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memori

MemoriLabs · Данни и AI

Agent-native memory for OpenClaw that structures memory from agent trace, execution history, decisions, tool calls, and conversations into durable long-term memory primitives.

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Платформи: Не са посочениНиво: Не е посоченоВ индекса: 20.09.2026 г.Обновен в GitHub: 18.09.2026 г.

Теми: agent · agent-memory · agenticai · ai · ai-memory · claude-code · enterprise · hermes · llm · long-short-term-memory · memory · memory-management · openclaw · python · rag

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Memori - Structured Long-term Memory for OpenClaw

Give 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.

Core Workflow

Memori operates on two parallel tracks through standard OpenClaw lifecycle hooks:

1. Advanced Augmentation (automatic)

After each interaction, Memori converts raw session data into structured, reusable memories asynchronously.

  • Transforms raw agent sessions into structured memory units
  • Captures the agent's actions, reasoning, tool usage, responses, corrections, and failures
  • Organizes into classes to enable efficient retrieval
  • Generates embeddings for semantic retrieval
  • Updates structured memory and the knowledge graph

This is how structured memory is continuously built and updated over time. It runs after the agent responds and does not impact latency.

2. Agent-Controlled-Intelligent Recall

Recall is explicit and initiated by the agent.

Memori separates memory creation from memory recall:

  • Creation is automatic (advanced augmentation)
  • Recall is intentional (agent-controlled)

Agents decide:

  • When to recall
  • What scope to recall from
  • How much history to include

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.

Installation

openclaw plugins install @memorilabs/openclaw-memori

Configuration

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:

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