdown/Obsidian. Fine for strict rules. Disaster as your only memory.
- B tier — Mem0. Great automation, kills your privacy and costs up to 7 cents per message.
- A tier — LanceDB. Fast, private, local. Black box though—hard to debug bad memories.
- A tier — Knowledge graphs (Graphiti). The future. Too experimental right now.
- A tier — SQLite. Not for conversation. Essential for structured data where accuracy matters.
- S tier — QMD. Free, local, surgical. Grabs only what the agent needs instead of loading everything. This is the one.
Recommended Setup
The actual winning setup is a stack: Obsidian as the human-readable layer, QMD to search it without token cost, SQLite for hard data. Run a nightly consolidation script and you basically never think about memory again.
📖 Read the full source: r/clawdbot
👀 See Also

How to fix OpenClaw 'Cannot find module' error after update
After updating OpenClaw from version 2026.3.24 to 2026.4.5, users are encountering a 'Cannot find module @buape/carbon' error. The solution involves manually running a post-installation script instead of installing the package globally.

DeepSeek-V4-Flash W4A16+FP8 with MTP Self-Speculation: 85 tok/s on 2x RTX PRO 6000 Max-Q
DeepSeek-V4-Flash quantized to W4A16+FP8 achieves 85.52 tok/s at 524k context on 2× RTX PRO 6000 Max-Q using a patched vLLM with retrofitted MTP head, up from 52.85 tok/s baseline.

Efficiently Managing OpenClaw Instances for Multiple Users
Explore strategies shared by users on r/openclaw for managing multiple OpenClaw instances. Learn how community members harness automation and load balancing for optimal performance.

Optimizing GLM-4.7-Flash on M4 Mac Mini with 24GB RAM
A developer shares specific configuration details for running GLM-4.7-Flash on an M4 Mac Mini with 24GB RAM, including Q3_K_XL quantization, 32k context size with MLA, and memory allocation realities for Metal.