My local LLM memory architecture finally stopped hallucinating

I finally managed to stabilize my long-term memory implementation for local LLM agents, and it took much more trial and error than I initially anticipated. If you have been trying to build a persistent AI workflow using Ollama or similar local setups, you know the nightmare of "context drift"—where the model starts forgetting user preferences or core project constraints after just a few dozen exchanges.

For a long time, I was stuck in a loop of trying to shove everything into the system prompt or just bloating the context window. The problem was obvious: the more "memory" I fed the model via a massive context window, the more diluted the actual reasoning became. I was seeing a massive spike in latency and a significant drop in instruction following.

The diagnosis: The "Context Stuffing" trap

I spent a week profiling my setup and realized the bottleneck wasn't the model's intelligence, but the retrieval mechanism. I was essentially using a naive RAG (Retrieval-Augmented Generation) approach that pulled too many irrelevant chunks. My error logs during testing looked something like this:

Error: Context window limit exceeded (32768/32768)
Warning: High perplexity detected in retrieved context segments
Status: Model ignoring user-defined persona constraints

The model wasn't "forgetting"; it was being overwhelmed by noise. Every time I tried to inject "memory," I was actually introducing contradictions.

The new approach: A hybrid tiered memory system

Instead of one big bucket of data, I moved to a three-tier architecture. This is a practical tutorial on how I restructured the logic:

1. The Ephemeral Layer (Short-term): This is just the raw conversation history. I keep this strictly limited to the last 5-10 exchanges to maintain high-speed reasoning and prevent the model from getting bogged down in old tangents.

2. The Semantic Layer (Mid-term): This is where my vector database lives. I use a lightweight embedding model to index specific facts. Instead of dumping everything, I implemented a "re-ranking" step. When a query comes in, I pull 10 chunks but use a secondary, tiny model to select only the top 3 most relevant ones to pass into the prompt.

3. The Entity Layer (Long-term): This was the missing piece. I realized that "facts" are different from "entities." I started using an LLM agent to extract structured JSON data about the user or the project.

{
  "user_preferences": {
    "coding_style": "functional",
    "preferred_language": "Rust",
    "verbosity": "concise"
  },
  "project_context": {
    "current_goal": "Refactoring the database module",
    "known_bugs": ["Race condition in connection pool"]
  }
}

Deployment results

By moving the "identity" of the session into a structured JSON object that is injected into the system prompt—rather than relying on the model to "remember" it from a text block—the stability skyrocketed. The model now treats these preferences as hard constraints rather than suggestions.

If you are building a complex LLM agent from scratch, don't just increase your context window size and hope for the best. Focus on the retrieval precision. A smaller, highly accurate context is infinitely more powerful than a massive, noisy one.