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Grok Bot+18 agentsAI Agent Memory Integration Skill
Integrate persistent vector memory, context management, and multi-tenant storage into AI agents.
$39.99
AI Agent Memory Integration Skill
Example session with this skill installed
I need to add long-term memory to a LangChain agent using pgvector. It needs to remember user preferences and past project details while keeping data isolated by user_id.
- Read your context and instructions
- Compiled the ai agent memory
I have designed the memory architecture for your LangChain agent. This implementation uses a repository pattern to separate the vector search logic from the core agent loop, ensuring strict user_id filtering on every query.
Memory Integration Plan
| Component | Specification |
|---|---|
| Vector Store | PGVector with SQLAlchemy |
| Embedding Model | text-embedding-3-small (1536 dims) |
| Memory Types | Semantic (Project details), User Preference (Explicit settings) |
| Isolation Strategy | Row-level filtering via tenant_id metadata field |
| Retention | Project details: Permanent; Preferences: User-controlled |
from langchain_community.vectorstores import PGVector
from langchain_core.documents import Document
class MemoryManager:
def __init__(self, connection_string, embeddings):
self.store = PGVector(
connection_string=connection_string,
embedding_function=embeddings,
collection_name="agent_memory"
)
def add_episodic_memory(self, text, user_id, metadata=None):
"""Stores a memory record with mandatory user isolation."""
meta = metadata or {}
meta["user_id"] = user_id
doc = Document(page_content=text, metadata=meta)
self.store.add_documents([doc])
def retrieve_context(self, query, user_id, k=5):
"""Retrieves top-k memories filtered by the active user."""
return self.store.similarity_search(
query,
k=k,
filter={"user_id": user_id}
)
Next steps
- Configure your PostgreSQL instance with the
vectorextension enabled. - Define the chunking strategy for project documents to optimize retrieval precision.
- Implement a cleanup job to handle user-requested data deletion (GDPR compliance).
Connects securely to your tools. The creator never sees your data.
About this skill
Integrating persistent memory into AI agents is a complex engineering challenge that goes far beyond simple prompting. Most agents lose context between sessions or struggle to retrieve relevant facts from large datasets without leaking sensitive info or blowing token budgets. This skill provides the architectural blueprints and implementation logic for professional memory systems.
What it does
- Memory classification defines working, episodic, semantic, and user preference memory types with distinct retention policies.
- Vector integration handles embedding workflows and retrieval logic for backends like pgvector, Qdrant, or Pinecone.
- Context assembly manages token budgets by selecting and summarizing relevant conversation history and retrieved facts.
- Lifecycle control implements TTL, expiry jobs, and version-controlled updates to prevent stale or conflicting data.
- Security enforcement applies multi-tenant isolation and access-control filters at the storage boundary.
How it works
- Define requirements by specifying the agent framework, embedding provider, and types of data the agent needs to remember.
- Schema design creates typed records including metadata, embeddings, and provenance fields.
- Pipeline implementation builds the ingestion and retrieval flows, including chunking strategies and reranking logic.
- Context management integrates the storage interfaces into the agent's prompt assembly loop.
Frameworks & tools
Works with Python or TypeScript agent frameworks. Supports vector databases including pgvector, Qdrant, Chroma, and Pinecone, alongside persistent stores for metadata. Compatible with OpenAI, Anthropic, or local embedding models.
Why this beats prompting it yourself
Writing "remember this" in a prompt fails as soon as the context window fills up or the session ends. This skill builds a durable engineering layer that handles data minimization, multi-tenant isolation, and conflict resolution which simple prompting cannot manage.
Use cases
- Personalized assistants that remember user preferences and past interactions across multiple sessions.
- Research agents that ingest large document sets into semantic memory for high-precision RAG.
- Long-running workflows that need to maintain authoritative state and progress across days or weeks.
Known limitations
Does not provide managed hosting for databases or embedding APIs. Developers must supply their own credentials and infrastructure. Logic is focused on architecture and integration code rather than raw model training.
Requires existing vector database and embedding API credentials. Implementation focus is on storage and retrieval logic, not model fine-tuning or training.
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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