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Grok Bot+18 agentsCodebase Understanding Skill
Architect and implement production-grade hierarchical memory for autonomous agents and conversational systems.
$29.99
Codebase Understanding Skill
Example session with this skill installed
Design a hierarchical memory system for a customer support agent using LangGraph and Pinecone. The agent needs to remember user names, past issues, and current session goals.
- Read your context and instructions
- Compiled the codebase understanding
I have designed a three-tier memory architecture for your LangGraph agent utilizing Pinecone for vector storage and a relational store for episodic metadata.
Customer Support Hierarchical Memory Design
1. Memory Taxonomy
| Tier | Storage | Retention | Scope |
|---|---|---|---|
| Working | LangGraph State | Session Only | Current Goal, Active Ticket ID |
| Episodic | Pinecone + Metadata | 90 Days | Past Resolution Steps, User Interaction Events |
| Semantic | PostgreSQL | Permanent (or until deleted) | User Name, Account Tier, Verified Preferences |
2. Episodic Schema (Pinecone Metadata)
{
"id": "ep_7721",
"text": "User struggled with password reset on mobile app.",
"metadata": {
"user_id": "u_991",
"timestamp": 1715606400,
"type": "event",
"outcome": "failed",
"confidence": 0.95,
"sensitivity": "low",
"tenant_id": "t_01"
}
}
3. Retrieval Pipeline Logic
def retrieve_memories(user_id, query_vector, tenant_id):
# 1. Mandatory Authorization Filter
filter_criteria = {
"user_id": {"$eq": user_id},
"tenant_id": {"$eq": tenant_id}
}
# 2. Staged Retrieval
candidates = index.query(
vector=query_vector,
filter=filter_criteria,
top_k=5,
include_metadata=True
)
# 3. Rank by Recency and Confidence
# Sort logic: (relevance * 0.7) + (recency_score * 0.3)
return ranked_results
Next steps
- Implement the
FactExtractornode in LangGraph to parse semantic facts from closed tickets. - Configure Pinecone index namespaces to enforce hard tenant isolation.
- Define a consolidation cron job to move repeated episodic failures into semantic "Pain Points".
Connects securely to your tools. The creator never sees your data.
About this skill
Designing agentic memory is more than just dumping chat history into a vector database. Most implementations fail by flooding the context window with irrelevant noise, losing track of user preferences, or leaking data across tenant boundaries. This skill provides a rigorous engineering framework for building three-tier hierarchical memory systems.
What it does
- Architectural design for short-term, episodic, and long-term semantic memory tiers.
- Data modeling for structured episodes, facts, and metadata including provenance and confidence scores.
- Pipeline construction for ingestion, extraction, deduplication, and consolidation of memories.
- Retrieval logic using staged filtering for authorization, recency, and task relevance.
- Lifecycle management covering expiration, TTL policies, and deletion propagation.
- Security auditing to identify cross-tenant leakage and memory poisoning risks.
How it works
- Define Policy: Establish memory boundaries, retention periods, and privacy requirements for your specific agent.
- Schema Generation: Design the relational or vector schemas for your chosen storage stack.
- Pipeline Scaffolding: Implement the logic for extracting facts from events and ranking retrieved candidates.
- Integration: Inject authorized, relevant memories into the agent context as evidence.
Frameworks & tools
The skill works with any agent framework including LangGraph, AutoGPT, or CrewAI. It supports storage patterns for Pinecone, Weaviate, PostgreSQL (pgvector), Redis, and standard NoSQL document stores.
Why this beats prompting it yourself
Generic prompts often result in "flat" memory that ignores metadata or mixes up temporary states with permanent facts. This skill enforces a professional engineering lifecycle, ensuring your agent distinguishes between what it observed, what it inferred, and what it is authorized to remember.
Use cases
- Build multi-session agents that remember user preferences without context bloat.
- Design autonomous agents that learn from task outcomes to improve future performance.
- Implement privacy-compliant memory for multi-tenant SaaS applications.
- Audit existing memory implementations for security vulnerabilities and stale data.
Known limitations
This skill provides architecture and code scaffolding but does not provision cloud infrastructure or perform live database migrations. Provisioning and deployment require user-managed tools.
Does not provision infrastructure or perform live migrations. Requires user-supplied runtime for execution. Design focuses on architecture rather than specific hosting providers.
How to use
- 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
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 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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