A framework for building persistent memory systems that allow AI agents to retain information across sessions and context window resets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-memory-design
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install agent-memory-design using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Agent Memory Design?
Persistent memory is critical for creating sophisticated AI systems that do not forget user preferences or project context. This skill provides a comprehensive blueprint for an externalized tiered memory system, ensuring that Openclaw Skills users can build agents capable of long-term recall and continuous learning. By moving beyond the transient context window, developers can implement architectures that categorize data into hot, warm, searchable, and archival storage, maintaining token efficiency while scaling knowledge indefinitely.
At its core, this approach treats memory as an external database rather than relying on the model's internal context. It addresses the inherent limitations of LLMs, which typically start every conversation with a blank slate. By utilizing Openclaw Skills patterns for memory management, developers can create agents that effectively remember facts, decisions, and workflows across multiple restarts and compactions.
Agent Memory Design Use Cases
- Developing long-running AI personal assistants that remember user history and preferences.
- Building coding agents that maintain context across complex project refactors and multi-file changes.
- Creating customer support bots that recall past interactions and ticket statuses without re-explanation.
- Designing research agents that accumulate knowledge over weeks of data gathering and synthesis.
- Implementing compaction safety to prevent data loss when LLM context limits are reached during intense tasks.
How Agent Memory Design Works
- Categorize incoming data into memory tiers based on frequency of use and token cost to optimize context usage.
- Implement active write triggers to record facts, decisions, and task statuses to persistent files immediately during a session.
- Configure the system prompt (Tier 1) to load core identity and essential rules for every interaction turn.
- Load session-specific context (Tier 2), such as active project plans and daily notes, into the workspace on demand.
- Query long-term records (Tier 3) using hybrid retrieval methods like semantic or keyword search when specific recall is required.
- Execute pre-compaction checklists to flush the active state to storage before the context window resets or summarizes.
Agent Memory Design Setup
To implement this memory architecture within your project, follow these structural setup steps:
- Create the standardized directory structure for your agent workspace.
- Initialize core knowledge files: MEMORY.md for Tier 1 and SESSION-STATE.md for Tier 2.
- Define the append-only logging format for daily memory files.
mkdir -p workspace/memory/{people,projects,rules,archive}
touch workspace/MEMORY.md workspace/SESSION-STATE.md
Ensure your agent is configured to treat these files as its source of truth, utilizing Openclaw Skills methodology for persistent updates.
Agent Memory Design Data Schema & Taxonomy
The architecture organizes data into a hierarchical file structure to ensure the agent can find information based on its urgency and relevance:
| File/Folder | Tier | Description |
|---|---|---|
| MEMORY.md | Tier 1: Hot | Core identity, user profile, and essential constraints loaded every turn. |
| SESSION-STATE.md | Tier 2: Warm | Active task context, PIDs, and current session goals. |
| memory/YYYY-MM-DD.md | Tier 3: Searchable | Append-only daily logs of specific facts and decisions learned. |
| memory/plans.md | Tier 2: Warm | Roadmaps and TODO lists for active projects. |
| memory/archive/ | Tier 4: Cold | Historical snapshots and old conversation transcripts for auditing. |
Agent Memory Design Advanced Features
- Compaction Safety Protocols: Automated checklists that trigger before context window resets to prevent state loss.
- Multi-Tier Retrieval: Support for keyword (BM25), semantic (Vector), and relationship-based (Knowledge Graph) searching.
- Tiered Token Optimization: Strategies to keep the system prompt lean while maintaining high agent intelligence.
- Maintenance Workflows: Weekly curation patterns to promote important learnings from daily logs to core MEMORY.md files.
- Scaling Roadmaps: Transition paths from simple Markdown files to high-performance Vector DBs as your Openclaw Skills implementation grows.
SKILL.md
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