Practical fixes for OpenClaw reliability issues

A developer on r/openclaw detailed their approach to overcoming common OpenClaw reliability problems, moving from a frustrating initial experience to a stable setup. Their solution centers on treating the agent as having no persistent memory between sessions and enforcing everything through structured files and scripts.

Key techniques that worked

• Use multiple installations for redundancy: They maintain two OpenClaw installs that can SSH into each other to fix broken configurations when issues arise.
• Treat built-in files as living documents: The developer emphasizes actively updating SOUL.md, AGENTS.md, USER.md, and MEMORY.md rather than setting them once. Important rules go in AGENTS.md, corrections go in MEMORY.md, and anything only in chat is lost between sessions.
• Build a 3-tier memory system: They added:
  • Tier 1 (hot): Daily logs (memory/YYYY-MM-DD.md) plus MEMORY.md for recent context and curated facts read every session.
  • Tier 2 (warm): OpenClaw's vector memory search for semantic retrieval across session transcripts and memory files.
  • Tier 3 (deep): A-Mem knowledge graph with 668 facts across 41 entities, activation scores, temporal decay, and cross-entity links. Cron jobs run nightly to extract new facts from conversations and update the graph, and weekly to decay stale facts and rebuild links.
  This enables retrieval of information mentioned up to six weeks prior.
• Fix retrieval quality with activation scores: They discovered all 691 facts in their knowledge graph had the same activation score (0.5), making search results random. They built an activation-boost script that increases a fact's score by +0.1 each time it's accessed and applies temporal decay to untouched facts, improving retrieval prioritization.
• Force a pre-compaction flush: To prevent context loss when OpenClaw compacts memory, they use the pre-compaction event to write WORKING.md with the full conversation state before the wipe, avoiding the loss of 12+ hours of context they experienced previously.
• Write corrections down immediately: They maintain a .learnings/LEARNINGS.md file that logs every significant correction with date, error, correction, and importance, ensuring future sessions inherit these learnings.
• Make instructions non-negotiable in files: They rewrote AGENTS.md rules with explicit language like "NON-NEGOTIABLE" and "NO EXCEPTIONS," including examples of failure, finding blunt language gets followed more consistently than polite suggestions.
• Script safety checks: Instead of relying on agent judgment for cron health checks, they replaced it with a shell script that handles comparison logic. The agent simply runs the script and reports output, removing judgment from the loop.
• Enforce verify-before-stating as a written rule: After the agent maintained a wrong assumption across sessions, they added a rule at the top of every AGENTS.md: never state something as fact without verifying it, research first, and say "I don't know" if unsure.

The core mental model is to treat the agent like someone who loses all memories every night, with files serving as the institutional memory.