I ripped out OpenClaw's default markdown memory and built a Node.js/Postgres API layer instead

A developer building a Cognitive Productivity Analyzer with OpenClaw hit a wall with the default MEMORY.md file after weeks of use. The flat text file turned into a hallucination engine — the agent recalled deprecated API designs as truth and forgot agreed-upon system tradeoffs. The fix: disable memory-core plugin and build a deterministic enforcement layer.

Architecture Details

• Database: PostgreSQL with strict schemas for system_architecture_rules, api_design_decisions, and feature_roadmaps.
• Middleware: Node.js/Express backend exposed to OpenClaw via custom tool calls.
• Typed Contracts: Agent must execute POST /memory/architecture with a TypeScript-validated JSON payload to record design tradeoffs or API routes.
• Immutable Context Injection: On query, the tool layer retrieves exact rows from Postgres and injects them into the context window before the agent reasons.

Results

Context drift dropped to absolute zero. The agent cannot hallucinate a database schema because it's constrained by relational data. Updating an architectural rule overwrites the old row — no conflicting notes. Tradeoff: ~150ms latency per tool call and a weekend of backend coding.

Takeaway

The developer argues that agent memory is not a storage problem but an API design problem. Forcing the LLM to categorize thoughts through strict tool boundaries prevents state corruption over hundreds of iterations. Considering open-sourcing the Node repository.