How to Build AI Applications for the Context Era

Context-first architecture: the core principle

A context-first AI application treats memory as a first-class system component, not an afterthought. The LLM is not the application — the context engine is. The LLM is the inference layer that processes what the context engine retrieves.

This inversion matters for how you build. In a context-first app, the most important decisions are about the memory layer: what to store, at what importance weight, with what metadata, with what graph relationships, with what decay parameters. These decisions determine the quality of what the LLM receives — and therefore the quality of the outputs.

The benchmark that motivates this: Feather DB with GPT-4o achieves 0.693 on LongMemEval vs. 0.640 for full-context GPT-4o. Same LLM. Different context quality. The 8.3% accuracy improvement comes entirely from better context architecture, not from a better model.

Step 1: Design your memory schema

The memory schema defines what gets stored and how it gets retrieved. A good schema has three components:

Semantic text. The text that gets embedded for retrieval. This should be semantically rich — it should capture not just a fact but enough context that the embedding reflects the full meaning. "User prefers TypeScript" is worse than "User is building a Node.js backend and has consistently chosen TypeScript over JavaScript for type safety, especially in API handler functions."

Metadata for filtering. Structured attributes that enable namespace isolation and faceted retrieval. At minimum: entity identifier (user_id, brand, agent_id), category or type, and timestamp. Add domain-specific attributes relevant to your application — platform, product category, audience segment, severity.

Importance weight. A float from 0.0 to 1.0 that represents the a priori reliability and value of the memory. High-confidence facts from trusted sources: 0.8–1.0. Inferences and summaries: 0.4–0.7. Speculative or low-confidence information: 0.1–0.3.

Step 2: Build the retrieval-augmented response loop

import feather_db as fdb
import uuid
from your_embedder import embed
from your_llm import call_llm

db = fdb.DB.open("app_context.feather", dim=768)

def context_first_response(
    query: str,
    entity_id: str,
    half_life: int = 30,
    time_weight: float = 0.3,
    k: int = 12
) -> str:
    query_vec = embed(query)

    # Retrieve: decay-adjusted, stickiness-tracked, namespace-scoped
    memories = db.search(
        query_vec,
        k=k,
        half_life=half_life,
        time_weight=time_weight,
        filters={"entity_id": entity_id}
    )

    # Optionally expand with graph context (2 hops)
    if memories:
        chain = db.context_chain(query_vec, k=3, hops=2,
                                  filters={"entity_id": entity_id})
        # Merge and deduplicate
        seen = {m.id for m in memories}
        for node in chain:
            if node.id not in seen:
                memories.append(node)
                seen.add(node.id)

    context_text = "\n---\n".join(
        [m.payload.get("text", "") for m in memories]
    )

    response = call_llm(
        system="You are an assistant with persistent memory of prior interactions.",
        context=context_text,
        query=query
    )

    # Store the new interaction
    mem_id = str(uuid.uuid4())
    meta = fdb.Metadata(importance=0.75)
    meta.set_attribute("entity_id", entity_id)
    meta.set_attribute("text", f"Q: {query}\nA: {response}")
    db.add(id=mem_id, vec=embed(f"{query} {response}"), meta=meta)

    # Link to the most relevant prior memory
    if memories:
        db.link(mem_id, memories[0].id,
                rel_type="same_session", weight=0.8)

    return response

Step 3: Tune your decay parameters

The half_life and time_weight parameters are the most important tuning decisions in a context-first application. Incorrect values produce two failure modes: too-aggressive decay (recent facts score too high, older but still-relevant facts are suppressed) or too-slow decay (stale facts continue to compete with fresh ones).

Step 4: Design your graph relationships

Graph edges are optional but add significant retrieval depth. The key insight: a semantic search retrieves what is semantically similar to the query. A graph traversal retrieves what is causally connected to the semantic matches — which is often what the agent actually needs to answer correctly.

# When storing a fact that supersedes an older one
db.link(
    from_id=new_fact_id,
    to_id=old_fact_id,
    rel_type="supersedes",
    weight=1.0
)

# When storing a resolution to a prior issue
db.link(
    from_id=resolution_id,
    to_id=issue_id,
    rel_type="resolved",
    weight=0.9
)

# When two memories belong to the same conversation
db.link(
    from_id=new_turn_id,
    to_id=prior_turn_id,
    rel_type="same_session",
    weight=0.7
)

The supersedes relationship is particularly important for correctness. When a fact changes — a user's preference updates, a prior recommendation is invalidated — storing a supersedes edge from the new fact to the old one demotes the old fact in retrieval without deleting it. The old fact can still be retrieved if explicitly searched, but it no longer competes with the fresh one for relevance-ranked retrieval.

Step 5: What to store, what not to store

Not everything belongs in the context engine. Storing too much degrades retrieval quality — the signal-to-noise ratio drops as low-relevance memories compete with high-relevance ones in the retrieval results.

Store with high importance (0.8–1.0): confirmed user preferences, explicit instructions, resolved issues with their resolution, key decisions and their rationale, high-confidence facts from authoritative sources.

Store with medium importance (0.4–0.7): interaction summaries, contextual background that grounded a useful response, inferences that proved correct in a follow-up interaction.

Do not store: raw LLM reasoning traces, intermediate tool call outputs, conversational filler, low-confidence speculations, duplicates of already-stored facts.

Performance characteristics to expect

With Feather DB in a production context-first application: 0.19ms p50 ANN retrieval on 500K vectors, 97.2% recall@10, 5–6× faster cold load with persisted HNSW (v0.16). The retrieval adds no perceptible latency to the agent loop. Total memory footprint for a 500K vector store at 768 dims: approximately 1.5GB on disk (including HNSW graph and metadata). For most per-user or per-entity stores, the size is orders of magnitude smaller.

Frequently Asked Questions

What is the minimum viable context-first implementation?

Minimum viable: embed the query, call db.search(query_vec, k=10, half_life=30, time_weight=0.3), pass retrieved texts to the LLM alongside the current query, store the interaction after each response. This alone captures the core benefit — decay-adjusted retrieval replacing context stuffing. Graph edges and fine-tuned decay parameters add incremental value beyond this baseline.

How do I choose the right value of k (memories to retrieve)?

k=10–15 is the standard range for most agent applications. Lower k (5–8) produces a tighter, higher-precision context window — good when the LLM performs better with focused context. Higher k (15–20) produces broader coverage — useful when queries span multiple topics or when causal context is important. LongMemEval testing with Feather DB used k=10, achieving 0.693 accuracy. The 97.2% recall@10 metric ensures that the top-10 relevant memories are retrieved with high reliability.

Should I use graph traversal for every application?

Graph traversal adds the most value in applications where causal relationships between facts matter — support agents (issue → resolution), research assistants (claim → evidence → counterevidence), multi-step workflow agents (task → subtask → completion). For applications where facts are relatively independent — question answering over a knowledge base, creative idea retrieval — flat retrieval without graph traversal is often sufficient.

How does Feather DB handle concurrent writes in multi-user applications?

Feather DB's embedded architecture is designed for single-process access. For multi-user applications with concurrent writes, the recommended pattern is to maintain one Feather DB file per user (or per namespace) and route writes to the correct file, or to use the self-hosted Docker deployment which handles concurrent access through a server layer. Cloud deployment (Q3 2026) will support native concurrent multi-user access.

What happens when the context engine has no relevant memories for a query?

When retrieval returns no results above the relevance threshold, the agent falls back to the LLM's general knowledge — the same behavior as a stateless agent. This is the correct behavior for novel queries in a new user or entity context. As the store accumulates memories for that entity, retrieval quality improves. The cold-start period (where retrieval adds little) transitions to a warm state as memories accumulate and high-importance facts establish recall stickiness.