mcpField
C
claude-memory-manager

claude-memory-manager

Local
github.com/NyxToolsDevMemory & Knowledge2Updated 29w ago

About claude-memory-manager

Claude Memory Manager provides cross-session memory for Claude Code by parsing session logs, extracting decisions, code changes, and fixes, and storing them in local SQLite. It combines semantic and keyword search and serves relevant context to Claude Desktop.

Key features

  1. 01Parses Claude Code session logs
  2. 02Extracts and categorizes important memories
  3. 03Combines semantic and keyword search
  4. 04Generates project context summaries
  5. 05Stores memories in local SQLite and exports them

Use cases

  1. 01Finding past architecture decisions and their rationale
  2. 02Reviewing bug fixes and error resolutions
  3. 03Generating a context summary for a project
  4. 04Watching for and ingesting new session logs

MCP support

Its MCP server gives Claude Desktop tools to search memories, recall project context, save memories, and view database statistics.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"nyxtoolsdev-claude-memory-manager": {
"command": "uvx",
"args": [
"claude-memory-manager"
]
}
}
}
Run commanduvx claude-memory-manager

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypiclaude-memory-manager0.1.1

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Claude Memory Manager

Cross-session memory for Claude Code — never lose context between sessions.

Claude Memory Manager automatically captures architectural decisions, code changes, bug fixes, and configuration choices from your Claude Code sessions, then intelligently retrieves relevant context when you start new sessions.

What It Does

Every time you use Claude Code, valuable context is created and lost when the session ends:

  • Which libraries you chose and why
  • Bug fixes and their root causes
  • Configuration decisions
  • File structure and naming conventions
  • Error resolutions

Claude Memory Manager solves this by:

  1. Parsing your Claude Code session logs (JSONL files)
  2. Extracting meaningful memories with importance scoring
  3. Embedding memories for semantic search
  4. Storing everything in a local SQLite database with FTS5
  5. Retrieving relevant context via hybrid semantic + keyword search
  6. Serving context to Claude Desktop via MCP protocol

Installation

pip install claude-memory-manager

For local embeddings (no API key needed):

pip install claude-memory-manager[local]

For development:

pip install claude-memory-manager[dev]

Quick Start

1. Initialize the Database
claude-memory init

This creates the SQLite database at ~/.claude-memory/memory.db and saves a config file.

2. Ingest Session Logs
# Ingest all sessions from the default path (~/.claude/projects/)
claude-memory ingest

# Ingest from a specific path
claude-memory ingest /path/to/sessions

# Watch for new sessions and auto-ingest
claude-memory ingest --watch
3. Search Memories
# Search across all memories
claude-memory search "authentication setup"

# Filter by project
claude-memory search "database schema" --project /path/to/project

# Filter by category
claude-memory search "cors" --category config
4. Generate Context Summary
# List all indexed projects
claude-memory context

# Generate summary for a specific project
claude-memory context /path/to/project

# With custom token limit
claude-memory context /path/to/project --max-tokens 3000
5. Connect to Claude Desktop (MCP)

Add to your Claude Desktop config (see MCP Setup):

{
  "mcpServers": {
    "claude-memory": {
      "command": "claude-memory-mcp",
      "args": []
    }
  }
}

CLI Reference

CommandDescription
claude-memory initInitialize the SQLite database
claude-memory ingest [PATH]Ingest session logs from path
claude-memory ingest --watchWatch and auto-ingest new sessions
claude-memory search "query"Hybrid semantic + keyword search
claude-memory context [PROJECT]Generate context summary
claude-memory listList all indexed sessions
claude-memory statsDatabase statistics
claude-memory prune --older-than 90dRemove old memories
claude-memory exportExport memories as JSON
claude-memory serveStart MCP server mode
Global Options
OptionDescription
--config PATHCustom config file path
--verbose / -vEnable debug logging
--versionShow version
Search Options
OptionDescription
--project / -pFilter by project path
--category / -cFilter by category
--limit / -nMax results (default: 5)
Categories

Memories are classified into these categories:

  • decision — Architectural and design decisions
  • code_change — Significant code modifications
  • bug_fix — Bug identification and resolution
  • config — Configuration and environment changes
  • error_resolution — Errors encountered and solved
  • preference — User preferences and conventions
  • discussion — General discussion summaries

MCP Setup

Claude Desktop
  1. Find your Claude Desktop config file:

    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json
  2. Add the memory server:

{
  "mcpServers": {
    "claude-memory": {
      "command": "claude-memory-mcp",
      "args": []
    }
  }
}
  1. Restart Claude Desktop.

See examples/claude-desktop-config.json for a complete example.

MCP Tools

Once connected, Claude Desktop can use these tools:

ToolDescription
memory_searchSearch memories by query with optional filters
memory_recallGet a formatted context summary for a project
memory_saveSave a new memory directly
memory_statsGet database statistics

Architecture

claude-memory-manager/
  src/claude_memory/
    cli.py              # Click CLI commands
    mcp_server.py       # MCP stdio server
    config.py           # Configuration management
    core/
      extractor.py      # Memory extraction from conversations
      embedder.py       # Embedding generation + caching
      indexer.py         # Pipeline: parse -> extract -> embed -> store
      retriever.py      # Hybrid semantic + keyword search
      summarizer.py     # Context summary generation
    parsers/
      jsonl_parser.py   # Claude Code session log parser
      diff_parser.py    # Unified diff parser
    storage/
      database.py       # SQLite + FTS5 operations
      models.py         # Pydantic data models
      migrations.py     # Schema versioning
    integrations/
      anthropic_embeddings.py  # Voyage AI API
      local_embeddings.py      # sentence-transformers
    utils/
      formatting.py     # CLI output formatting
      license.py        # License validation
Data Flow
Session Logs (.jsonl)
        |
  [JSONL Parser] -----> ParsedSession
        |
  [Extractor] --------> Memory objects (categorized, scored)
        |
  [Embedder] ----------> Embeddings (bytes for SQLite BLOB)
        |
  [Indexer] -----------> SQLite DB (with FTS5 index)
        |
  [Retriever] ---------> Search results (hybrid ranked)
        |
  [Summarizer] --------> Context summary (markdown)

Configuration

Configuration is loaded from (in priority order):

  1. Environment variables
  2. Config file (~/.claude-memory/config.json)
  3. Defaults
Environment Variables
VariableDescriptionDefault
CLAUDE_SESSIONS_PATHPath to session logs~/.claude/projects
CLAUDE_MEMORY_DB_PATHDatabase file path~/.claude-memory/memory.db
CLAUDE_MEMORY_EMBEDDING_PROVIDERanthropic, voyage, or locallocal
ANTHROPIC_API_KEYAnthropic API key—
VOYAGE_API_KEYVoyage AI API key—
CLAUDE_MEMORY_MAX_TOKENSMax tokens for context2000
CLAUDE_MEMORY_LOG_LEVELLog levelINFO
Embedding Providers
ProviderDimensionRequires
voyage1024VOYAGE_API_KEY
anthropic1024ANTHROPIC_API_KEY
local384pip install claude-memory-manager[local]

If no provider is available, a stub provider is used (keyword search still works, but semantic search is disabled).

FAQ

Where are my memories stored? In a SQLite database at ~/.claude-memory/memory.db. All data stays local.

Does this send my code to any API? Only if you configure the Voyage or Anthropic embedding provider. In that case, only memory text content (not full session logs) is sent to generate embeddings. Use local for fully offline operation.

How does deduplication work? Each memory's content is hashed (SHA-256). If a memory with the same hash already exists, it is skipped during ingestion.

How does hybrid search work? Results from cosine-similarity vector search (70% weight) are combined with SQLite FTS5 keyword search results (30% weight). Memories appearing in both get combined scores.

Can I export my memories? Yes: claude-memory export > memories.json or claude-memory export -o file.json.

How do I prune old memories? claude-memory prune --older-than 90d removes memories older than 90 days. Supports d (days), w (weeks), m (months), y (years).

Development

# Clone and install in development mode
git clone https://github.com/nyxtools/claude-memory-manager.git
cd claude-memory-manager
pip install -e ".[dev]"

# Run tests
pytest

# Type check
mypy src/

# Lint
ruff check src/ tests/

License

MIT License. Copyright (c) 2026 NyxTools.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.