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  1. Home
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  3. N3MemoryCore Lite (Working Memory)
N3MemoryCore Lite (Working Memory) logo
Health: ActiveRecent health check succeeded.Last checked 9/22/2026, 7:47:12 PM

N3MemoryCore Lite (Working Memory)

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View RepositoryVisit Website

Ephemeral 7-day hybrid vector+BM25 working memory, multilingual, backed by Redis Stack.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "n3memorycore-lite-working-memory": {
      "command": "uvx",
      "args": [
        "--from",
        "n3memorycore-mcp-lite",
        "n3mc-workingmemory"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Tool Schemas (6) Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Capabilities & Tool Schemas (6) ~111 tokensApproximate context cost of this server’s tool schemas (~4 chars/token), before any tool is called. Actual usage depends on your client and model.Self-reported Self-reportedParsed from the repository README, not verified against a live server β€” may be incomplete or out of date.

Inspect callable tools, capabilities, and parameters exposed to AI agents by N3MemoryCore Lite (Working Memory).

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` β€” wraps up a finished project

repair_memory

Re-create the RediSearch index if missing

Documentation Overview

N3MemoryCore MCP β€” 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 NeuralNexusNoteβ„’ product β€” 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.

πŸ’¬ 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 β€” 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.

πŸ‡―πŸ‡΅ ζ—₯本θͺžη‰ˆγ―こけら πŸ›‘οΈ Development Philosophy


πŸš€ Quickstart β€” 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 β€” Start Redis Stack

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

Step 2 β€” Install the package (choose one)

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

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

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


(a) From PyPI β€” most users:

Terminal
pip install n3memorycore-mcp-lite

(b) From a fork (you cloned this repo) β€” contributors / customizers:

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

(c) Zero-install via uvx β€” no global install, isolated env:

bash
# 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 β€” Wire it into your MCP client

ClientWhat to do
Claude Code (CLI), this repo's working tree.mcp.json is already committed β€” just cd into the repo and run claude. The CLI auto-connects on next prompt.
Claude Code (CLI), a different project directoryCopy .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-approvalOne 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 β€” 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 β€” the ~400 MB intfloat/multilingual-e5-base embedding model downloads to ~/.cache/huggingface/. Subsequent starts complete in seconds.


⚠️ Prerequisites (required before install)

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

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

    bash
    # 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 β€” 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 β€” the server is the source of truth for the ephemerality guarantee.

  2. uv on your PATH β€” 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.


Features

  • πŸ’Ύ Fully local β€” Your conversations stay in your own Redis instance. Nothing sent to the cloud.
  • πŸ” Semantic search β€” Finds relevant past conversations even when the exact words differ.
  • 🌐 Multilingual out of the box β€” CPU-only, no LLM/GPU required. NFKC fold (ο½±οΎ™οΎŒο½§β†”γ‚’γƒ«γƒ•γ‚‘, 123↔123, ligatures), bigram coverage for Japanese / Chinese / Korean / Thai / Lao / Myanmar / Khmer, diacritic cross-match for Latin scripts (café↔cafe).
  • πŸ›‘οΈ Encoding safety β€” stdio UTF-8 reconfigure on Windows (cp932 β†’ UTF-8), lone-surrogate sanitization on every input. Same defenses as the Free build.
  • πŸ”„ Context across sessions β€” Working memory that lasts 7 days (auto-expires via Redis TTL; pair with any persistent memory backend if you need longer retention).
  • ⚑ Works automatically β€” Saving and searching happen automatically. The MCP initialize response ships behavioral instructions, so no user action is required.
  • πŸ€– Multi-agent ready β€” 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 β€” 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 β€” 7-day auto-expiry means failed attempts and abandoned designs don't bleed into the next task. docker restart redis-stack wipes everything instantly.
  • πŸ’° Reduces token waste β€” No more re-explaining past context. Memory search uses local embeddings (intfloat/multilingual-e5-base) and costs zero Claude tokens, and accurate context injection means fewer corrections and back-and-forth.

How It Works

Code
User's message
    β”‚
    β–Ό
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”     β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”     β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚  1. Auto-save │────▢│ 2. Semantic   │────▢│ 3. Context    β”‚
β”‚  Save last    β”‚     β”‚    search     β”‚     β”‚    injection   β”‚
β”‚  response to  β”‚     β”‚  Find related β”‚     β”‚  Feed to       β”‚
β”‚  Redis        β”‚     β”‚  memories     β”‚     β”‚  Claude        β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜     β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜     β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                                                 β”‚
                                                 β–Ό
                                          Claude responds
                                          with full context

Everything runs automatically via the behavioral instructions shipped in the MCP initialize response. No Claude Code hooks are involved β€” 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-memoryN3MemoryCore RAG
StrengthsReliable, loads every session, great for fixed factsConversation context, detailed history
WeaknessesCannot capture conversation flow or contextDepends on search quality; not guaranteed to surface
Best forUser profile, folder paths, stable settingsConversation threads, past decisions, reasoning

Recommended usage:

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

Use cases β€” when working memory is the right tool

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

Last commit
3mo ago
Most recent push to the default branch.
Tools exposed
6
Callable tools this server registers over MCP.

Reviews

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Frequently Asked Questions about N3MemoryCore Lite (Working Memory)

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "n3memorycore-lite-working-memory": { "command": "uvx", "args": ["--from","n3memorycore-mcp-lite","n3mc-workingmemory"] } }

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Technical Specs & Signals

Category🧠Knowledge & Memory
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimePython
Last updatedJun 1, 2026
3/8 checks healthy over the last 45d
Views0
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars0
GitHub Star CountTotal stargazers on GitHub representing community popularity (0 stars).
Last commit3mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Jun 1, 2026
47Quality signal: Fair Β· 47/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools24/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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No high-severity advisories surfaced by our automated scan.

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Scanned 1d ago via OSV.dev Β· n3memorycore-mcp-lite (PyPI)

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