OpenClaw memory fix guide

Fix OpenClaw Memory Loss

Give OpenClaw persistent, searchable memory so your agent keeps decisions, context, and project state across restarts.

OpenClaw can index local files and retrieve useful snippets, but memory reliability still depends on your persistence strategy and retrieval discipline.

When teams rely only on prompt context plus a single MEMORY.md file, important decisions can disappear after compaction, session resets, or tooling interruptions.

ClawVault adds a durable memory layer built for CLI-driven agents: explicit store/search/checkpoint/wake workflows, local-first files, and semantic retrieval by meaning.

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Why teams lose context

  • •Context windows are finite, so long threads eventually compress or drop details.
  • •Session resets can break continuity when recent decisions are not persisted.
  • •Manual notes in MEMORY.md become noisy and hard to query at scale.
  • •Important context can be buried when retrieval relies on exact keyword matches.
  • •Teams repeatedly re-explain architecture, constraints, and preferences.

How ClawVault helps

  • •Persist memories intentionally with structured categories for decisions, projects, people, and lessons.
  • •Retrieve context semantically, not only by exact keywords, to improve recall reliability.
  • •Checkpoint before risky operations and wake with known state after interruption.
  • •Store data locally in inspectable Markdown files and indexes you can version-control.
  • •Keep a repeatable memory workflow that agents and humans can audit.

01Why OpenClaw can forget context

OpenClaw is strong at execution and contextual reasoning, but no agent can keep unlimited state in a single active prompt.

As sessions evolve, context compaction and prompt turnover can strip nuance from earlier decisions. Without a dedicated memory process, loss is gradual and hard to notice until it blocks delivery.

  • •Decision rationale fades first, then implementation details.
  • •Cross-session continuity becomes manual and error-prone.
  • •Repeated clarifications consume tokens and developer time.

02Why MEMORY.md alone often breaks down

A single memory file helps with simple projects, but it does not provide robust retrieval, relationship tracking, or recovery primitives.

As memory volume grows, one document becomes a changelog instead of a reliable decision graph.

  • •No semantic ranking by intent.
  • •No explicit checkpoint and wake lifecycle.
  • •No durable category schema for recall loops.

03Drop-in ClawVault flow for OpenClaw

ClawVault stays CLI-first and local-first so you can attach it to existing OpenClaw workflows without changing your orchestration stack.

Use this baseline loop as the standard for every multi-session project.

Recommended starter flow
$npm install -g clawvault
$clawvault store --category decisions --title "Auth Strategy" --content "JWT with rotating refresh tokens"
$clawvault vsearch "why did we choose JWT"
$clawvault checkpoint --working-on "shipping auth middleware"
$clawvault wake

04Reference architecture for durable agent memory

This architecture keeps memory explicit, queryable, and recoverable while preserving local ownership.

Logical memory architecture
OpenClaw runtime
-> clawvault store (structured memory write)
-> local markdown + index (durable state)
-> clawvault search/vsearch (intent-based recall)
-> memory injection back into active context

05Benchmark protocol for memory recall accuracy

If you need performance evidence for your team, run repeatable recall tests across real project traces. Keep the test public in your repo for trust and iteration.

  • •Create a fixed dataset of decisions and constraints from prior sessions.
  • •Run the same retrieval queries after context reset events.
  • •Measure correct recall rate, token overhead, and median lookup latency.
  • •Compare memory-only, MEMORY.md-only, and ClawVault-assisted workflows.

ClawVault vs ad-hoc OpenClaw memory patterns

Most teams start with prompts plus raw notes. This matrix shows why a dedicated memory layer is more reliable for production workflows.

CriterionClawVaultNative notes + scripts
Cross-session durabilityExplicit memory storage and retrieval loopDepends on manual note hygiene
Semantic recallIntent-based search and rankingMostly keyword matching
Recovery after interruptionCheckpoint and wake primitivesManual reconstruction of prior state
AuditabilityLocal files + transparent CLI workflowMixed prompts and scattered notes

When should I use ClawVault?

  • •Use ClawVault when OpenClaw sessions reset frequently and team members keep re-explaining context.
  • •Use ClawVault when you need durable decision history for compliance, audits, or incident review.
  • •Use ClawVault when your agents must recover reliably after risky operations or long-running tasks.

Frequently asked questions

01How do I fix OpenClaw forgetting things?
Add a persistent memory layer. Store important decisions in ClawVault as structured memories, retrieve context with semantic search, and restore working state with checkpoint/wake commands.
02What is the best way to add persistent memory to OpenClaw?
Use a CLI-first memory workflow that OpenClaw can call directly: store key context, retrieve by meaning, and enforce checkpoint recovery before risky work.
03Why does OpenClaw lose context across sessions?
Prompt context is limited and session history can compact over time. If memory is not persisted and indexed outside the active context window, details are eventually lost.
04Can ClawVault work as a drop-in OpenClaw memory fix?
Yes. ClawVault is framework-agnostic and CLI-native, so OpenClaw can use it without adopting a new hosted platform.

Related guides

Best OpenClaw Memory Plugin

Compare plugin options and choose a durable memory stack.

Persistent Memory for AI Agents

Design long-term memory loops that survive restarts.

How OpenClaw Memory Actually Works

Technical breakdown of memory flow and common failure points.

MEMORY.md vs ClawVault

When a single file is enough and when it is not.