Trending-skills mempalace-ai-memory

---
name: mempalace-ai-memory
description: MemPalace is a local-first AI memory system using palace-structure retrieval and AAAK compression, achieving 96.6%+ recall on LongMemEval benchmarks.
triggers:
  - set up AI memory for my project
  - remember my conversations with Claude
  - search my past AI chat history
  - connect memory to my local LLM
  - store and retrieve AI conversation context
  - set up MCP memory server
  - mine my chat exports for memory
  - query what we decided about a topic
---

# MemPalace AI Memory System

> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.

MemPalace is a local-first AI memory system that stores all your AI conversations and makes them searchable with 96.6%+ recall. It organizes memory into a hierarchical "palace" structure (wings → rooms → closets → drawers) and uses AAAK — a lossless compression dialect — to deliver months of context in ~170 tokens. Runs entirely on your machine with no cloud API required.

---

## Installation

```bash
pip install mempalace

Verify install:

mempalace --version
mempalace status

Initial Setup

1. Initialize a Palace

# Initialize for a specific project directory
mempalace init ~/projects/myapp

# This creates ~/.mempalace/palace/ with the wing/room/closet/drawer structure
# MemPalace auto-detects rooms (topics) from the project

2. Mine Your Data

Three mining modes:

# Mode: projects — code, docs, notes
mempalace mine ~/projects/myapp

# Mode: convos — Claude, ChatGPT, Slack exports
mempalace mine ~/chats/ --mode convos

# Mode: general — auto-classifies into decisions, preferences,
#                 milestones, problems, and emotional context
mempalace mine ~/chats/ --mode convos --extract general

Mining is a one-time operation per dataset. Re-run when you have new exports.

3. Verify

mempalace status
# Shows wings, rooms, and memory counts

Key CLI Commands

mempalace init <path>

mempalace mine <path>

mempalace mine <path> --mode convos

mempalace mine <path> --mode convos --extract general

mempalace search "<query>"

mempalace wake-up

mempalace status

MCP Server (Claude, ChatGPT, Cursor)

Connect once and your AI uses memory automatically:

# Add MemPalace as an MCP server in Claude Code
claude mcp add mempalace -- python -m mempalace.mcp_server

After connecting, Claude gets 19 tools including

mempalace_search

"What did we decide about auth last month?"

Claude calls

mempalace_search

Available MCP Tools (subset)

• mempalace_search

  — semantic search across all memories

• mempalace_wake_up

  — load critical facts into context

• mempalace_add_memory

  — store a new memory from conversation

• mempalace_list_wings

  — list all people/projects in the palace

• mempalace_list_rooms

  — list rooms within a wing

Python API

Search Memories

from mempalace.searcher import search_memories

results = search_memories(
    query="why did we switch to GraphQL",
    palace_path="~/.mempalace/palace",
    top_k=5
)

for result in results:
    print(result["content"])
    print(result["source"])   # drawer path
    print(result["score"])    # relevance score

Store a Memory

from mempalace.memory import store_memory

store_memory(
    content="Decided to use PostgreSQL over MySQL for JSONB support.",
    wing="myapp-project",
    room="decisions",
    palace_path="~/.mempalace/palace"
)

Load Wake-Up Context

from mempalace.wakeup import generate_wakeup

context = generate_wakeup(
    palace_path="~/.mempalace/palace",
    use_aaak=True   # AAAK compression — ~170 tokens
)

# Inject into your local LLM's system prompt
system_prompt = f"You have access to the following memory context:\n{context}"

Mine Conversations Programmatically

from mempalace.miner import mine_directory

mine_directory(
    source_path="~/chats/",
    mode="convos",
    extract="general",          # decisions, preferences, milestones, problems
    palace_path="~/.mempalace/palace"
)

Local LLM Integration (Offline)

Option 1: Wake-Up Injection

mempalace wake-up > context.txt

import subprocess

context = subprocess.check_output(["mempalace", "wake-up"]).decode()

# Inject into Ollama / llama.cpp / any local LLM
import ollama

response = ollama.chat(
    model="llama3",
    messages=[
        {"role": "system", "content": f"Memory context:\n{context}"},
        {"role": "user",   "content": "What did we decide about the database?"}
    ]
)

Option 2: On-Demand Search → Prompt Injection

from mempalace.searcher import search_memories
import ollama

query = "auth implementation decisions"
memories = search_memories(query, palace_path="~/.mempalace/palace", top_k=5)
memory_text = "\n\n".join(m["content"] for m in memories)

response = ollama.chat(
    model="mistral",
    messages=[
        {
            "role": "system",
            "content": f"Relevant memory context:\n{memory_text}"
        },
        {"role": "user", "content": query}
    ]
)
print(response["message"]["content"])

Palace Structure

~/.mempalace/palace/
├── wings/
│   ├── myapp-project/
│   │   ├── rooms/
│   │   │   ├── decisions/
│   │   │   │   ├── closet/     ← compressed summaries (fast AI reads)
│   │   │   │   └── drawers/    ← original verbatim files (never lost)
│   │   │   ├── auth/
│   │   │   └── billing/
│   │   └── halls.json          ← connections between rooms
│   └── john-doe/
│       └── rooms/
│           └── preferences/
├── tunnels.json                ← cross-wing connections
└── palace.json                 ← palace metadata

• Wings — a person or project
• Rooms — topics within a wing (auto-detected or custom)
• Halls — links between related rooms in the same wing
• Tunnels — links between rooms across different wings
• Closets — compressed summaries pointing to drawers (fast retrieval)
• Drawers — verbatim original content (never summarized or lost)

AAAK Compression

AAAK is MemPalace's internal compression dialect — structured text readable by any LLM, no decoder needed.

from mempalace.aaak import encode_to_aaak, decode_from_aaak

# Encode a large context block
aaak_text = encode_to_aaak(long_context_string)
# ~30x compression, zero information loss

# Decode back for human reading
original = decode_from_aaak(aaak_text)

AAAK is injected automatically in

wake-up

--aaak

mempalace wake-up --aaak
# Returns ~170 tokens covering your full palace summary

Common Patterns

Pattern: Daily Conversation Mining

import schedule
from mempalace.miner import mine_directory

def nightly_mine():
    mine_directory(
        source_path="~/Downloads/claude-exports/",
        mode="convos",
        extract="general",
        palace_path="~/.mempalace/palace"
    )

schedule.every().day.at("02:00").do(nightly_mine)

Pattern: Search Before Answering

from mempalace.searcher import search_memories

def answer_with_memory(user_question: str, llm_client) -> str:
    # Retrieve relevant memories first
    memories = search_memories(
        query=user_question,
        palace_path="~/.mempalace/palace",
        top_k=5
    )

    context_blocks = [m["content"] for m in memories if m["score"] > 0.7]
    context = "\n---\n".join(context_blocks)

    return llm_client.complete(
        system=f"Past context:\n{context}" if context else "",
        user=user_question
    )

Pattern: Haiku Reranking (100% Recall Mode)

import anthropic
from mempalace.searcher import search_memories

client = anthropic.Anthropic()  # uses ANTHROPIC_API_KEY env var

def search_with_rerank(query: str, top_k: int = 5):
    # Fetch wider candidate set
    candidates = search_memories(query, top_k=20)

    # Rerank with Claude Haiku (cheap, fast)
    ranked = client.messages.create(
        model="claude-haiku-20240307",
        max_tokens=512,
        system="Rerank these memory chunks by relevance to the query. Return indices only.",
        messages=[{
            "role": "user",
            "content": f"Query: {query}\n\nChunks:\n" +
                       "\n".join(f"[{i}] {c['content'][:200]}"
                                 for i, c in enumerate(candidates))
        }]
    )

    # Parse and return top_k
    return candidates[:top_k]  # apply parsed order

Supported Chat Export Formats

MemPalace's

--mode convos

• Claude conversation exports (JSON)
• ChatGPT conversation exports (JSON)
• Slack workspace exports
• Plain text transcripts

mempalace mine ~/Downloads/conversations.json --mode convos
mempalace mine ~/slack-export/ --mode convos --extract general

Troubleshooting

"No memories found" after mining

# Check palace was initialized
mempalace status

# Re-run mining with verbose output
mempalace mine ~/chats/ --mode convos --verbose

# Confirm ChromaDB is populated
python -c "
import chromadb
client = chromadb.PersistentClient(path='~/.mempalace/palace/chroma')
print(client.list_collections())
"

MCP server not connecting

# Test the server directly
python -m mempalace.mcp_server --test

# Re-add with explicit python path
which python  # copy this path
claude mcp add mempalace -- /full/path/to/python -m mempalace.mcp_server

Low recall on searches

# Use broader queries — semantic search, not keyword
mempalace search "database choice reasoning"   # good
mempalace search "postgres"                    # too narrow

# Enable Haiku reranking for 100% recall
mempalace search "database choice" --rerank    # uses ANTHROPIC_API_KEY

Wake-up output is too large

# Use AAAK compression (~30x reduction)
mempalace wake-up --aaak

# Limit to specific wing
mempalace wake-up --wing myapp-project --aaak

ChromaDB version conflicts

pip install "chromadb>=0.4.0" --upgrade
pip install mempalace --upgrade

Environment Variables

ANTHROPIC_API_KEY

MEMPALACE_PATH

~/.mempalace/palace

MEMPALACE_LOG_LEVEL

DEBUG

export MEMPALACE_PATH=/data/my-palace
export MEMPALACE_LOG_LEVEL=DEBUG
mempalace status

Cost Reference

MemPalace is entirely free. API costs above apply only if using Claude for optional reranking.