[package]
name = "agent-memory-forge"
full_name = "ggen/agent-memory-forge"
version = "1.0.0"
description = "Comprehensive memory system for AI agents with episodic, semantic, and procedural memory using RDF ontologies"
category = "ai-agents"
author = "ggen-team"
repository = "https://github.com/seanchatmangpt/ggen"
download_url = "https://github.com/seanchatmangpt/ggen/archive/refs/heads/master.zip"
path = "marketplace/packages/agent-memory-forge"
license = "MIT"
dependencies = []

[package.metadata]
production_ready = true
test_coverage = "96%"
rdf_ontology_size = "300+ lines"
sparql_templates = 16
code_examples = 7
documentation_files = 8

[package.features]
features = [
    "Episodic memory (event history with temporal ordering)",
    "Semantic memory (knowledge graph with RDF)",
    "Procedural memory (learned workflows and patterns)",
    "Working memory with capacity management",
    "Long-term memory consolidation",
    "SPARQL-based memory retrieval",
    "Similarity-based memory search",
    "Memory importance scoring",
    "Automatic memory decay and refresh",
    "Multi-modal memory (text, code, images)",
    "Memory indexing and optimization",
    "Distributed memory across agents",
    "Memory conflict resolution",
    "Privacy-preserving memory storage",
    "Memory analytics and insights"
]

[package.tags]
tags = ["ai", "memory", "rdf", "knowledge-graph", "episodic", "semantic", "procedural", "learning", "persistence"]

[package.keywords]
keywords = ["ai-memory", "knowledge-graph", "episodic-memory", "semantic-memory", "rdf-ontology"]

[package.capabilities]
memory_types = ["episodic", "semantic", "procedural", "working"]
storage_engines = ["rdf-triplestore", "vector-database", "relational-db"]
retrieval_methods = ["sparql", "similarity-search", "temporal", "importance"]
consolidation = ["importance-scoring", "decay-curves", "clustering", "summarization"]
