SMRITI Memory

SMRITI Memcore

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

www.smriti-memcore.com

Interactive Semantic Palace Graph Browser ( smriti ui ) — Category Room hub clustering, neural synaptic particle flow, and real-time dual inspector. Watch Full-Res Video

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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.

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SMRITI vs. Naive RAG & Vector Databases

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Key Capabilities

• Dual-Process Performance: Zero-blocking real-time loops. Write immediately, analyze when idle.
• Persona Palaces & On-Demand Hub: Download pre-curated cognitive memory palaces for historical figures (e.g., Albert Einstein, Isaac Newton) and converse with them with authentic mental models.
• 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.

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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) │ └──────────┘ └───────────────────┘

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Quick Start

1. Install SMRITI

Install the core memory engine and CLI:

pip install smriti-memcore

*(Optional: Ensure Ollama is running locally for 100% private, free LLM inference: ollama run mistral)*

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2. Instructions for Professors & Course Creators: Package Your Curriculum

Turn your raw lecture folder into a self-contained, offline-first Cognitive Palace Bundle (.smritibundle) that acts as a 24/7 Socratic Teaching Assistant for your students.

# Package a folder of slides, transcripts, readings, or notebooks into an offline bundle:

smriti pack ./my-course-folder -o cs101.smritibundle

Or package directly from a declarative YAML/JSON persona spec:

smriti pack ./course-spec.yaml -o cs101.smritibundle --install

• Supported Formats: PDF slides, WebVTT/SRT video lecture transcripts, Markdown, plain text, and Jupyter notebooks (.ipynb).
• Zero Cloud Costs: All vector embeddings are calculated locally ($0 OpenAI/HuggingFace API bills).
• Lightweight Distribution: Generates a self-contained archive (typically 5–15 MB) containing the vector index, full-text search, and semantic graph. Upload it directly to Canvas, Blackboard, Piazza, Google Drive, or email it to students.

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3. Instructions for Students: 24/7 Socratic Office Hours

Students download the professor's .smritibundle and converse with an interactive course mentor directly in their terminal—100% offline, private, and subscription-free.

# Step 1: Import the course bundle provided by your professor

smriti pull ./cs101.smritibundle

Step 2: Start your 24/7 Socratic Office Hours

smriti chat cs101

(Optional) Use a specific local Ollama model (e.g. Mistral, Qwen, Llama)

smriti chat cs101 --model mistral smriti chat cs101 --model qwen3.5

• Pinpoint Attribution: Asks about any concept and surfaces exact slide page numbers and video lecture transcript quotes.
• Socratic Pedagogy: Rather than simply giving away homework answers, the course persona guides students step-by-step from first principles using course analogies.
• 100% Local & Private: All conversations stay on the student's laptop—zero student data is sent to external servers.

Pre-Curated Mentors in Official Registry

You can also explore out-of-the-box mentors right away:

# List all available mentors and locally installed bundles

smriti list

Socratic Distributed Systems Professor (Raft, Paxos, CAP theorem)

smriti chat prof-systems

100% Private, Local CBT & Mindfulness Companion

smriti chat mindful-counsellor

Historical Thinkers

smriti chat einstein smriti chat newton smriti chat marcus-aurelius

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4. Smriti DevBrain: Universal Developer Memory (Cursor, Claude, Windsurf, Gemini, Zed)

SMRITI solves cross-agent amnesia by acting as a shared, local cognitive brain across all your AI coding tools. A bug solved in Claude Code or Gemini is immediately remembered by Cursor.

Step 1: Auto-Configure All Installed IDEs & Agents

Run one command to scan and configure Cursor, Claude Code, Windsurf, Gemini, and Zed in seconds:

smriti install

*(Or target specific tools: smriti install --client cursor,windsurf)*

Step 2: Initialize a Codebase Workspace

Inside any repository, run:

smriti init

This automatically:

• Creates a dedicated Semantic Room in the Palace for the project (repo: ).
• Scaffolds .cursorrules, .cursor/rules/smriti.mdc, CLAUDE.md, and project .cursor/mcp.json.
• Installs a lightweight, non-blocking git post-commit hook that records recent commits and architectural changes into memory.

Git Commit Memory Hook Management:

smriti hook install    # Enable automatic commit context logging

smriti hook uninstall # Remove the post-commit hook

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3. 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()

--- Or Load a Pre-Curated Persona (e.g. Albert Einstein) ---

einstein, manifest = SMRITI.from_persona("albert-einstein") memories = einstein.recall("What thought experiment led to Special Relativity?") for mem in memories: print(f"[{mem.strength:.2f}] {mem.content}")

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MCP Tool Reference

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

Core Tools

AMP v1.0 Alias Tools

These endpoi