clsplusplus 7.0.0

Brain-inspired, model-agnostic persistent memory for LLMs. Learn, recall, forget — like a brain. Works with OpenAI, Claude, Gemini, Llama.

CLS++ — Continuous Learning System++

Switch AI models. Never lose context.

Quick Start • Architecture • Documentation • Deployment • Contributing

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What is CLS++?

Every LLM in production today operates with amnesia. Sessions end, context windows clear, and the model forgets everything—preferences, corrections, facts established over months.

CLS++ is an external memory substrate that solves this at its root. Drawing from neuroscientific Complementary Learning Systems (CLS) theory, it implements:

Feature	Description
Four-store hierarchy	L0 (Working Buffer) → L1 (Indexing) → L2 (Schema Graph) → L3 (Deep Recess)
Biological consolidation	Salience, Usage, Authority, Conflict, Surprise signals
Sleep cycle	Nightly maintenance: rank, decay, deduplicate, consolidate
Reconsolidation gate	Belief revision only with evidence quorum
Model-agnostic	Any LLM plugs in via REST API—Claude, GPT-4, Gemini, Llama

Memory is external to the model. Switch models anytime. No reset.

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Quick Start

Install

pip install clsplusplus          # Python (lightweight: only httpx + pydantic)
npm install clsplusplus          # JavaScript / TypeScript (zero dependencies)

Python SDK

from clsplusplus import Brain

brain = Brain("alice")

# Teach it anything in natural language
brain.learn("I work at Google as a senior engineer")
brain.learn("I prefer Python over JavaScript")

# Ask it anything — semantic recall, not keyword matching
brain.ask("What's my job?")           # ["I work at Google as a senior engineer"]

# Get LLM-ready context for any prompt
brain.context("coding help")
# "Known facts about this user:\n- I work at Google..."

# Forget (GDPR right to be forgotten)
brain.forget("I work at Google as a senior engineer")

JavaScript / TypeScript SDK

import { Brain } from "clsplusplus";

const brain = new Brain("alice");

await brain.learn("I work at Google as a senior engineer");
const facts = await brain.ask("What's my job?");
const context = await brain.context("coding help");
await brain.forget("I work at Google as a senior engineer");

Use with OpenAI

from clsplusplus import Brain

brain = Brain("alice")

# Wrap any LLM function — auto-injects memory, auto-learns
@brain.wrap
def chat(system_prompt, user_message):
    return openai.chat(system=system_prompt, user=user_message)

response = chat("You are a helpful assistant", "Help me with Python")
# Brain auto-recalls relevant memory, injects into prompt,
# calls your LLM, learns from the exchange, returns response.

Full API

Method	Description
brain.learn(fact)	Teach a fact. Returns memory ID.
brain.ask(question)	Query for relevant facts. Returns list.
brain.context(topic)	Get LLM-ready context string.
brain.forget(fact)	Forget by text or ID.
brain.absorb(text)	Bulk-learn from document or conversation.
brain.who()	Auto-generated user profile.
brain.correct(old, new)	Update a belief.
brain.chat(message, llm)	Full conversation handler with memory.
brain.teach(dict)	Learn from structured data.
brain.watch(messages)	Learn from chat message history.
brain.wrap(fn)	Wrap any LLM function with auto-memory.

Run the Full Server Locally

git clone https://github.com/rajamohan1950/CLSplusplus.git
cd CLSplusplus
pip install -e ".[server]"

# Start infrastructure (Redis + PostgreSQL)
docker compose up -d redis postgres

# Start the API server
uvicorn clsplusplus.api:create_app --factory --host 0.0.0.0 --port 8080

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Try It Live

Try the demo — Tell Claude something, ask OpenAI. Same memory. No sign-up.

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Architecture

Client (any LLM) → POST /v1/memory/read (before inference)
                        ↓
              ┌─────────────────────────┐
              │   CLS++ Core Service    │
              │   L0: Working Buffer    │   ← Prefrontal Cortex
              │   L1: Indexing Store    │   ← Hippocampus
              │   L2: Schema Graph      │   ← Neocortex
              │   L3: Deep Recess       │   ← Thalamus
              │   Plasticity Engine     │
              │   Sleep Orchestrator    │
              └─────────────────────────┘
                        ↓
              POST /v1/memory/write (after inference)

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SaaS Mode (Memory-as-a-Service)

Enable API key auth and rate limiting for production:

export CLS_API_KEYS=cls_live_xxxxxxxxxxxxxxxxxxxxxxxx
export CLS_REQUIRE_API_KEY=true
export CLS_RATE_LIMIT_REQUESTS=100
export CLS_RATE_LIMIT_WINDOW_SECONDS=60

Product endpoints: POST /v1/memories/encode, POST /v1/memories/retrieve, DELETE /v1/memories/forget, GET /v1/health/score. See SaaS docs.

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Deployment

Platform	Guide
Render (free tier)	Deploy in 1 click • Setup guide
AWS Free Tier	CloudFormation • Step-by-step
AWS	CloudFormation
Azure	ARM template

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Documentation

Document	Description
API Reference	Endpoints, auth, examples
API Blueprint	SaaS API playbook (DX, security, billing)
SaaS Strategy	Memory-as-a-Service, pricing
Marketplace Integration	AWS, Azure, GCP, OCI
Productionization	Deployment, security, compliance
Commercialization	Go-to-market, licensing

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Status

Phase 1 (Foundation) — Complete

• Four stores (L0–L3) + Plasticity Engine
• Write/Read API + Python SDK
• Docker Compose + Render deploy
• Sleep cycle orchestrator
• Reconsolidation gate
• API key auth + rate limiting
• SaaS product endpoints

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Contributing

We welcome contributions. See CONTRIBUTING.md and the Wiki for details.

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License

Provisional patent filed October 2025. Apache 2.0 (see LICENSE).

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AlphaForge AI Labs • Rajamohan Jabbala • 2026