Memory Architecture

Add Persistent Memory to LM Studio

Give your LM Studio agent a brain. Smriti adds long-term, causal memory — retaining context across every session, understanding why events happened, not just what happened.

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O(1)
Connection Retrieval
S-V-O
Extraction Engine
Dual
pgvector Architecture
∞
Context Retention
The Problem

LM Studio agents lose all context when a session ends. Every conversation starts from zero. Users repeat themselves, agents forget preferences, and causal connections between events disappear entirely.

The Smriti Solution

Smriti integrates directly with LM Studio as a drop-in memory layer. It extracts Subject-Verb-Object triples from every interaction, stores them in a dual semantic and episodic pgvector space, and retrieves causal context in O(1) time — across any number of sessions.

How It Works with LM Studio

LM Studio calls Smriti's memory API before generating any response. Smriti checks its timeline, retrieves relevant causal context, and passes it as structured memory to the LM Studio prompt. The agent responds with full awareness of past events — not just the last message.

# Install Smriti pip install smriti # Initialize with lm-studio from smriti import SmritiMemory memory = SmritiMemory( backend="pgvector", mode="dual", # semantic + episodic svo_extraction=True # auto S-V-O tagging ) # Store any interaction memory.store("User bought flowers on Tuesday") # Retrieve with causal context result = memory.recall("why did we argue on Friday?") # Returns: Tuesday → flowers → context → Friday

Smriti vs Standard Vector DB

Feature Standard Vector DB Smriti
Long-term memory Partial Full timeline
Causal context (A caused B) No S-V-O graph
Cross-session retention Session only Persistent
Encrypted memory blocks No Block-level
Query speed O(n) scan O(1) via index

Why LM Studio needs long-term memory

LM Studio is a powerful AI framework but lacks persistent, cross-session memory by default. Each agent run begins with no knowledge of previous interactions. For production applications — customer support, personal assistants, research tools — this is a critical limitation. Smriti solves this by maintaining a chronological memory timeline that persists indefinitely.

How Smriti's S-V-O architecture improves LM Studio agents

Unlike simple vector search that retrieves semantically similar text, Smriti extracts the Subject-Verb-Object structure from every stored memory. This means a LM Studio agent can answer causal questions: not just 'what happened?' but 'why did it happen?' and 'what was the consequence?' This is the difference between a database and a timeline.

Getting started with Smriti and LM Studio

Smriti installs as a Python package and integrates with LM Studio in under 10 lines of code. It requires a PostgreSQL instance with pgvector enabled. Memory is stored in two parallel vector spaces — one for semantic meaning, one for episodic cause-and-effect — giving LM Studio agents both broad recall and precise causal reasoning.