Memory Map

memory_map

Persistent memory and conversation history MCP server for Claude Code — key-value context store, rolling MongoDB-backed history, and cross-project recall across sessions.

When to Activate

• Installing memory_map for the first time or on a new machine
• Writing CLAUDE.md session-setup instructions (load_memory, suggest_history)
• Deciding what belongs in memory vs history vs inline code comments
• Pruning stale or sensitive history chunks (delete_history)
• Using cross-project or global memory tools
• Configuring history compression or vector search
• Debugging why Claude starts a session without prior context
• Manually checkpointing conversation history with /mem_save

Architecture

memory_map_mcp/
├── server.py          — MCP server: 22 tools via FastMCP (stdio transport)
├── history_hook.py    — hook script: saves Q&A pairs on UserPromptSubmit / Stop / PreCompact
└── history_store.py   — storage layer: MongoDB CRUD, BM25, vector search, RRF, MMR

Storage:
├── MongoDB memory_map.history    — conversation chunks (suggest_history, save_history, delete_history)
├── MongoDB memory_map.memory     — key-value memory (save_memory / load_memory)
└── .mcp_memory.json              — per-project fallback when MongoDB not configured

MongoDB is required for all history features. Key-value memory works without it (falls back to .mcp_memory.json), but suggest_history, save_history, and delete_history all return an error if MEMORY_MAP_MONGO_URI is unset.

MCP registration makes all tools available globally. Per-project activation is controlled by CLAUDE.md — Claude only calls load_memory / suggest_history automatically if the instructions tell it to.

Installation

Step 1 — Install from PyPI

pip install memory-map-mcp

Or install from source for development:

git clone https://github.com/kid-sid/memory_map.git
cd memory_map
python -m venv venv

# Windows
venv\Scripts\pip install -e .

# Mac/Linux
source venv/bin/activate && pip install -e .

Step 2 — Set MongoDB URI

Add to your environment / shell profile:

export MEMORY_MAP_MONGO_URI="mongodb+srv://user:pass@cluster.mongodb.net/"

Free-tier MongoDB Atlas works. Without this, history tools are unavailable.

Step 3 — Register the MCP Server

# Global — available in every project (recommended)
claude mcp add -s user memory_map memory-map-mcp

If installed from source:

# Windows
claude mcp add -s user memory_map \
  C:/Users/yourname/memory_map/venv/Scripts/python.exe \
  C:/Users/yourname/memory_map/memory_map_mcp/server.py

# Mac/Linux
claude mcp add -s user memory_map \
  python3 /home/yourname/memory_map/memory_map_mcp/server.py

Registration scope options:

Always use -s user for memory_map — it stores files at local paths that differ per machine.

Verify: claude mcp list → should show memory_map.

Step 4 — Lifecycle Hooks

Add to ~/.claude/settings.json so history is captured automatically in every project:

{
  "hooks": {
    "UserPromptSubmit": [
      {
        "matcher": "",
        "hooks": [{
          "type": "command",
          "command": "memory-map-hook",
          "timeout": 10
        }]
      }
    ],
    "PreCompact": [
      {
        "matcher": "",
        "hooks": [{
          "type": "command",
          "command": "memory-map-hook --force",
          "timeout": 15
        }]
      }
    ],
    "Stop": [
      {
        "matcher": "",
        "hooks": [{
          "type": "command",
          "command": "memory-map-hook --force",
          "timeout": 15,
          "async": true
        }]
      }
    ]
  }
}

If installed from source, replace memory-map-hook with the full path to history_hook.py.

Step 5 — Enable Per-Project Memory

Add this block to CLAUDE.md in the project root:

## Session Setup (Required)
At the start of every session, before doing anything else:
1. Call `load_memory` with the current working directory
2. Call `suggest_history` with the current working directory and the user's first message
3. Read both outputs before exploring files or asking questions

Save or update memory entries whenever you learn something worth keeping across sessions.
If something loaded from memory is no longer accurate, update it with `save_memory` using the same key.
Use short, lowercase keys: `stack`, `current_work`, `gotchas`, `key_files`. Keep values concise.

Commit CLAUDE.md — teammates get session-start loading automatically.

Memory Tools

save_memory / load_memory / delete_memory

Per-project key-value store backed by MongoDB (or .mcp_memory.json without MongoDB).

save_memory(project_path, key, value)
load_memory(project_path, query="", top_k=10)   → compressed key-value output
delete_memory(project_path, key)

Key conventions:

Rules:

• Short, lowercase, underscore-separated keys
• Values: one or two sentences max — dense, not verbose
• Overwrite stale values with the same key; don't accumulate duplicates
• Convert relative dates to absolute: "Thursday" → "2026-05-15"

What NOT to save in memory:

• Code patterns and architecture (read the code)
• Git history / who changed what (git log / git blame)
• Fix recipes (the fix is in the code; commit message has context)
• Ephemeral task state (in-progress work, current conversation)
• Anything already in CLAUDE.md

Global Memory

Shared across all projects on the machine:

save_global_memory(key, value)
load_global_memory()

Use for: user identity, preferred tools, cross-project conventions. Never store secrets.

History Tools

Conversation history is stored in MongoDB (memory_map.history collection), one document per Q&A pair. Tags are extracted by local keyword matching — no LLM calls.

suggest_history — primary session-start tool

suggest_history(project_path, user_message, token_budget=2000, diversity=0.3)

Hybrid retrieval pipeline:

1. Concurrent fetch — vector search + BM25/tag scoring run in parallel
2. RRF merge — Reciprocal Rank Fusion combines both ranked lists
3. MMR re-ranking — penalises redundant chunks so results cover more distinct topics
4. Anchor — most recent chunk always included for session continuity
5. Token budget — fills remaining budget with most recent unselected chunks

Call at session start with the user's first message. Returns the most relevant chunks within the token budget.

save_history / load_history / get_history_chunks

save_history(project_path, dialogue, session_id="", tags="")
load_history(project_path, last_n=5)        → tag index (id, timestamp, tags, preview, tokens)
get_history_chunks(project_path, ids)       → full dialogue for comma-separated chunk IDs

save_history is called automatically by history_hook.py — no manual calls needed during normal use. Auto-summarises oldest chunks when total tokens exceed MCP_HISTORY_MAX_TOKENS (default 50 000).

load_history + get_history_chunks are for inspection only — listing what's saved or fetching a specific chunk by ID. Do not use them at session start — use suggest_history instead.

delete_history — prune stale or sensitive chunks

delete_history(project_path, ids="", older_than_days=0)

Remove chunks you no longer need. At least one filter must be provided:

# Delete specific chunks by ID (get IDs from load_history or suggest_history output)
delete_history("C:/projects/my-api", ids="6830a1f2e4b0c1234567abcd,6830a1f2e4b0c1234567ef01")
→ "deleted: 2 chunk(s)"

# Remove everything older than 30 days
delete_history("C:/projects/my-api", older_than_days=30)
→ "deleted: 7 chunk(s)"

# Both filters together (union — deletes chunks matching either condition)
delete_history("C:/projects/my-api", ids="6830a1f2e4b0c1234567abcd", older_than_days=30)

Deletion is scoped to the given project — other projects are never affected.

summarise_history

summarise_history(project_path, n=10)

Collapse the n oldest chunks into a single summary chunk. Triggered automatically by save_history when the token budget is exceeded; call manually to compact immediately.

backfill_history_embeddings / backfill_bm25_text

backfill_history_embeddings(project_path="", batch_size=20)
backfill_bm25_text(project_path="", batch_size=100)

Run once after enabling vector search or upgrading from an older version. Repeat until remaining=0. Pass project_path="" to backfill across all projects.

Vector Search (Optional)

Set MEMORY_MAP_EMBED_PROVIDER to enable semantic search in suggest_history:

Without MEMORY_MAP_EMBED_PROVIDER, suggest_history still works using BM25 and tag matching.