memori
You have access to Memori, agent-native memory infrastructure: an LLM-agnostic layer that structures memory from not just natural…
Agent-native memory for OpenClaw that structures memory from agent trace, execution history, decisions, tool calls, and conversations into durable long-term memory primitives.
Claude Code
$ npx -y skills add MemoriLabs/Memori --skill clawhub --agent claude-code
Codex
$ npx skills add MemoriLabs/Memori --skill clawhub -a codex -y
Cursor
$ npx skills add MemoriLabs/Memori --skill clawhub -a cursor -y
Gemini CLI
$ npx skills add MemoriLabs/Memori --skill clawhub -a gemini-cli -y
GitHub Copilot
$ npx skills add MemoriLabs/Memori --skill clawhub -a github-copilot -y
OpenCode
$ npx skills add MemoriLabs/Memori --skill clawhub -a opencode -y
Windsurf
$ npx skills add MemoriLabs/Memori --skill clawhub -a windsurf -y
name: memori
id: '@memorilabs/openclaw-memori'
description: Agent-native memory for OpenClaw that structures memory from agent trace, execution history, decisions, tool calls, and conversations into durable long-term memory primitives.
license: Apache-2.0
homepage: https://github.com/MemoriLabs/Memori
repository: https://github.com/MemoriLabs/Memori.git
compatibility:
- openclaw
metadata:
openclaw:
requires:
env:
- MEMORI_API_KEY
- ENTITY_ID
- PROJECT_ID
bins:
- memori
primaryEnv: MEMORI_API_KEY
externalServices:
- https://api.memorilabs.aiGive your OpenClaw agents persistent, structured memory derived from agent execution, tool usage, workflow history, and conversations. Memori integrates seamlessly in the background via lifecycle hooks and provides agents with the tools to retrieve context when it is relevant.
Memori operates on two parallel tracks through standard OpenClaw lifecycle hooks:
After each interaction, Memori converts raw session data into structured, reusable memories asynchronously.
This is how structured memory is continuously built and updated over time. It runs after the agent responds and does not impact latency.
Recall is explicit and initiated by the agent.
Memori separates memory creation from memory recall:
Agents decide:
To maintain an efficient context window, Memori equips the agent with specific tools to retrieve history when required for the conversation:
1. **`memori_recall`**: Searches the structured memory graph for specific facts, constraints, and prior decisions. 2. **`memori_recall_summary`**: Retrieves structured daily briefs and rolling summaries of prior sessions. 3. **`memori_compaction`**: Retrieves structured post-compaction brief to continue task without interruption. 4. **`memori_feedback`**: Reports on memory quality to improve extraction accuracy.
openclaw plugins install @memorilabs/openclaw-memori
Add to your `~/.openclaw/openclaw.json` or use the `openclaw memori init` CLI command to set up your workspace:
openclaw memori init \ --api-key "YOUR_MEMORI_API_KEY" \ --entity-id "your-entity-id" \ --project-id "your-project-id"
Alternatively, configure it directly via JSON:
{
"plugins": {
"entries": {
"openclaw-memori": {
"enabled": true,
"config": {
"apiKey": "${MEMORI_API_KEY}",
"entityId": "openclaw-user",
"projectId": "default-project"
}
}
}
}
}When this plugin is active, the agent is equipped with tools to manage long-term context. The agent should use its discretion to call these tools when helpful:
Check that the plugin is working and securely connected:
# Verify plugin is securely connected to the API openclaw memori status --check # Check for Memori logs in gateway output openclaw gateway logs --filter "[Memori]"
Check your current API quota:
memori quota
**Example output:**
__ __ _
| \/ | ___ _ __ ___ ___ _ __(_)
| |\/| |/ _ \ '_ ` _ \ / _ \| '__| |
| | | | __/ | | | | | (_) | | | |
|_| |_|\___|_| |_| |_|\___/|_| |_|
perfectam memoriam
memorilabs.ai
+ Maximum # of Memories: 100
+ Current # of Memories: 0
+ You are not currently over quota.Use this to monitor usage and upgrade if needed.
**Explicit Opt-In Required:** Memori requires the user to explicitly configure an API key (`MEMORI_API_KEY`) and an `entityId`. **No data is captured or transmitted unless these credentials are actively provided by the user.**
Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.
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