USP

Unlike other memory solutions, MemPalace offers superior retrieval recall (96.6% R@5 raw) without requiring any LLM or API calls for its core functionality, ensuring privacy and local operation. Its structured index and extensive MCP tools…

Use cases

01Storing and retrieving past Claude Code sessions
02Semantic search across project files and conversations
03Providing context for AI agents without external API calls
04Building a local knowledge graph for temporal entity tracking

Detected files (8)

.codex-plugin/skills/search/SKILL.mdskill

Show content (299 bytes)

---
name: search
description: Search your MemPalace — semantic search across all mined memories, projects, and conversations.
allowed-tools: Bash, Read
---

# MemPalace Search

Run the following command and follow the returned instructions step by step:

```bash
mempalace instructions search
```

.claude-plugin/skills/mempalace/SKILL.mdskill

Show content (841 bytes)

---
name: mempalace
description: MemPalace — mine projects and conversations into a searchable memory palace. Use when asked about mempalace, memory palace, mining memories, searching memories, or palace setup.
allowed-tools: Bash, Read, Write, Edit, Glob, Grep
---

# MemPalace

A searchable memory palace for AI — mine projects and conversations, then search them semantically.

## Prerequisites

Ensure `mempalace` is installed:

```bash
mempalace --version
```

If not installed:

```bash
pip install mempalace
```

## Usage

MemPalace provides dynamic instructions via the CLI. To get instructions for any operation:

```bash
mempalace instructions <command>
```

Where `<command>` is one of: `help`, `init`, `mine`, `search`, `status`.

Run the appropriate instructions command, then follow the returned instructions step by step.

.codex-plugin/skills/status/SKILL.mdskill

Show content (274 bytes)

---
name: status
description: Show MemPalace status — room counts, storage usage, and palace health.
allowed-tools: Bash, Read
---

# MemPalace Status

Run the following command and follow the returned instructions step by step:

```bash
mempalace instructions status
```

.codex-plugin/skills/mine/SKILL.mdskill

Show content (310 bytes)

---
name: mine
description: Mine a project or conversation into your MemPalace — extract and store memories for later retrieval.
allowed-tools: Bash, Read, Glob, Grep
---

# MemPalace Mine

Run the following command and follow the returned instructions step by step:

```bash
mempalace instructions mine
```

.codex-plugin/skills/init/SKILL.mdskill

Show content (301 bytes)

---
name: init
description: Initialize a new MemPalace — guided setup for your AI memory palace with ChromaDB backend.
allowed-tools: Bash, Read, Write, Edit
---

# MemPalace Init

Run the following command and follow the returned instructions step by step:

```bash
mempalace instructions init
```

.codex-plugin/skills/help/SKILL.mdskill

Show content (281 bytes)

---
name: help
description: Show MemPalace help — available commands, usage tips, and getting started guidance.
allowed-tools: Bash, Read
---

# MemPalace Help

Run the following command and follow the returned instructions step by step:

```bash
mempalace instructions help
```

.claude-plugin/marketplace.jsonmarketplace

Show content (444 bytes)

{
  "name": "mempalace",
  "owner": {
    "name": "milla-jovovich",
    "url": "https://github.com/MemPalace"
  },
  "plugins": [
    {
      "name": "mempalace",
      "source": "./.claude-plugin",
      "description": "AI memory system — mine projects and conversations into a searchable palace. 19 MCP tools, auto-save hooks, guided setup.",
      "version": "3.3.3",
      "author": {
        "name": "milla-jovovich"
      }
    }
  ]
}

.agents/plugins/marketplace.jsonmarketplace

Show content (350 bytes)

{
  "name": "mempalace",
  "interface": {
    "displayName": "MemPalace"
  },
  "plugins": [
    {
      "name": "mempalace",
      "source": {
        "source": "local",
        "path": "./.codex-plugin"
      },
      "policy": {
        "installation": "AVAILABLE",
        "authentication": "NONE"
      },
      "category": "Coding"
    }
  ]
}

README

[!CAUTION] Scam alert. The only official sources for MemPalace are this GitHub repository, the PyPI package, and the docs site at mempalaceofficial.com. Any other domain — including mempalace.tech — is an impostor and may distribute malware. Details and timeline: docs/HISTORY.md.

[!IMPORTANT] 🚨 Claude Code sessions expire in 30 days w/out auto-save hooks wired! Read this →

MemPalace

Local-first AI memory. Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.

What it is

MemPalace stores your conversation history as verbatim text and retrieves it with semantic search. It does not summarize, extract, or paraphrase. The index is structured — people and projects become wings, topics become rooms, and original content lives in drawers — so searches can be scoped rather than run against a flat corpus.

The retrieval layer is pluggable. The current default is ChromaDB; the interface is defined in mempalace/backends/base.py and alternative backends can be dropped in without touching the rest of the system.

Nothing leaves your machine unless you opt in.

Architecture, concepts, and mining flows: mempalaceofficial.com/concepts/the-palace.

Install

pip install mempalace
mempalace init ~/projects/myapp

Quickstart

# Mine content into the palace
mempalace mine ~/projects/myapp                    # project files
mempalace mine ~/.claude/projects/ --mode convos   # Claude Code sessions (scope with --wing per project)

# Search
mempalace search "why did we switch to GraphQL"

# Load context for a new session
mempalace wake-up

For Claude Code, Gemini CLI, MCP-compatible tools, and local models, see mempalaceofficial.com/guide/getting-started.

Benchmarks

All numbers below are reproducible from this repository with the commands in benchmarks/BENCHMARKS.md. Full per-question result files are committed under benchmarks/results_*.

LongMemEval — retrieval recall (R@5, 500 questions):

Mode	R@5	LLM required
Raw (semantic search, no heuristics, no LLM)	96.6%	None
Hybrid v4, held-out 450q (tuned on 50 dev, not seen during training)	98.4%	None
Hybrid v4 + LLM rerank (full 500)	≥99%	Any capable model

The raw 96.6% requires no API key, no cloud, and no LLM at any stage. The hybrid pipeline adds keyword boosting, temporal-proximity boosting, and preference-pattern extraction; the held-out 98.4% is the honest generalisable figure.

The rerank pipeline promotes the best candidate out of the top-20 retrieved sessions using an LLM reader. It works with any reasonably capable model — we have reproduced it with Claude Haiku, Claude Sonnet, and minimax-m2.7 via Ollama Cloud (no Anthropic dependency). The gap between raw and reranked is model-agnostic; we do not headline a "100%" number because the last 0.6% was reached by inspecting specific wrong answers, which benchmarks/BENCHMARKS.md flags as teaching to the test.

Other benchmarks (full results in benchmarks/BENCHMARKS.md):

Benchmark	Metric	Score	Notes
LoCoMo (session, top-10, no rerank)	R@10	60.3%	1,986 questions
LoCoMo (hybrid v5, top-10, no rerank)	R@10	88.9%	Same set
ConvoMem (all categories, 250 items)	Avg recall	92.9%	50 per category
MemBench (ACL 2025, 8,500 items)	R@5	80.3%	All categories

We deliberately do not include a side-by-side comparison against Mem0, Mastra, Hindsight, Supermemory, or Zep. Those projects publish different metrics on different splits, and placing retrieval recall next to end-to-end QA accuracy is not an honest comparison. See each project's own research page for their published numbers.

Reproducing every result:

git clone https://github.com/MemPalace/mempalace.git
cd mempalace
pip install -e ".[dev]"
# see benchmarks/README.md for dataset download commands
python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json

Knowledge graph

MemPalace includes a temporal entity-relationship graph with validity windows — add, query, invalidate, timeline — backed by local SQLite. Usage and tool reference: mempalaceofficial.com/concepts/knowledge-graph.

MCP server

29 MCP tools cover palace reads/writes, knowledge-graph operations, cross-wing navigation, drawer management, and agent diaries. Installation and the full tool list: mempalaceofficial.com/reference/mcp-tools.

Agents

Each specialist agent gets its own wing and diary in the palace. Discoverable at runtime via mempalace_list_agents — no bloat in your system prompt: mempalaceofficial.com/concepts/agents.

Auto-save hooks

Two Claude Code hooks save periodically and before context compression: mempalaceofficial.com/guide/hooks.

For per-message recall on top of the file-level chunks the hooks produce, run mempalace sweep <transcript-dir> periodically — it stores one verbatim drawer per user/assistant message, idempotent and resume-safe.

Requirements

Python 3.9+
A vector-store backend (ChromaDB by default)
~300 MB disk for the default embedding model

No API key is required for the core benchmark path.

Docs

Getting started → mempalaceofficial.com/guide/getting-started
CLI reference → mempalaceofficial.com/reference/cli
Python API → mempalaceofficial.com/reference/python-api
Full benchmark methodology → benchmarks/BENCHMARKS.md
Release notes → CHANGELOG.md
Corrections and public notices → docs/HISTORY.md

Contributing

PRs welcome. See CONTRIBUTING.md.

License

MIT — see LICENSE.