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BytesAgain β€Ί Skills β€Ί Engramai
πŸ¦€ ClawHub

Engramai

by @shing19

Neuroscience-grounded memory for AI agents. Add, recall, and manage memories with ACT-R activation, Hebbian learning, and cognitive consolidation.

Versionv1.0.0
Downloads440
#clawhub
TERMINAL
clawhub install engramai

πŸ“– About This Skill

name: engramai description: Neuroscience-grounded memory for AI agents. Add, recall, and manage memories with ACT-R activation, Hebbian learning, and cognitive consolidation. homepage: https://github.com/tonitangpotato/neuromemory-ai metadata: {"clawdbot":{"emoji":"🧠","requires":{"bins":["python3"],"packages":{"pip":["engramai"]}}}}

engramai 🧠

Cognitive memory system implementing ACT-R activation, Memory Chain consolidation, Ebbinghaus forgetting, and Hebbian learning.

Installation

pip install engramai

Quick Start

from engram import Memory
mem = Memory("./agent.db")
mem.add("User prefers concise answers", type="relational", importance=0.8)
results = mem.recall("user preferences", limit=5)
mem.consolidate()  # Daily maintenance

CLI Usage

# Add a memory
neuromem add "User prefers dark mode" --type preference --importance 0.8
Recall memories

neuromem recall "user preferences"
View statistics

neuromem stats
Run consolidation (like sleep)

neuromem consolidate
Prune weak memories

neuromem forget --threshold 0.01
List memories

neuromem list --limit 20
Show Hebbian links

neuromem hebbian "dark mode"

AI Agent Integration (Important!)

For AI agents to use engram correctly, follow these patterns:

When to Call What

| Trigger | Action | Example | |---------|--------|---------| | Learn user preference | store(type="relational") | "User prefers concise answers" | | Learn important fact | store(type="factual") | "Project uses Python 3.12" | | Learn how to do something | store(type="procedural") | "Deploy requires running tests first" | | Question about history | recall() first, then answer | "What did I say about X?" | | User satisfied | reward("positive feedback") | Strengthens recent memories | | User unsatisfied | reward("negative feedback") | Suppresses recent memories | | Daily maintenance | consolidate() + forget() | Run via cron or heartbeat |

What to Store

βœ… Store:

  • User preferences and habits
  • Important facts and decisions
  • Lessons learned
  • Procedural knowledge

    • ❌ Don't store:

  • Every conversation message (too noisy)
  • Temporary information
  • Publicly available facts
  • Sensitive data (unless requested)

    • Importance Guide

    | Level | Use For | |-------|---------| | 0.9-1.0 | Critical info (API keys location, absolute preferences) | | 0.7-0.8 | Important (code style, project structure) | | 0.5-0.6 | Normal (general facts, experiences) | | 0.3-0.4 | Low priority (casual chat, temp notes) |

    Hybrid Mode (Recommended)

    Use engram alongside file-based memory:

  • engram: Active memory β€” retrieval, associations, dynamic weighting
  • Files (memory/*.md): Logs β€” transparency, debugging, manual editing

    • Heartbeat Maintenance

    Add to your heartbeat or cron:

    ## Memory Maintenance (Daily)

  • [ ] engram.consolidate
    • 

  • [ ] engram.forget --threshold 0.01
    •

    Memory Types

  • factual β€” Facts and knowledge
  • episodic β€” Events and experiences
  • relational β€” Relationships and preferences
  • emotional β€” Emotional moments
  • procedural β€” How-to knowledge
  • opinion β€” Beliefs and opinions

    • MCP Server

    For Claude/Cursor/Clawdbot integration:

    python -m engram.mcp_server --db ./agent.db

    MCP Config (Clawdbot):

    mcp:
  servers:
    engram:
      command: python3
      args: ["-m", "engram.mcp_server"]
      env:
        ENGRAM_DB_PATH: ~/.clawdbot/agents/main/memory.db

    Tools: engram.store, engram.recall, engram.consolidate, engram.forget, engram.reward, engram.stats, engram.export

    Key Features

    | Feature | Description | |---------|-------------| | ACT-R Activation | Retrieval ranked by recency Γ— frequency Γ— context | | Memory Chain | Dual-system consolidation (working β†’ core) | | Ebbinghaus Forgetting | Natural decay with spaced repetition | | Hebbian Learning | "Neurons that fire together wire together" | | Confidence Scoring | Metacognitive monitoring | | Reward Learning | User feedback shapes memory | | Zero Dependencies | Pure Python stdlib + SQLite |

    Links

  • PyPI: https://pypi.org/project/engramai/
  • npm: https://www.npmjs.com/package/neuromemory-ai
  • GitHub: https://github.com/tonitangpotato/neuromemory-ai
  • Docs: https://github.com/tonitangpotato/neuromemory-ai/blob/main/docs/USAGE.md

    • πŸ’‘ Examples

    from engram import Memory
    mem = Memory("./agent.db")
mem.add("User prefers concise answers", type="relational", importance=0.8)
results = mem.recall("user preferences", limit=5)
mem.consolidate()  # Daily maintenance