A
licenseNot graded
qualityC
maintenanceEnables persistent memory for AI agents, combining episodic and semantic memory with LLM reasoning, accessible via MCP.
2
MIT
genpark-agent-hierarchical-episodic-memory-skill
by Alpha-Park
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Hierarchical Episodic Memory Skill for Autonomous Agents
Hierarchical episodic memory engine tailored for multi-agent autonomous systems. Organizes conversational flow across three tiered tiers: short-term working memory, mid-term session context, and long-term high-salience episodic reflections, using pure Python standard library token frequency scoring.
Core Capabilities
Tiered Memory Promotion: Automatically promotes high-salience experiences from short-term to mid-term and permanent long-term memory.
Pure Stdlib Information Retrieval: Fast BM25/TF-IDF token overlap scoring without requiring Pinecone, Weaviate, or heavy native C-extensions.
Salience Scoring: Ranks recall items by importance weighting and keyword match density.
Related MCP server: mnemosis-mcp
MCP Server Integration
Run standard stdio MCP server:
python mcp_server.pyAvailable MCP Tools
add_memory_event: Store an observation with importance weighting (0.0 to 1.0).retrieve_memories: Semantic retrieval of past events matching queries.inspect_memory_tiers: Return counts and items across short, mid, and long term tiers.
Tool Schema Changelog
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