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.

💬 The MCP protocol can only nudge the LLM to call save_memory, so which conversations actually get saved is ultimately up to the LLM. But if you ask Claude Code, it can also wire up hook-based auto-saving of every conversation. Just say "after every turn, automatically save the full Claude Code transcript to Lite" and Claude Code will drop a script under ~/.claude/hooks/ and add a Stop hook to ~/.claude/settings.json. The harness runs the hook deterministically — it does not depend on the LLM remembering to call save_memory, so Claude can never accidentally skip a save. See the Hook-based full-transcript saving section below for details.

🇯🇵 日本語版はこちら 🛡️ Development Philosophy

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🚀 Quickstart — connect to Claude Code in 3 steps

The fastest path from "nothing installed" to "Claude Code is using N3MC memory". Pick the install path that matches you (PyPI / fork / uvx), then add the server to your client config. Both Claude Code CLI and Claude Desktop are covered.

Step 1 — Start Redis Stack

docker run -d --name redis-stack -p 6379:6379 redis/redis-stack-server:latest
# (Subsequent sessions: `docker start redis-stack`)

Step 2 — Install the package (choose one)

Quickest path — Claude Code marketplace. Bundles install + MCP wiring in two commands. Run inside Claude Code:

/plugin marketplace add NeuralNexusNote/n3mcmcp-lite
/plugin install n3mc-workingmemory@neuralnexusnote

Then /reload-plugins and skip Step 3 — the plugin manifest handles MCP wiring. The manual options below remain available for forks, custom configs, and Claude Desktop.

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(a) From PyPI — most users:

pip install n3memorycore-mcp-lite

(b) From a fork (you cloned this repo) — contributors / customizers:

git clone https://github.com/<YOU>/n3mcmcp-lite
cd n3mcmcp-lite
pip install -e ".[dev]"

(c) Zero-install via uvx — no global install, isolated env:

# Just verify it runs; the actual launch is handled by your MCP client config:
uvx --from n3memorycore-mcp-lite n3mc-workingmemory --help

After step 2, the n3mc-workingmemory command is on your PATH. Run where n3mc-workingmemory (Windows) or which n3mc-workingmemory (macOS/Linux) to confirm.

Step 3 — Wire it into your MCP client

Client	What to do
Claude Code (CLI), this repo's working tree	.mcp.json is already committed — just cd into the repo and run claude. The CLI auto-connects on next prompt.
Claude Code (CLI), a different project directory	Copy .mcp.json into that project, or add the same n3mc-workingmemory block to its .mcp.json. See Claude Code (standalone CLI).
Claude Desktop (incl. its built-in "Code" tab)	Edit claude_desktop_config.json (path differs per OS). See Claude Desktop.
Claude Code with auto-tool-approval	One extra block in ~/.claude/settings.json so the AI never blocks on "Allow?" prompts. See Auto-approve tool calls.
uvx-launched (no global install needed)	Use the uvx-form command/args in your client config. See Claude Code (standalone CLI).

That's it. Once Claude Code is connected, the server's behavioral instructions take over — search_memory runs at the start of every turn and save_memory runs after each meaningful exchange, all automatically.

First call may take 30–60 seconds the first time only — the ~400 MB intfloat/e5-base-v2 embedding model downloads to ~/.cache/huggingface/. Subsequent starts complete in seconds.

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⚠️ Prerequisites (required before install)

This server does not run out of the box — you must prepare two things first:

1. Redis Stack on localhost:6379 — the Lite build stores memory in Redis + RediSearch. The easiest way is Docker:

   # First time only (creates the container):
   docker run -d --name redis-stack -p 6379:6379 redis/redis-stack-server:latest
   
   # Every subsequent session (container already exists):
   docker start redis-stack
   

   Re-running the docker run command after the container exists fails with Conflict. The container name "/redis-stack" is already in use. Use docker start from the second session onward.

   Why no persistence flags on the docker line: this build is deliberately volatile. Ephemerality is a design feature, not a missing capability — see the "Use cases" section below. Rather than rely on fragile shell-quoting for --save "" (which breaks on Windows PowerShell and cmd.exe), the MCP server enforces the ephemeral state at startup by issuing CONFIG SET appendonly no and CONFIG SET save "" on every connect. If you manually re-enable persistence between sessions, it is reverted on the next Lite run. The plain docker run above is sufficient — the server is the source of truth for the ephemerality guarantee.

2. uv on your PATH — required only for the Claude Code plugin / uvx install path. Not needed if you install from source.

The server refuses to start if Redis is unreachable, and the Claude Code plugin will fail to launch without uv. Install both before running /plugin install or any client-side config.

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Features

• 💾 Fully local — Your conversations stay in your own Redis instance. Nothing sent to the cloud.
• 🔍 Semantic search — Finds relevant past conversations even when the exact words differ.
• 🌐 Multilingual out of the box — CPU-only, no LLM/GPU required. NFKC fold (ｱﾙﾌｧ↔アルファ, １２３↔123, ligatures), bigram coverage for Japanese / Chinese / Korean / Thai / Lao / Myanmar / Khmer, diacritic cross-match for Latin scripts (café↔cafe).
• 🛡️ Encoding safety — stdio UTF-8 reconfigure on Windows (cp932 → UTF-8), lone-surrogate sanitization on every input. Same defenses as the Free build.
• 🔄 Context across sessions — Working memory that lasts 7 days (auto-expires via Redis TTL; pair with any persistent memory backend if you need longer retention).
• ⚡ Works automatically — Saving and searching happen automatically. The MCP initialize response ships behavioral instructions, so no user action is required.
• 🤖 Multi-agent ready — Multiple AI agents share one Redis. The b_local and b_session biases prioritize each project's own memories while still surfacing the team's collective knowledge.
• 🏢 Team & organization support — Deploy Redis on a shared server and point N3MC_REDIS_URL to it for team-wide memory sharing (⚠️ authentication must be handled at the Redis layer).
• 🧹 Ephemerality is a design feature — 7-day auto-expiry means failed attempts and abandoned designs don't bleed into the next task. docker restart redis-stack wipes everything instantly.
• 💰 Reduces token waste — No more re-explaining past context. Memory search uses local embeddings (intfloat/e5-base-v2) and costs zero Claude tokens, and accurate context injection means fewer corrections and back-and-forth.

How It Works

User's message
    │
    ▼
┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│  1. Auto-save │────▶│ 2. Semantic   │────▶│ 3. Context    │
│  Save last    │     │    search     │     │    injection   │
│  response to  │     │  Find related │     │  Feed to       │
│  Redis        │     │  memories     │     │  Claude        │
└──────────────┘     └──────────────┘     └──────────────┘
                                                 │
                                                 ▼
                                          Claude responds
                                          with full context

Everything runs automatically via the behavioral instructions shipped in the MCP initialize response. No Claude Code hooks are involved — the only client-side setup is adding the tools to permissions.allow. No user action required.

Relationship with Claude's built-in auto-memory

Claude Code has a built-in auto-memory system (~/.claude/projects/.../memory/). N3MemoryCore complements it rather than competing with it.

	Claude auto-memory	N3MemoryCore RAG
Strengths	Reliable, loads every session, great for fixed facts	Conversation context, detailed history
Weaknesses	Cannot capture conversation flow or context	Depends on search quality; not guaranteed to surface
Best for	User profile, folder paths, stable settings	Conversation threads, past decisions, reasoning

Recommended usage:

• Fixed information needed every session (folder paths, user preferences) → save to auto-memory
• Conversation context and history (discussion threads, past decisions) → N3MemoryCore accumulates automatically (7-day window; pair with a persistent memory backend if you need longer retention)

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Use cases — when working memory is the right tool

The 7-day TTL and volatile Redis storage are design features, not limitations. They make this server the right 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.
• Project-scoped working memory — pin a session_id per task / project to keep contexts cleanly separated.

If you need long-term, persistent knowledge accumulation across months or years, working memory is not the right layer. Pair this server with any persistent memory MCP — the official knowledge-graph server, your own SQLite-backed implementation, or an external service — to cover the long-term side.

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), 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, session_id boost
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 (cascades to chunks if id is a parent doc)
delete_memories_by_session	Bulk-delete every memory tied to a session_id — wraps up a finished project
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.

ID hierarchy

N3MemoryCore identifies the origin and context of every record with five ID fields. Most users only ever touch session_id (and rarely agent_name); the rest are filled in automatically.

ID	Stored in	Generated	Granularity	Purpose
id (PK)	Redis hash	Per record (UUIDv7, time-ordered)	One record	Unique identifier for each memory — used for delete_memory and dedup.
owner_id	config.json	First startup (UUIDv4)	Owner / installation	Identifies whose data this is. Validated on every save_memory; mismatched payloads are rejected with owner_id mismatch. Stored as a TAG field; filtering happens in Python (see spec §3.12).
local_id (agent_id)	config.json	First startup (UUIDv4)	Agent / install	UUIDv4 identifier for this install. Stored on every row for forward-compatibility with future persistent variants, but does NOT feed Lite's b_local ranking — b_local is computed from stored_importance + access_count only (see Ranking formula).
session_id	In-memory or supplied by client	Per task / project / conversation (string)	Task / project / conversation	Surfaces memories from the same task / project together. Drives the b_session ranking bias (b_session_match=1.0, b_session_mismatch=0.6) so the current chat's memories outrank unrelated cross-project rows in the same Redis instance. Also the filter key for delete_memories_by_session. Resolution order: per-call argument → N3MC_SESSION_ID env var → per-process UUIDv4 fallback.
agent_name	Redis hash	Per save_memory call (free-form string)	Agent display label	Human-readable label (e.g. "claude-code", "claude-desktop"). Not used in ranking — display/audit only.

owner_id  (one N3MC server / data owner)
  └── session_id  (one task / project / conversation)
        └── local_id  (the agent speaking inside that session)
              ├── agent_name  (its display name: "claude-code" etc.)
              └── id  (one memory record)

Practical guidance:

• You should pin session_id when working on a named project or task. Pass the same string (e.g. "proj-alpha", "task-refactor-auth") to both save_memory and search_memory. This both ranks-up the project's own memories and gives you a one-shot delete_memories_by_session for project teardown.
• You can leave agent_name empty for single-agent use. Set it ("claude-code", "cursor", …) when multiple agents share the same Redis so audit/list output stays readable.
• You should not pass owner_id unless you specifically need to prove ownership (the server validates it against config.json and rejects mismatches; an empty value means "use my own").

Prerequisites

1. Start Redis Stack

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

# First time only (creates the container):
docker run -d --name redis-stack -p 6379:6379 redis/redis-stack-server:latest

# Every subsequent session (container already exists):
docker start redis-stack

That's it — the container exposes Redis on localhost:6379 and the server will find it automatically. Re-running the docker run command after the first install produces Conflict. The container name "/redis-stack" is already in use; use docker start redis-stack thereafter.

2. Install the package

From PyPI (recommended):

pip install n3memorycore-mcp-lite

Or zero-install via uvx (the Claude Code plugin uses this path):

uvx --from n3memorycore-mcp-lite n3mc-workingmemory

From source (if you want to edit the code):

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.

First install requires internet access to three resources:

1. github.com — when /plugin marketplace add NeuralNexusNote/n3mcmcp-lite registers the plugin (skip this step if you install via uvx or from source instead).
2. pypi.org — when uvx --from n3memorycore-mcp-lite (or pip install) resolves the package.
3. huggingface.co — when the server first starts and downloads intfloat/e5-base-v2 (~400 MB) into ~/.cache/huggingface/.

All three fail with explicit, time-bounded errors when offline; none hang. Subsequent starts use only the local cache and require no internet.

Configure a client

Claude Desktop (and the "Code" tab inside Claude Desktop)

If you are using the Claude Desktop application — including its built-in Code tab — configure MCP via the desktop config file, NOT via .mcp.json (which is only read by the standalone claude CLI).

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

{
  "mcpServers": {
    "n3mc-workingmemory": {
      "command": "n3mc-workingmemory",
      "args": []
    }
  }
}

Windows tip: if Claude Desktop fails to spawn the server with the bare command name above (the hammer/tool icon never appears), replace "command" with the absolute path to the installed .exe, for example:

"command": "C:\\Users\\<YOU>\\AppData\\Local\\Programs\\Python\\Python312\\Scripts\\n3mc-workingmemory.exe"

Run where n3mc-workingmemory in a terminal to find the exact path on your machine.

After editing the config, fully quit Claude Desktop — closing the window is not enough. Right-click the Claude icon in the system tray (or use Task Manager) and terminate every Claude process, then relaunch.

Claude Code (standalone CLI)

This section applies ONLY to the claude command-line tool, not to the Claude Desktop "Code" tab (see above for that).

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

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

{
  "mcpServers": {
    "n3mc-workingmemory": {
      "type": "stdio",
      "command": "n3mc-workingmemory",
      "args": []
    }
  }
}

Auto-approve tool calls (Claude Code only)

By default, Claude Code prompts the user for each MCP tool call. For a fully automatic memory loop — so the connected AI never blocks on an "Allow?" prompt — the n3mc-workingmemory tools must be listed under permissions.allow in Claude Code settings.

Plugin install auto-configures this — when you install via /plugin install n3mc-workingmemory@neuralnexusnote, a SessionStart hook runs hooks/install_permissions.py which idempotently adds the six mcp__n3mc-workingmemory__* tools to ~/.claude/settings.json. No manual editing needed. The hook only writes if at least one entry is missing and never touches unrelated fields. The hooks.json command tries python → py (Windows Python Launcher) → python3 in a || fallback chain, so the hook works as long as any one of these is on PATH. It only exits non-zero — surfacing in Claude Code's /plugins Errors tab — when all three are missing, avoiding silent failure.

The same hook also performs a uvx pre-flight check — the plugin manifest launches the MCP server via uvx --from n3memorycore-mcp-lite n3mc-workingmemory, so a missing uvx would otherwise surface only as an opaque ENOENT in the MCP launcher. The hook calls shutil.which("uvx") and, if not found, writes a bilingual install hint to stderr (pipx install uv, curl -LsSf https://astral.sh/uv/install.sh | sh, plus the docs URL) so the user sees an actionable message in the /plugins Errors tab. The hook still exits 0 because the permission install itself succeeded.

If you installed without the plugin (e.g. claude mcp add or a manual .mcp.json), or no Python interpreter is available at all, add the block below manually to ~/.claude/settings.json (user-global, recommended) or .claude/settings.json (per-project):

{
  "permissions": {
    "allow": [
      "mcp__n3mc-workingmemory__search_memory",
      "mcp__n3mc-workingmemory__save_memory",
      "mcp__n3mc-workingmemory__list_memories",
      "mcp__n3mc-workingmemory__delete_memory",
      "mcp__n3mc-workingmemory__delete_memories_by_session",
      "mcp__n3mc-workingmemory__repair_memory"
    ]
  }
}

Without this, every save_memory / search_memory call surfaces an approval prompt and the AI blocks if the user is away. Claude Desktop has no per-tool permission gate, so this step is not needed there.

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%\n3mc-workingmemory\
macOS	~/Library/Application Support/n3mc-workingmemory/
Linux	~/.local/share/n3mc-workingmemory/

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:

{
  "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,
  "context_char_limit":       3000,
  "min_score":                0.2,
  "search_query_max_chars":   2000,
  "chunk_threshold":          400,
  "chunk_overlap":            100,
  "access_count_enabled":     true,
  "access_count_weight":      0.02,
  "access_count_max_boost":   0.5,
  "ttl_refresh_on_search":    true,
  "ttl_refresh_top_k":        5,
  "lexical_rerank_enabled":   true,
  "rerank_weight":            0.3,
  "rerank_phrase_weight":     0.2,
  "b_session_match":          1.0,
  "b_session_mismatch":       0.6,
  "skip_code_blocks":         false
}

• redis_url — connection URL; N3MC_REDIS_URL env var takes precedence.
• ttl_seconds — TTL on every new memory and sha-guard (default 7 d).
• chunk_threshold / chunk_overlap — sliding-window size and overlap (chars). Bodies longer than the threshold trigger the parent-document + chunks path for verbatim recall.
• access_count_* — access-frequency auto-importance; top-K search hits receive a capped boost on future queries.
• ttl_refresh_on_search / ttl_refresh_top_k — TTL reset for the top-K hits on each search (reset-only; no extension past a fresh save).
• lexical_rerank_* / rerank_weight / rerank_phrase_weight — lightweight post-fusion lexical reranker (CPU-only).
• b_session_match / b_session_mismatch — multiplicative ranking boost for rows whose stored session_id matches (default 1.0) vs. rows from other projects (0.6). Pass the same session_id to save_memory and search_memory to surface a project's memories above unrelated cross-project rows in the same Redis instance. Set both to 1.0 to disable the bias.
• skip_code_blocks — when true, save_memory rejects any payload containing a triple-backtick fence (```) and returns status: "skipped_code". Default false. Set to true if you want FastAPI-era N3MemoryCore-style code exclusion (keep code out of the memory index entirely — useful when your workflow already has git/IDE history for code and you only want prose decisions/plans in Redis).

See the spec §6 for the complete field-by-field reference.

Multilingual support

Built-in, CPU-only, no LLM and no GPU required. Search and dedup behave the same regardless of how the user types the same word:

Layer	What it does	Real-world example
NFKC normalization	Folds compatibility forms before SHA / embedding / BM25	ｱﾙﾌｧ ↔ アルファ, １２３ ↔ 123, fi ↔ fi
Bigram BM25 side channel	Overlapping bigrams emitted for space-less scripts	記憶装置 → 記憶 憶装 装置; same for Korean (안녕하세요), Thai (สวัสดี), Lao, Myanmar, Khmer
Diacritic fold	Latin/Greek/Cyrillic words also indexed without combining marks	café matches cafe, Ångström matches Angstrom
e5-base-v2 embedding	Multilingual semantic space across 100+ languages	Cross-language paraphrase retrieval

These run automatically on every save_memory and search_memory call. The raw content field is never rewritten — verbatim recall (spec §3.11) still returns the original bytes byte-for-byte.

Encoding safety

Two layers of defense run before any tool body executes (spec §3.13). Same guards as the Free build, ported one-to-one:

1. stdio UTF-8 reconfigure — at module import, sys.stdin / sys.stdout / sys.stderr are switched to encoding="utf-8". On Windows-Japanese hosts the default console code page is cp932, which would otherwise mangle every non-ASCII byte on the MCP JSON-RPC channel. POSIX systems are already UTF-8, so the call is a safe no-op.
2. Lone-surrogate sanitization — every save_memory.content and search_memory.query is passed through sanitize_surrogates() before any .encode("utf-8") call. Lone UTF-16 surrogate halves (U+D800–U+DFFF) appear when Windows subprocess pipes deliver UTF-8 bytes that Python's decoder maps with errors="surrogateescape" — they round-trip through json.loads but raise UnicodeEncodeError at SHA1 / Redis HSET / embedding time. Without the guard the entire write is silently lost. The function is recursive so JSON payloads with surrogates buried inside are cleaned in one pass.

If a save payload consists entirely of surrogates, sanitization collapses it to the empty string and the regular empty-content rejection path applies — {"status":"error","saved":false,"reason":"empty content"}.

Ranking formula

final_score = (0.7 * cosine_similarity + 0.3 * keyword_relevance) * time_decay * b_local * b_session

time_decay   = 2 ^ (-days_elapsed / half_life_days)       (default half-life: 3 days)
b_local      = clamp(0.5, 2.0, stored_importance + access_boost)
access_boost = min(0.5, access_count * 0.02)
b_session    = b_session_match (default 1.0)   if row.session_id == effective_session
             = b_session_mismatch (default 0.6) otherwise

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.

Auto-importance (access-frequency boost): each time search_memory returns a memory in its top 5 hits, that memory's access_count is incremented by 1 and b_local rises by 0.02 on future queries (capped at +0.5). No LLM judgement required — frequently-useful memories naturally float to the top through CPU-only self-tuning.

Development

# 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 — full design document (English)
• N3MemoryCore_MCP_Spec_JP.md — 日本語版

Appendix A of the spec lists optional extensions (cross-encoder reranker, save-time chunking, HyDE, Japanese morphological analysis) with drop-in points and library candidates. Use it as reference when you want to edit the code without breaking the TTL, dedup, or RediSearch contracts.

Why N3MemoryCore? (vs. built-in memory)

The auto-save reliability of N3MemoryCore is no better than the memory features built into modern LLM products (e.g. Claude's built-in memory) — both depend on the LLM voluntarily calling a save tool, and both share the non-determinism described in On compliance below. The differentiation sits elsewhere:

Aspect	Built-in memory	N3MemoryCore (Lite)
Data ownership	Vendor-hosted	Your own Redis Stack on your machine
Client surface	The vendor's product only	Any MCP-compliant client (Claude Code, Cursor, Cline, Goose, your own app)
Multi-AI collaboration	One AI's memory	session_id lets multiple agents share the same memory namespace; delete_memories_by_session wraps up a finished task
Verbatim recall	Opaque (may be summarized)	Parent-document contract — byte-exact full text returned
Search internals	Black box	Hybrid BM25 + e5 vectors + CJK bigram + time decay + lightweight reranker, all parameters visible and tunable
Inspect / control	UI only	list_memories / delete_memory / delete_memories_by_session operate on raw records
Persistence	Tied to the vendor's service lifetime	In-memory Redis with 7-day TTL — short-lived by design, but you own the container; pair with any persistent memory backend for long-term storage
Tunability	Fixed	half_life_days, chunk_threshold, dedup_threshold, rerank weights — all editable

So the value of running N3MemoryCore Lite is not "more reliable auto-save" — it is owning a transparent, multi-client working-memory layer that several AIs can collaborate on under a shared session_id, where search behaviour is editable and verbatim recall is contractually guaranteed. (For long-term, persistent storage of user-invested artifacts, pair it with any persistent memory backend.)

If those properties matter to your workflow, Lite earns its keep. If you only need "the LLM remembers something across sessions" inside one vendor's product, the built-in memory is simpler.

On compliance — MCP can persuade, not force

This server cannot make the LLM call its tools. The MCP protocol gives a server only three persuasion levers:

1. Tool descriptions in tools/list — visible to the LLM on every turn.
2. The instructions field sent at session start — usually surfaced to the LLM as a system-level hint.
3. Tool response text — read by the LLM when it does call a tool.

We use all three: tool descriptions are explicit, instructions lays out a rule set, and search_memory / save_memory responses end with short reminders that re-anchor the auto-save discipline mid-turn. Even with all of that, whether the LLM follows through is non-deterministic. Compliance depends on the model's tool-calling bias, the MCP client's prompt construction (some clients summarize or drop the instructions field), and competing instructions from the user prompt, CLAUDE.md, etc.

In practice: most turns will auto-save correctly, but some won't — especially short answers, fact-correction turns, or turns where the LLM is heavily focused on the user's question. If a fact you wanted saved is missing next session, just say "save this" — the server is still ready to take it.

When you need a guaranteed save

Within the MCP framing, three paths bypass this non-determinism:

Path 1 — ask the LLM explicitly in your prompt (operational workaround, immediate). Write "save this to N3MemoryCore" or "record this in memory" into your prompt. LLMs almost always honour explicit user requests. Pros: zero infrastructure, works today, works with every MCP client. Cons: cognitive load — you must remember to say it; not automatic.

Hook-based full-transcript saving

Path 2 — Claude Code hook that saves the full transcript (Claude Code only, deterministic). Claude Code exposes harness-level hooks (Stop, etc.) that the harness runs deterministically — they do not depend on the LLM remembering anything. Setup is one prompt to Claude Code:

"After every turn, automatically save the full Claude Code transcript to Lite."

Claude Code then provisions:

• A script at ~/.claude/hooks/save_transcript.py that reads transcript_path from hook input, imports n3mc_mcp.database.Database directly, and calls save_memory on the Lite DB (no MCP round-trip).
• A hooks.Stop block in ~/.claude/settings.json that runs the script after every assistant turn with async: true (so model load never blocks the UI).

Behavioral notes:

• Claude can never accidentally skip a save — the harness fires the hook regardless of what the LLM does.
• No MCP round-trip overhead; the hook talks to Redis directly.
• As a session grows, the per-turn transcripts collide via near-duplicate detection (dedup_threshold), so the DB stays close to one entry per session instead of one per turn.
• Transcripts shorter than ~200 chars are skipped as noise.
• Pros: deterministic / independent of model behavior / no save anxiety.
• Cons: Claude Code only (Cursor / Windsurf need a different approach) / the hook process loads the embedding model each turn (async, so no UI block, but there is CPU/IO cost) / Lite's 7-day TTL still applies, so transcripts saved this way still expire within a week — point the same hook at any persistent memory backend when long-term retention matters.

Path 3 — bypass MCP and call the first-party Anthropic Messages API yourself (architecture change). Step outside MCP clients (Claude Code, etc.) and drive messages.create tool_use directly from your own application code; you can then fire save_memory deterministically every turn regardless of what the LLM "decided" to do. Pros: deterministic / works with any model and any client. Cons: you have to write the orchestration application.

The convenience of "MCP + LLM handles it for me" and the guarantee of "every turn saves" sit at opposite ends of a tradeoff. This server packs its persuasion levers as hard as the protocol allows; any stronger guarantee is your call as the user or client implementer (and if you're on Claude Code, Path 2 is by far the lowest-cost option).

Forking & contributing

This repository is public and Apache-2.0 licensed — fork, modify, and run it freely. The fork-and-run path is:

git clone https://github.com/<YOU>/n3mcmcp-lite
cd n3mcmcp-lite
docker run -d --name redis-stack -p 6379:6379 redis/redis-stack-server:latest
python -m venv .venv && source .venv/bin/activate    # Windows: .venv\Scripts\Activate.ps1
pip install -e ".[dev]"
pytest tests/ -q                                      # 105 tests, ~30s warm

CI runs the same matrix on every push and PR — see .github/workflows/test.yml. Read CONTRIBUTING.md for the full developer guide (EN + JP) including coding conventions, the spec-as-contract policy, and PR checklist.

To actually use the fork from Claude Code, you do NOT need any additional setup beyond the pip install -e ".[dev]" above:

1. The n3mc-workingmemory command is now on your PATH (run which n3mc-workingmemory to confirm).
2. The repository's .mcp.json already declares the server, so the moment you cd n3mcmcp-lite && claude, the CLI auto-connects.
3. For other client surfaces (Claude Desktop, a different project's .mcp.json, auto-tool-approval), the Quickstart Step 3 table lists the exact action.

If you intend to publish your fork under a new package name, also edit the name, [project.urls], and console-script names in pyproject.toml before re-uploading to PyPI.

Troubleshooting

Windows: pip install --upgrade fails with WinError 32 (file in use)

Symptom:

ERROR: Could not install packages due to an OSError: [WinError 32]
The process cannot access the file because it is being used by another process:
'...\Scripts\n3mc-workingmemory.exe' -> '...\Scripts\n3mc-workingmemory.exe.deleteme'

Cause: an MCP client (Claude Code / Claude Desktop) is currently holding n3mc-workingmemory.exe open as a child process, so pip cannot replace the binary.

Fix — pick one:

1. Fully quit the MCP client first. Closing the window is not enough on Windows. Open Task Manager and end every claude / n3mc-workingmemory.exe / python.exe process whose command line includes n3mc-workingmemory, then re-run pip install --upgrade.
2. Use uvx instead of a global install — uvx --from n3memorycore-mcp-lite n3mc-workingmemory runs in an isolated ephemeral environment per session, so there is no system-level .exe to lock.

This is a Windows file-locking quirk, not a packaging defect — the wheel itself installs cleanly into a fresh venv (python -m venv .venv && .venv/Scripts/pip install n3memorycore-mcp-lite).

~3memorycore-mcp-lite warnings during pip install

If you see lines like:

WARNING: Ignoring invalid distribution ~3memorycore-mcp-lite

that is pip flagging a previous install that was interrupted mid-write (typically by the file-lock issue above). The leftover directory is named with a leading ~ and is harmless but noisy. Delete it manually:

# Windows
rmdir /s "%LOCALAPPDATA%\Programs\Python\Python312\Lib\site-packages\~3memorycore_mcp_lite-1.5.0.dist-info"

(Adjust the path to match your Python installation.)

License

Apache License 2.0 — see LICENSE.

---

_{MCP Registry: mcp-name: io.github.NeuralNexusNote/n3mc-workingmemory}