Metadata-Version: 2.4
Name: n3memorycore-mcp-lite
Version: 1.1.0
Summary: N3MemoryCore MCP Lite — ephemeral (Redis-backed, 7d TTL) hybrid (vector + BM25) memory for Claude and other MCP clients
Project-URL: Homepage, https://github.com/NeuralNexusNote/n3mcmcp-lite
Project-URL: Issues, https://github.com/NeuralNexusNote/n3mcmcp-lite/issues
Author: NeuralNexusNote, ArnolfJp019
License:                                  Apache License
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License-File: LICENSE
License-File: NOTICE
Keywords: anthropic,claude,embedding,lite,mcp,memory,rag,redis,redis-stack,vector-search
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Requires-Dist: mcp>=1.2.0
Requires-Dist: numpy>=1.24
Requires-Dist: platformdirs>=4.0.0
Requires-Dist: redis>=5.0.0
Requires-Dist: sentence-transformers>=2.7.0
Requires-Dist: uuid-utils>=0.9.0
Provides-Extra: dev
Requires-Dist: pytest>=8.0; extra == 'dev'
Description-Content-Type: text/markdown

# N3MemoryCore MCP — Lite (Ephemeral)

> **N3MC-MCP-Lite is an "external memory server" used by MCP-compatible
> editors such as Claude Code, Cursor, and Windsurf.**
> It runs as an MCP Server so AI can save and search conversation and
> code context across sessions.

> A NeuralNexusNote™ product — **free Lite** build: ephemeral hybrid
> (vector + BM25) memory exposed as a Model Context Protocol server, backed
> by Redis Stack with a 7-day TTL per entry.

> 🇯🇵 **[日本語版はこちら](./README_JP.md)**
> 🛡️ **[Development Philosophy](./PHILOSOPHY.md)**

---

## Lite vs. Paid

| Build                   | Storage                           | Durability        | Where                |
| ----------------------- | --------------------------------- | ----------------- | -------------------- |
| **Lite (this repo)**   | Redis Stack (RediSearch)          | 7d TTL, volatile | Claude Marketplace   |
| Paid                    | SQLite + sqlite-vec (local file)  | Permanent         | Separate distribution |

Same MCP surface (five tools, same ranking formula). The 7-day TTL and
volatile Redis storage are **design features, not limitations** —
they make the Lite build the better fit for:

- **Agentic code-generation loops** — failed attempts and abandoned
  designs don't bleed into the next task; `docker restart redis-stack`
  wipes the slate clean.
- **Multi-agent collaboration** — decisions made during one task don't
  contaminate unrelated follow-ups.
- **Experimental / throwaway prototyping** — leave it alone and memory
  evaporates in 7 days, no pruning needed.

The Paid build targets the opposite use case: long-term knowledge
accumulation where persistence is the feature. Pick the Lite for
**project-scoped memory**; pick the Paid for **continuous memory**.

## What is this?

`n3memorycore-mcp-lite` is a local-only MCP server that gives Claude (and
any other MCP-compatible client) short-lived memory across conversations.
It stores text entries in a local Redis Stack instance with both a BM25
full-text index and a 768-dimension vector index
([`intfloat/e5-base-v2`](https://huggingface.co/intfloat/e5-base-v2)), and
returns hybrid-ranked results.

Every operation runs on the user's machine. No API calls, no cloud
storage.

## Tools exposed

| Tool             | Purpose                                                       |
| ---------------- | ------------------------------------------------------------- |
| `search_memory`  | Hybrid (vector + BM25) search, ranked & time-decayed          |
| `save_memory`    | Persist a short entry (7d TTL, dedup: exact + near-duplicate) |
| `list_memories`  | Most-recent entries, newest first                             |
| `delete_memory`  | Remove a specific entry by id                                 |
| `repair_memory`  | Re-create the RediSearch index if missing                     |

The server also ships **behavioral instructions** via MCP's `initialize`
response, asking the client to `search_memory` at the start of each turn
and `save_memory` after each meaningful exchange — so "auto-save" is
preserved without any Claude Code hooks.

## Prerequisites

### 1. Start Redis Stack

The Lite build requires Redis Stack (Redis + RediSearch module). The
easiest way is Docker:

```bash
docker run -d --name redis-stack -p 6379:6379 redis/redis-stack-server:latest
```

That's it — the container exposes Redis on `localhost:6379` and the
server will find it automatically.

### 2. Install the package

Install from source (PyPI distribution is not yet available):

```bash
git clone https://github.com/NeuralNexusNote/n3mcmcp-lite
cd n3mcmcp-lite
pip install -e .
```

The first run downloads the ~400 MB embedding model from Hugging Face
into the standard `~/.cache/huggingface/` directory.

## Configure a client

### Claude Desktop

Add to `~/Library/Application Support/Claude/claude_desktop_config.json`
(macOS) or `%APPDATA%\Claude\claude_desktop_config.json` (Windows):

```json
{
  "mcpServers": {
    "n3memorycore-lite": {
      "command": "n3mc-mcp-lite",
      "args": []
    }
  }
}
```

### Claude Code

**`.mcp.json` is already included in this repository.** Clone the repo,
install the package, and Claude Code connects automatically — no manual
configuration needed.

For other projects, add the following to that project's `.mcp.json`:

```json
{
  "mcpServers": {
    "n3memorycore-lite": {
      "type": "stdio",
      "command": "n3mc-mcp-lite",
      "args": []
    }
  }
}
```

## Data location

The Lite build does not store a database on disk — memories live in
Redis and expire automatically. Only a small `config.json` sits in the
platform-standard user data directory:

| OS      | Path                                                       |
| ------- | ---------------------------------------------------------- |
| Windows | `%LOCALAPPDATA%\n3memorycore-lite\`                       |
| macOS   | `~/Library/Application Support/n3memorycore-lite/`        |
| Linux   | `~/.local/share/n3memorycore-lite/`                       |

Override with the `N3MC_DATA_DIR` environment variable.

## Configuration

On first run, `config.json` is auto-generated with random UUIDs for
`owner_id` and `local_id`. Editable defaults:

```json
{
  "owner_id": "<uuid>",
  "local_id": "<uuid>",
  "redis_url": "redis://localhost:6379/0",
  "ttl_seconds": 604800,
  "dedup_threshold": 0.95,
  "half_life_days": 3,
  "bm25_min_threshold": 0.1,
  "search_result_limit": 20,
  "min_score": 0.2,
  "search_query_max_chars": 2000
}
```

`redis_url` can also be supplied via the `N3MC_REDIS_URL` environment
variable (takes precedence over the config file).

## Ranking formula

```
final_score = (0.7 * cosine_similarity + 0.3 * keyword_relevance) * time_decay

time_decay = 2 ^ (-days_elapsed / half_life_days)       (default half-life: 3 days)
```

With a default 3-day half-life (shorter than the 7-day TTL), `time_decay`
is meaningful in the Lite build: a fresh memory scores 1.0, a 3-day-old
one exactly 0.5, and a 7-day-old (near-expiry) entry ≈ 0.20 — pushing
recent context ahead in the ranking.

## Development

```bash
# Start Redis Stack first (see Prerequisites), then:
pip install -e ".[dev]"
pytest tests/ -q
```

Tests target Redis DB index `0` (configurable via `N3MC_REDIS_TEST_URL`)
and `FLUSHDB` it before/after each test. RediSearch refuses to create
indexes outside DB 0 (`Cannot create index on db != 0`), so a separate
test DB isn't an option — run the test suite against a **dedicated**
Redis container, never one that holds data you care about. Tests refuse
to run if Redis isn't reachable.

## Extending the Lite build

If you want to modify behavior (change the ranking formula, drop in a cross-encoder reranker, plug in a Japanese morphological tokenizer, etc.), start from the design spec shipped in this repository:

- [`N3MemoryCore_MCP_Spec_EN.md`](https://github.com/NeuralNexusNote/n3mcmcp-lite/blob/main/N3MemoryCore_MCP_Spec_EN.md) — full design document (English)
- [`N3MemoryCore_MCP_Spec_JP.md`](https://github.com/NeuralNexusNote/n3mcmcp-lite/blob/main/N3MemoryCore_MCP_Spec_JP.md) — 日本語版

Appendix B of the spec lists optional extensions (cross-encoder reranker, save-time chunking, HyDE, Japanese morphological analysis) with drop-in points and library candidates. The spec gives an AI (or human) enough context to edit the code without breaking the TTL, dedup, or RediSearch contracts — it is the source of truth for design intent.

## License

Apache License 2.0 — see [LICENSE](./LICENSE).
