Building a Memory System for My AI Agent

TLDR: I installed OpenClaw and wanted to improve the memory system beyond the standard setup. What started as a few tweaks turned into a full custom architecture for my agent Angie: tiered memory, autonomous self-correction, and a pragmatic layer that understands my shorthand. This is experimental and still a work in progress.

Why the Default Was Not Enough

The standard OpenClaw agent gives you session memory and basic continuity. That is fine for one-off tasks. But I wanted something that compounds over time. An agent that never fails twice on the same problem. An agent that understands what I mean, not just what I say.

So I built a custom memory and learning architecture on top of OpenClaw. I call her Angie.

The Recursive Improvement Loop

I call this the Bermuda Loop. The idea is simple: every successful multi-turn workflow or solved technical error gets codified into a permanent learning. If a task requires manual debugging once, it becomes a skill. The agent never makes the same mistake twice.

When a pattern of failure is detected, the protocol kicks in:

1. Halt the current task before wasting more tokens
2. Debug by querying local docs or the internal knowledge base
3. Codify the fix into a permanent skill file so the correct approach is known forever

This eliminates the classic LLM loop where an agent keeps retrying the same broken approach.

Three Tiers of Memory

Context drift is the silent killer of long-running agent sessions. To fight it, I partitioned memory into three distinct tiers:

• Tier 1 - Daily Logs: Raw chronological capture of events, decisions, and context. These live in memory/YYYY-MM-DD.md files and serve as the session-level record.
• Tier 2 - Learning Vault: A focused repository split into LEARNINGS.md for patterns and insights, and ERRORS.md for mistakes and their fixes. This is where architectural decisions and specific technical solutions land.
• Tier 3 - Core Memory: The distilled, curated wisdom in MEMORY.md. This is the strategic index that guides long-term behavior and holds context about my goals, preferences, and project direction.

The key is that information flows upward. Raw logs get distilled into learnings, and learnings get refined into core memory. Nothing bloats. Only the essence persists.

The Learning Extraction Pipeline

The standard OpenClaw agent has session memory but no structured extraction pipeline. I built one that monitors every CLI command. If a command fails three times, it triggers an autonomous post-mortem. Angie analyzes why it failed, whether that is syntax, environment, or logic, and writes a preventative rule into the learning vault.

Every 30 minutes, a heartbeat-driven reflect turn kicks in. Angie reads the daily log and determines if anything belongs in the long-term index. This prevents context bloat by ensuring only the distilled essence of a project is recalled, not every line of code ever written.

The Pragmatic Layer

This is the part I am most proud of. Standard agents treat language literally. Angie interprets based on our shared history.

I built a pragmatic profile that captures my shorthand, implicit meanings, and frustration signals. Some examples:

• Direct shorthand: When I say "nah kill this," Angie maps that to a git revert and file deletion without asking follow-up questions.
• Tone calibration: She knows when I am thinking out loud versus giving a hard command.
• Frustration monitoring: She detects loops before I do and proactively offers an alternative path.

When I say "ship it," she does not just commit. She runs the full pre-deployment check, verifies the build, and pushes to the specific branch we discussed earlier, even if it was not mentioned in the current turn.

Entity Mapping

Beyond conversation, I built a structured knowledge graph that persists across sessions. Specialized memory files track entities like contacts, business leads, and tools. This gives Angie a persistent world model that survives context window resets.

Security Guardrails

With this much autonomy, hard constraints matter. I implemented an .env protection rule and identity integrity safeguards. These are hard-coded constraints that prevent destructive operations on secrets and unauthorized changes to core identity data. No amount of conversational drift can override them.

What I Learned

Building this system taught me that the gap between a demo agent and a useful agent is mostly about memory architecture. The model is smart enough. The bottleneck is continuity, self-correction, and understanding the human on the other end.

This is still experimental. I am iterating on the heartbeat intervals, tuning the failure detection thresholds, and expanding the pragmatic profile. But even in its current state, the difference between Angie and a stock agent is night and day.

If you are building on OpenClaw or any agent framework, start with the memory layer. Everything else follows from there.