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    Hierarchical AI Agent Memory Builder

    1

    Architect and implement multi-tiered memory systems for autonomous agents with structured retrieval and privacy.

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    Hierarchical AI Agent Memory Builder

    Hierarchical AI Agent Memory Builder

    Example session with this skill installed

    Design a hierarchical memory system for a multi-tenant travel agent using LangChain and pgvector. We need to store user preferences and past trip itineraries safely.

    • Read your context and instructions
    • Compiled the hierarchical ai agent

    I have designed a three-tier memory architecture for your travel agent. This setup ensures that specific trip events (Episodic) inform general travel preferences (Semantic) while maintaining strict tenant isolation.

    Hierarchical Memory Architecture for Travel AI

    TierPurposeStorage RecommendationRetention Policy
    WorkingCurrent booking flow / flight searchRedis (Key-Value)Session end or 24h
    EpisodicSpecific past trips (e.g., Tokyo 2023)pgvector (JSONB + Embeddings)5 years
    SemanticDurable preferences (e.g., "Prefers aisle")pgvector (Relational)Indefinite / User-deleted

    Data Schema (SQL / pgvector)

    -- Semantic Memory for User Preferences
CREATE TABLE user_preferences (
    memory_id UUID PRIMARY KEY,
    tenant_id UUID NOT NULL,
    user_id UUID NOT NULL,
    fact_key TEXT, -- e.g., 'seat_preference'
    fact_value TEXT, -- e.g., 'aisle'
    confidence FLOAT,
    last_verified TIMESTAMP,
    deleted BOOLEAN DEFAULT FALSE
);

-- Episodic Memory for Trip History
CREATE TABLE trip_episodes (
    episode_id UUID PRIMARY KEY,
    tenant_id UUID NOT NULL,
    user_id UUID NOT NULL,
    summary TEXT,
    itinerary_data JSONB,
    embedding vector(1536),
    timestamp TIMESTAMP DEFAULT NOW(),
    sensitivity_level INT -- 1: Public, 2: Internal, 3: Private
);

    Retrieval Logic

    1. Scope Filter: Apply WHERE tenant_id = :tid AND user_id = :uid to all queries.
    2. Context Injection: Retrieve the last 3 episodes and all active preferences.

    Conflict Resolution: If a new episode contradicts a semantic preference (e.g., user booked window instead of aisle), flag for consolidation logic to update the semantic record.

    Next steps

    1. Implement the consolidate_memories function to move facts from trip_episodes to user_preferences.
    2. Set up RLS (Row Level Security) in Postgres to enforce the tenant_id boundary at the database level.
    3. Integrate the LangChain VectorStoreRetriever using the pgvector provider.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Building persistent agents requires more than just a messages array. Without a structured memory strategy, agents forget user preferences, repeat mistakes, or leak sensitive data across sessions. This skill provides the engineering framework to build a hierarchical memory system that balances short-term context with long-term semantic knowledge.

    What it does

    • Tiered Architecture Design defining short-term working memory, episodic event logs, and long-term semantic fact storage.
    • Data Modeling creating schemas for memory records including provenance, confidence scores, and retention metadata.
    • Retrieval Pipelines designing multi-stage processes for identity-scoped filtering, vector search, and relevance ranking.
    • Consolidation Logic defining how episodic patterns transform into durable semantic facts while resolving contradictions.
    • Privacy Enforcement mapping deletion, expiration, and tenant-isolation controls into the memory lifecycle.

    How it works

    1. Requirements Mapping identifying agent type, data sensitivity, and storage constraints like vector versus relational databases.
    2. Schema Generation producing structured definitions for episodes, facts, and working state objects.
    3. Pipeline Construction detailing the ingestion, extraction, and retrieval logic required for the specific stack.
    4. Validation Planning creating test suites for retrieval precision, stale-memory rates, and cross-tenant leakage.

    Frameworks & tools

    This skill works with LangChain, LlamaIndex, and Haystack. It supports storage designs for Pinecone, Weaviate, Milvus, PostgreSQL (pgvector), and Redis.

    Why this beats prompting it yourself

    Generic prompts often result in flat text files that grow indefinitely or lose context. This skill enforces engineering rigor by treating memory as a managed database with explicit lifecycle, authorization, and consolidation policies.

    Use cases

    • Building a customer support agent that remembers past resolutions across tickets.
    • Designing a personal assistant that learns user preferences without re-prompting.
    • Implementing a multi-tenant agent system requiring strict data isolation.

    Known limitations

    Does not perform live database provisioning or infrastructure deployment. Implementation requires user-provided runtime environments and authorized API access.

    Requires user-provided infrastructure. Does not perform live migrations or database provisioning. Implementation depends on developer-supplied SDKs and authorized runtime access.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Listed6 days ago

    What's inside

    Frequently Asked Questions

    This skill provides an engineering framework and structured schemas to transition from simple chat history to a hierarchical memory system, ensuring agents remember user preferences and facts without hitting context limits or leaking data.

    The skill is framework-agnostic and designed to work with popular ecosystems like LangChain, LlamaIndex, and Haystack, as well as various vector and relational databases like Pinecone or PostgreSQL.

    You receive comprehensive architecture designs, structured data schemas for memory records, multi-stage retrieval logic, and validation plans for testing memory precision and privacy.

    Yes, the architecture includes explicit designs for identity-scoped filtering and tenant-isolation controls to ensure that memory data is never accessible across different user sessions or accounts.

    The skill provides the logic, schemas, and architectural blueprints; however, you will need to provide your own infrastructure, such as a vector database or runtime environment, to host the implementation.

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