From Vector Store to Living Engine: A Migration Guide

Theory · Living Context Engine Series · May 2026

You Don't Have to Rip and Replace

A team running a production vector store does not need to throw it out to move toward a Living Context Engine. The migration is a sequence of additive changes — each one independently useful, each one a step closer to the full architecture. This post documents the order we recommend, based on what produces the most quality gain per unit of engineering effort.

Step 1: Add Decay State to Every Node

The single highest-leverage change: attach inserted_at, recall_count, and importance to every node. Most vector stores support arbitrary metadata; use those fields. You do not need a new store for this — just a discipline at write time.

Update your retrieval wrapper to re-rank top-k results by a composite score that combines similarity with decay. The implementation is one function:

def composite_score(sim, age_days, recall_count,
                    importance, half_life=90, tw=0.3):
    stickiness    = 1 + math.log(1 + recall_count)
    effective_age = age_days / stickiness
    recency       = 0.5 ** (effective_age / half_life)
    return ((1 - tw) * sim + tw * recency) * importance

Apply this re-rank on every retrieval. Increment recall_count on confirmed-useful results. That single change typically buys 15–30% quality lift on production workloads measured against held-out evaluation sets.

Step 2: Add Typed Edges

Identify the three or four most-meaningful relationship types in your domain. For a marketing context store: derived_from, responds_to, variant_of. For a support context store: resolves, duplicate_of, escalated_to.

Add an edges field to your node payload (or a sidecar table). Backfill edges where you have ground truth — every campaign's brief edges to its executions, every support ticket edges to its resolution.

Your retrieval is still vector-only, but consumers can now follow edges in application code. This is the architectural foundation for the next step.

Step 3: Implement a Two-Phase Retrieval

Wrap your retrieval in a two-phase function:

1. Phase 1: ANN search for top-k seed nodes.
2. Phase 2: For each seed, traverse outgoing edges up to a fixed hop budget, scoring each hop with the composite function from Step 1.

Return the union of seeds and traversed nodes, deduped, scored. You have just implemented context_chain at the application layer over your existing vector store. The retrieval shape now matches what a Living Context Engine produces natively.

Step 4: Close the Loop

Add a write-back path. When the agent produces an output that uses retrieved context, write the output back as a new node with responds_to or derived_from edges to the inputs. When the output gets a downstream signal (the user clicked, the campaign performed, the ticket got resolved), update the importance or recall count of the related nodes.

This is the step that turns retrieval into memory. The system now learns from being used.

Step 5: Tune Half-Lives Per Category

Once the closed loop is running, you have enough data to tune half-lives per category. Look at which categories of context get recalled frequently for long after insertion (long half-life) and which get recalled rarely after a short window (short half-life). Encode the values explicitly per-node.

When to Switch Stores

Steps 1–5 are achievable on most vector stores via application-layer wrappers. Switching to a native Living Context Engine like Feather DB is justified when one or more of these are true:

• Your retrieval latency budget is tight (a fused kernel is 2–5x faster than a wrapped two-phase implementation).
• You want per-agent isolation (single-file storage gives you that natively).
• You are operating multiple decay regimes in the same store and need per-node half-lives in the retrieval kernel itself.
• You want the audit trail of a single-file context snapshot per high-stakes decision.

If none of those apply, the application-layer wrapper version of a Living Context Engine over your existing store is a valid endpoint.

The Migration Mindset

The point of the migration sequence is to be honest about leverage. Step 1 — decay-weighted scoring — captures most of the available quality gain. Step 4 — the closed loop — is what makes the system improve over time. The other steps amplify those two. None of this requires a rewrite. All of it requires discipline at write time and a clear model of what a Living Context Engine is supposed to do.

Build it incrementally. Measure each step. The architecture earns its keep one decay curve at a time.

Part of the Living Context Engine series. Next: the Context Engine Loop series.