memotrail 0.1.0

Persistent memory layer for AI coding assistants. Every conversation remembered, every decision searchable.

🔍 MemoTrail

Every AI coding conversation you've ever had — searchable and remembered.

MemoTrail is a persistent memory layer for AI coding assistants. It automatically records your sessions, indexes them semantically, and surfaces the most relevant past context in every new session.

"Why did we switch from Postgres to Redis 3 months ago?" → MemoTrail finds the exact conversation where you decided that.

The Problem

Every time you start a new Claude Code session, it's a blank slate. Your AI doesn't remember that you spent 3 hours yesterday debugging that auth flow, or that you decided to use Redis instead of Memcached, or that you tried approach X and it failed.

You repeat yourself. Every. Single. Day.

The Solution

MemoTrail runs as an MCP server that gives your AI coding assistant a real memory:

• 🔍 Search past conversations — "What did we discuss about authentication?"
• 🧠 Automatic indexing — Every session is recorded and searchable
• 📝 Decision tracking — Important choices are preserved
• 🔒 100% local — Your data never leaves your machine
• ⚡ 5 min setup — One install, one command

Quick Start

# Install
pip install memotrail

# Index your existing Claude Code history
memotrail index

# Add to Claude Code
claude mcp add memotrail -- memotrail serve

That's it. Start a new Claude Code session and ask:

"What did we work on last week?"

How It Works

You code with Claude Code
        ↓
MemoTrail records the conversation
        ↓
Messages are chunked and embedded
        ↓
Stored locally (ChromaDB + SQLite)
        ↓
    Next session...
        ↓
Claude Code calls MemoTrail tools
        ↓
Relevant past context surfaces automatically

Available Tools

Once connected, Claude Code can use these tools:

Tool	What it does
search_chats	Search past conversations semantically
get_decisions	List recorded architectural decisions
get_recent_sessions	See what you worked on recently
get_session_detail	Deep dive into a specific session
save_memory	Manually save important facts
memory_stats	See how much is indexed

CLI Commands

# Index existing Claude Code history
memotrail index

# Search from terminal
memotrail search "redis caching decision"

# View stats
memotrail stats

# Run MCP server (usually called by Claude Code)
memotrail serve

Architecture

~/.memotrail/
├── chroma/          # Vector embeddings (semantic search)
└── memotrail.db     # Session metadata (SQLite)

Everything runs locally. No cloud, no accounts, no API keys needed.

• Embedding model: all-MiniLM-L6-v2 (~80MB, runs on CPU)
• Vector DB: ChromaDB (persistent, local)
• Metadata: SQLite (single file)

Roadmap

• Claude Code session indexing
• Semantic search across conversations
• MCP server with 6 tools
• CLI for indexing and searching
• Automatic decision extraction
• Session summarization
• Cursor collector
• Copilot collector
• VS Code extension
• Cloud sync (Pro)
• Team memory (Team)

Development

# Clone
git clone https://github.com/melihhopa/memotrail.git
cd memotrail

# Install in dev mode
pip install -e ".[dev]"

# Run tests
pytest

# Lint
ruff check src/

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

Good first issues:

• Add Cursor session collector
• Add Copilot session collector
• Improve chunking strategy
• Add BM25 keyword search alongside semantic search

License

MIT — see LICENSE

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Built by Halil Hopa | memotrail.ai