Feature deep dive

Structured Graph Categories for AI Agent Memory

Organize agent memory into clear categories and graph links so retrieval stays fast, precise, and explainable as memory grows.

Unstructured memory eventually turns into noise. As the memory set grows, retrieval quality drops because important context is mixed with low-signal notes.

Structured graph categories solve this by keeping memory records predictable. Agents can quickly find decision rationale, project constraints, people context, and lessons learned.

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Why teams lose context

  • •Flat note systems become hard to query at scale.
  • •High-value decisions get buried under incidental details.
  • •Without categories, retrieval ranking drifts over time.
  • •Teams struggle to audit what the agent actually remembers.

How ClawVault helps

  • •Define a stable category taxonomy for durable memory writes.
  • •Link related memories to preserve relationship context.
  • •Use category-aware retrieval before injecting context.
  • •Review category quality as part of regular release hygiene.

01Why memory taxonomy matters

A memory graph without categories behaves like a giant unindexed notebook. It can look complete while still failing retrieval at critical moments.

A practical taxonomy improves both machine recall and human review because each memory has a clear purpose and ownership.

02Recommended category model

Start with a small category set, then expand only when retrieval quality demands it. Keep naming stable so queries remain predictable over time.

  • •decisions: architecture and implementation tradeoffs
  • •projects: scope, milestones, owners, blockers
  • •preferences: user and team working rules
  • •lessons: incidents, postmortems, and recurring fixes
  • •people: stakeholders and collaboration context

03Link memories into a graph

Category labels create order, but links create reasoning paths. Connect related memories so retrieval can follow intent chains instead of isolated snippets.

Category + graph workflow
$clawvault store --category decisions --title "Auth Session Model" --content "Use short-lived access tokens with rolling refresh"
$clawvault store --category lessons --title "Token Revocation Incident" --content "Add immediate revoke path on credential leak"
$clawvault vsearch "why did we choose rolling refresh"

04Operational safeguards

Treat category quality as an engineering standard. Poorly categorized memories reduce trust and increase token waste.

  • •Reject ambiguous categories during memory write reviews.
  • •Merge duplicate records that represent the same decision.
  • •Archive stale memories that no longer reflect production behavior.

When should I use ClawVault?

  • •Use structured categories when memory volume grows beyond a single file.
  • •Use structured categories when teams need repeatable recall quality.
  • •Use structured categories when incident review requires explainable memory traces.

Frequently asked questions

01What are structured graph categories in AI memory?
They are stable labels and links that organize memory records into predictable groups like decisions, projects, preferences, and lessons.
02Why do categories improve memory retrieval?
Categories reduce ambiguity, improve ranking quality, and help agents focus on relevant context for each task.
03How many categories should I start with?
Start small with 4-6 high-signal categories, then expand carefully based on real retrieval failure patterns.
04Can structured categories work with semantic search?
Yes. Categories and semantic retrieval complement each other: categories add precision while semantic ranking adds intent coverage.

Related guides

Persistent Memory

Build durable recall loops on top of structured categories.

Semantic Search Memory

Combine category precision with intent-based retrieval.

Markdown Memory System

Store category records in transparent local files.

How OpenClaw Memory Works

Technical context for memory flow and retrieval quality.