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ClaudeWave

Brain-inspired persistent memory for LLMs. Model-agnostic — switch between Claude, GPT-4, Gemini, Llama without losing context. Sleep consolidation, belief protection, 4-layer memory hierarchy. Apache 2.0.

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<!-- mcp-name: io.github.rajamohan1950/cls-memory -->
<p align="center">
  <img src="https://img.shields.io/badge/CLS%2B%2B-Memory%20for%20LLMs-6366f1?style=for-the-badge&logo=github" alt="CLS++" />
</p>

<h1 align="center">CLS++ — Continuous Learning System++</h1>
<p align="center">
  <strong>Switch AI models. Never lose context.</strong>
</p>

<p align="center">
  <a href="#-quick-start">Quick Start</a> •
  <a href="#-architecture">Architecture</a> •
  <a href="#-documentation">Documentation</a> •
  <a href="#-deployment">Deployment</a> •
  <a href="#-contributing">Contributing</a>
</p>

<p align="center">
  <a href="https://pypi.org/project/clsplusplus/"><img src="https://img.shields.io/pypi/v/clsplusplus?style=flat-square&label=PyPI&color=6366f1" alt="PyPI" /></a>
  <a href="https://www.npmjs.com/package/clsplusplus"><img src="https://img.shields.io/npm/v/clsplusplus?style=flat-square&label=npm&color=cb3837" alt="npm" /></a>
  <a href="https://pypi.org/project/clsplusplus/"><img src="https://img.shields.io/pypi/pyversions/clsplusplus?style=flat-square" alt="Python" /></a>
  <a href="https://www.clsplusplus.com/docs"><img src="https://img.shields.io/badge/API-Live%20at%20www.clsplusplus.com-22c55e?style=flat-square" alt="API" /></a>
  <a href="https://github.com/rajamohan1950/CLSplusplus/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache%202.0-blue?style=flat-square" alt="License" /></a>
  <a href="https://github.com/rajamohan1950/CLSplusplus"><img src="https://img.shields.io/badge/Patent-Oct%202025-blue?style=flat-square" alt="Patent" /></a>
</p>

---

## 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)](https://en.wikipedia.org/wiki/Complementary_learning_systems) 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.

---

## Quick Start

### Install

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

### Python SDK

```python
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

```typescript
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

```python
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

```bash
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
```

---

## Try It Live

**[Try the demo](https://www.clsplusplus.com)** — Tell Claude something, ask OpenAI. Same memory. No sign-up.

The Chrome extension (Web Store, v6.0.1) captures user messages from
ChatGPT, Claude, and Gemini chat pages automatically and feeds them through
the same memory pipeline. Host permissions: `chatgpt.com`,
`chat.openai.com`, `claude.ai`, `gemini.google.com`. The Link Account popup
differentiates 401 / 403 / network / unknown errors so you know whether the
key is wrong, the account is unlinked, or the server is unreachable.

---

## Architecture

```
Browser (extension/capture.js)          Any LLM client (SDK / REST)
        ↓                                       ↓
        ↓               www.clsplusplus.com (Vercel, Next.js)
        ↓                         │  rewrites /api/v1/*, /api/admin/*
        ↓                         ▼
        └──────────────►  Render-hosted FastAPI (clsplusplus-api)
                                  │  middleware: auth, rate limit, abuse-guard
                                  ▼
              ┌─────────────────────────────────────────┐
              │   CLS++ Core Service                    │
              │   L0: Redis working buffer              │ ← Prefrontal cortex
              │   L1: PostgreSQL+pgvector episodic      │ ← Hippocampus
              │   L2: Schema graph (crystallized)       │ ← Neocortex
              │   L3: Deep archive                      │ ← Thalamus
              │   PhaseMemoryEngine (gas→liquid→        │
              │     solid→glass, auto tier-compression) │
              │   SleepOrchestrator (replay + REM)      │
              │   ReconsolidationGate (belief revision) │
              │   Weblab (PostHog flags + auto-rollback)│
              │   Pricing control plane (memory-stored) │
              └─────────────────────────────────────────┘
```

Every user message captured in the extension and every SDK `learn()` call
lands in L0, is promoted through the phase engine by the same thermodynamic
rules, and is persisted to L1 in the background. There is no separate
"explicit store" path — capture is continuous, tier compression is
automatic.

---

## SaaS Mode (Memory-as-a-Service)

Enable API key auth and rate limiting for production:

```bash
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

# Abuse-guard (env-tunable; defaults shown)
export CLS_ABUSE_AUTHFAIL_THRESHOLD=60        # auth failures per IP per window
export CLS_ABUSE_AUTHFAIL_WINDOW_SECONDS=600  # 10 minutes
export CLS_ABUSE_WHITELIST_IPS=               # comma-separated operator IPs
```

Requests carrying a valid API key are exempt from auth-fail flood counting
(see `src/clsplusplus/abuse_guard.py`).

Product endpoints: `POST /v1/memories/encode`, `POST /v1/memories/retrieve`, `DELETE /v1/memories/forget`, `GET /v1/health/score`. See [SaaS docs](docs/SAAS_MEMORY_AS_SERVICE.md).

### Pricing

CLS++ launched in India and bills in **INR via Razorpay (UPI / QR)** — not
USD, not Stripe. Tiers: **Pro ₹299**, **Business ₹999**, **Enterprise
₹4,999**. The pricing control plane lives inside the CLS++ memory layer
itself (reserved namespace `__cls_pricing__`) and is operator-tunable at
runtime via `POST /admin/pricing/config`. The default config — currency,
margin floor/target, infra spend, dynamic-demand toggle — is seeded by
`src/clsplusplus/pricing_store.py` on first read. `GET /v1/pricing` is a
public, abuse-exempt endpoint.

---

## Deployment

| Platform | Guide |
|----------|-------|
| **Render** (free tier) | [Deploy in 1 click](https://render.com/deploy?repo=https://github.com/rajamohan1950/CLSplusplus) • [Setup guide](docs/DEPLOY_RENDER.md) |
| **AWS Free Tier** | [CloudFormation](infrastructure/aws/cloudformation-free-tier.yaml) • [Step-by-step](infrastructure/aws/FREE_TIER_GUIDE.md) |
| **AWS** | [CloudFormation](infrastructure/aws/cloudformation.yaml) |
| **Azure** | [ARM template](infrastructure/azure/arm-template.json) |

---

## Documentation

| Document | Description |
|----------|-------------|
| [API Reference](docs/API_DOCUMENTATION.md) | Endpoints, auth, examples |
| [API Blueprint](docs/API_BLUEPRINT.md) | SaaS API playbook (DX, security, billing) |
| [SaaS Strategy](docs/SAAS_MEMORY_AS_SERVICE.md) | Memory-as-a-Service, pricing |
| [Marketplace Integration](docs/MARKETPLACE_INTEGRATION.md) | AWS, Azure, GCP, OCI |
| [Productionization](docs/PRODUCTIONIZATION_ROADMAP.md) | Deployment, security, compliance |
| [Commercialization](docs/COMMERCIALIZATION_STRATEGY.md) | Go-to-market, licensing |

---

## Status

**Phase 1 (Foundation)** — Complete

- [x] Four stores (L0–L3) + Plasticity Engine
- [x] Write/Rea
aiclscontinuous-learningembeddingsfastapilangchainllmllm-memorymachine-learningmemorymodel-agnosticneurosciencepersistent-memorypythonrag

What people ask about CLSplusplus

What is rajamohan1950/CLSplusplus?

+

rajamohan1950/CLSplusplus is tools for the Claude AI ecosystem. Brain-inspired persistent memory for LLMs. Model-agnostic — switch between Claude, GPT-4, Gemini, Llama without losing context. Sleep consolidation, belief protection, 4-layer memory hierarchy. Apache 2.0. It has 0 GitHub stars and was last updated today.

How do I install CLSplusplus?

+

You can install CLSplusplus by cloning the repository (https://github.com/rajamohan1950/CLSplusplus) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.

Is rajamohan1950/CLSplusplus safe to use?

+

rajamohan1950/CLSplusplus has not been audited yet by our security agent. Review the original repository on GitHub before using it in production.

Who maintains rajamohan1950/CLSplusplus?

+

rajamohan1950/CLSplusplus is maintained by rajamohan1950. The last recorded GitHub activity is from today, with 439 open issues.

Are there alternatives to CLSplusplus?

+

Yes. On ClaudeWave you can browse similar tools at /categories/tools, sorted by popularity or recent activity.

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