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Engram

Persistent semantic memory for AI agents — local, fast, free. Use when agent needs to recall past decisions, store new facts/preferences, search conversation history, or maintain context across sessions.

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版本0.2.0
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name: engram description: "Persistent semantic memory for AI agents — local, fast, free. Use when agent needs to recall past decisions, store new facts/preferences, search conversation history, or maintain context across sessions." metadata: openclaw: emoji: "🧠" requires: bins: ["engram"] install: - id: node kind: node package: engram-memory bins: ["engram"] label: "Install Engram (npm)"

Engram — Agent Memory

Local semantic memory with biological decay, typed memories, and relationship graphs. No API keys. No cloud.

Boot Sequence

engram search "<current task or context>" --limit 10

Always recall before working. Accessed memories get salience-boosted.

Storing

engram add "Client uses React with TypeScript" --type fact --tags react,client
engram add "We decided to pause ads" --type decision --tags ads
echo "Raw conversation text" | engram ingest

Types: fact, decision, preference, event, relationship

Searching

engram search "what tech stack"
engram search "pricing decisions" --type decision
engram search "client status" --agent client-agent

Relationships

engram relate <src> <tgt> --type supports
engram auto-relate <id>
engram relations <id>

Types: related_to, supports, contradicts, caused_by, supersedes, part_of, references

Key Concepts

  • Decay: Unused memories lose salience daily. Recalled ones get boosted.
  • Types: Filter by fact, decision, preference, event, relationship.
  • Scoping: global, agent, private, shared.
  • Dedup: >92% similarity auto-merges.

Quick Reference

engram stats
engram recall --limit 10
engram export > backup.json
engram import backup.json

如何使用「Engram」?

  1. 打开小龙虾AI(Web 或 iOS App)
  2. 点击上方「立即使用」按钮,或在对话框中输入任务描述
  3. 小龙虾AI 会自动匹配并调用「Engram技能完成任务
  4. 结果即时呈现,支持继续对话优化

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