Title: I ripped out OpenClaw's default markdown memory and built a strict Node.js/Postgres API layer instead. The hallucination drop is insane.

Like a lot of you, I hit a massive wall with OpenClaw’s default [`MEMORY.md`](http://MEMORY.md) file after a few weeks of heavy use. For context, I’m currently building a Cognitive Productivity Analyzer (full-stack application tracking deep work, generating focus heatmaps, and running burnout risk predictions). I've been using OpenClaw to help me map out the system architecture and write the project documentation. But relying on OpenClaw's native memory was a disaster for a project this complex. Because the memory is just a flat text file, it turned into a hallucination engine. The agent would confidently recall deprecated API designs from three weeks ago as absolute truth, or completely forget the system tradeoffs we had just agreed on regarding the database structure. When an LLM dumps text into a `.md` file, there are no structural guarantees. So, I disabled the `memory-core` plugin entirely and built a deterministic enforcement layer. Instead of letting the agent write to a file, I spun up a lightweight Node.js/Express middleware backed by a strict PostgreSQL database. I exposed this to OpenClaw entirely through custom tool calls. **Here is how the architecture actually works in practice:** * **Strict Database Schemas:** I set up specific Postgres tables for `system_architecture_rules`, `api_design_decisions`, and `feature_roadmaps`. * **Typed API Contracts:** If the agent wants to remember a design tradeoff or log a new API route, it *must* execute a `POST /memory/architecture` tool call with a strict JSON payload that validates against my TypeScript interfaces. * **Immutable Truth:** When I ask OpenClaw a question about the project, the tool layer does a quick retrieval from Postgres and injects the *exact, typed* rules into the context window before the agent even begins to reason. **The Tradeoffs:** Yes, it adds about \~150ms of latency per tool call, and I had to spend a weekend writing the backend logic to handle the API routing. **The Result:** Context drift went to absolute zero. The agent can no longer hallucinate a database schema because it is physically constrained by the relational data sitting in Postgres. If an architectural rule is updated, it overwrites the old row in the database, meaning the agent never gets confused by conflicting notes in a massive markdown log. I think we are treating agent memory as a "storage" problem when it's actually an "API design" problem. You can't just give an LLM a blank text document and expect it to maintain state accurately over hundreds of iterations. You have to force it to categorize its thoughts through strict tool boundaries. Curious if anyone else has completely abandoned the native memory plugins for external databases yet? I'm considering open-sourcing the Node repository if this is a headache other people are dealing with.