OpenMemo Memory – Persistent Memory for OpenClaw Agents

Provides OpenClaw agents with local, scene-aware, persistent structured memory for task deduplication and long-term workflow recall.

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:

• Repeat the same tasks — the agent deploys successfully, but runs the entire workflow again next time because it never recorded the result
• Store memory as large documents — chat history and MEMORY.md files help retrieve text, but agents also need to remember tasks they completed, decisions they made, and workflows that succeeded

What OpenMemo Adds

OpenMemo introduces a structured memory layer designed for agent workflows. Instead of storing raw conversation text, OpenMemo records experience events.

text

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.

text

OpenClaw Agent
      |
      v
OpenMemo Skill
      |
      v
OpenMemo Adapter (local)
      |
      v
OpenMemo Memory Engine

Example

Without OpenMemo:

text

> deploy backend
  → agent rebuilds everything again

With OpenMemo:

text

> 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 recall
• scene (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 store
• scene (string, optional): Scene context
• type (string, optional): Memory type — fact, decision, observation, preference

check_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 check

Rules

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:

text

pip install openmemo openmemo-openclaw
openmemo serve

Restart your agent. The Skill will automatically detect the adapter and activate persistent memory.

Best Use Cases

• Coding agents
• DevOps automation
• Research agents
• Multi-step AI workflows

Links

• GitHub: https://github.com/openmemoai/openmemo
• Adapter: https://github.com/openmemoai/openmemo-openclaw-adapter