contextfs 0.2.16

Universal AI memory layer - cross-client, cross-repo context management with RAG

ContextFS

Persistent Memory for AI Agents - Give your AI tools memory that persists across sessions.

Full Documentation | Get Started

Install

# With pip
pip install contextfs

# With uv (recommended)
uv pip install contextfs

# Run directly without installing
uvx contextfs

Quick Start

# Initialize your repo for indexing
contextfs index init

# Save a memory
contextfs memory save "Use PostgreSQL for database" --type decision

# Search memories
contextfs memory search "database"

# Index your codebase for semantic search
contextfs index index

MCP Integration

Add to your AI tool's MCP config:

{
  "mcpServers": {
    "contextfs": {
      "command": "uvx",
      "args": ["contextfs"]
    }
  }
}

Works with: Claude Code, Claude Desktop, Cursor, VS Code, and any MCP-compatible client.

See tool-specific setup guides for detailed instructions.

Key Features

• Semantic Search - Find relevant memories using natural language
• Auto Code Indexing - Index your entire codebase for context-aware AI
• Cross-Session Memory - Decisions, facts, and patterns persist across conversations
• Multi-Tool Sync - Share memory between Claude, Cursor, VS Code, and more

Python SDK

from contextfs import ContextFS

ctx = ContextFS()

# Save
ctx.save("Use JWT for auth", type="decision", tags=["auth"])

# Search
results = ctx.search("authentication")

Cloud Sync

Enable cross-device memory sync:

contextfs cloud login
contextfs cloud sync

Sign up at contextfs.ai for cloud features.

Documentation

Visit contextfs.ai/docs for:

• Installation guides for each AI tool
• API reference
• Memory types and best practices
• Cloud sync setup

License

MIT - Matthew Long and The YonedaAI Collaboration