SMRITI Memcore

Enterprise-grade, privacy-first Long-Term Memory (LTM) engine for LLM agents, multi-agent frameworks, and MCP clients.

🌐 www.smriti-memcore.com

💡 What is SMRITI?

SMRITI is a high-performance, neuro-inspired long-term memory layer designed to give AI agents persistent, adaptive recall without blocking their real-time execution loop.

Inspired by human Dual-Process cognitive theory, SMRITI splits memory operations into:

1. System 1 (Immediate Heuristics): Decoupled, millisecond-level ingestion of raw interactions into an append-only Episode Buffer.
2. System 2 (Async Consolidation): Background LLM-driven consolidation that extracts knowledge graphs, resolves contradictions, identifies skills, and decays weak memories.

⚔️ SMRITI vs. Naive RAG & Vector Databases

🚀 Key Capabilities

• 🧠 Dual-Process Performance: Zero-blocking real-time loops. Write immediately, analyze when idle.
• 🔒 Privacy-First (Private Rooms): Create local semantic rooms whose memories are automatically excluded from shared/team-wide sync.
• 🔌 Model Context Protocol (MCP): Native MCP server integration with Claude Code, Claude Desktop, Gemini Antigravity, and Codex.
• 📦 AMP v1.0 Spec Compliant: Drop-in compatibility with any agent framework conforming to the Agent Memory Protocol.
• 📊 Visual Graph Explorer: Clean D3.js-based visualization interface with Prometheus metrics monitoring.
• 📂 Obsidian Vault Integration: Automatically syncs your agent's memory graph into an Obsidian vault for human curation.
• 🧩 Framework Agnostic: Integrates natively with LangChain, LlamaIndex, CrewAI, and AutoGen.

🧠 Core Architecture

                           ┌─────────────────────────────────┐
                           │    Asynchronous Consolidation   │
                           │      (8 Background Processes)   │
                           │  • Chunking      • Cross-Ref.   │
                           │  • Conflict Res. • Skill Ext.   │
                           │  • Forgetting    • Spaced Rep.  │
                           │  • Reflection    • Defragment.  │
                           └────────────────┬────────────────┘
                                            │ background
  ┌──────────┐   ┌──────────┐   ┌───────────▼─────────┐   ┌──────────┐
  │  Input   │──▶│ Attention │──▶│   Episode Buffer    │──▶│ Semantic │
  │  Text    │   │   Gate    │   │  (append-only log)  │   │  Palace  │
  │  └───────┘   │ (salience │   └─────────────────────┘   │  Graph   │
  │              │  filter)  │                              │ G=(V,E)  │
  └──────────┘   └──────────┘                              └────┬─────┘
                                                                │
  ┌──────────┐   ┌──────────┐   ┌───────────────────┐           │
  │  Query   │──▶│ Retrieval│──▶│  Working Memory   │◀──────────┘
  │          │   │  Engine  │   │   (7 ± 2 slots)   │
  └──────────┘   │ Q(v) =   │   └───────────────────┘
                 │ β₁cos +  │
                 │ β₂decay+ │   ┌───────────────────┐
                 │ β₃freq + │──▶│    Meta-Memory    │
                 │ β₄sal    │   │ (confidence map)  │
                 └──────────┘   └───────────────────┘

🏁 Quick Start

1. Unified MCP Server (Claude Code, Gemini, Codex)

SMRITI can be used as a global, persistent memory layer across all your MCP-enabled developer clients.

Method A: One-Line Installer (Recommended)

Run the setup script directly in your terminal:

bash <(curl -s https://raw.githubusercontent.com/smriti-memcore/smriti-memcore/main/install_smriti_mcp.sh)

Method B: Via PyPI

Install the package and run the setup CLI:

pip3 install smriti-memcore
smriti_install

2. Python SDK

For application developers building custom agent loops.

pip install smriti-memcore[faiss] # FAISS is recommended for accelerated vector search

from smriti import SMRITI, SmritiConfig

# Initialize memory engine with OpenAI
config = SmritiConfig(
    storage_path="./my_agent_memory",
    llm_model="gpt-4o",
    openai_api_key="your-api-key-here"
)
memory = SMRITI(config=config)

# Ingest observations
memory.encode("User prefers using PyTorch for neural networks.")
memory.encode("User is allergic to shellfish.", context="medical")

# Recall relevant context using multi-factor retrieval
results = memory.recall("What framework does the user prefer?")
for mem in results:
    print(f"[{mem.strength:.2f}] {mem.content}")

# Manually trigger System 2 background consolidation
memory.consolidate()
memory.save()

🛠️ MCP Tool Reference

SMRITI exposes 19 tools (13 native + 6 AMP aliases) for clients:

Core Tools

AMP v1.0 Alias Tools

These endpoints ensure complete conformance with the standard Agent Memory Protocol specification:

🔌 Framework Integrations

LangChain Integration

Use SmritiLangChainMemory as a drop-in replacement for default chat buffers. It limits active context using Working Memory and offloads the conversational history to the Semantic Palace graph in the background.

from langchain.chains import ConversationChain
from smriti.integrations.langchain_memory import SmritiLangChainMemory
from smriti import SMRITI

smriti_engine = SMRITI(storage_path="./langchain_smriti_db")
smriti_memory = SmritiLangChainMemory(smriti_client=smriti_engine, top_k=3)

conversation = ConversationChain(
    llm=my_llm,
    memory=smriti_memory,
)
conversation.predict(input="I prefer backend APIs in Python.")

📊 Benchmarks & Performance

1. LoCoMo (Multi-System Context Retrieval)

Tested against four architectures on the LoCoMo long-context dialogue dataset (28 turns, 15 evaluation questions):

SMRITI retains high recall while drastically reducing query context size. Consolidation runs in the background and does not block client interactions.

2. LongMemEval (Long-Term Chat Sessions)

Evaluated over 50+ chat sessions using the LongMemEval harness:

⚙️ Configuration Parameters

Initialize SmritiConfig with custom parameters to tune the cognitive weights:

from smriti import SmritiConfig