Claude Memory Manager

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 pathclaude-memory ingest /path/to/sessions
# Watch for new sessions and auto-ingestclaude-memory ingest --watch

3. Search Memories

# Search across all memoriesclaude-memory search "authentication setup"
# Filter by projectclaude-memory search "database schema" --project /path/to/project
# Filter by categoryclaude-memory search "cors" --category config

4. Generate Context Summary

# List all indexed projectsclaude-memory context
# Generate summary for a specific projectclaude-memory context /path/to/project
# With custom token limitclaude-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

Global Options

Search Options

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": []    }  }}

3. Restart Claude Desktop.

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

MCP Tools

Once connected, Claude Desktop can use these tools:

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

Embedding Providers

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 modegit clone https://github.com/nyxtools/claude-memory-manager.gitcd claude-memory-managerpip install -e ".[dev]"
# Run testspytest
# Type checkmypy src/
# Lintruff check src/ tests/

License

MIT License. Copyright (c) 2026 NyxTools.