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agenticforge-memory

Configure and deploy AgenticFORGE memory systems across AI agent workflows.

75|4|Updated Mar 3, 2026
Search Tags:#memory#semantic-memory#episodic-memory#memory-manager#working-memory#agenticforge#rag-pipeline
One-click install
npx skills add https://github.com/LittleBlacky/AgenticFORGE --skill agenticforge-memory
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agenticforge-memory
Source: https://github.com/LittleBlacky/AgenticFORGE/tree/main/skills/agenticforge-memory
Command: npx skills add https://github.com/LittleBlacky/AgenticFORGE --skill agenticforge-memory

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AgenticFORGE memory systems provide structured, multi-layered memory for AI agents, enabling persistent context, historical recall, and scalable knowledge access across sessions.

Core Features & Use Cases

  • WorkingMemory for session context
  • EpisodicMemory for past conversations and events
  • SemanticMemory for vector search over knowledge
  • MemoryManager for unified memory orchestration
  • RAGPipeline for ingesting and retrieving contextual data
  • Seamless integration with SkillRunner and agents

Quick Start

Create a MemoryManager instance configured with WorkingMemory, EpisodicMemory, and SemanticMemory, then integrate it with your agent to persist and retrieve context.

Frequently Asked Questions about agenticforge-memory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I configure persistent memory for AI agents across multiple sessions?▼

To configure persistent memory for AI agents, deploy a MemoryManager instance with WorkingMemory, EpisodicMemory, and SemanticMemory, using optional adapters for persistent stores to maintain context and historical recall across sessions.

What is the difference between working memory, episodic memory, and semantic memory in AI agents?▼

Working memory handles session context, episodic memory stores past conversations and events, and semantic memory enables vector search over knowledge, combining to provide structured multi-layered memory for AI agents.

How do I build a RAG pipeline for ingesting and retrieving contextual data?▼

You can build a RAG pipeline by using the createRagPipeline functionality to ingest and retrieve contextual data through SemanticMemory, enabling scalable vector search over your stored knowledge base.

Can I use in-memory stores instead of persistent adapters for agent memory?▼

Yes, the memory system supports optional adapters for either persistent or in-memory stores, allowing you to choose based on your scalability needs and whether you require context retention after sessions end.

How do I integrate a memory manager with an existing AI agent workflow?▼

You integrate a memory manager by creating a MemoryManager instance and connecting it with your agent and SkillRunner, orchestrating memory layers and ensuring proper memory item structure throughout the workflow.

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