OpenMemo Memory – Persistent Memory for OpenClaw Agents
by @openmemoai
Provides OpenClaw agents with local, scene-aware, persistent structured memory for task deduplication and long-term workflow recall.
clawhub install openmemo-clawhub-skill📖 About This Skill
OpenMemo - Persistent Memory for OpenClaw Agents
Stop agents from repeating tasks. Give your AI long-term memory.
The Problem
OpenClaw provides a basic memory system, but in real-world usage agents still:
What OpenMemo Adds
OpenMemo introduces a structured memory layer designed for agent workflows. Instead of storing raw conversation text, OpenMemo records experience events.
Backend deployed using Docker Compose
Scene: deployment
Type: task_execution
Agents recall actions and results, not just text.
Comparison
| Feature | Typical Long-Term Memory | OpenMemo Memory | |---|---|---| | Memory type | Document memory | Experience memory | | Storage | Notes and logs | Structured events | | Retrieval | Vector search | Scene + task recall | | Task deduplication | No | Yes | | Workflow reuse | No | Yes |
Core Capabilities
Persistent Memory
OpenMemo records structured experience from agent workflows: tasks completed, decisions made, workflows validated. These memories persist across sessions and can be recalled when similar tasks appear. Over time the agent accumulates long-term operational knowledge.
Task Deduplication
OpenMemo introduces task fingerprinting. Before executing a task, the agent checks memory. If the task already succeeded, the agent can reuse the result, skip execution, or continue from the previous step. This prevents duplicate execution, wasted tokens, and repeated workflows.
Scene-Aware Memory
OpenMemo detects the working context: coding, research, debugging, deployment. Only the most relevant memories are retrieved for the current task. This keeps context focused and efficient.
Memory Inspector
A built-in dashboard lets you see what the agent remembers, memory ranking and recall results, and system health. The memory system becomes transparent instead of a black box.
Local-First Architecture
All memory operations happen locally. No external dependencies, no cloud required, full privacy, lower latency.
OpenClaw Agent
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v
OpenMemo Skill
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v
OpenMemo Adapter (local)
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OpenMemo Memory Engine
Example
Without OpenMemo:
> deploy backend
→ agent rebuilds everything again
With OpenMemo:
> deploy backend
→ agent detects previous deployment
→ reuses workflow
The agent stops behaving like a script and starts behaving like a system.
Tools
recall_memory
Retrieve relevant memory from OpenMemo. Use this to recall past experience, decisions, and knowledge before executing tasks.
Parameters:
query (string, required): The search query for memory recallscene (string, optional): Scene context (e.g., coding, debug, research, deployment)write_memory
Store structured memory event in OpenMemo. Use this after completing important tasks to save experience for future use.
Parameters:
content (string, required): The memory content to storescene (string, optional): Scene contexttype (string, optional): Memory type — fact, decision, observation, preferencecheck_task_memory
Check if a task has already been executed. Use this FIRST before starting any task to avoid duplication.
Parameters:
task_description (string, required): Description of the task to checkRules
When executing tasks, follow these memory operating rules:
1. BEFORE starting any task, call check_task_memory with the task description. If a successful previous execution exists, reuse the result or skip.
2. Use recall_memory to retrieve relevant past experience before making decisions.
3. After completing important tasks, call write_memory to store structured experience: decisions made, successful approaches, errors resolved, key observations.
4. Always include the scene context (coding, debug, research, deployment) for better recall accuracy.
Setup
Install the OpenMemo adapter locally:
pip install openmemo openmemo-openclaw
openmemo serve
Restart your agent. The Skill will automatically detect the adapter and activate persistent memory.
Best Use Cases
Links
💡 Examples
Without OpenMemo:
> deploy backend
→ agent rebuilds everything again
With OpenMemo:
> deploy backend
→ agent detects previous deployment
→ reuses workflow
The agent stops behaving like a script and starts behaving like a system.
⚙️ Configuration
Install the OpenMemo adapter locally:
pip install openmemo openmemo-openclaw
openmemo serve
Restart your agent. The Skill will automatically detect the adapter and activate persistent memory.
🔒 Constraints
When executing tasks, follow these memory operating rules:
1. BEFORE starting any task, call check_task_memory with the task description. If a successful previous execution exists, reuse the result or skip.
2. Use recall_memory to retrieve relevant past experience before making decisions.
3. After completing important tasks, call write_memory to store structured experience: decisions made, successful approaches, errors resolved, key observations.
4. Always include the scene context (coding, debug, research, deployment) for better recall accuracy.